阅读说明：本技术 用于针对所需的且可遗传的性状来挑选反刍动物的方法 (Method for selecting ruminants for desirable and heritable traits ) 是由 伊扎克·米兹拉希 于 2018-08-06 设计创作，主要内容包括：本发明公开一种用于挑选一反刍动物的方法,所述反刍动物具有一所需的且可遗传的性状。所述方法包含：分析在所述动物的一微生物组中的一可遗传的微生物的数量,所述可遗传的微生物与所述可遗传的性状有关,其中所述可遗传的微生物的所述数量指示出所述动物是否具有一所需的且可遗传的性状。(The present invention discloses a method for selecting a ruminant having a desirable and heritable trait. The method comprises the following steps: analyzing a quantity of a heritable microorganism in a microbiome of the animal, said heritable microorganism being associated with the heritable trait, wherein the quantity of the heritable microorganism is indicative of whether the animal has a desired and heritable trait.)

1. A method for selecting a ruminant animal having a desirable and heritable trait, the method comprising: the method comprises analyzing the quantity of a heritable microorganism in a microbiome of the animal, the heritable microorganism being associated with the heritable trait, wherein the quantity of the heritable microorganism is indicative of whether the animal has a desirable and heritable trait.

2. The method of claim 1, wherein: the heritable microorganism comprises a heritable bacterium.

3. The method of claim 2, wherein: the heritable bacteria are at least one of a plurality of Operational Taxonomic Units (OTUs) listed in table 1.

4. The method of claim 2, wherein: the heritable bacteria are bacteroidetes and firmicutes, wherein the quantity indicates whether the animal has a desirable and heritable trait.

5. The method of claim 2, wherein: the heritable bacteria are of the order Bacteroides and clostridiales.

6. The method of claim 3, wherein: the at least one OTU is selected as any one of SEQ ID NOs 1, 3, 5, 7, 8, 9, 11 to 17, 19, 20 or 21.

7. The method of any of claims 3 or 6, wherein: the at least one heritable bacterium is selected from the group consisting of a heritable bacterium genus, a heritable family of bacteria, and a heritable order of bacteria.

8. The method of any of claims 3, 6 or 7, wherein: the bacterium exhibits a sequence of the 16S rRNA gene selected from the group consisting of SEQ ID NOS: 1 to 22.

9. The method of any of claims 3, 6, 7 or 8, wherein: the at least one OTU comprises at least five OTUs.

10. The method of any of claims 3 to 9, wherein: the ruminant is a cow.

11. The method of any of claims 3 to 10, wherein: the method further comprises: breeding using said selected animals.

12. The method of any of claims 3 to 11, wherein: the desirable and heritable trait is selected from the group consisting of milk protein, dry matter intake, methane production, feed efficiency, and milk fat.

13. The method of any of claims 3 to 12, wherein: the microbiome is a non-pathogenic microbiome.

14. The method of any of claims 3 to 13, wherein: analyzing the amount is performed by analyzing the expression of at least one gene of a genome of the at least one bacterium.

15. The method of any of claims 3 to 13, wherein: analyzing the amount is carried out by sequencing DNA from a sample of the microbiome.

16. A method for determining reproductive purity of a plurality of ruminants, comprising: the method comprises the following steps: analyzing a microbiome of the plurality of ruminants, wherein a similarity in number of a bacterium in the microbiome indicates the reproductive purity.

17. The method of claim 16, wherein: the bacterium is at least one of a plurality of Operational Taxonomic Units (OTUs) listed in table 1.

18. The method of claim 17, wherein: the at least one OTU is selected from the group consisting of genus, family and order.

19. The method of claim 17, wherein: the bacterium exhibits a sequence of the 16S rRNA gene selected from the group consisting of SEQ ID NOS: 1 to 22.

20. The method of any of claims 16 to 19, wherein: the at least one OTU comprises at least five OTUs.

21. The method of any of claims 16 to 20, wherein: the ruminant is a cow.

22. The method of any of claims 3 to 21, wherein: the microbiome comprises a rumen microbiome or a fecal microbiome.

23. A method for breeding a ruminant animal, comprising: the method comprises the following steps: fertilizing a female ruminant, which has been selected according to the method of any one of claims 1 to 15, with semen from a male ruminant, thereby breeding the ruminant.

24. The method of claim 23, wherein: the male ruminant has been selected according to the method of any one of claims 1 to 15.

25. A method for breeding a ruminant animal, comprising: the method comprises the following steps: fertilizing a female ruminant with semen from a male ruminant which has been selected according to the method of any one of claims 1 to 15, thereby breeding the ruminant.

26. A method for increasing the number of a plurality of ruminants having a desired microbiome, comprising: the method comprises the following steps: propagating a male and a female of the plurality of ruminants, wherein the ruminal microbiome of any one of the male ruminant and/or the female ruminant comprises above a predetermined level of a heritable microorganism, thereby increasing the number of the plurality of ruminants having a desired microbiome.

27. The method of claim 26, wherein: the heritable microorganism is associated with a heritable trait.

28. The method of claim 26, wherein: the heritable microorganisms affect a relative number of a plurality of microorganisms of the microbiome.

29. The method of claim 26, wherein: the ruminal microbiome of both the male ruminant or the female ruminant comprises a heritable microorganism above a predetermined level.

30. The method of claim 26, wherein: the heritable microorganism is a bacterium.

31. The method of claim 30, wherein: the bacterium exhibits a sequence of the 16S rRNA gene selected from the group consisting of SEQ ID NOS: 1 to 22.

Technical field and background

In some embodiments of the invention, the invention relates to a method of selecting a ruminant for a desired and heritable trait based on the presence of specific bacteria in a microbiome of the ruminant.

The rumen microbiome of cattle essentially enables the host ruminant to digest its feed through degradation and fermentation. In this sense, this relationship is unique and distinct from host-microbiome interactions that have evolved in humans and non-herbivores, in which case this dependency does not exist. This strict and mandatory host-microbiome relationship, established approximately fifty million years ago, is believed to play an important role in host physiology. Despite its great importance, the impact of natural genetic variation in the host-caused by sexual reproduction and meiosis-on the complex relationships between the multiple constituents of the rumen microbiome and the physiological traits of multiple hosts is poorly understood. It is known that there are several associations between specific compositions of the rumen microbiome and animal physiology, exemplified primarily by the ability of the animal to harvest energy from feed [ Kruger Ben Shabat S et al 2016.ISME J10: 2958-.

These recent findings have located the rumen microbiome of such cattle as a completely new area of endeavour to improve the feed efficiency of cows. As the population continues to increase, this may have important implications on multiple issues of food safety, for example, efforts to replenish multiple food sources available for human consumption while reducing environmental impact on a global scale. Although it is very important, little is understood about the complex relationships between the multiple components of the rumen microbiome and the genetics and physiology of the host.

Background art includes Guan LL et al, 2008.FEMS Microbiology L meters 288:85-9, Roeher et al, 2016.P L oS Genet 12: e1005846, L i Z et al, 2016.Microbiology Reports8:1016- > 102, and WO 2017/187433.

Disclosure of Invention

According to one aspect of the present invention there is provided a method for selecting a ruminant having a desirable and heritable trait, the method comprising: analyzing a quantity of a heritable microorganism in a microbiome of the animal, said heritable microorganism being associated with the heritable trait, wherein the quantity of the heritable microorganism is indicative of whether the animal has a desired and heritable trait.

According to an aspect of the present invention, there is provided a method for propagating a ruminant, the method comprising: fertilizing a female ruminant with semen from a male ruminant selected by the methods described herein to breed the ruminant.

According to an aspect of the present invention, there is provided a method for increasing the number of a plurality of ruminants having a desired microbiome, the method comprising: propagating a male and a female of the plurality of ruminants, wherein the ruminal microbiome of any one of the male ruminant and/or the female ruminant comprises above a predetermined level of a heritable microorganism, thereby increasing the number of the plurality of ruminants having a desired microbiome.

According to an aspect of the present invention, there is provided a method for determining reproductive purity of a plurality of ruminants, the method comprising: analyzing a microbiome of the plurality of ruminants, wherein a similarity in number of a bacterium in the microbiome indicates the reproductive purity.

According to an aspect of the present invention, there is provided a method for propagating a ruminant, the method comprising: fertilizing a female ruminant, which has been selected according to the methods described herein, with semen from a male ruminant, thereby breeding the ruminant.

According to some embodiments of the invention, the heritable microorganism comprises a heritable bacterium.

According to some embodiments of the invention, the heritable bacteria is at least one of a plurality of Operational Taxonomic Units (OTUs) listed in table 1.

According to some embodiments of the invention, the heritable bacteria are bacteroidetes and firmicutes, wherein the amount indicates whether the animal has a desirable and heritable trait.

According to some embodiments of the invention, the heritable bacteria are of the order bacteroidales and clostridiales.

According to some embodiments of the invention, the at least one OTU is selected as any one of SEQ ID NOs 1, 3, 5, 7, 8, 9, 11 to 17, 19, 20 or 21.

According to some embodiments of the invention, the at least one heritable bacterium is selected from the group consisting of a heritable bacterium, a heritable family of bacteria, and a heritable order of bacteria.

According to some embodiments of the invention, the bacterium exhibits a sequence of the 16S rRNA gene selected from the group consisting of SEQ ID NOs 1 to 22.

According to some embodiments of the invention, the at least one OTU comprises at least five OTUs.

According to some embodiments of the invention, the ruminant is a dairy cow.

According to some embodiments of the invention, the method further comprises: breeding using said selected animals.

According to some embodiments of the invention, the desirable and heritable trait is selected from the group consisting of milk protein, dry matter intake, methane production, feed efficiency, and milk fat.

According to some embodiments of the invention, the microbiome is a non-pathogenic microbiome.

According to some embodiments of the invention, analyzing the amount is performed by analyzing the expression of at least one gene of a genome of the at least one bacterium.

According to some embodiments of the invention, analyzing the amount is carried out by sequencing DNA from a sample derived from the microbiome.

According to some embodiments of the invention, analyzing the amount is carried out by sequencing DNA from a sample derived from the microbiome.

According to some embodiments of the invention, the bacterium is at least one of a plurality of Operational Taxonomic Units (OTUs) listed in table 1.

According to some embodiments of the invention, the at least one OTU is selected from the group consisting of genus, family and order.

According to some embodiments of the invention, the bacterium exhibits a sequence of the 16S rRNA gene selected from the group consisting of SEQ ID NOs 1 to 22.

According to some embodiments of the invention, the at least one OTU comprises at least five OTUs.

According to some embodiments of the invention, the ruminant is a dairy cow.

According to some embodiments of the invention, the microbiome comprises a rumen microbiome or a fecal microbiome.

According to some embodiments of the invention, the male ruminant has been selected according to a plurality of methods described herein.

According to some embodiments of the invention, the heritable microorganism is associated with a heritable trait.

According to some embodiments of the invention, the heritable microorganisms affect a relative number of a plurality of microorganisms of the microbiome.

According to some embodiments of the invention, the ruminal microbiome of both the male ruminant or the female ruminant comprises a heritable microorganism above a predetermined level.

According to some embodiments of the invention, the heritable microorganism is a bacterium.

According to some embodiments of the invention, the bacterium exhibits a sequence of the 16S rRNA gene selected from the group consisting of SEQ ID NOs 1 to 22.

Unless defined otherwise, all technical and/or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of various embodiments of the present invention, a variety of exemplary methods and/or materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the various materials, methods, and examples are illustrative only and not intended to be necessarily limiting.

Drawings

Some embodiments of the invention are described herein, by way of example only, with reference to the accompanying drawings. Referring now in specific detail and in detail to the various figures, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of various embodiments of the present invention. In this regard, the description taken with the drawings make it apparent to those skilled in the art how the various embodiments of the invention may be practiced.

In the drawings:

FIG. 1: a heritable portion of the rumen microbiome of cattle is closely related phylogenetically. An average pairwise similarity among sequences of 16S rDNA genes of randomly chosen multiple ruminal OTU groups of the same size (n-22) was compared to the average pairwise similarity of the 22 heritable OTUs. The Y-axis represents the number of the plurality of clusters and the X-axis represents the sequence similarity. Pink depicts the cohort of heritable OTUs with a calculated average similarity of 72% in the sequence of the 16S rDNA gene. Depicted in blue is the distribution of multiple cohorts of randomly chosen multiple ruminal OTUs. All randomized groups showed a lower 16S rDNA similarity (P < 0.01);

FIG. 2: multiple heritable OTUs showed high rates of presence. A number of OTUs with categorical annotations are listed on the left. A relative abundance of each OTU along a cow cohort is presented in the left panel, and the presence of each OTU is shown in the right panel. Green indicates an OTU from bacteroidales (bacterioids), while brown indicates an OTU from clostridiales;

FIG. 3: multiple heritable OTUs are associated with multiple host attributes and multiple rumen metabolites. A heat map describes the correlation between the relative abundances (rows) of heritable OTUs in the rumen and selected indices (columns) representing the physiological attributes of different hosts or the rumen metabolites. Multiple OTUs were color-coded by order (green for bacteroidales, brown for clostridiales). The plurality of physiological attributes are colored in black, and the plurality of rumen metabolites are color-coded according to the following four groups: amino acids (blue), sugars (yellow), Volatile Fatty Acids (VFA) (green) and all other metabolites measured (grey). Each represents a plurality of nominal (nominal) p values less than 0.05, 0.005, 0.0005;

fig. 4A to 4B: multiple heritable OTUs are more tightly linked to the physiological and rumen metabolites of the host than other rumen microorganisms. (A) The mean absolute correlation of the heritable OTUs for a particular index (Spearman) is compared to the mean absolute correlation of the entire microbiome. Multiple asterisks represent significant differences in mean (t-test, p < 0.05). The multiple red bars represent the association of the heritable microbiome. While the multiple blue bars represent the association of the entire microbiome. (B) A ratio of differences (O.R.) between the heritable OTUs and all OTUs for an OTU that is associated with a particular index (nominal spearman p < 0.05). Y-axis: difference ratio, X-axis: p-values derived from Fisher-exact test (Fisher-exact test). The red vertical line segment defines the 0.05 significance threshold for Bonferroni corrected (Bonferroni-corrected). The plurality of color points represent categories according to the legend;

FIG. 5: a previous study (Shabat, Sheerli Kruger Ben et al, ISME J10: 2958-. In this study, 6 of the 22 heritable OTUs were associated with different dairy production indicators, namely Dry Matter Intake (DMI), milk protein and feed efficiency, measured as Residual Feed Intake (RFI). The plurality of OTUs and their classification bits are on a plurality of rows and the plurality of production indicators are on a plurality of columns. In the present examples, "& symbol" in a block indicates that a significant correlation was found between the heritable OTU and the production indicator;

FIG. 6: a histogram shows the false discovery rate of the heritability test under the assumption of 1,00 randomly arranged permutations. An X axis: the number of heritable OTUs detected. Y-axis: the number of permutations. A red vertical line segment represents the number of OTUs detected as heritable in the actual (not replaced) data.

FIG. 7: a null model (null-model) for testing the significance of the actual Pesmann relevance r values of the plurality of heritable OTUs for each of the plurality of indices. The plurality of red bars represent a plurality of values generated from a data graph (profile) of the actual relative abundance of the plurality of heritable OTUs; the black bars represent values from 1,000 permutations. In each of these permutations, the abundance data plots for each OTU were randomly shuffled;

FIG. 8: the 50 OTUs with high heritability estimates were ranked by day (sampling the microbiome on three different days), where the multiple heritability estimates were found to be the major classification key of importance and the average heritability estimate was taken as the minor classification key. An X axis: the number of days of sampling. Y-axis: OTU classification of microorganisms. The red line segment represents a threshold above which OTUs are considered heritable;

FIG. 9: a heat map depicts multiple abundance data plots (average over three sampling days) for multiple heritable microbial OTUs along the entire study group. An X axis: animal identification number in the experiment. Y-axis: classification of various microorganisms OTU. Coloring the heat map according to the relative abundance of a particular said OTU in a particular said animal. The relative abundance was multiplied by 1M to simplify readability. The horizontal line segment in red is represented as a threshold above which OTUs are considered heritable;

FIG. 10: a heat map depicts a plurality of abundance data plots (average over three sampling days) for a plurality of heritable microbial OTUs along a genotyped subset of the entire study group. An X axis: animal identification number in the experiment. Y-axis: classification of various microorganisms OTU. Coloring the heat map according to the relative abundance of a particular said OTU in a particular said animal. The relative abundance was multiplied by 1M to simplify readability. The horizontal line segment in red is represented as a threshold above which OTUs are considered heritable; and

FIG. 11: a plurality of heritability estimates for a plurality of host traits. An X axis: and (5) properties. Y-axis: an estimate of heritability. An asterisk on the top of the bar indicates significant genetic makeup.

Detailed Description

In some embodiments of the invention, the invention relates to a method of selecting a ruminant for a desired and heritable trait based on the presence of specific bacteria in a microbiome of the ruminant.

Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not necessarily limited in its application to the details set forth in the following description or illustrated by the examples. The invention is capable of other embodiments or of being practiced or carried out in various ways.

Ruminants have maintained a permanent and mandatory relationship with their rumen microbiome for over five million years. In this unique host-microbiome relationship, the ability of the host to digest its feed is entirely dependent on its co-evolving microbiome. This particular consortium raises questions about the dependence between the genetics and physiology of ruminants and the structure, composition and metabolism of the ruminal microbiome. To elucidate this relationship, the present inventors investigated the association of the genetics of the host with the phylogenetic and functional composition of the rumen microbiome. They completed this survey by studying a population of 78 dairy Holstein-Friesian cows using data from the rumen microbiota and other phenotypes from each animal in combination with genotypic data from a subset of 47 animals. More specifically, by applying SNP-based heritability estimates incorporating amplified sequencing data, multiple host traits, and multiple ruminal metabolites, the inventors identified 22 Operational Taxa (OTUs) whose abundances were associated with multiple ruminal metabolic traits and multiple host physiological traits and showed measurable heritability (see fig. 8).

The current results show that multiple heritable microbial species represent a relevant phylogenetic group (FIG. 1). This finding corresponds to a basic ecological concept in that multiple organisms sharing a similar niche are more likely to be systematically close to each other than organisms that do not share a similar niche. The plurality of metabolites and the plurality of measured physiological parameters are typically clustered together according to their classification based on their association data chart (profile) hierarchical clustering (columns in heat map, fig. 3) of the different heritable OTUs. For example, most of the amino acids clustered together, some of the volatile fatty acids paired together, and six of the nine production indices were adjacent to each other along the heat map. At this point, from the perspective of the heritable OTUs, even within the distinguishable regions of the heritable OTUs identified in this study, one can observe that the OTUs were clustered according to their abundance data plots and were highly segregated according to their taxonomic membership, e.g., eight of nine unknown bacteroidetes were clustered together and three of five Prevotella (Prevotella) were clustered together.

The inventors further found that the heritable bacteria contained a high proportion of microorganisms associated with multiple host traits and with multiple rumen metabolic parameters (fig. 4A and 4B). These findings imply that genetic variation in a host can have a measurable effect on multiple physiological traits and rumen metabolism of the host by potentially modulating the abundance of different ruminal microbiota groups. These findings indicate that host genetics is associated with specific ruminal bacteria, which potentially more readily affect ruminal metabolism and host physiology. Notably, the plurality of metabolites and the plurality of host traits found associated with heritable bacteria are also linked together by their metabolism. This can be measured in terms of methane production, propionate: a number of correlation values for acetate ratio, lactate, propionate, butyrate and energy harvesting efficiency (expressed as RFI) of the host were observed. Of particular interest is the observation that the heritable bacteria are mostly associated with the propionate: acetate ratio (average | r | ═ 0.64), the propionate: the acetate ratio is inversely proportional to methanogenesis and lactic acid, and is also proportional to RFI, which estimates the efficiency of energy capture (fig. 3 and 4). Another remarkable finding is that the milk protein trait associated with multiple heritable microorganisms and showing the highest rate of difference indicates an enrichment of heritable bacteria associated with this host trait (fig. 4). These observations further reinforce the concept of a triangular relationship between the host's genotype, rumen bacteria, and host traits.

Thus, according to one aspect of the present invention, there is provided a method for selecting a ruminant having a desirable and heritable trait, the method comprising: analyzing a quantity of a heritable microorganism in a microbiome of the animal, said heritable microorganism being associated with the heritable trait, wherein the quantity of the heritable microorganism is indicative of whether the animal has a desired and heritable trait.

A variety of ruminants contemplated by the present invention include, for example, cattle (e.g., cows), goats, sheep, giraffes, bison, european bison, yaks, buffalo, deer, camels, alpacas, vicunas, antelopes, pronghorn antelopes, and blue-ox antelopes.

According to a particular embodiment, the ruminant is a cow or bull of the family bovidae-e.g. a common cow or a holstein-fries blue cow.

According to a particular embodiment, the animal selected is a newborn animal, generally no more than one day old. According to another embodiment, the animals are selected to be no more than two days old. According to another embodiment, the animals are selected to be no more than three days old. According to another embodiment, the animals are selected to be no more than one week old. According to another embodiment, the animal is selected to be no more than two weeks old. According to another embodiment, the animals are selected to be no more than one month old. According to another embodiment, the animals are selected to be no more than three months old. According to yet another embodiment, the animal is selected as an adult.

The phrase "heritable trait" (also referred to as a heritable trait) as used herein refers to a trait in which variation between individuals in a particular population is due in part (or in whole) to genetic variation. Due to these genetic variations, the relative or absolute abundance of a plurality of specific microorganism populations (as markers) in the microbiome is similar from one generation to the next in a statistically significant manner.

A microorganism can be classified as heritable when changes in abundance of the microorganism in a group of animals can be explained by genetic variation between animals.

A number of statistical methods that may be used within the context of the present invention include, but are not limited to, the one-component GRM method, MAF-layered GREM L (GREM LL MS), LL 0D LL 1 and MAF-layered GREM L (GREM L L L DMS), L D-tunable genetic relationships of single-component and MAF-layered (L DAK-SC and L DAK-MS), extended pedigree with thresholded GRMs, Chelette (treelet) covariant smoothing (TCS), LD-score regression, and BOLT-REML.

In one embodiment, the trait relates to rumen metabolism, examples of which include, but are not limited to, propionate: acetate ratio, methane content/concentration in the rumen, propionic acid content/concentration in the rumen, and valeric acid content/concentration in the rumen. In addition, the trait may also be the content/concentration of an amino acid in the rumen, such as glycine, aspartic acid and tyrosine.

In another embodiment, the trait relates to a property of a host, examples of which include, but are not limited to, the content of protein or fat present in the milk of the animal, dry matter intake, or feed efficiency.

Various examples of the various heritable traits shown by the inventors are at least partially heritable, including feed efficiency and methane production.

As used herein, the term "feed efficiency" refers to the ability of the animal to extract energy from food. The feed efficiency refers to the difference between the actual feed intake of an animal and the predicted feed intake based on its production level and body weight. Thus, an animal with "a high" feed efficiency is one that produces more milk or weighs more than predicted based on its feed intake. An animal with a "negative" feed efficiency is one that produces less milk or weighs less than would be predicted based on its feed intake. In one embodiment, the Residual Feed Intake (RFI) method (Koch et al, 1963, J Anim Sci,22, 486-. The plurality of expected RFI values for each cow may be calculated based on a multiple regression equation.

According to an embodiment, an animal may be classified as having a low RFI (or high feed efficiency) when the animal has at least 0.05 standard deviations below the average RFI of the herd (a herd having at least 15 animals).

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when the animal has at least 0.05 standard deviations below the average RFI of the herd (a herd having at least 15 animals).

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when the animal has at least 1 standard deviation below the average RFI of the herd (a herd having at least 15 animals).

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when the animal has at least 2 standard deviations below the average RFI of the herd (a herd having at least 15 animals).

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when the animal has at least 3 standard deviations below the average RFI of the herd (a herd having at least 15 animals).

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when the animal has at least 4 standard deviations below the average RFI of the herd (a herd having at least 15 animals).

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when the animal has at least 5 standard deviations below the average RFI of the herd, which is a herd having at least 15 animals.

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when the animal has at least 6 standard deviations below the average RFI of the herd (a herd having at least 15 animals).

According to an embodiment, an animal may be classified as having a high RFI (or low feed efficiency) when the animal has at least 0.05 standard deviations above the average RFI of the herd (a herd having at least 15 animals).

According to an embodiment, an animal may be classified as having a high RFI (or low energy efficiency) when the animal has at least 0.05 standard deviations above the mean RFI of the herd (a herd having at least 15 animals).

According to an embodiment, an animal may be classified as having a high RFI (or low energy efficiency) when the animal has at least 1 standard deviation above the mean RFI of the herd (a herd having at least 15 animals).

According to an embodiment, an animal may be classified as having a high RFI (or low energy efficiency) when the animal has at least 2 standard deviations above the average RFI of the herd, which is a herd having at least 15 animals.

According to one embodiment, an animal may be classified as having a high RFI (or low energy efficiency) when the animal has at least 3 standard deviations above the average RFI of the herd, which is a herd having at least 15 animals.

According to an embodiment, an animal may be classified as having a high RFI (or low energy efficiency) when the animal has at least 4 standard deviations above the mean RFI of the herd (a herd having at least 15 animals).

According to an embodiment, an animal may be classified as having a high RFI (or low energy efficiency) when the animal has at least 5 standard deviations below the average RFI of the herd (a herd having at least 15 animals).

According to an embodiment, an animal may be classified as having a high RFI (or low energy efficiency) when the animal has at least 6 standard deviations below the average RFI of the herd (a herd having at least 15 animals).

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when its Dry Matter Intake (DMI) is less than 1 kg per day, as compared to the DMI predicted from its expected food intake (calculated as a function of weight and milk production, as described herein above).

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when its dry food intake (DMI) is less than 2 kilograms per day compared to the DMI predicted from its expected food intake.

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when its dry food intake (DMI) is less than 4 kilograms per day compared to the DMI predicted from its expected food intake.

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when its dry food intake (DMI) is less than 8 kilograms per day compared to the DMI predicted from its expected food intake.

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when its dry food intake (DMI) is less than 16 kg per day, as compared to the DMI predicted from its expected food intake.

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when its dry food intake (DMI) is less than 32 kilograms per day compared to the DMI predicted from its expected food intake.

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when compared to the amount of milk that is produced 1.5 times or weighed 1.5 times according to its predicted feed intake.

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when compared to producing 2 times the amount of milk or weighing 2 times its weight as predicted by its feed intake.

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when compared to the amount of milk produced 2.5 times or weighing 2.5 times according to its predicted feed intake.

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when compared to producing 3 times the amount of milk or weighing 3 times its weight as predicted from its feed intake.

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when the animal produces 3.5 times the amount of milk or weighs 3.5 times more than predicted based on its feed intake.

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when compared to producing 4 times the amount of milk or weighing 4 times its weight as predicted by its feed intake.

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when the animal produces 4.5 times the amount of milk or weighs 4.5 times its weight as compared to what is predicted from its feed intake.

According to one embodiment, an animal may be classified as having a low RFI (or high energy efficiency) when compared to the amount of milk produced 5 times or the weight weighed 5 times as predicted from its feed intake.

The term "methane production" refers to the amount of methane emitted by the plurality of animals themselves or produced by the microbiome. It may be measured in units of grams per day or grams per kilogram of dry matter intake.

According to one embodiment, an animal may be classified as a "high methane producer" when the animal has at least 0.05 standard deviations above the average methane production of the herd.

According to one embodiment, an animal may be classified as a "high methane producer" when the animal has at least 0.5 standard deviations above the average methane production of the herd.

According to one embodiment, an animal may be classified as a "high methane producer" when the animal has at least 1 standard deviation above the average methane production of the herd.

According to one embodiment, an animal may be classified as a "high methane producer" when the animal has at least 2 standard deviations above the average methane production of the herd.

According to one embodiment, an animal may be classified as a "high methane producer" when the animal has at least 3 standard deviations above the average methane production of the herd.

According to one embodiment, an animal may be classified as a "high methane producer" when the animal has at least 4 standard deviations above the average methane production of the herd.

According to one embodiment, an animal may be classified as a "high methane producer" when the animal has at least 5 standard deviations above the average methane production of the herd.

According to one embodiment, an animal may be classified as a "high methane producer" when the animal has at least 6 standard deviations above the average methane production of the herd.

The term "low methane production" means that the microbiome (e.g., rumen microbiome/fecal microbiome) of the animal produces less than 100 grams per day or 10 grams per kilogram of dry matter intake.

According to one embodiment, an animal may be classified as a "low methane producer" when the animal has at least 0.05 standard deviations below the average methane production of the herd.

According to one embodiment, an animal may be classified as a "low methane producer" when the animal has at least 0.5 standard deviations below the average methane production of the herd.

According to one embodiment, an animal may be classified as a "low methane producer" when the animal has at least 1 standard deviation below the average methane production of the herd.

According to one embodiment, an animal may be classified as a "low methane producer" when the animal has at least 2 standard deviations below the average methane production of the herd.

According to one embodiment, an animal may be classified as a "low methane producer" when the animal has at least 3 standard deviations below the average methane production of the herd.

According to one embodiment, an animal may be classified as a "low methane producer" when the animal has at least 4 standard deviations below the average methane production of the herd.

According to one embodiment, an animal may be classified as a "low methane producer" when the animal has at least 5 standard deviations below the average methane production of the herd.

According to one embodiment, an animal may be classified as a "low methane producer" when the animal has at least 6 standard deviations below the average methane production of the herd.

The term "microbiome" as used herein refers to a collective group of multiple microorganisms (bacteria, fungi, protists), their genetic elements (genomes) in a defined environment.

A sample of a microorganism comprises a sample of a plurality of microorganisms from a microbiome and or components or products thereof.

According to a particular embodiment, the microbiome is a rumen microbiome. In other embodiments, the microbiome is a fecal microbiome.

According to another embodiment, the microbiome is derived from a healthy animal (i.e., the microbiome is a non-pathogenic microbiome).

To analyze the plurality of microorganisms of a microbiome, a sample of a microorganism phase is collected from the animal. This may be done in any manner that allows for the recovery of multiple microorganisms or their components or products, and is suitable for the relevant microbiome source, e.g., rumen.

Rumen may be collected by using a number of methods known in the art, and include, for example, using a gastric tube with a rumen pinhole sampler. Typically, rumen is collected after feeding.

In some embodiments, instead of analyzing a sample of a stomach, a stool sample is used that reflects the microbiome of the rumen. Thus, in this example, a fecal microbiome was analyzed.

According to one embodiment of this aspect of the invention, the abundance of a particular bacterial taxa is analyzed in a sample of microbial organisms.

Described herein below are various methods of quantifying the levels of various microorganisms (e.g., bacteria) of various taxa.

In some embodiments, determining a level or set of levels of one or more types of microorganisms, or components or products thereof, comprises: a level and a set of levels of one or more DNA sequences are determined. In some embodiments, the one or more DNA sequences comprise any DNA sequence that can be used to distinguish between different microorganism types. In certain embodiments, the one or more DNA sequences comprise sequences of a plurality of 16S rRNA genes. In certain embodiments, the one or more DNA sequences comprise a plurality of sequences of the 18S rRNA gene. In some embodiments, 1,2, 3, 4,5, 10, 15, 20, 25, 50, 100, 1,000, 5,000 or more sequences are amplified.

A suitable computer program (e.g. B L AST) may be used to classify the plurality of species against a suitable reference database (e.g. a 16SrRNA reference database).

In the case of determining whether a Nucleic acid or protein is substantially homologous to a sequence of the invention or shares a certain percentage of sequence similarity, the sequence similarity may be defined by conventional algorithms which generally allow the introduction of a small number of gaps for achieving the best fit, in particular the "percent similarity" of two polypeptides or of two Nucleic Acids using the algorithm of kallin and alzheimer's library (Karlin and Altschul) is determined (proc. natl. acad. sci. usa 87: 2264. 2268,1993) which is incorporated into the B L ASTX and B L asrs program of alzheimer's et al (j.mol. biol.215: 403: 410,1990) using the B L ASTN program B5638 nucleotides are searched for obtaining a plurality of nucleotide sequences homologous to a Nucleic acid molecule of the invention using the B L, or the corresponding B3627 st t protein obtained using the algorithm of ascl 3662B 3655, the corresponding gap score of astr 2, the algorithm described in the database 363662, L, the database of the invention using the algorithm of ascl L, the database L, the algorithm described for comparison of the polynucleotide sequence of ascl, the invention using the nucleotide sequence of ascl 3562, the nucleotide sequence obtained by the algorithm of the invention.

According to one embodiment, in order to classify a microorganism as belonging to a particular genus, it must comprise at least 90% sequence homology, at least 91% sequence homology, at least 92% sequence homology, at least 93% sequence homology, at least 94% sequence homology, at least 95% sequence homology, at least 96% sequence homology, at least 97% sequence homology, at least 98% sequence homology, at least 99% sequence homology with a reference microorganism known to belong to the particular genus. According to a particular embodiment, the sequence homology is at least 95%.

According to another embodiment, in order to classify a microorganism as belonging to a particular species, it must comprise at least 90% sequence homology, at least 91% sequence homology, at least 92% sequence homology, at least 93% sequence homology, at least 94% sequence homology, at least 95% sequence homology, at least 96% sequence homology, at least 97% sequence homology, at least 98% sequence homology, at least 99% sequence homology with a reference microorganism known to belong to said particular species. According to a particular embodiment, the sequence homology is at least 97%.

In some embodiments, a level or set of levels of one or more DNA sequences in a sample of a microbial organism is determined directly. In some embodiments, DNA is isolated from a sample of a microorganism organism and a level or set of levels of one or more DNA sequences of the isolated DNA is determined. Various methods for isolating microbial DNA are well known in the art. Examples include, but are not limited to, phenol-chloroform extraction and a wide variety of commercially available kits, including the QJAampDN stool (stool) mini kit (Qiagen, valencia, california).

In some embodiments, a level or set of levels of one or more DNA sequences is determined by amplifying a plurality of DNA sequences using PCR (e.g., standard PCR, semi-quantitative PCR, or quantitative PCR). In some embodiments, a level or set of levels of one or more DNA sequences is determined by amplifying a plurality of DNA sequences using quantitative PCR. These and other basic DNA amplification methods are well known to practitioners in the art and are described in Auseebel et al (Ausubel F M, Brent R, Kingston R E, Moore D, Seidman J G, Smith J A, Struhl K (editor). 1998. modern guidelines for molecular biology. Weiri: New York).

In some embodiments, a plurality of DNA sequences are amplified using a plurality of primers specific for one or more sequences that can distinguish a plurality of individual microorganism types from other, different microorganism types. In some embodiments, a plurality of primers specific for a plurality of 16S rRNA sequences are used to amplify a sequence of a plurality of 16S rRNA genes or a plurality of fragments thereof. In some embodiments, a plurality of 18S DNA sequences are amplified using a plurality of primers specific for the plurality of 18S DNA sequences.

In some embodiments, a level or set of levels of the sequence of one or more 16s rrna genes is determined using a phylogenetic chip (phylochip) technique. The use of phylogenetic chips is well known in the art and is described in hessian et al ("plume of deep sea oil enriches local oil degrading bacteria". Science,330,204-208,2010), the entire contents of which are incorporated herein by reference. Briefly, sequences of 16S rRNA genes were amplified and labeled from DNA extracted from a sample of a microorganism. Next, the amplified DNA was hybridized with an array of multiple probes containing genes for 16S rRNA of multiple microorganisms. The extent of binding to each probe was then quantified to provide a sample level of microorganism type corresponding to the sequence of the probed 16S rRNA gene. In some embodiments, phylogenetic chip analysis is performed by a commercial vendor. Examples include, but are not limited to, second genome, ltd (san diego, california).

In some embodiments, determining a level or set of levels of one or more microorganism types or components or products thereof comprises: determining a level or set of levels of one or more microbial RNA molecules (e.g., transcripts). Various methods of quantifying the levels of multiple RNA transcripts are well known in the art and include, but are not limited to, northern blot analysis, semi-quantitative reverse transcriptase PCR, and microarray analysis. These or other basic RNA transcription detection protocols are described in Osebel et al (Ausubel F M, Brent R, Kingston R E, Moore D, Seidman J G, Smith J A, Struhl K (editor). 1998. modern guidelines for molecular biology. Weiri: New York).

In some embodiments, determining a level or set of levels of one or more microbial types or components or products thereof comprises determining a level or set of levels of one or more microbial proteins methods of quantifying protein levels are well known in the art and include, but are not limited to, western blotting and mass spectrometry methods these or all other basic protein detection procedures (Ausubel F M, Brent R, Kingston R E, MoD, Seidman J ore, Smith J A, Struhl K (editor). 1998. modern step guidelines for molecular biology:. Weiri: New York.) in some embodiments, determining a level or set of levels of one or more microbial metabolites comprises determining a level or set of levels of one or more microbial metabolites by mass spectrometry in some embodiments, determining a level or set of levels of a plurality of metabolites by resonance spectroscopy in some embodiments, determining a level or set of levels of a plurality of metabolites by spectrophotometric spectroscopy in some embodiments, determining a level or set of a plurality of metabolites by enzyme-linked immunosorbent assay.

In some embodiments, what is determined is the distribution of microorganism families within the microbiome. However, if desired (including, for example, the presence or absence of various genetic elements of a gene, the presence or absence of plastids, etc.), the characteristics can be carried out to a number of more detailed levels, for example, to the genus and/or species level, and/or to the strains or variations (e.g., variants) within a species level. Alternatively, a plurality of high-order classification designs, such as phyla, classes, or orders, may be used. The aim is to identify what microorganisms are present in the sample from the ruminant and the relative distribution of those microorganisms, for example, expressed as a percentage of the total number of microorganisms present, thereby creating a pattern or signature of a micro floral for the animal to be tested.

In other embodiments of the present invention, when considering a number of taxa, the overall pattern of the floret is evaluated, i.e., not only identifying a particular taxa, but also typically or alternatively comparing all taxa detected with each other, also considering the percentage of taxa of each component. Those skilled in the art will recognize that there are many possible ways of representing or compiling such data, all of which are encompassed by the present invention. For example, a "pie chart" format may be used to depict the identification of a miniature flower; or the relationships may be represented digitally or graphically as ratios or percentages of all detected taxa, etc. Further, the data can be manipulated such that only a plurality of sub-groups of the taxonomic group chosen (e.g., a plurality of key indicators having strong positive correlations) are considered. The data may be expressed, for example, as a percentage of the total number of detected microorganisms, or as a weight percentage.

It is known in the art that various methods of analyzing the similarity of the genetic background of two ruminants can be carried out by using various genotyping assays.

The term "genotyping" as used herein refers to a process of determining multiple genetic variations between individuals in a species, and is found in more than 1% of the population according to the differences defined as single base on a specific locus, SNPs are found in both coding and non-coding regions of the genome, and may be associated with a phenotypic trait of interest, e.g., a quantifiable phenotypic trait of interest, as well as a multiple marker for a plurality of quantifiable phenotypic traits, as such, SNPs may be used as multiple markers for a plurality of phenotypic traits of interest, another common type for genotyping is "InDels" (or multiple insertions and deletions of multiple nucleotides of different lengths, as well as a plurality of methods for genotyping and InDels, as described by the multiple sequencing process of the multiple sequencing process, such as the multiple sequencing process for determining genotypes between individuals, or multiple sequencing steps of the multiple sequencing process for high throughput of the sample, or multiple sequencing process for high throughput assay of the multiple sequencing process, as described by the multiple sequencing process of the multiple sequencing process for the multiple sequencing process of sequencing of the sample, including the multiple sequencing process of sequencing of the multiple samples, the multiple dna sequencing process, the process of sequencing process, the process of sequencing, the multiple dna sequencing process of sequencing, the process of sequencing, or sequencing of sequencing.

Multiple low-density and high-density chips are contemplated for use with the present invention, including multiple SNP arrays containing 3,000 to 800,000 SNPs by way of example, a "50K" SNP chip measures approximately 50,000 SNPs and is often used in the animal husbandry to establish genetic value or Genomically Estimated Breeding Values (GEBVs). in certain embodiments of the present invention, any of the following multiple SNP chips may be used — bovine SNP50 v1 microbead chip (with lumena), bovine SNP v2 microbead chip (with lumena), bovine 3K microbead chip (with lumena), bovine L D microbead chip (with lumena), bovine HD microbead chip (with lumena), gene search^RTMGenome profiler^TML D bead chip of (1), or Gene search^RTMGenome profiler^TMThe HD bead chip of (1).

In one embodiment, to measure genetic similarity between the plurality of animals, a genetic association between the plurality of animals based on the SNP data is calculated. For this purpose, a matrix can be generated that estimates the genetic correlation between each pair of unique animals. This matrix is based on the number of multiple shared alleles and is weighted by the rarity of the alleles:

wherein Ajk represents an estimate of said genetic relationship between animals j and k, and xij and xik are counts of alleles of said plurality of references in animals j and k, respectively; pi is the ratio of alleles of the reference in the population; and n is the total number of the plurality of SNPs used for the correlation estimation.

To identify multiple microbial species, a portion of which are highly variable in multiple abundance data plots attributable to multiple heritable genetic factors, samples of the microbial species were analyzed to show taxonomic groups (e.g., species) of multiple microbes of similar abundance (absolute or relative) in multiple animals sharing a similar genetic background.

In one embodiment, microorganisms or OTUs are considered to exhibit a significant heritable composition, provided that their heritability estimates are greater than 0.01 and the P-value is less than 0.1. It should be understood that the level of confidence may be increased or decreased depending on the stringency of the test. Thus, for example, in another embodiment, multiple microorganisms are considered to exhibit a significant heritable composition, provided that their heritability estimates are greater than 0.01 and the P-value is less than 0.05. Other expected heritability estimates contemplated by the inventors include: p values greater than 0.02 and less than 0.1, P values greater than 0.03 and less than 0.1, P values greater than 0.04 and less than 0.1, P values greater than 0.05 and less than 0.1, P values greater than 0.06 and less than 0.1, P values greater than 0.07 and less than 0.1, P values greater than 0.08 and less than 0.1, P values greater than 0.09 and less than 0.1, P values greater than 0.1 and less than 0.1, P values greater than 0.2 and less than 0.1, P values greater than 0.3 and less than 0.1, P values greater than 0.4 and less than 0.1, P values greater than 0.5 and less than 0.1, P values greater than 0.6 and less than 0.1, P values greater than 0.7 and less than 0.1, P values greater than 0.8 and less than 0.1.

Other expected heritability estimates contemplated by the inventors include: p values greater than 0.02 and less than 0.05, P values greater than 0.03 and less than 0.05, P values greater than 0.04 and less than 0.05, P values greater than 0.05 and less than 0.05, P values greater than 0.06 and less than 0.05, P values greater than 0.07 and less than 0.05, P values greater than 0.08 and less than 0.05, P values greater than 0.09 and less than 0.05, P values greater than 0.1 and less than 0.05, P values greater than 0.2 and less than 0.05, P values greater than 0.3 and less than 0.05, P values greater than 0.4 and less than 0.05, P values greater than 0.5 and less than 0.05, P values greater than 0.6 and less than 0.05, P values greater than 0.7 and less than 0.05, P values greater than 0.8 and less than 0.05.

According to a particular embodiment, the heritability estimate is greater than 0.7 and the P-value is less than 0.05.

To increase the confidence of the analysis, the heritability analysis may be exclusively limited to taxa of bacteria present in at least 20%, 25%, 30%, 40%, 50% or higher of the genotyped subgroup. In addition, the heritability analysis for each bacterial taxa can be performed several times, e.g., on a number of different sampling days (e.g., 2, 3, 4,5 or more days). Only bacterial taxa exhibiting a significant heritable composition (e.g., heritability estimates greater than 0.7 and p-values less than 0.05) on all individual sampling days were considered heritable.

In one embodiment, the heritable bacteria belong to bacteroidetes and/or firmicutes, wherein the number of phyla indicates whether the animal has a desirable and heritable trait.

In another embodiment, the heritable bacteria are of the Bacteroides and/or Clostridiales (bacterioids), wherein the number of phyla indicates whether the animal has a desirable and heritable trait.

As mentioned above, the present invention identifies 22 OTUs that meet heritable standards in carrying out the various procedures described herein above.

The term "OTU" as used herein refers to a terminal leaf of a phylogenetic tree and is defined by a Nucleic acid sequence, e.g., the entire genome or a specific genetic sequence, and all sequences sharing sequence identity with this Nucleic acid sequence at the species level in some embodiments, the specific genetic sequence may be the 16S sequence or a portion of the 16S sequence in other embodiments, the entire genome of two entities is sequenced and compared in another embodiment, a plurality of selected regions, e.g., a multi-locus sequence tag (M26 ST), a plurality of specific genes or a plurality of genomes, are considered to be genetically compared in embodiments of 16S, a plurality of OTU' S sharing an average nucleotide identity of more than 97% throughout the variable regions of 16S or the 16S are considered to be the same OTU.No. see e.g., Claion et al, Cladon et al, a plurality of serially variable 16S gene regions are considered to resolve the composition of highly complex biological organisms as a plurality of the same biological species as a plurality of the consensus gene of the microbial species, e.g., the same biological species, such as the multiple nucleotide sequence found to be more than the consensus gene of the biological species Sotinan.g., the molecular species No. Sotinan.g., the molecular strain No. 7, the same molecular strain No. 7, the general molecular strain No. 7-polymorphic genes, the same molecular sequence, the general molecular sequence of the molecular species, the same molecular species, the multiple molecular species are considered to be more than the multiple molecular species by the multiple molecular sequence of the multiple nucleotide sequence identity of the multiple molecular species No. found to be more than the multiple molecular species No. found by the molecular species No. found to be the multiple molecular species No. 1. No. 7-polymorphic genes found to be more than the general molecular species No. 1. No. 7-polymorphic genes found to be more than the same as the multiple molecular strain No. found to be more than the general molecular strain No. 35, the general molecular species No. 35, the multiple molecular species No. 7-polymorphic genes found to be the same as the general molecular strain No. 35, the general molecular strain No. found to be the molecular strain No. found by the general molecular strain No. found to be the multiple molecular strain No. found to be more than the general molecular strain No. found to be the general molecular strain No. 7-polymorphic, No. 35, No. found to.

In one embodiment, the OTU is listed in table 1 herein below.

TABLE 1

In one embodiment, the OTU is described in any one of the plurality of sequences listed in SEQ ID NOs 1 to 22. In another embodiment, the OTU is illustrated in SEQ ID NOs 1, 3, 5, 7, 8, 9, 11 to 17, 19, 20 or 21.

A number of specific OTUs for a number of specific traits are summarized in table 2 herein below. In the table, (1) is indicated as a positive correlation, (-1) is indicated as a negative correlation, and (0) is indicated as no significant correlation.

TABLE 2

The present invention further contemplates analyzing a plurality of the above-described OTUs. Thus, at least one OTU, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 11, at least 12, at least 13, at least 14, at least 15, or all of the plurality of the above-described OTUs were analyzed.

It will be appreciated that once the animal has been classified as having a sufficient number of heritable microorganisms associated with a desired phenotype, the animal may be selected (isolated from the remainder of the herd) and classified as having that phenotype. According to one embodiment, the animal is marked so that it clearly contains this phenotype.

In one embodiment, the animal is selected as a candidate for breeding. Thus, the animal may be considered suitable as a gamete donor for natural mating, artificial insemination or in vitro fertilization.

Thus, according to another aspect of the present invention, there is provided a method for propagating a ruminant, the method comprising: fertilizing a female ruminant, which has been selected according to the methods described herein, with semen from a male ruminant, thereby breeding the ruminant.

According to another aspect of the present invention there is provided a method for propagating a ruminant, the method comprising: fertilizing a female ruminant with semen from a male ruminant which has been selected according to the methods of any one of claims 1 to 14, thereby breeding the ruminant.

Preferably, the breeding of one or more bulls and cows is performed by artificial insemination, but may alternatively be performed by natural fertilization.

According to a further aspect of the present invention, there is provided a method for determining reproductive purity of a plurality of ruminants, the method comprising: analyzing a microbiome of the plurality of ruminants, wherein a similarity in number of a bacterium in the microbiome indicates the reproductive purity.

Thus, two animals of the same species can be considered to be the same breed when the number (e.g., incidence) of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or even at least ten or all of the microbiomes of the plurality of bacterial OTUs listed in table 1 is statistically significantly similar. According to another embodiment, two animals of the same species may be considered as the same breed when the number (e.g. relative ratio) of the microbiome belonging to bacteroidetes and/or firmicutes, or at least 10%, 20%, 30%, 40%, 50%, 60% or even 70% of the bacteria belonging to bacteroidetes and/or clostridiales is the same.

Since the present inventors have shown that the abundance of a plurality of specific microorganisms in the rumen microbiome is heritable, the present inventors further contemplate that it is possible to breed a plurality of ruminants having the abundance of specific microorganisms in their microbiome. It will be appreciated that this may be done without knowledge of the trait or purpose associated with the heritable bacteria.

Thus, according to a further aspect of the present invention, there is provided a method for increasing the number of a plurality of ruminants having a desired microbiome, the method comprising: propagating a male and a female of the plurality of ruminants, wherein the ruminal microbiome of any one of the male ruminant and/or the female ruminant comprises above a predetermined level of a heritable microorganism, thereby increasing the number of the plurality of ruminants having a desired microbiome.

As further described above, the selection of a plurality of animals having a plurality of ruminal microbiomes comprising a plurality of heritable microorganisms (e.g., bacteria) may be performed by analyzing a plurality of samples of the plurality of ruminal microbiomes.

As mentioned above, the heritable microorganism may be associated with a known heritable trait. The above provides a number of examples of a number of heritable traits.

Additionally or alternatively, the heritable microorganisms may affect the relative amounts of the various microorganisms of the microbiome of the animal (i.e., the composition of the microbiome as a whole).

Examples of heritable microorganisms (e.g., bacteria) are described above-e.g., bacteria exhibiting the sequence of a 16S rRNA gene selected from the group consisting of SEQ ID NOs: 1 to 22.

As used herein, the term "about" refers to ± 10%.

The various terms "comprising", "including", "having" and their equivalents mean "including but not limited to".

The term "consisting" means "including and limited to".

The term "consisting essentially of" means that a composition, method, or structure may include additional ingredients, steps, and/or portions, but only if the additional ingredients, steps, and/or portions do not materially alter the basic and novel characteristics of the composition, method, or structure as claimed.

As used herein, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. For example, the term "a compound" or "at least one compound" may include a plurality of compounds, and include mixtures thereof.

Throughout this application, various embodiments of this invention may be presented in a range format. It should be understood that the description in form of the ranges is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub-ranges as well as individual numerical values within that range. For example, a range described as from 1 to 6 should be considered to have specifically disclosed various sub-ranges, e.g., from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., as well as various individual values within that range, e.g., 1,2, 3, 4,5, and 6. This applies regardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to include any number of the recited digits (fractional or integer) within the indicated range limits. The terms "ranging/ranging between" a first indicating number and "a second indicating number" and "ranging/ranging from" a first indicating number "to" a second indicating number are used interchangeably herein and are meant to include the first indicating number and the second indicating number as well as all fractions and integers therebetween.

As used herein, the term "method" refers to manners, means, techniques and procedures for accomplishing a particular task including, but not limited to, those manners, means, techniques and procedures either known or readily developed from known manners, means, techniques and procedures by practitioners of the chemical, pharmacological, biological, biochemical and medical arts.

When referring to a plurality of specific sequence listings, said reference is understood to also encompass sequences that substantially correspond to their complements, as well as including minor sequence variations due to, for example, sequencing errors, cloning errors, or other alterations that result in base substitutions, base deletions, or base additions, such variations being less frequent than 1 of 50 nucleotides, alternatively, less than 1 of 100 nucleotides, alternatively, less than 1 of 200 nucleotides, alternatively, less than 1 of 500 nucleotides, alternatively, less than 1 of 1000 nucleotides, alternatively, less than 1 of 5,000 nucleotides, alternatively, less than 1 of 10,000 nucleotides.

It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination or in any other described embodiment or embodiments as suitable for the invention. Certain features described within the context of various embodiments are not considered essential features of those embodiments unless the embodiments are inoperable without those elements.

Various embodiments and aspects of the present invention as described above and as claimed in the claims section below are supported experimentally in the following examples.

Examples of the invention

Reference is now made to the following examples, which together with the above descriptions illustrate some embodiments of the invention in a non-limiting manner.

Generally, the nomenclature used herein and the protocols utilized herein include molecular, biochemical, microbial, recombinant DNA techniques which are explained in detail in the literature, see, for example, "molecular cloning: A laboratory Manual" Sambrook et al (1989), "modern procedures in molecular biology" Ausubel, R.M., editors (1994), "practical guidelines for molecular cloning," practical guidelines in John Wiley and Sons, Balm, Maryland, 1988; Watson et al, "recombinant DNA", U.S. Kokoku, New York, Birren et al (editor) gene body analysis: A laboratory Manual series ", 1 to 4, Cold spring Press, New York (1998), and methods for cell synthesis, including methods described in the methods of cell culture, and methods described in the procedures of cell culture, and methods of cell culture, and cell culture, including procedures of cell culture, and methods of cell culture, and cell culture, methods, including methods of cell culture, and cell culture, methods, and cell culture, methods, including methods, cell culture, and cell culture, methods, and cell culture, and cell culture, cell culture, and cell culture, cell culture, cell, and cell culture, cell culture, cell.

Materials and methods

Extracting microbial DNA: the microbial fraction of rumen fluid was isolated following The method of Stevenson and Weimer, 2007, Applied microbiology and Biotechnology75: 165-. DNA was extracted as described by Stevenson and Weimer (supra).

And (3) extracting genome DNA: 500 microliters of whole blood from each individual animal was mixed with 500 microliters of Tris-HCl saturated phenol (pH 8.0) and 500 microliters of DDW (diafiltered water). The mixture was shaken at room temperature for 4 hours, then centrifuged at 7500g for 5 minutes, and the aqueous phase was transferred to a fresh tube with 500 microliters of Tris-HCl saturated phenol-chloroform (1:1), followed by centrifugation at 7500g for 5 minutes. The aqueous solution phase containing the DNA is transferred to a fresh tube for further processing.

Genotyping of animals: the plurality of animals are members of a wolcanni center herd of the israel agricultural research organization. Among the multiple genotyped animals, there were 11 cohorts, each of which shared a common male parent (half-sib cohort). One such group consists of four half-siblings, another consists of three half-siblings, and the remainder of the groups consists of two half-siblings. In addition, there are two pairs of siblings sharing a common female parent. There are no full siblings (full-filing) in the multiple genotyped animals. The genetic association between the cows is assessed only on the basis of their genomic information.

Genomic DNA extracts from the multiple animals were loaded onto a bovine SNP chip 50K targeting 54,609 common SNPs (in ruminan) evenly distributed along the bovine genome. The SNP chip model used was a Luminan bovine SNP50-24 v3.0 catalog number 20000766 and was processed following the manufacturer's step guidelines of the genomics center of the biomedical core facility at Israel institute of technology (Adler et al, 2013, JoVE (journal of visual experiments): e50683-e 50683).

16S rRNA sequencing and analysis amplification of the V2 region of 16S was performed using multiple primers CCTACGGGAGGCAGCAG (SEQ ID NO: 1; forward) and CCGTCAATTCMTTTRAGT (SEQ ID NO: 2; reverse). Next, multiple databases were pooled and then 251 cycles of sequencing were performed starting from each end of multiple fragments in a single MiSeq flow cell (with Lumina) followed by analysis with Casava 1.8. A total of 49,760,478 paired end reads were obtained from all samples, each sample having on average 106,325 paired end reads.

Quality control of genotype data genotype from 47 individuals currently analyzed was combined with a reference set of 2,691 individual genotypes collected from multiple Holstein-Flisland dairy cows across Israel and Netherlands farms (ICBA, provided by the Israel Dairy Association), the reference set of genotypes allowed a more robust Quality Control (QC) and creation of a general relationship matrix, the QC was performed using the P L INK program with parameters of-cow-file isotype _ all-maf 0.05-gene 0.05-mind 0.05-outgene type _ all-QC-recode 12

Removing a plurality of SNPs that were not genotyped in more than 5% of the plurality of individuals. Similarly, if the genotype of a plurality of individuals is less than 95% of the locus (SNPs) covered by the SNP chip, the plurality of individuals is removed from the analysis.

354 individuals, one of which belongs to the study group, were removed due to low genotyping, and 3,797 SNPs were removed due to "missing" in the multiple genotyped ethnic groups, and 11,290 SNPs did not meet the MAF criteria. The total number of SNPs passing QC was 40,812.

Generation of genetic correlation matrix: all animals and SNPs that passed QC were used to generate a matrix that estimated the genetic correlation between each unique pair of animals. GCTA (Yang et al 2011, The American journal of Human Genetics 88:76-82) software is used to calculate The relationship matrix. The matrix is based on counts of multiple shared alleles and is weighted by the rarity of the alleles:

wherein A is_jkRepresents the estimate of the genetic relationship between animals j and k. x is the number of_ijAnd x_ikCounts of the multiple reference alleles in animals j and k, respectively. P_iIs the ratio of the reference alleles in the population. N is the total number of the plurality of SNPs used for the correlation estimation.

Heritability estimate: establishing a heritability estimate for each species from a distribution of the relative abundances of the plurality of species under a plurality of doubts coupled with the estimated genetic relatedness between the plurality of animals. The estimation is performed using the soft GCTA. The model used by this software is called the total heritability, and reflects the heritability accounting for all of the multiple SNPs that pass QC.

The model is as follows:

y＝Xβ+Wu+

where y is a vector of observations (phenotypes), β is a vector of fixed influences (study covariates), X is a designed matrix, u is a vector of influences of SNPs, W is a normalized matrix of genotypes, and the influence of individuals (residual).

The variation of the model can then be attributed to two sources, genetic error and random error, by:

wherein V is the variation of the whole. I is the identity matrix (n x n).Is a variation caused by inheritance (overall SNP effect).The variation (residual) caused by the influence of multiple individuals.

Next, GCTA estimationAndand then estimating the genetic force as:

comparing the distances of phylogeny in a plurality of heritable bacteria OTUs to the distances of phylogeny in the entire rumen microbiome: DNA similarity (in percent) between each unique pair of the 22 heritable bacterial OTUs was calculated using clustalw2(44), followed by calculation of the average of these similarities. A reference range of multiple average similarities was calculated by randomly sampling 100 equal-sized subgroups, each of which (n-22) was obtained from a pool of OTUs present in at least 12 genotyped animals (9K). A number of pairs of DNA similarities and their averages were calculated for each random subgroup. To obtain significance for the average similarity in a population of multiple heritable bacterial OTUs, their average similarity was ranked among all 100 average similarity values obtained from multiple random subgroups.

Difference ratio of OTU correlation:

wherein hc is a count of a plurality of heritable OTUs that are related to the index, hn is a count of a plurality of heritable OTUs that are not related to the index, nc is a count of a plurality of non-heritable OTUs that are related to the index, and nn is a count of a plurality of non-heritable OTUs that are not related to the index. In this context, an OTU is associated with the index if the OTU has a nominal spearman (nominal spearman) with p < 0.05.

Counting and drawing: statistical analysis was performed using R software, and multiple charts were generated using ggplot2 and the pheatmap software package.

Experiment design: the main goal is to identify microbial species where a large portion of their variation in the abundance data graph is attributable to multiple heritable genetic factors. To achieve this, the inventors analyzed information on a plurality of common SNP genotypes of 47 dairy holstein cows. This information was combined with additional data for these animals from a recent study (Kruger et al, 2016, ISME J10: 2958-. For each animal, the 16S rRNA gene was sequenced from multiple samples for three consecutive days. A plurality of rumen metabolites are also quantified and a plurality of rumen metabolic activity assays are performed, such as measurements of methane production and fiber digestion in the rumen in vitro. Metadata (metadata) of a plurality of production indicators of the individual cow is merged with a plurality of physiological indicators. A QC pipeline (see materials and methods) was used to remove multiple low quality and non-informative SNPs. The sequencing analysis of the 16S amplicons was performed using QIIME lines. Classification data plots for rumen microbiome represented by multiple OTUs at 85K species level (three samples per animal) were correlated with genomic data represented by genotyping of common SNP loci (see methods). Notably, the inventors focused on the identification of multiple heritable microorganisms OTUs, rather than the intensity of multiple such heritability estimates. This approach is more robust to multiple heritage estimates, which typically have multiple smaller sample sizes in the estimation process. The plurality of microbial OTUs found to be associated with the genome of the animal is further associated with the metabolomics of the plurality of microbiomes, as well as with the physiology and various productivity parameters of the animal.

Results

Heritable species with high phylogenetic relevance and enriched in Bacteroides

The first step of the present analysis is to identify heritable bacterial species, i.e., microbial species, where a significant portion of their variation in the abundance data plot is attributable to a number of heritable genetic factors. This will be reflected by a highly similar abundance of certain species in multiple animals sharing a similar genetic background. Thus, the correlation between pairs of animals in the cohort was estimated. This estimation is done by taking into account the counts and frequencies of the plurality of alleles (SNPs) in the plurality of reference genotypes. These paired estimates of genetic relationships were used with the abundance data plots for each species to calculate their estimates of heritability.

To increase the confidence of this analysis, the heritability analysis exclusively limited the number of OTUs present in at least 12 genotyped animals (25% of the genotyped subgroup), as previously described (Benson AK et al, 2010, Proceedings of the National Academy of Sciences107: 18933-. In addition, three independent heritability analyses were performed for each OTU, one on each sampling day. Only those OTUs that exhibited a significant heritable composition (heritability estimates greater than 0.7, and p-values less than 0.05) over all three separate sampling days were considered heritable OTUs. Following this protocol, the analysis yielded 22 heritable OTUs meeting these criteria (fig. 8), all belonging to the bacterial domain. Although the significance evaluation procedure of the heritability is based on a parametric test, the inventors examined the robustness of this finding by examining the distribution of false finding rates of the test under multiple permutation hypotheses. For this purpose, they generate a null-model (null-model) with 100 iterations (iterations) in which they repeat multiple genetic force analyses after randomly shuffling the permutations of the multiple genetic data charts in each of the iterations. In 94% of the permutations, the number of OTUs detected as heritable is less than 22, whereas in most permutations, the number of OTUs detected as heritable is less than 5 (fig. 6).

Notably, the heritable OTUs exhibited a high presence in animals ranging between 50% to 100% of the animals, with the majority occurring in 70% to 100% of the animals examined (figures 2, 9 and 10). Said abundance data plots for said plurality of heritable microorganisms are correlated with their presence data plots (spearman correlation between counts present and total abundance: r-0.75, p<5x10^-5)。

When the phylogenetic distance between these OTUs was measured, they were found to be highly phylogenetically related based on the similarity of their 16S nucleotide sequences (fig. 1).

These OTUs belong to the two main phyla of the gastric neoplasia biota, bacteroidetes and firmicutes, and are grouped according to two predominant orders in the rumen, bacteroidales and clostridiales (fig. 2).

The inventors further asked whether this phylogenetic composition of heritable OTUs represents a phylogenetic composition of overall species composition in the rumen. Species of order bacteroides were found to be represented more in the plurality of heritable OTUs than in the overall ruminal microbiome (trend, Fisher-exact test) p-value < 0.053).

Heritable bacterial abundance is correlated with host traits and metabolic parameters of the rumen, and can significantly account for a high proportion of variation between animals

The present inventors hypothesize that heritable taxa associated with the host's genome will potentially be involved in rumen metabolism as well as in the physiology of the host. Thus, the inventors sought a correlation between heritable microorganisms and all measured physiological parameters of the animals, as well as metabolic parameters of the rumen. In detail, they correlated abundance data plots along a cohort of 78 cows per heritable OTU with data plots for each measured index (by rumen metabolite or other index). Then, they compare the average relevance of the plurality of heritable OTUs for each of the plurality of rumen metabolites and plurality of host attributes to a zero model. In each of 1,000 iterations of the zero model, they shuffled the abundance data plots for each heritable OTU and again calculated their average correlations for each of the multiple rumen metabolites and multiple host attributes. This analysis revealed that the heritable OTUs showed a strong and significant correlation with a number of the ruminal metabolic parameters and the physiological attributes of the host (fig. 3, fig. 7).

The most strong associations for the plurality of heritable OTUs, in terms of rumen metabolism, are: acetate ratio (maximum intensity r 0.86, average | r 0.64), methane metabolism (maximum intensity r 0.69, average | r 0.49), propionic acid (maximum intensity r-0.6245274, average | r 0.44) and valeric acid (maximum intensity r-0.57, average | r 0.39), and the concentrations of some amino acids, i.e. glycine, aspartic acid and tyrosine (having maximum intensities r 0.51, 0.5, -0.53, and average | r 0.32, 0.39, 0.36, respectively). With respect to attributes of multiple hosts, the most relevant parameters are: milk protein (maximum intensity r ═ 0.46, average value | r | ═ 0.33), dry intake (maximum intensity r ═ 0.41, average value | r | ═ 0.28), feed efficiency (expressed as residual feed intake (RFI, maximum intensity r ═ 0.26, average value | r | _ 0.39), and milk fat (maximum intensity r ═ 0.39, average value | r | _ 0.25). Furthermore, when analyzing the individual associations of the heritable OTUs for propionate to acetate ratio, methane metabolism, propionic acid and valeric acid, it was found that most of these OTUs were positively or negatively correlated with these parameters (fig. 4A). With respect to multiple physiological attributes of the host, most heritable OTUs are positively correlated with RFI, DMMI and milk proteins.

These findings raise the question whether the fraction of heritable microorganisms associated with host physiology and rumen metabolism is different from the fraction found in the bulk gastroma microbiome. To this end, for each index, the inventors calculated the rate of difference in the OTU correlation (see materials and methods) and, for many parameters, identified a significantly higher difference for an OTU associated with a particular index in the heritable microbiome. It is particularly true here that these heritable microorganisms show a high correlation with the various parameters mentioned.

In a previous study (Kruger Ben Shabat S et al, 2016.ISME J10: 2958-. In addition, five other heritable OTUs phylogenetically related to Bacteroides, Prevotella, Clostridiales and Flavobacterium species (Flavesacies) were found to be highly related to milk proteins in this study, and an OTU of the aforementioned Prevotella was also found to be significantly related to Dry Matter Intake (DMI) in this previous study (FIG. 5).

Rumen and animal physiological characters show different heritability estimated values

After identifying multiple heritable microbial species that exhibit associations with multiple host traits, the inventors began to estimate the heritability of multiple different important host and rumen metabolic traits associated with the multiple heritable microorganisms they found (fig. 11). The efficiency measurements of propionate, succinate and valerate of Volatile Fatty Acids (VFA) and of milk proteins with RFI and DMI present significant estimates of the heritability.

Reference to the literature

Reyniers JA.1959 pure culture concepts and Annals of the New YorkAcadmay of Sciences 78:3-16.

Gilbert JA, Neufeld JD.2014. P L oS biology12: e1002020, living in a world without microorganisms.

Mizrahi I.2013 symbiosis of rumen, p 533-.

4.Kittelmann S,CS, Seedorf H, Kirk MR, Ganesh S, McEwanJC, Janssen pH.2014 two different bacterial community types are associated with the sheep' S low methane emission trait P L oS One9: e103171.

Shi W, Moon CD, &lttttransformation = L "&tttL &ttt/t &ttteahy SC, Kang D, Froula J, Kittelmann S, Fan C, Deutsch S, Gagic D, Seedorf H.2014 methane production phenotype Genome response 24: 1517-.

Ross EM, Moate PJ, Marett L C, Cocks BG, Hayes BJ.2013. prediction of metagenomics Complex health and environmental phenotypes from microbiome to humans and cattle PloS one 8: e73056.

The microbial genome of the rumen is related to high methane production in cattle BMC genetics 16:839 in Wallace RJ, Rooke JA, McKain N, Duthie C-A, Hyslop JJ, Ross DW, Waterhouse A, Watson M, Roehe R.2015.

A potential role of the bovine rumen microbiome in regulating milk composition and feed efficiency P L oS One9: E85423.

The effect of residual feed intake and feed content in the diet on the rumen microflora of beef cattle Applied and environmental microbiology 78: 4949-.

Role of rumen microbiology in determining feed efficiency in dairy cows, p 203-.

Guan LL, Nkrumah JD, Basarab JA, Moore SS.2008. association of Microbiology of rumen with feed efficiency of cattle FEMS Microbiology L ets 288:85-91.

McCann JC, Wiley L M, Forbes TD, Rouquette Jr FM, Tedeschi L o.2014. relationship between rumen microbiome and residual feed intake-efficiency of salomonas bulls raised on a bermuda grazing land P L oSOne 9: e91864.

Kruger Ben Shabat S, Sasson G, Doron-Faigenboim A, Durman T, YaacobyS, Berg Miller ME, White BA, Shterzer N, Mizrahi I.2016. depending on the mechanism of the particular microbiome, a basis for energy harvesting efficiency in ruminants is ISME J10: 2958-.

Fernando SC, Purvis H, Najar F, Sukharnikov L, Krehbiel C, Nagaraja T, Roe B, DeSilva U.2010. dynamic Applied and environmental Microbiology 76: 7482-.

Phylogenetic diversity of bacterial communities in the rumen of cattle affected by diet and microenvironment, Folia microbiology 56:453.

Kittelmann S, Janssen pH.2011 the composition of the rumen ciliate community of domestic sheep, deer and cattle fed on different diets was characterized by PCR-DGGE and clone bank as FEMS Microbiology Ecology 75:468-481.

17 Kocherginskaya SA, Aminov RI, White BA.2001, analysis of diversity of rumen bacteria Anerobe 7:119-134 under two different dietary conditions using denaturing gradient colloid electrophoresis, random sequencing and statistical ecology methods.

Tajima K, Aminov R, Nagamine T, Matsui H, Nakamura M, Benno Y.2001. the diet-dependent changes in the bacterial population of the rumen were revealed using instant PCR 67:2766-2774.

Friedman N, Shriker E, Gold B, Durman T, Zarechki R, Ruppin E, MizrahiI.2017 diet-induced redox potential changes are the basis for compositional changes in the archaebacteria community of the rumen 19:174-184.

The effect of a silage mixture of pomegranate pulp and dry feed on digestibility and milk production performance of Dairy cows, Journal of Dairy Research 83:35-41, Shaani Y, Eliyahu D, Mizrahi I, Yose E, Ben-Meir Y, Nikbac M, Solomon R, Mabjeesh SJ, Miron J.2016.

Roehe R, Dewhhurst RJ, Duthie C-A, Rooke JA, McKain N, Ross DW, HyslopJJ, Waterhouse A, Freeman TC, Watson M.2016. genetic variation in bovine hosts based on gene abundance in metagenome influences methane production P L oS Genet 12: e1005846 by ruminal microorganisms with optimal selection criteria for low methane emission and efficient feed transformed hosts.

22, L i Z, Wright ADG, Si H, Wang X, Qian W, Zhang Z, L i G.2016. changes in rumen microbiome and metabolites reveal the effect of host genetics on hybridization Environmental Microbiology Reports8: 1016-.

23.Caporaso JG,Kuczynski J,Stombaugh J,Bittinger K,Bushman FD,Costello EK,Fierer N,AG, Goodrich JK, Gordon JI.2010.QIIME allowed the analysis of high throughput community sequencing data Nature methods 7: 335-.

The individuality of the microbial phase composition of the gut is a complex polygenic trait influenced by environmental and host genetic factors Proceedings of the National Academy of Sciences107: 18933-.

VanRaden P, Van Tassell C, Wiggans G, Sonstegard T, Schnabel R, Taylor J, Schenkel F.2009. reviews of Invitation: the reliability of the genome prediction for North American Holstein bull Journal of the family science 92:16-24.

Variation of residual feed intake of Holstein-Flysian lactating heifers in south Australia Journal of Dairy Science 94: 4715-.

Benson AK.2016. gut microbiome-an emerging complex trait Nature genetics 48: 1301-.

Felsenstein J.1985 phylogenetic and comparative method The American Naturalist125:1-15.

Ungerfeld EM, Rust SR, Burnett R.2003. use of some novel alternative electron acceptors to inhibit rumen methanogenesis differentiation Development 43: 189-.

30.M.1991 conversion of acetate, propionate and butyrate to methane Water Research 25:1509-1513 in a batch reactor at elevated temperature.

Bryant M.1979. production of methane by microorganisms-theoretical aspect Journal of Animal Science48:193-201.

Analysis of rumen metagenome and metatranscript in sheep with low methane production in Kamke J, kittel mann S, Soni P, L i Y, Tavendale M, Ganesh S, Janssen PH, Shi W, Froula J, Rubin em.2016. analysis of the metatranscript revealed that the genome enriched in charcot bacteria had a characteristic Microbiome 4:56 for lactic acid formation and utilization.

Rogers J, Peirce-Sandner S, Papas A, Polan C, Sniffen C, Muscott, Staples C, Clark J.1989 production reaction Journal of dairy science 72: 1800-.

Relative quantitative real-time PCR revealed the predominance of Prevotella in the rumen of cattle and the low abundance of typical ruminal bacterial species Applied Microbiology and Biotechnology75:165- & 174.

Jami E, Israel A, Kotser A, Mizrahi I.2013, The gastric bacterial community exploring bovine neoplasia from birth to adult, The ISME journal 7: 1069-.

Ron M, Blanc Y, Band M, Ezra E, Weller J.1996, the misconception rate of the Tageted Dairy cattle herd and its effect on genetic improvement Journal of Dairy Science 79: 676-.

Adler AJ, Wiley GB, Gaffney PM.2013. Infinium assay for large scale SNP genotyping applications JoVE (journal of Visualized experiments) e50683-e50683.

Edgar RC.2010 search and clustering are orders of magnitude faster than B L AST Bioinformatics 26: 2460-.

Altschul SF, Gish W, Miller W, Myers EW, &lTtTtranslation = L "&gtTtTL &lttT/T &tttipman DJ.1990. basic local alignment search tool Journal of molecular biology 215: 403-.

Cole JR, Wang Q, Cardenas E, Fish J, Chai B, Farris RJ, Kulam-Syed-Mohideen A, McGarrell DM, Marsh T, Garrity GM.2009, ribosoma database project: improved alignments and novel tool Nucleic acids research 37: D141-D145 for rRNA analysis.

Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, De Bakker PI, Daly MJ.2007.P L INK, The set of tools for whole-genome association and swarm-based linkage analysis, The American Journal of Human Genetics 81: 559-.

Yang J, L ee SH, Goddard ME, Visscher PM.2011.GCTA The tool for analysis of Whole genome Complex traits, The American Journal of Human Genetics 88:76-82.

SNPs common to Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyhold DR, MaddenPA, Heath AC, Martin NG, Montgomery GW.2010, explain a large part of the human height heritability 42: 565-.

L arkin MA, Blackshifts G, Brown N, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, L opez R.2007.Clustal W and Clustal X version 2.0bioinformatics 23: 2947-.

45.Team RC.2016. R: one language and environment for statistical computing, vienna: r-fund for statistical calculations; 2014.

wickham h.2016.ggplot 2: the exquisite pattern Springer for data analysis.

Kolde r.2015. heatmap: beautiful heatmap R package version 1.0.2.

Sequence listing

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<400>12

ttcggcatat tggtcaatgg gcgcgagcct gaaccagcca agtagcgtgc aggatgacgg 60

ccctatgggt tgtaaactgc ttttatatag ggataaagtc ggggacgtgt ccccgtttgt 120

aggtactata tgaataagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180

tccgggcgtt atccggattt attgggttta aagggagcgc aggccggagg ctaagcgtga 240

cgtgaaatgt 250

<210>13

<211>250

<212>DNA

<213>Artificial sequence

<220>

<221>gene

<222>(1)..(250)

<223> nucleic acid sequence for manipulating taxonomic units (OTUs)

<400>13

tgaggaatat tggacaatgg ccgaaaggct gatccagcca tgccgcgtgc gggaagacgg 60

ccctatgggt tgtaaaccgc ttttgttggg gagcaataag ggccacgtgt gacccgatga 120

gagtacccag cgaataagca tcggctaact ccgtgccagc agccgcggta atacggagga 180

tgcaagcgtt atccggattt attgggttta aagggtgcgt aggcggacga ttaagcgtga 240

ggtgaaatgc 250

<210>14

<211>250

<212>DNA

<213>Artificial sequence

<220>

<221>gene

<222>(1)..(250)

<223> nucleic acid sequence for manipulating taxonomic units (OTUs)

<400>14

tgaggaatat tggtcaatgg gcggaagcct gaaccagcca agtagcgtga aggatgacgg 60

ccctacgggt tgtaaacttc ttttatgcgg gaacaaagtg cgccacgcgt ggcgttttgc 120

gcgtaccgca ggaaaaagca ccggctaatt ccgtgccagc agccgcggta atacggaagg 180

tgcgagcgtt atccggattc attgggttta aagggagcgt aggcggagcg ccaagtcagc 240

tgtgaaatcc 250

<210>15

<211>250

<212>DNA

<213>Artificial sequence

<220>

<221>gene

<222>(1)..(250)

<223> nucleic acid sequence for manipulating taxonomic units (OTUs)

<400>15

tggggaatat tgcacaatgg aggaaactct gatgcagcga cgccgcgtga gtgaagaagt 60

atttcggtat gtaaagctct atcagcaggg aagataatga cggtacctga ataagaagca 120

ccggctaaat acgtgccagc agccgcggta atacgtatgg tgcaagcgtt atccggattt 180

actgggtgta aagggagtgc aggcggtctg aaaagtcaga tgtgaaagcc cggggctcaa 240

ccccgggact 250

<210>16

<211>250

<212>DNA

<213>Artificial sequence

<220>

<221>gene

<222>(1)..(250)

<223> nucleic acid sequence for manipulating taxonomic units (OTUs)

<400>16

tggggaatat tgcacaatgg gggaaaccct gatgcagcga cgccgcgtga gtgaagaagt 60

atttcggtat gtaaagctct atcagcaggg aagaaaatga cggtacctga ctaagaagcc 120

ccggctaact acgtgccagc agccgcggta atacgtaggg ggcaagcgtt atccggattt 180

actgggtgta aagggagcgc agacggaaga acaagtctga tgtgaaatgc gggggctcaa 240

ctcctgaatt 250

<210>17

<211>250

<212>DNA

<213>Artificial sequence

<220>

<221>gene

<222>(1)..(250)

<223> nucleic acid sequence for manipulating taxonomic units (OTUs)

<400>17

tgaggaatat tggtcaatgg gcgagagcct gaaccagcca agtagcgtgc aggaagacgg 60

ccctatgggt tgtaaactgc ttttatatag ggataaagtc ggggacgtgt ccccgtttgt 120

aggtactata tgaataagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180

tccgggcgtt atccggattt attgggttta aagggagcgc aggccggctt ttaagcgtga 240

cgtgaaatgt 250

<210>18

<211>250

<212>DNA

<213>Artificial sequence

<220>

<221>gene

<222>(1)..(250)

<223> nucleic acid sequence for manipulating taxonomic units (OTUs)

<400>18

tgaggaatat tggtcaatgg gcgcgagcct gaaccagcca agtagcgtgc aggatgacgg 60

ccctatgggt tgtaaactgc ttttggaggg gaataaagtc gtctacgtgt aggtgtttgc 120

atgtaccctc agaataagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180

tcctggcgtt atccggattt attgggttta aagggagcgc aggcgggcga ttaagcgtga 240

cgtgaaatgc 250

<210>19

<211>250

<212>DNA

<213>Artificial sequence

<220>

<221>gene

<222>(1)..(250)

<223> nucleic acid sequence for manipulating taxonomic units (OTUs)

<400>19

tgaggaatat tggtcaatgg ccgcgaggct gaaccagcca agtagcgtgc aggatgacgg 60

ccctctgggttgtaaactgc ttttatgcgg gaacaaaggc gtctacgtgt agtcgtgtgc 120

gtgtaccgca ggaaaaagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180

tccgggcgtt atccggattt attgggttta aagggagcgc aggctgaagc gcaagccggc 240

tgtaaaattt 250

<210>20

<211>250

<212>DNA

<213>Artificial sequence

<220>

<221>gene

<222>(1)..(250)

<223> nucleic acid sequence for manipulating taxonomic units (OTUs)

<400>20

tgaggaatat tggtcaatgg gcggaagcct gaaccagcca agtcgcgtga aggatgaagg 60

tattatgtat tgtaaacttc tttagctgtg gagaaataag gtgctcgtga gcaccgatgc 120

tagtacacag agaataaggg tcggctaact ccgtgccagc agccgcggta atacggagga 180

cccgagcgtt atccggattc attgggttta aagggtgcgc aggcggcttc ttaagtcagc 240

ggtaaaatcg 250

<210>21

<211>250

<212>DNA

<213>Artificial sequence

<220>

<221>gene

<222>(1)..(250)

<223> nucleic acid sequence for manipulating taxonomic units (OTUs)

<400>21

tggggaatct tccgcaatgg gcgaaagcct gacggagcaa cgccgcgtga gtgaagaagg 60

tcttcggatc gtaaagctct gttgaagggg acgcacggcg cctgttacaa gatagcaggt 120

gaatgacggt acccttcgag gaagccacgg ctaactacgt gccagcagcc gcggtaatac 180

gtaggcggca agcgttgtcc ggaatcattg ggcgtaaagg gagcgcaggt ggacgtatag 240

gtccttctta 250

<210>22

<211>250

<212>DNA

<213>Artificial sequence

<220>

<221>gene

<222>(1)..(250)

<223> nucleic acid sequence for manipulating taxonomic units (OTUs)

<400>22

tggggaatat tgcacaatgg gggaaaccct gatgcagcga tgccgcgtgg aggaagaagg 60

ttttcggatt gtaaactcct gtcttaaagg acgataatga cggtacttta ggaggaagct 120

ccggctaact acgtgccagc agccgcggta atacgtaggg agcgagcgtt gtccggaatt 180

actgggtgta aagggagcgt aggcgggagt gcaagtcaga tgtgaaatac atgggctcaa 240

cccatgggct 250

<210>23

<211>17

<212>DNA

<213>Artificial sequence

<220>

<221>STS

<222>(1)..(17)

<223> Single-stranded DNA oligonucleotide

<400>23

cctacgggag gcagcag 17

<210>24

<211>18

<212>DNA

<213>Artificial sequence

<220>

<221>STS

<222>(1)..(18)

<223> Single-stranded DNA oligonucleotide

<400>24

ccgtcaattc mtttragt 18