阅读说明：本技术 用于在胎儿孕育中预测胎龄和早产的无创分子钟 (Noninvasive molecular clock for predicting gestational age and preterm delivery in fetal pregnancy ) 是由 米拉·N·穆法雷 翠·T·M·努 琼·卡蒙纳斯-索勒 马斯·梅尔比 斯蒂芬·R·夸克 于 2018-10-23 设计创作，主要内容包括：本发明涉及预测胎儿的胎龄的方法。本发明还涉及识别妇女有早产风险的方法。在一些方面,所述方法包含对来自妇女的生物学样本中的一种或多种胎盘或胎儿组织特异性基因进行定量。(The present invention relates to a method of predicting the gestational age of a fetus. The invention also relates to a method of identifying a woman at risk of preterm birth. In some aspects, the method comprises quantifying one or more placental or fetal tissue-specific genes in a biological sample from a woman.)

1. A method of estimating gestational age of a fetus comprising analyzing a maternal sample to determine an expression profile from a panel comprising one or more placental genes.

2. The method of claim 1, wherein said expression profile is from a panel comprising three (3) or more placental genes.

3. The method of claim 1 or2, wherein said expression profile is from a panel comprising placental genes only.

4. The method of any one of claims 1 to 3, wherein the expression level of each of said placental genes is altered during pregnancy.

5. The method of any one of claims 1-4, wherein the expression level of at least one placental gene is higher during the first pregnancy than during the third pregnancy.

6. The method of any one of claims 1-4, wherein the expression level of at least one placental gene is lower during the first pregnancy than during the third pregnancy.

7. The method of claim 5, wherein the expression level of all of said placental genes is lower during the first pregnancy than during the third pregnancy.

8. The method of any of the preceding claims, wherein said placental genes are selected from the genes in Table 1.

9. The method of claim 8, wherein said placental gene is selected from the group consisting of CGA, CAPN6, CGB, ALPP, CSHL1, PLAC4, PSG7, PAPPA and LGALS 14.

10.The method of any of the preceding claims, wherein expression profiles of three (3) to nine placental genes are determined.

11. The method according to any one of claims 1 to 10, wherein the expression profile is determined by measuring cell-free rna (cfrna) in the maternal sample.

12. The method of any one of claims 1 to 10, wherein said expression profile is determined by measuring placental protein in said maternal sample.

13. The method of claim 11 or 12, wherein the maternal sample is maternal blood, plasma, serum, or urine.

14. The method of claims 1-13, wherein the maternal sample is obtained from the mother during the third pregnancy of pregnancy.

15. The method of claims 1-13, wherein the maternal sample is obtained from the mother during the second pregnancy of pregnancy.

16. The method of any one of claims 1 to 15, comprising:

comparing the expression profile to a plurality of reference profiles, wherein each reference profile is characteristic of a particular gestational age;

determining which of the plurality of reference profiles corresponds to the expression profile based on the comparison, an

Deriving an estimated gestational age of the fetus at the time the maternal sample was obtained based on the particular gestational age of the corresponding reference spectrum.

17. A method for estimating gestational age of a fetus, comprising:

(a) obtaining a maternal expression profile of a sample comprising expression levels of the genomic set according to any one of claims 1 to 11;

(b) comparing the expression level of the genomic complement to a reference expression level, wherein the reference expression level is obtained from a term labor population to determine whether the maternal expression profile is similar to or different from a reference expression level within a threshold value.

18. The method of claim 17, wherein one or more reference expression levels for the term population are established using machine learning techniques.

19. The method of claim 18, further comprising:

obtaining a plurality of training samples, each training sample labeled as preterm or term;

obtaining one or more measured expression levels of the genomic suite for each of the plurality of training samples;

iteratively adjusting the one or more reference expression levels using the machine learning technique to increase the number of training samples that are correctly classified as a result of comparing the one or more measured expression levels to the one or more reference expression levels.

20. The method of claims 16-18, further comprising: comparing the expression level to other reference expression levels of the genomic suite, wherein the other reference expression levels are obtained from a preterm population to determine whether the maternal expression profile is similar to or different from the other reference expression levels within a threshold.

21. A method for estimating gestational age of a fetus comprising the steps of: (i) determining a maternal expression profile of a set comprising at least one placental RNA, and (ii) comparing said maternal expression profile to a reference profile, wherein comparison of said maternal expression profile to said reference profile enables estimation of gestational age.

22. The method of claim 21, wherein the gestational age of the reference spectra is known.

23. The method of claim 21, wherein the comparison of the maternal expression profile to the reference profile is made by comparing the maternal expression profile to a pregnancy function that provides gestational age based on an input of one or more expression levels, wherein the pregnancy function is determined by fitting a model to a plurality of calibration samples having measured expression levels and known gestational age.

24. The method of claim 23, wherein the model is a regression model.

25. The method of claim 21, wherein the spectral stack is as in any one of claims 1-15.

26. The method of any preceding claim, performed by a computer.

27. A method, comprising: determining a first gestational age using a first maternal sample according to the method of claim 16, 17 or 18, and determining a second gestational age using a second maternal sample obtained later in gestation according to the method of claim 16, 17 or 18.

28. The method of claim 11, wherein the expression level of each placental gene is determined by qPCR or massively parallel sequencing.

29. The method of claim 12, wherein the expression level of each placental gene is determined by mass spectrometry or using an antibody array.

30. The method of any one of claims 1 to 29, wherein the expression of at least one additional gene is determined and said additional gene is not a placental gene.

31. A composition comprising primers for multiplex amplification of at least 3 and no more than fifty placental genes selected from the genes in table 1.

32. A kit comprising primers for multiplex amplification of at least 3 and no more than fifty placental genes selected from the genes in table 1.

33. An antibody array for detecting at least three and no more than one hundred placental proteins isolated from maternal blood or urine.

34. A method for assessing the risk of preterm birth in a pregnant woman, comprising analyzing a maternal sample to determine an expression profile from a panel comprising one or more genes selected from table 2.

35. The method of claim 34, wherein the panel comprises three or more genes from table 2.

36. The method of claim 34 or 35, wherein the gene is expressed at a higher level in the preterm population than in the term population.

37. The method of claim 34 or 35, wherein the gene is selected from CLCN3, DAPP1, POLE2, PPBP, LYPLAL1, MAP3K7CL, MOB1B, RAB27B, RGS18, and TBC1D 15; and optionally selected from CLCN3, DAPP1, PPBP, MAP3K7CL, MOB1B, RAB27B, and RGS 18.

38. The method of claim 37, wherein the panel comprises three (3) genes selected from any combination of three of CLCN3, DAPP1, POLE2, PPBP, LYPLAL1, MAP3K7CL, MOB1B, RAB27B, RGS18, and TBC1D 15; wherein optionally, the kit comprises a nucleic acid sequence selected from the group consisting of (1) RGS 18; DAPP 1; PPBP; (2) RGS 18; RAB 27B; PPBP; (3) RGS 18; MOB 1B; PPBP; (4) RGS 18; PPBP; MAP3K7 CL; (5) RGS 18; PPBP; CLCN 3; (6) DAPP 1; RAB 27B; PPBP; (7) DAPP 1; MOB 1B; PPBP; (8) DAPP 1; PPBP; CLCN 3; (9) RAB 27B; MOB 1B; PPBP; (10) RAB 27B; PPBP; MAP3K7 CL; (11) RAB 27B; PPBP; CLCN 3; (12) MOB 1B; PPBP; MAP3K7 CL; (13) MOB 1B; PPBP; three genes of CLCN 3.

39. The method of any one of claims 34 to 38, wherein the expression profile of a panel of three to ten genes is determined.

40. The method of claim 39, wherein the expression profile is a panel comprising exactly three genes.

41. The method of claim 39, wherein the expression profile of a panel of exactly three genes is determined.

42. The method according to any one of claims 34 to 41, wherein the expression profile is determined by measuring cell-free RNA (cfRNA) in the maternal sample.

43. The method of any one of claims 34 to 41, wherein the expression profile is determined by measuring proteins in the maternal sample.

44. The method of any one of claims 34 to 43, wherein the maternal sample is maternal blood, plasma, serum, or urine.

45. The method of any one of claims 34 to 44, wherein the maternal sample is obtained more than 28 days prior to preterm delivery.

46. The method of claim 45, wherein the maternal sample is obtained more than 45 days prior to preterm delivery.

47. The method of claim 45, wherein the maternal sample is obtained after the second and before the eighth month of pregnancy.

48. The method of any one of claims 34 to 47, wherein the maternal sample is obtained during the second pregnancy of pregnancy.

49. The method of any one of claims 34 to 47, wherein the maternal sample is obtained during the third pregnancy of pregnancy.

50. A method according to any one of claims 34 to 49, wherein the maternal sample is obtained at a specified gestational week, the method comprising:

comparing the expression profile to a time-matched reference profile, wherein the time-matched reference profile is characteristic of a normal term pregnancy at a specified gestational week;

confirming that the pregnant woman is at an elevated risk of preterm birth if the expression profile differs significantly from the time-matched reference profile within a threshold value.

51. A method according to any one of claims 34 to 49, wherein the maternal sample is obtained at a specified gestational week, the method comprising:

comparing the expression profile to a time-matched reference profile, wherein the time-matched reference profile is characteristic of preterm birth;

confirming that the pregnant woman is at an elevated risk of preterm birth if the expression profile is significantly similar to the time-matched reference profile within the threshold value.

52. A method for assessing the risk of preterm birth in a pregnant woman, comprising:

(a) obtaining a maternal expression profile or sample comprising the expression levels of the genomic set according to claim 37 or 38;

(b) comparing the expression level to a reference expression level for the genomic suite, wherein the reference expression level is obtained from a preterm population, a term delivery population, or both, to determine whether the maternal expression profile is similar to or different from the reference expression level within a threshold value.

53. A method according to claim 51 or 52, wherein one or more reference levels are established using machine learning techniques.

54. A method as claimed in claims 51 to 53, performed by a computer.

55. A method comprising performing the steps of claims 50-52 on two or more maternal samples obtained at different times during pregnancy.

56. The method of claim 42, wherein the expression level of each gene is determined by qPCR or massively parallel sequencing.

57. The method of claim 43, wherein the expression level of each gene is determined by mass spectrometry or using an antibody array.

58. A composition comprising primers for multiplex amplification of at least 3 and no more than one hundred genes selected from the genes in table 2.

59. A composition comprising (1) a cfRNA having a cfRNA sequence corresponding to at least 2 genes in table 2, or an amplicon of the cfRNA sequence or a cDNA from the cfRNA sequence, and (2) a primer for amplifying the cfRNA sequence or amplicon or cDNA, or a probe for detecting the cfRNA sequence or amplicon or cDNA, with the proviso that the composition does not include a cfRNA having: a cfRNA sequence corresponding to 200 different genes from a human genome, amplicons of the 200 different genes, or cdnas from the 200 different genes; and does not include primers for amplifying the 200 different genes, amplicons, or cdnas; and does not include probes for detecting more than 200 different cfRNA sequences or amplicons or cdnas as described above.

60. The composition of claim 59, wherein the probe is a nucleic acid probe.

61. The composition of claim 59 or 60, the cfRNA sequences corresponding to at least 2, 3, 4,5, 6, 7, 8, 9, or 10 genes in Table 2.

62. The composition of claims 59 to 61, wherein the composition does not comprise cfRNA having: a cfRNA sequence corresponding to more than 100 (optionally more than 75 or more than 50) different genes from the human genome, amplicons of the 200 different genes, or cdnas from the 200 different genes.

63. A kit comprising primers for multiplex amplification of at least 3 and no more than one hundred genes selected from the genes in table 2.

64. A method of estimating time of labor comprising analyzing a maternal sample to determine an expression profile from a panel comprising one or more placental genes.

65. The method of claim 64, wherein said expression profile is from a panel comprising three (3) or more placental genes.

66. The method of claim 64 or 65, wherein said expression profile is from a panel comprising placental genes only.

67. The method of any one of claims 64-66, wherein the expression level of each said placental gene is altered during pregnancy.

68. The method of any of claims 64-67, wherein the expression level of at least one placental gene is higher during the first pregnancy than during the third pregnancy.

69. The method of any of claims 64-67, wherein the expression level of at least one placental gene is lower during the first pregnancy than during the third pregnancy.

70. The method of claim 69, wherein the expression level of all of said placental genes is lower during a first pregnancy than during a third pregnancy.

71. The method of any of the preceding claims, wherein said placental genes are selected from the genes in Table 1.

72. The method of claim 71, wherein said placental gene is selected from the group consisting of CGA, CAPN6, CGB, ALPP, CSHL1, PLAC4, PSG7, PAPPA and LGALS 14.

73. The method of claim 56, wherein the collection of placental genes comprises at least one gene other than CGA and CGB.

74. The method of any of the preceding claims, wherein expression profiles of three (3) to nine placental genes are determined.

75. The method according to any one of claims 64 to 74, wherein the expression profile is determined by measuring cell-free RNA (cfRNA) in the maternal sample.

76. The method of any one of claims 64-74, wherein said expression profile is determined by measuring placental protein in said maternal sample.

77. The method of claim 75 or 76, wherein the maternal sample is maternal blood, plasma, serum, or urine.

78. The method of claims 64-67 or 71-77, wherein the maternal sample is obtained from the mother during the third pregnancy of pregnancy.

79. The method of claims 64-67 or 71-77, wherein the maternal sample is obtained from the mother during the second pregnancy of pregnancy.

80. The method of any one of claims 64-79, comprising:

comparing the expression profile to a plurality of reference profiles, wherein each reference profile is characteristic of labor time;

determining which of the plurality of reference profiles corresponds to the expression profile, an

Estimating an estimated time of delivery at which the maternal sample was obtained based on the time of delivery of the corresponding reference spectrum.

81. A method for estimating labor time, comprising:

(a) obtaining a maternal expression profile of a sample comprising expression levels of the genomic cassette of claim 56;

(b) comparing the expression level to a reference expression level for the genome, wherein the reference expression level is obtained from a term delivery population to determine whether the maternal expression profile is similar or different to the reference expression level within a threshold.

82. The method of claim 80 or 81, wherein one or more reference levels of the term population are established using machine learning techniques.

83. The method of claim 82, performed by a computer.

84. A method, comprising: determining a first time of delivery using a first maternal sample according to the method of claim 80 or 81; and determining a second delivery time according to the method of claim 80 or 81 using a second maternal sample obtained later in gestation.

85. The method of claim 75, wherein the expression level of each placental gene is determined by qPCR or massively parallel sequencing.

86. The method of claim 76, wherein the expression level of each placental gene is determined by mass spectrometry or using an antibody array.

87. The method of any one of claims 64-86, wherein the expression of at least one additional gene is determined and said additional gene is not a placental gene.

88. A composition comprising primers for multiplex amplification of at least 3 genes and no more than one hundred placental genes selected from table 1.

89. A kit comprising primers for multiplex amplification of at least 3 placental genes and no more than one hundred placental genes selected from table 1.

90. An antibody array for detecting at least 3 placental proteins from table 1 and no more than 100 placental proteins isolated from maternal blood or urine.

91. A method for reducing the risk of preterm birth in a pregnant woman, comprising:

(i) assessing the risk of preterm labor according to any one of claims 34 to 57, and

(ii) treatment is administered to delay the onset of preterm birth.

92. A method for reducing the risk of preterm birth in a pregnant woman, comprising:

(i) assessing the risk of preterm labor according to any one of claims 34 to 57, and

(ii) the fetus is imaged with ultrasound at least once every four weeks.

93. A computer-implemented method for estimating gestational age of a fetus, comprising:

(a) obtaining one or more expression profiles from a maternal sample of a pregnant woman carrying a fetus, wherein the expression profiles correspond to expression of cfRNA transcripts from a first genomic set;

(b) comparing, using a computer system, the expression profile to one or more reference profiles characteristic of a particular gestational age to estimate the gestational age of the fetus, wherein the reference profiles characteristic of the particular gestational age are determined using a machine learning model that analyzes a first training sample that is a cfRNA expression profile tagged with a particular gestational age;

(c) updating, using the computer system, the reference spectrum by:

(1) receiving a second training sample, wherein the second training sample is a cfRNA expression profile tagged with a specific gestational age, an

(2) Iteratively adjusting, via a machine learning model, the reference spectrum to increase a number of correctly classified first and second training samples.

94. The method of claim 93, wherein the reference spectra may form a line or a curve or be discrete values.

95. The method of any one of claims 93-94, wherein the first genomic set comprises any combination of: any combination of genes disclosed herein, comprising a placental gene, a placental gene set forth in Table 1, and at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, or 9 genes selected from CGA [ SEQ ID NO:1], CAPN6[ SEQ ID NO:2], CGB [ SEQ ID NO:3], ALPP [ SEQ ID NO:4], CSHL1[ SEQ ID NO:5], PLAC4[ SEQ ID NO:6], PSG7[ SEQ ID NO:7], PAPPA [ SEQ ID NO:8], and LGALS14[ SEQ ID NO:9 ].

96. A computer system, comprising: (a) a database comprising reference profiles, each reference profile comprising expression levels of cfRNA transcripts corresponding to a first genomic set and to a particular gestational age in a pregnant woman population; (b) a user interface configured to interact with a client computer over a network and receive an expression profile comprising expression levels of cfRNA transcripts corresponding to the first genomic set in a pregnant woman carrying a fetus; and (c) one or more processors configured to analyze the reference profile and the expression profile, including comparing the reference profile and the expression profile to determine the gestational age of the fetus; and (d) a network interface that transmits the gestational age of the fetus to the client computer.

97. The system of claim 96, wherein the reference profile and expression profile comprise expression levels of a cfRNA panel of any combination disclosed herein, comprising transcripts from placental genes; placental genes listed in table 1; and at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, or 9 genes selected from CGA [ SEQ ID NO:1], CAPN6[ SEQ ID NO:2], CGB [ SEQ ID NO:3], ALPP [ SEQ ID NO:4], CSHL1[ SEQ ID NO:5], PLAC4[ SEQ ID NO:6], PSG7[ SEQ ID NO:7], PAPPA [ SEQ ID NO:8], and LGALS14[ SEQ ID NO:9 ].

98. A computer-implemented method for assessing the risk of preterm birth in a pregnant woman, comprising:

(a) obtaining one or more expression profiles from a maternal sample of a pregnant woman, wherein the expression profiles correspond to expression of a plurality of cfRNA transcripts from a first genomic set;

(b) comparing, using a computer system, the expression signature to one or more reference profiles characteristic of women having (a) a high risk of preterm birth or (b) a low risk of preterm birth or having a defined length of pregnancy, wherein the reference profiles are determined using a machine learning model that analyzes a first training sample that is a cfRNA expression profile of preterm or full term or labeled with length of pregnancy,

(c) updating, using the computer system, the reference spectrum by:

(1) receiving a second training sample, wherein the second training sample is a cfRNA expression profile labeled as preterm or term or labeled as length of gestation, and

(2) iteratively adjusting, via a machine learning model, the reference spectrum to increase a number of correctly classified first and second training samples.

99. The method of claim 98, wherein the reference spectra may form a line or a curve or be discrete values.

100. The method of any one of claims 98-99, wherein the first genomic set comprises any combination of: any combination of genes disclosed herein, comprising the genes listed in table 1, and a sequence selected from CGA [ SEQ ID NO:1], CAPN6[ SEQ ID NO:2], CGB [ SEQ ID NO:3], ALPP [ SEQ ID NO:4], CSHL1[ SEQ ID NO:5], PLAC4[ SEQ ID NO:6], PSG7[ SEQ ID NO:7], PAPPA [ SEQ ID NO:8], and LGALS14[ SEQ ID NO:9], at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8 or 9 genes, or selected from CLCN3[ SEQ id no:10], DAPP1[ SEQ ID NO:11], PPBP [ SEQ ID NO:13], MAP3K7CL [ SEQ ID NO:15], MOB1B [ SEQ ID NO:16], RAB27B [ SEQ ID NO:17] and RGS18[ SEQ ID NO:18], at least 2, at least 3, at least 4, at least 5, at least 6 or 7 genes.

101. The method of claim 100, wherein said first genomic set comprises at least one combination selected from the group consisting of: (1) RGS 18; DAPP 1; PPBP; (2) RGS 18; RAB 27B; PPBP; (3) RGS 18; MOB 1B; PPBP; (4) RGS 18; PPBP; MAP3K7 CL; (5) RGS 18; PPBP; CLCN 3; (6) DAPP 1; RAB 27B; PPBP; (7) DAPP 1; MOB 1B; PPBP; (8) DAPP 1; PPBP; CLCN 3; (9) RAB 27B; MOB 1B; PPBP; (10) RAB 27B; PPBP; MAP3K7 CL; (11) RAB 27B; PPBP; CLCN 3; (12) MOB 1B; PPBP; MAP3K7 CL; and (13) MOB 1B; PPBP; CLCN 3.

102. A computer system, comprising: (a) a database comprising reference profiles, each reference profile comprising expression levels of cfRNA transcripts corresponding to a first genomic set and risk of preterm birth in a pregnant woman population; (b) a user interface configured to interact with a client computer over a network and receive an expression profile comprising expression levels of cfRNA transcripts corresponding to the first genomic set in a pregnant woman; and (c) one or more processors configured to analyze the reference profile and expression profile, including comparing the reference profile and expression profile to determine the risk of preterm birth; and (d) a network interface that transmits the pre-term birth risk to the client computer.

103. The system of claim 102, wherein the reference profile and expression profile comprise expression levels of a cfRNA panel disclosed herein, e.g., genes listed in table 1 and at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, or 9 genes selected from CGA [ SEQ ID No. 1], CAPN6[ SEQ ID No. 2], CGB [ SEQ ID No. 3], ALPP [ SEQ ID No. 4], CSHL1[ SEQ ID No. 5], PLAC4[ SEQ ID No. 6], PSG7[ SEQ ID No. 7], PAPPA [ SEQ ID No. 8], and LGALS14[ SEQ ID No. 9], or at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, or 9 genes selected from CLCN3[ SEQ ID No. 10], DAPP1[ SEQ ID No. 11], PPBP [ SEQ ID No. 13], MAP3K CL [ SEQ ID No. 15], MOB 1[ SEQ ID No. 16], RAB27 [ RAB B ] and at least 18RGS [ SEQ ID No. 4934 ] of the CLCN 14[ SEQ ID No. 9] At least 3, at least 4, at least 5, at least 6, or 7 genes.

Technical Field

The present invention relates to the field of medicine.

Cross Reference to Related Applications

This application claims the benefit of U.S. provisional applications No.62/576,033 (filed on 2017, month 10, 23) and No.62/578,360 (filed on 2017, month 10, 27), each of which is incorporated herein by reference in its entirety.

Sequence listing

This application contains a sequence listing that has been submitted electronically in ASCII format and is incorporated herein by reference in its entirety. The ASCII copy was created at 17.10.2018 under the name 103182-1107145 (000300PC) _ sl. txt, 159,304 bytes in size.

Background

For thousands of years, the timing and planning of human inoculation (pregnancy) has been a topic of great interest. In ancient times, ancient Hirschausts had a striking detailed understanding of various details of the fetal pregnancy stage, and they developed mathematical theories in an attempt to explain the time of important markers during pregnancy (including delivery of infants) (Hanson 1995; Hanson 1987; Parker 1999). In modern times, biologists bring together detailed cellular and molecular images (cellular and molecular portraits) of fetal and placental inoculations. However, these results are often associated with pregnancy and do not result in molecular detection, which may enable monitoring of pregnancy and labor predictions for a given set of parents. The most widely used indicator of the pregnancy molecules is the determination of the levels of Human Chorionic Gonadotropin (HCG) and alpha-fetoprotein (AFP), which can be used to detect pregnancy and fetal complications, respectively; however, no single or combined molecules were found to accurately determine gestational age (Dugoff et al 2005; Yefet et al 2017).

Due to the lack of useful molecular testing, most clinicians use ultrasound imaging or patient evaluation of the Last Menstrual Period (LMP) to determine gestational age and to roughly estimate the date of labor. However, these methods are neither particularly accurate nor can they be used to predict preterm birth, which is a significant contributor to mortality and cost in prenatal care. Moreover, inaccurate dates may even mislead the assessment of fetal pregnancy for normal term pregnancies, which has been shown to eventually lead to unnecessary induction and caesarean delivery, prolonged postpartum care and increased expendable medical costs (Bennett et al, 2004; Whitworth et al, 2015).

It would be useful to develop a more accurate method to measure the gestational age of a fetus at various points of pregnancy and more generally to monitor abnormal or signs of preterm pregnancy in the fetus and placenta. There are approximately 1500 million newborns born each year around the world (Blencowe et al, 2013). As a major cause of neonatal death and a second cause of death in children under 5 years old (Liu et al, 2012), Premature delivery is estimated to cause losses to the United states of up to $ 262 billion per year [ Institute of Medicine (US) Committee on pursuing survival Birth bit and administration health issues 2007 ]. Complications continue throughout life, while premature birth is the major cause of poor health, disability, or loss of life due to premature death (Murray et al, 2012). Two thirds of Premature births are naturally occurring (occur epidemic), the only predictor factors being the history of Premature births, multiple births and vaginal bleeding [ Institute of Medicine (US) Committee on acquiring prognosis Birth and administration health issues 2007 ]. Efforts to find genetic causes have met with only limited success (Ward et al, 2005; York et al, 2009), and thus most efforts have focused on phenotypic and environmental causes (Muglia and Katz, 2010).

Summary of The Invention

Gestational age or time may be determined by: (a) generating an expression profile using cfRNA or protein from a maternal sample, and (b) comparing the expression profile to one or more reference profiles reflecting expression profiles characteristic of a defined gestational age.

The risk of preterm birth can be determined by: (a) generating an expression profile using cfRNA (or protein) from a maternal sample, and (b) determining whether the expression profile is characteristic of a population with a history of preterm birth: and/or whether the expression profile is characteristic of a population with a history of term labor.

In a first aspect, the present disclosure provides a method of estimating gestational age of a fetus comprising analyzing a maternal sample to determine an expression profile from a panel (panel) comprising one or more placental genes.

In some embodiments, the method comprises an expression profile comprising three or more placental genes. In some embodiments, the method comprises expression profiles from a panel consisting of placental genes alone.

In some embodiments, the method further comprises altering the expression level of each placental gene during the course of pregnancy. In some embodiments, the method comprises increasing the expression level of at least one placental gene during the first pregnancy compared to the third pregnancy. In some embodiments, the expression level of all placental genes in the first pregnancy is lower than in the third pregnancy. In some embodiments, the method comprises a decreased expression level of at least one placental gene during the first pregnancy compared to the third pregnancy.

In some embodiments, the method comprises a placental gene selected from the genes in table 1. In some embodiments, the method comprises a placental gene selected from the group consisting of CGA, CAPN6, CGB, ALPP, CSHL1, PLAC4, PSG7, PAPPA, and LGALS 14.

In some embodiments, the method comprises determining the expression profile of three to nine placental genes. In some embodiments, the method comprises determining the expression profile by measuring cell-free rna (cfrna) in a maternal sample. In some embodiments, the method comprises determining the expression profile by measuring placental protein in a maternal sample.

In some embodiments, the method comprises a maternal sample from blood, plasma, serum, or urine. In some embodiments, the method comprises obtaining a maternal sample from the mother during the third pregnancy of the pregnancy. In some embodiments, the method comprises obtaining a maternal sample from the mother during the second pregnancy of the pregnancy.

In some embodiments, the method comprises the steps of: comparing the expression profile to a plurality of reference profiles, wherein each reference profile is characteristic of a defined gestational age, determining which of the plurality of reference profiles corresponds to the expression profile based on the comparison, and deriving an estimated gestational age of the fetus at the time the maternal sample was obtained from the defined gestational age of the corresponding reference profile.

In a second aspect, the present disclosure provides a method for estimating the gestational age of a fetus, the method comprising the steps of: (a) obtaining a maternal expression profile of a sample comprising the expression level of the genomic set according to any embodiment of the first aspect, and (b) comparing the expression level of the genomic set to a reference expression level, wherein the reference expression level is obtained from a term delivery population, to determine whether the maternal expression profile is similar to or different from the reference expression level within the threshold value.

In some embodiments, the method comprises establishing one or more reference expression levels for the term population using machine learning techniques. In some approaches, the method further comprises obtaining a plurality of training samples, each training sample labeled as preterm or term; obtaining one or more measured expression levels of a genomic set for each of a plurality of training samples; and iteratively adjusting the one or more reference expression levels using a machine learning technique to increase the number of training samples that are correctly classified by comparing the one or more measured expression levels to the one or more reference expression levels.

In some embodiments, the method further comprises the steps of: the expression level of the genomic set is compared to other reference expression levels obtained from a preterm population to determine whether the maternal expression profile is similar to or different from the other reference expression levels within the threshold.

In a third aspect, the present disclosure provides a method for estimating gestational age of a fetus, the method comprising the steps of: (i) determining a maternal expression profile of a set comprising at least one placental RNA, and (ii) comparing the maternal expression profile to a reference profile, wherein the comparison of the maternal expression profile to the reference profile enables estimation of gestational age. In some embodiments, the gestational age of the reference profile is known. In some embodiments, the comparison of the maternal expression profile to the reference profile is performed by comparing the maternal expression profile to a gestational age function that provides gestational age based on an input of one or more expression levels, wherein the gestational age function is determined by fitting a model to a plurality of calibration samples with measured expression levels and known gestational age. In some approaches, the method uses a regression model.

In some embodiments, the method comprises the profile panel as described in any embodiment of the first aspect. In some embodiments, the method is performed by a computer.

In some embodiments, the method comprises determining a first gestational age using a first maternal sample according to the method of the first or second aspect and determining a second gestational age using a second maternal sample obtained at a later stage of pregnancy according to the method of the first or second aspect.

The method of the first aspect, wherein the expression level of each placental gene is determined by qPCR or massively parallel sequencing.

The method of the first aspect, wherein the expression level of each placental gene is determined by mass spectrometry or using an antibody array.

The method of the first, second or third aspect, wherein the expression of at least one further gene is determined and said further gene is not a placental gene.

In a fourth aspect, the present disclosure provides a composition comprising primers for multiplex amplification of at least three and no more than fifty placental genes selected from table 1.

In a fifth aspect, the present disclosure provides a kit comprising primers suitable for multiplex amplification of at least three and no more than fifty placental genes selected from table 1.

In a sixth aspect, the present disclosure provides an antibody array for detecting at least three and not more than one hundred placental proteins isolated from maternal blood or urine.

In a seventh aspect, the present disclosure provides a method for assessing the risk of preterm birth in a pregnant woman, the method comprising analyzing a maternal sample to determine an expression profile from a panel comprising one or more genes selected from table 2.

In some embodiments, the method comprises a set comprising three or more genes from table 2. In some embodiments, the method comprises a gene having an expression level that is higher in the preterm population than in the term population. In some embodiments, the method comprises a gene selected from CLCN3, DAPP1, POLE2, PPBP, LYPLAL1, MAP3K7CL, MOB1B, RAB27B, RGS18, and TBC1D15, or selected from CLCN3, DAPP1, PPBP, MAP3K7CL, MOB1B, RAB27B, and RGS 18. In some embodiments, the method comprises a panel comprising three genes selected from CLCN3, DAPP1, POLE2, PPBP, LYPLAL1, MAP3K7CL, MOB1B, RAB27B, RGS18, and TBC1D15 (ten transcript panels), or any combination of three genes selected from CLCN3, DAPP1, PPBP, MAP3K7CL, MOB1B, RAB27B, and RGS18 (seven transcript panels).

In some embodiments, the method comprises wherein the expression profile of a panel of three to ten genes is determined. In some embodiments, the method comprises wherein an expression profile is determined for a panel comprising exactly three genes.

In some approaches, the method comprises determining the expression profile by measuring cell-free rna (cfrna) in the maternal sample. In some embodiments, the method comprises determining the expression profile by measuring the protein in the maternal sample.

In some embodiments, the method comprises a maternal sample from blood, plasma, serum, or urine. In some embodiments, the method comprises obtaining a maternal sample more than 28 days prior to preterm birth. In some embodiments, the method comprises obtaining a maternal sample more than 45 days prior to preterm birth. In some embodiments, the method comprises obtaining maternal samples obtained after the second and before the eighth month of gestation. In some embodiments, the method comprises obtaining a maternal sample obtained during the second pregnancy of the pregnancy.

In some embodiments, the maternal sample is obtained during the third pregnancy of pregnancy.

In some embodiments, the method of the seventh aspect comprises obtaining a maternal sample at a specified gestational week, the method comprising the steps of: comparing the expression profile to a time-matched reference profile, wherein the time-matched reference profile is characteristic of a normal term pregnancy at a specified gestational week, and confirming that the pregnant woman is at an elevated risk of preterm birth if the expression profile is significantly different from the time-matched reference profile within the threshold.

In some embodiments, the method of the seventh aspect comprises obtaining a maternal sample at a specified gestational week, the method comprising the steps of: comparing the expression profile with a time-matched reference profile, wherein the time-matched reference profile is characteristic of preterm birth, and confirming that the pregnant woman is at elevated risk of preterm birth if the expression profile is significantly similar to the time-matched reference profile within the threshold.

In an eighth aspect, the present disclosure provides a method for assessing the risk of preterm birth in a pregnant woman, the method comprising the steps of: (a) obtaining a maternal expression profile of a sample comprising the expression level of the genomic set according to the seventh aspect of the present disclosure, and (b) comparing the expression level of the genomic set to a reference expression level, wherein the reference expression level is obtained from a preterm population, a term delivery population, or both, to determine whether the maternal expression profile is similar to or different from the reference expression level within the threshold value.

In some embodiments, the method uses machine learning techniques to establish the one or more reference levels.

In some embodiments, the method of the seventh or eighth aspect is performed by a computer.

In a ninth aspect, the present disclosure provides a method comprising performing the steps of the claims provided in the seventh or eighth aspect using two or more maternal samples obtained at different times during pregnancy.

The method of the seventh aspect, wherein the expression level of a single gene is determined by qPCR or massively parallel sequencing.

The method of the seventh aspect, wherein the expression level of each gene is determined by mass spectrometry or an antibody array.

In a tenth aspect, the present disclosure provides a composition comprising primers for multiplex amplification of at least three genes selected from table 2 and no more than one hundred different genes.

In an eleventh aspect, the present disclosure provides a kit comprising primers for multiplex amplification of at least three genes selected from table 2 and no more than one hundred different genes.

In a twelfth aspect, the disclosure provides a method of estimating time to delivery comprising analyzing a maternal sample to determine an expression profile from a panel comprising one or more placental genes.

In some embodiments, the method comprises an expression profile from a panel comprising three or more placental genes.

In some embodiments, the method comprises expression profiles from a panel comprising only placental genes.

In some embodiments, the method comprises altering the expression level of each placental gene during pregnancy. In some embodiments, the method comprises the expression level of at least one placental gene is higher during the first pregnancy than during the third pregnancy. In some embodiments, the method comprises the expression level of at least one placental gene is lower during the first pregnancy than during the third pregnancy. In some embodiments, the expression level of all placental genes is lower during the first pregnancy compared to the third pregnancy.

In some embodiments, the method comprises determining the expression profile by measuring cell-free rna (cfrna) in a maternal sample. In some embodiments, the method comprises determining the expression profile by measuring placental protein in a maternal sample.

In some embodiments, the method comprises a maternal sample from blood, plasma, serum, or urine.

In some embodiments, the method comprises obtaining a maternal sample from the mother during the third pregnancy of the pregnancy.

In some embodiments, the method comprises obtaining a maternal sample from the mother during the second pregnancy of the pregnancy.

In some embodiments, the method comprises the steps of: comparing the expression profile with a plurality of reference profiles, wherein each reference profile is characteristic of the time of labor, determining which of the plurality of reference profiles corresponds to the expression profile, and deriving an estimated time of labor at which the maternal sample was obtained based on the time of labor of the corresponding reference profile.

In a thirteenth aspect, the present disclosure provides a method for estimating labor time, the method comprising the steps of: (a) obtaining a maternal expression profile of a sample comprising the expression level of the genomic set according to any one of the embodiments of the ninth and seventh aspects, and (b) comparing the expression level to a reference expression level of the genomic set, wherein the reference expression level is obtained from a term labor population, to determine whether the maternal expression profile is similar to or different from the reference expression level within the threshold value.

In some embodiments, the method comprises establishing one or more reference levels for a term population using machine learning techniques. In some embodiments, the method is performed by a computer.

In some embodiments, the method comprises determining a first time of delivery using a first maternal sample according to the method of the twelfth or thirteenth aspect and determining a second time of delivery using a second maternal sample obtained later in pregnancy according to the method of the twelfth or thirteenth aspect.

The method of the twelfth aspect, wherein the expression level of each placental gene is determined by qPCR or massively parallel sequencing.

The method of the twelfth aspect, wherein the expression level of each placental gene is determined by mass spectrometry or an antibody array.

The method of the twelfth or thirteenth aspect, wherein the expression of at least one additional gene is determined, and said additional gene is not a placental gene.

In a fourteenth aspect, the present disclosure provides a composition comprising primers for multiplex amplification of at least three placental genes selected from table 1 and no more than one hundred different genes.

In a fifteenth aspect, the present disclosure provides a kit comprising primers for multiplex amplification of at least three genes selected from table 1 and no more than one hundred placental genes.

In a sixteenth aspect, the present disclosure provides an antibody array for detecting at least three and not more than one hundred placental proteins isolated from maternal blood or urine.

Drawings

Fig. 1A and 1B are time charts showing collection timelines of pregnant women from three different queues: denmark (fig. 1A), pennsylvania, and alabama (fig. 1B). The squares, inverted triangles and lines represent sample collection, date of labor and individual patients, respectively.

Figure 2A shows data from a representative gene expression array of placenta, immune or organ specific genes (last row). Gene-specific patient interpumary mean ± Standard Error of Mean (SEM) plotted during pregnancy (grey shading).Genes representing data available for only 21 patients.

Fig. 2B is a heat map showing the correlation between gene-specific estimated transcript counts. Genes were listed in the same order as in fig. 2A, while genes for which only data of 21 patients were available were omitted. Placental genes (rows/columns 1-20), immune genes (rows/columns 21-29) and organ-specific genes (rows/columns 30-36) are shown.

Fig. 2C-2D show solid lines and shading representing the linear fit and 95% confidence interval, respectively. Fig. 2C shows an exemplary random forest model prediction of labor time for training data (n 21, R0.91, P)<2.2x 10^-16Cross validation). Fig. 2D shows an exemplary random forest model prediction of labor time for validation data (n 10, R0.89, P)<2.2x 10^-16)。

Fig. 2E is a graph showing a comparison of expected delivery date predictions during a second pregnancy, a third pregnancy, or both, by the ultrasound or cell-free RNA methods of the invention.

Fig. 3A shows a heatmap of 40 differentially expressed genes (p <0.001) between preterm and normal delivery. RNA-Seq was performed on samples from Pennsylvania.

Fig. 3B shows an individual plot of 10 genes identified and validated in an independent cohort from alabama, which accurately predicted preterm birth using any unique combination of 3 genes from this set (set). All reported p values were calculated using fisher's exact test (FDR < 5%). Indicates significance levels below 0.05, 0.005 and 0.0005 respectively.

Fig. 3C is a graph showing the predicted performance of 10 validated preterm biomarkers in a unique combination of 3 genes from fig. 3B. Area under the curve (AUC) values are highlighted for both the discovery (pa and denmark) and validation (alabama) cohorts.

Figure 4 shows data from a representative gene expression array of placenta or immune genes. Gene-specific patient interpumary mean ± mean Standard Error (SEM) plotted during pregnancy (grey shading).Genes representing data available for only 21 patients.

Figure 5 shows that the random forest model constructed using 9 placental genes outperformed the random forest model constructed using 51 genes of placental, immune and tissue-specific organ origin to predict gestational age by Root Mean Square Error (RMSE).

Fig. 6A and 6B show solid lines and shading representing the linear fit and 95% confidence interval, respectively. Fig. 6A shows an exemplary random forest model prediction of gestational age for training data (n-21, R-0.91, P)<2.2x 10^-16Cross validation), and fig. 6B shows an exemplary random forest model prediction for gestational age of validation data (n 10, R0.90, P)<2.2x10^-16)。

Fig. 7A and 7B show solid lines and shading representing the linear fit and 95% confidence interval, respectively. Training and validation data are recorded above each graph. Random forest model prediction of gestational age and time of delivery for normal and preterm samples showed that while this model is very effective for predicting gestational age for normal delivery (RMSE ═ 4.5) and preterm delivery (RMSE ═ 4.7) (fig. 7A), it does not accurately predict time of delivery in the case of preterm delivery (RMSE ═ 10.5 weeks) (fig. 7B); while accurately predicting the time of delivery for normal delivery (fig. 7B).

FIG. 8 shows that RT-qPCR measurements are consistent with previously determined RNA-Seq values.

FIG. 9 shows a standard curve generated from C using a common set of external RNA controls developed by the external RNA control Association (ERCC)_tThe values were back-calculated to obtain C for each gene evaluated_tAnd (6) counting. Controls consisted of a pool of unlabeled polyadenylated transcripts designed to be added to the RNA analysis experiments after sample isolation and before interrogation. ERCC Spike-In Control mixtures are commercially available pre-formulated blends of 92 transcripts designed to be 250 to 2,000 nucleotides In length that mimic natural eukaryotic mRNA (e.g., ERCC RNA Spike-In Mix from Invitrogen, Calif., Cat. No. 4456740).

Fig. 10A-10D provide exemplary lists of genes that clearly differ between natural preterm (preterm) and normal labor samples using three statistical analyses.

Detailed Description

1. Introduction to the design reside in

We have discovered a set of genetic biomarkers for non-invasively predicting fetal age or time of delivery of a pregnant woman. We have also found genetic biomarkers of the orthogonal set (orthogonal set) for non-invasively predicting whether a woman is at risk for fetal preterm delivery. The discovery of a set of genetic markers for predicting fetal age or time to delivery of a fetus is of great significance, in part, because of the potential advantage of replacing ultrasound as the gold standard for predicting fetal age and thus avoiding the significant healthcare costs associated with ultrasound and sonographers. Moreover, the discovery of a collective set of genetic markers for predicting whether a woman is at risk of preterm birth is also of great significance, in part because of the potential advantages of prophylactically treating a woman at risk of preterm birth and thus eliminating the substantial healthcare costs associated with Neonatal Intensive Care Unit (NICU).

We performed a high resolution study of normal pregnancy in humans by measuring cfRNA longitudinally in the blood of pregnant women during each week of pregnancy. Analysis of tissue-specific transcripts in these samples enabled us to track the pregnancy of fetuses and placentas with high resolution and sensitivity, and to detect gene-specific responses of the maternal immune system to pregnancy. The data from this study established a "clock" for normal human pregnancy and enabled a direct molecular approach to determine the expected date of labor, with accuracy comparable to ultrasound, but at a lower cost. We have also identified an orthogonal gene set that accurately distinguishes women at risk of preterm birth up to two months prior to parturition, thereby establishing the basis for screening or diagnostic testing for risk of preterm birth.

2. Definition of

As used herein, the term "cell-free RNA" or "cfRNA" refers to RNA, particularly mRNA, expressed by cells of the mother, fetus and/or placenta and recoverable from the acellular portion of the maternal blood, and comprises fragments of full-length RNA transcripts. In some embodiments, a "cfRNA" does not comprise rRNA. In some embodiments, a "cfRNA" does not comprise a miRNA. In some embodiments, "cfRNA" refers to mRNA. Cf RNA can also be recovered from maternal urine.

As used herein, the term "placental gene", "placental gene product", "placental cfRNA" or "placental protein" refers to a gene or corresponding gene product that is expressed in the placenta, but not expressed (or expressed at significantly lower levels) by maternal or fetal tissue. There are common resources available for identifying placental genes, including databases such as tissue-specific gene expression and regulation (TiGER), which identify 377 RefSeq (NCBI reference sequence database) genes (http:// biolnfo. wilmer. jhu. edu/TiGER) that are preferentially expressed in the placenta. Other databases, such as Expression Atlas (https:// www.ebi.ac.uk/gxa/home), may also be used to identify placental genes. The placental gene products comprise mRNA and protein.

As used herein, the term "expression profile" refers to the expression level of one or more gene products obtained from a maternal sample. The gene product may be cfRNA or a protein. For gene products recovered from maternal plasma, the expression level can be expressed as the number of transcripts of a particular RNA per mL of maternal plasma, the mass of a particular polypeptide per mL of maternal plasma, the number of transcripts calculated from RNA-Seq, or any other suitable unit. Similar units can be used for gene products obtained from other maternal samples (e.g., urine). Expression of the gene product can be determined using any suitable method (e.g., as described below). The measurements are typically normalized to account for variations in sample quantity and quality, reverse transcription efficiency, and the like. When the expression profile reflects expression from multiple different gene products (e.g., different cfRNA transcripts), the gene products may be given different weights when generating or comparing the expression profile or reference profile. For example, when comparing an expression profile comprising cfRNA 1 and cfRNA 2 in a sample from a pregnant woman to a reference profile (discussed below), a 2-fold difference in cfRNA 1 value may be given greater weight than a 2-fold difference in cfRNA 2 when determining the degree of similarity or difference between the expression profile and the reference profile. Expression profiles from maternal (e.g., patient) samples are sometimes referred to as "maternal expression profiles," while maternal expression profiles in samples collected at a given time may be referred to as "[ time ] maternal expression profiles," e.g., "24-week maternal expression profiles.

As used herein, a "reference profile" is an expression profile derived from a reference population. For illustration, examples of reference populations are pregnant women, pregnant women delivered at term or pregnant women delivered prematurely. In some embodiments, the reference population is a subpopulation of pregnant women characterized by maternal age (e.g., 20-25 year old women delivered at term), race or ethnicity (e.g., african american women delivered at term). The reference profile is generated by combining the expression profiles of a statistically significant number of women in a population, and for a given gene product, may reflect the average transcript level in the population, the median transcript level in the population, or may be determined using any of a number of methods known in the epidemiological and medical arts. The reference population will typically include at least 10 subjects (e.g., 10-200 subjects), sometimes 50 or more subjects, sometimes 1000 or more subjects.

As used herein, the term "panel" refers to a collection of gene products measured in a particular assay. For example, in an assay for six (6) different cfrnas ("RNA a-F"), the six cfrnas will be a set of profiles. Likewise, in the assay for six (6) different proteins from maternal plasma or urine, these six proteins would be a spectral stack. As another illustration, in an assay where expression data for a large number of gene transcripts is collected (e.g., an entire transcriptome or a large number of placental gene transcripts), a subset used to estimate gestational age or labor time or to assess risk of preterm birth may be referred to as a spectral panel. It will be appreciated that the measured values of RNA or protein not included in the stack may be used as controls to normalize the measured values within or between samples, or for similar uses. In some embodiments, a repertoire can comprise a collection of gene products that comprises both cfRNA and protein. The spectrum stack is sometimes referred to as a "stack".

As used herein, the terms "preterm delivery," "term pregnancy," "term delivery," and "normal term pregnancy" have their normal meaning. Term refers to delivery after the fetus reaches 37 weeks gestational age, while preterm delivery refers to delivery before the fetus reaches 37 weeks gestational age. In some instances, preterm birth refers to delivery during 16 to 35 weeks gestational age or 24 to 30 weeks gestational age. The preterm population used in the study discussed below (see examples) delivered the fetus prior to 29 weeks gestational age in one case (pennsylvania cohort) and at 33 weeks gestational age in another case (alabama cohort). See fig. 1.

As used herein, "maternal sample" refers to a sample of bodily fluid obtained from a pregnant woman. The body fluid is typically serum, plasma or urine, and is typically serum. In some embodiments, samples of different body fluids may be used, such as saliva, cerebrospinal fluid, pleural effusion, and the like. Maternal samples can be obtained at various time points during pregnancy and stored (e.g., frozen) until assayed. It should be understood that the date of collection of the maternal sample is an indispensable characteristic of the sample.

As used herein, "labor time" refers to the number of weeks from a specified time (current time, date of maternal sample collection) to the date of labor or expected date of labor. The time to delivery is calculated by subtracting (gestational age at time of delivery) from (gestational age at time of sample collection).

As used herein, the terms "protein" and "polypeptide" are used interchangeably. Reference to a protein obtained from a maternal sample does not necessarily mean that the protein is the full-length gene expression product. Portions, fragments and cleavage products may be detected and confirmed according to the present invention.

3. Illustrative methods and embodiments Using cell-free RNA expression profiling

The present invention relates to the discovery of a high resolution molecular clock for fetal inoculation, and the invention of methods for determining time of delivery, gestational age of a fetus, and risk of preterm delivery. In one aspect, methods and materials for estimating gestational age or time of delivery of a fetus using expression profiles of placental genes are described. In another aspect, methods and materials for assessing the risk of preterm birth are described.

For purposes of illustration and not limitation, gestational age or labor time may be determined by: (a) generating an expression profile using cfRNA (or protein) from a maternal sample, and (b) comparing the expression profile to one or more reference profiles reflecting expression profiles characteristic of a defined gestational age. To illustrate, maternal expression profiles are compared to 37 reference profiles (characteristic of gestational age of 1 to 37 weeks) and gestational age or time of delivery is estimated based on their correlation to one of the 37 reference profiles. For purposes of illustration and not limitation, the risk of preterm birth may be determined by: (a) generating an expression profile using cfRNA (or protein) from a maternal sample, and (b) determining whether the expression profile is characteristic of a population with a history of preterm birth and/or whether the expression profile is characteristic of a population with a history of term labor. In another approach, machine learning (e.g., random forest regression, support vector machine, elastic network, lasso) is used to predict gestational age, time of delivery, and risk of preterm birth based on maternal expression profiles generated from maternal samples.

3.1 obtaining maternal samples

A maternal sample (e.g., plasma or urine) can be collected and cfRNA can be isolated from the sample immediately or after storage. See example 1 below. Methods known in the art may be employed to protect the RNA portion from degradation, including, for example, the use of special collection tubes (e.g., PAXgene RNA tubes from Preanalytix, Tempusblood RNA tubes from Applied Biosystems) or additives to stabilize the RNA portion (e.g., RNAlater from Ambion, RNAsin from Promega).

Multiple maternal samples may be collected. For example, maternal samples may be collected once per pregnancy, or monthly for a certain period of time during pregnancy (e.g., 3-8 months). When noted, maternal samples may be collected more frequently. For example, gestational age or delivery time may be monitored frequently (e.g., once every two weeks) as a means of monitoring fetal health.

As another example, a woman identified as at risk of preterm birth at 24 weeks may choose to take an assay every two weeks to monitor risk. In the case of intervention to avoid preterm birth (e.g., progesterone supplementation), a maternal sample can be obtained after initiation of the intervention to assess whether the intervention has altered the maternal expression profile. Significantly, the method of the invention can be used to accurately distinguish women up to two months prior to delivery, i.e. at risk of preterm birth. See example 6. In some embodiments of the invention, maternal samples are obtained more than 28 days prior to preterm birth. In some embodiments of the invention, maternal samples are obtained more than 45 days prior to preterm birth. In some embodiments, maternal samples are obtained after the second and before the eighth month of pregnancy. In some embodiments, the maternal sample is obtained during the second pregnancy of the pregnancy. In some embodiments, the maternal sample is obtained during the third pregnancy of pregnancy. As discussed above, in many cases, maternal samples can be obtained and assayed more than once during the course of a pregnancy.

3.2 isolation of cfRNA

Cell-free RNA can be isolated from maternal samples using techniques well known in the art. See example 1 below. Isolation of cfRNA from blood or blood fractions is described in Qin et al, BMC res. 380(2013) and Mersy et al, clin. chem.,61(12)1515-23(2015), both incorporated herein by reference. Kits for isolating cfRNA from Blood are known and commercially available [ e.g., PaxGene Blood RNA kit (Qiagen, Cat. No. 762164)]. Kits for isolating cfRNA from plasma/serum are known and commercially available [ e.g., plasma/serum RNA purification kit from Norgen Biotek, canada, catalog No.: 56900, and Quick-cfRNA from Zymo Research^TMSerum&Plasma, catalog No.: r1059; NextPrep Magnazol cfRNA isolation kit (bioscientistic); Quick-cfRNA^TMSerum&Plasma kit (Zymo Research) andcirculating nucleic acid kit (Qiagen)]。

The isolation of cfRNA from urine has been described (see, e.g., Zhao et al, 2015, int.J. cancer, 1; 136(11):2610-5, incorporated herein by reference, describing the use of cfRNA for the confirmation of biomarkers and monitoring disease conditions). Kits for isolating cfRNA from urine are known and commercially available (e.g., urine cell-free circulating RNA purification kit from Norgen Biotek, canada, cat # 56900).

3.3 quantification of cfRNA transcripts

Quantification of specific transcripts in cell-free RNA samples can be accomplished in a variety of ways, including, but not limited to, array-based methods, amplification-based methods (e.g., RT-qPCR), and high-throughput sequencing (RNA-Seq). The method of the present invention is not limited to a specific quantitative method.

3.3.1RT-qPCR assay

The RT-qPCR assay is described in example 1 below. Briefly, RNA is transcribed from total RNA or messenger RNA (mrna) to complementary dna (cdna) by reverse transcriptase. Alternatively, cDNA is generated using template-specific primers specific for the selected RNA transcript (e.g., one or more of SEQ ID NOS: 1-19). The cDNA was then used as template for the qPCR reaction.

RT-qPCR can be performed in a one-step or two-step process. The one-step assay uses reverse transcriptase and DNA polymerase in combination with reverse transcription and PCR in a single tube and buffer. One-step RT-qPCR uses only sequence-specific primers. In a two-step assay, the reverse transcription and PCR steps are performed in separate tubes using different optimization buffers, reaction conditions and primer strategies [ e.g. using random primers, oligo- (dT) or sequence specific primers in reverse transcription followed by sequence specific primers in the qPCR step ]. As mentioned above, it is clear that the RT-qPCR referred to herein comprises one or two steps of RT-qPCR assays.

RT-qPCR can be performed using various buffers and optimization methods. See example 1 below. The isolation of cfRNA from blood and subsequent analysis by RT-qPCR is known in the art (see, e.g., U.S. patent publication No. 20140199681, incorporated herein by reference). Kits for performing one-step RT-qPCR are known and commercially available [ e.g., TaqPath^TM1 step RT-qPCR Master Mix, CG (Thermo Fisher Scientific, Cat. No. A15299)]. Kits for performing two-step RT-qPCR are known and commercially available [ e.g., Maxima First Strand cDNA Synthesis kit for RT-qPCR (Thermo Fisher Scientific, Cat. No. K1641)]。

3.3.2RNA-Seq assay

RNA-Seq (RNA sequencing) assays, also known as whole transcriptome shotgun sequencing, reveal the presence and quantity of RNA in a sample at a given time point using Next Generation Sequencing (NGS) [ see Zhong et al nat. rev. gen.10(1): 57-63 (2009), which is incorporated herein by reference). The RNA-Seq assay is described in example 1 below. RNA-Seq facilitates the observation of changes in gene expression over time or differences in gene expression among different groups or treatments [ see Maher et al Nature.458(7234): 97-101 (2009), which is incorporated herein by reference ].

An exemplary method of analyzing cfRNA isolated from a maternal body fluid sample is set forth below. Briefly, cfRNA is isolated from a maternal sample, e.g., using sequence-specific primers, oligo (dt), or random primers, to generate cDNA molecules. In one method, cDNA is generated using template-specific primers specific for a selected RNA transcript (e.g., corresponding to one or more of the genes listed in tables 1 and 2; SEQ ID NOS: 1-19). The cDNA molecules can be fragmented and optimized such that sequencing adapters are added to the 3 'and 5' ends of the cDNA molecules to generate a sequencing library. cfRNA typically does not require fragmentation. The optimized cDNA was then sequenced using the NGS sequencing platform. Suitable kits for amplifying cDNA and analyzing sequencing products according to the methods of the invention include, for example, the ovation RNA-Seq system (NuGen). Other methods of preparing RNA-Seq libraries for sequencing platforms are known, for example, Podnar et al, 2014, "Next-Generation sequencing RNA-Seq Library Construction" Current protocol Mol biol.2014, 14 months 4; 106:4.21.1-19.doi:10.1002/0471142727.mb0421s 106; schuierer et al, 2017, "expression of RNA-Seq protocols for degraded and low-yield analysis. BMC genetics.2017, 6 months and 5 days; 18(1) 442.doi 10.1186/s 12864-017-3827-y; hrdlickova R,2017, RNA-Seq methods for transcriptome analysis, WileyInterdiscip Rev RNA.2017, 1 month; 8(1), doi:10.1002/wrna.1364), which is incorporated herein by reference in its entirety.

Sequencing libraries suitable for use with RNA-Seq assays can comprise cDNA derived from cfRNA isolated from a maternal sample. It will also be apparent that the sequencing library may comprise cDNA derived from other RNA species (e.g., mirnas) that may be collected during total RNA isolation rather than cfRNA isolation procedures. Thus, partial or complete transcriptome analysis of RNA content obtained from maternal samples may be performed. In one embodiment, it is preferred that only cfRNA obtained from a maternal sample is used as input material for the preparation of cDNA suitable for RNA-Seq.

3.4 Spectrum set

The inventors have found that certain combinations of gene products are particularly useful in the practice of the present invention. That is, it has been determined that certain combinations of gene products are sufficient or preferred to provide an accurate estimate of gestational age, time of delivery, or the likelihood of predicting preterm birth. For example, as described in example 4, a subset of 9 placental genes has greater predictive power in estimating gestational age or time of delivery than a larger genomic set.

It should be understood that while certain features of the stack are discussed in this section, the invention is not limited to these specifically described embodiments. It will also be appreciated that although this section describes the kits by reference to cfRNA transcript expression, kits based on the expression levels of circulating proteins encoded by those gene sets may also be used to determine gestational age or time of delivery and identify women at risk for preterm birth. See section 4 below.

In some methods, a plurality of different sets of profiles are used during pregnancy of a woman. For example, a first spectrum set may be used during a second pregnancy, while a different spectrum set may be used during a third pregnancy.

3.4.1 Spectrum suite for determining gestational age or delivery time

In one aspect, the invention provides a method for estimating gestational age or time of delivery of a fetus by analyzing a maternal sample to determine an expression profile of placental genes (e.g., cfRNA or proteins encoded by the placental genes). An appropriate stack may be selected based on the information provided in the present disclosure. In one embodiment, the panel comprises 1, at least 2, or at least 3 placental genes. In some embodiments, the panel can comprise at least 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 placental genes. In some embodiments, a panel of profiles can comprise exactly 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 placental genes. In some embodiments, the panel comprises less than 100 genes, e.g., less than 100 placental genes, sometimes less than 50 placental genes, sometimes less than 20 placental genes, sometimes less than 15 placental genes, sometimes less than 10 placental genes, and sometimes less than 5 placental genes.

In some embodiments, the expression level of each placental gene in the repertoire of profiles is altered during pregnancy. See the examples below. Thus, in one embodiment, the expression level of at least one placental gene in the set of the first pregnancy is higher compared to the third pregnancy. In some embodiments, the expression level of most or all placental genes in the set of first pregnancy is higher compared to the third pregnancy. In some embodiments, the expression level of the at least one placental gene is lower in the first pregnancy compared to the third pregnancy. In some embodiments, the expression level of most or all placental genes in the set of first pregnancy is lower compared to the third pregnancy.

In some embodiments, the at least one placental gene is selected from the genes in table 1. In some embodiments, all placental genes in the repertoire are the genes listed in table 1.

In some embodiments, the expression profile comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, or 9 genes selected from CGA [ SEQ ID NO:1], CAPN6[ SEQ ID NO:2], CGB [ SEQ ID NO:3], ALPP [ SEQ ID NO:4], CSHL1[ SEQ ID NO:5], PLAC4[ SEQ ID NO:6], PSG7[ SEQ ID NO:7], PAPPA [ SEQ ID NO:8], and LGALS14[ SEQ ID NO:9 ]. In some embodiments, the expression profile comprises 1, 2, 3, 4,5, 6, 7, 8, or 9 genes selected from CGA [ SEQ ID NO:1], CAPN6[ SEQ ID NO:2], CGB [ SEQ ID NO:3], ALPP [ SEQ ID NO:4], CSHL1[ SEQ ID NO:5], PLAC4[ SEQ ID NO:6], PSG7[ SEQ ID NO:7], PAPPA [ SEQ ID NO:8], and LGALS14[ SEQ ID NO:9 ]. In one method, the collection of placental genes comprises at least one gene other than CGA and CGB. In one method, the panel of spectra comprises three (3) to nine (9) cfRNAs selected from SEQ ID NOS: 1-9.

In one embodiment, gestational age is determined using a panel of panels of the following 9 genes: CGA, CAPN6, CGB, ALPP, CSHL1, PLAC4, PSG7, PAPPA and LGALS 14. We trained several different models for a subset of women (i.e., non-pregnant or multiparous women, women carrying male or female births) to determine the importance of the 9 genes that constitute the confirmed transcriptome signatures. 4 different models trained on women carrying either male or female foetuses and non-or multi-parted women revealed that 2 of the 9 genes identified in the text were sufficient to predict the time of delivery for women carrying either male (CGA, CSHL1) or female (CGA, CAPN6) and multi-foetal (CGA, CSHL 1). However, all 9 genes are essential for optimal prediction of the time until delivery of the non-parturient woman, highlighting the importance of the identified transcriptome signatures. In some embodiments of the invention, the kit comprises CGA and CSHL1 or CGA and CAPN 6.

The model weights the nine transcripts used to predict gestational age in the following order of importance (from highest to lowest): CGA, CAPN6, CGB, ALPP, CSHL1, PLAC4, PSG7, PAPPA and LGALS 14. Thus, in some embodiments, a determined individual gene expression level is given a different weight (or coefficient) when compared to expression in a reference profile. For example, when all 9 genes or a subset comprising less than 9 (e.g., 2, 3, 4,5, 6, 7, or 8) genes are included in the group, the expression values for each gene are ranked CGA > CAPN6> CGB > ALPP > CSHL1> PLAC4> PSG7> papa > LGALS 14.

In one embodiment, the panel comprises 1, at least 2, or at least 3 genes from table 1. In some embodiments, the panel of profiles can comprise at least 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 genes from table 1. In certain embodiments, a panel of profiles can comprise exactly 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 genes from table 1. In certain embodiments, the panel comprises less than 100 genes, sometimes less than 50 genes, sometimes less than 20 genes, sometimes less than 15 genes, sometimes less than 10 genes, and sometimes less than 5 genes. In some methods, the panel of genes includes a gene number in the range of 1-100 genes, 1-50 genes, 1-25 genes, 3-100 genes, 3-50 genes, 3-25 genes, or 3-10 genes.

In some embodiments, the placental genes are selected from the genes in table 1. In some embodiments, the placental gene is selected from the group consisting of CGA, CAPN6, CGB, ALPP, CSHL1, PLAC4, PSG7, PAPPA, and LGALS 14. In some embodiments, the gene comprises at least one gene other than CGA. In some embodiments, the gene comprises at least two, three, four, five, six, seven or eight genes other than CGA. In some embodiments, the gene comprises at least one gene other than CGB. In some embodiments, the gene comprises at least two, three, four, five, six, seven or eight genes other than CGB. In some embodiments, the gene comprises at least one gene other than CGA and CGB. In some embodiments, the method comprises determining the expression profile of three (3) to nine placental genes.

3.4.2 Spectrum suite for determining the risk of preterm birth

In one aspect, the present invention provides a method of estimating the risk of preterm birth by analyzing a maternal sample to determine an expression profile. In one embodiment, the panel for such determination comprises one or more cfRNA transcripts having a higher expression level in the preterm population than in the term population. In one embodiment, the preterm population refers to a group of women who delivered a fetus prior to 37 weeks gestational age. In another embodiment, the preterm population is a woman who delivered a fetus 33 weeks before gestational age. In another embodiment, the preterm population is a woman who delivered a fetus prior to 29 weeks gestational age. In yet another embodiment, the preterm population is a woman who delivered a fetus between 12 and 33 weeks gestational age. In another embodiment, the preterm population refers to a group of women who delivered a fetus between 16 and 29 weeks gestational age. In one embodiment, the preterm population refers to a group of women who delivered a fetus between 16 and 33 weeks gestational age. As mentioned above, one preterm delivery population used in the examples consists of women who delivered a fetus before 29 weeks gestational age, and this population (or a sub-population thereof) is preferably used for the preparation of a reference profile that is characteristic of high risk of preterm delivery. The examples also show that biomarkers found in a population of women who delivered a fetus before 29 weeks are useful as biomarkers for a population of women who delivered before 33 weeks gestational age.

In one method, the panel of profiles comprises 1 or more genes, preferably 3 or more genes, listed in table 2.

In one method, the repertoire comprises three (3) or more genes selected from the ten transcript repertoires CLCN3[ SEQ ID NO:10], DAPP1[ SEQ ID NO:11], POLE2[ SEQ ID NO:12], PPBP [ SEQ ID NO:13], LYPLAL1[ SEQ ID NO:14], MAP3K7CL [ SEQ ID NO:15], MOB1B [ SEQ ID NO:16], RAB27B [ SEQ ID NO:17], RGS18[ SEQ ID NO:18], and TBC1D15[ SEQ ID NO:19 ]. In one method, the panel of profiles includes three (3) or more genes. In one method, the panel of profiles comprises three (3) or more genes selected from SEQ ID NOS: 10-19. In one method, the panel of panels comprises exactly three (3) genes selected from SEQ ID NOS: 10-19. In some embodiments, the set includes only genes selected from SEQ ID NOs 10-19. For example, in various embodiments, a spectral stack will include the following combinations: (i) CLCN3, DAPP1, POLE 2; (ii) DAPP1, poll 2, PPBP; (iii) POLYLE 2, PPBP, LYPLAL 1; (iv) PPBP, LYPLAL1, MAP3K7 CL; (v) LYPLAL1, MAP3K7CL, MOB 1B; (vi) MAP3K7CL, MOB1B, RAB 27B; (vii) MOB1B, RAB27B, RGS 18; and (viii) RAB27B, RGS18, TBC1D 15. It will be appreciated that a complete list of combinations of 3 genes selected from SEQ ID NOs 10-19 is readily generated and that this paragraph is intended to convey possession of each of the 3 combinations of genes.

In one method, the repertoire comprises three (3) or more genes selected from seven transcript repertoires CLCN3[ SEQ ID NO:10], DAPP1[ SEQ ID NO:11], PPBP [ SEQ ID NO:13], MAP3K7CL [ SEQ ID NO:15], MOB1B [ SEQ ID NO:16], RAB27B [ SEQ ID NO:17], and RGS18[ SEQ ID NO:18 ]. In one method, the panel of profiles includes three (3) or more genes. In one method, the panel of profiles comprises three (3) or more genes selected from SEQ ID NOs: 10, 11, 13, and 15-18. In one method, the panel of profiles comprises exactly three (3) genes selected from SEQ ID NOs 10, 11, 13, and 15-18. In some embodiments, the set includes only genes selected from SEQ ID NOs 10, 11, 13, 15, and 16-18.

In one approach, the panel of profiles comprises exactly three genes selected from table 2. In one approach, the panel of spectra includes exactly three genes selected from SEQ ID NOs 10-19. In one approach, the panel of spectra includes exactly three genes selected from SEQ ID NOs 10, 11, 13, 15, and 16-18.

The model weights seven transcripts for identifying women at higher risk or preterm birth in the following order of importance (highest to lowest): RAB27B > PPBP > DAPP1> RGS18> (MOB1B, MAP3K7CL and CLCN3), with MOB1B, MAP3K7CL and CLCN3 ranked equally. Thus, in some embodiments, a determined individual gene expression level is given a different weight (or coefficient) when compared to expression in a reference profile. For example, when all 7 genes or a subset of less than 7 genes (e.g., 2, 3, 4,5, 6) are included in the group, the expression values for each gene are ranked as RAB27B > PPBP > DAPP1> RGS18> (MOB1B, MAP3K7CL, and CLCN 3).

In one aspect, the invention provides a method of determining the risk of preterm birth by analyzing a maternal sample to determine the expression profile of a set of genes (e.g., cfRNAs or proteins) listed in Table 2 (e.g., SEQ ID NOS: 10, 11, 13, 15, and 16-18). In one embodiment, the panel comprises at least 1, at least 2, or at least 3 genes from table 2. In some embodiments, the panel of profiles can comprise at least 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 genes from table 2. In certain embodiments, a panel of profiles can comprise exactly 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 genes from table 2. In certain embodiments, the panel includes less than 100 genes, sometimes less than 50 genes, sometimes less than 20 genes, sometimes less than 15 genes, sometimes less than 10 genes, and sometimes less than 5 genes. In some methods, the panel of genes includes a number of genes in the range of 1-100 genes, 1-50 genes, 1-25 genes, 3-100 genes, 3-50 genes, 3-25 genes, or 3-10 genes. In one approach, at least one gene in the repertoire is not listed in FIG. 3A and/or FIG. 3B and/or FIG. 4 of U.S. patent publication No. 2013/0252835.

In one method, a maternal sample is obtained at a specified gestational week and the maternal expression profile is compared to a time-matched reference profile, wherein the time-matched reference profile is characteristic of a term pregnancy profile at the specified gestational week. In one method, a maternal sample is obtained at a specified pregnancy (e.g., first, second, or third pregnancy) of the pregnancy and the maternal expression profile is compared to a time-matched reference profile, where the time-matched reference profile is characteristic of a term pregnancy profile at the specified pregnancy of the pregnancy. Significant deviation of the maternal profile from the reference profile indicates that the woman is at elevated risk of preterm birth. It is immediately apparent that in an alternative method, a maternal sample is obtained at a given gestational week and the maternal expression profile is compared to a time-matched reference profile, wherein the time-matched reference profile is characteristic of the preterm delivery profile at the given gestational week. Significant similarity between the maternal and reference profiles indicates that the woman is at elevated risk of preterm birth. In one approach, a machine learning model is used to compare the maternal and reference spectra.

4. Illustrative methods and embodiments Using expression of circulating proteins

4.1 isolation of proteins from maternal blood or urine

Proteins can be isolated from maternal samples using methods well known in the art. In one method, whole proteins are derived from a maternal blood fraction or urine, and the presence and/or amount of a particular protein is determined. In one method, the protein fraction (e.g., a fraction enriched in the protein of interest) is used for the assay. In one method, the assay is performed using one or more purified proteins. The separation and fractionation of proteins is performed by fractionation using methods such as molecular weight, protein charge, solubility/hydrophobicity, isoelectric point (pI) of the protein, affinity purification (e.g., using antisense ligands such as antibodies or aptamers specific to the protein), and the like. Kits for isolating proteins from blood are known and commercially available (e.g., whole protein assay kit from ITSIbiosciences, cat. No.: K-0014-20). Kits for separating proteins from plasma/serum are known and commercially available [ e.g., antibody serum purification kit from Abcam (protein a), cat No.: ab109209 ]. Kits for isolating proteins and RNA from samples are also known [ e.g., protein and RNA isolation system (PARIS) from Thermo Fisher Scientific, catalog No. AM1921 ].

4.2 detection of proteins from maternal samples

Well known methods may be used to confirm and/or quantify a particular protein from a maternal sample, including enzyme-linked immunosorbent assays (ELISAs); radioimmunoassay (RA) [ see, e.g., Anthony et al, Ann. Clin. biochem.,34: 276. 280(1997), which describes the detection of difficult to detect low levels of proteins using comparable ELISA conditions, incorporated herein by reference ]; ortho-ligation and ortho-extension assays (see, e.g., U.S. patent publication nos. 20170211133; 20160376642; 20160369321; 20160289750; 20140194311; 20140170654; 20130323729; and 20020064779, incorporated herein by reference), protein binding arrays (e.g., antibody or aptamer arrays), Mass Spectrometry [ see, e.g., Han, x. et al (2008), incorporated herein by reference. Mass Spectrometry for protein Opinion in Chemical Biology,12(5), 483-490. http:// doi.org/10.1016/j.cbpa.2008.07.024; serang, O et al (2012), Areview of static methods for protein identification using a standard mass spectrometry, statistics and ItsInterface,5(1), 3-20, herein incorporated by reference. Any suitable method may be used.

Protein-binding arrays can be used to detect and quantify proteins, including but not limited to antibody-Based arrays and Aptamer-Based arrays (see, e.g., Gold L, et al (2010) Aptamer-Based Multiplexed technology for Biomarker discovery. plos ONE5(12): e15004.https:// doi. org/10.1371/journal. point. 0015004, incorporated herein by reference). An antibody array (also known as an antibody microarray) is a particular form of protein array. In this technique, a series of capture antibodies are immobilized on a solid surface such as glass, plastic, membrane or silicon chip, and the interaction between the antibodies and their target antigens is detected (see, e.g., U.S. Pat. Nos. 4,591,570; 4,829,010; and 5,100,777, which are all incorporated herein by reference). Antibody arrays can be used to detect Protein expression from various biological fluids including serum, plasma, urine, and cell or tissue lysates [ see Knickerbocker T., MacBeath G.detecting and Quantifying Multiple Proteins in clinical Samples in High-through Using Antibody Microarray. in: Wu C. (eds.) Protein Microarray for analysis. methods in Molecular Biology (methods and Protocols), Vol.723. Humana Press (2011), incorporated herein by reference ].

Kits for performing antibody arrays are known and commercially available (e.g., custom antibody arrays or predetermined antibody arrays from noctilus RayBiotech, georgia).

5. Statistical analysis

The maternal expression profile can be compared to the reference profile in a variety of ways. In one approach, a comparison between two data sets is performed to determine whether one data set is different or similar (e.g., within a statistical significance range) to the other data set. In one embodiment, the first data set can include maternal expression profiles and the second data set can include reference profiles, where the first data set and the second data set comprise one or more data points (e.g., median values) of gene expression data for one or more genes collected at one or more time points during pregnancy (e.g., once per week or once per pregnancy during the course of pregnancy). In some embodiments, the second data set comprises a plurality of data points from a preterm maternal sample or a maternal sample with a known gestational age.

Thus, a maternal data set may be a measure of the expression level of one or more genes, where the expression level may be determined from the individual expression values of each gene, for example as an average, weighted average or median of the individual expression levels. In other embodiments, the individual expression levels can be considered as different dimensions of the multi-dimensional data points, e.g., for clustering. To determine gestational age or time of delivery, a comparison may be made between the measured maternal sample expression level and a reference expression level of a plurality of references with different known gestational ages to identify the closest cohort or representative data point (e.g., the smallest difference in gap between the measured expression level and the reference expression level). The known gestational age of the closest reference sample (or representative data points of a group of reference samples all having the same gestational age) may be used as the gestational age or time of delivery of the maternal sample. Such comparison may be performed by comparing the measured expression levels to a pregnancy function determined from a reference sample, such as a linear function defining a functional relationship between the expression levels (e.g., a three-dimensional space where the individual expression levels correspond to different dimensions, or a two-dimensional plot where the individual expression levels are combined to provide a single metric).

In embodiments where a term sample and a preterm sample are distinguished, the comparison may involve determining whether the measured expression level is more similar to a preterm reference level or a term reference level. Such comparison may involve determining which reference level cluster is closest to the measured expression level. One or more values may be used to determine whether the measured expression levels are sufficiently close (e.g., as measured by gaps or weight gaps, where differences along one dimension are weighted differently) to be considered the measured levels of a portion of a full term or preterm sample cluster. If the expression levels are not close enough, uncertain classification may result. The threshold value may be used to determine whether the measured expression level is sufficiently close to a reference expression level for a term or preterm population. As will be apparent to those skilled in the art, the threshold may be selected based on the desired sensitivity and specificity.

To determine the reference level, the training sample set may be labeled with different classifications (e.g., term or preterm delivery). The reference level may then be selected as a value representing the classification or as a value separating different classifications, e.g. as a cutoff value assigning different classifications to new samples. Machine learning techniques can analyze different expression levels of different genes to determine which set of expression levels (features) provides the best discrimination against an optimized reference level set. The trade-off between specificity and sensitivity can be optimized, for example, by the ROC (receiver operating characteristic) curve. In some embodiments, a plurality of training samples may be obtained, each training sample being labeled as preterm or term. In some embodiments, the training sample is labeled as non-productive, multiparous women, carrying male tires, carrying female tires, and the like. For each of a plurality of training samples, one or more measured expression levels of the genomic suite may be obtained. Using machine learning techniques (e.g., by optimizing a cost function defined by the model), one or more reference expression levels can be iteratively adjusted to increase the number of training samples that are correctly classified as a result of comparing one or more measured expression levels to the one or more reference expression levels.

In some aspects, the first data set and the second data set may be analyzed to establish a relative difference or similarity (e.g., fold increase or fold decrease) between the data sets (e.g., expression levels of the data sets). Such a procedure may be performed when determining a single expression level of a genomic set. On the other hand, a pairwise comparison of the expression levels of each gene at each time point throughout the pregnancy can be used to identify which reference levels are most similar, where each set of reference levels can correspond to a different gestational age. In some embodiments, the pair-wise comparison (e.g., between expression levels of different genes and/or between reference levels at different times) may comprise statistical analysis via a series of statistical methods, including but not limited to fisher's exact test, Wilcox rank test, permutation test, linear regression, generalized linear model, and pseudo-likelihood test, in combination with appropriate multiple hypothesis correction (e.g., Benjamini Hochberg).

In one embodiment, differentiating gene activity throughout pregnancy (e.g., between preterm and term maternal samples, see example 1 and fig. 11A-11D) may comprise a conditional maximum likelihood approach using quantile adjustment, a Generalized Linear Model (GLM) likelihood ratio test, and/or a pseudo-likelihood F test, which is performed in R using edgeR software (Bioconductor, available from htps:// Bioconductor.

Alternatively, the sample dataset may be analyzed using a random forest model generated using the second dataset [ see, e.g., Chen and Ishwaran, Genomics,99: 323-. See the examples. Random forest is a form of machine learning that randomly selects a training set to build multiple models (e.g., decision trees or regression models) and uses the outputs of such a full set of models to determine the final output (e.g., via majority voting on term/preterm classifications or averages in determining gestational age or time of delivery). Each model may have the same or different characteristics (e.g., expression levels of genes), but different reference levels as determined from randomly selected different training sets. It will be appreciated that other machine learning techniques may be used to compare the two data sets, including but not limited to support vector machines, elastic networks, lasso, or neural networks. It will also be apparent that Machine Learning models (e.g., supervised Machine Learning; see, e.g., Mohri et al (2012), reasons of Machine Learning, The MITPress, incorporated herein by reference) can be developed to explain certain attributes of a population, such as ethnicity, and that a variety of models (e.g., eastern european model and north african model) can be prepared based on different needs.

In one aspect, a machine learning model may be prepared as follows (e.g., to predict gestational age or labor time):

(1) constructing a training set of markers (e.g., knowing the gestational age of each sample);

(2) iterating (e.g., recursive feature selection) by selecting a target feature;

(3) building a regression model (e.g., a random forest) based on the selected features; and

(4) the regression model and feature subset are selected using the cross-validation data (e.g., the accuracy of the retained data is evaluated by retaining a portion of the training set and determining the regression model).

In one embodiment, once the regression model is prepared, it can be stored and used for future data interpretation. In other embodiments, a single regression model may be determined, for example, by fitting a straight line or curve to a set of measured expression levels measured at known gestational age. For example, when a model (e.g., a linear or non-linear function) is fitted to the expression levels of a plurality of calibration samples having measured expression levels and known gestational age, the regression model may be considered to be a pregnancy function. Thus, comparison of maternal expression profiles to reference profiles may be performed by comparing maternal expression profiles to a pregnancy function that provides gestational age based on input of one or more expression levels.

Alternatively, the first and second datasets can be analyzed using SAMS (molecular sub-phenotype scoring algorithm) available from http:// statweb. stanford. edu/. about. tibs/SAM/[ see Tusher et al, PNAS,98: 5116-. SAMS is a classification algorithm for gene expression data generated by the calculation of two scores (e.g., an upper score and a lower score). In one embodiment, a maternal expression profile dataset of the invention (e.g., cfRNA) can be compared to a reference expression profile dataset, and maternal samples with an upper score above the median value (compared to the reference expression profile) and a lower score above the median value (compared to the reference expression profile) can be classified as statistically significant [ see, e.g., Herazo-Maya, Lancet Respir Med, 20 months 9, (2017) doi: org/10.1016/S2213-2600(17)30349-1 and Dinu et al, BMC Bioinformatics,8:242(2007), both incorporated herein by reference ]. Additional evaluations of the first and second data sets using SAMS may be performed according to the SAMS user Manual (available from http:// www-stat. stanford. edu/. tbs/SAM/SAM. pdf).

There are various additional statistical analyses for comparison of first and second datasets against gene expression data (e.g., preterm dataset versus mother body sample), including, for example, the methods set forth by Efren and Tibshirani [ On Testing the signaling of Sets of genes. an. appl. Stat.,1.107-129(2007) ], and Zhao et al [ genetic expression profiling summary in comparative cell cartioma, PLOS Medicine,3.E13.13.10.1371/journal. pmed.0030013.(2006), both incorporated herein by reference.

As discussed above, the maternal expression profile can be compared to a reference profile, and a measure of similarity or difference can be made. In one method, comparing the maternal expression profile to a reference profile comprises compiling gene expression data (e.g., number or relative number of transcripts of a specified cfRNA sequence on a computer readable medium) and processing the data on the computer to confirm the degree of similarity and difference between the profiles.

6. Medical intervention in women at risk of preterm birth

Women identified as at risk for preterm birth may choose medical intervention (e.g., progesterone supplementation, cervical cerclage), behavioral modification (smoking cessation), or ultrasound imaging to monitor and reduce the likelihood of preterm birth or to extend the term of pregnancy as much as possible. See Newnham et al, "Strategies to Present Preterm birth," Frontiers in Immunology 5(2014):584, incorporated herein by reference. Progesterone is useful for the treatment and/or prevention of the onset of preterm labor in women identified as at risk for preterm labor. In some embodiments, progesterone may be administered to a pregnant woman, for example, as a vaginal gel in an amount sufficient to prolong pregnancy by delaying cervical shortening or sloughing (effacing). The frequency of administration may be once weekly or 4 times daily. For certain women with preterm precursors, namely premature rupture of membranes (PROM), antibiotic therapy (amoxicillin, ampicillin, erythromycin, azithromycin, and cephalosporin) is recommended, and may be administered to women identified as at risk for preterm birth. When a woman is identified as at risk for preterm birth, the healthcare provider may recommend that at least one ultrasound exam be performed every four weeks, every two weeks, or every week.

7. Therapeutic and prognostic use of the invention for women at risk of preterm birth

In some embodiments, the methods described herein are for treatment. In one method, a first maternal expression profile is obtained from a woman at risk of preterm birth at a first time point, a medically appropriate step (e.g., a medical intervention) is initiated or performed, and then a second maternal expression profile is obtained from the woman at a second time point. Each maternal expression profile is compared to an appropriate reference profile (e.g., time-matched, population-matched, etc.). If the difference between the second maternal expression profile and the appropriate corresponding reference profile is less than the difference between the first maternal expression profile and its appropriate corresponding reference profile, it is an indication that the step performed has a beneficial therapeutic effect. In some cases, the first and second maternal expression profiles are compared to the same reference profile. In one approach, the procedure can be performed without any medical intervention, in which case a natural improvement can be observed.

In some embodiments, the methods described herein are used for prognosis. It is believed that certain maternal expression profiles are indicative of a particular prognosis. For example, certain maternal expression profiles can be used to estimate the time to preterm delivery (without intervention). Reference profiles for this purpose can be generated from sub-populations grouped by specific pregnancy outcome (date of preterm birth), by genetic risk, or by phenotypic factors such as age and previous pregnancy history. The methods disclosed herein may also be used to confirm and monitor a fetus with a congenital defect; in some cases, the method can be used to inform decisions regarding intrauterine treatment. Maternal expression profiles can be used to estimate the time of delivery and gestational age of the fetus, and the results can be used to provide recommendations or treatment for the mother or fetus. Similarly, with appropriately selected genes, such profiles can be used to estimate the risk of adverse events such as preterm birth.

8. Computer-implemented method & reference value database

The method of the present invention may be implemented using a computer-based system. As used herein, "computer-based system" refers to hardware devices, software devices, and data storage devices used to analyze the information of the present invention. The minimal hardware of the computer-based system of the present invention comprises a Central Processing Unit (CPU), input devices, output devices, and data storage devices. Those skilled in the art will readily appreciate that any computer-based system currently available is suitable for use with the present invention. The data storage device may comprise any article of manufacture including a recorded article of manufacture of current information as described above, or a memory access device that can access such an article of manufacture.

In some embodiments, a database comprising reference spectra is used in the methods of the invention. In some embodiments, a database is provided comprising expression data from a plurality of women, and optionally different subpopulations of women. Accordingly, aspects of the present invention provide systems and methods for the use and development of databases. In some approaches, the database is used in conjunction with an algorithm that enables the generation of new reference spectra based on the feature selection of individual women.

Any computer system mentioned herein may utilize any suitable number of subsystems. In some embodiments, the computer system comprises a single computer device, wherein the subsystems may be components of the computer device. In other embodiments, a computer system may contain multiple computer devices with internal components, each computer device being a subsystem. Computer systems may include desktop and laptop computers, tablets, mobile phones, and other mobile devices.

A computer system may contain multiple identical components or subsystems, connected together through an external interface, through an internal interface, or via removable storage that may be connected or removed from one component to another. In some embodiments, computer systems, subsystems, or devices may communicate over a network. In this case, one computer may be considered a client and another computer may be considered a server, where each computer may be part of the same computer system. A client and server may each comprise multiple systems, subsystems, or components.

Aspects of the embodiments may be implemented in the form of control logic using hardware circuitry (e.g., an application specific integrated circuit or a field programmable gate array) and/or using computer software with a generally programmable processor, in a modular or integrated manner. As used herein, a processor may include a single-core processor, a multi-core processor on the same integrated chip, or multiple processing units on a single circuit board or networked, as well as dedicated hardware. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will know and appreciate other ways and/or methods to implement embodiments of the present invention using hardware and a combination of hardware and software.

Any of the software components or functions described in this application may be implemented as software code to be executed by a processor using, for example, conventional or object-oriented techniques using any suitable computer language such as Java, C + +, C #, Objective-C, Swift, or a scripting language such as Perl or Python. The software code may be stored on a computer readable medium as a series of instructions or commands for storage and/or transmission. Suitable non-transitory computer readable media may include Random Access Memory (RAM), Read Only Memory (ROM), magnetic media such as a hard drive or floppy disk, or optical media such as a Compact Disc (CD) or DVD (digital versatile disc), flash memory, and the like. A computer readable medium may be any combination of such storage or transmission devices.

Databases may be provided in various forms or media to facilitate their use. "Medium" means an article of manufacture containing expressive information of the invention. The database of the present invention may be recorded on a computer-readable medium, such as any medium that can be directly read and accessed by a computer (e.g., an internet database). Such media include, but are not limited to: magnetic storage media such as floppy disks, hard disk storage media, and magnetic tape; optical storage media such as CD-ROM; electrical storage media such as RAM and ROM; and mixtures of these classes, such as magnetic/optical storage media. Those skilled in the art will readily understand how any currently known computer readable medium may be used to create an article of manufacture that includes a record of current database information. "recorded" refers to the process of storing information on a computer-readable medium using any such method known in the art. Any convenient data storage structure may be selected based on the means for accessing the stored information. Various data processor programs and formats may be used for storage, such as word processing text files, database formats, and the like.

Such programs may also be encoded and transmitted using carrier wave signals adapted for transmission over wired, optical, and/or wireless networks conforming to various protocols including the internet. Thus, a computer readable medium may be created using data signals encoded with such a program. The computer readable medium encoded with the program code may be packaged with a compatible device or provided separately from other devices (e.g., via Internet download). Any such computer readable medium may reside (reside) on or within a single computer product (e.g., a hard drive, a CD, or an entire computer system), and may exist on or within different computer products within a system or network. The computer system may contain a monitor, printer, or other suitable display for providing any of the results mentioned herein to a user.

Any of the methods described herein may be performed in whole or in part with a computer system comprising one or more processors, which may be configured to perform the steps. Thus, embodiments may relate to a computer system configured to perform the steps of any of the methods described herein, potentially with different components performing the respective steps or groups of the respective steps. Although presented as numbered steps, the steps of the methods herein may be performed simultaneously or at different times or in a different order. Additionally, portions of these steps may be used with portions of other steps of other methods. Also, all or part of the steps may be optional. Additionally, any of the steps of any of the methods may be performed by a module, unit, circuit or other device of a system for performing such steps.

9. Primers, probes and compositions

In the practice of aspects of the invention, primers and probes can be used that specifically hybridize or amplify cfRNA from placental genes (including the genes in table 1) and other informative-genes (including the genes in tables 1 and 2). In particular, useful primers and probes include those that specifically hybridize to or amplify SEQ ID NOS: 1-19. These primers and probes are used for amplification (including multiplex PCR, multiplex RT-qPCR or other amplification methods), reverse transcription, construction of sequencing libraries (e.g., RNA-seq libraries), addition of aptamer sequences, hybridization capture of target RNA, construction of nucleic acid arrays, primer extension, and for other uses known to practitioners having knowledge in the art. Designing probes and primers for their intended use is well within the ability of one of ordinary skill in the art, given the methods of amplification (e.g., addition of aptamers or universal primers), target sequence composition, base composition, avoidance of artifacts such as primer dimer formation, and fragmentation properties of cfRNA.

For example, the use of SEQ ID NOS: 1-19 to design primers, primer pairs, and probes specific for each gene and functional for their intended purpose (e.g., for use in a multiplex reaction) is within the ability of one of ordinary skill in the art. It will be appreciated that for each RNA transcript there are many different primers and primer combinations that can amplify at least a portion of the transcript. Thus, one skilled in the art can design primer combinations to amplify the informative sequence of any one of SEQ ID NOs 1-19, or any combination thereof, as well as other gene sequences identified in tables 1 and 2. Exemplary primers and probes are described in tables 3-5. The probe may be a nucleic acid probe, such as an RNA or DNA probe. The primer or probe may be immobilized (e.g., for capture-based enrichment) or detectably labeled (e.g., with a fluorescent, enzymatic, or chemiluminescent moiety, etc.).

9.1 gestational age or delivery time composition

In one aspect, the invention provides primers for multiplex amplification of at least 3 and no more than 50, optionally no more than 25, optionally no more than 10 genes selected from the genes of table 1. In some embodiments, the invention provides primers for multiplex amplification of at least 3mRNA transcripts provided in table 1. In another embodiment, the invention provides primers for multiplex amplification of any combination of at least 3mRNA transcripts selected from SEQ ID NOS 1-9. In one embodiment, the primers are used in multiplex amplification, wherein the primers comprise at least one pair, and optionally three or more primer pairs. Exemplary primer pairs are provided in table 3. In another embodiment, the primers used for multiplex amplification comprise at least three and no more than 100 primer pairs, optionally no more than 50, optionally no more than 25, optionally no more than 10 primer pairs selected from any of the primer pairs provided in table 3.

In a related aspect, the invention provides a composition comprising a primer or primer pair as described above. The composition may be a mixture. The composition may be a solution. The composition may additionally comprise one or more of the following: (a) maternal cfRNA, (b) buffer, (c) enzyme (e.g., one or a combination of reverse transcriptase, DNA polymerase, RNA, or DNA ligase), (d) dntps.

In one aspect, a composition is provided, comprising: (1) a cfRNA having a cfRNA sequence corresponding to at least 2 genes in table 1, an amplicon of said cfRNA sequence or a cDNA from said cfRNA sequence, and (2) a primer for amplifying said cfRNA sequence or amplicon or cDNA, or a probe for detecting said cfRNA sequence or amplicon or cDNA, with the proviso that the composition does not include a primer for amplifying more than a threshold number of different genes, amplicons, or cdnas; and does not include probes for detecting more than a threshold number of different cfRNA sequences or amplicons or cdnas. In one embodiment, the composition does not include cfrnas having sequences corresponding to cfrnas from a human genome (human genome) that exceed a threshold number of different genes, amplicons of, or cdnas from, that exceed a threshold number of different genes. In some embodiments, the threshold number is 200. In some embodiments, the threshold number is 150. In some embodiments, the threshold number is 100. In some embodiments, the threshold number is 50. In some embodiments, the threshold number is 25.

In a related aspect, the invention provides a nucleic acid array comprising a primer, primer pair or probe as described above.

9.2 premature birth Risk composition

In one aspect, the invention provides primers for multiplex amplification of at least 3 and no more than 100 genes, optionally no more than 50, optionally no more than 25, optionally no more than 10 genes selected from the genes of table 2. In some embodiments, the invention provides primers for multiplex amplification of at least 3mRNA transcripts (i.e., RefSeq identifiers) provided in table 2. In another embodiment, the invention provides primers for the multiplex amplification of any combination of at least 3mRNA transcripts selected from SEQ ID NOs 10 to 19, or alternatively at least 3mRNA transcripts selected from SEQ ID NOs 10, 11, 13 and 15-18. In one embodiment, the primers are used in multiplex amplification, wherein the primers comprise at least one pair, and optionally three or more primer pairs. Exemplary primer pairs are provided in table 3. In another embodiment, the primers used for multiplex amplification comprise at least three and no more than 100 primer pairs, optionally no more than 50, optionally no more than 25, optionally no more than 10 pairs selected from any of the primer pairs provided in table 3.

In a related aspect, the invention provides a composition comprising a primer or primer pair as described above. The composition may be a mixture. The composition may be a solution. The composition may additionally include one or more of the following: (a) parent cfRNA, (b) buffer, (c) enzyme (e.g., reverse transcriptase, DNA polymerase, RNA or DNA ligase), (d) dntps.

In a related aspect, the invention provides a kit comprising a primer or primer pair as described above packaged together. In one method, a mixture of different primers is combined into a single mixture. In another method, primers specific for a single cfRNA are packaged in separate vials. The kit may additionally comprise one or more of: (a) parent cfRNA, (b) buffer, (c) enzyme (e.g., reverse transcriptase, DNA polymerase, RNA or DNA ligase), (d) dntps.

In one aspect, a composition is provided that includes (1) a cfRNA having a cfRNA sequence corresponding to at least 2 genes in table 2, an amplicon of the cfRNA sequence or a cDNA from the cfRNA sequence, and (2) a primer for amplifying the cfRNA sequence or amplicon or cDNA; or a probe for detecting the cfRNA sequence or amplicon or cDNA, with the proviso that the composition does not include primers for amplifying more than a threshold number of different genes, amplicons, or cdnas; and does not include probes for detecting more than a threshold number of different cfRNA sequences or amplicons or cdnas. In one embodiment, the composition does not include cfrnas having sequences corresponding to cfrnas of a threshold number of different genes from the human genome, amplicons of more than a threshold number of different genes, or cdnas from more than a threshold number of different genes. In some embodiments, the threshold number is 200. In some embodiments, the threshold number is 150. In some embodiments, the threshold number is 100. In some embodiments, the threshold number is 50. In some embodiments, the threshold number is 25.

In a related aspect, the invention provides a nucleic acid array comprising a primer or primer pair as described above.

10. Method of producing a composite material

This section describes the implementation of methods for determining gestational age and risk of preterm birth. The examples in this section are for illustration only and are in no way limiting.

In one method, maternal samples are collected, frozen, and shipped to a central laboratory for analysis. In one method, the methods of the invention are optionally performed in a local medical facility (e.g., a hospital laboratory) using a kit for isolating cfRNA, generating cDNA, qPCR, and/or sequencing. In one method, the kit comprises reagents for cfRNA isolation. The use of standard kits is advantageous to ensure consistency in sample collection, cfRNA isolation and analysis by qPCR or transcriptome sequencing. The kit may comprise reagents for cfRNA, cDNA generation, qPCR and/or sequencing, and primers or probes described herein for determining the expression level of a cfRNA transcript or transcript combination described herein. In one approach, cfRNA, cDNA, or libraries are produced and shipped to a central laboratory for analysis.

In one approach, maternal samples are collected, expression profiles are determined using a distributed system (which includes a server system and a client system communicating on a server-client over a computer network), frozen and shipped to a central laboratory for analysis. The server system may include a database of reference profiles, and may receive data (e.g., expression profile information) from the client system. The expression profile information from the patient is compared to the reference profile using a computer product, for example comprising a computer readable medium storing a plurality of instructions for controlling a computer system to perform the method of the invention (the method according to any one of the preceding claims). The database of reference spectra may be generated using the machine learning methods described herein. Advantageously, this information can be used as training data when collecting expression profiles from individual patients. This may be particularly useful when training and validation data is collected from demographically distinct patient populations (e.g., populations identified by age, race or ethnicity, geographic location, or other criteria).

A patient expression profile will be most useful when it correlates with a particular outcome (e.g., term delivery or preterm birth) or gestational age at birth. Thus, in one aspect, the invention relates to (1) collecting cfRNA from a pregnant woman one or more times during pregnancy, determining an expression profile (i.e. an expression profile corresponding to a set of genes identified herein, e.g. genes or combinations or subsets described herein from table 1, table 2 or table 6) using the cfRNA; and recording the expression profile, e.g., on a suitable non-transitory computer readable medium; then (2) determining the date of labor for the woman, classifying the labor as term or preterm (by days if preterm), or otherwise characterizing the outcome of the pregnancy, and (3) correlating the information in (2) with the expression profile in (1), e.g., by linking the information and the expression profile in a computer readable medium.

Determination of gestational age

In one method, there is provided a computer-implemented method for estimating gestational age of a fetus, the method comprising: (a) obtaining one or more expression profiles from a maternal sample of a pregnant woman carrying a fetus, wherein the expression profiles correspond to expression of cfRNA transcripts from a first genomic set; (b) comparing, using a computer system, the expression profile to one or more reference profiles defining gestational age characteristics to estimate the gestational age of the fetus, wherein the reference profiles are determined as characteristics defining gestational age using a machine learning model that analyzes a first training sample that is an expression profile with cfrnas defining gestational age markers; (c) updating, using a computer system, a reference spectrum by: (1) receiving a second training sample, wherein the second training sample is a cfRNA expression profile labeled with a particular gestational age, and (2) iteratively adjusting the reference profile via a machine learning model to increase the number of correctly classified first and second training samples. The reference spectra may form a straight line or a curve, or be discrete values. In some embodiments, the first genomic set comprises any combination of any of: any combination of fetal age-predictive genes disclosed herein, comprising a placental gene, a placental gene set forth in Table 1, and at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, or 9 genes selected from CGA [ SEQ ID NO:1], CAPN6[ SEQ ID NO:2], CGB [ SEQ ID NO:3], ALPP [ SEQ ID NO:4], CSHL1[ SEQ ID NO:5], PLAC4[ SEQ ID NO:6], PSG7[ SEQ ID NO:7], PAPPA [ SEQ ID NO:8], and LGALS14[ SEQ ID NO:9 ].

There is also provided a computer system comprising: (a) a database comprising reference profiles, each reference profile comprising expression levels of cfRNA transcripts in a pregnant woman population corresponding to a first genomic set and to a defined gestational age; (b) a user interface configured to interact with a client computer over a network and receive an expression profile comprising expression levels of cfRNA transcripts corresponding to a first genomic set in a pregnant woman carrying a fetus; and (c) one or more processors configured to analyze the reference profile and the expression profile, including comparing the reference profile and the expression profile to determine the gestational age of the fetus; and (d) a network interface that transmits the gestational age of the fetus to the client computer. In one embodiment, the reference profile and expression profile comprise the expression levels of the cfRNA panel of any combination disclosed herein, comprising transcripts from placental genes; the placental genes listed in table 1; and at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, or 9 genes selected from CGA [ SEQ ID NO:1], CAPN6[ SEQ ID NO:2], CGB [ SEQ ID NO:3], ALPP [ SEQ ID NO:4], CSHL1[ SEQ ID NO:5], PLAC4[ SEQ ID NO:6], PSG7[ SEQ ID NO:7], PAPPA [ SEQ ID NO:8], and LGALS14[ SEQ ID NO:9 ].

Risk of premature birth

In one method, there is provided a computer-implemented method for assessing the risk of preterm labor in a pregnant woman, the method comprising: (a) obtaining one or more expression profiles from a maternal sample of the pregnant woman, wherein the expression profiles correspond to expression of a plurality of cfRNA transcripts from a first genomic set; (b) comparing, using a computer system, the expression signature to one or more reference signatures characteristic of a woman with (a) a high risk of preterm birth or (b) a low risk of preterm birth or characteristic of a woman with a defined length of gestation, wherein a reference profile is determined using a machine learning model that analyzes a first training sample that is a preterm or full term cfRNA expression profile or labeled with a length of gestation, (c) updating, using a computer system, the reference profile by: (1) receive a second training sample, wherein the second training sample is a cfRNA expression profile labeled as preterm or term or labeled with gestational duration, and (2) iteratively adjust the reference profile via the machine learning model to increase the number of correctly classified first and second training samples. The reference spectrum may form a straight line or a curve, or be discrete values. In some embodiments, the first genomic complement comprises any combination of genes disclosed herein that predict risk of preterm birth, comprising the genes listed in Table 1, and at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, or 9 genes selected from CGA [ SEQ ID NO:1], CAPN6[ SEQ ID NO:2], CGB [ SEQ ID NO:3], ALPP [ SEQ ID NO:4], CSHL1[ SEQ ID NO:5], PLAC4[ SEQ ID NO:6], PSG7[ SEQ ID NO:7], PAPPA PA [ SEQ ID NO:8], and LGALS14[ SEQ ID NO:9], or at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, or 9 genes selected from CLCN3[ SEQ ID NO:10], DAPP1[ SEQ ID NO:11], PPBP [ SEQ ID NO:13], MAP3K7CL [ SEQ ID NO:15], MOB1B [ SEQ ID NO:16], RAB [ RAB 24 [ SEQ ID NO: 27B ] and RAB 18[ RGO [ SEQ ID NO: 3618 ] of SEQ ID NO: 3618 ] and LGS, At least 3, at least 4, at least 5, at least 6, or 7 genes. In some embodiments, the first genomic set comprises a polynucleotide selected from the group consisting of (1) RGS 18; DAPP 1; PPBP; (2) RGS 18; RAB 27B; PPBP; (3) RGS 18; MOB 1B; PPBP; (4) RGS 18; PPBP; MAP3K7 CL; (5) RGS 18; PPBP; CLCN 3; (6) DAPP 1; RAB 27B; PPBP; (7) DAPP 1; MOB 1B; PPBP; (8) DAPP 1; PPBP; CLCN 3; (9) RAB 27B; MOB 1B; PPBP; (10) RAB 27B; PPBP; MAP3K7 CL; (11) RAB 27B; PPBP; CLCN 3; (12) MOB 1B; PPBP; MAP3K7 CL; and (13) MOB 1B; PPBP; at least one combination of CLCN 3.

To determine the risk of preterm birth, maternal samples can be labeled as "preterm birth" and "term"; or to mark the gestational age of the child at birth; or to mark the length of pregnancy (e.g. the week of delivery), combinations of these, or a label suitable for use in quantitatively or qualitatively distinguishing term delivery from preterm delivery.

There is also provided a computer system comprising: (a) a database comprising reference profiles, each reference profile comprising expression levels of cfRNA transcripts corresponding to a first genomic set and risk of preterm birth in a pregnant woman population; (b) a user interface configured to interact with a client computer over a network and receive an expression profile comprising expression levels of cfRNA transcripts corresponding to a first genome set in a pregnant woman; and (c) one or more processors configured to analyze the reference profile and the expression profile, including comparing the reference profile and the expression profile to determine a risk of preterm birth; and (d) a network interface that transmits the risk of preterm birth to the client computer. In some embodiments, the reference profile and expression profile include the expression levels of the cfRNA panel of any combination disclosed herein, comprising the genes listed in table 1, and at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, or 9 genes selected from CGA [ SEQ ID NO:1], CAPN6[ SEQ ID NO:2], CGB [ SEQ ID NO:3], ALPP [ SEQ ID NO:4], CSHL1[ SEQ ID NO:5], PLAC4[ SEQ ID NO:6], PSG7[ SEQ ID NO:7], PAPPA [ SEQ ID NO:8], and LGALS14[ SEQ ID NO:9], or at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, or 9 genes selected from CLCN3[ SEQ ID NO:10], DAPP1[ SEQ ID NO:11], PPBP [ SEQ ID NO:13], MAP3K CL [ SEQ ID NO:15], MOB 1[ SEQ ID NO:16], RAB27 [ RAB B [ SEQ ID NO:18] and RGS [ 4934 ] of SEQ ID NO:18, At least 3, at least 4, at least 5, at least 6, or 7 genes.

11. Examples of the embodiments

11.1 example 1-materials and Experimental methods

Sample collection

Blood samples were collected from danish pregnant women (high resolution cohort) once a week, and from pennsylvania university (preterm cohort) and university of alabama bermingham (preterm cohort) at one time point during the second or third pregnancy, according to protocols approved by the institutional review board. The risk of natural preterm labor in women in pennsylvania and alabama involved in the study was high. All preterm women experienced natural preterm birth, except for one pennsylvania patient (preeclampsia). All women with a history of preterm birth received weekly injections of progesterone according to the standard of care. Blood samples were collected into EDTA-coated evacuated blood collection tubes (Becton Dickinson, new jersey). Plasma was separated from the blood using standard clinical blood centrifugation protocols.

Cell-free RNA (cfRNA) isolation

Cell-free RNA was extracted from 0.75-2mL of plasma using plasma/serum circulating RNA and an exosome purification kit (Norgen Biotek, Canada, Cat. No. 42800). The DNA residue was digested with Baseline-ZERO DNase (Epicentre, Wisconsin), then passed through RNAclean and Concentrator^TM-5 kit (ZymoResearch, Calif.) washes. The resulting RNA was eluted to 12. mu.l in elution buffer.

RT-qPCR detection

The RT-qPCR assay consists of two main reactions: reverse transcription/pre-amplification of extracted cfRNA and qPCR of pre-amplified cDNA. Primers for our genome were designed and synthesized by Fluidigm corporation, california (table 3). Using CellsDirect^TMOne-Step RT-qPCR kit (Invitrogen, Calif. catalog # 11753-100) and 96 primer pair pools from Table 3, 1-2. mu.l or 10. mu.l of 12. mu.l of total purified RNA was used for the reverse transcription/pre-amplification reaction. Pre-amplification was performed for 20 cycles and the remaining primers of the digestion reaction were treated with exonuclease I. Multiplex qPCR reactions were performed on 96 samples of 96 primer pairs using a 96x96 dynamic array chip on a BioMark System (Fluidigm, california). BioMark kinetic array chips individual samples (cdnas) and individual reagents (primer pairs) were loaded into wells on a kinetic array chip, respectively. The integrated fluidic circuit controller pushes the sample and reagents into the channel until full;coordinated release and closure of the fluid values then enables mixing of the sample and reagents into the various compartments within the chip. The 96x96 dynamic array chip can simultaneously analyze up to 9,216 reactions. Threshold cycles (Ct values) of qPCR reactions were extracted using Fluidigm real-time PCR analysis software.

cfRNA-Seq library preparation

Cell-free RNA sequencing libraries were prepared by the smart strended TotalRNAseq-Pico Input mammalian kit (Clontech, ca, catalog No. 634413) using 6 μ Ι of eluted cfRNA according to the manufacturer's manual. In Illumina NextSeq^TMShort read sequencing was performed on the (2x75bp) platform (Illumina, CA) at depths exceeding ten million reads per sample.

Statistical analysis

cfRNA-Seq differential expression analysis

28 samples (14 term and 14 preterm) cfRNA samples from the preterm discovery cohort were sequenced. The sequencing reads were mapped to a human reference genome using a STAR aligner (hg 38). Duplicate entries are deleted by Picard and the unique reads are then quantified using htseq-count. After pretreatment, 16 samples containing sequencing reads mapped to over 3000 genes were used for subsequent statistical analysis. The difference genes between term and preterm samples were confirmed using the quantile adjusted conditional maximum likelihood method, the Generalized Linear Model (GLM) likelihood ratio test, and the pseudo-likelihood F test performed in R using the edgeR package.

RT-qPCR sample analysis

Quantizing original C in absolute terms_tThe value is obtained. Absolute quantification the transcript count contained in each sample was estimated based on the cycle threshold of a known amount of ERCC (fig. 9). The dilution of the estimated transcript counts, the sample size, was then adjusted and normalized by the amount of plasma treated.

Multivariate random forest modeling

Recursive feature selection and model building is performed in R using the insert symbol package. The longitudinal data were smoothed using a 3-week centered moving average, and divided into 21 patient training sets and 10 patient validation sets. Model selection was performed using 10-fold cross-validation with 10 replicates.

Expected delivery date estimation

The expected delivery date is derived from random forest model predictions. The centered moving average is not used to smooth the longitudinal data of this application. For any given sampling period [ second pregnancy (T2), third pregnancy (T3), or both (T2& T3) ], the labor time estimate is transferred to a designated reference time point and then averaged using median to establish the expected labor date.

Selection and validation of preterm birth biomarker candidates

The absolute RT-qPCR values were normalized using a modified median multiple method that was not temporally and epidemiologically variable, as applied in Rose and Mennuti (Fetal Medicine, West J Med., 1993; 159: 312-. Median term patients were quantified by the third pregnancy at cohort levels for each gene. Biomarker discovery was performed as described by Sweeney et al (j. pediatricinfect. dis. soc.,2017, doi: 10.1093/jpeg/pix 021, incorporated by reference) using a combination of criteria of effect size and significance threshold calculated using Hedges 'g and fisher's exact test, respectively. Using an effect size threshold of 0.8 and a False Discovery Rate (FDR) of 5%, significant differences between the cohorts of genes were considered. Candidate gene biomarkers were then tested in 3 unique combinations to assess their ability to detect true and false positives. A combination of true positive rate greater than 0.75 and false positive rate less than 0.05 was selected using independent queues for further validation. The ROC curve is based on the scores of biomarker combinations, where all genes show at least a 2.5-fold increase over median expression.

11.2 example 2-longitudinal data on expiration dates from three different groups

We performed a high resolution study of normal human pregnancy by measuring cfRNA longitudinally in the blood of pregnant women every week of gestation. cfRNA provides a window to the phenotypic state of pregnancy by providing information about gene expression in fetal, placental, and maternal tissues. Koh et al describe the use of tissue-specific genes to directly measure tissue health and physiology, and these measurements are consistent with known physiology of pregnancy and fetal pregnancy at low temporal resolution [ Koh et al PNAS, Vol.111, 20: 7361-. Analysis of tissue specific transcripts in this sample enabled us to track the pregnancy of fetuses and placentas with high resolution and sensitivity, as well as to detect gene specific responses of the maternal immune system to pregnancy. The data from this study established a "clock" for normal human pregnancy and enabled a direct molecular approach to determine time of delivery and gestational age using 9 placental genes. We demonstrate that cfRNA samples from the second and third gestational periods of pregnancy can predict the expected date of delivery with accuracy comparable to ultrasound, thus laying the foundation for an inexpensive portable date determination method.

We recruited 31 denmark pregnant women from the denmark national biobank, each of whom agreed to donate blood once a week, thereby producing 521 plasma samples for analysis (fig. 1A). All pregnant women delivered normally at term (defined as delivery with gestational age equal to or greater than 37 weeks) and their medical history showed no abnormal health changes during pregnancy (table 8). Each sample was analyzed by highly multiplexed real-time PCR using a selected genomic set specific to placenta, fetal tissue or the immune system.

TABLE 8

11.3 example 3 Gene expression of maternal, placental, and fetal tissue-specific genes in maternal plasma samples from normal term parturition

Cell-free RNA was isolated from blood samples of individuals in each cohort of danish as described in example 1. RT-qPCR assays were performed on isolated cfRNA essentially as described in example 1. Each gene primer pair listed in figure 9 was added to an aliquot of the cfRNA sample and the Ct value was calculated using the appropriate control.

Mean values between months ± Standard Error of Mean (SEM) for gene-specific patients were plotted during pregnancy (fig. 2A). The time course of gene expression averaged out interesting behavior due to gene function (FIG. 2A and FIG. 4). Placental and fetal genes (blue and yellow) showed significant increases throughout pregnancy, with slightly different trajectories for the genes. Some of these genes reach a stationary phase before delivery, with one (CGB) falling off the peak of the first pregnancy. Immune genes that are controlled by the maternal immune system but may also contain fetal contributions have a more complex interpretation but typically show a change over time at measurable baselines during the early stages of pregnancy and after delivery. Then, we calculated the correlation between all genes and gene values for all pregnancies (fig. 2B), and found that the genes within each set (i.e. placenta, immune, fetus) were highly correlated with each other. Furthermore, we found that the placental and fetal genes also showed moderate cross-correlation, indicating that placental cfRNA can provide an accurate estimate of fetal conception and gestational age throughout pregnancy.

11.4 example 4 prediction model & comparison with gold Standard of labor time

The results of the gene expression assays prompted us to apply machine learning methods to build models that would predict gestational age or time of delivery from cfRNA measurements. We used a random forest model and could show that a subset of nine placental genes provides more predictive power than using the full set of measured genes (fig. 5). Using these 9 genes (CGA, CAPN6, CGB, ALPP, CSHL1, PLAC4, PSG7, PAPPA and LGALS14), we accurately predicted the time from sample collection to delivery (Pearson correlation coefficient r is 0.91, P is 0.91, and LGALS14)<2.2x10^-16) This is an objective criterion not affected by ultrasound estimated gestational age (fig. 2C). The performance of our model was significantly improved during pregnancy [ Root Mean Square Error (RMSE) ═ 6.0(T1),3.9(T2),3.3(T3),3.7(PP) weeks]. Notably, in the verification phase [ RMSE ═ 5.4(T1),4.2(T2),3.8(T3),2.7(PP) weeks]During (fig. 2D), our model performed equally well in the reservation queue for 10 women (r 0.89, P)<2.2x10-16)。

We also built a separate model to predict gestational age (by ultrasound estimation), and by usingThe same nine placental genes, the model was trained (r ═ 0.91, P<2.2x10^-16) And verification data (r ═ 0.90, P)<2.2x10^-16) Both aspects performed well (fig. 6A and 6B).

The random forest model selects the placental genes that best predict time and gestational age from sample collection until delivery. Although several of these genes showed similar time traces, their detection rates at early pregnancy differed, suggesting that redundancy may improve accuracy at early time points when both placental and fetal cfRNA are low and lead to drop-out effects. As cfRNA increases during pregnancy, the accuracy of the model improves. This is in contrast to the efficacy of the ultrasound date determination, which relies on the assumption that a constant fetal growth rate-deteriorates over time (Savitz et al 2002; Papageorghiou et al 2016).

Further investigative drivers of this model revealed markers with known effects during pregnancy. The two main model drivers CGA and CGB behaved differently from CAPN6 than the other genes in the model. CGA and CGB are two subunits of HCG, known to play a major role in the initiation and progression of pregnancy, and are involved in the differentiation of trophoblasts (Jaffe et al, 1969). Trends in both genes were observed to coincide with known trends in protein levels during pregnancy (Cocquebert et al, 2012). Free CGB and PAPPA were also used as biochemical markers of risk for down syndrome in first pregnancy (Wald and Hackshaw1997), and other genes selected by this model were associated with trophoblast inoculation (e.g., LGALS14, PAPPA).

We then used our model to estimate the expected date of labour from samples taken during the second, third or both pregnancy periods (fig. 2E). We found that patients of 32% (T2), 23% (T3), 45% (T2& T3) and 48% (T1 ultrasound) delivered within one week of their expected delivery date (table 9).

TABLE 9

Previous studies reported that it was possible to determine, under normal circumstances, the week a woman may give birth using ultrasound to an accuracy of 57.8% and LMP to an accuracy of 48.1% (Savitz et al, 2002). The results are comparable to the results of ultrasonic measurement, the cost is low, and the used method is easier to be transferred to the environment with insufficient resources.

To predict gestational age, we trained several different models for a subset of women (i.e., non-pregnant or multiparous women, women carrying male or female births) to determine the importance of the 9 genes that constitute the identified transcriptome signatures. Training 4 different models for women pregnant with either male or female foetus revealed that 2 of the 9 genes identified in the text were sufficient to predict the time of delivery for women pregnant with either male (CGA, CSHL1) [ Root Mean Square Error (RMSE) of the second and third pregnancy 5.43 and 4.80 respectively ] or female (CGA, CAPN6) (RMSE of the second and third pregnancy 5.58 and 4.60 respectively) and multiple (CGA, CSHL1) (RMSE of the second and third pregnancy 5.22 and 4.56 respectively). However, all 9 genes are essential to predict the time to delivery of a non-parturient woman, highlighting the importance of the identified transcriptome signatures. The model weights nine transcripts for predicting gestational age in the following order of importance (from highest to lowest): CGA, CAPN6, CGB, ALPP, CSHL1, PLAC4, PSG7, PAPPA and LGALS 14. See table 10.

Watch 10

In summary, we have discovered a molecular clock of fetal pregnancy that reflects the roadmap of gestating gene expression in the placenta and fetus and that can predict time to delivery, gestational age, and expected date of delivery with accuracy comparable to ultrasound. Our method has several advantages over ultrasound, namely cost and applicability in the late stages of pregnancy. The cfRNA measurement results can be easily transferred to resource-scarce environments, at a fraction of the cost of ultrasound. Even in countries where ultrasound is frequently used, cfRNA remains an attractive accurate alternative to ultrasound, especially in the second and third pregnancy when ultrasound prediction is reduced to estimated days of labor of 15(T2) or 27(T3) (Altman and Chitty, 1997). It is expected that this clock will also help in finding and monitoring fetuses with congenital defects that can be treated in utero, which represents a rapidly growing part of maternal medicine.

11.5 example 5-identification of genes differentially expressed between Normal and preterm birth

Although the first generation "clock" models are able to predict gestational age and time of delivery for normal pregnancy, we are also interested in testing its performance at preterm delivery. Therefore, we used two cohorts to test the performance of preterm birth, recruited from the high-risk communities of preterm birth, recruited by pennsylvania university and bermingham university, alabama university, respectively (see fig. 1 and table 1). We found that although this model demonstrated performance in normal pregnancy (RMSE ═ 4.3 weeks), it was generally not predictive of time to delivery in preterm samples (RMSE ═ 10.5 weeks) (fig. 7). This indicates that the contents of the model reflect the normal inoculation program and may not account for various abnormal physiological events that may lead to premature delivery. In other words, from a molecular point of view, a preterm fetus does not appear to have reached term pregnancy, and thus preterm delivery may not be caused by an over-maturation signal of the fetus or placenta (which provides a sensation of reaching term). This conclusion is supported by the following observations: pharmacologic agents aimed at preventing or slowing uterine contractions prevent small amounts of premature labor (Romero et al 2014; Conde-Agudelo and Romero 2016).

To further investigate this problem and develop a second generation "clock" model capable of predicting preterm birth, we performed RNAseq (essentially as described in example 1) on plasma sample cfRNA obtained from term (n-7) and preterm (n-9) women collected from a preterm-enriched population (pennsylvania) (see fig. 1 and table 1) to identify genes that can distinguish preterm from normal delivery.

Analysis of this RNAseq data indicated that nearly 40 genes could separate term from preterm birth with statistical significance (p <0.001) (see fig. 3A and fig. 10A to 10D). When recalculated to exclude a preeclamptic woman (see examples), the determination of 37 genes can separate term from preterm birth with statistical significance.

Then, we created a PCR panel (PCR panel) with the highest scoring candidate preterm biomarkers and other immune and placental genes. We confirmed that the differential expression observed in RNAseq was also observed with the qPCR stack (figure 8).

11.6 example 6 prediction model for preterm birth

The first ten genes from this panel (CLCN3, DAPP1, POLE2, PPBP, LYPLAL1, MAP3K7CL, MOB1B, RAB27B, RGS18, TBC1D15) (FDR ≦ 5%, Hedge's g ≧ 0.8) (FIG. 3B) were accurately classified for 7 (78%) of 9 preterm samples and only 1 (4%) of 26 term samples from both Pennsylvania and Denmark were misclassified with a mean AUC of 0.87 (FIG. 3C).

When used in combination, these ten genes also showed successful validation in an independent preterm enrichment cohort from alabama, with 4 out of 6 preterm samples (66%) classified accurately and 3 out of 18 term samples (17%) misclassified (see fig. 1).

Furthermore, this independent validation cohort indicates that it is possible to differentiate preterm delivery from term pregnancy up to 2 months prior to delivery, with an AUC of 0.74 (fig. 3C). Several genes in response signature (response signature) were significantly highly expressed in premature women (FDR. ltoreq.5%, Hedge's g. gtoreq.0.8), respectively, demonstrating the robustness of their action (FIG. 3B). Our data indicate that the genes associated with natural preterm labor are different from those most predictive of gestational age and normal time of delivery.

In a subsequent refinement, we determined that one woman in the cohort experienced preterm birth due to preeclampsia rather than natural preterm birth. We deleted data points associated with her plasma samples. Running the analysis with this sample removed yielded 7 transcripts (CLCN3, DAPP1, PPBP, MAP3K7CL, MOB1B, RAB27B, RGS18) instead of 10, with a true positive rate of greater than 75% and a misclassification rate of greater than 5% when 3 were used in combination.

As described in example 7 below, we identified several sub-combinations of 7 transcripts that could be used to determine the likelihood or risk of preterm birth in women. Thus, in certain methods, one or more of the following sets are used to assess the likelihood of term or preterm birth: (1) RGS 18; DAPP 1; PPBP; (2) RGS 18; RAB 27B; PPBP; (3) RGS 18; MOB 1B; PPBP; (4) RGS 18; PPBP; MAP3K7 CL; (5) RGS 18; PPBP; CLCN 3; (6) DAPP 1; RAB 27B; PPBP; (7) DAPP 1; MOB 1B; PPBP; (8) DAPP 1; PPBP; CLCN 3; (9) RAB 27B; MOB 1B; PPBP; (10) RAB 27B; PPBP; MAP3K7 CL; (11) RAB 27B; PPBP; CLCN 3; (12) MOB 1B; PPBP; MAP3K7 CL; and (13) MOB 1B; PPBP; CLCN 3.

We found that PPBP, DAPP1 and RAB27B were elevated alone (FDR ≦ 5%, Hedge's g ≧ 0.8) in women born preterm in both the Pennsylvania and Alabama cohorts, demonstrating the robustness of their action. The ranking of the weight order (from highest to lowest) is RAB27B > PPBP > DAPP1> RGS18> (MOB1B, MAP3K7CL and CLCN 3).

In summary, we have found and validated a set of biomarkers that are capable of predicting the time of labor in a patient at risk of preterm birth. Furthermore, our model of preterm birth indicates that the physiology of preterm birth differs from normal pregnancy, which lays the foundation for the first screening or diagnostic test of the risk of preterm birth.

11.7 example 7-combination of genes that meet the criteria of 75% true Positive Rate and less than 5% false Positive Rate

Seven target transcripts, RAB27B, PPBP, DAPP1, RGS18, MOB1B, MAP3K7CL, CLCN37, can be grouped into 35 unique combinations of genes. We filtered these combinations using a standard of true positive rate of 75% and false positive rate of less than 5%. This resulted in 13 combinations shown in table 11. We generated ROC curves to determine which combinations predicted risk of preterm birth.

TABLE 11

Combination of	Gene 1	Gene 2	Gene 3
				1	RGS18	DAPP1	PPBP
2	RGS18	RAB27B	PPBP
				3	RGS18	MOB1B	PPBP
4	RGS18	PPBP	MAP3K7CL
				5	RGS18	PPBP	CLCN3
6	DAPP1	RAB27B	PPBP
				7	DAPP1	MOB1B	PPBP
8	DAPP1	PPBP	CLCN3
				9	RAB27B	MOB1B	PPBP
10	RAB27B	PPBP	MAP3K7CL
				11	RAB27B	PPBP	CLCN3
12	MOB1B	PPBP	MAP3K7CL
				13	MOB1B	PPBP	CLCN3

Each of these 13 combinations of 3 genes can be used as a panel to assess the risk of preterm birth. Thus, in some embodiments, a set comprising one or more of the following combinations of genes is used to determine the following sets. Thus, in some methods, a kit comprising one or more of the following combinations of genes is used to assess the likelihood of term or preterm birth: (1) RGS 18; DAPP 1; PPBP; (2) RGS 18; RAB 27B; PPBP; (3) RGS 18; MOB 1B; PPBP; (4) RGS 18; PPBP; MAP3K7 CL; (5) RGS 18; PPBP; CLCN 3; (6) DAPP 1; RAB 27B; PPBP; (7) DAPP 1; MOB 1B; PPBP; (8) DAPP 1; PPBP; CLCN 3; (9) RAB 27B; MOB 1B; PPBP; (10) RAB 27B; PPBP; MAP3K7 CL; (11) RAB 27B; PPBP; CLCN 3; (12) MOB 1B; PPBP; MAP3K7 CL; and (13) MOB 1B; PPBP; CLCN 3.

11.8 example 8-Body Mass Index (BMI) does not affect the level of cell-free RNA (cfRNA)

We tested the effect of BMI on circulating cfRNA levels using estimated transcript counts of GAPDH per ml plasma and found no significant differences between individuals who were under-weighted (BMI <18.5), normal (18.5. ltoreq. BMI <25), overweight (25. ltoreq. BMI <30) and obese (BMI > 30) before and after Bonferroni correction using the Wilcoxon rank sum test.

P values for different tests of GAPDH levels before and after Bonferroni correction were as follows: (1) excess weight versus normal weight (P ═ 0.58,1), excess weight versus overweight (P ═ 0.12,0.80), insufficient weight versus obesity (P ═ 0.26,1), normal weight versus overweight (P ═ 0.06,0.35), normal weight versus obesity (P ═ 0.16,0.95), and overweight versus obesity (P ═ 0.72, 1). Similar results were obtained for placenta-specific cfrnas, such as CAPN6, CGA, and CGB.

All comparisons are made in queues so that differences in BMI distribution between queues are not confused.

12. Selected reference file

Altman,D.G.,&Chitty,L.S.(1997).New charts for ultrasound dating ofpregnancy.Ultrasound in Obstetrics&Gynecology,10(3),174–191.doi:10.1046/j.1469-0705.1997.10030174.x

Barr,W.B.,&Pecci,C.C.(2004).Last menstrual period versus ultrasoundfor pregnancy dating.International Journal of Gynaecology and Obstetrics,87(1),38–39.doi:10.1016/j.ijgo.2004.06.008

Bennett,K.A.,Crane,J.M.G.,O’shea,P.,Lacelle,J.,Hutchens,D.,&Copel,J.A.(2004).First trimester ultrasound screening is effective in reducingpostterm labor induction rates:a randomized controlled trial.American Journalof Obstetrics and Gynecology,190(4),1077–1081.doi:10.1016/j.ajog.2003.09.065

Blencowe,H.,Cousens,S.,Chou,D.,Oestergaard,M.,Say,L.,Moller,A.-B.,…Born Too Soon Preterm Birth Action Group.(2013).Born too soon:the globalepidemiology of 15 million preterm births.Reproductive Health,10 Suppl 1,S2.doi:10.1186/1742-4755-10-S1-S2

Cocquebert,M.,Berndt,S.,Segond,N.,Guibourdenche,J.,Murthi,P.,Aldaz-Carroll,L.,…Fournier,T.(2012).Comparative expression of hCGβ-genes in humantrophoblast from early and late first-trimester placentas.American Journal ofPhysiology.Endocrinology and Metabolism,303(8),E950–8.doi:10.1152/ajpendo.00087.2012

Conde-Agudelo,A.,&Romero,R.(2016).Vaginal progesterone to preventpreterm birth in pregnant women with a sonographic short cervix:clinical andpublic health implications.American Journal of Obstetrics and Gynecology,214(2),235–242.doi:10.1016/j.ajog.2015.09.102

Dugoff,L.,Hobbins,J.C.,Malone,F.D.,Vidaver,J.,Sullivan,L.,Canick,J.A.,…FASTER Trial Research Consortium.(2005).Quad screen as a predictor ofadverse pregnancy outcome.Obstetrics and Gynecology,106(2),260–267.doi:10.1097/01.AOG.0000172419.37410.eb

Hanson,A.E.(1987).The Eight Months’Child and the Etiquette of Birth:Obsit Omen！Bulletin of the History of Medicine.

Hanson,A.E.(1995).Paidopoiia:Metaphors for conception,abortion,andgestation in the Hippocratic Corpus.Clio Medica(Amsterdam,Netherlands).

Institute of Medicine(US)Committee on Understanding Premature Birthand Assuring Healthy Outcomes.(2007).Preterm Birth:Causes,Consequences,andPrevention.(R.E.Behrman&A.S.Butler,Eds.).Washington(DC):National AcademiesPress(US).

Jaffe,R.B.,Lee,P.A.,&Midgley,A.R.(1969).Serum gonadotropins before,atthe inception of,and following human pregnancy.The Journal of ClinicalEndocrinology and Metabolism,29(9),1281–1283.doi:10.1210/jcem-29-9-1281

Koh,W.,Pan,W.,Gawad,C.,Fan,H.C.,Kerchner,G.A.,Wyss-Coray,T.,…Quake,S.R.(2014).Noninvasive in vivo monitoring of tissue-specific global geneexpression in humans.Proceedings of the National Academy of Sciences of theUnited States of America,111(20),

7361–7366.doi:10.1073/pnas.1405528111

Liu,L.,Johnson,H.L.,Cousens,S.,Perin,J.,Scott,S.,Lawn,J.E.,…ChildHealth Epidemiology Reference Group of WHO and UNICEF.(2012).Global,regional,and national causes of child mortality:an updated systematic analysis for2010 with time trends since 2000.The Lancet,379(9832),2151–2161.doi:10.1016/S0140-6736(12)60560-1

Lund,S.P.,Nettleton,D.,McCarthy,D.J.,&Smyth,G.K.(2012).Detectingdifferential expression in RNA-sequence data using quasi-likelihood withshrunken dispersion estimates.Statistical Applications in Genetics andMolecular Biology,11(5).doi:10.1515/1544-6115.1826

McCarthy,D.J.,Chen,Y.,&Smyth,G.K.(2012).Differential expressionanalysis of multifactor RNA-Seq experiments with respect to biologicalvariation.Nucleic Acids Research,40(10),4288–4297.doi:10.1093/nar/gks042

Muglia,L.J.,&Katz,M.(2010).The enigma of spontaneous pretermbirth.The New England Journal of Medicine,362(6),529–535.doi:10.1056/NEJMra0904308

Murray, C.J.L., Vos, T.A., Lozano, R.A., Naghavi, M.A., Flaxman, A.D., Michaud, C.A., … et al (2012). Disability-adjusted life areas (DALYs) for291diseases and injuis 21regions, 1990-a systematic analysis for The Global Burden of The disease study 2010.The Lancet,380(9859), 2197-2223. doi:10.1016/S0140-6736(12)61689-4

Papageorghiou,A.T.,Kemp,B.,Stones,W.,Ohuma,E.O.,Kennedy,S.H.,Purwar,M.,…International Fetal and Newborn Growth Consortium for the 21st Century(INTERGROWTH-21st).(2016).Ultrasound-based gestational-age estimation in latepregnancy.Ultrasound in Obstetrics&Gynecology,48(6),719–726.doi:10.1002/uog.15894

Parker,H.(1999).Greek Embryological Calendars and a Fragment from theLost Work of Damastes,on the Care of Pregnant Women and of Infants.TheClassicalQuarterly.

Robinson,M.D.,McCarthy,D.J.,&Smyth,G.K.(2010).edgeR:a Bioconductorpackage for differential expression analysis of digital gene expressiondata.Bioinformatics,26(1),139–140.doi:10.1093/bioinformatics/btp616Robinson,M.D.,&Smyth,G.K.(2008).Small-sample estimation of negative binomialdispersion,with applications to SAGE data.Biostatistics,9(2),321–332.doi:10.1093/biostatistics/kxm030

Romero,R.,Dey,S.K.,&Fisher,S.J.(2014).Preterm labor:one syndrome,manycauses.Science,345(6198),760–765.doi:10.1126/science.1251816

Rose,N.C.,&Mennuti,M.T.(1993).Maternal serum screening for neuraltube defects and fetal chromosome abnormalities.The Western Journal ofMedicine,159(3),312–317.

Savitz,D.A.,Terry,J.W.,Dole,N.,Thorp,J.M.,Siega-Riz,A.M.,&Herring,A.H.(2002).Comparison of pregnancy dating by last menstrual period,ultrasoundscanning,and their combination.American Journal of Obstetrics and Gynecology,187(6),1660–1666.doi:10.1067/mob.2002.127601

Sweeney,T.E.,Haynes,W.A.,Vallania,F.,Ioannidis,J.P.,&Khatri,P.(2017).Methods to increase reproducibility in differential gene expression viameta-analysis.Nucleic Acids Research,45(1),e1.doi:10.1093/nar/gkw797

Wald,N.J.,&Hackshaw,A.K.(1997).Combining ultrasound and biochemistryin first-trimester screening for Down’s syndrome.Prenatal Diagnosis,17(9),821–829.doi:10.1002/(SICI)1097-0223(199709)17:9<821::AID-PD154>3.0.CO；2-5

Ward,K.,Argyle,V.,Meade,M.,&Nelson,L.(2005).The heritability ofpreterm delivery.Obstetrics and Gynecology,106(6),1235–1239.doi:10.1097/01.AOG.0000189091.35982.85

Whitworth,M.,Bricker,L.,&Mullan,C.(2015).Ultrasound for fetalassessment in early pregnancy.Cochrane Database of Systematic Reviews,(7),CD007058.doi:10.1002/14651858.CD007058.pub3

Yefet,E.,Kuzmin,O.,Schwartz,N.,Basson,F.,&Nachum,Z.(2017).PredictiveValue of Second-Trimester Biomarkers and Maternal Features for AdversePregnancy Outcomes.Fetal Diagnosis and Therapy.doi:10.1159/000458409

York,T.P.,Strauss,J.F.,Neale,M.C.,&Eaves,L.J.(2009).Estimating fetaland maternal genetic contributions to premature birth from multiparouspregnancy histories of twins using MCMC and maximum-likelihoodapproaches.Twin Research and Human Genetics,12(4),333–342.doi:10.1375/twin.12.4.333

Zhang, G., et al, (2017) Genetic associates with geographic duration and spacing previous birth. the New England Journal of Medicine,377(12), 1156-1167. doi:10.1056/NEJMoa1612665

Rose and Mennuti (Fetal Medicine, West J Med., 1993; 159:312-

Sweeney et al (J.Pediatric Infect.Dis.Soc.,2017, doi: 10.1093/jpeg/pix 021.)

13. Tables 1 to 5

Table 1: predicting time of labor

Table 2: predicting preterm delivery

Table 3: exemplary primer pairs

TABLE 4

Key words: the "forward primer comprises a sequence identical to SEQ ID NO: a sequence corresponding to bases a-b of X. For example, the forward primer includes bases 30-45 of SEQ ID NO. 1. The "reverse primer comprises the reverse complement of the sequence corresponding to bases c-d of SEQ ID NO: X. For example, the reverse primer includes the reverse complement of bases 500-520 of SEQ ID NO. 1.

TABLE 5

Key words: the probe comprises a sequence corresponding to bases a-b of SEQ ID NO X, or a complement thereof.

Table 6: list of exemplary mRNA transcripts:

table 7: sequence of exemplary mRNA transcripts:

1CGA mRNA transcript 861bp SEQ ID NO

3604bp of mRNA transcript of 2CAPN6 SEQ ID NO

3CGB mRNA transcript 933bp SEQ ID NO

2883bp of mRNA transcript of 4ALPP in SEQ ID NO

5CSHL1mRNA transcript 661bp

6PLAC4mRNA transcript 10009bp SEQ ID NO

7PSG7mRNA transcript 2046bp SEQ ID NO

8PAPPA mRNA transcript 11025bp SEQ ID NO

9LGALS14mRNA transcript 794bp SEQ ID NO

10CLCN3mRNA transcript 6299bp

3006bp of the mRNA transcript of 11DAPP1 SEQ ID NO

12POLE2mRNA transcript 1861bp SEQ ID NO

1307bp of mRNA transcript of SEQ ID NO 13PPBP

14LYPLAL1mRNA transcript 1922bp SEQ ID NO

15MAP3K7CL mRNA transcript 2269bp

16MOB1B mRNA transcript 7091bp SEQ ID NO

7003bp mRNA transcript of SEQ ID NO 17RAB27B

18RGS18mRNA transcript 2158bp of SEQ ID NO

5852bp of the mRNA transcript of 19TBC1D15 SEQ ID NO

Ngo et al, Science 360, 1133-1136 (2018) are incorporated herein by reference.

Although the foregoing invention has been described in some detail for purposes of clarity and understanding, it will be clear to one skilled in the relevant art, once they are familiar with this disclosure, that various changes in form and detail can be made without departing from the true scope of the invention as set forth in the following claims. Accordingly, the invention is not limited to the exact components or details of methods or configurations set forth above. No particular order to the steps or stages of a method or process described in this disclosure, including the figures, is intended or implied except to the extent necessary or inherent in the process itself. In many cases, the order of process steps may be altered without changing the purpose, effect or importance of the methods described.

All publications and patent documents cited herein are incorporated by reference to the same extent as if each such publication or document were specifically and individually indicated to be incorporated by reference. Citation of publications and patent documents (patents, published patent applications, and unpublished patent applications) is not intended as an admission that any such document is pertinent prior art, nor does it constitute any admission as to the contents or date thereof.

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acaataatta actcttcctc tcttctgata aattccccat acctcccaat ccaagtagca 2280

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gcaggacaat aacaagaggc tggacaccct acagatgtct ttgatgtttt cagttgtttg 2460

atatatctcc cctgtagggc atgttgagga aggaggaggg ctgatcaagg ccaagctggt 2520

ctagcctgac atcctagctc ctgactgaac actatagact tcccagcagc atttcaccca 2580

gcagccagag ccggctttaa gtccccaacc cttacagaca ccactgccac caccaccaac 2640

cacgaccacc accaccacca ccactcacca ccatcatcac ctccggaaag tgtagtcctg 2700

ccctaaccca agtcaccccc gacagtaaat tttaccttca tgttgagaaa gcttcctggt 2760

gcttaatcaa gagctggagt tcaatgagtc ctagacagtg agaggggcct gagcttcagc 2820

tcaatggaag cctgctgtgt gccacaagac ggaaaagtgg aagaagctgc agtgggagac 2880

aaagcctcgg tcccccaccc atccacacac acctacactc acacacgcgc acatgggcgc 2940

gcacgaacta ccattcaggc agtcagtggg caagaggaaa gataagtaag taccatacac 3000

acctaaaaga tgagagaatt catccagaca tattacagcc agtttggggc ccctgactgc 3060

aatgtgaaac ctctcgctgc tgctaggttt acaaacaagc ccattgtcct gtgcctccta 3120

atatcatttg tactgaagac cccatctggg gacttgagac tttggtccca gcccagactc 3180

ctcagacttt tctctcagtt gggatgcttc actcgctggg ggtgtttgtt tgccctctca 3240

tttttcagta cttctacaga attttctcta gagtcagtca ttatgaaatg tacttccctc 3300

catcttaacc tatcaacttt ctgcccctcc ttcaaggccc agtataaatg ccacctcctc 3360

catgaagcct tccctaattc caccccaaac ccccaccttc aacaatattt caacgcttct 3420

gcaatgatga aaaagaaaca tagttgtagt acttagccta cctagaccag caagcattca 3480

tttttagctc gctcattttt taccatgttt tccagtctgt ttaacttctg cagtgccttc 3540

actacactgc cttacataaa ccaaatcaca ataaagttca tattcagtac attgaaaaaa 3600

aaaa 3604

<210>3

<211>933

<212>DNA

<213> Intelligent people

<400>3

tgcaggaaag cctcaagtag aggagggttg aggcttcagt ccagcacctt tctcgggtca 60

cggcctcctc ctggctccca ggaccccacc ataggcagag gcaggccttc ctacacccta 120

ctccctgtgc ctccagcctc gactagtccc tagcactcga cgactgagtc tctgaggtca 180

cttcaccgtg gtctccgcct cacccttggc gctggaccag tgagaggaga gggctggggc 240

gctccgctga gccactcctg cgcccccctg gccttgtcta cctcttgccc cccgaggggt 300

tagtgtcgag ctcaccccag catcctatca cctcctggtg gccttgccgc ccccacaacc 360

ccgaggtata aagccaggta cacgaggcag gggacgcacc aaggatggag atgttccagg 420

ggctgctgct gttgctgctg ctgagcatgg gcgggacatg ggcatccaag gagccgcttc 480

ggccacggtg ccgccccatc aatgccaccc tggctgtgga gaaggagggc tgccccgtgt 540

gcatcaccgt caacaccacc atctgtgccg gctactgccc caccatgacc cgcgtgctgc 600

agggggtcct gccggccctg cctcaggtgg tgtgcaacta ccgcgatgtg cgcttcgagt 660

ccatccggct ccctggctgc ccgcgcggcg tgaaccccgt ggtctcctac gccgtggctc 720

tcagctgtca atgtgcactc tgccgccgca gcaccactga ctgcgggggt cccaaggacc 780

accccttgac ctgtgatgac ccccgcttcc aggactcctc ttcctcaaag gcccctcccc 840

ccagccttcc aagcccatcc cgactcccgg ggccctcgga caccccgatc ctcccacaat 900

aaaggcttct caatccgcaa aaaaaaaaaa aaa 933

<210>4

<211>2883

<212>DNA

<213> Intelligent people

<400>4

tcagccagtg tggcttcagg tcaagaggct gggcagggtc aaggtggcaa cgaggggaga 60

agccgggaca cagttctccc tgatttaaac ccgggcagcc tggagtgcag ctcatactcc 120

atgcccagaa ttcctgcctc gccactgtcc tgctgccctc cagacatgct ggggccctgc 180

atgctgctgc tgctgctgct gctgggcctg aggctacagc tctccctggg catcatccca 240

gttgaggagg agaacccgga cttctggaac cgcgaggcag ccgaggccct gggtgccgcc 300

aagaagctgc agcctgcaca gacagccgcc aagaacctca tcatcttcct gggcgatggg 360

atgggggtgt ctacggtgac agctgccagg atcctaaaag ggcagaagaa ggacaaactg 420

gggcctgaga tacccctggc catggaccgc ttcccatatg tggctctgtc caagacatac 480

aatgtagaca aacatgtgcc agacagtgga gccacagcca cggcctacct gtgcggggtc 540

aagggcaact tccagaccat tggcttgagt gcagccgccc gctttaacca gtgcaacacg 600

acacgcggca acgaggtcat ctccgtgatg aatcgggcca agaaagcagg gaagtcagtg 660

ggagtggtaa ccaccacacg agtgcagcac gcctcgccag ccggcaccta cgcccacacg 720

gtgaaccgca actggtactc ggacgccgac gtgcctgcct ccgcccgcca ggaggggtgc 780

caggacatcg ctacgcagct catctccaac atggacattg acgtgatcct aggtggaggc 840

cgaaagtaca tgtttcgcat gggaacccca gaccctgagt acccagatga ctacagccaa 900

ggtgggacca ggctggacgg gaagaatctg gtgcaggaat ggctggcgaa gcgccagggt 960

gcccggtatg tgtggaaccg cactgagctc atgcaggctt ccctggaccc gtctgtgacc 1020

catctcatgg gtctctttga gcctggagac atgaaatacg agatccaccg agactccaca 1080

ctggacccct ccctgatgga gatgacagag gctgccctgc gcctgctgag caggaacccc 1140

cgcggcttct tcctcttcgt ggagggtggt cgcatcgacc atggtcatca tgaaagcagg 1200

gcttaccggg cactgactga gacgatcatg ttcgacgacg ccattgagag ggcgggccag 1260

ctcaccagcg aggaggacac gctgagcctc gtcactgccg accactccca cgtcttctcc 1320

ttcggaggct accccctgcg agggagctcc atcttcgggc tggcccctgg caaggcccgg 1380

gacaggaagg cctacacggt cctcctatac ggaaacggtc caggctatgt gctcaaggac 1440

ggcgcccggc cggatgttac cgagagcgag agcgggagcc ccgagtatcg gcagcagtca 1500

gcagtgcccc tggacgaaga gacccacgca ggcgaggacg tggcggtgtt cgcgcgcggc 1560

ccgcaggcgc acctggttca cggcgtgcag gagcagacct tcatagcgca cgtcatggcc 1620

ttcgccgcct gcctggagcc ctacaccgcc tgcgacctgg cgccccccgc cggcaccacc 1680

gacgccgcgc acccggggcg gtccgtggtc cccgcgttgc ttcctctgct ggccgggacc 1740

ctgctgctgc tggagacggc cactgctccc tgagtgtccc gtccctgggg ctcctgcttc 1800

cccatcccgg agttctcctg ctccccacct cctgtcgtcc tgcctggcct ccagcccgag 1860

tcgtcatccc cggagtccct atacagaggt cctgccatgg aaccttcccc tccccgtgcg 1920

ctctggggac tgagcccatg acaccaaacc tgccccttgg ctgctctcgg actccctacc 1980

ccaaccccag ggactgcagg ttgtgccctg tggctgcctg caccccagga aaggaggggg 2040

ctcaggccat ccagccacca cctacagccc agtgggtacc aggcaggctc ccttcctggg 2100

gaaaagaagc acccagaccc cgcgccccgc tgatctttgc ttcagtcctt gaatcacctg 2160

tgggacttga ggactcggga tcttcaggac gcctggagaa gggtggtttc ctgccaccct 2220

gctggccaag gaggctcctg gggtggggat caccaggggg attttgacac agccttcggc 2280

tgccccccac taagctaatt ccacacccct gtaccccccc agggggccct ctgcctcatg 2340

gcaaaggctt gccccaaatc tcaacttctc agacgttcca tacccccaca tgccaatttc 2400

agcacccaac tgagatccga ggagctcctg ggaagccctg ggtgcaggac actggtcgag 2460

agccaaaggt ccctccccag acatctggac actgggcata gatttctcaa gaaggaagac 2520

tcccctgcct ccccagggcc tctgctctcc tgggagacaa agcaataata aaaggaagtg 2580

tttgtaatcc cagcactttg ggaggccgag gtgggcggat cacgaggtca ggagatggag 2640

accatcctgg ctaacacggt gaaacccctt atctatgcgc ctgtagtccc agctacccag 2700

gaggctgaag caggataatc gcttgaaccc gggcggcgga gattgcagtg agccgaggtc 2760

atgccactgc actgcagcct gggcgacaga gcgagattct gcctcaaaaa taaacaaata 2820

aattttaaaa ataaataaat aataaaagga agtgttagac aatgtaaaaa aaaaaaaaaa 2880

aaa 2883

<210>5

<211>661

<212>DNA

<213> Intelligent people

<400>5

agcatcccaa ggcccgactc cccgcaccac tcagggtcct gtggacagct cacctagcgg 60

caatggctgc aggaagaagc ctatatcaca aaggaacaga agtattcatt cctgcatgac 120

tcccagacct ccttctgctt ctcagactct attccgacat cctccaacat ggaggaaacg 180

cagcagaaat ccaacttaga gctgctccac atctccctgc tgctcatcga gtcgcggctg 240

gagcccgtgc ggttcctcag gagtaccttc accaacaacc tggtgtatga cacctcggac 300

agcgatgact atcacctcct aaaggaccta gaggaaggca tccaaatgct gatggggagg 360

ctggaagacg gcagccacct gactgggcag accctcaagc agacctacag caagtttgac 420

acaaactcgc acaaccatga cgcactgctc aagaactacg ggctgctcca ctgcttcagg 480

aaggacatgg acaaggtcga gacattcctg cgcatggtgc agtgccgctc tgtggagggc 540

agctgtggct tctaggggcc cgcgtggcat cctgtgaccc ctccccagtg cctctcctgg 600

ccctgaaggt gccactccag tgcccaccag ccttgtctta ataaaattaa gttgtattgt 660

t 661

<210>6

<211>10009

<212>DNA

<213> Intelligent people

<400>6

cgtagctcat aatccatttt tataacacct tgctatctat atttacacct ttaaagaaca 60

cgggaattta agagggaaga gtaactaggc ttttgctaaa cttgggctaa taaaaccctc 120

tgtagagaga tccttaatat aggcatgggg acaacaagga gtatcccaag ggactcgccg 180

ctagggtgtc ttttaagcta ttggagcaaa ttcaaatttg gcttaaagaa aaagaaactc 240

attttgtatt gcaacaccat ttgggttaaa tacaagttag atgacgaata tatctggcct 300

aaacatggtt ctatatacta tagtgatatt ttacgattag gcttattttg taaaagagaa 360

ggaaaatggg aagagatccc ttatgtacag gcttttatgg ctctatactg gatcacgtta 420

cttccaggca ttagaatgcc atgcataagg gatccccacc tagctgctcc ccatagaaag 480

ttcataagcc tccccagagt ctcttcagtc ccccagtcct gagtgggggt tctcgccaat 540

tccctaatga gattccaccc caatatcatc aggcaccttt cccccttatc caactagccc 600

tagcctatac cctctgctgc ccaagaaaat gagcccaacc agtacaccag gagtggggct 660

ccatatcagc ccctaaggtc aagcctgtgt ccactgtgga aagtagttga tggaaatgag 720

ggaacactca aagagtacat atgccacttt ccatgtctaa ttagacctta taaaaggaaa 780

gaattggcca gttttcagat aaaccagaaa agcttataca agagtttgtt acgttgacta 840

tgttcttcaa attgccacga tttacaaata ttgtcatccg cttgctgtgc tgtggggaaa 900

aaaaagtaga ggaaaaagtg tgtggttaag ccagtcaatt atgacaaggt taaagaagta 960

actcggggaa aagatgaaaa tcccgctctg tttcagggtc ttttagttga agcactcagg 1020

aaatatacta atgcaggccc agacacccca gaagggcaag ctctcctggg tatacatttt 1080

ctcattcaat cttctcctga cattaggagg aatctacaaa aagcagcaat gggaccttca 1140

agtcctatga aacgacgctt aaacatagcc tttaaagttt acaacaacag ggacagggca 1200

aaagagggga gtaaaaagaa atagccaaaa agtacaattg ttaacagtga ctttaagcct 1260

ccttgcccct caggattact catcttgaga aaatgttaca aaattagcat ctgggatgcc 1320

tagacaagac ttgatgcctg acttgctgac ccctgggcca gaatcactgc gcctactata 1380

cgcaaaaggg cccctggcaa tgcaaatgtc ctaactgctc tggtgagaga gaacaataac 1440

aacaaaaagc ttccatcaat actagagcta accttctcct actagcccca gtgagctgct 1500

tagctcaagt aagtttactg tcccagagga cagctttcca cagtggcaga taagcagccg 1560

cctgaacatt tttctttggt atttccacca ctgagtgtgc tctccagtgg cgtggggact 1620

ccagaatctc cttttgagca atgcagtttg cttcctcccc tttttagttg atgctatggg 1680

attccctgtc ctgccttttc ctgttttcca tacctatcgg ggcaaacaaa atttggccag 1740

gtagatgggt cccagttctg taaataactt gaatccagtt gtcttgtata ggtcatttta 1800

tttaatatgt ttttgggtat atgtacatgt attgtgatgt gtgttacatc tagcgtgctg 1860

tcaaactggc ttatagataa aagaacactc atacattcaa caaataagac tactgaaagc 1920

ttattagttt gaagagaatc ttgtatcttc taaaatttaa ctttaggatt tttacctagg 1980

taagtcactg atgttcatag gctttaaaat ggttaaaatg gctttaaatg gtgaccagct 2040

ttgcatggta ccttggttct cggtgatcta gataaagtta aaagtgaaat aattaaatac 2100

acgtaaatgg gatatgctta atgtgtggtt taaaatcata aaatggtaga atggttctca 2160

gttatagaat gacaatgtct agtgtgaagt tcatgacttc ttccttccta ggtttccata 2220

aaatgtgcta aagaaatgta ttctttattg agaaaaaatt ttttgtctaa tccggaagtt 2280

actaaatggg aggttcaaaa catgagtgaa ccagtgagta gaaaagagag atgtaaagaa 2340

tattatgaat agaaaatgta ttttttgttt gttttgcaag gaaggatata aagaaagagt 2400

aattttatat gtggaggaat cctgtatagt aaattcccta tcctagagta aaataacttt 2460

aagaaagagg tagtatagaa catgtcagga aattcagcta tgttgtagat ggtctgtgta 2520

agtcatctgc acagtgcatg agtgtggagg tgggcgggca ctcattggcc cttgaactcc 2580

ttttgagcag tatggaagcc aagaactaga agccaggaaa tggggttgta aaactgattt 2640

gtctatggat tttatgtgtt gagctgctgt ggtcttggct tgtagtaatt acctatatga 2700

accttccccc ctccccttta gaatttagga caggttcaaa aggccctcca atataaaaat 2760

aaaatactgt ccttccccac aaaggaaaaa atagctcccc ggttcaacca ggagacttag 2820

tcttgctaaa accttaaaga cagggtaaag acagggatac cccaagaatc aattacaatg 2880

aaatggaagg ggccttatca ggtattgtta agtaccccca ctgctgttaa acttcaggga 2940

acacctactt gggcacacag atccaggact aaacctgttt cttatgagtc acaggcacaa 3000

aggaagggca ctacaaccac aaccaatatc agtaaagctt tggaagacct ctgctaccta 3060

tttaaaataa tcaacactca gccagaagag gtaatgtaat gctgtagatg ggaataggag 3120

cattgatctt gctcttcttc ctgactgtag tacttccttt ctatggcttt aaccagccac 3180

ctcctcctgg gaaacatctc ctgtgggctt gttgggtata gaagctactc taagacccaa 3240

ccagatacca tgatgccact gttaattctg tttgctcttc taattaacct aagctagtgt 3300

gtatgtggac agggagggtg gacaaaattc tacagtaaat atttcaaaaa ttatagcatc 3360

atagaatcat ctttatggct gccagatttg tcatcaacac ccccaggata gacagtttca 3420

tcttccgacc tatctggaaa atctcaggac catgtcccca gacctcctaa ctaaccatag 3480

caccccaaaa tacccaaacc cctattgtga agtggaactc ttccccactt agtggatccc 3540

ccctggaccc tgctgtcccc ctgccctgac cactattatc ggaatctggg aagttgggca 3600

tctatatctc cagtgcactc ataactctaa catttgcatc cactcttgca ttaatgacac 3660

aaaagtggaa gcttccctgc gatgctctgg tccaactcta gttgccaagt ttccaagacc 3720

acggggaggt aaatgagatt ccatttgtga gtgaaaagac catatatggt accttctccc 3780

ggatgggaac atacaaagga aaaacaactg cctgatctgg gaaggtgaca gtactacctt 3840

cttctagaaa acaaagattg ttcaaccacc accatgagaa caggtggaaa atatctctat 3900

agacccaacc tggcaatgaa gtataaacat cgcaccccgc agggcttctc ttggtgccct3960

agttgggttc atttttgttt gtgactatga atgggaagaa gtcacaccct gtaaccactc 4020

caactcccta aggagtcacc tcttctttaa ggaatagctt tcccttgtat ctaaaaaact 4080

tggaactgac atgaatgaac gttggccact cttacccctc caggggtcac aatctataac 4140

gcctaggacc caagaatatc agaaataagt aagcaataaa actaattctg gcaggaatca 4200

gggtggcaat aggactagca gcaccctggg gtggctttgc ctaccatgag ttaacgctaa 4260

agaacttggc tcaaatccta gaatccttag ccaccaacgg agatcaggca ttaaagagaa 4320

ttcaagagtt ccccagactc tggaaaatgt agttgttgat aacagactag cattggatta 4380

tttactagct gaacaaggtg gggtcttgtg cagttattaa taaaacctgc tgcacatata 4440

ttaactctgg acaggttgag gttaacattc aaaagatcta tgagcaagct acctagttac 4500

atagatataa ccagggcact gcccccaact atatctggtc aaccatcaaa agtgccttcc 4560

caagtctcac ctgtttttca cctcttctag gacctttgac aactgtcttg ttacaaatgt 4620

ttggtccttg cttctttaac ctcttagtaa agtttgtgta ttctagatta ccacagttcc 4680

agagacaatg ctggcacaag gcttccagcc catcctgtcc actgacacgg agaatgaaat 4740

cgtcctgcct ctgggctcct tagatcaggt atccagagat ttttactcct ccagtgccag 4800

gcagggccta cgtccataaa ctcagcagga agtagttacg gaaaacagat ctccgccctt 4860

ctgcagcccc cttaagatta aggaggagta tctaatctct gaagggggaa tgaggtagga 4920

ggtgggactc aactctggaa gtggggctca ggcactcaga ccaaactgag cactagctaa 4980

aataggtcca gggcagatgc tagtttccat aggacacacc gacctgtgtc aagtcagttc 5040

accatggctc tggcagcacc cagaagttac caccctcacc ctggaaatgt ctgcataaac 5100

tgccccttca tttgcatata attaaaagtg gatacaaata ccactgcaga actgcctctg 5160

agctgctact gtgggcgcac agcctgtagg gcagccctgc tttgcaagga gcagcgcctc 5220

tgctgctgct gtgcacagcc ggccgcttca ataaaagttg ctaacaccac tggcttgccc 5280

ttgagttcct tcctgggcaa agctaagaac cctcccgggc tatgcttcaa tcttagggct 5340

cgcctgtcct gcatcactgg gatcatctcc cagtaaacta gccacactta catccatgtg 5400

tcagggacat ttctggagaa agcagcccag gacactgttg aataaaacac acaatagtct 5460

ctgtggtctt ctccacccca ccccacacca ggcaccctca gcttgattct cctttttaat 5520

tgcctgtaag cagggaagca caatgttttc acattctttg taaggccttt gttctactaa 5580

aatctaacct cagagcacaa ttttaaacta gatgaaagag ttgctgcgcc tgaagcactg 5640

caaacacctc ctcaccacac atgtgcactc accctggaca ccctcactca ccctgacacc 5700

ctcactcctc accctggaca ccctcactca ccccagacac cgtcactcct caccctggac 5760

acctcactct gcaccctgga caccctcact caccctggac acgttcactc accctgacac 5820

cctcactcac cctggacacc ctcactcacc ctggataccc tcactcctca ccctggacac 5880

cctcactcac cctggatacc ctcactcctc accctggaca ctctcactca ccctgacacc 5940

ctcaatcctc accctggact ccctcactcc tcaccctgga ctccctcact cctcaccctg 6000

gacaccctca ctcctcatcc tggacaccct cactcaacct ggacaccctc actcctcacc 6060

ctgacaccct cactcctcac cctggacacc ctcactcctc accctgacac cctcactcct 6120

caccctggca ccctcagtca ccctgacacc ctcactcctc accctgacac cctcaagtct 6180

tcacctccct ggctgcagcc tgggacacgc tttccctaac ttctgaaggc tcagtcctcc 6240

tcaagccaat ctcatctcaa attgcacctc ctcagagagg tcttccataa ccgcccttat 6300

aaagcaggat tctttcacca ataccccttc ccacatggca ctgtctcaca gcactcctct 6360

aaaagtctgt ttacttcctt gacaatctgt cttccttata aggggaggtt ctgtaaaagc 6420

caagactctc tctgtctagt tgactgttgc ataccagggc ttagaccaag gccctgacat 6480

gcagtaggtg cttaatatgt tttgaggcaa ggtcttgctc tgttgcacat gctggagtgc 6540

agtggcacaa tcgtaattca ttgcagcctt gaactcctga gctcaagtga tcctcctgcc 6600

tcagcctcct gagtagctgg gactacaggc atgcaccacc aagcttggct aatttaaaaa 6660

aaaaattata tagataggga cttgctatgt tgcctaggct gatcttgaac tcctaacctc 6720

aagcaatcct cccacctcgg ccttccaaag tgctgggata ataggcatgg agccgccaca 6780

cccagccaat gtgccgaaga aagaaagaaa aacatgctca tcctttgagt caggttcaaa 6840

ttttttctcc tctttaaccc ccagtcactc cagttataag tgatttttaa ctcttctcac 6900

actttaatgc atctggcaag aagatccacg tggtgttagg aacaatacag gaccttaagg 6960

atgggggaat cagcaggtgt cagcgtgccc tgtatgctca gggcagctgt ttccactgga 7020

cattctccct ttgcctctct gggcagcaac tcctaggcca gccgacctgc tgtgtcgagt 7080

aaccaggatt tctcaatctt ggcatggttg ccattttgga ccagatcgtt ctttgttgtg 7140

ggggctgccc tgtacggcaa agaatgccga gcagcacttc cagtctccac ccacaggacg 7200

ccagtagcac cctctaagtt gtgagaactc aaaatgtccc cagaggatgc cagatgtccc 7260

ctggggtggg gacacaatca ccccaggttg agatccatgg agccaggtct gtttgccacc 7320

aaggggtaaa gctccattcc caccttagga gggctaggag gcagcatcgt ggggccacag 7380

aaggcctggg tttgcagtca gaggacagga tgcacattcc ttcaagatac agacccagat 7440

tgttgggcat ctagttcttg ggttttctgt tgttgctgtt ccgttttgtc tgtcttccct 7500

cctttgttta ctagcagcct ggaatttgcc actttttcta aacgaagatt tatggaacac 7560

ttaccacacg gctgacgctg cgcgaggcta aggttctaat acaccgcagc tcacttaact 7620

ctcgcaatac cataaacgca cactgtttca tcttgaccct ttcttgggaa ggtgacagag 7680

aggtaggagg gcaaacatct tgtgtgcccc gtcccaaggg tattactggt ggaataatat 7740

ccgcccccca ccccagtttc taatttgctg taggctgtga cgctgtgggg caagactagg 7800

agtcctgttg aaattaggaa taagtgtgct gtgagggaag ggctgcctta ttttagagca 7860

cagattttct gaatatctat tttgacaggt tcgatcctct ccccttcctg ccttccttct 7920

gtcgattttc aatgtcttga tggtgtccca cctgagtggc ctttagagat gtgagttgtg 7980

aggcactggg gaggcaggca cacgtcctcc agcccaagac tgcctaattt aacagggatt 8040

tctgcattct ggaacaagcc tccattttcc ccaagcagga ttactccaga gggcaaaaca 8100

cagcccaata gtatcacatt tcctttctgc tttagcaaaa ataaccactg tctcattcat 8160

gggaaaaggc cgccaaacaa atttgttact ggaaccattt gtaacaactt ctagtttgca 8220

ctgccttgga gcaagcacac tttgtagagg agggatttgc agttacttgg gcaacaaggt 8280

aaccactgat cattacagga agcttcagaa accgtgggac cagtgtagaa gaatggacta 8340

tctgtccaaa ctaagaataa aaagaatgac acttgtattt tgtatgtctt tttcactttg 8400

cctttctagt aattcatttt tcttgatatt tacaccttgt ggccctgtga tagactggaa 8460

atctcaaaaa cacacgttca gcaccaagat tttcagcagc accgcctcag aatgagaccc 8520

ctagaaaaaa ctgcgtgttt tccacttgcc caacacgagg agtttttgga acacgacctg 8580

cttgaggtgg agattttcta gatgggcaaa gagaaggaaa cacttaacct aggaagagta 8640

tttaggaaga agaaagaaca cagcctttct gcacaggaaa ccgccgagca gaggggcatc 8700

tggcctctgc agtggcctcc aaatagagtc caatggctgg ggccagcgtg gctgcttaaa 8760

ggggactcaa gggatataat aaaatgcaga ttctcaggtc ctagtgcaga caggctcacc 8820

caataagtct ggactgcata tgggaatctc tatttctagg cccttctgca aggtattcct 8880

gctctttcca ggaaccatcg gcagctggtt tggggaaaga agcaacgact ccaagtgtga 8940

cctgtgagct ggcagcagcc accctcagct ctgctctcgg tcactgaatc cgattctgca 9000

ttttaacagg accccaggtg ttgcacccac acaaagctga agcagattgg tctgggggca 9060

aaaaattaga gctatggaga ttctctcaaa tgaaatagat gatatcattg actgttagag 9120

cttctagaag gaatctgagg tcacttgttc aaattccctg atttacagat gaggaaacag 9180

aggctcagac agctcaaatg acttctctcc aatacccaac attcgacaag tagcagctct 9240

gggactagta cccaaagcac ctagctctcc aatcactgcg caagccacac aattctgtct 9300

gcttgtcagt ggcttttctg attcaaaaaa agcttaggaa tttccccagg aggcagcacg 9360

atgtagtggg aagggctctggatgtctctc caaggcttct ggaattcatg cccacctcca 9420

ccaagaagcc actttcctgc cagctacagg tgctcacctg aaaagcaagc cagaccatat 9480

taaccctggc attgctggta cctggaagac tttctgattc aatgctttcc acctcctcct 9540

acccctcacc acccccgtgg catgaaatcc tgggggctgc tttagaaatt gttttctttg 9600

gctgctggtg ggggtgctgc tggtgggggt ttgcacagct ggcacactgc accagtctgg 9660

tgggggtttg cacagctggc acactgcacc agtctcctgc ctgctgccaa caaggccatt 9720

tcccaagcac tggctttgga gaagttgggg ctctgaagtg ggaacacaag gctgcctttt 9780

gcaggccagg tgtaaattct ccccctgcca ctttcagcct agcgtgaaac agatggagtg 9840

tgcattccca cttcccttta tggtaccctg gaatgatgga gctgcccagg gcatcgccac 9900

gttactctct agacagtctc tttgtcttcc tgcaatggca gcgccgaggt tgtatatttc 9960

taggtgcagg tatatgattg ccatataata aaaatctgaa aacatccca 10009

<210>7

<211>2046

<212>DNA

<213> Intelligent people

<400>7

agtgcagaag gaggaaggac agcacagctg acagccgtgc tcaggaagat tctggatcct 60

aggctcatct ccacagagga gaacacgcag ggagcagaga ccatggggcc cctctcagcc 120

cctccctgca cacagcatat aacctggaaa gggctcctgc tcacagcatc acttttaaac 180

ttctggaacc cgcccaccac agcccaagtc acgattgaag cccagccacc aaaagtttcc 240

gaggggaagg atgttcttct acttgtccac aatttgcccc agaatcttac tggctacatc 300

tggtacaaag gacaaatcag ggacctctac cattatgtta catcatatat agtagacggt 360

caaataatta aatatgggcc tgcatacagt ggacgagaaa cagtatattc caatgcatcc 420

ctgctgatcc agaatgtcac ccaggaagac acaggatcct acactttaca catcataaag 480

cgaggtgatg ggactggagg agtaactgga cgtttcacct tcaccttata cctggagact 540

cccaaaccct ccatctccag cagcaatttc aaccccaggg aggccacgga ggctgtgatt 600

ttaacctgtg atcctgagac tccagatgca agctacctgt ggtggatgaa tggtcagagc 660

ctccctatga ctcacagctt gcagctgtct gaaaccaaca ggaccctcta cctatttggt 720

gtcacaaact atactgcagg accctatgaa tgtgaaatac ggaacccagt gagtgccagc 780

cgcagtgacc cagtcaccct gaatctcctc ccgaagctgc ccaagcccta catcaccatc 840

aataacttaa accccaggga gaataaggat gtctcaacct tcacctgtga acctaagagt 900

gagaactaca cctacatttg gtggctaaat ggtcagagcc tcccggtcag tcccagggta 960

aagcgacgca ttgaaaacag gatcctcatt ctacccagtg tcacgagaaa tgaaacagga 1020

ccctatcaat gtgaaatacg ggaccgatat ggtggcatcc gcagtgaccc agtcaccctg 1080

aatgtcctct atggtccaga cctccccaga atttaccctt cattcaccta ttaccattca 1140

ggacaaaacc tctacttgtc ctgctttgcg gactctaacc caccggcaca gtattcttgg 1200

acaattaatg ggaagtttca gctatcagga caaaagcttt ctatccccca gattactaca 1260

aagcatagcg ggctctatgc ttgctctgtt cgtaactcag ccactggcaa ggaaagctcc 1320

aaatccgtga cagtcagagt ctctgactgg acattaccct gaattctact agttcctcca 1380

attccatctt ctcccatgga acctcaaaga gcaagaccca ctctgttcca gaagccctat 1440

aagtcagagt tggacaactc aatgtaaatt tcatgggaaa atccttgtac ctgatgtctg 1500

agccactcag aactcaccaa aatgttcaac accataacaa cagctgctca aactgtaaac 1560

aaggaaaaca agttgatgac ttcacactgt ggacagcttt tcccaagatg tcagaataag 1620

actccccatc atgatgaggc tctcacccct cttagctgtc cttgcttgtg cctgcctctt 1680

tcacttggca ggataatgca gtcattagaa tttcacatgt agtataggag cttctgaggg 1740

taacaacaga gtgtcagata tgtcatctca acctcagact tttacataac atctcaggag 1800

gaaatgtggc tctctccatc ttgcatacag ggctcccaat agaaatgaac acagagatat 1860

tgcctgtgtg tttgcagaga agatggtttc tataaagagt aggaaagctg aaattatagt 1920

agactcccct ttaaatgcac attgtgtgga tggctctcac catttcctaa gagatacatt 1980

gtaaaacgtg acagtaagac tgattctagc agaataaaac atgtactaca tttgctaaaa 2040

aaaaaa 2046

<210>8

<211>11025

<212>DNA

<213> Intelligent people

<400>8

gagcatcttt tggggggagg gaattcagcg gatcagtctt aagaggagct tttttttgga 60

gcgagaaatc atataaaata aaatgaaata aaacaaggag gaaggcaacc agctgttagg 120

ggaaaaataa ggcagataaa ggagcgggga gagaaattaa ttgccaacca ggaggagttg 180

ggctgtattt ttcaaaggtg gggagagtgg agcacacacc ttgaggagga aagcgagaaa 240

gaaaagaaaa aagcaagtgg aaaggggggc tcgcccaaga agggtgaaga agcgaagaaa 300

gtcgaggcgc cgaggctccc aaagctggca gctccgggtg gcggtgcagg ggcgaagggg 360

gggcgggggg aaccgtcgga catgcggctc tggagttggg tgctgcacct ggggctgctg 420

agcgccgcgc tgggctgcgg gctggccgag cgtccccgcc gggcccggag agacccgcgg 480

gccggccgac ccccgcgccc cgccgccggc ccggccacct gcgccacccg ggcggcccgc 540

ggccgccgcg cctcgccgcc gccgccgccg ccgccgggcg gtgcctggga agccgtgcgc 600

gtcccccggc ggcggcagca gcgggaggcg aggggcgcca ccgaggagcc gagcccgccg 660

agccgggcgc tctatttcag cgggcgaggc gagcagctgc gcctccgggc cgacctcgag 720

ctgccccggg acgcgttcac gctgcaagtg tggctgcgag cggagggggg ccagaggtct 780

ccggcagtga tcacagggct gtatgacaaa tgttcttata tctcacgtga ccgaggatgg 840

gtcgtgggca ttcacaccat cagtgaccaa gacaacaaag acccacgcta ctttttctcc 900

ttgaagacag accgagcccg gcaagtgacc accatcaatg cccaccgcag ctacctccca 960

ggccagtggg tatacctagc tgccacctat gatgggcagt tcatgaagct ctatgtgaat 1020

ggtgcccagg tggccacctc tggggaacaa gtgggtggca tattcagccc actgacccag 1080

aagtgcaaag tgctcatgtt agggggcagt gccctgaatc acaactaccg gggctacatc 1140

gagcacttca gtctgtggaa ggtggccagg actcagcggg agatactgtc tgacatggaa 1200

acccatggcg cccacactgc tctacctcag ctcctcctcc aggagaactg ggacaatgtg 1260

aagcatgcct ggtcccccat gaaggatggc agcagcccca aagtggaatt cagcaatgcc 1320

cacggctttc tgctggacac gagtctggag cctcctctgt gcggacagac attgtgtgac 1380

aacacagagg tcattgccag ctacaatcag ctctcaagtt tccgccagcc caaggtggtg 1440

cgctaccgcg tggtcaacct ctatgaagat gatcataaga acccgacggt gacgcgcgag 1500

caggtggact tccagcacca tcagctggct gaggccttca agcaatacaa catctcctgg 1560

gagctggacg tgctggaggt gagcaactcc tcccttcgcc gccgcctcat cctggccaac 1620

tgtgacatca gcaagattgg ggatgagaac tgtgaccccg agtgcaacca cacgctgacg 1680

ggccacgacg gcggggattg ccgccacctg cgccaccctg ccttcgtgaa gaagcagcac 1740

aacggggtgt gtgacatgga ctgcaactat gaacggttca actttgatgg tggagagtgc 1800

tgtgaccctg aaatcaccaa tgtcactcag acttgctttg accccgactc tccacacaga 1860

gcctacttgg atgttaatga gctgaagaac attcttaaat tggatggatc aacacatctc 1920

aatattttct ttgcaaaatc ctcagaggag gagttggcag gagtagcaac ttggccatgg 1980

gacaaggagg ccctgatgca cttaggtggc attgtcttga acccatcttt ctatggcatg 2040

cctgggcaca cccacaccat gatccatgag attggtcaca gcctgggcct ctatcacgtc 2100

ttccgaggca tctcagaaat ccagtcctgc agtgacccct gcatggagac agagccctcc 2160

ttcgagactg gagacctctg caatgatacc aacccagccc ctaaacacaa gtcctgtggt 2220

gacccagggc caggaaatga cacctgtggc tttcatagct tcttcaacac tccttacaac 2280

aacttcatga gctatgcaga tgacgactgt acggactcct tcacgcccaa tcaagtcgcc 2340

agaatgcact gttacctgga cctggtctac cagggctggc agccctccag gaaaccagcg 2400

cctgttgccc tcgcccccca agttctgggc cacacaacgg actctgtgac actggagtgg 2460

ttcccaccta tagatggcca tttctttgaa agagaattgg gatcagcatg tcatctttgc 2520

ctggaaggga gaatcctggt gcagtatgct tccaacgctt cctccccaat gccctgcagc 2580

ccatcaggac actggagccc tcgtgaagca gaaggtcatc ctgatgttga acagccctgt 2640

aagtccagtg tccgcacctg gagcccaaat tcagctgtca acccacacac ggttcctcca 2700

gcctgccctg agcctcaagg ctgctacctc gagctggagt tcctctaccc cttggtccct 2760

gagtctctga ccatttgggt gacctttgtc tccactgact gggactctag tggagctgtc 2820

aatgacatca aactgttggc tgtcagtggg aagaacatct ccctgggtcc tcagaatgtc 2880

ttctgtgatg tcccactgac catcagactc tgggacgtgg gcgaggaggt gtatggcatc 2940

caaatctaca cgctggatga gcacctggag atcgatgctg ccatgttgac ctccactgca 3000

gacaccccac tctgtctaca gtgtaagccc ctgaagtata aggtggtccg ggaccctcct 3060

ctccagatgg atgtggcctc catcctacat ctcaatagga aattcgtaga catggatcta 3120

aatcttggca gtgtgtacca gtattgggtc ataactattt caggaactga agagagtgag 3180

ccatcacctg ctgtcacata catccatgga agtgggtact gtggcgatgg cattatacaa 3240

aaagaccaag gtgaacaatg cgacgacatg aataagatca atggtgatgg ctgctccctt 3300

ttctgccgac aagaagtctc cttcaattgt attgatgaac ccagccggtg ctatttccat 3360

gatggtgatg gggtatgtga ggagtttgaa caaaaaacca gcattaagga ctgtggtgtc 3420

tacacgcccc agggattcct ggatcagtgg gcatccaatg cttcagtatc tcatcaagac 3480

cagcaatgcc caggctgggt catcatcgga cagccagcag catcccaggt gtgtcgaacc 3540

aaggtgatag atctcagtga aggcatttcc cagcatgcct ggtacccttg caccatcagc 3600

tacccatatt cccagctggc tcagaccact ttttggctcc gggcgtattt ttctcaacca 3660

atggttgccg cagctgtcat tgtccacctg gtgacggatg ggacatatta tggggaccaa 3720

aagcaggaga ccatcagcgt gcagctgctt gataccaaag atcagagcca cgatctaggc 3780

ctccatgtcc tgagctgcag gaacaatccc ctgattatcc ctgtggtcca tgacctcagc 3840

cagcccttct accacagcca ggcggtacgt gtgagcttca gttcgcccct ggtcgccatc 3900

tcgggggtgg ccctccgttc cttcgacaac tttgaccccg tcaccctgag cagctgccag 3960

agaggggaga cctacagccc tgccgagcag agctgcgtgc acttcgcatg tgagaaaact 4020

gactgtccag agctggctgt ggagaatgct tctctcaatt gctccagcag cgaccgctac 4080

cacggtgccc agtgtactgt gagctgccgg acaggctacg tgctccagat acggcgggat 4140

gatgagctga tcaagagcca gacgggaccc agcgtcacag tgacctgtac agagggcaag 4200

tggaataagc aggtggcctg tgagccagtc gactgcagca tcccagatca ccatcaagtc 4260

tatgctgcct ccttctcctg ccctgagggc accacctttg gcagtcaatg ttccttccag 4320

tgccgtcacc ctgcacaatt gaaaggcaac aacagcctcc tgacctgcat ggaggatggg 4380

ctgtggtcct tcccagaggc cctgtgtgag ctcatgtgcc tcgctccacc ccctgtgccc 4440

aatgcagacc tccagaccgc ccggtgccga gagaataagc acaaggtggg ctccttctgc 4500

aaatacaaat gcaagcctgg ataccatgtg cctggatcct ctcggaagtc aaagaaacgg 4560

gccttcaaga ctcagtgtac ccaggatggc agctggcagg agggagcttg tgttcctgtg 4620

acctgtgacc cacctccacc aaaattccat gggctctacc agtgtactaa tggcttccag 4680

ttcaacagtg agtgtaggat caagtgtgaa gacagtgatg cctcccaggg acttgggagc 4740

aatgtcattc attgccggaa agatggcacc tggaacggct ccttccatgt ctgccaggag 4800

atgcaaggcc agtgctcggt tccaaacgag ctcaacagca acctcaaact gcagtgccct 4860

gatggctatg ccatagggtc ggagtgtgcc acctcgtgcc tggaccacaa cagcgagtcc 4920

atcatcctgc caatgaacgt gaccgtgcgt gacatccccc actggctgaa ccccacacgg 4980

gtagagagag ttgtctgcac tgctggtctc aagtggtatc ctcaccctgc tctgattcac 5040

tgtgtcaaag gctgtgagcc cttcatggga gacaattatt gtgatgccat caacaaccga 5100

gccttttgca actatgacgg tggggattgc tgcacctcca cagtgaagac caaaaaggtc 5160

accccattcc ctatgtcctg tgatctacaa ggtgactgtg cttgtcggga cccccaggcc 5220

caagaacaca gccggaaaga cctccgggga tacagccatg gctaaggaag gacaagaagt 5280

tgtcaaagaa ttcccaacgc caggacccac atccctttgg tattgatttc acagtcagct 5340

gctcaacgga atggcctctc cacaccaggg atccttagca cccaaccggt ctgcctttaa 5400

ttttacccag gaaggactca cattggggcg aatgaaccaa gtttcgccat gctggatgat 5460

gaaatggatt cccatcccaa agtctgagat ggattgcata tacagtgtgc agtcccagag 5520

cctcctaaaa ttctagccat ttgtcacaca accacagcaa gaaacgtgtt ctatatctag 5580

agtgtgccca tctgtgttta gtacacatgc atgcatacac acccatacaa acatctgtgt 5640

gagggcagtt ctggagatga gcagagagag accggaataa actcaatctt ttctttccca 5700

agctcctagc caacactatc cttgggagaa agaaatttgc agaaactgct aagaccaagt 5760

gtggagatgt caagctagtt cacactctga ggctcagaat atgtaggaca tgcacaattg 5820

tgcagtcctt tgggattgga agtgaaacag tctgtgatcc cctaccttct agggaactag 5880

gacctaggaa gaggtaaaga ttatcaggta tgcaaagcgc cccaattctt ctgctgccat 5940

gggggatttt accccaactc cagggttcga ggccaatctg agaatggctt aggattgcaa 6000

tgtcaaggta ttatatcagc cccttgcttg aggcttgagg tcataatatc cctctaggac 6060

ttacctgttc ccccagatct tgccttggga ccacatttgc tgctactttt cctgctgctc 6120

tatcctatac attgaataat ccaagatggt agaactaggt taggaaaaat tccacacaac 6180

caaacagtct gccttaaaag tgacccacat ttttccatag ctcctcactt tttagccctt 6240

ctgcaagaga aaaaccctca tgggtccaca tggtgagaag ttaagtttcc tgtaagtggg 6300

cctctcaccc tggaaaggag ttgagggaca tcagatgctg gaaccctcac tgaaagtcca 6360

gaatgtctaa gccagtgtta gattttgtaa acaagtggaa cagtgttaaa tttctatgat 6420

gttggagcca tccagagact actggaattg tcgagacttt tggattatta tccttatcct 6480

tatcctaatc ttcctagccc ttcaggctag agtaggcttc gatcctgaga accttgctgt 6540

tgctctgagg agatataatt ctgggagaaa gaatctttta taagaacagt acagattgtt 6600

ctcaagaggg ccatcagaag gaagccaaag agttcacagc ctcagcacca acaactcaac 6660

atggtcatca tgttttctat atggtttttc cagctagcag tactcccttc catacctgtg 6720

actgggcagt gcttttctct ctcccatgtc tagcctccaa aagttaagtg aaaattagtc 6780

aactgcacgt ggaagccccc accactttgg ggatctcttt atttcttttc agccagggac 6840

ctgtccactc cctttgaatt aatatgggaa gaaattaata caggatgaac tggagagaag 6900

ggttgagtgt ggcatacttt ctgaaacctg gagctgggaa ttgcggagaa gggaaggtct 6960

agactagtta catcacatag ggattactgt aaatcaagtc atctcaagtc tagtgaagac 7020

agccaacaga aacaaaacct agcataggga tagaaaatac catgcacgtg tgcagcccca 7080

cctaattcct gcatccaagg caggtgttgt taatctatca tagcacttaa aaaaaaaaaa 7140

aaaaagagac caaaaataac tttaggaacc accatattat atcactccca atagcactga 7200

cctggtgatc aaaaacactt gagaagacat ctattggcca tctctggcca attacactaa 7260

gaaacatatc aaggtgcttt tggcacaggt gcccacaaat acggatgcag tgctgagata 7320

gtttatgaga cttgtaccat ttcacaaact ctgaaattgg gttccatatt ggcaaggctg 7380

ccacagttgt taagaataat cctctatgtt tcttcctcac aaaaccatat ctcatttata 7440

tccagaccat tacttcacta taattacaag gacaaattat tagcaagaaa taagaatagt 7500

attagaagaa ttgatcctat tttgaacccc tctccagtat cttcacactc ttgtcaactc 7560

tccaggcctc tctcttgccc tgagttatca gcctgtgtgg tgttaactac cttagaaggt 7620

acaagctaag aaatgtaaca gtatcaaccc tcccagttgc ttaattatac ccataggtaa 7680

tacaaaaagc tctgaagacc caaagatgac attactaatg atgtgatttc aggagccaca 7740

gaagaacctt accagcttcc ctcaaatcag tccttatcct ctttctatct tcactcccat 7800

catcatctat tttcacacta tccagctaag caaagattcc tggaggctga cttgtatctt 7860

cagactcaca gagtgaattcagctcttctg aatcaagacc cacccagtct ctttcattca 7920

gacctgttgc taacaaattt atatttgcca aggatattag gcaaaagagg ctacttgatt 7980

ggtggccaac ctcgtgccca catggaaggt atctttaata gggtcttttc aaaccttagt 8040

ggaggagggt cagctcaatt tgggcaatgc atttgttccc agtttcattt tcttcctggg 8100

aattaactcg tcatttcatt ccttcagtca tcttctgtgt aggtgaccgg agcactgaga 8160

ggcagctctg atgcactatt gtgtgtcagc agctcaaagg ccctaaaaca ctgaaggttc 8220

tgcatctgaa gtattagatt gttagcagca aaatatgaaa gatgaggtgg acagtcctct 8280

aagccctatt tagggaagct tttccaagcc acaatcttaa ctacctaccc aaaggatttg 8340

cattaccccc agattctgtg ccaacaacct tttaaggaaa tacagtcctt gggaaatgag 8400

ttttgatggt gaattggggt gttaaggaag ggaaagattg tcatagatgg tagggctttg 8460

aaaatgcagg gtatcagctg ccactcctgg cttcaacaca ttgagtcact gcctagacgg 8520

ttctcttggt cttattccca tcctggccaa tgcttaaata ctatttgttg aaaataattc 8580

tttgagacag atttcagcta cctcccttcc aggttcgatt taacttggtt gtaattgtca 8640

atttgttgtt ataggtctta cctgtgtgaa agaaagaaaa agaaagaaag aaagaaagag 8700

aaaggaaatt ataaggtcaa gttaacagtt ttgaggtttt gtgttttttt ctggaactac 8760

ttcaagtgag aaaataaaaa aaaatggtga caaagctgta cagatagaga taatagaaga 8820

caaagagatt aaaaggaaat aaaaatgcat gattaaaaac taagaataaa aaacctattt 8880

ttatgtttcc taaaggaaat tgtttattct acagcctcag taggtagaca caaacataaa 8940

gatttcccta gaagacatag agtgggattt gataacactg tctgttattt tctgtacatt 9000

gtggtaggtc caggaaatat gacattttcc cccttgatgt gttattgttg ttgttgggtg 9060

gggtgggcat tttgtttatt tgtttggtgg caatcagtgg tagtagggag tgggagggct 9120

tatattggtt tttccagcta ttaaggggac atattgtgtc gttgtgcttt tcacgttata 9180

aaatgtttat atttaccagt acagcactgg gctttataaa gactgcactc agaaccacac 9240

tgcacagtcc agttttttaa aaagctgcta catgacagac aggtaatccc actgagtgag 9300

ttttgagaaa caaatcaaac gaagtaaaca agaaacataa aaaccaaata gcaaatgaat 9360

aaaagcctgt tcttgtaact tattcaactt ttgccaaatt cctaccaatc acttgctttt 9420

taaaagaaat gtataatagc caaaagagaa attatgtccc tgttgtacag aagttagaat 9480

ttttgactcc aggcagcagt ttgctcagtg atcttgaaca agttatccaa ttgcctctac 9540

atttgcatca gtttctctag ctgcaaaatg gggataatac tatataccta cctcacagtg 9600

ggagggcagg agattttgag gccctgaggt tttaggtggg ctgtgagggc caacgcttga 9660

cacaaagtcc atgggttatt attcaagaat gcacaggccc atcggccttt tagaaagaca 9720

agacagggag tgcttgtttg atatttcaag gaataaagcc ggagctcctg aattgtagtc 9780

caccttaaaa gagagacctg tattggagaa tattttattt ttttggcaaa tttgatctta 9840

ccctttacca gttctataat ttggttaaaa gctgattatg tcctacaatg tcaaagtcag 9900

ctaactgtcg tctacttaag acttctggtc atttccaact tatagaggaa gggagtctct 9960

aaaatctctt cttcagaagg cacctcactt ctcagactta aaattccaca tcaagtgttc 10020

cattaaaaga agataaggca ttctgagtgc aaacaaatgg gggcttctta aactacacac 10080

cagcagtcag tgaggaaaac tttgaacaat tattgagttg ctttcttggg tctctataat 10140

caataacctg tctgcagata tctatctata taaagatatt atatataaat ataaatttac 10200

atatatatgc acatgtatat atagttgtac atatatgtgt gtatatatat acttaaatgt 10260

aatatttaca aaataaaact gtgatctcgt ctagagaaaa tgtattcata ttacaaactg 10320

ctcttccata tttatgtacc atattatacc tttttattat tgttataatt attatgggta 10380

tttctaatta atatgatgtt gaaacctgtt tggcaccttc tggaagctac caaaaaaatg 10440

acactccatt gaagtgctta aaagctgttc tcataagaat tctactggcc tattgtaaaa 10500

aagaaaaaaa aaaagaaaaa gaagaaagac acaaagaaaa taatctaaac accaaaaact 10560

aaacacaatt ccaatccttt ttctgtacct cacgcgcata aatttgctgc tcctattttt 10620

ttttctgttt atgtgttttt atggatctaa gttaaatctt ttggcaatat ataaaaatgt 10680

aaatagtaaa ctttatttat taagaatgtc atctttttta atttatattt acacaattgt 10740

tcatctaatt tattttttct atacagtttt aaatactcag acatattttg ctgttcatga 10800

tatttttatc ctgttctcat ggatttgttt tcccatactg ttttctctga tctcaattac 10860

aggttggatc tcacaaataa taatgtcaga gacagaaata ttttgccact gttgattact 10920

atactttaaa gttctatatt atgaaaatat ataatagctt gtacgcttca aaaaaaaaaa 10980

aaaaaaaaaa aaaaaaaaaa aaaaaaaaaa aaaaaaaaaa aaaaa 11025

<210>9

<211>794

<212>DNA

<213> Intelligent people

<400>9

gctgcattac agacacagac ctgcaaacat ctatggttgt gacagagttt ctttctgaca 60

cctgagtctt tctcctgctg cacggaaagc ttgctgggag gggcttggaa tctggcatga 120

agccaaaggg catctctgag ttgcagcatt taaatgatcc cactcagaga ttcacacaga 180

agactggaca caattccgaa gagctgccca gaaggagaga acaatgtcat cactacccgt 240

accatacaca ctgcctgttt ccttgcctgt tggttcgtgc gtgataatca cagggacacc 300

gatcctcact tttgtcaagg acccacagct ggaggtgaat ttctacactg ggatggatga 360

ggactcagat attgctttcc aattccgact gcactttggt catcctgcaa tcatgaacag 420

ttgtgtgttt ggcatatgga gatatgagga gaaatgctac tatttaccct ttgaagatgg 480

caaaccattt gagctgtgca tctatgtgcg tcacaaggaa tacaaggtaa tggtaaatgg 540

ccaacgcatt tacaactttg cccatcgatt cccgccagca tctgtgaaga tgctgcaagt 600

cttcagagat atctccctga ccagagtgct tatcagcgat tgagggagat gatcagactc 660

ctcattgttg aggaatccct ctttctacct gaccatggga ttcccagagc ctactaacag 720

aataatccct cctcacccct tcccctacac ttgatcatta aaacagcacc aaacttcaaa 780

aaaaaaaaaa aaaa 794

<210>10

<211>6299

<212>DNA

<213> Intelligent people

<400>10

gtgacgtcac gcgtcgacgc tggggcgtac ctttcgggct cctgactcct gccgcttctc 60

ttccccttcc gtgggtcagg gccggtccgg tccggaacct gcagcccctt tcccagtgtt 120

ctagttcgcc cgtgacccgg aataatgagc aaggagggtg tggtgggttg aaagccatcc 180

tactttactc ccgagttaga gcatggattc agttttagtc ttaaggggga agtgagattg 240

gagattttta tttttaattt tgggcagaag caggttgact ctagggatct ccagagcgag 300

aggatttaac ttcatgttgc tcccgtgttt gaaggaggac aataaaagtc ccaccgggca 360

aaattttcgt aacctctgcg gtagaaaacg tcaggtatct tttaaatcgc gatagttttc 420

gctgtgtcag gctttcttcg gtggagctcc gagggtagct aggttctagg tttgaaacag 480

atgcagaatc caaaggcagc gcaaaaaaca gccaccgatt ttgctatgtc tctgagctgc 540

gagataatca gacagctaaa tggagtctga gcagctgttc catagaggct actatagaaa 600

cagctacaac agtataacaa gtgcaagtag tgatgaggaa cttttagatg gagcaggtgt 660

tattatggac tttcaaacat ctgaagatga caatttatta gatggtgaca ctgcagttgg 720

aactcattat acaatgacaa atggaggcag cattaacagt tctacacatt tactggatct 780

tttggatgaa ccaattccag gtgttggtac atatgatgat ttccatacta ttgattgggt 840

gcgagaaaaa tgtaaagaca gagaaaggca tagacggatc aacagcaaaa agaaagaatc 900

agcatgggaa atgacaaaaa gtttgtatga tgcgtggtca ggatggctag tagtaacact 960

aacaggattg gcatcagggg cactggccgg attaatagac attgctgccg attggatgac 1020

tgacctaaag gagggcattt gccttagtgc gttgtggtac aaccacgaac agtgctgttg 1080

gggatctaat gaaacaacat ttgaagagag ggataaatgt ccacagtgga aaacatgggc 1140

agaattaatc ataggtcaag cagagggtcc tggttcttat atcatgaact acataatgta 1200

catcttctgg gccttgagtt ttgcctttct tgcagtttcc ctggtaaagg tatttgctcc 1260

atatgcctgt ggctctggaa ttccagagat taaaactatt ttaagtggat tcatcatcag 1320

aggttacttg ggaaaatgga ctttaatgat taaaaccatc acattagtcc tggctgtggc 1380

atcaggtttg agtttaggaa aagaaggtcc cctggtacat gttgcctgtt gctgcggaaa 1440

tatcttttcc tacctctttc caaagtatag cacaaacgaa gctaaaaaaa gggaggtgct 1500

atcagctgcc tcagctgcag gggtttctgt agcttttggt gcaccaattg gaggagttct 1560

ttttagcctg gaagaggtta gctattattt tcctctcaaa actttatgga gatcattttt 1620

tgctgcttta gtggctgcat ttgttttgag gtccatcaat ccatttggta acagccgtct 1680

ggtccttttt tatgtggagt atcatacacc atggtacctt tttgaactgt ttccttttat 1740

tcttctaggg gtatttggag ggctttgggg agcctttttc attagggcaa atattgcctg 1800

gtgtcgtcga cgcaagtcca cgaaatttgg aaagtatccc gttctggaag tcattattgt 1860

tgcagccatt actgctgtga tagccttccc taatccatac actaggctaa acaccagtga 1920

actgatcaaa gagcttttta cagactgtgg tcccctggaa tcctcttctc tttgtgacta 1980

cagaaatgac atgaatgcca gtaaaattgt cgatgacatt cctgatcgtc cagcaggcat 2040

tggagtatat tcagctatat ggcagttatg cctggcactc atatttaaaa tcataatgac 2100

agtattcact tttggcatca aggttccatc aggcttgttc atccccagca tggccattgg 2160

agcgatcgca ggaaggattg tggggattgc ggtggagcag cttgcctact atcaccacga 2220

ctggtttatc tttaaggagt ggtgtgaggt cggggctgat tgcattacacctggccttta 2280

tgccatggtt ggtgctgctg catgcttagg tggtgtgaca agaatgactg tctccctggt 2340

ggttattgtt tttgagctta ctggaggctt ggaatatatt gttcccctta tggctgcagt 2400

catgaccagt aaatgggttg gagatgcctt tggcagggaa ggcatttatg aagcacacat 2460

ccgattaaat ggataccctt tcttggatgc aaaagaagaa ttcactcata ccaccctggc 2520

tgctgacgtt atgagacctc gaaggaatga tcctccctta gctgtcctga cacaggacaa 2580

tatgacagtg gatgatatag aaaacatgat taatgaaacc agctacaatg gatttcctgt 2640

cataatgtca aaagaatctc agagattagt gggatttgcc ctcagaagag acctgacaat 2700

tgcaatagaa agtgccagga aaaaacaaga aggtatcgtt ggcagttctc gggtgtgttt 2760

tgcacagcac accccatctc ttccagcaga aagtcctcgg ccattgaagc ttcgaagcat 2820

tcttgacatg agccctttta cagtgacaga ccacacccca atggagatcg tggtggatat 2880

tttccgaaag ctgggactga ggcagtgcct tgtaactcac aatgggattg tcttggggat 2940

catcacaaag aagaacatat tagagcatct cgagcaacta aagcagcacg tcgaaccctt 3000

ggcgcctcct tggcattata acaaaaaaag atatcctccg gcatatggcc cagacggcaa 3060

accaagaccc cgcttcaata atgttcaact gaatctcaca gatgaggaga gagaagaaac 3120

ggaagaggaa gtttatttgt tgaatagcac aactctttaa cctgagggag tcatctactt 3180

ttttttcctc ctttacaaaa aaagaaagga aatataaaag ccgggttttt gcaacatggt 3240

ttgcaaataa tgctggtgga atggaggagt tgtttgggga gggaaaggag agagaaggaa 3300

aggagtgagg tatttcccgt ctaacagaaa gcagcgtatc aactcctatt gttctgcact 3360

ggatgcattc agctgaggat gtgcctgata gtgcaggctt gcgcctcaac agagatgaca 3420

gcagagtcct cgagcacctg gcctgttgct ccaacattgc aaagacacat tatcagtccc 3480

tatttctaga gggattactt tgaattgagc catctataaa actgcaaggt cttgcccttt 3540

tttttaatca aaactgttct gtttaattca tgaattgtat agttaagcat tacctttcta 3600

cattccagaa gagcctttat ttctctctct ctctctctct ctctctctct ctctactgag 3660

ctgtaacaaa gcctctttaa atcggtgtat ccttttgaag cagtcctttc tcatattgag 3720

atgtactgtg attttactga ggtttcatca caagaaggga gtgtttcttg tgccattaac 3780

catgtagttt gtaccatcac taaatgcttg gaacagtaca catgcaccac aacaaaggct 3840

catcaaacag gtaaagtctc gaaggaagcg agaacgaaat ctctcattgt gtgccgtgtg 3900

gctcaaaacc gaaaacaatg aagcttggtt ttaaaggata aagttttctt ttttgttttc 3960

ctctcagact ttatggataa tgtgaccggg tcttatgcaa attttctatt tctaaaacta 4020

ctactatgat atacaagtgc tgttgagcat aattaaataa aatgctgctg ctttgacagt 4080

aaagagaagg aagtattctg attagctgta tctggtatta attgcatgtt aaaacactgg 4140

aatttttaaa attgaaatta gatcagtcat tcttttcttt tctcaagata tctcatggct 4200

gacactgaag aagaaatgta attcataact tgcactaaat gtatattttt tttcttaaaa 4260

atttaccatt cttatttata tttttatgga ttaaaattta taaaatacag atcagttaat 4320

attgcactta agtaatttta cctttttaat gtgattttta tagaataatt cagacttaca 4380

aatacagaga tatgaacaaa gtttacagtg ggaacaaagg tttaaaaaaa ggttgtggtt 4440

ctctctctgt gatccagtgt gcacataaac ctttctctga tctttcactg ccatcctctg 4500

gattatgtct tctgacctgt ccattttgac ccattaactg gaaagttgaa aaactacatt 4560

aactggaaag ttgaaaaact acattacttt ggagaataaa accgaaagtt cgtgtatacc 4620

ttcttaaaaa aaaaatcaaa ccaaaaatgt gaaaacaata gaattgcaaa gatagcagtt 4680

aaaattttaa tctgaaaata acctttgaat ctcgggctag gttacgtcca tatttgaagt 4740

ggtcagtgat ggtttgaaca ttttttgcag gatgagtgaa aatgcactgg attatatttg 4800

ggatttttgt ttttggaatt gtctgtttta atcacagcct taattcacaa ttggcaaagg 4860

cagtttactc aaaggactgg gctaaatatt ctgtaattat gcatttttga taggaaaatg 4920

aaatttttgc aaacagacat tttctttttt tttggctgga gtgcagtggg gcatggtctt 4980

ggctcactgc agcgttgacc acctgggctc aagtgatact cccgcctcag ccacccaagt 5040

agctggcact acgggcacac gccaccatgc ccagctaatt tttttgtatt tttagtagag 5100

atggggtttt gccatgctgc ccaggctggt ctcaactcct cagctcaagc aatctgcctg 5160

cgtgagcctc ccaaagtggt ggaattacag gcgtgggcca ctgcgcctgg cccagacaga 5220

cattttctga aacacaactg gcaatgagct gtttttacat tttgaaagtg attcttcact 5280

tcctagttct taattatagt atacctatta agatctgtaa gatcctgaag acataagatc 5340

atgaagccat ataagaatga ggattgaaag ttgagcaaaa ttttcgggat tttgggaaac 5400

attcttagct gtgctatctg cctaaaatta ttccttatta cttctctcct ttgacagact 5460

tcaagttttc ttcatagccc tttcaaagtt ttttgagcca tccagagtaa aatcatttct 5520

aaatgatagt tctgtatatc tccaactcgt cttaagtgta tttgcctgtg tgcaacgtat 5580

tgctagacta tgaactcctc agcatggctg ctggataact taattgtcct gagttaatag 5640

ccttcaaagg acaaatcggt ttctttgcag atagcttcgt aaaacttcac atggagttta 5700

ttttatcata tttccctttt ttatttctgc tcctccttta attgcccatc ttgcttcaga 5760

gactgacatt tcagggtgga tattaattaa agcattaatt ttgttttttg gtatatttct 5820

atccctagta tttctatctt actgctaaaa tacaggaaaa gtgccgtatt tttaatgcat 5880

ttagtggttt tctttggtgt tatctgttcc atttttcttt ttcatacatt gaagtgtgtc 5940

tccttttcaa ccaaaataat gaaatagtgg agaccatgaa attgttgtgc ctggctaatt 6000

ggcaaattaa tttaccaata taataagtgt agcgccttgt ttgaataccc tttttgagaa 6060

ggtatgatga gaatgggcaa gggtgtcagc atctcttctt cttaataatt aattgttttc 6120

agttttggtt cacgaagaat gcttagttaa tctgtaatgt tgcctagagc tgtatttatc 6180

tgtttttatt tatactagtg tagtaaagct gcatatcatt acagtaaaaa cgactactgt 6240

gatgagttaa tcagaaaatc tattaaaatc tatatgacaa tgaaaaaaaa aaaaaaaaa 6299

<210>11

<211>3006

<212>DNA

<213> Intelligent people

<400>11

gcaggctgct gtctcacaga gcgagaaggt gtcaggagca gcccagttgt gtctctctct 60

ctacctctgt gaagggcgcg aatgggcaga gcagaacttc tagaagggaa gatgagcacc 120

caggatccct cagatctgtg gagcagatcc gatggagagg ctgagctgct ccaggacttg 180

gggtggtatc acggcaacct cacacgccat gctgctgaag ctcttctcct ctcaaatgga 240

tgtgacggca gctaccttct gagggacagc aatgagacca ccgggctgta ctctctctct 300

gtgagggcca aagattctgt taaacacttt catgttgaat atactggata ttcatttaaa 360

tttggcttta atgaattctc atctttgaag gattttgtca agcattttgc aaatcagcct 420

ttgattggaa gcgagacagg cactctgatg gttctaaaac atccctaccc aagaaaagtg 480

gaagaaccct ccatttatga atctgtccgg gttcacacag caatgcagac aggaagaaca 540

gaagatgacc ttgtgcccac agcaccttct ctgggcacca aagaaggtta cctcaccaaa 600

cagggaggcc tggtcaagac ctggaaaaca agatggttta ctctgcacag gaatgaactg 660

aaatacttca aagaccagat gtcaccagaa ccaattcgga tcctagacct aacagaatgt 720

tcagctgtac aattcgatta ttcacaagaa agggtaaact gtttttgttt ggtatttcca 780

ttcaggacat tttatctctg tgcaaagacc ggagtagaag ctgatgagtg gatcaagata 840

ttacgctgga aattggtcaa ggacaaaagc tgatttattt tgtctgctct ctgtatatct 900

cccgaggaga agactgatca caaataagaa aacagctcaa ccaaggggaa ggcacgatcc 960

gatctcggtc gttcatcttt aaatagatct ttcttgccaa ggaatgctct ggcccaggag 1020

caaggtggaa tgtttccctg acgctgtgat ctgcagcagg cttcaaatga aaaccgacta 1080

aggattttct ttcaaaaaca aatcagaagc agatgctgat tgggacccat ataccacgtt 1140

gctgactcac gttgctgccc ttccatgatg ttgccatctc cttgagaaca ctgaagcaat 1200

caccattctg atagaaagtg cttaaaccac cactcttagg tctgctcact cttagaacac 1260

acaatggaag aggaagggtt tttgttttca ctcattgtgg tccccaagcc tattgacact 1320

agttgcctag agtcccactg tgagtcatgg tcagcctgtc tgacatccag gttgtgctat 1380

taaccaagaa ggaaacagat acttggaggc ttagatgact tctgcaggat ttatattcag 1440

atagaaaaca tcaaatattt tcaggggaga ggtttttttt tttaattttt ccccctttat 1500

acaaaaaaaa aagaacattt ccaaaactaa aatagaaaat gcttgtggca tttattttct 1560

ctttttaaaa ggttcagaaa tttggcaggt cctttgcttc taatgacaaa actgtgagag 1620

ctagatgtcc tatgggcaat taggtagtat aataaaggta aatgaaggta caatttttaa 1680

accattattt tcaccctgtt ggggtaaatg ttttaaagag tgagaaaaca taaattgaga 1740

aagggtgata aagtaataga taacttttag tttaataata attattgtta ttatactact 1800

aataatagag cacttgtaag cactaagtta tctttatcca acatttctcc aaatggactg 1860

aaagaaactt ttcaaggaca gtgtattata acaatccctt tcccagaatt agttgtatag 1920

ggttggccca agagatgtaa gaaaaatctc gcattgctcc ctaagcaccc tgggccttat 1980

taaagagcaa cttctatttc cagtcggggg agtaacacta aagctacaag aaatatgtaa 2040

taatgatagg taataatgtg ttccaaagct ttttcaaact agaataagga ggcaaataga 2100

agaatgagat actgatgtcc acagttcatt ggcagaatct aaccccttct gttatctttt 2160

ttaatactat ttttgtttag atagaagttt caaagaagat aaaaatgctt gaagagcctg 2220

agagtaaaaa gattatgctg caaagctatg atataaactg ctcttgcagt ccaaagggat 2280

acctgattaa agaagtttct tatttaaaca tctcagacgc aaaaattaca ttaaattttt 2340

gtatatttca acaacatttt aaatgtattt tgttatgttt gtattatata ggataaagca 2400

aatgtcaagt taaaatgtat tgtgttgttt gtaaagtaag aagttactgg ccaggagcgg 2460

cggctcatgc ctgtaatccc aggactttgg taggccaaga caagcagatc acttgaggtc 2520

aggagttcaa catcagcctg gccaacatga tgaaaccttg tctttactaa aaatacaaaa 2580

attagctggg catggtggca ggcgcctgta atcccagcta ctcaggaggc tgaggcagga 2640

gaattgcttg aacccgggag gtggaggttg cagtgaacca agatcgcggc gctgcactct 2700

agcctgggtg acagagtcag actccgtccc aaaaaaacaa acaaacaaaa caaaacaaaa 2760

aaaaacagaa gttacaaatg aatactcacg gatatgtata gttttatgtt tgttttctta 2820

gaaacaaatg tgtttctttg ggtgggtaat attgtgtttt actatgttta ccttttataa 2880

aacataacct gtttatttat attctttggc tttgtttatt aaaaagcatg attttgctgt 2940

gcatgtacca ttttgctatt aaaatttatt tttaatattt gtaacttgaa aaaaaaaaaa 3000

aaaaaa 3006

<210>12

<211>1861

<212>DNA

<213> Intelligent people

<400>12

agcctactcg gtccggggtt gcgaactgta aggtctgagt tgctgcggcg caggcagcgg 60

agaccaagca gggatcttaa cagggtttag cgccacgcgg gccagggccg aggccggagc 120

tgggaggggc gcgcccggga aggggcggag ctgcggcggt ggcgccaaat cgcaaatatg 180

gcgccggagc ggctgcggag ccgggcgctc tccgccttca agttgcgggg cttgctgctc 240

cgtggtgaag ctattaagta cctcacagaa gctcttcagt ctatcagtga attagagctt 300

gaagataaac tggaaaagat aattaatgca gttgagaagc aacccttgtc atcaaacatg 360

attgaacgat ctgtggtgga agcagcagtc caggaatgca gtcagtctgt tgatgaaact 420

atagagcacg ttttcaatat cataggagca tttgatattc cacgctttgt gtacaattca 480

gaaagaaaaa aatttcttcc tctgttaatg accaaccacc ctgcaccaaa tttatttgga 540

acaccaagag ataaagcaga gatgtttcgt gagcgatata ccattttgca ccagaggacc 600

cacaggcatg aattatttac tcctccggtg ataggttctc accctgatga aagcggaagc 660

aaattccagc ttaaaacaat agaaacctta ttgggtagta caaccaaaat cggagatgcg 720

attgttcttg gaatgataac gcagttaaaa gagggaaaat tttttctgga agatcctact 780

ggaacagtcc aactagacct tagtaaagct cagttccata gtggtttata cacagaggca 840

tgctttgtct tagcagaagg ttggtttgaa gatcaagtgt ttcatgtcaa tgcctttgga 900

tttccaccca ctgagccctc tagtactact agggcatact atggaaatat taattttttt 960

ggaggtcctt ctaatacatc tgtgaagact tctgcaaaac taaaacagct agaagaggag 1020

aataaagatg ctatgtttgt gtttttatct gatgtttggt tggaccaggt ggaagtattg 1080

gaaaaacttc gcataatgtt tgctggttat tcaccagcac ctccaacctg ctttattctg 1140

tgtggtaatt tttcatctgc accatatgga aaaaatcaag ttcaagcttt gaaagattcc 1200

ctaaaaactt tggcagatat aatatgtgaa tacccagata ttcaccaaag tagtcgtttt 1260

gtgtttgtac ctggtccaga ggatcctgga tttggttcca tcttaccaag gccaccactt 1320

gctgaaagca tcactaatga attcagacaa agggtaccat tttcagtttt tactactaat 1380

ccttgcagaa ttcagtactg tacacaggaa attactgtct tccgtgaaga cttagtaaat 1440

aaaatgtgca gaaactgcgt ccgttttcct agcagcaatt tggctattcc taatcacttt 1500

gtaaagacta tcttatccca aggacatctg actcccctac ctctttatgt ctgcccagtg 1560

tattgggcat atgactatgc tttgagagtg tatcctgtgc ccgatctact tgtcattgca 1620

gacaaatatg atcctttcac tacgacaaat accgaatgcc tctgcataaa ccctggctct 1680

tttccaagaa gtggattttc attcaaagtt ttttatcctt ctaataagac agtagaagat 1740

agcaaacttc aaggcttttg agattcttaa agatcatctg aagaaaattc atcagttttc 1800

tgcttaactc tatatcttat gtgattctga tattacaata aaattatggt aaactttagg 1860

a 1861

<210>13

<211>1307

<212>DNA

<213> Intelligent people

<400>13

acttatctgc agacttgtag gcagcaactc accctcactc agaggtcttc tggttctgga 60

aacaactcta gctcagcctt ctccaccatg agcctcagac ttgataccac cccttcctgt 120

aacagtgcga gaccacttca tgccttgcag gtgctgctgc ttctgtcatt gctgctgact 180

gctctggctt cctccaccaa aggacaaact aagagaaact tggcgaaagg caaagaggaa 240

agtctagaca gtgacttgta tgctgaactc cgctgcatgt gtataaagac aacctctgga 300

attcatccca aaaacatcca aagtttggaa gtgatcggga aaggaaccca ttgcaaccaa 360

gtcgaagtga tagccacact gaaggatgggaggaaaatct gcctggaccc agatgctccc 420

agaatcaaga aaattgtaca gaaaaaattg gcaggtgatg aatctgctga ttaatttgtt 480

ctgtttctgc caaacttctt taactcccag gaagggtaga attttgaaac cttgattttc 540

tagagttctc atttattcag gatacctatt cttactgtat taaaatttgg atatgtgttt 600

cattctgtct caaaaatcac attttattct gagaaggttg gttaaaagat ggcagaaaga 660

agatgaaaat aaataagcct ggtttcaacc ctctaattct tgcctaaaca ttggactgta 720

ctttgcattt ttttctttaa aaatttctat tctaacacaa cttggttgat ttttcctggt 780

ctactttatg gttattagac atactcatgg gtattattag atttcataat ggtcaatgat 840

aataggaatt acatggagcc caacagagaa tatttgctca atacattttt gttaatatat 900

ttaggaactt aatggagtct ctcagtgtct tagtcctagg atgtcttatt taaaatactc 960

cctgaaagtt tattctgatg tttattttag ccatcaaaca ctaaaataat aaattggtga 1020

atatgaatct tataaactgt ggttagctgg tttaaagtga atatatttgc cactagtaga 1080

acaaaaatag atgatgaaaa tgaattaaca tatctacata gttataattc tatcattaga 1140

atgagcctta taaataagta caatatagga cttcaacctt actagactcc taattctaaa 1200

ttctactttt ttcatcaaca gaactttcat tcatttttta aaccctaaaa cttataccca 1260

cactattctt acaaaaatat tcacatgaaa taaaaatttg ctattga 1307

<210>14

<211>1922

<212>DNA

<213> Intelligent people

<400>14

gtgcgcggcc ccgcgcggca acgcaggggc ggaaccgcat gactggcagt ggcatcagcg 60

atggcggctg cgtcggggtc ggttctgcag cgctgtatcg tgtcgccggc agggaggcat 120

agcgcctctc tgatcttcct gcatggctca ggtgattctg gacaaggatt aagaatgtgg 180

atcaagcagg ttttaaatca agatttaaca ttccaacaca taaaaattat ttatccaaca 240

gctcctccca gatcatatac tcctatgaaa ggaggaatct ccaatgtatg gtttgacaga 300

tttaaaataa ccaatgactg cccagaacac cttgaatcaa ttgatgtcat gtgtcaagtg 360

cttactgatt tgattgatga agaagtaaaa agtggcatca agaagaacag gatattaata 420

ggaggattct ctatgggagg atgcatggca atacatttag catatagaaa tcatcaagat 480

gtggcaggag tatttgctct ttctagtttt ctgaataaag catctgctgt ttaccaggct 540

cttcagaaga gtaatggtgt acttcctgaa ttatttcagt gtcatggtac tgcagatgag 600

ttagttcttc attcttgggc agaagagaca aactcaatgt taaaatctct aggagtgacc 660

acgaagtttc atagttttcc aaatgtttac catgagctaa gcaaaactga gttagacata 720

ttgaagttat ggattcttac aaagctgcca ggagaaatgg aaaaacaaaa atgaatgaat 780

caagagtgat ttgttaatgt aagtgtaatg tctttgtgaa aagtgatttt tactgccaaa 840

ttataatgat aattaaaata ttaagaaata acactttcct gactttttta ttattaaaat 900

gcttatcact gtagacagta gctaatctta ttaatgaaaa acaatagaca aacatctgtg 960

cataattttt cagacacaat tctgtaaata tttggaaacc ttttaagtat ttaaactttt 1020

aaatttttga aataaagtat tctaaactaa tataaataag gacaatgaaa aaacatgaaa 1080

ggacttagca taatgttatt ttatcttttc tacaactttg tttaaattac ctttccaaag 1140

atatttgtgt ttatgtaatt ttccacggaa taacattaat actctaggtt tataaaccgg 1200

tttcacatta tttcatttga tcatcacaag agctttgtga agtaagccga gaagttgtta 1260

ctggtattta ataatagcaa tagaggagtt aaagactttc ccacagcttg caggtcaaga 1320

caagaaattc aggtctccta attctcagtg gagctctatt tctgttaacc caaattgctg 1380

ctctgtttta ggtctcaatt tcatctgtaa aatgatacta atagtactta tcccattgga 1440

tttttgttga gatttaaata aatagccaaa agccaataca taataaacac tcaataaaga 1500

ttaaccataa ggagagtcat gatctggttc caggaataca ttgttagatg actgaaaaat 1560

tgtattactt caatgaaaat actataaata ataacatttt catatattag ttggttctca 1620

tgcatacata atctaatttt atttgatcct cacaactgtt taagttttat taaatataca 1680

ttatccctat ttgtataaat agaatcatac aatacctgcc tgctttcatt caacaaaatt 1740

atcatgagat ttttccatgt tgtgtacatc aatagttcat ctattttatt gctcagtaat 1800

attccattgt gtggatgtat cactatttgt ttacacattc accactgata tataagttgc 1860

ttccagtgtg aggctgtttt aaataaagct gctatgaata ttcatgtaag aaaaaaaaaa 1920

aa 1922

<210>15

<211>2269

<212>DNA

<213> Intelligent people

<400>15

cgcagccccg gttcctgccc gcacctctcc ctccacacct ccccgcaagc tgagggagcc 60

ggctccggcc tcggccagcc caggaaggcg ctcccacagc gcagtggtgg gctgaagggc 120

tcctcaagtg ccgccaaagt gggagcccag gcagaggagg cgccgagagc gagggagggc 180

tgtgaggact gccagcacgc tgtcacctct caatagcagc ccaaacagat taagacatgg 240

gaggtgaaag acaacttgag tggttaaatt actgtcatgc aaagcgacta gatggttcag 300

ctgattgcac ctttagaagt tatgtggaac gaggcagcag atcttaagcc ccttgctctg 360

tcacgcaggc tggaatgcag tggtggaatc atggctcact acagccctga cctcctgggc 420

ccagagatgg agtctcgcta ttttgcccag gttggtcttg aacacctggc ttcaagcagt 480

cctcctgctt ttggcttctt gaagtgcttg gattacagta tttcagtttt atgctctgca 540

acaagtttgg ccatgttgga ggacaatcca aaggtcagca agttggctac tggcgattgg 600

atgctcactc tgaagccaaa gtctattact gtgcccgtgg aaatccccag ctcccctctg 660

gattgtcagt ggctgctatg cagcaggtgc agcctggtct ctcactgagt ctctactcca 720

caaaggcaac gactggccaa ggcagtggct ggctctgggt tacacaagtg cagacactca 780

actaagtgag ctggaagacc caggagaagg cggaggctca ggtgcccaca tgatcagcac 840

agccagggta cctgctgaca agcctgtacg catcgccttt agcctcaatg acgcctcaga 900

tgatacaccc cctgaagact ccattccttt ggtctttcca gaattagacc agcagctaca 960

gcccctgccg ccttgtcatg actccgagga atccatggag gtgttcaaac agcactgcca 1020

aatagcagaa gaataccatg aggtcaaaaa ggaaatcacc ctgcttgagc aaaggaagaa 1080

ggagctcatt gccaagttag atcaggcaga aaaggagaag gtggatgctg ctgagctggt 1140

tcgggaattc gaggctctga cggaggagaa tcggacgttg aggttggccc agtctcaatg 1200

tgtggaacaa ctggagaaac ttcgaataca gtatcagaag aggcagggct cgtcctaact 1260

ttaaattttt cagtgtgagc atacgaggct gatgactgcc ctgtgctggc caaaagattt 1320

ttattttaaa tgaatagtga gtcagatcta ttgcttctct gtattaccca catgacaact 1380

gtctataatg agtttactgc ttgccagctt ctagcttgag agaagggata ttttaaatga 1440

gatcattaac gtgaaactat tactagtata tgtttttgga gatcagaatt cttttccaaa 1500

gatatatgtt tttttctttt ttaggaagat atgatcatgc tgtacaacag ggtagaaaat 1560

gataaaaata gactattgac tgacccagct aagaatcgtg ggctgagcag agttaaacca 1620

tgggacaaac ccataacatg ttcaccatag tttcacgtat gtgtattttt aaatttcatg 1680

cctttaatat ttcaaatatg ctcaaattta aactgtcaga aacttctgtg catgtattta 1740

tatttgccag agtataaact tttatactct gatttttatc cttcaatgat tgattatact 1800

aagaataaat ggtcacatat cctaaaagct tcttcatgaa attattagca gaaaccatgt 1860

ttgtaaccaa agcacatttg ccaatgctaa ctggctgttg taataataaa cagataaggc 1920

tgcatttgct tcatgccatg tgacctcaca gtaaacatct ctgcctttgc ctgtgtgtgt 1980

tctgggggag gggggacatg gaaaaatatt gtttggacat tacttgggtg agtgcccatg 2040

aaaacatcag tgaacttgta actattgttt tgttttggat ttaaggagat gttttagatc 2100

agtaacagct aataggaata tgcgagtaaa ttcagaattg aaacaatttc tccttgttct 2160

acctatcacc acattttctc aaattgaact ctttgttata tgtccatttc tattcatgta 2220

acttcttttt cattaaacat ggatcaaaac tgacaaaaaa aaaaaaaaa 2269

<210>16

<211>7091

<212>DNA

<213> Intelligent people

<400>16

gctacccact tccgccccct ccccctgcca ttggaactag ctgagccgaa ctagttgcgg 60

ccaccgagca gccggctctc ggcacctcct cctccgcctc cctgtctcct gttccattcg 120

cctttcctct tctttcctgg cccacgccgc tccgaggcct cgcgaccgcc gagcctgcag 180

cctgccccgc ggccaacatg agcttcttgt tgagttctca gcctgaagtt gactggaact 240

ttcagttaac aagtatttat cgaatacctg atctgtagtg ttggacttag acctatggaa 300

ggagctactg atgtgaatga aagtggtagt cgctcttcta aaacttttaa accaaagaag 360

aacattccag agggttctca ccagtatgag ctcttaaaac acgcagaagc cacacttggc 420

agtggcaacc ttcggatggc tgtcatgctt cctgaagggg aagatctcaa tgaatgggtt 480

gcagttaaca ctgtggattt cttcaatcag atcaacatgc tttatggaac tatcacagac 540

ttctgtacag aagagagttg tccagtgatg tcagctggcc caaaatatga gtatcattgg 600

gcagatggaa cgaacataaa gaaacctatt aagtgctctg caccaaagta tattgattac 660

ttgatgactt gggttcagga ccagttggat gatgagacgt tatttccatc aaaaattggt 720

gtcccgttcc caaagaattt catgtctgtg gcaaaaacta tactcaaacg cctctttagg 780

gtttatgctc acatttatca tcagcatttt gaccctgtga tccagcttca ggaggaagca 840

catctaaata catctttcaa gcactttatt ttttttgtcc aggaattcaa ccttattgat 900

agaagagaac ttgcaccact ccaagaactg attgaaaaac tcacctcaaa agacagataa960

aaggatgcag agctgtgcaa attgttcctc aaatgaagca gtgtggagtg tattggggat 1020

tttgttatat tttgttttta tctggattgt ttttgtccta ggtttggggg cgggggcttg 1080

tttgggttcc tttttcttta ttctgattat gtgaaaccat attctattgc taggggaagc 1140

caagaaccat tctctataca cttgataagg gtaaatttat cttagtgttt ttaaacttgg 1200

ttttggttac ttgaggagtt ttttaataat attgtgtgct gcaagaaagt gcttgttgat 1260

tgaactgccg atggattggt ttctgtgtgg tataaattgt ggcccattta tgaagtcccc 1320

aaaagagtta tgtttttaag tgccttggca ggctcacttc tgaggtgcaa aacatagata 1380

tagaactgaa cagggcttga aacaatatta ggattactac ccagggcact tactggtgca 1440

tgttgtaaca tatctatgat aaaagccata gtttacctaa aatggtgatt tccagccttt 1500

actgctttga agaaacagaa tttgtaaagg tatgcatgta gaacataaaa aatatttctt 1560

aattattttt tatattgatg gtaatatatt acgttcaaca atgcttaaag ctctacaagc 1620

aggtcttttc ccacctcttg atatctgtga tactgaaact tgaggatgtt gaaatgtatt 1680

acattttggc ttctttctac atgttaactg cactgtagat gtaaaaattc aggttatata 1740

taggattgcc atcttcagag gtgatgctga actgtgaggt ttcctagtaa ttgccaaatg 1800

agccgtaagt ctgcagaatt cccttctact ttgaagagaa ggggatagga atgtatattt 1860

ggctgggggc atggagatgt tcgtatgtat gaggagttag ggatggggag tcaagttcta 1920

gaaagttttg tctgaaaacc tttgaataga atggcatgaa gattttaatc aattacttat 1980

aaacaaagtc ttagagactt ccttttagga atcaacttcc atgagaagtt aaaaataaat 2040

tattaatttt aggtacagac attaaacatg gaatttaagg actgttgggg gaaattgatc 2100

acttcttagc atttccattc agtgaatgga gctgatgttt gcctgtcatt ttaagatgat 2160

accatacctt ctttggttat tataggtcca gtttgaagca ttctgacttc tggtttttcc 2220

accctgaaag gaaatgtttt tctttgcagc agtattagat aatgaaaaat gctaattcag 2280

tagttattaa cttctaaatt ttattcgcca tgactttcta gtgaattatt accataaata 2340

acaatttcag aaacttagtt tttagaataa atattaattt ttccacttca gttttattct 2400

agaaaatacc ctttttagaa atccagtttt agttttgtca ttttcgataa atctttcttc 2460

agttagaaat atatatcctt ccttcagttg aaacatatac ctttttcaca tctaggaaga 2520

aatgcttgct ctgaaatagt atagattaaa aacactcagt agaaaagaat ctaaaattaa 2580

atgaatttgt tttgccatta aagtagagca gtgatacaat ttaatgccat tacaattatg 2640

ttgactagaa actgcctttt tctccacttc atttctagca attatttacc aagtaccaac 2700

agtagaagta acaggaaagc ctggcagagt taaatatctt ggacatttat tggtaaagct 2760

tatttataaa ctgcagtcag agctagttaa tttccttaaa tctttttgta ttcagataga 2820

taatatgaat cattatgggt tgattcagaa ataaaatttg tgaggtgatt ttgaatcttg 2880

tccatatagg aaaatgaagc acagaattac tcagtcttcc atattgtatt tgacttcata 2940

tcaatctagt aaaaaaggag ttgcaatagc caagtataga gagaatagtg aaaaattaat 3000

cttgcccttt caagccttat acagtagtac actgtacttg tttttagtag taagacctac 3060

tttcccacta tatgtagata gtttgttttc actgtgccag aatctcaggt gcctgcttag 3120

agtatttctt taatcacagt cactgggaag taaggagatg tatatatgtg tatatatggt 3180

aacaaagcat agcagttctc taggggagag gcctggcatt gcacatggtg ttacatggct 3240

acaagtaagg aaaaaatcag aaagtgaaag aactgatgta ataaaaggtt gatttggttg 3300

gttcccatga aagttagtaa gatgcccttt taaatataag gatcagtgct ttgttctgca 3360

gcagagtttg ctgataaatg tctgttggat tctttttgga tttctttaat taatttgtaa 3420

gtaaccaaga taattatttt cccccttgct ctctatatta atacgtagct ataaagcaac 3480

agttggtttt cttatccttt gataaaagca tcccataaaa tataaagtag taagttaaca 3540

tagtattatt gtcacacaca atgctttttt tggttaaatg ttgatacgaa gcaatgtttt 3600

ggaattactt taattgatgg agtagtggtg gtagagagaa attaataaca aaaagagtga 3660

aaatatttta attagcagta gatggtgcta ctggctttca tttgctgact tgattattcc 3720

ctttctctta aaaaccatgg cattagactg cactaaatta acaagcatgt tagttgctgg 3780

tagaggtttt ggaggttaat ttaccttaaa ttggaagact tttaattgca gtctctttct 3840

accttccctc tgttagtcat ttgtaaattc taaatggtca ccataaaatg tattaggtag 3900

gagaagatac gttttatgta taatatatct cagactgagt tactgcctgt cttatcagga 3960

tggataaaac actacagtct cttatcagga aatagagatg atgtggatat ttatatatta 4020

catatataac caccagactc cattttatat attagcattt tccttgctta tgggaaaata 4080

gcaaaacaac atttcattta tacttttgtt tacccctctc tgagacaggt tttgataacc 4140

actgaaatgg tagaatatgt gagatacaaa tattgagttg tagaactttc tttttaaggt 4200

gaataagtca tgccttaaca tccaaataag agttcatctt cagagtggtt cttttgggag 4260

cactgtttat tccagctata ctgcaaaagt ataatgtttt tggaactgtt ctagagcata 4320

ccatgaaaag cagtttgtta ttatgcagga aaatcagttt catcatttta gttacactaa 4380

acacttttgg cagcttaata tgaccttttt aaattttttt tatttttttt atttttattt 4440

ctttaagatg gagtcttgct ctgttgcccg ggctggagta caatggcatg atctcagctc 4500

actgcaacct ccacctcctg ggttcaagca tttctcctgc ctcagcctcc caagtagctg 4560

ggattacagg cagcaccaca cctggctaat tttcatattt ttagtagaga tggggtttca 4620

acatattggc caggctggtc tcaaactcct gacctcaagt gatccgccct ccccagcctc 4680

ccaaagtgct gggattacag gtgtgagcca ccacagccag ccagtatgac ctatcttaat 4740

catcagctca actgtaattt aaatttggct gttctctgga gctaaaccat tagggaagtt 4800

caaaggaatg tgccatgatt tccgaatttg cacaagagaa tgttttaagc attggtagca 4860

taattgaata aaagaatagt ttcctgatgt cactattttg aagtggaaat tatcacttgg 4920

atgtggaggt tttacttttt aaaaacattc agcttaatta ccttacctta attaccttag 4980

ttagatatac taatggaaaa aaaccaagtc ctttctctag aacttgtttt ctatttttgt 5040

tccttttcat gaaaacttct caatttaatt ttaactactg taggatagta ttgattgaat 5100

ggatactatg gaaaagtgga tccaatattt aagatagaag tagtttaagg agacaacagc 5160

ctttactgcc attttttttt aaatgttttc actcagatga acaatttgac tttaataaaa 5220

gactggagat ttttgtacaa agaaatagga ataagtttca tatactaatt atgctgagtt 5280

ttaagcctac atatcacaaa atatttagaa ttgtataacc ttttcatata tttataactt 5340

ttaatgtctt tttaaaagat gtgggaccaa aaatatattt ataatttgga aatgtgactg 5400

cataccaata agaaaactta ccttattttg aaatttatct gggatattaa agaatctacc 5460

aattcttaaa aacacagatt tatactttaa gcttatttta aaattaaaga atatatacca 5520

attcttagaa acactttaag gactactctt aaataactta aatatcagag ttttgttgta 5580

atattaaaat ttaccgtgga aatcactgtt gttcagctat caccttaatt gtgtatgata 5640

tgataaatgt ttagcagtaa agctatctta agatttaatg gaaaagttta atttgaagat 5700

gtaacaaaaa ttctgaccac agttgattct gaatttttaa ggctttccta ataggctgat 5760

cacagagaat aatccatttt gaaggtataa aactgcactg tatgtctgtc acttgtagct 5820

gaactgattc acattttgac aaaagagaga aaatacaaaa atgagttttg caaatgtaat 5880

aactttttct gcatatagaa ctaaataatt gaaaaatatg ggctatagtt ctcaaaggta 5940

gatagtaaaa tcactggctt tttccagctg tatgtttttc cactgtgcgt gtacacacac 6000

actggaaaat aattaggctg attttgcagg tcttcattgt tagagattct gaagtattta 6060

ctgtcaattc ataggtttca gtttatttag gaaattagtg tttgacagct ttttttaaat 6120

tatttcactg aagctgagat tattagtgat acaaagttaa aatttcaata tttaatttct 6180

ctatatatta ttaatattaa attgtttttt acttataaat tcatgttctc atctgattta 6240

atattaaatt tgtataggtg ggcgtttctt accattttgc acaagttttt gtttttctga 6300

aatacttaat tgtgcaggtt gtaaaaaaga ttagtgcatt ttcattttaa ggatgctttg 6360

ctccttaaat tgttcgacagaaatgacttt ttagggaaag tagttttttt ggagctacta 6420

acttgtattt attattgtac atgcataacc agggtggtga gggcactaat cttgtaggaa 6480

acacttactt gatgttttat ttgaactttt cctataggtt taacttttac tgcatagaat 6540

taacactagg aacagtgtca tgaaatctgg gttgaaggag aatacagtat atatgagaac 6600

acttaaagtt caaatagaaa tcatttctga agacaaaagc agaggaatat tgtcagtgcc 6660

aagtaatgga agaataaggg cggcatttac actgtgcaag tattgagaag agtgcataaa 6720

gacagggaac tactctcatg gagacagttt ctctcttata atcaagtaac tagaagggga 6780

aaaatcatct aagttatgaa atccaacata ggcgctatat tacaaactgt gccggattat 6840

gcaaattgta gttgttactg atcaaagttt aattgcttca tttttgttta aaaagggata 6900

ctgatgtcag aaaatctgta atatgtttta ttcaaaagat gtaaataatg tatacagact 6960

tgtatgtgat gggatgggaa atatttaaat tctaggtgtt tttttttttt taaagaagaa 7020

actcaatgtt tataagaaaa aaatgaataa atagttacgt ttggccatga atcctgaaaa 7080

aaaaaaaaaa a 7091

<210>17

<211>7003

<212>DNA

<213> Intelligent people

<400>17

actcgcagtc ctgacgggca ggggctgcgg accgcccggc cttggaccca tccggagcca 60

caggttggag gagataagta gctgtccccg tgctcatcgc cctgtggagc agatcctgtc 120

tccttgctga cggtggagcc cgggagttcc agggcttggg aaggggaagg aaacctctct 180

gaaatctgac acctgctctc ccggcaagga aacttcgcag gctgaccgac caagaccatc 240

actatgaccg atggagacta tgattatctg atcaaactcc tggccctcgg ggattcaggg 300

gtggggaaga caacatttct ttatagatac acagataata aattcaatcc caaattcatc 360

actacagtag gaatagactt tcgggaaaaa cgtgtggttt ataatgcaca aggaccgaat 420

ggatcttcag ggaaagcatt taaagtgcat cttcagcttt gggacactgc gggacaagag 480

cggttccgga gtctcaccac tgcatttttc agagacgcca tgggcttctt attaatgttt 540

gacctcacca gtcaacagag cttcttaaat gtcagaaact ggatgagcca actgcaagca 600

aatgcttatt gtgaaaatcc agatatagta ttaattggca acaaggcaga cctaccagat 660

cagagggaag tcaatgaacg gcaagctcgg gaactggctg acaaatatgg cataccatat 720

tttgaaacaa gtgcagcaac tggacagaat gtggagaaag ctgtagaaac ccttttggac 780

ttaatcatga agcgaatgga acagtgtgtg gagaagacac aaatccctga tactgtcaat 840

ggtggaaatt ctggaaactt ggatggggaa aagccaccag agaagaaatg tatctgctag 900

actctacata gaaactgaac atcaagaacc ccaccaaaat attactttta aaaacaatga 960

caaaccacac aattgttgtt gagtaaacca cgcacaatgg catgtctttc tttttctgcc 1020

agaaaatcta ttttaagaaa ccagaatagt caacagtgtt caaaagaatt gactagttat 1080

ccctgaggcc ctttcaaaca tgatcaaaga tttcccaatg tgatctcatc atcatggata 1140

ctcaatttgt tttttcttat agagaaaatg agtatataag acaatataca agaagaaata 1200

tcagtgagtt ttaaatcaga acaagttacc tgtcacattg aagaaaaggg taggcactaa 1260

agggagaaca cagaaagaag aatttctaaa atattggatt tacttcttatattgagtcag 1320

atgcatactt ttagatttgc attggggaaa atgtactagc taaaaatgga tacacaatga 1380

agaattctat ttggctaatt aagaatgata tactatgtac acccaataag ctgtactaga 1440

atgaataaat tactgataag gttacaaata ggtaaatgtc acacttctgt taaaatgcag 1500

gaggtagtgt cataatgccg tctttatatt cttaataaat agcactttga caagaacagg 1560

actgtaaatg atgaagtaca agacaaatac cctgggaaaa aaaatgaaag tatgagaaat 1620

tggcatttct acagctgaaa ttcaatgtat ctgttagaga tgtctggaag ggttacttag 1680

ccaaatttta ctcaagccaa ttaggagctg atattatcag ttggaattaa gagaactcca 1740

gaggtttcca tttcaaacaa aattttagaa attggtttgg tgttcagctt cacatttcat 1800

tttttcttag cacatgttga taaaatagtc acaaggagaa attaccagtt acggtttatt 1860

aaatctcttt taaaatgcag tcaaggaaaa ctagccttga atttttttta gataaaataa 1920

gatggtgata tgaaacaaaa agtggcaatt attgcaggtt tccttttagt ttacaaaagt 1980

actggaaact aaatcatatt tcttccctcc aaatttcacc cattcctgac tttgaatcaa 2040

ttgcagaaat gcaggtgtgt tactttgttg atcaataact ttggaacaat tatggatcaa 2100

ttctatggtc actctgaatt ttcatgtcat taatcacata aaaattgata atacctcatt 2160

ctgtattaca atatgatttt attttgccaa aggcaagaca cctatagttg agctgtattt 2220

tgggggattg ggtgaggaag gacttctgat cttatctcaa caaaaaactg gccagtattt 2280

ttgttaatgt aaagcttcct tttctttcta aaaaatagta acaaaattat ttttcattgg 2340

cctattctgt tcttgtgtct aaactaacat tacattaatt tttaatctta gtttctgata 2400

aacacaagcc attcctatca aaatattatt tatttcagtc aattttacca aataacaaag 2460

acaatatatt ttcgtttttt tttattatga gcatatgatt ttttgacagg ctgtttcctc 2520

gtcgtataga ttttttccaa tcaaacctac tttttccata ctctgtgcat attttttgtg 2580

aagttataca cattgaagac cctaaaaatc ccagtccatc attcagctta cctctgcgaa 2640

cttctatctg gtattgaatc agtttcagaa acacagacag atccaaggaa atgtctcttt 2700

ataatgttct taggatggac tagacccata aatgtgccat gaatcaaaat attaataatt 2760

tgaaagcttt catgctgtta gcccctgatg aaattctcag cattaactgg ccagctcctc 2820

tgatttctgc agcatcgcaa caggttcgaa gatgggttgt ggctgggtat tccctcccat 2880

ggtgtttcct ctgggatgct cttcattatc tcaatgcctg tgccatgaag atagaaaact 2940

gtaagctaac atttaagatg tttcttctgg aaggaaagtg agcaggaaca agttatattg 3000

ccactgctgt ggcaaatttt ggtgaacttt tggggtcatt atatcaattt tttctttgga 3060

ttcaaattgt aatgtcccct gcatttcctt aatagggaat gtgaaacctt tataaaactc 3120

taaaagtatt ctgttttgat atgtcttttt gtttctattc attttcagtt atatgattga 3180

tttacttatg ccaagattct gtcactgtca gttatttaat gagtgttttt tcagggtctg 3240

ttttaagatc attatttgat agctgtagca tgaagcagag gttgatgatg cccataattg 3300

caagactatt cctgtaaaaa taacaattat tgggtaataa cttcaagagg aatgagaagt 3360

gacaaaattg atttaaaata ttgttctact tataaataaa tgcttgatat aaaaaatttt 3420

ctccataaag tttgacatct gaccccagat tctatgtaat cattattaga aattccttct 3480

ctcattattt caggattagt agttctgtgt aattcatttt acaatttcaa attgttctgg 3540

tgccataaag tatacagact actttaaaga tttccaaatc ccctaattta ccccacaaca 3600

gcatgtaatt ttagccaaga tatgtcctgt tactaagtat ctcccaatgc tttagtaaaa 3660

cgtatttagg agaaatgttg aaaatgtaca tgaagctcct ttctgatata gaaaccattt 3720

ctggagtatt tacactggtt tgatgtttac attgctctaa ctcggtgcct cagatacctc 3780

tgtgaccaaa tttgtctcca accacatagc tcatttccta taatgttata tcataggaag 3840

ccctcacaga gacactaaca cagctaaaga tcttctgata ttatcagcaa gggatgcaag 3900

gactttattg gaatctggag agtttaactg ccttctcttg gtctcctcac ttacttctta 3960

tgaagttggc attacctgag actcttagct gtgattaggt acaagcttac cttttagggt 4020

agaaaaagaa agatcatttg aaaaatgtat ctaaaataat ccagagaaca taatgtttgt 4080

cttggtctga taatgataag aagtcaagga ttggcagaga aaatactaaa cgccaagagt 4140

tgagcctgtg ggtctctcca taagagtttt aaaactcttg ccagttacca ctttatccaa 4200

tttgctatca ttttcgtatt atcagctatc gccctgtaaa atattcaaaa ctagctattt 4260

ctaaagtaaa cattttatct gttactttta accagatagg tgtctttgtc atccttctac 4320

tataaattgt tctttgccaa cctgtacagg tagatgaacc aggcgagagt tttaatcagc 4380

cttttcttgt cccctttgta agaaagagat gcttgccata gagaaggaca tgagtacatt 4440

aaaaataatt taatagccac aatatgatgt tctttaagct gcaaattgag tacactggga 4500

atcaacaaat ttgatgaagc ctgtctgtct cttcaccagt ggagtgagtg cagcagttag 4560

aaagagaagc aatattgtgc aactggtgca gtggtgagtt aatcatagtg tataaccttg 4620

tgttcatgaa acaggttgtt cattgttctg catctctctt catttaaaaa ggatacacaa 4680

ttctttcctc attgcatatt acaccaaacg tttgagggaa aaatcctcat tcgtaaagga 4740

ttttggatgt ataatctaaa actcaacaat aaagaaataa tattccaagt ctctggtttc 4800

ctaagataca taataactgt ttataaagaa ggtctaagag ctgatatttg ccaaagtgat 4860

agaagagttg ttttttcctc tctactacca agctttaaga cattaaaaga agtctagtgt 4920

atttgaatat tttagagaaa gctttatcat tttttaagat gccaagatgc tgcctacgtt 4980

tgcaaaagtt gtctaagaat tcaccatgag ctatattttc ttctggatct ttgaccaagg 5040

tgatgtcagc ttatttctgg ggaaggtgtt gagctcttat acatgaaaat ggatataggc 5100

tattctctgg gatgagtgtc atttcaatgc tttataaatc catgaagctg cttgtctcat 5160

aaagtagaac tgatacaaat tttggttgga tatatagaga attttataaa tgtattgcct 5220

tagaatttct gggtggagac ccaactacaa tgacattgtc atgccagaac tataaagata 5280

attagagtta aaagttgttt aaattgtgcc cttaaataca gcagaacctg gagaaggtca 5340

tacttcaaag gtcgattttg agtccgaata aagaaagacc tagtaacaga tagttttttt 5400

ttgttcattt tcttctacca agtagaggtt tatgccctca gaactaaact agtaaaaata 5460

tctgaacaaa aaacctttcg ttgttggcat aaaaatgtga tacacttaga gacattttgt 5520

ttattgcata taaatctaat ttttccataa attagattta tgatattttc ataaagcact 5580

tgattagttt ttcaaggcgt accatcacaa agatgctttc ctgcagagtt ctttgtatca 5640

acagcctatg gttgagatgt tttctcattt cctgtagaga gagaatacca ctaacaaaca 5700

aacaaaaact ttagtgccaa aatagtggaa ctattttgtc atcttttgag aaaaaaatat 5760

acaaagaagt catcttttca ttaagtggat tccctggttc ctttccagct ggttgtggaa 5820

gtaatggcta acatccttca gctgactttg tctacaagga ttattagcaa attctgtagg 5880

agcaagcatg tctgacctta acttaatgga tcccttattc aatcagtggc ttctgtcttt 5940

atgtctgttg gcatatcaaa atggtttctg ttcctagaaa agtaataaca tatgcttatc 6000

tttattcttt ttccaggtga ttttgttttc aaatgctcct tgtgaaaaca cctagtgttg 6060

tagaaaggaa agtggccaga aagaacaact tgggaccatg agtaggtcat taaatagctt 6120

agtgatttat cctcatatag ggcttataaa ccctgtatgt gtttatatgt gcttcacaga 6180

gttcgtgtca ggctcaaagg agatatgtat aagaaagtgg tttgtaaatt atgttccatt 6240

tcataaatag acactattca caaactaaaa tctaataaaa aaccacagtt gtaatttaaa 6300

ctgcttgata taaaaagagg tatcatagca gggaaaacac actaattttc atacagtaga 6360

ggtattgaaa actgaaaatg ggaaggcaac ttgaagtcat tgtatttgat tgaaaatgtt 6420

taatacatct cattattgac aaaatatgtc atcttgtatt tatttcaagg aaaccaatga 6480

attctaggta gtatattaca agttggtcaa aatattccat gtacaaatag ggcttctgtg 6540

tccatagcct tgtaagagat actgattgta tctgaaatta ttttttaaaa aaataaatta 6600

tcctgcttta gttagtgtgt taaaagtaga cgatgttcta atataacact gaagtgcttc 6660

attgtatccc aacagtttac cttcaagtaa tattatcttt atttttaggc taagcacgtt 6720

tgattattttgtctgtctcc tatatagatc tgttttgtct agtgctatga atgtaactta 6780

aaactataaa cttgaagttt ttattctata tgccccttaa tagactgtgg ttcctgacgc 6840

acactgttag gtcattattt tgttgtacca aagttctagt ggcttcagaa atcatagcat 6900

ccaatgattt tttggtgtct ggctatgaat actatggttg agaattgtat tcagtgattg 6960

tttctgcaca cttttcaaat aaaaaatgaa tttttatcaa tta 7003

<210>18

<211>2158

<212>DNA

<213> Intelligent people

<400>18

agttctgcat ttctgcagag acagaaagaa acgcagctct tgacttcttt tttgtaaaca 60

ttactgtaag agttgtgata actttttatt ctactatgta tatgtatgga atagtattaa 120

taaatgaact agggaaggat gtaataaatt agacatctct tcattttaga gagaagatgg 180

aaacaacatt gcttttcttt tctcaaataa atatgtgtga atcaaaagaa aaaacttttt 240

tcaagttaat acatggttca ggaaaagaag aaacaagcaa agaagccaaa atcagagcta 300

aggaaaaaag aaatagacta agtcttcttg tgcagaaacc tgagtttcat gaagacaccc 360

gctccagtag atctgggcac ttggccaaag aaacaagagt ctcccctgaa gaggcagtga 420

aatggggtga atcatttgac aaactgcttt cccatagaga tggactagag gcttttacca 480

gatttcttaa aactgaattc agtgaagaaa atattgaatt ttggatagcc tgtgaagatt 540

tcaagaaaag caagggacct caacaaattc accttaaagc aaaagcaata tatgagaaat 600

ttatacagac tgatgcccca aaagaggtta accttgattt tcacacaaaa gaagtcatta 660

caaacagcat cactcaacct accctccaca gttttgatgc tgcacaaagc agagtgtatc 720

agctcatgga acaagacagt tatacacgtt ttctgaaatc tgacatctat ttagacttga 780

tggaaggaag acctcagaga ccaacaaatc ttaggagacg atcacgctca tttacctgca 840

atgaattcca agatgtacaa tcagatgttg ccatttggtt ataaagaaaa ttgattttgc 900

tcatttttat gacaaactta tacatctgct tctaacatat cgcatgttta tgttaagatt 960

tggtcccatc ctttaaactg aaatatgtca tgtgaaatta ttttaaaaat gtaaaaacaa 1020

aactttctgc taacaaaata catacagtat ctgccagtat attctgtaaa accttctatt 1080

tgatgtcatt ccatttataa tcagaaaaaa aacttatttc ttaatcaaaa ggcagtacaa 1140

aaaaagtaat aatgttttat aagattgtag agttaagtaa aagttaagct tttgcaaagt 1200

tgtcaaaagt tcaaacaaaa gtctagttgg gattttttac caaagcagca taatatgtgt 1260

tatataaaca taataatact cagatatcca aatgttcaga tagcattttt cataatgaat 1320

gttctctttt ttttggtaat agtgtagaag tgatctggtt cttacaatgg gagatgaaga 1380

acatttatta ttgggttact actaaccctg tcccaagaat agtaatatca cctctagtta 1440

taagccagca acaggaactt ttgtgaagac acattcatct ctacagaact tcagattaaa 1500

tataatctag attaatgact gagaataaga tccacatttg aactcattcc taagtgaaca 1560

tggacgtacc cagttataca aagtacttct gttggtcaca gaaacatgac cagattttgc 1620

atatctccag gtagggaact aagtagacta ccttatcacc ggctaagaaa acttgctact 1680

aaactattag gccatcaatg gcttgaataa aaaccagaga aggtttttcc caggacgtct 1740

catgtttggc cctttagaat tggggtagaa atcagaaatg agatgagggg aagaagcaag 1800

gagtctaagg ccctagcgat ttgggcatct gccacattgg ttcatattca gaaagtgtta 1860

tctcattgat tatattcttg ttaagcaaat ctccttaagt aattattatt caaataagat 1920

tatactcata catctatatg tcactgtttt aaagagatat ttaattttta atgtgtgtta 1980

catggtctgt aaatatttgt atttaaaaat gccatgcatt aggctttgga aatttaatgt 2040

tagttgaaat gtaaaatgtg aaaactttag atcatttgta gtaataaata tttttaactt 2100

cattcataca gttaagttta tctgacaata aaagctctga ctgaaaaaaa aaaaaaaa 2158

<210>19

<211>5852

<212>DNA

<213> Intelligent people

<400>19

ttttgccgga tgttgttgta tgtccgagag acacgtgagg ttctgctacg tcattaccag 60

gcacgcgcag gaaacatggc ggcggcgggt gttgtgagcg ggaaggtttt tggtttcttc 120

ttgattcaat cttgataagt agtatgtgtc caggacttta tccatactcc agtttgttgg 180

agtatggtag gagtatgatt atatatgaac aagaaggagt atatattcac tcatcttgtg 240

gaaagaccaa tgaccaagac ggcttgattt caggaatatt acgtgtttta gaaaaggatg 300

ccgaagtaat agtggactgg agaccattgg atgatgcatt agattcctct agtattctct 360

atgctagaaa ggactccagt tcagttgtag aatggactca ggccccaaaa gaaagaggtc 420

atcgaggatc agaacatctg aacagttacg aagcagaatg ggacatggtt aatacagttt 480

catttaaaag gaaaccacat accaatggag atgctccaag tcatagaaat gggaaaagca 540

aatggtcatt cctgttcagt ttgacagacc tgaaatcaat caagcaaaac aaagagggta 600

tgggctggtc ctatttggta ttctgtctaa aggatgacgt cgttctccct gctctacact 660

ttcatcaagg agatagcaaa ctactgattg aatctcttga aaaatatgtg gtattgtgtg 720

aatctccaca ggataaaaga acacttcttg tgaattgtca gaataagagt ctttcacagt 780

cttttgaaaa tcttcttgat gagccagcat atggtttaat acaaaaaatt aaaaaggacc 840

cttatacggc aactatgata ggattttcca aagtcacaaa ctacattttt gacagtttga 900

gaggcagcga tccctctaca catcaacgac caccttcaga aatggcagat tttcttagtg 960

atgctattcc aggtctaaag ataaatcaac aagaagaacc aggatttgaa gtcatcacaa 1020

gaattgattt gggggaacgc cctgttgttc aaaggagaga accggtatca ctggaagaat 1080

ggactaagaa cattgattct gaaggaagaa ttttaaatgt agataatatg aagcagatga 1140

tatttagagg gggacttagt catgcattga gaaagcaagc atggaaattt cttctgggtt 1200

attttccctg ggacagtacc aaggaggaaa gaacccaatt acaaaagcaa aaaactgatg 1260

aatacttcag aatgaaactg cagtggaaat ccatcagcca ggaacaagag aaaagaaatt 1320

cgaggttaag agattataga agtcttatcg aaaaagatgt taacagaaca gatcgaacaa 1380

acaagtttta tgaaggccaa gataatccag ggttgatttt acttcatgac attttgatga 1440

cctactgtat gtatgatttt gatttaggat atgttcaagg aatgagtgat ttactttccc 1500

ctcttttata tgtgatggaa aatgaagtgg atgccttttg gtgctttgcc tcttacatgg 1560

accaaatgca tcagaatttt gaagaacaaa tgcaaggcat gaagacccag ctaattcagc 1620

tgagtacctt acttcgattg ttagacagtg gattttgcag ttacttagaa tctcaggact 1680

ctggatacct ttatttttgc ttcaggtggc ttttaatcag attcaaaagg gaatttagtt 1740

ttctagatat tcttcgatta tgggaggtaa tgtggaccga actaccatgt acaaatttcc 1800

atcttcttct ctgttgtgct attctggaat cagaaaagca gcaaataatg gaaaagcatt 1860

atggcttcaa tgaaatactt aagcatatca atgaattgtc catgaaaatt gatgtggaag 1920

atatactctg caaggcagaa gcaatttctc tacagatggt aaaatgcaag gaattgccac 1980

aagcagtctg tgagatcctt gggcttcaag gcagtgaagt tacaacacca gattcagacg 2040

ttggtgaaga cgaaaatgtt gtcatgactc cttgtcctac atctgcattt caaagtaatg 2100

ccttgcctac actctctgcc agtggagcca gaaatgacag cccaacacag ataccagtgt 2160

cctcagatgt ctgcagatta acacctgcat gatcactgtt cttgcttttt tgggaagaga 2220

cactttgttg caaccctttt tcaagtactt gaaagttgaa aatttgaaat cttggtattg 2280

atcatgcttt aaggtttatg taaagaaagt gtactgatgt tcttacatta aagctttaca 2340

aagatttaaa ctaattattt ttgtagttac ttctaccaaa tagcctttcc ttttcgataa 2400

cattcctcag tatttttata gccaagtaca ttttattttc ttgctgatga actggaattg 2460

gataaatatt gcaagtggat gagttggaaa ttatgcactt tgaaaaacat tcactttgtt 2520

taagcttatt gggtttcaga tttgattaaa ttaaatgtgg aggctttcta tagcattcta 2580

agctgagaag tagattgtta cccagtaatg aaataaaaaa taaaaataaa aggatttttt 2640

tctctattgt ttacgacagt actcagctta aatatttatg ctggtcaaat gtgatttaaa 2700

ttggacattt tcatcaatgc agtctaatgt gtagataaat atttcaacca taataagtgg 2760

attggcagta tattttttac attgaacttt tcttcacttg tatataaaga ttatatataa 2820

gtacttattt atgagtataa gaaaggttag gcatattttc attaactgaa taaacgactt 2880

gatttatata acctggttta tcaaaattta acatggcttc agtatgagat ctttttcaaa 2940

actattttct taaacattta tttcatgaga ttatgttcaa ccctgtacct ggtgtaattt 3000

taaaattaat tgcttgtaac ctcactttac taataatgtt tattatcttt cctaataatg 3060

cattaactga ttaatcaggt gtttaaattt ttataaaata ctcttgcaaa aagtttattt 3120

gaaaaatttc tagatggtct catgagtttc aaaataataa tttttgtgta tgaacaaagc 3180

tgttgttttt accatgcagt attgcatgat tttaagttat gtggaattaa cataactgat 3240

tttgttttaa ttgtaagttg ttaactcctg tatatatcat taaaataaat ctgaagttga 3300

agtagtgttt ttagttaaat tatacttaga aatagtctgc ttttttaaaa ttttttttct 3360

tgagaaagag tcttgctctg ttgcccaggc tggagtgcag tggcgcagtc ctggctcact 3420

gcagcctccg ccttctgggt tcaagcgatt ctcctgtctc agcctcccga gcagctggga 3480

ctacaggctt gtgccatcgc gcctgactaa tttttgtatt ttgagtagag atggggtttc 3540

accatgttgg ccaggctggt ctcgaactct tgacctcaag tgatccactc gcttcagcct 3600

cccaaagtgc tgagattaca ggtgtgagcc actgtgcccg gctaattctt taatagaaga 3660

aaaaacatcc aagatggacc tcaattcatc tcttattttt atatgattaa aatgataatc 3720

tggccgggcg cggtggctca cgcctgtaat cccagcactt tgggaggccg aggcgggcgg 3780

atcacgaggt caggagatcg agaccatccc ggctaaaacg gtgaaacccc gtctctacta 3840

aaaatacaaa aaattagccg ggcgtagtgg cgggcgcctg tagtcccagc tacttgggag 3900

gctgaggcag gagaatggcg tgaacccggg aggcggagct tgcagtgagc cgagatcccg 3960

ccactgcact ccagcctggg cgacagagcg agactccgtc tcaaaaaaaa aaaaaaaaaa 4020

atgataatct gaataagtta tggaaatgaa aaccatcctt tttataactg aaaaaaaatt 4080

ttcattagca tggaaatggg cacagtgttg ccttgaaaga tacagttatt tgactcagta 4140

aagcagctta ttacaactga tgctaatagt atagagaaaa aagttgtgca gttctaaaat 4200

ggtcctagag attgactttt ttcccccaag aaagttaggg aacaaaacga acttttttcc 4260

tggttgagca ttaactgaca atcacgacag tagaaccgtt agagtttagt ttttaatatt 4320

atgtgtgtta tctttcatca gttaataatg agtaagccta ttcagaaaaa gaacataaac 4380

tgatcaaaaa ctcagcatct ccagcctttc atttcctgct attcaggaaa ttgcttagaa 4440

catcttgatg tcctccttgt tcttcctgga cagtgacttt ttgggagttt gttcctgctg 4500

cgtaatgtga tacccacttc agattttttt tttatcaata catttagtaa gttgaacttc 4560

tgtcaagttt tattacaaaa ttacttgtta aaacaatttt tactaaactg catttctatc 4620

tagcatattt ttgatatgga agtgatagta tagtatagtt ccaggagaag tcttaaatca 4680

gtccacagag tccagttagc aaatactctg tgccattaag attgctaaaa tacacagttc 4740

aggtaaattt actagtgttt tttaaaggtt tatttgtttt cacaagatgc tctgtccaca 4800

cccttataac atgtaaaata ttgtgtgctg tattatgtgg taaagttgtt aaaattcagt 4860

ttctaacatt aacttaaaag tacagacaat ctaacatgat gatttgactt acaaactttc 4920

aactaaattt atgatggctt taaagcagtg cactgaatag aaaccatact ttgagtaccc 4980

atacagccat ttttcacttt tactacaata ttctataaat cacatgagat atttaacact 5040

ttattataaa ataggctttg tgttagatga ttttgcccaa atgtaaacta atgtagtgtt 5100

ctgagcatgt ttaagttagg gtaggctaaa ctatgtttgg taggttagat gtattaaaag 5160

catttttgat taatgatgtc ttcaatttat gatgtgttta ttggaacata acctcaatat 5220

aagttgaaaa gcatatgtat tttcaattct ggcatgaacc tatgggaatc ttttgcattt 5280

aagaacctcc ccattttaat aatttcatgg gtctaagatt cttcatctgt ttataaggaa 5340

ctttagtctt agtgattaga gactaaattt ttttttgagc agtaagaaaa cagccttttg 5400

ggacagatag tgagtgattc ttaggaactt gacattgcca agaaatttta tagatgccga 5460

agaattctta tgtgaaattc acataagcat gcccattact aaagacagtt tgtataaagt 5520

aaccctaaat gtttattgag gaacctacag cttcaactga cttacgtgca gatatgtacc 5580

aggagaacat cattttagct tgggcgtctt tacttggggt tttcagagga tccaggaacc 5640

tcactgtatg caaagtcttg tggatgtacc tgaatgtttt tggaggcagg tcacatagtt 5700

tctgaaagtg ttctcttatt ttcctcaaat gtaggtaacc attgttacaa gttatttaac 5760

aggagaatag taacaatgtc taacttatgc taatgatttt gtgtgctgag ctcccattaa 5820

ttaaaatgtc ttcagaaaaa aaaaaaaaaa aa 5852

<210>20

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>20

ccaaccgcga gaagatgac 19

<210>21

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>21

tagcacagcc tggatagcaa 20

<210>22

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>22

tgagaaagga ggctgcatca 20

<210>23

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>23

ctgctgcaac tgctgaaca 19

<210>24

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>24

gcctcttcca gaaactagga gaa 23

<210>25

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>25

ggggctttct ttgtgtaagc aa 22

<210>26

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>26

gacagctgcc aggatcctaa 20

<210>27

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>27

gtctggcaca tgtttgtcta ca 22

<210>28

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>28

aagtgcgcca caagcaaa 18

<210>29

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>29

tgccttatgg cgagttcca 19

<210>30

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>30

cagcggcagc tgattgttaa 20

<210>31

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>31

cagagagatc acccttcaag tca 23

<210>32

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>32

acccagatga ctcccacaaa 20

<210>33

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>33

caaggattgg ctgctgctta 20

<210>34

<211>17

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>34

aaggccgtgg tcctgac 17

<210>35

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>35

tcagctggct gaagtagtcc 20

<210>36

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>36

gcaaggtggc agaagtcaa 19

<210>37

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>37

atggccagag atgcttcca 19

<210>38

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>38

gcgcctttct tcatcatcca 20

<210>39

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>39

gatggtgatg gtagggtttt cc 22

<210>40

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>40

tcctggaact gaagcactca 20

<210>41

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>41

gcagcacaag aatgggtaca 20

<210>42

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>42

ccccttcctg gctctcagta 20

<210>43

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>43

ggtctgggct gctctcca 18

<210>44

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>44

ggacagtgac cctcaacca 19

<210>45

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>45

cagcagggca aatctcttgt ta 22

<210>46

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>46

tggaaaggtg gtgtggaaac 20

<210>47

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>47

gtcagctggt ggttgctaa 19

<210>48

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>48

tgatgtcagt gctgctactc c 21

<210>49

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>49

ctgtgtatcc aagacagcag tca 23

<210>50

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>50

ctcagttcag gcttcctaca 20

<210>51

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>51

tcttttggca caaggcttac 20

<210>52

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>52

cacaatagaa ccttcagcag ac 22

<210>53

<211>26

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>53

gaaaagtgtc ttcatgtatc cagtta 26

<210>54

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>54

attccagctt caacccctca 20

<210>55

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>55

atgactaggt gcctgggaac 20

<210>56

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>56

ccaactaatg ccaccaccaa 20

<210>57

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>57

cgaagagact ggctgttgac 20

<210>58

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>58

ccccttgcct acaagaagct a 21

<210>59

<211>17

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>59

tcccgttggg ccaatcc 17

<210>60

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>60

agcaagaacg ccaaggacaa 20

<210>61

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>61

cggccacgat tctcttccaa 20

<210>62

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>62

agattgcatg tcccctggaa 20

<210>63

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>63

gggtgggttc cagaaggtta 20

<210>64

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>64

tatgcctgcc acaccactaa 20

<210>65

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>65

gccaggagaa cttccttgta cta 23

<210>66

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>66

tcaaccgccc tgaacaca 18

<210>67

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>67

acaccgacaa tgtgaccaga a 21

<210>68

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>68

agccttccaa gcccatcc 18

<210>69

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>69

tgcggattga gaagccttta 20

<210>70

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>70

cgtggtcagg atggctagta 20

<210>71

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>71

ccaatcggca gcaatgtcta 20

<210>72

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>72

gtcctctgtg aaggggtcaa a 21

<210>73

<211>25

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>73

tcttcaggca agttagagtt ggtta 25

<210>74

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>74

tgacaaccca gagctcatcc 20

<210>75

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>75

ggacgccatg ttgtttggaa 20

<210>76

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>76

cgtccaggtg tcagaggatt a 21

<210>77

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>77

accttgttct ccaggatacc c 21

<210>78

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>78

tgaagtggct ggttacatcc 20

<210>79

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>79

agaggctggt catagggtaa 20

<210>80

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>80

gtcttgacca gcctctctca 20

<210>81

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>81

acggtgcttt gagggataca 20

<210>82

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>82

acaagagacc ggctctagga 20

<210>83

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>83

ttgccactag gtgagctgtc 20

<210>84

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>84

cgttccgtta tccaggcttt t 21

<210>85

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>85

actcctggta ggtgtcaatg g 21

<210>86

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>86

ttagagctgc tccacatctc c 21

<210>87

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>87

accaggttgt tggtgaaggt a 21

<210>88

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>88

tccatcacca agagggtgaa 20

<210>89

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>89

caggatgctt tccccaaaca 20

<210>90

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>90

acgtgcattg tgctctctca 20

<210>91

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>91

cagcactgat ttggtcatct cc 22

<210>92

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>92

tgggcaccaa agaaggtta 19

<210>93

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>93

ttcctgtgca gagtaaacca 20

<210>94

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>94

atctctacgc ccaccagtcc 20

<210>95

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>95

agcgagtccg agtcatccaa 20

<210>96

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>96

gacgaaagct tggctccaaa 20

<210>97

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>97

gttccatagc gacgttctcc 20

<210>98

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>98

tgggataaaa gctctgctct tca 23

<210>99

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>99

tgttcgccgg cagaatacta 20

<210>100

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>100

acaaggccaa gaattccctc a 21

<210>101

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>101

tgccggggct tcttaaaca 19

<210>102

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>102

ttcgtcccca gccaacaa 18

<210>103

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>103

tggcttcccg ttcactatcc 20

<210>104

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>104

gcagtgactt tctcagcaac a 21

<210>105

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>105

ttgggacagc ttggatcaca 20

<210>106

<211>17

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>106

ctctgccgtc gcagcaa 17

<210>107

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>107

tggattagcc cgttgcagac 20

<210>108

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>108

gccggagcaa aacttaggaa a 21

<210>109

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>109

aggcggagtt cacaccaata 20

<210>110

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>110

gccaagctct ggaggaagaa 20

<210>111

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>111

gagaagaaca ggccgatggt ta 22

<210>112

<211>17

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>112

atcctggtgc tgctgac 17

<210>113

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>113

ggccagatct tctgcttca 19

<210>114

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>114

agttcagaag agcccgagac 20

<210>115

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>115

gcgctgtctt ttggtgaaaa c 21

<210>116

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>116

taccgggaga actacgtatc ca 22

<210>117

<211>17

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>117

atgcgccggt tctggaa 17

<210>118

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>118

aggaatgtga gctggagaca 20

<210>119

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>119

ttgtcacctt ccaactgaac c 21

<210>120

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>120

gctacctgga agctgaccaa 20

<210>121

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>121

ccacctgcct agtggcaaa 19

<210>122

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>122

ggaaaggggt ggatgtgtca 20

<210>123

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>123

cacccaggat gtccttgttc ta 22

<210>124

<211>24

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>124

atgttagagc tcagttggtt gata 24

<210>125

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>125

tactgcatga ccctcgacaa 20

<210>126

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>126

atattgtcgc accccatgca 20

<210>127

<211>26

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>127

acctcctttc ctgataattt cctcac 26

<210>128

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>128

gccagcagca ctttgagtta 20

<210>129

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>129

tgagtcaaca cggagctgta 20

<210>130

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>130

gaaacccagc cagaaagcaa 20

<210>131

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>131

aggtggtggt gtcagacaaa 20

<210>132

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>132

cctctgccct ccacttaatg tta 23

<210>133

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>133

cagctataga gccttccacc aa 22

<210>134

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>134

ccggacctga gcgtcatgta 20

<210>135

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>135

gcacaccacg tgctcaca 18

<210>136

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>136

gaacgggaag cttgtcatca a 21

<210>137

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>137

atcgccccac ttgattttgg 20

<210>138

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>138

tcagtttgga aacctgcaga a 21

<210>139

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>139

gctgcccata gctctttgaa 20

<210>140

<211>16

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>140

cccgtcgcct gtacca 16

<210>141

<211>24

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>141

tgttggaata gactctgaga agca 24

<210>142

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>142

cggctacgac ctgaaactct a 21

<210>143

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>143

tgagtgaggt gttgatgaac ca 22

<210>144

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>144

ccagaggcag tgcaaacata a 21

<210>145

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>145

agaaattggg tccggggtta 20

<210>146

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>146

actccggaca gcacataca 19

<210>147

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>147

ccaactgaag cactccgaaa 20

<210>148

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>148

aagacctggc aacggatgac 20

<210>149

<211>24

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>149

ttgagaatcc tttccaaccc agac 24

<210>150

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>150

acaacttgtc catcgtttca c 21

<210>151

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>151

accagccatc acacacaa 18

<210>152

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>152

agaactgaac agcgcaaca 19

<210>153

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>153

gctgggtatt caccatggaa 20

<210>154

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>154

ggtgaccgtt ttggcaatcc 20

<210>155

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>155

cactggccac tccagtcac 19

<210>156

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>156

gcctggcgca ttacaacaa 19

<210>157

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>157

caattccccg ggtagcagta 20

<210>158

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>158

cggcaaagct gaaggagaag ta 22

<210>159

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>159

caggaccctt tgcaccatca 20

<210>160

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>160

acacagtgct aggtgcagtt a 21

<210>161

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>161

tccatactcc cagcctttca c 21

<210>162

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>162

aagttcatcc ggcccttca 19

<210>163

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>163

ggtgtcttca tccacctcca 20

<210>164

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>164

tggacgtaag ggactcaaaa tcc 23

<210>165

<211>17

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>165

cccaggcctg cgttaca 17

<210>166

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>166

tgtgccttac gacccatgta 20

<210>167

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>167

gttgttcagg gcctcgttta 20

<210>168

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>168

gcaagaaggt ggcattgttt cta 23

<210>169

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>169

tctggggaaa gtgaggcaaa 20

<210>170

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>170

agagaagaga aggctggtga ac 22

<210>171

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>171

tccaggttgt caggagcaaa 20

<210>172

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>172

tcccatctca aagcccatta ca 22

<210>173

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>173

ctcgtctgag cgggagaa 18

<210>174

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>174

ctggcaggac tgtgagtaca a 21

<210>175

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>175

atttcgtact gctcctcttc cc 22

<210>176

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>176

tgaccgacga gatcaacttc c 21

<210>177

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>177

tgtgccttga cctcagcaa 19

<210>178

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>178

tgaaggcatt ggggctgtg 19

<210>179

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>179

agcctgacac gcagaggt 18

<210>180

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>180

tgtgcatcta tgtgcgtcac 20

<210>181

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>181

ggaatcgatg ggcaaagttg ta 22

<210>182

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>182

caaaagacgg gcctcaccaa 20

<210>183

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>183

cgtaagaggt tgcgcctgaa 20

<210>184

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>184

catcggtgga acgcacaa 18

<210>185

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>185

gggcacttcc ctcaaacaaa 20

<210>186

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>186

gccttggttg gactggaaaa 20

<210>187

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>187

tttgaagagc aacatggggt ac 22

<210>188

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>188

ctcccaggaa ccgtacttca 20

<210>189

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>189

ctctgataaa agccacgtct cc 22

<210>190

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>190

catcaagatg tggcaggagt a 21

<210>191

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>191

tgcagtacca tgacactgaa ata 23

<210>192

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>192

gactccattc ctttggtctt tcc 23

<210>193

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>193

ccatggattc ctcggagtca 20

<210>194

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>194

tggtctgatg ggtggagacc 20

<210>195

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>195

tgagtttcgg ggattgccat ac 22

<210>196

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>196

tctcacaatg ggattctctc ca 22

<210>197

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>197

caggcaagtt cctgaagtcc 20

<210>198

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>198

tgccgaagaa acatggacca a 21

<210>199

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>199

agccccaaag aatggccaaa 20

<210>200

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>200

agaaggccaa accccagaa 19

<210>201

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>201

atgctgaggc cttgtttgc 19

<210>202

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>202

gagagttgtc cagtgatgtc a 21

<210>203

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>203

gtcctgaacc caagtcatca 20

<210>204

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>204

catcggtacc cagttcagga a 21

<210>205

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>205

tgctgcatgc tgaacacac 19

<210>206

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>206

tcctctccaa caccaaagtc c 21

<210>207

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>207

aggattccgg cacagtcaa 19

<210>208

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>208

tggaagccac ggtggataa 19

<210>209

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>209

tgtgcggata tgcttgttcc 20

<210>210

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>210

tctgctccct tccattccc 19

<210>211

<211>26

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>211

gaattcttca ttcccttgaa ctgaac 26

<210>212

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>212

cgcctcatgt tctgctaca 19

<210>213

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>213

tagaagagtg cgttggtcac 20

<210>214

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>214

cgagccgacc atgtcttca 19

<210>215

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>215

aagctgaagg ttcggagcaa 20

<210>216

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>216

ccaggctttc caaggttacc a 21

<210>217

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>217

tggctttctg ggcaagca 18

<210>218

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>218

actggataca ttcaaaccct cca 23

<210>219

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>219

tggtgcacag actggtgtta 20

<210>220

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>220

gtactgtggc gatggcatta tac 23

<210>221

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>221

agaaaaggga gcagccatca 20

<210>222

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>222

acagtggaag cctgggttaa 20

<210>223

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>223

acagtgtggg agcagttatc a 21

<210>224

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>224

ctggcatcca gttgacgaaa 20

<210>225

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>225

catcagggcc tagcaggtaa 20

<210>226

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>226

gtgcagcact accacatgaa 20

<210>227

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>227

tatacgagcc cgtcttctcc 20

<210>228

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>228

gccagctgct atatcacaca aa 22

<210>229

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>229

aaacccaggg ctacttccaa 20

<210>230

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>230

gccacatttc aaaggaaact gac 23

<210>231

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>231

tccctgcagc caatcagata 20

<210>232

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>232

ccaccaagaa gccactttcc 20

<210>233

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>233

taccagcaat gccagggtta 20

<210>234

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>234

agaaactgcg tccgttttcc 20

<210>235

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>235

ggagtcagat gtccttggga taa 23

<210>236

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>236

cggatcaaac tgggatttac cc 22

<210>237

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>237

cgagaacacg ttgccataca 20

<210>238

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>238

tctggcttcc tccaccaaa 19

<210>239

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>239

cagcggagtt cagcatacaa 20

<210>240

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>240

gttcggctcc tggctgat 18

<210>241

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>241

caaagatgga caccagcgaa tc 22

<210>242

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>242

ggggcagttt ctgctcttca 20

<210>243

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>243

tcatcctcag gcagcgtctt a 21

<210>244

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>244

gcaggatcct acaccttaca ca 22

<210>245

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>245

tgctggagat ggagggctta 20

<210>246

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>246

ctggcgagga aagctcca 18

<210>247

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>247

cagaaatgac atcacagctg cta 23

<210>248

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>248

ctccccagca tttacccttc a 21

<210>249

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>249

ggttagactc ggcgaagca 19

<210>250

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>250

acccagtcac cctgaatgtc 20

<210>251

<211>23

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>251

gcaggacaag tagaggtttt gtc 23

<210>252

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>252

gtcggtctgc tggtctcc 18

<210>253

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>253

tgtgtcttgc agtgctcaac 20

<210>254

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>254

aggcacagat atgggacaca 20

<210>255

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>255

ataaggcacc tacagctcca 20

<210>256

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>256

accagatcag agggaagtca 20

<210>257

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>257

cagttgctgc acttgtttca 20

<210>258

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>258

ggaagcagga tggatgaaca 20

<210>259

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>259

ctcgggtatg gaatgtagtc c 21

<210>260

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>260

tgaagacacc cgctccagta 20

<210>261

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>261

ccccatttca ctgcctcttc a 21

<210>262

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>262

tgggaaggtg gtcatcacac 20

<210>263

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>263

cagcacccgc tgagatca 18

<210>264

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>264

gccaagatcc catctctcca 20

<210>265

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>265

aggcacttca gctcaggaaa 20

<210>266

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>266

ctggctgtgg gtgtggtact 20

<210>267

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>267

cgctccactc cctctaggc 19

<210>268

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>268

gctagagacc gagtgtcctc a 21

<210>269

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>269

ccagaatgag gaactcctgg aa 22

<210>270

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>270

tcaaagagct ggtgcgaaaa 20

<210>271

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>271

atttgtgtcc aggtcctcca 20

<210>272

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>272

gaaggagcta ccaggcttcc 20

<210>273

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>273

agcaatttat ccacggcatc c 21

<210>274

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>274

cttcgagaag tcttgcaacc 20

<210>275

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>275

gccagaataa gagggaagct a 21

<210>276

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>276

tttgccaaag tggctgcaaa 20

<210>277

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>277

tttctgggct tgggttctcc 20

<210>278

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>278

tgcaccagtg ggtgtcata 19

<210>279

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>279

gtggaactga aggtgccaaa 20

<210>280

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>280

agaagtggaa ccgcttacta ca 22

<210>281

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>281

agtgtggctc caaggtcata 20

<210>282

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>282

gctgccatcg tgtatttcta ca 22

<210>283

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>283

agacctcatc caccggaaaa 20

<210>284

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>284

gggagcactt ggcacttttc a 21

<210>285

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>285

gcaggatgtg ccacagatca 20

<210>286

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>286

ggtccttccc atctacagtg aa 22

<210>287

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>287

agcatccccg tgatggaaat a 21

<210>288

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>288

tgctgccgct catcttca 18

<210>289

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>289

caaaggttgc cttggcatca 20

<210>290

<211>19

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>290

gacctggcgc tccagttta 19

<210>291

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>291

cctctgtgaa gcatctcagc ta 22

<210>292

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>292

aagacggctt gatttcagga a 21

<210>293

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>293

gcatcatcca atggtctcca 20

<210>294

<211>21

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>294

tgttaagcag gacgactttc c 21

<210>295

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>295

cctttctggc tgggcttaaa 20

<210>296

<211>18

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>296

ggtgcctctg ccttccaa 18

<210>297

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>297

ttgtgccagc catagtcaca 20

<210>298

<211>22

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>298

agagtgaagg tgtgatgctg aa 22

<210>299

<211>20

<212>DNA

<213> Artificial sequence

<220>

<223> description of Artificial sequences Synthesis

Primer and method for producing the same

<400>299

gcacggtttg tgacaggtac 20