Methods for nucleic acid analysis of milk

Abstract

This application relates to a method for determining the proportion or amount of DNA contributed by individual animals to a volume of milk collected from a plurality of individual animals, using shallow whole genome sequencing (SWGS) allelic sampling for DNA sequence polymorphisms in DNA extracted from samples of the volume of milk. The method is useful, for example, for detecting mastitis or subclinical mastitis in animals contributing milk to the volume of milk.

Description

[Technical Field] 【0001】 This invention broadly relates to the fields of livestock farming and veterinary medicine, and in particular to the health management of dairy animals. This invention provides a method for nucleic acid analysis of milk, for example, tank milk, and moreover, a method that can provide useful information regarding the health status of the individual animals that contribute to that milk. [Background technology] 【0002】 Mastitis is the most common and most important health problem in dairy farming. The losses due to antibiotic treatment and reduced milk yield and quality amount to approximately 80 euros per cow per year in the Dutch dairy industry (Hogeveen et al., 2011). 【0003】 The milk of affected cows is characterized by an increase in somatic cell concentration, i.e., the migration of immune cells into the udder and milk, which is conveniently expressed as the “somatic cell score” (SCS). A normal (healthy) SCS value is less than 100,000 cells / mL of milk, but in cows with clinical mastitis, the SCS can reach several million cells / mL. Cows with subclinical mastitis may already have 200,000 cells / mL before showing obvious symptoms, and the latter SCS value is commonly used as a cutoff between subclinical mastitis and normal health. Cows with subclinical mastitis typically show reduced milk yield, and if added to tanks, their milk has a detrimental effect on the milk quality of the entire herd. Therefore, the detection of cows with clinical mastitis and subclinical mastitis is of paramount importance. 【0004】 Currently, the most common method for monitoring the SCS of individual cows is through milk recording. Milk samples from individual cows are collected, for example, monthly and sent to a milk recording center for analysis of milk components, SCS, and bacterial contamination. However, an increasing number of dairy farmers are refraining from or reducing the frequency of milk recording to control costs. High-performance milking machines monitor udder health by measuring milk conductivity, but these are expensive and unaffordable for many farms. Therefore, there is an urgent need for cost-effective alternatives to monitoring udder health on farms. 【0005】 Dairy farms typically have dozens to hundreds of cows, and their milk is stored in large containers or "tanks" until it is collected daily by a dairy processing plant. Recently, European Patent Application Publication 2597159 and Blud et al., 2012, disclose bulk single nucleotide polymorphism (SNP) genotyping of such tank milk (i.e., eliminating the need to perform separate measurements for each animal) as a method for determining the SCS of individual milking cows that contributed milk to the tanks. This method initially required SNP genotyping of all milking cows on the farm (the authors note that the number of farms SNP genotyping all of their cows was rapidly increasing due to the increasing use of genomic selection (GS) in selecting animals, including milking cows). SNP genotyping of tank milk yielded estimates of "B allele frequencies" (Bfreq) for tens of thousands to hundreds of thousands of markers, depending on the SNP array used. The Bfreq of tank milk for a given SNP is the number of known B alleles (g) in each cow. i ;=0, 1, or 2) and the unknown ratio of the contributing DNA (p i The sum of the products (for all cows) is shown. i This can be estimated from this ensemble of linear equations using the least squares method or other methods. Combining the known or estimated milk volume contributed by individual cows with the total known SCS in the tank, p iThis can be converted to an SCS for each individual cow. Figure 1 schematically shows the method of European Patent Application Publication No. 2597159 and Blud et al., 2012, which uses an SNP array to determine the genotype of tank milk, combined with the SNP array generated genotype for the individual cows contributing to the tank milk. The number of SNPs required to achieve adequate accuracy depends on the number of cows on the farm. Tens of thousands of SNPs were sufficient for farms with dozens of cows, but hundreds of thousands of SNPs were required for farms with hundreds of cows. 【0006】 The most commonly used SNP arrays for cow genotyping are low-density arrays that examine thousands of SNPs, such as the GoldenGate® Bovine3K Genotyping BeadChip (2,900 SNPs) or the BovineLD v2.0 Genotyping Bead Chip (7,931 SNPs), both developed by Illumina. When the number of SNPs examined is relatively small, the accuracy of the above tank milk genotyping methods may be limited, especially on large farms (note that the average farm size is increasing worldwide, and the actual proportion of dairy cow herds already kept on farms exceeds 250 cows in the US, Europe, and Australia). The authors also suggest the use of high-density SNP arrays, such as Illumina's 50,000 BovineSNP50 Genotyping BeadChip (current version featuring 53,714 SNPs) or BovineHD Genotyping BeadChip (current version featuring 777,962 SNPs). However, the use of such high-density SNP arrays significantly increases costs and therefore may greatly reduce the appeal of these methods to farmers. [Overview of the project] 【0007】 The present invention addresses and / or provides improvements to the bulk nucleic acid analysis of milk, particularly tank milk. Such a method can, among other things, identify milk-contributing cows having subclinical or clinical mastitis, and / or determine whether a particular cow(s) contributed milk to the tank (this may be useful, for example, for monitoring compliance with not putting milk from cows, such as those receiving treatment, into the tank). 【0008】 DNA sequencing has emerged as an alternative to SNP array-based genotyping. However, generating reliable genotypes that can potentially replace the SNP array-generated genotypes in the aforementioned existing methods for bulk genotyping of tank milk requires increasing sequencing depth, and farmers may face unrealistic costs. 【0009】 Against this backdrop, the inventors unexpectedly demonstrated that, despite the fact that most of the allele information for most polymorphisms revealed by shallow whole-genome sequencing (SWGS) is incomplete or "ambiguous," SWGS can still be used for bulk allele sampling of DNA polymorphisms in milk (e.g., tank milk) and, combined with allele data for the aforementioned DNA sequence polymorphisms in milk-contributing animals, for example, can be used to estimate the proportion or amount of DNA contributed to the milk by an individual animal with desirable accuracy. The inventors demonstrated that bulk SWGS allele sampling of milk yields useful results when combined with various methods for generating allele data for DNA sequence polymorphisms in milk-contributing animals, for example, in combination with SNP array genotyping with or without computer imputation of genotypes at uninvestigated SNPs, or when combined with SWGS allele sampling data. 【0010】 Therefore, one embodiment is a method for determining the ratio or amount of DNA contributed by individual animals to a certain volume of milk collected from multiple individual animals, a) A step of sampling alleles for multiple DNA sequence polymorphisms in DNA extracted from a certain volume of milk sample by shallow whole-genome sequencing (SWGS); b) A step of determining the ratio or amount of DNA contributed by an individual animal to a certain volume of milk, based on allele sampling for the DNA sequence polymorphism from step a) and allele data for the DNA sequence polymorphism in the individual animal. This provides a method that includes [something]. 【0011】 A further embodiment is a method for identifying one or more animals having subclinical mastitis or clinical mastitis from a group of individual animals contributing milk to a certain volume of milk, a') A method comprising determining the ratio or amount of DNA contributed by an individual animal to a certain volume of milk by performing allel sampling for multiple DNA sequence polymorphisms in DNA extracted from a certain volume of milk sample using SWGS, b') A step of determining the somatic cell concentration in the milk of an individual animal based on the ratio or amount of DNA contributed by the individual animal to a certain volume of milk, as determined in step a'); c') If the somatic cell concentration in the milk of one or more animals determined in step b') exceeds a predetermined threshold, the step of identifying that one or more animals have subclinical mastitis or clinical mastitis. This provides a method that includes [something]. 【0012】 These and further aspects of the present invention, as well as preferred embodiments, are described in the following sections and in the appended claims. The subject matter of the appended claims is thus incorporated in detail herein. [Brief explanation of the drawing] 【0013】 [Figure 1]Figure 1 schematically illustrates the method proposed in European Patent Application Publication No. 2597159 and by Blud et al., 2012, for estimating somatic cell score (SCS) and detecting (latent) mastitis in individual dairy cows that contribute milk to a milk tank. This method uses bulk single nucleotide polymorphism (SNP) genotyping of tank milk by SNP array in combination with SNP array-generated genotyping of individual cows that contribute milk to the tank. [Figure 2(A)] Figure 2 shows the accuracy of estimating the somatic cell score (SCS) for individual cows by bulk DNA analysis of tank milk using three different schemes: (A) SNP array for genotyping DNA polymorphisms in both tank milk and the individual cows contributing to the tank milk (reference scheme "A"). [Figure 2(B)] Figure 2 shows the accuracy of estimating the somatic cell score (SCS) for individual cows by bulk DNA analysis of tank milk using three different schemes: (B) SWGS for allele sampling for DNA polymorphisms in tank milk, and SNP array for genotyping of DNA polymorphisms in individual cows contributing to tank milk (scheme "B" embodying the principle of the present invention). [Figure 2(C)-1] Figure 2 shows the accuracy of estimating the somatic cell score (SCS) for individual cows by bulk DNA analysis of tank milk using three different schemes: (C) SWGS for allelic sampling for DNA polymorphisms in both tank milk and the individual cows contributing to the tank milk (scheme "C" embodying the principle of the present invention). [Figure 2(C)-2] Figure 2 shows the accuracy of estimating the somatic cell score (SCS) for individual cows by bulk DNA analysis of tank milk using three different schemes: (C) SWGS for allelic sampling for DNA polymorphisms in both tank milk and the individual cows contributing to the tank milk (scheme "C" embodying the principle of the present invention). [Figure 3]Figure 3 shows the correlation between predicted SCS and measured SCS for individual cows using two different genotyping schemes: an SNP array for genotyping DNA polymorphisms in both tank milk and the individual cows contributing to the tank milk (reference scheme "A"); SWGS for allele sampling for DNA polymorphisms in tank milk, and an SNP array and computer imputation for genotyping DNA polymorphisms in the individual cows contributing to the tank milk (scheme "B" embodying the principle of the present invention). [Figure 4] Figure 4 shows the correlation between predicted and measured SCS for individual cows using two different genotyping schemes: an SNP array for genotyping DNA polymorphisms in both tank milk and the individual cows contributing to the tank milk (reference scheme "A"); SWGS at various depths (0.4×, 1×, 2×, 4×) for allelic sampling for DNA polymorphisms in tank milk, and an SNP array and computer imputation for genotyping DNA polymorphisms in the individual cows contributing to the tank milk (scheme "B" embodying the principles of the present invention). [Figure 5] Figure 5 shows the accuracy of identifying animals with SCS above and below a selected threshold using two different genotyping schemes: an SNP array for genotyping DNA polymorphisms in both tank milk and individual cows contributing to tank milk (reference scheme "A"); SWGS at various depths (0.4×, 1×, 2×, 4×) for allel sampling for DNA polymorphisms in tank milk, and an SNP array and computer imputation for genotyping DNA polymorphisms in individual cows contributing to tank milk (scheme "B" embodying the principle of the present invention). [Figure 6(A)]Figure 6(A) shows the estimated SCS values ​​for the milk of 120 cows over a 21-week period (diamonds: SCS measured at week 12; triangles: SCS predicted by bulk genotyping from tank milk sequencing at week 12; large circles: average predicted SCS over 21 weeks; small circle dots: individual measurements). [Figure 6(B)] Figure 6(B) shows the correlation between measured SCS at week 12 and average predicted SCS over 21 weeks. [Modes for carrying out the invention] 【0014】 As used herein, the singular forms "a," "an," and "the" include both singular and plural referents unless the context explicitly indicates otherwise. As used herein, the terms “comprising,” “comprises,” and “comprised of” are synonymous with “including,” “includes,” or “containing,” and are comprehensive or open-ended, not excluding additional unlisted members, elements, or steps of the method. These terms also encompass “consisting of” and “consisting essentially of,” which enjoy the meanings established in patent terminology. 【0015】 The detailed explanation of numerical ranges by endpoints includes all numbers and fractions included within each range, as well as the endpoints described. As used herein, the terms “about” or “approximately” mean, when referring to measurable values ​​such as parameters, quantities, or time periods, to include variations of a given value and variations from that value, for example, variations of + / -10%, preferably + / -5%, more preferably + / -1%, and even more preferably + / -0.1%, insofar as such variations are appropriate for implementation in the disclosed invention. It should be understood that the values ​​that “about” in a modifying phrase also mean are themselves disclosed in detail and preferably disclosed. 【0016】 The terms “one or more” or “at least one,” for example, one or more members, or at least one member of a group of members, are self-evident, but by further example, the terms encompass, among other things, any one of the members, or any two or more of the members, for example, any ≥3, ≥4, ≥5, ≥6, or ≥7 of the members, and all of the members below that. In another example, “one or more” or “at least one” could mean 1, 2, 3, 4, 5, 6, 7, or more. 【0017】 The background discussions of the invention as described herein are included for the purpose of explaining the context of the invention. This does not constitute an acknowledgment that anything referred to was published, publicly known, or part of the ordinary general knowledge in any country as of the priority date of any of the claims. 【0018】 Throughout this disclosure, various publications, patents, and published patent specifications are referenced by citation. All documents referenced herein are incorporated herein in their entirety by reference. In particular, teachings or sections of such documents referenced in detail herein are incorporated by reference. 【0019】 Unless otherwise defined, all terms used in the disclosure of this invention, including technical and scientific terms, have meanings that are generally understood by those skilled in the art to which this invention belongs. Further guidance includes definitions of terms to better understand the teachings of this invention. Where a particular term is defined in relation to a particular aspect or embodiment of this invention, such meanings are to apply throughout this specification, i.e., in the context of other aspects or embodiments of this invention, unless otherwise defined. 【0020】 Different aspects or embodiments of the present invention are defined in more detail in the following sections. Each of the aspects or embodiments defined in this way may be combined with any other aspect or embodiment unless otherwise expressly indicated. In particular, any feature indicated as preferred or advantageous may be combined with any other one or more features indicated as preferred or advantageous. 【0021】 Throughout this specification, references to “one embodiment” or “embodiment” mean that any particular feature, structure, or characteristic described in relation to an embodiment is included in at least one embodiment of the present invention. Therefore, the appearance of the phrase “in one embodiment” or “in an embodiment” in various places throughout this specification does not necessarily refer to all of the same embodiments, but it may. Furthermore, certain features, structures, or characteristics may be combined in any suitable manner in one or more embodiments, as will be obvious to those skilled in the art from this disclosure. Moreover, some embodiments described herein include some features included in other embodiments but not others, and combinations of features from different embodiments mean that, as will be understood to those skilled in the art, they fall within the scope of the present invention and form different embodiments. For example, in the appended claims, any of the claimed embodiments may be used in any combination. 【0022】 The inventors have unexpectedly demonstrated that shallow whole-genome sequencing (SWGS) can be used for bulk allele sampling of DNA polymorphisms in milk (e.g., tank milk), and combined with allele data for the aforementioned DNA sequence polymorphisms in the milk-contributing animals, for example, to estimate the proportion or amount of DNA contributed by the animals to the milk with desirable accuracy and reliability. 【0023】 A method for embodying the inventors' recognition is, advantageously, to identify, for example, cows contributing to tank milk that have subclinical or clinical mastitis; and / or to determine whether a particular cow(s) has contributed milk to the tank (this may be useful for monitoring compliance with not putting milk from cows, for example, cows receiving treatment, into the tank). 【0024】 Therefore, one embodiment is a method for determining the ratio or amount of DNA contributed by individual animals to a certain volume of milk collected from multiple individual animals, a) A step of sampling alleles for multiple DNA sequence polymorphisms in DNA extracted from a certain volume of milk sample by shallow whole-genome sequencing (SWGS); b) A step of determining the ratio or amount of DNA contributed by an individual animal to a certain volume of milk, based on allele sampling for the DNA sequence polymorphism from step a) and allele data for the DNA sequence polymorphism in the individual animal. This provides a method that includes [something]. 【0025】 A further embodiment is a method for identifying one or more animals having subclinical mastitis or clinical mastitis from a group of individual animals contributing milk to a certain volume of milk, a') A method comprising determining the ratio or amount of DNA contributed by an individual animal to a certain volume of milk by performing allel sampling for multiple DNA sequence polymorphisms in DNA extracted from a certain volume of milk sample using SWGS, b') A step of determining the somatic cell concentration in the milk of an individual animal based on the ratio or amount of DNA contributed by the individual animal to a certain volume of milk, as determined in step a'); c') If the somatic cell concentration in the milk of one or more animals determined in step b') exceeds a predetermined threshold, the step of identifying that one or more animals have subclinical mastitis or clinical mastitis. This provides a method that includes [something]. 【0026】 The terms “quantity,” “amount,” and “level” are synonymous and are generally well understood in the art. In certain embodiments, these terms may mean absolute or relative quantitative determination of an analyte in an object or material of interest. Quantification may require analysis of the object or material of interest, and more typically, analysis of a sample of the object or material of interest. The absolute amount of an analyte in an object or material of interest can be appropriately expressed as weight, molar amount, or more generally as concentration, for example, as weight per unit volume or moles per unit volume. The relative amount of an analyte in an object or material of interest can be favorably expressed in comparison to a suitable reference variable. In non-limiting examples, the amount of an analyte in an object or material of interest can be expressed in comparison to the amount of a second analyte in the object or material of interest, and may also be appropriately expressed by a weight ratio or molar ratio between the analytes. In non-limiting examples, the amount of an analyte in an object or material of interest can be shown in comparison to the total amount of all analytes of the same chemical class in the object or material of interest, and can be appropriately expressed by a weight ratio or molar ratio (proportion or percentage) between the analyte and the total. In yet another non-limiting example, the amount of an analyte in an object or material of interest can be shown in comparison to the amount of the same analyte in another object or material of interest, and can be appropriately expressed by a ratio of the amounts, or by a multiple increase or decrease between the amounts. Performing a relative comparison between a first variable and a second variable (e.g., a first amount and a second amount) may, but is not required, require determining the absolute values ​​of the first and second variables. For example, but not limited to, a quantification method may allow for the determination of the relative contribution (e.g., weight ratio or molar ratio, proportion or percentage) of two or more analytes of the same chemical class to the total amount of all such analytes in the object or material of interest, without requiring the determination or calculation of the absolute amount of an individual analyte or the absolute total amount of all such analytes. 【0027】 Therefore, in certain embodiments, the method may enable the determination of the absolute amount of DNA contributed by an individual animal to the milk, which can be appropriately expressed as the weight of DNA from a given animal per unit volume of milk (w / v) or the concentration of DNA from a given animal in the milk (mol / v). 【0028】 In a more typical embodiment, the method determines the relative contribution of an individual animal to the DNA in milk (more specifically, to the pool of DNA from the animal in the milk). The relative contribution of a given animal to the DNA in milk can be appropriately expressed by the proportion (ratio or percentage) of milk DNA originating from or attributable to the animal. The method can conveniently estimate this proportion as a unitless variable. 【0029】 Therefore, in a particular preferred embodiment, the method of the present invention determines the ratio of DNA contributed by an individual animal to a certain volume of milk. As used herein, the term "DNA" means deoxyribonucleic acid. This term includes DNA present in cells, released from cells, and at least partially purified or extracted from such sources. In this method, this term means, in particular, DNA from an animal, more specifically from somatic cells of an animal, and even more specifically from nucleated somatic cells of an animal. Genomic (nuclear) DNA is specifically intended herein. This term includes any form of DNA suitable for performing the technical operations associated with this method, such as SWGS and SNP array genotyping. As an example without limitation, this term may encompass double-stranded DNA; single-stranded DNA, e.g., denatured DNA; intact DNA; fragmented DNA, e.g., DNA fragmented by the application of physical forces (e.g., acoustic shear, sonication, hydrodynamic shear) or by enzymatic methods (e.g., DNase I restriction endonuclease or other restriction endonucleases, nonspecific nucleases, transposases); chemically modified DNA, e.g., labeled DNA; fragmented DNA ligated with an adapter sequence; fragmented DNA ligated into a vector; and so on. 【0030】 As used herein, the term “animal” encompasses any milk-producing animal, and in particular means female lactating mammals, preferably non-human lactating mammals. In certain embodiments, the animal is a lactating livestock, and more specifically, a female lactating livestock mammal. The terms “livestock” or “livestock mammal” encompass any livestock species of animal or mammal that is kept and raised for profit or use, for example, but not limited to, consumption, indirect consumption (i.e., production of food such as dairy products), leather production, fur production, breeding, research, or as a pack animal. 【0031】 In certain embodiments, the animal is a Bovidae or a hybrid of Bovidae. Bovidae are even-toed, ectounger mammals belonging to the Bovidae family. Members include, but are not limited to, wild and domestic cattle, bison (American buffalo), African buffalo, water buffalo, antelope, gazelle, sheep, goat, musk ox, and yak. ​​Hybrids of two different members of the Bovidae family include, for example, hybrids of American bison or European bison and domestic cattle, domestic cattle / yak hybrids, domestic cattle / buffalo hybrids, bison / yak hybrids, and so on. 【0032】 In certain preferred embodiments, the animal is cattle (subfamily Bovidae), including wild and domestic cattle, bison, African buffalo, water buffalo, and yak. ​​Preferably, the animal may belong to the genus Bos. 【0033】 In certain more preferred embodiments, the animal is a cattle, more specifically, a domesticated cattle, such as a Bos taurus or Bos indicus breed, including any bovine breed, or a hybrid cattle. Therefore, a particularly preferred animal is a dairy cow. 【0034】 In a particular, more preferred embodiment, the animal is a buffalo, such as an American buffalo or an African buffalo. In certain further embodiments, the animal is a sheep, preferably an Ovis aries. 【0035】 In certain further embodiments, the animal is a goat, preferably a Capra aegagrus hircus. The phrase "a certain volume of milk collected from multiple individual animals" broadly refers to any mix or pool of milk collected from such multiple individual animals. This phrase does not impose any limitations on the quantity or volume (L) of milk, or the storage or container in which the milk is kept or stored. For example, it includes any bulk quantity of milk collected and pooled from multiple animals. 【0036】 As used herein, the term “plural” has its usual meaning of being greater than one (>1). Preferably, the term “plural individual animals” shall encompass individual dairy animals, such as typically classified groups or herds of cattle, hybrid cattle, buffalo, sheep, or goats. Thus, in certain embodiments, the “plural individual animals” in the phrase may represent five or more (≧5), ≧10, ≧15, ≧20, ≧25, ≧30, ≧40, ≧50, ≧60, ≧70, ≧80, ≧90, ≧100, ≧150, ≧200, ≧250, ≧300, ≧400, ≧500, ≧600, ≧700, ≧800, ≧900, ≧1000, ≧1500, or ≧2000 individual animals. In certain further embodiments, the phrase “multiple individual animals” may refer to 5000 or fewer individuals, for example, 4000 or fewer, 3000 or fewer, 2500 or fewer, 2000 or fewer, 1500 or fewer, or 1000 or fewer individuals. In certain further embodiments, the phrase “multiple individual animals” may refer to individuals between 10 and 25, or between 25 and 50, or between 50 and 100, or between 100 and 250, or between 250 and 500, or between 500 and 1000 individuals. In certain embodiments, the method can be applied to a certain volume of milk (e.g., tank milk) collected from multiple individual animals from one farm. In certain embodiments, the method can be applied to a certain volume of milk (e.g., tank milk) collected from multiple individual animals from two or more farms. In certain embodiments, the method can also be applied to a certain volume of milk (e.g., tank milk) from a milking plant, which may typically include milk collected from a considerable number of farms. Therefore, in certain embodiments, the phrase “multiple individual animals” can also refer to more than 5,000 individual animals. In this context, the term “multiple individual animals” can typically refer to animals that are all of the same species, though not exclusively. The term can also typically refer to animals that are all of the same subspecies, or the same variety, or the same breeding species, though not exclusively. As an exemplary example, the term could refer to multiple milking cows, Boss Taurus.Other exemplary examples include the term meaning multiple milking Holstein-Friesian cows, or multiple milking Norwegian Red cows, or multiple milking Kostroma cows, or multiple milking Brown Swiss cows, or multiple milking Swedish Red cows, or multiple milking Ayrshire cows, or multiple milking Angeln cows, or multiple milking Guernsey cows, or multiple milking Shorthorn cows, or multiple milking Pie Rouge des Plaines cows. 【0037】 In certain other embodiments, the term “multiple individual animals” may refer to animals that are all of the same species or subspecies but not of the same variety or breeding species. For example, “multiple” could include two or more varieties or breeding species, e.g., 2 to 5, e.g., 3 or 4 different varieties or breeding species of animals. 【0038】 In certain embodiments, a certain volume of milk may refer to tank milk, i.e., milk collected from dairy animals on a farm and stored in containers conventionally called “milk tanks.” Commercial milk tanks typically allow the collected milk to be refrigerated and mixed before being collected daily by milk carriers. Milk tanks vary considerably in size, from the smallest tanks of 100 liters (L) to silo tanks of 150,000 L. Milk silos can typically have capacities of 25,000 to 150,000 L, while intermediate milk tanks typically have capacities between 1,000 and 10,000 L, and small milk tanks typically have capacities between 150 and 3,000 L. 【0039】 Step a) of this method involves allelic sampling for multiple DNA sequence polymorphisms in DNA extracted from a certain volume of milk sample by shallow whole-genome sequencing (SWGS). 【0040】 The term "sample" is understood as conventionally, and more specifically, means a limited quantity, portion, or specimen that exhibits the quality (i.e., representative or characteristic properties) of the whole (e.g., object or material) that is taken out or obtained. In this context, a sample could be a particular volume of milk. Those skilled in the art can select an appropriate quantity of sample so that the method can be carried out. 【0041】 DNA, particularly genomic DNA, can be extracted or isolated from DNA-containing samples by methods known in the art. The terms “extract” or “isolate” in relation to a specific component (e.g., DNA) of a composition or mixture (e.g., a milk sample) encompass the process or technique by which such component is separated from one or more or (substantially) all other components. This term does not require absolute purity. Instead, the isolation of a component results in a separation environment in which the abundance of the component is greater than the initial abundance of the composition or mixture, compared to one or more or all other components. The separation environment may refer to a single medium, e.g., a single solution, dispersion, gel, precipitate, etc. The amount of nucleic acid can be determined by measuring absorbance A260. The purity of nucleic acid can be determined by measuring absorbance A260 / A280, or by agarose or polyacrylamide gel electrophoresis and ethidium bromide or similar staining. Conventional techniques for extracting or isolating DNA, particularly genomic DNA, include, but are not limited to, organic (phenol-chloroform) extraction, non-organic (proteinase K and salting-out) extraction, ion-exchange resin extraction, or silica-exchange resin extraction. See, in particular, Sambrook and Russell, 2001; and Sharma 1993. 【0042】 The term "DNA sequence polymorphism" refers to the occurrence of two or more genetically determined alternative sequences or "alleles" at a locus (polymorphic locus or polymorphic site) in a natural population. In this specification, sequence polymorphisms in genomic (chromosomal) DNA are particularly intended. Polymorphic loci or sites may, but are not limited to, exhibit two, three, four, five, or more different alleles in a natural population. Typically, a locus or site can be considered polymorphic (and therefore useful for genetic analysis) if the minor or rare allele at the locus or site has a frequency of 0.01 (1%) or higher. On the other hand, polymorphic loci with minor allele frequencies (MAFs) of less than 0.01 (1%), e.g., MAFs of 0.001 (0.1%) or higher, 0.002 (0.2%) or higher, or 0.005 (0.5%) are also applicable in this method. Diploid organisms, particularly somatic cells of diploid organisms, can be homozygous or heterozygous for an allele at a given polymorphic locus or polymorphic site. For example, if two different alleles (A and B) are present at a polymorphic locus or polymorphic site in a natural population, individuals from that population may be homozygous for allele A (genotype AA), homozygous for allele B (genotype BB), or heterozygous (genotype AB or BA). 【0043】 DNA sequence polymorphisms include, but are not limited to, single nucleotide polymorphisms (SNPs); restriction fragment length polymorphisms (RFLPs); variable number of tandem repeats (VNTRs), including "microsatellites" or short tandem repeats (STRs) (e.g., dinucleotide repeats, trinucleotide repeats, or tetranucleotide repeats) and "minisatellites" (e.g., repeats of DNA motifs 10 to 100 bp long); indels (insertions or deletions of multiple nucleotides); hypervariable regions; short interspersed elements (SINEs), such as Alu elements; and copy number variations (CNVs). Any type or any combination thereof of such DNA sequence polymorphisms can be used in this method. 【0044】 In certain preferred embodiments, DNA sequence polymorphisms are single nucleotide polymorphisms (SNPs). SNPs occur at polymorphic sites occupied by a single nucleotide, which are sites of variation between allele sequences. SNPs typically arise from the substitution of one nucleotide for another at a polymorphic site, including transitions (substitution of purine with another purine, or pyrimidine with another pyrimidine) and transversions (substitution of purine with pyrimidine, or pyrimidine with purine). SNPs can also arise from nucleotide deletions (single nucleotide deletions) or nucleotide insertions (single nucleotide insertions) relative to a reference allele. SNPs are typically the most abundant type of genomic DNA sequence polymorphism, and their density can range from 1 SNP per tens of base pairs to 1 SNP per hundreds of base pairs, for example, 1 SNP per 100 base pairs. 【0045】 Currently, there are millions to tens of millions of SNPs reported for many animals, more specifically dairy farm animals, and these, or subsets thereof, may be useful in the methods of the present invention, for example, but are not limited to: - Cow (http: / / www.ncbi.nlm.nih.gov / SNP / snp_batchSearch.cgi?org=9913&type=SNP); - Sheep (https: / / www.ncbi.nlm.nih.gov / SNP / snp_batchSearch.cgi?org=9940&type=SNP); or, - Goat (https: / / www.ncbi.nlm.nih.gov / SNP / snp_batchSearch.cgi?org=9925&type=SNP) Low-density and high-density SNP genotyping arrays, which typically contain SNPs equally spaced across each genome to enable whole-genome studies, are also commercially available for such animals and may represent a preferred subset of SNPs useful in this method, for example, but not limited to, Illumina's - GoldenGate® Bovine 3K Genotyping BeadChip (2,900 SNP) - UshiLD v2.0 Genotyping BeadChip(7,931SNP); - Cow SNP50 Genotyping BeadChip (53,714 SNPs); - Cow HD Genotyping BeadChip (777,962SNP); - Sheep (Ovine) SNP50 BeadChip (54,241 SNP); or - Goat SNP50 BeadChip (>50,000 SNP) There is. 【0046】 As used herein, the phrase "plurality of DNA sequence polymorphisms" when so intended broadly means any number of polymorphisms that enable the method to determine or estimate the desired result (e.g., the ratio or amount of DNA contributed by an individual animal to a volume of milk, or the somatic cell concentration in the milk of an individual animal, etc.) with a desired accuracy. By way of example, and not limitation, such accuracy can be represented as the correlation (r) between the result determined or estimated by the method, calculated by an appropriate statistical technique (Pearson, Spearman, or Kendall's correlation), and the actual situation (e.g., when the DNA amount or cell concentration in the milk is separately measured for individual dairy cows). Preferably, r may be ≧0.75, more preferably ≧0.80, even more preferably ≧0.85, even more preferably ≧0.90, e.g., 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99 or even 1.00. As another example, and not limitation, such accuracy can be represented as the ability to identify animals having a somatic cell score (SCS) above a certain threshold measured as (T p +T N ) / m (wherein T p represents the number of true positives, T N represents the number of true negatives, and m represents the total number of animals) for cases below the threshold. The number of polymorphisms can depend on several variables, e.g., and not limitation, the required accuracy, the animal species, subspecies, variety or breeding stock, the number of animals contributing to a volume of milk, the depth of SWGS sequencing, the type and degree of polymorphism heterozygosity, etc. 【0047】 In certain embodiments, the method is at least 5000, or at least 10,000, or at least 25,000, or at least 50,000, or at least 100,000, or at least 250,000, or at least 500,000, or at least 750,000, or at least 1.0×10 6 or at least 2.0×10 6 or at least 3.0×10 6 or at least 4.0×106 , or at least 5.0 × 10 6 , or at least 6.0 × 10 6 , or at least 7.0 × 10 6 , or at least 8.0 × 10 6 , or at least 9.0 × 10 6 , or at least 1.0 × 10 7 , or at least 1.5 × 10 7 , or at least 2.0 × 10 7 Individual DNA sequence polymorphisms can be analyzed. In a particular preferred embodiment, this method allows for the analysis of at least 1 × 10⁶ DNA sequence polymorphisms. 5 Preferably at least 5 × 10 5 , more preferably at least 1 × 10 6 More preferably, at least 5 × 10 6 , or at least 8 × 10 6 This method can analyze individual DNA sequence polymorphisms. In a particular embodiment, this method can analyze 2.0 × 10⁻⁶ DNA sequences. 7 Less than, or 1.5 × 10 7 Less than, or 1.0 × 10 7 DNA sequence polymorphisms of less than 1.0 × 10¹⁶, e.g., 1.0 × 10¹⁶. 6 From 1.0 × 10 7 DNA sequence polymorphisms between individuals, for example, approximately 1.0 × 10⁻⁶. 6 , or approximately 2.0 × 10 6 , or approximately 3.0 × 10 6 , or approximately 4.0 × 10 6 , or approximately 5.0 × 10 6 , or approximately 6.0 × 10 6 , or approximately 7.0 × 10 6 , or approximately 8.0 × 10 6 , or approximately 9.0 × 10 6 , or approximately 1.0 × 10 7 It is possible to analyze individual DNA sequence polymorphisms. 【0048】 The term “whole-genome sequencing” (WGS) broadly refers to methods and procedures in which the entire genome of an organism or the genomes of multiple organisms (e.g., genomic DNA in particular) are typically subjected to a sequencing workflow in a single step. Therefore, it does not involve any step of intentionally targeting or selecting only specific parts of the genome(s) for sequencing, while excluding or reducing the representation of other parts. The term does not mean that the entire genome of each organism is effectively sequenced, but rather that the entire genomic DNA in a sample is sequenced to a given depth without controlling selection or priority for specific parts of the genomic DNA (e.g., coding sequences or exons). Therefore, for example, the selection depth of whole-genome sequencing may be such that not all of the genome(s) are perfectly sequenced, but the unsequenced parts are substantially random or normally distributed. Also, certain parts of the genome(s) may not be readily adaptable to sequencing (e.g., repetitive sequences), and such parts may unintentionally be present only in small amounts within the sequence. Whole-genome sequencing makes it possible to sequence polymorphic alleles in substantially the entire genome(s) or end-to-end, i.e., polymorphic alleles in, but not limited to, a specific part(s) of the genome(s). Various sequencing methods known to those skilled in the art, particularly next-generation or high-throughput sequencing (NGS), now enable whole-genome sequencing of DNA, such as genomic DNA. Commercial examples include Illumina's dye sequencing, pyrosequencing (Qiagen), or SMRT sequencing (PacBio). These methods utilize well-known whole-genome shotgun methods. Third-generation methods use, for example, scanning tunneling electron microscopy (TEM), fluorescence resonance energy transfer (FRET), single-molecule detection, or nanopore systems. See, for example, Niedringhaus et al., 2011. 【0049】 The term “shallow whole-genome sequencing” is commonly used in the art to mean WGS configured to provide relatively low or shallow sequencing coverage or depth. The terms “sequencing depth,” “read depth,” “sequencing coverage,” or “coverage” are used in their conventional sense. Generally, these terms indicate the average number of times a given nucleotide in a sequence was read or sequenced. Thus, for example, a sequencing coverage or depth of 0.25, 1.0, 10, or 30 means that, on average, each nucleotide was sequenced 0.25, 1.0, 10, or 30 times, respectively. Sequencing depth can be conveniently calculated as N × L / G from the length of the sequenced genome (G), the number of reads (N), and the average read length (L). Sequence coverage is further described in Sims et al., 2014. For example, but not limited to, the sequencing depth of the SWGS may be 10 or less. In a particularly preferred embodiment, the sequencing depth of the SWGS may be 5 or less, for example, about 5.0, about 4.0, about 3.0, or about 2.0, and more preferably 1.0 or less, for example, about 1.0, about 0.95, about 0.90, about 0.85, about 0.80, about 0.75, about 0.70, about 0.65, about 0.60, about 0.55, about 0.50, about 0.45, about 0.40, about 0.35, about 0.30, about 0.25, about 0.20, about 0.15, or about 0.10, or about 0.05. In a particularly preferred embodiment, the sequencing depth of the SWGS may be between 0.10 and 1.0, for example, between 0.25 and 1.0. Sequencing depths of approximately 1.0 or less can be conveniently referred to as "low," while sequencing depths of approximately 0.25 or less (for example, between 0.05 and 0.25, or between 0.10 and 0.25) can be conveniently referred to as "very low." 【0050】 The sequencing depth can be selected or optimized based, in particular, on the size of the farm, i.e., the number of animals contributing to milk production. For example, in a particular embodiment, a milk sequencing depth of 0.25 may be sufficient to provide satisfactory accuracy for farms with 100 or fewer animals (e.g., cows), a sequencing depth of 0.5 for farms with 250 or fewer animals (e.g., cows), a sequencing depth of 2.0 for farms with 500 or fewer animals (e.g., cows), and a sequencing depth of 5.0 for farms with 1000 or fewer animals (e.g., cows). 【0051】 In certain embodiments, in step a) and / or step b) or step a') of the Method, the sequencing depth of the SWGS is 0.10 to 10.0, preferably 0.25 to 5.0, for example, a sequencing depth of about 0.25, about 0.50, about 0.75, about 1.0, about 2.0, about 3.0, about 4.0, or about 5.0. In certain further embodiments, in step a) and / or step b) or step a') of the Method, the sequencing depth of the SWGS is 0.25 to 1.0, preferably about 0.25 or about 1.0. 【0052】 The term "alleletic sampling" for multiple DNA sequence polymorphisms using SWGS broadly means that SWGS generates or collects at least some information about at least some alleles (or more) of the said multiple DNA sequence polymorphisms. Due to the shallow sequencing depth of SWGS, alleletic information tends to be largely incomplete or "ambiguous." For example, the average number of reads for alleles of a SNP is expected to correspond to the sequencing depth of the SWGS. Therefore, fractions of polymorphic loci may be covered by no reads, fractions with one read, and fractions with two or more reads. As an example, but not limited to, based on a Poisson distribution with an average equal to the sequencing depth: - For a sequencing depth of 1.0, 63% of polymorphic loci are sampled (i.e., at least one read), 37% of polymorphic loci are not sampled (i.e., no read), and of the sampled loci, only one allele (one read) may be sampled for 58% of loci, and two or more reads may be sampled for 42% of loci; or, - For a sequencing depth of 0.25, 22% of polymorphic loci are sampled (i.e., at least one read), 78% of polymorphic loci are not sampled (i.e., no reads), and of the sampled loci, those with only one allele (one read) are sampled for 88% of the loci, while those with two or more reads may be sampled for 12% of the loci. 【0053】 Therefore, in certain embodiments, the allele sampling of DNA sequence polymorphisms by SWGS as intended herein may be low-allele sampling (in particular, obtained or can be obtained by an SWGS sequencing depth of about 1.0 or less), or very low-allele sampling (in particular, obtained or can be obtained by an SWGS sequencing depth of about 0.25 or less, for example, between 0.05 and 0.25, or between 0.10 and 0.25). 【0054】 For example, in one particular embodiment, approximately 10% to 80% of polymorphic loci are sampled by at least one read, while approximately 90% to 20% are not sampled (no read). In a further embodiment, approximately 10% to 70% of polymorphic loci are sampled by at least one read, while approximately 90% to 30% are not sampled (no read). In a further embodiment, approximately 15% to 65% of polymorphic loci are sampled by at least one read, while approximately 85% to 35% are not sampled (no read). In a further embodiment, approximately 20% to 60% of polymorphic loci are sampled by at least one read, while approximately 80% to 40% are not sampled (no read). For example, in a particular embodiment: approximately 15%, or approximately 20%, or approximately 25%, or approximately 30%, or approximately 35%, or approximately 40%, or approximately 45%, or approximately 50%, or approximately 55%, or approximately 60%, or approximately 65%, or approximately 70% of polymorphic loci are sampled in at least one read, while approximately 85%, or approximately 80%, or approximately 75%, or approximately 70%, or approximately 65%, or approximately 60%, or approximately 55%, or approximately 50%, or approximately 45%, or approximately 40%, or approximately 35%, or approximately 30% of polymorphic loci are not sampled (no read). In a particular embodiment, the method may require that subsequent analyses include only polymorphisms sampled by a pre-selected number of reads (e.g., one read, or two or fewer reads, or three or fewer reads). 【0055】 In a particular step, the allele sampling of the DNA sequence polymorphism by SWGS in step a) or step a') is low or very low allele sampling. 【0056】 Despite the fact that SWGS may yield no allele information, or only partially or relatively inaccurate information, for any given polymorphism (e.g., SNP), the inventors have found that the incorporation of this "ambiguous" information from SWGS allele sampling for a large number of polymorphisms, e.g., hundreds of thousands to millions, can still be used to obtain very useful and accurate estimates of the proportion or amount of DNA contributed by an individual animal to a given volume of milk, or the somatic cell concentration in the milk of an individual animal. 【0057】 Step b) of this method includes allelic sampling of the DNA sequence polymorphism from step a) and determining the proportion or amount of DNA contributed by the individual animal to a certain volume of milk, based on the allelic data for the DNA sequence polymorphism in the individual animal. 【0058】 In this context, the term “allele data” broadly encompasses allele information about the polymorphism in individual animals, such as distinct genotypes (i.e., the identity of both alleles present at the polymorphic locus), the composition or quantity of the alleles, or the quality and quantity of allele sampling data from SWGS (i.e., SWGS allele sampling data). 【0059】 Therefore, in a particular embodiment, the allele data for the DNA sequence polymorphism of an individual animal in step b) includes or consists of the genotype or allele quantity for the DNA sequence polymorphism in the individual animal, and / or SWGS allele sampling data for the DNA sequence polymorphism in the individual animal. 【0060】 In a particular embodiment, the allele data for the DNA sequence polymorphism in an individual animal in step b) includes or consists of the genotype for the DNA sequence polymorphism in the individual animal. 【0061】 In a particular embodiment, the allele data for the DNA sequence polymorphism in an individual animal in step b) includes or consists of SWGS allele sampling data for the DNA sequence polymorphism in the individual animal. 【0062】 In a particular preferred embodiment, the SWGS allele sampling data of individual animals in step b) may be from an SWGS having a sequencing depth of 5 or less, for example, about 5.0, about 4.0, about 3.0, or about 2.0, more preferably 1.0 or less, for example, about 1.0, about 0.95, about 0.90, about 0.85, about 0.80, about 0.75, about 0.70, about 0.65, about 0.60, about 0.55, about 0.50, about 0.45, about 0.40, about 0.35, about 0.30, about 0.25, about 0.20, about 0.15, or about 0.10, or about 0.05; or particularly preferably from an SWGS having a sequencing depth between 0.10 and 1.0, for example between 0.25 and 1.0. 【0063】 In certain embodiments, the SWGS allele sampling data of individual animals in step b) are from low (in particular obtained or obtainable by SWGS sequencing depths of 1.0 or less) or very low (in particular obtained or obtainable by SWGS sequencing depths of 0.25 or less, e.g., between 0.05 and 0.25, or between 0.10 and 0.25) allele sampling by SWGS, as described elsewhere in this specification. 【0064】 In a particular preferred embodiment, the individual animal SWGS allele sampling data in step b) may be from an SWGS having a sequencing depth that is the same as or substantially the same as the sequencing depth of the SWGS used for allele sampling in step a). In this context, “substantially the same” may mean that the respective sequencing depths differ by 0.50 or less, preferably 0.40 or less, more preferably 0.30 or less, for example, 0.25 or less, 0.20 or less, 0.15 or less, 0.10 or less, or 0.05 or less. 【0065】 This method generally assumes the prior existence of allele data for the aforementioned DNA sequence polymorphisms in individual animals. Such allele data can be generated by known methods, for example, by genotyping or (essentially as described above) by allele sampling using SWGS. For this purpose, DNA extracted from animals, particularly from somatic cells, and even more particularly from somatic cells, such as DNA extracted from blood samples, is used. 【0066】 The term "genotyping" refers to the process of determining, as in the past, which alleles of one or more polymorphic regions are present in the analyzed DNA sample, for example, in a DNA sample from an individual animal. Depending on the situation, genotyping methods can identify individual genotypes or provide the composition or quantity of alleles, such as the relative amount of alleles in the examined DNA sequence polymorphism. Preferably, genotyping methods can identify individual genotypes. 【0067】 Genotyping techniques are commonplace and generally well-known in the art. For example, but not limited to, custom or commercially available genotyping arrays, such as SNP genotyping arrays, can genotype a large number of SNPs in parallel. Non-limiting examples of such SNP arrays useful for implementing certain embodiments of the present invention include Illumina's GoldenGate® Bovine3K Genotyping BeadChip (2,900 SNPs), BovineLD v2.0 Genotyping Bead Chip (7,931 SNPs), BovineSNP50 Genotyping BeadChip (53,714 SNPs), BovineHD Genotyping BeadChip (777,962 SNPs), OvineSNP50 BeadChip (54,241 SNPs), or Goat SNP50 BeadChip (>50,000 SNPs). Further genotyping methods include sequencing, such as targeted genome sequencing or whole-genome sequencing. Whole-genome sequencing (WGS) may be preferred. Preferably, in the context of genotyping, WGS can use sequencing depths that allow for substantially clear genotyping (calling), such as 10 or more, or 20 or more, or 30 or more, for example, about 40, about 50, about 60, about 70, or about 80 (e.g., “deep” WGS). 【0068】 Therefore, in certain embodiments, the genotype for the DNA sequence polymorphism of the individual animal used in step b) was determined at least partially by a genotyping array, such as an SNP genotyping array, by genome sequencing, such as targeted genome sequencing or whole genome sequencing, or any combination thereof. 【0069】 In a particular preferred embodiment, the genotype for the DNA sequence polymorphism of the individual animal used in step b) was determined at least partially by a genotyping array. In a particular preferred embodiment, the genotype for the DNA sequence polymorphism of the individual animal used in step b) was determined at least partially by a SNP genotyping array. 【0070】 In certain embodiments, the genotype for the DNA sequence polymorphism of the individual animal used in step b) was at least partially determined by whole-genome sequencing. In certain embodiments, the genotype for the DNA sequence polymorphism of the individual animal used in step b) was at least partially determined by deep whole-genome sequencing. 【0071】 Typically, such SNP genotyping arrays can enable the examination of thousands of SNPs (e.g., at least 2,500, at least 5,000, or at least 7,500 SNPs), tens of thousands of SNPs (e.g., at least 10,000, at least 25,000, at least 50,000, or at least 70,000 SNPs), or hundreds of thousands of SNPs (e.g., at least 100,000, at least 250,000, at least 500,000, or at least 750,000 SNPs). However, SNPs of relatively low density may be more acceptable to farmers due to their lower cost. Therefore, in certain embodiments, the genotype for the aforementioned DNA sequence polymorphisms of individual animals was at least partially determined by an SNP genotyping array capable of examining 100,000 or fewer SNPs, preferably 50,000 or fewer SNPs, more preferably 10,000 or fewer SNPs. The inventors have demonstrated that in certain embodiments of this method, the accuracy of the prediction may be only slightly affected by the type of SNP array used (for example, in the data shown in Figure 2B, the 10K SNP array was performed as if it were a 50K array). 【0072】 The genotype for the aforementioned DNA sequence polymorphisms in an individual animal may be the "actual" genotype (i.e., the experimentally detected genotype) and / or the genotype determined by a computer-based genotype imputation process known in the art (see Marchini and Howie, 2010). Genotype imputation involves statistical inference of unobserved genotypes using known haplotypes in a population (e.g., native populations of the same species, subspecies, variety, or breeding species) based on genotypes experimentally observed in a sample (e.g., an individual animal). Thus, genotype imputation typically requires that a reference population has been pre-typed by high-density SNP arrays or by deep (e.g., >10) whole-genome sequencing, a situation that is becoming increasingly common for livestock, particularly for important global breeding species (e.g., Holstein-Friesian, Jersey, or Brown Swiss cattle breeds), although reference populations can be just as readily genotyped for other breeding species. Software packages for performing genotype imputation are publicly available, such as Beagle (Browning et al., 2009), MaCH (Li et al., 2010), or IMPUTE2 (Howie et al., 2012). 【0073】 Therefore, in certain embodiments, the genotype or allele quantity for the DNA sequence polymorphism of the individual animal used in step b) is at least partially imputed. 【0074】 When genotypes are imputed, some uncertainty exists in the actual underlying genotype. Therefore, in imputed data, the allele amounts are often not 0, 0.5, or 1, and may deviate somewhat from these values. Such allele amounts can be converted to genotypes that yield the most probable genotype. Alternatively, when analyzing imputed data, methods can be used that allow for such allele amounts or genotypes, thereby gaining some power when the imputed allele amounts are used in place of the most probable genotypes. 【0075】 Genotype imputation can significantly increase the number of individual animal polymorphisms that can be used in step b) of this method. Considering that allele sampling by SWGS in step a) generates information on a very large number of polymorphic sites (e.g., hundreds of thousands to millions), genotype imputation can favorably increase the number of individual animal polymorphisms by the same order of magnitude, enabling more useful statistical analysis. 【0076】 Therefore, in certain embodiments, the allele data for the DNA sequence polymorphism of an individual animal in step b) includes or consists of a genotype for the DNA sequence polymorphism of the individual animal, which is partially experimentally detected and partially imputed. 【0077】 In a particular embodiment, the genotypes for the DNA sequence polymorphisms of the individual animals used in step b) are partially determined by genotype arrays, e.g., SNP genotype arrays, by genome sequencing, e.g., targeted genome sequencing or whole genome sequencing, or any combination thereof, and are partially imputed based on experimentally determined genotypes. 【0078】 In a particular preferred embodiment, the genotype of the DNA sequence polymorphism of the individual animal used in step b) is partially determined by a genotype array, such as an SNP genotype array, and is partially imputed based on experimentally determined genotypes. 【0079】 For example, but not limited to, the genotypes of the DNA sequence polymorphisms of the individual animals used in step b) may be partially determined by a (SNP) genotype array capable of examining thousands of SNPs (e.g., at least 2,500, at least 5,000, or at least 7,500 SNPs), tens of thousands of SNPs (e.g., at least 10,000, at least 25,000, at least 50,000, or at least 70,000 SNPs), or even hundreds of thousands of SNPs (e.g., at least 100,000, at least 250,000, at least 500,000, or at least 750,000 SNPs), and the total number of genotype DNA polymorphisms may be determined by applying genotype imputation to at least 1.0 × 10¹⁶ 6 , or at least 2.0 × 10 6 , or at least 3.0 × 10 6 , or at least 4.0 × 10 6 , or at least 5.0 × 10 6 , or at least 6.0 × 10 6 , or at least 7.0 × 10 6 , or at least 8.0 × 10 6 , or at least 9.0 × 10 6 , or at least 1.0 × 10 7 , or at least 1.5 × 10 7 , or at least 2.0 × 10 7 Increase it to this point. 【0080】 The proportion or amount of DNA contributed by individual animals to a given volume of milk can be determined herein based on bulk allele sampling of DNA sequence polymorphisms in the milk and known allele data of said DNA sequence polymorphisms in individual animals. Thus, the composition of DNA extracted from milk can be determined in terms of the relative contribution of each animal (e.g., cow), i.e., what proportion of the DNA in the milk is contributed by 1, 2, 3, ..., n animals. This can be achieved using various mathematical approaches generally known in the art, such as least squares analysis, non-negative least squares analysis, weighted least squares analysis, maximum likelihood estimation, and Bayesian methods. 【0081】 In certain embodiments, a linear model can be used. In certain embodiments, a set of linear equations can be defined that include variables corresponding to the ratio of DNA in tank milk contributed by a given animal, and the ratio can be estimated by least squares analysis of the equations. 【0082】 For example, assuming a dialleletic polymorphism with a reference (R) allele and a substitute (A) allele (and therefore possible genotypes are RR, RA, or AA), the set of m linear equations can be defined in the following form: 【0083】 【number】 【0084】 In the formula, f i This is the ratio of DNA in the milk contributed by animal i, and d ij This is the "quantity" of alternative allele A in animal i and polymorphism j, and ε j This is the error term of polymorphism j. When milk is sampled for alleles using SWGS, 【0085】 【number】 【0086】 This corresponds to the ratio of readouts A at the corresponding genomic location. Regarding the genotypes of individual animals detected experimentally, d ij These correspond to 0, 0.5, or 1 for genotypes RR, RA, and AA, respectively. For the genotypes of individual animals obtained by imputation, d ij This is the amount of A allele estimated by an appropriate imputation algorithm, such as the Beagle software package. For allele sampling in individual animals performed by SWGS, d ij =0.5 × P("RA"|nr R ,nr A ,q j )+P("AA"|nr R ,nr A ,q j ) was conducted, and in the ceremony, nr R (each nr A ) are numerical values ​​R (read A respectively) relating to polymorphism j and animal i, and q j This represents the population frequency of the A allele of polymorphism j. 【0087】 【number】 【0088】 Next, f i This can be determined by least squares analysis, that is, 【0089】 【number】 【0090】 It can be estimated by minimizing f. When allele sampling in milk is performed by SWGS, weighted least squares analysis can be performed, i.e., f i teeth, 【0091】 【number】 【0092】 It can be estimated by minimizing the following, where W j is coverage (nr R +nr A ) Furthermore, since animal somatic cells are the primary source of animal genomic DNA in their milk, the relative contribution of an individual animal to the DNA in pooled milk (e.g., tank milk) also responds to or reflects, and can therefore be translated into the relative contribution of an individual animal to the somatic cell concentration or somatic cell score (SCS) in the pooled milk. From there, the concentration of somatic cells in an individual animal's milk can be calculated or estimated, converted to individual volumes of milk, based on the proportion of DNA contributed by the individual animal to the pooled milk, the actual concentration of somatic cells in the pooled milk (which is determined by the dairy plant collecting the milk, as milk pricing is often influenced by this parameter), and the individual animal's relative contribution to the pooled milk (this information is generally available when today's milking machines automatically collect the amount of milk obtained from each animal, e.g., cow). As an example, the somatic cell score (SCS) of individual animal i can be calculated. i ) is the f explained above. i From this, it can be calculated as follows: SCS i =SCS tank ×V tank ×f i / V i In the formula, V tank V is the total pool volume of milk (e.g., liters), i V is the volume of milk in the same unit (e.g., liters) contributed by individual animal i to the pooled milk. i This can be measured directly, or it can be estimated from a standard lactation curve that captures the estimated annual production of a given animal and the change in daily milk yield (or volume) as a function of the number of days during lactation, i.e., postpartum. 【0093】 Therefore, in certain embodiments, the method may further include the step of determining the somatic cell concentration in the milk of an individual animal based on the ratio or amount of DNA contributed by the individual animal to a certain volume of milk. In certain embodiments, the somatic cell concentration can be expressed by a somatic cell score (SCS). 【0094】 An increase in somatic cell concentration (SCS) in milk is a characteristic or sign of mastitis and subclinical mastitis. Mastitis involves inflammation of the mammary glands. It can affect any mammal, non-ruminant and ruminant, such as cows, sheep, and goats. It is the most common and costly disease affecting dairy cows worldwide. Mastitis is typically caused by mammary infections caused by Gram-positive and Gram-negative bacteria. Major pathogens that cause mastitis include Escherichia coli (E. coli), Streptococcus uberis (S. uberis), and Staphylococcus aureus (S. aureus). Other organisms have also been identified as possible mastitis pathogens, though less common. Other bacteria that cause bovine mastitis include, but are not limited to, Streptococcus agalactiae, Klebsiella pneumoniae, Klebsiella oxytoca, and Pseudomonas aeruginosa. These organisms are present as the main pathogens most commonly associated with clinical mastitis in dairy cows. They infect the udder cistern through the teat ducts, inhibiting milk secretion for a moderate to long period and inducing inflammation of the milk-producing tissue. If scar tissue is involved, a permanent decrease in milk production occurs. Mammary infections alter the composition, quantity, appearance, and / or quality of the milk. Depending on the type of infection, common sources of microbial infection include unsanitary milking equipment, milkers, other animals, bedding, and the animals' own excrement (feces). The distinction between clinical mastitis and subclinical mastitis infections can be determined by whether the symptoms are visible to the naked eye, for example, whether they can be observed by the farmer without the use of instruments or tests. Such symptoms may include swelling and redness of the udder, as well as a decrease or change in the quality or quantity of milk produced. 【0095】 Therefore, in certain embodiments of this method, somatic cell concentration or somatic cell score (SCS) in the milk of an individual animal exceeding a predetermined threshold identifies the animal as having subclinical mastitis or clinical mastitis. In preferred embodiments, somatic cell concentration or SCS in the milk of an individual animal exceeding a predetermined threshold may identify the animal as having subclinical mastitis. Therefore, this method is particularly useful and applicable for detecting or diagnosing subclinical mastitis or clinical mastitis in individual animals. 【0096】 Such useful thresholds may depend on the animal species, subspecies, variety, or breed, but are generally known to those skilled in the art. For example, in healthy cows, the somatic cell concentration or SCS in their milk is typically less than 100,000 cells / mL, but a somatic cell concentration or SCS of 200,000 cells / mL may be considered a usable threshold indicating that cows with 200,000 cells / mL or less have subclinical or clinical mastitis. The somatic cell concentration or SCS of cows with clinical mastitis can typically reach several million cells / mL, by which point the cow may be removed from milking. 【0097】 In certain embodiments, the absolute amount of DNA contributed by individual animals to the pooled milk can also be estimated by converting it to individual volumes of milk, based on the ratio of DNA contributed by individual animals to the pooled milk, the actual concentration of DNA in the pooled milk, and the relative contribution of individual animals to the pooled milk. 【0098】 In consideration of the above teachings, another aspect of the present invention is a method for identifying one or more animals having subclinical or clinical mastitis from a group of individual animals that contribute milk to a certain volume of milk, a') Determining the proportion or amount of DNA contributed by an individual animal to a certain volume of milk by a method including, for example, low or very low allele sampling, in which allele sampling is performed for multiple DNA sequence polymorphisms across the genome in DNA extracted from a certain volume of milk sample using SWGS, b') A step of determining the somatic cell concentration in the milk of an individual animal based on the ratio or amount of DNA contributed by the individual animal to a certain volume of milk, as determined in step a'); c') If the somatic cell concentration in the milk of one or more animals determined in step b') exceeds a predetermined threshold, the step of identifying that one or more animals have subclinical mastitis or clinical mastitis. This includes methods. 【0099】 This application also provides the aspects and embodiments shown in the following statement. Description 1. A method for determining the ratio or amount of DNA contributed by individual animals to a certain volume of milk collected from multiple individual animals, a) A step of sampling alleles for multiple DNA sequence polymorphisms in DNA extracted from a certain volume of milk sample by shallow whole-genome sequencing (SWGS); b) A step of determining the ratio or amount of DNA contributed by an individual animal to a certain volume of milk, based on allele sampling for the DNA sequence polymorphism from step a) and allele data for the DNA sequence polymorphism in the individual animal. Methods that include... 【0100】 The method according to Description 1, wherein in step b), the allele data for the DNA sequence polymorphism in an individual animal includes, or comprises, genotype or allele quantity for the DNA sequence polymorphism in an individual animal, and / or SWGS allele sampling data for the DNA sequence polymorphism in an individual animal. 【0101】 Description 3. The method according to Description 1 or 2, wherein the proportion of DNA contributed by an individual animal to a given volume of milk is determined. Description 4. The method according to any one of Descriptions 1 to 3, further comprising the step of determining the somatic cell concentration in the milk of an individual animal based on the ratio or amount of DNA contributed by the individual animal to a certain volume of milk, wherein the somatic cell concentration is expressed by a somatic cell score (SCS), for example. 【0102】 Description 5. The method according to any one of Descriptions 1 to 4, wherein the allele sampling for the DNA sequence polymorphism by SWGS in step a) is low or very low allele sampling. 【0103】 The method according to any one of Descriptions 2-4, wherein the SWGS allele sampling data of individual animals in step 6) is from low or very low allele sampling by SWGS. 【0104】 Description 7. The method according to any one of Descriptions 1 to 6, wherein in step a) and / or step b), the sequencing depth of the SWGS is 0.10 to 10.0, preferably 0.25 to 5.0, for example, a sequencing depth of about 0.25, about 0.50, about 0.75, about 1.0, about 2.0, about 3.0, about 4.0 or about 5.0. 【0105】 Description 8. The method according to any one of Descriptions 1 to 6, wherein in step a) and / or step b), the sequencing depth of the SWGS is 0.25 to 1.0, preferably about 0.25 or about 1.0. 【0106】 Description 9. The method described in any one of Descriptions 1 to 8, wherein the DNA sequence polymorphism is a single nucleotide polymorphism (SNP). Description 10. The method according to any one of Descriptions 2 to 9, wherein the genotype of the DNA sequence polymorphism in an individual animal is determined at least partially by a genotyping array, such as an SNP genotyping array, by genome sequencing, such as targeted genome sequencing or whole genome sequencing, or by any combination thereof. 【0107】 Description 11. The method according to Description 10, wherein the genotype for the DNA sequence polymorphism in an individual animal is at least partially determined by an SNP genotyping array capable of examining 100,000 or fewer SNPs, preferably 50,000 or fewer SNPs, and more preferably 10,000 or fewer SNPs. 【0108】 Description 12. The method according to any one of Descriptions 2 to 11, wherein the genotype or allele quantity of the DNA sequence polymorphism in an individual animal is at least partially imputed. 【0109】 Description 13. At least 1 × 10 5 Preferably at least 5 × 10 5 , more preferably at least 1 × 10 6 More preferably, at least 5 × 10 6 , or at least 8 × 10 6 A method described in one of descriptions 1 to 12, wherein DNA sequence polymorphisms are analyzed. 【0110】 Description 14. The method described in any one of Descriptions 1-13, wherein the animal is a lactating livestock. Description 15. The method according to any one of Descriptions 1 to 14, wherein the animal is a Bovidae or Bovidae hybrid; preferably a cattle; more preferably a livestock cattle, e.g., Bos taurus), Bos indicus or a hybrid livestock cattle; or a buffalo. 【0111】 Description 16. The method described in any one of Descriptions 1-14, wherein the animal is a sheep or a goat. Description 17. The method according to any one of Descriptions 1 to 16, wherein a certain volume of milk is tank milk. 【0112】 Description 18. The method according to any one of Descriptions 4 to 17, wherein a somatic cell concentration or somatic cell score (SCS) in the milk of an individual animal exceeding a predetermined threshold identifies the animal as having subclinical mastitis or clinical mastitis. 【0113】 Description 19. A method for identifying one or more animals having subclinical mastitis or clinical mastitis from among multiple individual animals contributing milk to a certain volume of milk, a') Determining the ratio or amount of DNA contributed by an individual animal to a certain volume of milk by a method including allele sampling for multiple DNA sequence polymorphisms in DNA extracted from a certain volume of milk sample by SWGS, for example, low or very low allele sampling, preferably by the method described in 1. b') A step of determining the somatic cell concentration in the milk of an individual animal based on the ratio or amount of DNA contributed by the individual animal to a certain volume of milk, as determined in step a'); c') If the somatic cell concentration in the milk of one or more animals determined in step b') exceeds a predetermined threshold, the step of identifying that one or more animals have subclinical mastitis or clinical mastitis. Methods that include... 【0114】 While the present invention has been described in conjunction with its specific embodiments, it will be obvious to those skilled in the art that many alternatives, modifications, and variations are apparent in light of the foregoing description. Therefore, all alternatives, modifications, and variations are to be included, as is the spirit and broad scope of the appended claims. The aspects and embodiments of the present invention disclosed herein are further supported by the following non-limiting examples. [Examples] 【0115】 material and method Simulation data Reference Scheme (A): The inventors simulated a farm with n cows (25, 50, 100, 250, 500, and 1,000) contributing milk to a tank. The cows were genotyped without error using SNP arrays for m (10K, 50K, or 750K) markers. Minor allele frequencies (MAF) were sampled from a uniform [0,0.5] distribution, and genotypes were sampled from the corresponding Hardy-Weinberg distribution. Somatic cell score (SCS) of individual cows (SCSi The exact B allele frequency (BAF) of SNPs in milk individuals was simulated by sampling values ​​from a Weibull distribution with a scale parameter α=1 and a shape parameter β=2, and the resulting value was multiplied by 200,000. j The ) were determined for each SNPj based on the cellular contribution to the milk of n cows and their genotype combinations. The B allele frequencies were estimated using a normal distribution error N(0,0.0025) (i.e., SE=0.05), and m 【0116】 【number】 【0117】 It was presumed that this would be obtained. Scheme B: With the same settings as the reference scheme, the following were added. For the female cattle genotyped with 10K or 50K arrays, the inventors simulated imputation by increasing the data up to 8 million (M) genotypes using an error model that mimicked the actual MAF-dependent imputation accuracy. The error model was constructed using the actual dataset for 800 unrelated Holstein-Friesian individuals genotyped against the 777K SNP HD array. This dataset was split into a set of 200 individuals and a set of 600 individuals. The set of 200 individuals was first reduced to genotypes initially examined by 7K Illumina BovineLDv2.0 and then to genotypes examined by 50K Illumina BovineSNP50v3 SNP array. The reduced SNP set was imputed back to the content of the Illumina BovineHD 777K SNP array using the 600 individuals as the reference population. The frequency of imputing a given genotype according to the actual genotype was scored separately for the LD and 50K array data for MAF bins of 0.01. The inventors simulated allele sampling in tank milk by SWGS as follows. For each position of 8M SNPs, the inventors sampled the local read depth (r ∈ integer) from the Poisson distribution of the average C, where C is the average coverage of the whole genome (0.25, 0.5, 1, 2, or 5). Then, the inventors sampled the read r with the probability = BAF j (calculated above) for each allele B 【0118】 Scheme C: The SNP genotypes of individuals and the B allele frequency (BAF jThe alleles were obtained in the same manner as the reference scheme (genotype at 8M SNP locations). Allele sampling in tank milk was assumed to have been performed by SWGS with mean coverage of C (0.25, 0.5, 1, 2, or 5), and allele sampling in cows was assumed to have been performed by SWGS with mean coverage of C (0.25, 0.5, or 1). Allele sampling in individual cows was simulated by (i) sampling the local read depth (rε integer) from the mean Poisson distribution of C for each 8M SNP location, and (ii) sampling the read r with a probability of 0, 0.5, or 1 that it is the alternative allele (A) depending on the cow's genotype (RR, RA, or AA). Allele sampling in tank milk was performed in the same manner as scheme B. 【0119】 Actual data Dataset 1: The inventors obtained milk tank samples from 133 milking Holstein-Friesian cows from a farm in France. All were genotyped using a customized Illumina BovineLDv2 array that examined 17K SNPs. For all cows, the genotype was imputed to HD (777K) density using Beagle software (v3.3.2) (Browning and Browning 2009) and a reference population of 800 Holstein-Friesian animals genotyped with the Illumina BovineHD array (777K SNPs). Individual milk readings, including volume and SCS (cells / mL), were obtained for all cows that contributed milk to the tank. DNA was isolated from 1.5 ml of tank milk using the NucleoMag® Blood kit (Macherey-Nagel, catalog number: 744501.1). The DNA from the tank milk was first genotyped using a customized Illumina BovineLDv2 to examine 17K SNPs. Next, an Illumina-compatible NGS library was prepared using 50 ng of genomic DNA with the KAPA HyperPlus kit (Roche, catalog number: KK8510). Sequencing was performed using a NextSeq500 instrument (Illumina), corresponding to approximately 3.5× genomic coverage. * 60 million pair-end reads of 75 bp were obtained. The reference (R) allele and surrogate (A) allele were counted at 777K SNP locations on the HD array using the Bam-ReadCount tool (https: / / github.com / genome / bam-readcount.git), resulting in 699,402 locations covered with at least one read with an average coverage of 2.8. The read depth distribution closely resembled a Poisson distribution with a mean of 2.8 (r=.98), with 18% of loci covered with one read and 70% of loci covered with two or more reads. 【0120】 Datasets 2 and 3: The inventors obtained tank milk samples from a Belgian farm, containing milk from 520 and 120 Holstein-Friesian cows, respectively. Milk volume and SCS (cells / ml) were obtained for all cows that contributed milk to the tank. All cows were genotyped using a standard procedure with an Illumina BovineLDv2 array to examine 17K SNPs, and the whole genome was imputed using Beagle software (v5.0) (Browning et al., 2018) with whole genome sequencing data (mean depth: 15×; range: 4×~48×) (M. Georges, unpublished) from 743 Holstein-Friesian animals as a reference, to obtain allele amounts for a total of 13 million SNPs. DNA extraction from tank milk samples, genotyping using Illumina BovineLDv2 (17K) and BovineSNP50v3 (50K) arrays, and sequencing (4x coverage) were performed in the same manner as for dataset 1. 【0121】 Dataset 4: In addition to obtaining tank milk samples on the day of milk reading (i.e., acquisition of SCS measured using a cell counter) at a Belgian farm with 120 cows, the inventors collected an additional 11 tank milk samples weekly before a period totaling approximately 3 months, and 9 samples weekly after that period. The corresponding DNA samples were sequenced using the same procedure as for Dataset 1. 【0122】 Statistical models The inventors have defined a set of m linear equations of the following form: 【0123】 【number】 【0124】 In the formula, f i This is the ratio of DNA in the tank milk contributed by cow i, and d ij This is the "amount" of alternative allele A in cow i and marker j, and ε jis the error term for marker j. When genotyping tank milk using an array, 【0125】 【Number】 【0126】 corresponds to the B allele frequency estimated by Genome Studio (Illumina). When allele sampling of tank milk is performed by SWGS, 【0127】 【Number】 【0128】 corresponds to the ratio of read A at the corresponding genomic position. For the genotypes of cows obtained from the array, d ij corresponds to 0, 0.5, or 1 for genotypes RR, RA, and AA, respectively. For the cow genotypes obtained by imputation, d ij is the amount of A allele estimated by Beagle (v3.3.2) (Browning & Browning 2009). For allele sampling in cows performed by SWGS, d ij = 0.5 × P(”RA”|nr R ,nr A ,q j ) + P(”AA”|nr R ,nr A ,q j ) is implemented, where nr R (nr A respectively) are the numerical values R (read A respectively) for marker j and cow i, and q j is the within-population frequency of the A allele of marker j. 【0129】 【Number】 【0130】 For cow i, if there is no usable information (e.g., failure to determine genotype or lack of covering reads) SNP j, then d ij teeth 【0131】 【number】 【0132】 I set it up that way. fi is obtained by least squares analysis, that is, 【0133】 【number】 【0134】 This was estimated by minimizing the following. When allele sampling in tank milk was performed by SWGS, the inventors also performed weighted least squares analysis, that is, the inventors 【0135】 【number】 【0136】 By minimizing f i We estimate that, in the formula, W j is coverage (nr R +nr A ) SCS i is, f i It was calculated from that. SCS i =SCS tank ×V tank ×f i / V i In the formula, V tank and V i These represent the volume of milk in the tank (e.g., in liters) and the volume of milk contributed to the tank by cow i (in the same units, e.g., in liters), respectively. 【0137】 V iIt can also be measured directly, as some milking machines record this information. i If the actual value of is unknown (for example, because the milking machine used on the farm does not provide that information), V i This can be estimated from the estimated annual production of a cow and the standard lactation curve, which shows the change in daily milk yield (or volume) as a function of the number of days after calving. 【0138】 The accuracy of the prediction is (i) the Pearson correlation (r) between the actual SCSi and the estimated SCSi, and / or (ii) (T p +T N It can be measured by the ability to distinguish between animals with an SCS above and below a certain threshold, measured as ) / m, where T p This indicates the number of true positives, T N indicates the number of true negatives, and m indicates the total number of cows. 【0139】 To test the impact of sequence depth on accuracy, the inventors sampled readings that overlapped SNP locations with probability x such that E(C×x)=D (where D is the desired sequence depth). 【0140】 result Simulation data The inventors first re-evaluated the accuracy of estimating the number of SCSs in individual cows by assuming bulk genotyping of tank milk under the "reference scheme" (A), i.e., genotyping individual cows and tank milk with the same three most commonly used bovine SNP arrays examining 10K, 50K, or 750K SNPs, respectively. As is evident from Figure 2A, with 10K SNPs, the prediction accuracy is satisfactory for farms with 100 cows or fewer (r≧0.9), with 50K SNPs for farms with 250 cows or fewer, and with 750K SNPs for farms with 1,000 cows or fewer contributing milk to a single tank. Furthermore, genotyping an entire herd with a 50K array, let alone a 750K array, is currently prohibitively expensive. 【0141】 Therefore, the inventors investigated a first set of alternative scheme (B), in which (i) individual cows are genotyped using one of 10K, 50K, or 750K SNPs, and the genotypes are augmented to 8 million (8M) SNPs by imputation, while (ii) allele sampling in tank milk is performed by SWGS with sequencing depths ranging from 0.25 to 5. Figure 2B first shows that the accuracy of prediction is only slightly affected by the type of SNP array used, i.e., the 10K SNP array is performed similarly to the 50K and 750K arrays (not shown). Most importantly, a sequencing depth of 0.25 (for tank milk) is sufficient to provide satisfactory accuracy for farms with 100 cows or fewer, a sequencing depth of 0.5 for farms with 250 cows or fewer, a sequencing depth of 2 for farms with 500 cows or fewer, and a sequencing depth of 5 for farms with 1,000 cows or fewer. 【0142】 The inventors further investigated a third set of scheme (C) in which allele sampling in both tank milk and individual cows is performed by SWGS. As is evident from Figure 2C, a sequencing depth of 0.25× is sufficient for milk and cows in a farm with 25 cows. If the sequencing depth for cows is maintained at 0.25×, the sequencing depth for milk is preferably increased to 1× and 5× in farms with 50 and 100 cows, respectively. To be applicable in a farm with 250 cows, the sequencing depth for cows is preferably increased to 0.5× and the sequencing depth for milk to 5×. 【0143】 Actual data The SCS for 133 dairy cows was first estimated under the "reference" scenario (A), i.e., using only the genotypes of 17K SNPs examined by the Illumina LD array. The correlation between the actual SCS and the estimated SCS was 0.91. Next, the inventors repeated the calculation under scheme B, i.e., using the imputed genotype amounts at 777K SNP locations (covered by the Illumina HD array) for cows, and also estimated the B allele frequency at the corresponding locations for milk from the SWGS data (3.5 × coverage) described in Materials and Methods. The accuracy of the predicted SCS increased to 0.96. Visual inspection of the correlation plot revealed one cow with over 3 million SSC / ml (i.e., obvious mastitis), which caused a sharp increase in the r value. The inventors removed this outlier and repeated the analysis. The correlation between actual SCS and estimated SCS was 0.79 under Scheme A, i.e., using only information from 17K SNPs examined by LD arrays, and rose to 0.93 under Scheme B, i.e., when information from imputed cow genotypes and milk SWGS was added (Figure 3). This clearly demonstrates that Scheme B is superior to A. 【0144】 Next, the inventors tested the effect of increasing the number of cows contributing milk to the tank using dataset 2 (520 cows). Under scheme A, i.e., under milk and cows genotyped with SNP arrays, the correlation between predicted SCC and measured SCC decreased to 0.47, as predicted by simulation (Figure 4). Next, the inventors applied scheme B, i.e., imputing cows with more than 10M SNPs and performing whole-genome sequencing on the milk. The correlation increased to 0.91 when all available sequence information was used, i.e., at a 4× sequencing depth. The inventors downsampled the sequencing data to depths of 2×, 1×, and 0.4×. The correlation never fell below 0.86 at a 0.4× coverage. 【0145】 Farmers typically use a selective SCS threshold, and if this threshold is exceeded, they can take interventions such as antibiotic treatment. Therefore, we also evaluated the accuracy of our method in distinguishing animals with SCS above and below the selected threshold. Using a commonly used threshold of 1 million SCS values, Scheme B achieved an accuracy of 0.9 or higher, even when sequencing milk at a depth of 0.4× (Figure 5). In summary, these results demonstrate that the proposed Scheme B is effective in detecting cows with subclinical mastitis, even on very large farms. Indeed, cow genotyping using a 17K array, when combined with cow genotyping imputation of the whole genome and shallow (e.g., 0.4×) sequencing of tank milk, is a conveniently beneficial and cost-effective proposal. 【0146】 The inventors ultimately desired to monitor the progression of individual SCS over a three-month period at one-week intervals using the proposed method. They collected tank milk samples from a farm with 120 cows over 21 consecutive weeks (Dataset 4). Actual SCS was measured for individual cows at week 12. The results showed that SCS measured at week 12 was a low predictor (r=0.56) of the average predicted SCS over approximately three months centered around week 12. Indeed, some cows with high SCS measured at week 12 had a generally very acceptable SCS over the three months, while some cows with low SCS measured at week 12 had an average high to very high SCS over the three months (Figures 6A, B). Therefore, this analysis demonstrated that quarterly milk records are a relatively inadequate indicator of an animal's actual SCS at the interval of milk recording, and that the actual average SCS may be significantly underestimated or overestimated. Furthermore, clinical symptoms do not always correlate well with SCS. Cows with very high SCS may be completely asymptomatic, and their milk may be added to tank milk, thereby compromising its quality and value. These facts further highlight the importance and enhanced informational value of the genetic analysis protocol taught in this application for detecting subclinical mastitis in animals. 【0147】 List of citations Blard et al. J. Dairy Sci. 2012, vol. 95 :4109-4113 Browning & Browning. Am J Hum Genet 2009, vol. 84, 210-223 Browning et al. Am J Hum Genet 2018, vol. 103, 338-348 Hogeveen et al. NZ Vet. J. 2011, vol. 59, 16-23 Howie et al. Nat Genet. 2012, vol. 44, 955-9 Li et al. Genet Epidemiol. 2010, vol. 34, 816-34 Marchini & Howie. Nat Rev Genet 2010, vol. 11, 499-511 Niedringhaus et al. Anal Chem. 2011, vol. 83: 4327-4341 Sambrook and Russell, Molecular Cloning: A Laboratory Manual, the third edition, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York, 1.31-1.38, 2001 Sharma. BioTechniques. 1993, vol. 14, 176-178 Sims et al. Nat. Rev. Gen. 2014, vol. 15, 121-132

Claims

[Claim 1] A method for determining the ratio or amount of DNA contributed by individual animals to a certain volume of milk collected from at least 100 individual animals, a) A step of allele sampling for multiple DNA sequence polymorphisms in DNA extracted from a certain volume of milk sample by shallow whole-genome sequencing (SWGS), wherein the sequencing depth of the SWGS is 10 or less, and the multiple DNA sequence polymorphisms include at least 1 × 10⁵ DNA sequence polymorphisms; b) A step of determining the ratio or amount of DNA contributed by an individual animal to a certain volume of milk, based on allele sampling for the DNA sequence polymorphism from step a) and allele data for the DNA sequence polymorphism in the individual animal; and including, method. [Claim 2] The method according to claim 1, wherein in step b), the allele data for the DNA sequence polymorphism in an individual animal includes or consists of the genotype or allele amount of the DNA sequence polymorphism in an individual animal, and / or SWGS allele sampling data for the DNA sequence polymorphism in an individual animal. [Claim 3] The method according to claim 1 or 2, further comprising the step of determining the somatic cell concentration in the milk of an individual animal based on the ratio or amount of DNA contributed by the individual animal to a certain volume of milk. [Claim 4] The method according to claim 3, wherein the concentration of somatic cells is expressed by a somatic cell score (SCS). [Claim 5] The allele sampling for the DNA sequence polymorphism by SWGS in step a) is low or very low allele sampling, and / or the SWGS allele sampling data in individual animals in step b) is from low or very low allele sampling by SWGS, The low-allele sampling is defined as having an SWGS sequencing depth of 1.0 or less, and the very low-allele sampling is defined as having an SWGS sequencing depth of 0.25 or less. The method according to any one of claims 1 to 3. [Claim 6] The method according to any one of claims 1 to 4, wherein in step a) and / or step b), the sequencing depth of the SWGS is 0.10 to 10.0, 0.25 to 5.0, 0.25 to 5.0, 0.25 to 1.0, about 0.25, about 0.50, about 0.75, about 1.0, about 2.0, about 3.0, about 4.0, or about 5.

0. [Claim 7] The method according to any one of claims 1 to 6, wherein the DNA sequence polymorphism is a single nucleotide polymorphism (SNP). [Claim 8] The method according to any one of claims 2 to 7, wherein the genotype of the DNA sequence polymorphism in an individual animal is at least partially determined by a genotyping array, by genome sequencing, or by any combination thereof, and / or the genotype or allele quantity of the DNA sequence polymorphism in an individual animal is at least partially imputed. [Claim 9] The method according to claim 8, wherein the genotyping array is an SNP genotyping array and / or the genome sequencing is targeted genome sequencing or whole genome sequencing. [Claim 10] The method according to claim 8 or 9, wherein the genotype of the DNA sequence polymorphism in an individual animal is at least partially determined by an SNP genotyping array capable of examining 100,000 or fewer SNPs, 50,000 or fewer SNPs, or 10,000 or fewer SNPs. [Claim 11] At least 5 × 10 ５ , at least 1 × 10 ６ , at least 5 × 10 ６ , or at least 8 × 10 ６ The method according to any one of claims 1 to 10, wherein DNA sequence polymorphisms are analyzed. [Claim 12] The method according to any one of claims 1 to 11, wherein the animal is a lactating livestock. [Claim 13] Animals, Bovidae or Bovidae hybrids; Cow; Cattle; Bos taurus, Bos indicus, or hybrid cattle; or The method according to any one of claims 1 to 12, wherein the material is a water buffalo. [Claim 14] The method according to any one of claims 1 to 12, wherein the animal is a sheep or a goat. [Claim 15] The method according to any one of claims 1 to 14, wherein a certain volume of milk is tank milk. [Claim 16] The method according to any one of claims 3 to 15, wherein a somatic cell concentration or somatic cell score (SCS) in the milk of an individual animal that exceeds a predetermined threshold identifies the animal as having subclinical mastitis or clinical mastitis. [Claim 17] A method for identifying one or more animals having subclinical mastitis or clinical mastitis from at least 100 individual animals contributing milk to a certain volume of milk, a') A step of determining the ratio or amount of DNA contributed by an individual animal to a certain volume of milk by a method comprising allele sampling for multiple DNA sequence polymorphisms in DNA extracted from a certain volume of milk sample using SWGS, wherein the sequencing depth is 10 or less, and the multiple DNA sequence polymorphisms include at least 1 × 10⁵ DNA sequence polymorphisms. b') A step of determining the somatic cell concentration in the milk of an individual animal based on the ratio or amount of DNA contributed by the individual animal to a certain volume of milk, as determined in step a'); c') If the somatic cell concentration in the milk of one or more animals determined in step b') exceeds a predetermined threshold, the step of identifying that one or more animals have subclinical mastitis or clinical mastitis. Methods that include... [Claim 18] The method according to claim 17, wherein the allele sampling is low or very low allele sampling, the low allele sampling has a SWGS sequencing depth of 1.0 or less, and the very low allele sampling has an SWGS sequencing depth of 0.25 or less. [Claim 19] The method according to claim 17 or 18, wherein the ratio or amount of DNA contributed by an individual animal to a certain volume of milk in step a') is determined by the method according to claim 1.

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