Classifications

• • A—HUMAN NECESSITIES
  • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
  • A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
  • A01K67/00—Rearing or breeding animals, not otherwise provided for; New or modified breeds of animals
  • A01K67/02—Breeding vertebrates
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
  • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q2600/00—Oligonucleotides characterized by their use
  • C12Q2600/156—Polymorphic or mutational markers
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q2600/00—Oligonucleotides characterized by their use
  • C12Q2600/172—Haplotypes

Definitions

• the present invention relates to the enhancement of desirable characteristics in dairy cattle. More specifically, it relates to the use of genes and genetic markers in methods for improving dairy cattle with respect to fitness and/or productivity traits using genetic markers, including simultaneous application of multiple genetic markers and interactions between specific alleles at those markers.
• Genomics offers the potential for greater improvement in productivity and fitness traits through the discovery of genes, or genetic markers linked to genes, that account for genetic variation and can be used for more direct and accurate selection. Close to 1000 markers with associations with productivity and fitness traits have been reported (see www.bovineqtl.tamu.edu/ for a searchable database of reported QTL), however, the resolution of QTL location is still quite low which makes it difficult to utilize these QTL in marker-assisted selection (MAS) on an industry scale.
• MAS marker-assisted selection
• the inventors have identified markers associated with novel traits in important genes in dairy cows, as well as numerous interaction effects including epistatic effects between these genes, which can be used to substantially improve the accuracy of genetic evaluations, prediction, and selection.
• Various embodiments of the invention provide methods for evaluating an animal's genotype at 1 or more positions in the animal's genome.
• the animal's genotype is evaluated at positions within a segment of DNA (an allele) that contains at least two SNPs selected from the SNPs described in the Tables and Sequence Listing. For each of the SNPs listed in tables 1 and 3, details regarding SNP location, SNP length, and alleles can be found in Table [0015]
• Other embodiments of the invention provide methods for allocating animals for use according to their predicted marker breeding value for productivity and/or fitness traits.
• Various aspects of this embodiment of the invention provide methods that comprise: a) analyzing the animal's genomic sequence at two or more polymorphisms (where the alleles analyzed each comprise at least two SNP) to determine the animal's genotype at each of those polymorphisms; b) analyzing the genotype determined for each polymorphisms to determine which allele of the SNP is present; c) allocating the animal for use based on its genotype at two or more of the polymorphisms analyzed.
• Various aspects of this embodiment of the invention provide methods for allocating animals for use based on a favorable association between the animal's genotype, at two or more polymorphisms disclosed in the present application, and a desired phenotype. Alternatively, the methods provide for not allocating an animal for a certain use because it has two or more SNP alleles that are either associated with undesirable phenotypes or are not associated with desirable phenotypes.
• Other embodiments of the invention provide methods for selecting animals for use in breeding to produce progeny.
• Various aspects of these methods comprise: A) determining the genotype of at least two potential parent animals at two or more locus/loci, where at least two of the loci analyzed contains an allele of a SNP selected from the group of SNPs described in Tables 1 and 3. B) Analyzing the determined genotype at two or more positions for at least two animals to determine which of the SNP alleles is present. C) Correlating the analyzed allele(s) with two or more phenotypes. D) Allocating at least two animals for use to produce progeny.
• Alternative embodiments include analyzing the animal's genotype at two or more loci wherein the analysis comprises evaluating interaction effects.
• Offspring animals provide methods for producing offspring animals (progeny animals). Aspects of this embodiment of the invention provide methods that comprise: breeding an animal that has been selected for breeding by methods described herein to produce offspring.
• the offspring may be produced by purely natural methods or through the use of any appropriate technical means, including but not limited to: artificial insemination; embryo transfer (ET), multiple ovulation embryo transfer (MOET), in vitro fertilization (IVF), or any combination thereof.
• Other embodiments of the invention provide for methods of selecting animals for use in breeding to produce progeny wherein interaction effects between multiple markers are applied in the analysis.
• allelic association preferably means: nonrandom deviation of f(A;B j ) from the product of f(A;) and f(B j ), which is specifically defined by r 2 >0.2, where r 2 is measured from a reasonably large animal sample (e.g., >100) and defined as
• Ai represents an allele at one locus
• Bi represents an allele at another locus
• f(A]Bi) denotes frequency of having both Ai and Bi
• f(Ai) is the frequency of Ai
• f(Bi) is the frequency of Bi in a population.
• the terms "allocating animals for use” and “allocation for use” preferably mean deciding how an animal will be used within a herd or that it will be removed from the herd to achieve desired herd management goals. For example, an animal might be allocated for use as a breeding animal or allocated for sale as a non- breeding animal (e.g. allocated to animals intended to be sold for meat). In certain aspects of the invention, animals may be allocated for use in sub-groups within the breeding programs that have very specific goals (e.g. productivity or fitness). Accordingly, even within the group of animals allocated for breeding purposes, there may be more specific allocation for use to achieve more specific and/or specialized breeding goals. [0022] As used herein the terms “animal” or “animals” preferably refer to dairy cattle.
• fit preferably refers to traits that include, but are not limited to: pregnancy rate (PR), daughter pregnancy rate (DPR), productive life (PL),somatic cell count (SCC) and somatic cell score (SCS).
• PR and DPR refer to the percentage of non-pregnant animals that become pregnant during each 21 -day period.
• PL is calculated as months in milk in each lactation, summed across all lactations until removal of the cow from the herd (by culling or death).
• SCS log 2 (SCC/100,000)+3, where SCC is somatic cells per milliliter of milk.
• growth refers to the measurement of various parameters associated with an increase in an animal's size/weight.
• linkage disequilibrium preferably means allelic association wherein Ai and Bi (as used in the above definition of allelic association) are present on the same chromosome.
• MAS marker-assisted selection
• marker breeding value MBV
• PMBV predicted marker breeding value
• natural breeding preferably refers to mating animals without human intervention in the fertilization process. That is, without the use of mechanical or technical methods such as artificial insemination or embryo transfer. The term does not refer to selection of the parent animals.
• net merit preferably refers to a composite index that includes several commonly measured traits weighted according to relative economic value in a typical production setting and expressed as lifetime economic worth per cow relative to an industry base. Examples of a net merit indexes include, but are not limited to, $NM or TPI in the USA, LPI in Canada, etc (formulae for calculating these indices are well known in the art (e.g. $NM can be found on the USDA/AIPL website: www.aipl.arsusda.gov/reference.htm).
• predicted value preferably refers to an estimate of an animal's breeding value or transmitting ability based on its genotype and pedigree.
• productivity and “production” preferably refers to yield traits that include, but are not limited to: total milk yield, milk fat percentage, milk fat yield, milk protein percentage, milk protein yield, total lifetime production, milking speed and lactation persistency.
• quantitative trait is used to denote a trait that is controlled by multiple (two or more, and often many) genes each of which contributes small to moderate effect on the trait. The observations on quantitative traits often follow a normal distribution.
• QTL quantitative trait locus
• reproductive material includes, but is not limited to semen, spermatozoa, ova, and zygote(s).
• single nucleotide polymorphism refers to a location in an animal's genome that is polymorphic within the population. That is, within the population some individual animals have one type of base at that position, while others have a different base. For example, a SNP might refer to a location in the genome where some animals have a "G” in their DNA sequence, while others have a "T”.
• hybridization under stringent conditions and “stringent hybridization conditions” preferably mean conditions under which a "probe” will hybridize to its target sequence to a detectably greater degree than to other sequences (e.g., at least 5-fold over background).
• Stringent conditions are target-sequence-dependent and will differ depending on the structure of the polynucleotide. By controlling the stringency of the hybridization and/or washing conditions, target sequences that are 100% complementary to the probe can be identified (homologous probing). Alternatively, stringency conditions can be adjusted to allow some mismatching in sequences so that lower degrees of similarity are detected (heterologous probing).
• stringent conditions will be those in which the salt concentration is less than about 1.5 M Na ion, typically about 0.01 to 1.0 M Na ion concentration (or other salts) at pH 7.0 to 8.3 and the temperature is at least about 30 0 C for short probes (e.g., 10 to 50 nucleotides) and at least about 60 0 C for long probes (e.g., greater than 50 nucleotides). Stringency may also be adjusted with the addition of destabilizing agents such as formamide.
• Exemplary moderate stringency conditions include hybridization in 40 to 45% formamide, 1 M NaCl, 1% SDS at 37 0 C, and a wash in 0.5X to IX SSC at 55 to 60 0 C.
• Exemplary high stringency conditions include hybridization in 50% formamide, 1 M NaCl, 1% SDS at 37°C, and a wash in 0.1X SSC at 60 to 65 0 C.
• the duration of hybridization is generally less than about 24 hours, usually about 4 to about 12 hours.
• T m the thermal melting point
• % GQ-0.61 (% form)-500/L the thermal melting point
• M the molarity of monovalent cations
• % GC the percentage of guanine and cytosine nucleotides in the DNA
• % form is the percentage of formamide in the hybridization solution
• L the length of the hybrid in base pairs.
• the T m is the temperature (under defined ionic strength and pH) at which 50% of a complementary target sequence hybridizes to a perfectly matched probe. T m is reduced by about 1°C for each 1% of mismatching; thus, T 1n , hybridization, and/or wash conditions can be adjusted to hybridize to sequences of the desired identity. For example, if sequences with 90% identity are sought, the T m can be decreased 10 0 C. Generally, stringent conditions are selected to be about 5°C lower than the T m for the specific sequence and its complement at a defined ionic strength and pH.
• T n a temperature at which the desired degree of mismatching results in a T n , of less than 45°C (aqueous solution) or 32°C (formamide solution).
• SSC concentration a temperature at which a higher temperature can be used.
• An extensive guide to the hybridization of nucleic acids is found in Tijssen (1993) Laboratory Techniques in Biochemistry and Molecular Biology—Hybridization with Nucleic Acid Probes, Part I, Chapter 2 (Elsevier, N.Y.); and Ausubel et al., eds. (1995) Current Protocols in Molecular Biology, Chapter 2 (Greene Publishing and Wiley- Interscience, New York). See also Sambrook et al. (1989) Molecular Cloning: A Laboratory Manual (2d ed., Cold Spring Harbor Laboratory Press, Plainview, N.Y.).
• marker breeding value MBV
• PMBV predicted marker breeding value
• whole-genome analysis preferably refers to the process of QTL mapping of the entire genome at high marker density (i.e. approximately one marker per cM) and detection of markers that are in population- wide linkage disequilibrium with QTL.
• WGS whole-genome selection
• MAS marker-assisted selection
• interaction effect preferably refers to an alteration of the predicted phenotypic effect of a first marker, depending on the allelic state of a second marker. For example, if SNPl has an effect estimate of 10 for a positive allelic association when SNP2 is an A, but SNPl has an effect estimate of 5 for a positive allelic association when SPP2 is a T, the change in effect estimate from 10 to 5 would be considered an interaction effect. Marker-based interaction effects must involve at least two markers.
• epistatic interaction preferably refers to interactions between alleles of genes, for example when the action of one gene is modified by one or several genes that assort independently (but may be linked).
• Various embodiments of the present invention provide methods for evaluating an animal's (especially a dairy animal's) genotype at 1 or more positions in the animal's genome. Aspects of these embodiments of the invention provide methods that comprise determining the animal's genomic sequence at 1 or more locations (loci) that contain single nucleotide polymorphisms (SNPs). Specifically, the invention provides methods for evaluating an animal's genotype by determining which of two or more alleles for the SNP are present for each of 1 or more SNPs selected from the group consisting of the SNPs described in Tables 1 and 3 of the instant application.
• the animal's genotype is evaluated to determine which allele is present for 10 or more SNPs selected from the group of SNPs described in Tables 1 and 3. More, preferably the animal's genotype is determined for positions corresponding with 2, 10, 100, 200, 500, or 1000, or more of SNPs, at least two of which are described in Tables 1 and 3. In some embodiments of this invention, interactions between two SNPs are used in analysis of the animal's genotype.
• the animal's genotype is analyzed with respect to at least 1 or more SNPs that have been shown to be associated with productivity and/or fitness (see Table 1 for a list of the SNPs associated with these traits). Further, embodiments of the invention provides a method for genotyping 2 or more, 10 or more, 10 or more, 50 or more, 100 or more, 200 or more, or 500 or more, or 1000 or more SNPs, at least one of which has been determined to be significantly associated with a productivity or fitness trait as described in Table 1.
• aspects of the present invention also provides for both whole-genome analysis and whole genome-selection (WGS) (that is marker-assisted selection (MAS) on a genome-wide basis).
• WGS whole-genome analysis
• MAS marker-assisted selection
• Various aspects of this embodiment of the invention provide for either whole-genome analysis or WGS wherein the makers analyzed for an animal span the animal's entire genome at moderate to high density. That is, the animal's genome is analyzed with markers that on average occur, at least, approximately every 1 to 5 centimorgans in the genome.
• the invention provides that of the markers used to carry out the whole-genome analysis or WGS, including 2 or more, 10 or more, 10 or more, 50 or more, 100 or more, 200 or more, 500 or more, or 1000 or more markers, at least one of which are selected from the markers described in Tables 1 and 3.
• the markers may be associated with fitness or productivity traits, or may be associated with both fitness and productivity traits.
• the genomic sequence at the SNP locus may be determined by any means compatible with the present invention. Suitable means are well known to those skilled in the art and include, but are not limited to direct sequencing, sequencing by synthesis, primer extension, Matrix Assisted Laser Desorption /Ionization-Time Of Flight (MALDI-TOF) mass spectrometry, polymerase chain reaction-restriction fragment length polymorphism, microarray/multiplex array systems (e.g. those available from Affymetrix, Santa Clara, California), and allele-specific hybridization.
• MALDI-TOF Matrix Assisted Laser Desorption /Ionization-Time Of Flight
• Other embodiments of the invention provide methods for allocating animals for subsequent use (e.g. to be used as sires or dams or to be sold for meat or dairy purposes) according to their predicted value for productivity or fitness.
• Various aspects of this embodiment of the invention comprise determining at least two animal's genotype for at least two SNPs selected from the group of SNPs described in Tables 1 and 3 (methods for determining animals' genotypes for two or more SNPs are described supra).
• the animal's allocation for use may be determined based on its genotype at one or more, 2 or more, 10 or more, 10 or more, 50 or more, 100 or more, 300 or more, or 500 or more, or 1000 or more SNPs.
• the animal's allocation may further include an analysis of interaction effects between at least two SNPs.
• the instant invention provides embodiments where analysis of the genotypes of the SNPs described in Tables 1 and 3 is the only analysis done.
• Other embodiments provide methods where analysis of the SNPs disclosed herein is combined with any other desired type of genomic or phenotypic analysis (e.g. analysis of any genetic markers beyond those disclosed in the instant invention).
• the SNPs analyzed may be selected from those SNPs only associated productivity, only associated with fitness, or the analysis may be done for SNPs selected from any desired combination of fitness and productivity. SNPs associated with various traits are listed in Table 1.
• the animal's genetic sequence for the selected SNP(s) are evaluated to determine which allele of the SNP is present for at least two of the selected SNPs.
• the animal's allelic complement for all of the determined SNPs is evaluated.
• the animal is allocated for use based on its genotype for two or more of the SNP positions evaluated.
• the allocation is made taking into account the animal's genotype at each of the SNPs evaluated, but its allocation may be based on any subset or subsets of the SNPs evaluated.
• the animal's genetic sequence for the selected SNP(s) are determined, this information is evaluated to determine which allele of the SNP is present for at least two of the selected SNPs.
• the animal's allelic complement for all of the determined SNPs is evaluated.
• An analysis of the allelic orientations of the SNPs is performed, and preferably, the result of the analysis includes information related to at least one interaction effect.
• the animal is allocated for use based on its genotype for two or more of the SNP positions evaluated.
• the allocation is made taking into account the animal's genotype at each of the SNPs evaluated, but its allocation may be based on any subset or subsets of the SNPs evaluated.
• the allocation may be made based on any suitable criteria. For any SNP, a determination may be made as to whether one of the allele(s) is associated/correlated with desirable characteristics or associated with undesirable characteristics. Furthermore, this determination may preferably include information related to interaction effects between multiple makers. This determination will often depend on breeding or herd management goals. Determination of which alleles are associated with desirable phenotypic characteristics can be made by any suitable means. Methods for determining these associations are well known in the art; moreover, aspects of the use of these methods are generally described in the EXAMPLES, below. [0055] Phenotypic traits that may be associated with the SNPs of the current invention include, but are not limited to; fitness traits and productivity traits.
• Fitness traits include but are not limited to: pregnancy rate (PR), daughter pregnancy rate (DPR), productive life (PL), somatic cell count (SCC) and somatic cell score (SCS).
• Productivity traits include but are not limited to: total milk yield, milk fat percentage, milk fat yield, milk protein percentage, milk protein yield, total lifetime production, milking speed and lactation persistency
• allocation for use of the animal may entail either positive selection for the animals having the desired genotype(s) (e.g. the animals with the desired genotypes are selected for productivity traits), negative selection of animals having undesirable genotypes (e.g. animals with an undesirable genotypes are culled from the herd), or any combination of these methods.
• animals identified as having SNP alleles associated with desirable phenotypes are allocated for use consistent with that phenotype (e.g. allocated for breeding based on phenotypes positively associated with fitness).
• animals that do not have SNP genotypes that are positively correlated with the desired phenotype (or possess SNP alleles that are negatively correlated with that phenotype) are not allocated for the same use as those with a positive correlation for the trait.
• Other embodiments of the invention provide methods for selecting potential parent animals (i.e., allocation for breeding) to improve fitness and/or productivity in potential offspring.
• Various aspects of this embodiment of the invention comprise determining at least two animal's genotype for at least two SNPs selected from the group of SNPs described in Tables 1 and 3. Furthermore, determination of whether and how an animal will be used as a potential parent animal may be based on its genotype at two or more, 2 or more, 10 or more, 50 or more, 100 or more, 300 or more, or 500 or more, including at least one of the SNPs described in Tables 1 and 3.
• various aspects of these embodiments of the invention provide methods where the only analysis done is to genotype the animal for two or more of the SNPs described in Tables 1 and 3.
• Other aspects of these embodiments provide methods where analysis of two or more SNPs disclosed herein is combined with any other desired genomic or phenotypic analysis (e.g. analysis of any genetic markers beyond those disclosed in the instant invention).
• the SNP(s) analyzed may all be selected from those associated only with fitness traits or only with productivity traits. Conversely, the analysis may be done for SNPs selected from any desired combination of these or other traits.
• the animal's genetic sequence at the site of the selected SNP(s) have been determined, this information is evaluated to determine which allele of the SNP is present for at least two of the selected SNPs.
• the animal's allelic complement for all of the sequenced SNPs is evaluated.
• the animal's allelic complement is analyzed and correlated with the probability that the animal's progeny will express two or more phenotypic traits.
• the animal is allocated for breeding use based on its genotype for two or more of the SNP positions evaluated and the probability that it will pass the desired genotype(s)/allele(s) to its progeny.
• the breeding allocation is made taking into account the animal's genotype at each of the SNPs evaluated. However, its breeding allocation may be based on any subset or subsets of the SNPs evaluated.
• the breeding allocation may be made based on any suitable criteria. For example, breeding allocation may be made so as to increase the probability of enhancing a single certain desirable characteristic in a population, in preference to other characteristics, (e.g. increased fitness, or even specifically lowering somatic cell score (SCS) as part of fitness); alternatively, the selection may be made so as to generally maximize overall production based on a combination of traits.
• the allocations chosen are dependent on the breeding goals.
• Sub-categories falling within fitness include, inter alia: daughter pregnancy rate (DPR), productive life (PL), and somatic cell score.
• Sub-categories falling within productivity include, inter alia: milk fat percentage, milk fat yield, total milk yield, milk protein percentage, and total milk protein.
• the animals used to produce the progeny are those that have been allocated for breeding according to any of the embodiments of the current invention. Those using the animals to produce progeny may perform the necessary analysis or, alternatively, those producing the progeny may obtain animals that have been analyzed by another.
• the progeny may be produced by any appropriate means, including, but not limited to using: (i) natural breeding, (ii) artificial insemination, (iii) in vitro fertilization (IVF) or (iv) collecting semen/spermatozoa and/or at least two ovum from the animal and contacting it, respectively with ova/ovum or semen/spermatozoa from a second animal to produce a conceptus by any means.
• the progeny are produced by a process comprising natural breeding.
• the progeny are produced through a process comprising the use of standard artificial insemination (AI), in vitro fertilization, multiple ovulation embryo transfer (MOET), or any combination thereof.
• AI artificial insemination
• MOET multiple ovulation embryo transfer
• Other embodiments of the invention provide for methods that comprise allocating an animal for breeding purposes and collecting/isolating genetic material from that animal: wherein genetic material includes but is not limited to: semen, spermatozoa, ovum, zygotes, blood, tissue, serum, DNA, and RNA.
• the various embodiments of the instant invention provide for databases comprising all or a portion of the sequences corresponding to at least 2 SNPs described in Tables 1 and 3.
• the databases comprise sequences for 1 or more, 5 or more, 10 or more, 20 or more, 50 or more, or substantially all of the SNPs described in Tables 1 and 3.
• inventions provide methods wherein two or more of the SNP sequence databases described herein are accessed by two or more computer- executable programs. Such methods include, but are not limited to, use of the databases by programs to analyze for an association between the SNP and a phenotypic trait, or other user-defined trait (e.g. traits measured using two or more metrics such as gene expression levels, protein expression levels, or chemical profiles), and programs used to allocate animals for breeding or market.
• a phenotypic trait e.g. traits measured using two or more metrics such as gene expression levels, protein expression levels, or chemical profiles
• Other embodiments of the invention provide methods comprising collecting genetic material from an animal that has been allocated for breeding. Wherein the animal has been allocated for breeding by any of the methods disclosed as part of the instant invention.
• kits or other diagnostic devices for determining which allele of a SNP is present in a sample; wherein the SNP(s) are selected from the group of SNPs described in Tables 1 and 3.
• the kit or device provides reagents/instruments to facilitate a determination as to whether nucleic acid corresponding to the SNP is present. Such kit/or device may further facilitate a determination as to which allele of the SNP is present.
• the kit or device comprises at least two nucleic acid oligonucleotide suitable for DNA amplification (e.g. through polymerase chain reaction).
• the kit or device comprises a purified nucleic acid fragment capable of specifically hybridizing, under stringent conditions, with at least two allele of at least two SNPs described in Tables 1 and 3.
• the kit or device comprises at least two nucleic acid array (e.g. DNA micro-arrays) capable of determining which allele of two or more of the SNPs described in Tables 1 and 3 is present in a sample.
• nucleic acid array e.g. DNA micro-arrays
• Preferred aspects of this embodiment of the invention provide DNA micro-arrays capable of simultaneously determining which allele is present in a sample for 2 or more SNPs.
• the DNA micro-array is capable of determining which SNP allele is present in a sample for 10 or more, 50 or more, 100 or more, 200 or more, 500 or more, or 1000 or more SNPs. Methods for making such arrays are known to those skilled in the art and such arrays are commercially available (e.g. from Affymetrix, Santa Clara, California).
• Genetic markers for fitness and/or productivity that are in allelic association with any of the SNPs described in the Tables may be identified by any suitable means known to those skilled in the art. For example, a genomic library may be screened using a probe specific for any of the sequences of the SNPs described in the Tables. In this way clones comprising at least a portion of that sequence can be identified and then up to 300 kilobases of 3' and/or 5' flanking chromosomal sequence can be determined. By this means, genetic markers in allelic association with the SNPs described in the Tables will be identified.
• chromosomal location of a SNP associated with a particular phenotypic variation can be determined, by means well known to those skilled in the art. Once the chromosomal location is determined genes suspected to be involved with determination of the phenotype can be analyzed. Such genes may be identified by sequencing adjacent portions of the chromosome or by comparison with analogous section of the human genetic map (or known genetic maps for other species).
• Other embodiments of the invention provide methods for identifying causal mutations that underlie two or more quantitative trait loci (QTL).
• QTL quantitative trait loci
• Various aspects of this embodiment of the invention provide for the identification QTL that are in allelic association with two or more of the SNPs described in Tables 1 and 3. Once these SNPs are identified, it is within the ability of skilled artisans to identify mutations located proximal to such SNP(s). Further, one skilled in the art can identify genes located proximate to the identified SNP(s) and evaluate these genes to select those likely to contain the causal mutation. Once identified, these genes and the surrounding sequence can be analyzed for the presence of mutations, in order to identify the causal mutation.
• the population frequencies of haplotypes are used in estimating breeding value of an haplotype.
• the frequency of a haplotype is different from the product of corresponding allelic frequencies. In this case, it is more appropriate to use haplotype frequencies for breeding value estimation.
• Interaction effects can also be used to produce genetically superior crossbred or hybrid animals for higher efficiency of commercial production.
• genotype AiA 2 BiB 2 is the best genotype and is better than the summation of the breeding values (and genotypic values) of genotype AiA 2 and BiB 2 .
• One way to utilize it is to create two lines with genotype AiAiBiBi and A2A2B2B2 respectively. These two lines could be from different breeds to create an ideal crossbred, or from within an existing breed population to create an ideal hybrid. Crossbreds or hybrids created from these two lines will all have genotypes A1A2B1B2, which improves the efficiency of commercial production.
• Interaction effects can also be used within computer mating programs to produce genetically superior offspring for higher efficiency of commercial production.
• genotype A1A2B1B2 is the best genotype and is better than the summation of the breeding values (and genotypic values) of genotype Ai A 2 and B 1 B 2 .
• One way to utilize it is to identify which cows and bulls have genotype AiAiBiBi and A 2 A 2 B 2 B 2 .
• the interaction effects could also be included when calculating the estimated breeding value of potential offspring.
• the new linkage mapping tools build on the basic mapping principles programmed in CRIMAP to improve efficiency through partitioning of large pedigrees, automation of chromosomal assignment and two-point linkage analysis, and merging of sub-maps into complete chromosomes.
• the resulting whole- genome discovery map included 6,966 markers and a map length of 3,290 cM for an average map density of 2.18 markers/cM. The average gap between markers was 0.47 cM and the largest gap was 7.8 cM. This map provided the basis for whole-genome analysis and fine-mapping of QTL contributing to variation in productivity and fitness in dairy cattle.
• Systems for discovery and mapping populations can take many forms.
• the most effective strategies for determining population-wide marker/QTL associations include a large and genetically diverse sample of individuals with phenotypic measurements of interest collected in a design that allows accounting for non-genetic effects and includes information regarding the pedigree of the individuals measured.
• an outbred population following the grand-daughter design (Weller et al, 1990) was used to discover and map QTL: the population, from the Holstein breed, had 529 sires each with an average of 6.1 genotyped sons, and each son has an average of 4216 daughters with milk data.
• DNA samples were collected from approximately 3,200 Holstein bulls and about 350 bulls from other dairy breeds; representing multiple sire and grandsire families.
• Dairy traits under evaluation include traditional traits such as milk yield (“MILK”) (pounds), fat yield (“FAT”) (pounds), fat percentage (“FATPCT”) (percent), productive life (“PL”) (months), somatic cell score (“SCS”) (Log), daughter pregnancy rate (“DPR”) (percent), protein yield (“PROT”) (pounds), protein percentage (“PROTPCT”) (percent), and net merit (“NM”) (dollar).
• MILK milk yield
• FAT fat yield
• FATPCT fat percentage
• PL productive life
• SCS somatic cell score
• DPR daughter pregnancy rate
• PROT protein yield
• PROTPCT protein percentage
• net merits of these traits defined as PTA (predicted transmitting ability) were estimated using phenotypes of all relatives.
• y s (vy) is the PTA of the i th bull (PTA of the j th son of the i th sire); s; is the effect of the i th sire; (SPTA); is the sire's PTA of the i th bull of the whole sample; ⁇ is the population mean; PTAd, (PTAd y ) is the residual bull PTA.
• Equation 2 is referred to as the sire model, in which sires were fitted as fixed factors.
• sires were fitted as fixed factors.
• a considerably large number of sires only have a very small number of progeny tested sons (e.g., some have one son), and it is clearly undesirable to fit sires as fixed factors in these cases.
• the USA Holstein herds have been making steady and rapid genetic progress in traditional dairy traits in the last several decades, implying that the sire's effect can be partially accounted for by fitting the birth year of a bull.
• sires were replaced with son's birth year in Equation 2.
• Equation 3 is referred to as the SPTA model, in which sire's PTA are fitted as a covariate. Residual PTA (PT Adj or PTAdy) were estimated using linear regression.
• linkage disequilibrium (LD) mapping was performed in the aforementioned discovery population using statistical analyses based on probabilities of individual ordered genotypes estimated conditional on observed marker genotypes.
• the first step was to estimate sire's ordered genotype probabilities at all linked markers conditional on grandsire's and offspring marker genotype data.
• the exact calculation quickly becomes computationally infeasible as the size and complexity of the pedigree and number of linked markers increases. For example, there are, in total 2 k ordered genotypes for all linked loci when a sire has k linked heterozygous loci.
• a stepwise procedure developed based on a likelihood ratio test was used for estimating probabilities of sire's ordered genotypes at all linked markers.
• M) is the probability of sire having a pair of haplotypes (or order genotype) H sa H db at all linked loci conditional on the observed genotype data M
• P ⁇ H s ⁇ k H dlk I H sa H db , M) is the probability of a son having ordered genotype H s ⁇ k H dlk at loci of interest conditional on sire's ordered genotype H sa H db at all linked loci and the observed genotype data M.
• haplotypes of neighboring (and/or non-neighboring) markers across each chromosome were defined by setting the maximum length of a chromosomal interval and minimum and maximum number of markers to be included.
• haplotype evaluation The association between pre-adjusted trait phenotypes and haplotype (or pair of haplotype that is alternatively termed as ordered genotypes) was evaluated via a regression approach with the following models:
• PTAd k is the preadjusted PTA of the k ⁇ bull as defined in Equation 3 under the sire model and can be replaced with PT Adi as defined in Equation 3 under the SPTA model, and ⁇ k is the residual;
• P(H s ,k) and P(Hdik) are the probability of paternal and maternal haplotype of individual k being haplotype i;
• P(H S ikH d ,k) is the probability of individual k has paternal haplotype i and maternal haplotype j that can be estimated using Equation 4; all ⁇ are corresponding regression coefficients.
• Equations 5, 6, 7, and 8 are designed to model paternal haplotype, maternal haplotype, additive haplotype, and genotype effects, respectively.
• Least-squares methods were used to estimate the effect of a haplotype or haplotype pair on a phenotypic trait and the regular F-test used to test the significance of the effect. Permutation tests were performed based on phenotype permutation (20,000) within each paternal half-sib family to estimate Type I error rate (p value).
• Equation 9 is referred to as the sire model, in which sires were fitted as fixed factors.
• a considerably large number of sires only have a very small number of progeny tested sons (e.g., some have one son), and it is clearly undesirable to fit sires as fixed factors in these cases.
• the USA Holstein herds have been making steady and rapid genetic progress in traditional dairy traits in the last several decades, implying that the sire's effect can be partially accounted for by fitting the birth year of a bull. For sires with ⁇ 10 progeny tested sons, sires were replaced with son's birth year in Equation 9.
• Equation 10 is referred to as the SPTA model, in which sire's PTA are fitted as a covariate. Residual PTA (PTAd 1 or PTAd 1 ,) were estimated using SAS PROC GLM procedure and used for further candidate gene analysis in this study. [0094] Candidate sene interaction analysis. The association between SNP and residual PTA of each dairy trait was analyzed using the following linear models:
• PTAd 1 is the preadjusted PTA of the i th bull as defined in Equation 10 under the sire model and can be replaced with PTAd 1 as defined in Equation 9 under the SPTA model;
• ⁇ k is the effect of genotype indicator l n ⁇ , and
• ⁇ kh is the interaction effect between genotype indicator I,i k at the 1 st SNP and genotype indicator I 12I1 at the 2 nd SNP;
• genotype indicator I ⁇ is defined as 1 if genotype being k at the jth SNP .
• markers with significant association to that trait can be used in selection of breeding animals.
• use of animals possessing a marker allele (or a haplotype of multiple marker alleles) in population- wide LD with a favorable QTL allele will increase the breeding value of animals used in breeding, increase the frequency of that QTL allele in the population over time and thereby increase the average genetic merit of the population for that trait.
• This increased genetic merit can be disseminated to commercial populations for full realization of value.
• a progeny-testing scheme could greatly improve its rate of genetic progress or graduation success rate via the use of markers for screening juvenile bulls.
• a progeny testing program would use pedigree information and performance of relatives to select juvenile bulls as candidates for entry into the program with an accuracy of approx 0.5.
• young bulls could be screened and selected with much higher accuracy.
• DNA samples from potential bull mothers and their male offspring could be screened with a genome- wide set of markers in linkage disequilibrium with QTL, and the bull-mother candidates with the best marker profile could be contracted for matings to specific bulls. If superovulation and embryo transfer (ET) is employed, a set of 5-10 offspring could be produced per bull mother per flush procedure. Then the marker set could again be used to select the best male offspring as a candidate for the progeny test program.
• E superovulation and embryo transfer
• a centralized or dispersed genetic nucleus (GN) population of cattle could be maintained to produce juvenile bulls for use in progeny testing or direct sale on the basis of MBVs.
• a GN herd of 1000 cows could be expected to produce roughly 3000 offspring per year, assuming the top 10-15% of females were used as ET donors in a multiple-ovulation and embryo-transfer (MOET) scheme.
• MOET multiple-ovulation and embryo-transfer
• markers could change the effectiveness MOET schemes and in vitro embryo production.
• MOET nucleus schemes have proven to be promising from the standpoint of extra genetic gain, but the costs of operating a nucleus herd together with the limited information on juvenile animals has limited widespread adoption.
• the first step in using a SNP for estimation of breeding value and selection in the GN is collection of DNA from all offspring that will be candidates for selection as breeders in the GN or as breeders in other commercial populations (in the present example, the 3,000 offspring produced in the GN each year).
• One method is to capture shortly after birth a small bit of ear tissue, hair sample, or blood from each calf into a labeled (bar-coded) tube. The DNA extracted from this tissue can be used to assay an essentially unlimited number of SNP markers and the results can be included in selection decisions before the animal reaches breeding age.
• EBVj j ⁇ gj + Oj [Equation 19]
• EBVy is the Estimated Trait Breeding Value for the i th animal
• n is the total number of markers (haplotypes) under consideration
• U 1 is the polygenic breeding value for the i th animal after fitting the marker genotype(s).
• a denotes to a polygenic random effect
• (A t A j ; B 5 B k ) denotes to a genotype configuration consisting of genotypes at A and B
• ⁇ (A t A j ; B s B k ) is the regression coefficient for genotype configuration (A t A j ; B 5 B k )
• I(A t A j ; B 5 B k ) is an index function defined as: 1 if genotype is (A 1 A 1 ; B, B L ) y V ' J s k [Equation
• Equation [20] can be used for both detection and utilization of interaction effects.
• the effect of genotype configuration (A 1 A j -; B 5 B k ) in Equation 20 can be fitted as fixed effects or a random effect.
• the breeding value of an animal with genotype configuration (A J AJ; B k B 5 ) can be calculated as:
• BV(A 1 A j JB k B 5 ) 2[P(A 1 BJa(A 1 BJ + P(A 1 BJa(A 1 B 8 )
• p(AjB j ) is the probability of a gamete produced by this animal having gamete haplotype AjBj. It should be noted that the sum of probabilities of all possible haplotypes is equal to 1 and that the value of p( AjB j ) is a function of the recombination fraction between QTL A and B in case of a genotype configuration being heterozygous at both loci. To explain the linkage effect further, consider an animal with genotype A 1 B 1 /A 2 B 2 (i.e. consisting of haplotypes AiBi and A 2 B 2 ). The probabilities of four different haplotypes for this animal can be calculated as
• ⁇ AB represents the recombination fraction between locus A and B.
• a nucleic acid sequence contains a SNP of the present invention if it comprises at least 20 consecutive nucleotides that include and/or are adjacent to a polymorphism described in Tables 1 and 3 and the Sequence Listing.
• a SNP of the present invention may be identified by a shorter stretch of consecutive nucleotides which include or are adjacent to a polymorphism which is described in Tables 1 and 3 and the Sequence Listing in instances where the shorter sequence of consecutive nucleotides is unique in the bovine genome.
• a SNP site is usually characterized by the consensus sequence in which the polymorphic site is contained, the position of the polymorphic site, and the various alleles at the polymorphic site.
• Consensus sequence means DNA sequence constructed as the consensus at each nucleotide position of a cluster of aligned sequences. Such clusters are often used to identify SNP and Indel (insertion/deletion) polymorphisms in alleles at a locus.
• Consensus sequence can be based on either strand of DNA at the locus, and states the nucleotide base of either one of each SNP allele in the locus and the nucleotide bases of all Indels in the locus, or both SNP alleles using degenerate code (IUPAC code: M for A or C; R for A or G; W for A or T; S for C or G; Y for C or T; K for G or T; V for A or C or G; H for A or C or T; D for A or G or T; B for C or G or T; N for A or C or G or T; Additional code that we use include I for "-"or A; O for "-” or C; E for "-” or G; L for "-” or T; where "-” means a deletion).
• IUPAC code M for A or C; R for A or G; W for A or T; S for C or G; Y for C or T; K for G or T; V for A or C or G
• Such SNP have a nucleic acid sequence having at least 90% sequence identity, more preferably at least 95% or even more preferably for some alleles at least 98% and in many cases at least 99% sequence identity, to the sequence of the same number of nucleotides in either strand of a segment of animal DNA which includes or is adjacent to the polymorphism.
• the nucleotide sequence of one strand of such a segment of animal DNA may be found in a sequence in the group consisting of SEQ ID NO:1 through SEQ ID NO: 175.
• sequence identity can be determined for sequence that is exclusive of the polymorphism sequence.
• the polymorphisms in each locus are described in Tables 1 and 3.
• tcttacacatcaggagatagytccgaggtggatttctacaa ss38333809 is SEQ ID NO:172 and ss38333810 is SEQ ID NO:173
• ss38333809 tcttacacatcaggagatagytccgaggtggatttctacaa
• Il I I I I I I I I I I I I I I I I I I I I I I M ss38334335 tcttacacatcaggagatggytccgaggtggatttctacaa ss38333809 is SEQ ID NO: 174 and SS38334335 is SEQ ID NO: 175
• Quantifying production traits can be accomplished by measuring milk of a cow and milk composition at each milking, or in certain time intervals only.
• USDA yield evaluation the milk production data are collected by Dairy Herd Improvement Associations (DHIA) using ICAR approved methods.
• Genetic evaluation includes all cows with the known sire and the first calving in 1960 and later and pedigree from birth year 1950 on. Lactations shorter than 305 days are extended to 305 days. All records are preadjusted for effects of age at calving, month of calving, times milked per day, previous days open, and heterogeneous variance. Genetic evaluation is conducted using the single-trait BLUP repeatability model.
• the model includes fixed effects of management group (herd x year x season plus register status), parity x age, and inbreeding, and random effects of permanent environment and herd by sire interaction.
• PTAs are estimated and published four times a year (February, May, August, and November). PTAs are calculated relative to a five year stepwise base i.e., as a difference from the average of all cows born in 2000. Bull PTAs are published estimating daughter performance for bulls having at least 10 daughters with valid lactation records.
• CE calving ease
• SB stillbirths
• DPR daughter pregnancy rate
• CE is scored by the owner on a scale of 1 to 5, 1 meaning no problems encountered or unobserved birth and 5 meaning extreme difficulty.
• the CE PTAs for sires are expressed as percent difficult births in primiparous daughter heifers (%DBH), where difficult births are those scored as requiring considerable force or being extremely difficult (4 or 5 on a five point scale).
• SB is scored by the owner on a scale of 1 to 3, 1 meaning the calf was born alive and was alive 48 h postpartum, 2 meaning the calf was born dead, and 3 indicating the calf was born alive but died within 48 h postpartum. SB scores of 2 and 3 are combined into a single category for evaluation.
• the SB PTAs for sires are expressed as percent stillbirths in daughter heifers (%SBH), where stillborn calves are those scored as dead at birth or born alive but died within 48 h of birth (2 or 3 on a three point scale).
• Pregnancy rate is a function of the number of days open, which is the number of days between calving and a successful breeding.
• DPR is defined as the percentage of nonpregnant cows (daughters) that become pregnant during each 21 -day period.
• a DPR PTA of "1" implies that daughters from this bull are 1% more likely to become pregnant during that estrus cycle than a bull with a DPR PTA of zero.
• Productive life is defined as the length of time a cow remains in a milking herd before removal by voluntary or involuntary culling (due to health or fertility problems), or death. PL is usually measured as the number of days, months, or days in milk (DIM) from the first calving to the day the cow exits the herd (due to death, culling, or selling to non-dairy purposes). Because some cows are still alive at the time of data collection, their records are projected (VanRaden, P.M. and EJ. H. Klaaskate. 1993) or treated as censored (Ducrocq, 1987).
• the USDA genetic evaluation for PL includes all cows with first calving in 1960 and later (born in 1950 and later for the pedigree). Cows born at least 3 years prior to evaluation, with a valid sire ID and first lactation records are considered. PL is considered to be completed at 7 years of age. Records are extended for cows that have not had the opportunity to reach 7 years of age because they are still alive, were sold for dairy purposes, or the herd discontinued testing. Cows sold for dairy purposes or in herds that discontinued testing receive extended records if they had opportunity to reach 3 years of age; otherwise their records are discarded.
• the method of genetic evaluation is a single trait BLUP animal model. The statistical model includes effects of management group (based on herd of first lactation and birth date) and sire by herd interaction..
• Sires' PTAs for PL are calculated relative to a five year stepwise base i.e., as a difference from the average PL of all cows born in 2000.
• Example 9 Quantification of somatic cell score in daughters (cows) and sires' PTAs
• SCS somatic cell score
• SNPs single nucleotide polymorphisms
• candidate genes leptin, poulFl, kappa casein, osteopontin, beta2-adrenergic receptor, growth hormone receptor, proteinase inhibitor, breast cancer resistance protein, diacylglycerol acyltransferase
• SNPs single nucleotide polymorphisms
• candidate genes leptin, poulFl, kappa casein, osteopontin, beta2-adrenergic receptor, growth hormone receptor, proteinase inhibitor, breast cancer resistance protein, diacylglycerol acyltransferase
• [012I]AIl SNPs used in this study have two alleles, resulting in a total of three unordered genotypes for each SNP (two homozygotes and one heterozygote). If ⁇ 300 bulls are homozygous for the minor allele, the minor allele homozygote class can be merged with the heterozygote to form a composite genotype (genotype iiij is denoted to both genotype ii and ij) or excluded from analyses. Consequently, analyses can be performed using original genotypes, composite genotypes, and data that excludes the least frequent genotype when the number of bulls with least frequent genotype is smaller than 300.
• yj (yy) is the PTA of the i th bull (PTA of the j th son of the i th sire); s; is the effect of the i th sire; (SPT A)j is the sire's PTA of the i th bull of the whole sample; ⁇ is the population mean; PTAd; (PT Ad y ) is the residual bull PTA.
• PTAd is the preadjusted PTA of the i th bull as defined in Equation 26 under the sire model and can be replaced with PTAd j as defined in Equation 27 under the SPTA model
• Xj is the number of copies of a specific SNP allele that the i th bull has
• ⁇ 2 is the regression coefficient for XJ
• n g is the number of unordered genotypes
• e ⁇ is the residual effect
• ⁇ k is the effect of genotype indicator Ijk that is defined as 1 if genotype being k [Equation 0 otherwise
• Equations 26 and 27 were preadjusted using all bulls evaluated by USDA (Equations 26 and 27), and the preadjusted PTA was analyzed using Equations 28 and 29 for statistical associations between SNP and trait.
• Equations 26 and 28, 26 and 29, 27 and 28, and 27 and 29 was referred as to the sire_allele, sire_genotype, SPTA_allele, SPTA_genotype model, respectively.
• Table 1 The following table describes genes, markers, trait associations, and interactions effects resulting from the experiments described herein.
• Example 11 Discovery of New Markers in the CATSPER, CD14, IGF2R, LIF, OSM, RCN3, RIM2, and TLE4 Genes and Association with Dairy Productivity Traits
• a whole-genome scan was conducted using 3000 Holstein bulls to identify quantitative trait loci (QTL) for dairy productivity traits on all bovine chromosomes.
• This invention concerns QTL (and selected candidate genes) on chromosomes BTA07 (CD14), BTA08 (TLE4) BTA09 (IGF2R), BTA14 (RIM2), BTAl 7 (LIF, OSM), BTAl 8 (RCN3), and BTA29 (CATSPER). Flanking sequences of the SNPs used in the whole-genome scan that were found to be associated with dairy productivity traits were used to BLAST against the public bovine genome sequence assembly.
• Table 2 The following table describes genes correlated NCBI GeneID numbers.
• Table 3 The following table includes a list of novel markers, gene names, and SEQ ID numbers resulting from the experiment described above.
• Ciobanu DC, Bastiaansen, JWM, Longergan, SM, Thomsen, H, Dekkers, JCM, Plastow, GS, and Rothschild, MF, (2004) J. Anim. Sci. 82:2829-39.

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