WO2011028134A9

WO2011028134A9 - Biological markers and uses therefor

Info

Publication number: WO2011028134A9
Application number: PCT/NZ2010/000172
Authority: WO
Inventors: Richard John Spelman; Stephen Richard Davis; Michel Alphonse Jumien Georges; Wouter Robert Herman Coppieters; Latifa Karim; Li Lin; Tom Jean-Marc Druet; Bevin Lyal Harris; Michael Dominic Keehan; Mathew Douglas Littlejohn; Juan Antonio Castro Arias; Haruko Takeda
Original assignee: Livestock Improvement Corporation Limited
Priority date: 2009-09-02
Filing date: 2010-08-31
Publication date: 2011-05-05
Also published as: WO2011028134A1

Abstract

The application describes methods for inferring the size potential of an animal and/or its offspring, particularly but not exclusively, methods for identifying and selecting animals on the basis of their liveweight and/or growth rate potential. Biological markers suitable for use in such methods are also described, said markers being located within a region of the Bovine Chromosome 14 encompassing the RPS20, MOS, PLAGl, CHCHD7, RDHE2, SDRl 6C6 and PENK genes.

Description

BIOLOGICAL MARKERS AND USES THEREFOR

FIELD

The present invention relates generally to methods for inferring the size potential of an animal and/or its offspring, particularly but not exclusively, methods for identifying and selecting animals on the basis of their liveweight and/or growth rate potential. The invention also relates to biological markers suitable for use in such methods.

BACKGROUND

Traditional breeding programmes for livestock have involved selecting animals based on the presence of certain phenotypic characteristics. Recent methods have incorporated selection for genotypic characteristics known to be associated with improved production traits. Selection of animals on the basis of genotypic characteristics allows for earlier and more specific selection of animals of interest than does selection on the basis of phenotypic characteristics.

The size of an animal may have an effect on its value for a particular purpose. For example, for beef farming it is desirable for animals to have a relatively larger weight or growth rate, whereas for dairy farming efficiencies can be gained with smaller animals, as less feed is required to support milk production.

A number of researchers have reported an association between certain genetic regions, Quantitative Trait Loci (QTL), and/or certain polymorphisms and weight in bovine species (Mizoshita et al. (2004), Takasuga et al. (2007), Buchanan et al. (2005), Kneeland et al. (2004), Nkrumah et al. (2007), Mizoshita et al. (2005)). Gudbjartsson et al., (2008) and Lettre et al., (2008) have reported certain genetic regions being associated with height in humans. However, there is a need to identify specific markers and genotypes which will allow for improvements in the ability to predict the size (including weight and/or height) potential of livestock, for breeding, selection, and farm management purposes for example.

Bibliographic details of the publications referred to herein are collected at the end of the description. OBJECT

It is an object of the present invention to provide improved methods for selecting animals, for predicting the size potential of an animal and/or its offspring, of screening a population of animals, breeding animals, managing animals, and/or estimating the worth of an animal and/or its offspring, or to at least to provide the public with a useful choice.

STATEMENT OF INVENTION

The inventors have identified a number of genetic markers whose sequence or genotype can be used to predict or infer the potential size of an animal and/or its offspring. Such information may be used in methods for selecting, screening and breeding animals, farm management, and for estimating an animals worth to a particular industry, for example.

In one aspect, the invention provides a method for inferring the size potential of an animal and/or its offspring, the method comprising at least the step of analysing a nucleic acid from said animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471, or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof, wherein the nucleotide present at the one or more positions infers the size potential of the animal and/or its offspring.

In a particular embodiment, the invention provides a method for inferring the growth rate potential of an animal and/or its offspring, the method comprising at least the step of analysing a nucleic acid from said animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471, or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof, wherein the nucleotide present at the one or more positions infers the growth rate potential of the animal and/or its offspring.

In a particular embodiment, the invention provides a method for inferring the liveweight potential of an animal and/or its offspring, the method comprising at least the step of analysing a nucleic acid from said animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471, or to determine the nucleotide sequence of one or, more genetic marker in linkage disequilibrium . with one or more thereof, wherein the nucleotide present at the one or more positions infers the liveweight potential of the animal and/or its offspring.

In one embodiment, the presence of one or more of 22986260T, 23186380T, 23186648A, 23187238A, 23205280A, 23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11,

23264854G, 23272034A, and 2327347 IT infers a larger size for the animal and/or its offspring. In one embodiment, the presence of one or more of these markers infers a higher growth rate and/or higher liveweight for the animal and/or its offspring.

In one embodiment, the presence of one or more of 22986260C, 23186380C, 23186648G, 23187238G, 23205280G, 23211989G, 23215765G, 23218552C, 23228064T,

23232324G, 23235239G, 23243588TT, 23243734G, 23264810(CCG)9, 23264854A, 23272034G, and 23273471C infers a smaller size for the animal and/or its offspring. In one embodiment, the presence of one or more of these markers infers a lower growth rate and/or lower liveweight for the animal and/or its offspring.

In another aspect the invention provides a method for selecting or rejecting an animal, the method comprising at least the step of analysing a nucleic acid from said animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471, or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof, wherein the nucleotide present at the one or more positions infers the size potential of the animal and/or its offspring.

In one embodiment, the method comprises at least the steps of: a) analysing a nucleic acid from the animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471 or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof; and,

b) selecting or rejecting an animal based on the nucleotide present at the one or more positions.

In one embodiment, the presence of one or more of 22986260T, 23186380T, 23186648 A, 23187238A, 23205280A, 23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11,

23232324G, 23235239G, 23243588TT, 23243734G, 23264810(CCG)9, 23264854A, 23272034G, and 23273471C infers a smaller size for the animal and/or its offspring. In one embodiment, the presence of one or more of these markers infers a lower growth rate and/or lower liveweight for the animal and/or its offspring. In another aspect, the invention provides a method of identifying animals which are more likely or less likely to produce one or more desirable trait the method comprising at least the step of analysing a nucleic acid from the animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471 or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof.

b) identifying the animals which are more likely or less likely to produce one or more desirable trait on the basis of the nucleotide present at the one or more positions.

In one embodiment, the one or more desirable trait is size. In one embodiment, the trait is growth rate. In one embodiment, the trait is liveweight. In one embodiment, animals which are more likely to have a smaller size are identified on the basis of the presence of one or more of 22986260C, 23186380C, 23186648G,

23187238G, 23205280G, 23211989G, 23215765G, 23218552C, 23228064T,

23232324G, 23235239G, 23243588TT, 23243734G, 23264810(CCG)9, 23264854A, 23272034G, and 23273471C. In one embodiment, animals which are more likely to have a lower growth rate and/or lower liveweight are identified on the basis of the presence of one or more of these markers.

In one embodiment, animals which are more likely to have a larger size are identified on the basis of the presence of one or more 22986260T, 23186380T, 23186648A,

23187238A, 23205280A, 23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11, 23264854G, 23272034A, and 2327347 IT. In one embodiment, animals more likely to have a higher growth rate and/or higher liveweight are identified on the basis of the presence of one or more of these markers.

In one particular embodiment, the invention provides a method for identifying an animal having a higher growth rate potential and/or higher liveweight potential, the method comprising at least the step of analysing a nucleic acid from said animal to determine the nucleotide sequence at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471, or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof, wherein the presence of one or more of 22986260T, 23186380T, 23186648 A, 23187238A, 23205280A,

23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11, 23264854G, 23272034A, and 2327347 IT infers a higher growth rate and/or higher liveweight potential. In another particular embodiment, the invention provides a method for identifying an animal having a lower liveweight potential and/or a lower growth rate potential, the method comprising at least the step of analysing a nucleic acid from said animal to determine the nucleotide sequence at one or more of the following positions on

chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471, or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof, wherein the presence of one or more of 22986260C, 23186380C, 23186648G, 23187238G, 23205280G, 23211989G, 23215765G, 23218552C, 23228064T, 23232324G,

23235239G, 23243588TT, 23243734G, 23264810(CCG)9, 23264854A, 23272034G, and 23273471C infers a lower liveweigh and/or lower growth rate potential.

In another aspect, the invention provides a method for estimating the worth of an animal and/or its offspring, the method comprising at least the step of analysing a nucleic acid from the animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471 or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof, wherein the nucleotide present infers of the size potential of the animal and/or its offspring.

In particular embodiment, the invention provides a method for estimating the worth of an animal and/or its offspring, the method comprising at least the step of analysing a nucleic acid from the animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471 or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof, wherein the nucleotide present infers of the growth rate potential of the animal and/or its offspring.

In one particular embodiment, the invention provides a method for estimating the worth of an animal and/or its offspring, the method comprising at least the step of analysing a nucleic acid from the animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471 or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof, wherein the nucleotide present infers of the liveweight potential of the animal and/or its offspring.

In one embodiment, the presence of one or more of 22986260T, 23186380T,_23186648A, 23187238A, 23205280A, 23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11,

23264854G, 23272034A, and 2327347 IT infers a larger size potential of the animal and/or its offspring. In one embodiment, the presence of one or more of these markers infers a higher growth rate and/or higher liveweight for the animal and/or its offspring.

In one embodiment, the present of one or more of 22986260C, 23186380C, 23186648G, 23187238G, 23205280G, 23211989G, 23215765G, 23218552C, 23228064T,

23232324G, 23235239G, 23243588TT, 23243734G, 23264810(CCG)9, 23264854A, 23272034G, and 23273471C infers a smaller size potential of the animal and/or its offspring. In one embodiment, the presence of one or more of these markers infers a lower growth rate and/or lower liveweight for the animal and/or its offspring.

In another aspect, the invention provides a method for breeding animals to produce offspring, which comprises selecting at least a first animal that has one or more of the following genotypes 22986260T, 23186380T, 23186648 A, 23187238 A, 23205280A, 23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11, 23264854G, 23272034A, and 2327347 IT (read in relation to chromosome 14 of Bos Taurus) and mating said first animal with a second animal to produce offspring.

In one embodiment, the second animal is selected on the basis it has one or more of the following genotypes 22986260T, 23186380T, 23186648A, 23187238A, 23205280A, 23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11, 23264854G, 23272034A, and 23273471T.

In another broad aspect, the invention provides a method for breeding animals to produce offspring, which comprises selecting at least a first animal that has one or more of the following genotypes 22986260C, 23186380C, 23186648G, 23187238G, 23205280G, 23211989G, 23215765G, 23218552C, 23228064T, 23232324G, 23235239G,

23243588TT, 23243734G, 23264810(CCG)9, 23264854A, 23272034G, and 23273471C (read in relation to chromosome 14 of Bos Taurus) and mating said parent with a second animal to produce offspring.

In one embodiment, the second animal is selected on the basis it has one or more of the following genotypes 22986260C, 23186380C, 23186648G, 23187238G, 23205280G, - 23211989G, 23215765G, 23218552C, 23228064T, 23232324G, 23235239G,

23243588TT, 23243734G, 23264810(CCG)9, 23264854A, 23272034G, and 23273471C.

In one embodiment, the methods of the invention further involve taking a sample from the animal.

In one embodiment, the methods further involve analysing one or more additional biological markers.

Preferably, analysis of the nucleotide sequence of the one or more genetic markers occurs using one or more of: polymerase chain reaction (PCR); gel electrophoresis; Southern blotting; nucleic acid sequencing; restriction fragment length polymorphism (RFLP); single-strand confirmation polymphism (SSCP); LCR (ligase chain reaction); denaturing gradient gel electrophoresis (DGGE); allele-specific oligonucleotides (ASOs); proteins which recognize nucleic acid mismatches; RNAse protection; oligonucleotide array hybridisation; denaturing HPLC (dHPLC); high resolution melting (HRM); and, matrix- assisted laser desorption/ionisation time-of-flight mass spectroscopy (MALDI-TOF MS).

In another aspect the invention provides a method for inferring the size potential of an animal and/or its offspring, the method comprising at least the step of observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; wherein the level infers the size potential of the animal and/or its offspring.

In another aspect the invention provides a method for inferring the growth rate potential of an animal and/or its offspring, the method comprising at least the step of observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; wherein the level infers the growth rate potential of the animal and/or its offspring.

In another aspect the invention provides a method for inferring the liveweight potential of an animal and/or its offspring, the method comprising at least the step of observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; wherein the level infers the liveweight potential of the animal and/or its offspring.

In another aspect, the invention provides a method of identifying animals which are more or less likely to produce one or more desirable trait the method comprising at least the step of observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK. In one embodiment, the one or more desirable trait is size. In one embodiment, the trait is growth rate. In one embodiment, the trait is liveweight. In one embodiment, the methods involve comparing the level of expression of one or more of said proteins, precursor thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding said proteins against a standard, wherein a difference in the level of expression in the sample compared to the standard infers the size potential, growth rate potential and/or liveweight potential of the animal and/or its offspring. In one embodiment, an increase in the level of expression infers a higher growth rate and/or higher liveweight potential for the animal and/or its offspring. In one particular embodiment, the invention provides a method for identifying an animal having a higher growth rate potential and/or a higher liveweight potential, the method comprising at least the step of observing the level of expression of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK, wherein an increase in the level of expression compared to a standard infers a higher growth rate potential and/or higher liveweight potential.

In another particular embodiment, the invention provides a method for identifying an animal having a lower growth rate potential and/or a lower liveweight potential, the method comprising at least the step of observing the level of expression of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK, wherein a decrease or substantially no increase in the level of expression compared to a standard infers a lower growth rate potential and/or lower liveweight potential.

In another broad aspect the invention provides a method for selecting or rejecting an animal the method comprising at least the step of observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; and selecting or rejecting an animal based on the level of one or more of said proteins, precursors, isoforms or fragments thereof, and/or nucleic acids encoding same. In one embodiment, the method involves comparing the level of expression of one or more of said proteins, precursor thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding said proteins against a standard, wherein a difference in the level of expression in the sample compared to the standard infers the size potential, growth rate potential and/or liveweight potential of the animal and/or its offspring. In one embodiment, an increase in the level of expression infers a higher growth rate and/or higher liveweight potential for the animal and/or its offspring. In one embodiment, a decrease or substantially no increase in the level of expression compared to the standard infers a lower growth rate potential and/or lower liveweight potential for the animal and/or its offspring.

In one embodiment, the method is performed for the purpose of selecting or rejecting an animal for milking purposes. In one embodiment, the method is performed for the purpose of selecting or rejecting an animal for beef farming. In another embodiment, the method is performed for the purpose of selecting or rejecting an animal for breeding purposes.

In one embodiment, where the method is performed for the purpose of selecting or rejecting an animal for milking purposes, the animal is selected if there is a decrease or no substantial increase in the level of expression compared to the standard or the animal is rejected where there is an increase in the level of expression compared to the standard.

In one embodiment, where the method is performed for the purpose of selecting an animal for beef farming, the animal is selected if there is an increase in the level of expression compared to the standard or the animal is rejected where there is a decrease or no substantial increase in the level of expression compared to the standard.

In another aspect the invention provides a method for estimating the worth of an animal and/or its offspring, the method comprising at least the step of observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; wherein the level infers the size potential of the animal and/or its offspring. In one embodiment, the level infers the growth rate potential and/or the liveweight potential of the animal and/or its offspring. In one embodiment, the method involves comparing the level of expression of one or more of said proteins, precursor thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding said proteins against a standard, wherein a difference in the level of expression in the sample compared to the standard infers the size potential, growth rate potential and/or liveweight potential of the animal and/or its offspring. In one embodiment, an increase in the level of expression infers a higher growth rate potential and/or higher liveweight potential for the animal and/or its offspring. In one embodiment, a decrease or substantially no increase in the level of expression compared to the standard infers a lower growth rate potential and/or lower liveweight potential for the animal and/or its offspring.

In another aspect, the invention provides a method for breeding animals to produce offspring, the method comprising observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1 ; CHCHD7; RDHE2; SDR16C6; and, PENK; in at least a first animal, selecting the first animal where there is an increase in the expression of one or more of said proteins, precursors, isoforms, fragments and/or nucleic acids encoding same compared to a standard, and mating said first animal with a second animal to produce offspring.

In one embodiment, the method further comprises observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; in the second animal, and selecting the second animal where there is an increase in the expression of one or more of said proteins, precursors, isoforms, fragments and/or nucleic acids encoding same compared to a standard.

In another aspect, the invention provides a method for breeding animals to produce offspring, the method comprising observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; in at least a first animal, selecting the first animal where there is a decrease or substantially no increase in the expression of one or more of said proteins, precursors, isoforms, fragments and/or nucleic acids encoding same compared to a standard, and mating said first animal with a second animal to produce offspring.

In one embodiment, the method further comprises observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or ^" nucleic acids encoding the proteins:. RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; in the second animal, and selecting the second animal where there is a decrease or substantially no increase in the expression of one or more of said proteins, precursors, isoforms, fragments and/or nucleic acids encoding same compared to a standard.

In one embodiment the methods comprise at least the steps of:

a) taking a sample from an animal;

b) detecting one or more of the proteins RPS20, MOS, PLAG1, CHCHD7,

RDHE2, SDR16C6, and, PENK, a precursor thereof, an isoform thereof, a fragment thereof, and a nucleic acid encoding any one or more thereof in the sample; and,

c) comparing the level of the one or more proteins, precursors, fragments, isoforms and/or nucleic acids against a standard.

Preferably at least an approximately 1.2 fold increase in the level of one or more of the proteins, a precursor thereof, an isoform thereof, a fragment thereof, and/or a nucleic acid encoding same, is indicative of a higher growth rate potential and/or a higher liveweight potential of the animal and/or its offspring.

In one embodiment the methods further involve analysis of one or more additional biological markers.

In certain embodiments of the invention the level of the one or more proteins, precursors, fragments, isoforms and nucleic acids encoding same is determined using an

immunoassay, separation based on characteristics such as molecular weight and isoelectric point, gel electrophoresis, Western Blotting or mass spectroscopy. Preferably the immunoassay is an ELISA. Preferably the gel electrophoresis is 2D gel electrophoresis or gel-free systems based on microfluidics technologies. In one embodiment of the methods of the invention the animal is bovine. In a particular embodiment the bovine animal is Bos taurus or Bos indicus. In a particular embodiment, the animal is chosen from the group consisting Jersey, Holstein, Friesian or crossbred dairy cattle. In another embodiment, the animal is Simmental.

The invention may also be said broadly to consist in the parts, elements and features referred to or indicated in the specification of the application, individually or collectively, in any or all combinations of two or more of said parts, elements or features, and where specific integers are mentioned herein which have known equivalents in the art to which the invention relates, such known equivalents are deemed to be incorporated herein as if individually set forth.

FIGURES

These and other aspects of the present invention, which should be considered in all its novel aspects, will become apparent from the following description, which is given by way of example only, with reference to the accompanying figures, in which:

Figure 1: Structure of the Holstein-Friesian (HF) x Jersey (J) intercross generated for QTL mapping. Following conventional genetic formalism matings are described mentioning females first and males second. Hence "HF x J" corresponds to a mating between HF dams and J sires. Males are labeled with squares/rectangles, females with circles/ovals. Number of individuals of a given genotype are given in parentheses.

Figure 2: (A) Location scores (F-values) obtained across the entire bovine genome when analyzing weight at birth, 6 (6M), 12 (12 M), 18 (18 M) and 24 months (24M) and height at 18 months in the HFxJ inter-cross with a line-cross model (Haley et al., 1994). (B) Location scores (F-values) obtained for BTA14 when analyzing weight and height in the HFxJ inter-cross with a paternal half-sib pedigree model implemented with HSQM (Coppieters et al., 1998). The lines correspond to weight at birth, 6, 12, 18 and > 24 (=live weight) months and "height" as labelled. The black dotted line corresponds to the 5% genome-wide significance threshold determined with a permutation test (Churchill & Doerge, 1994). The lower horizontal bar (CI) corresponds to the 95% CI for the QTL location determined by bootstrapping (Visscher et al., 1996). The higher horizontal bar (SIAG) shows the position of the 1.1 MB critical interval defined by Mizoshita et al. (2005). (C) Highest chromosome-wide log(l/p) values obtained within each of the six sire families for height (white bars) and live weight (black bars). The corresponding map positions are given above the bars. Note the very distinct map positions for sire 4. The black dotted line marks the 5% chromosome-wide significance threshold. The black arrows point towards the four sire families segregating for the QTL. The corresponding sires (1, 2, 3 and 5) are therefore predicted to be of "Qq" genotype. (D) Sire-specific allele substitution effects on weight (gray bars of increasing darkness: birth, 6, 12, 18 months and live weight) and height (white bars) expressed in Kgs and mm, respectively. Family- specific allele substitution effects were determined at the most significant QTL position as determined in the across-family analysis.

Figure 3: (A) Black line (labeled "HSQM"): Linkage-based QTL analysis of live weight in the HFxJ intercross using HSQM (Coppieters et al., 1998) and a 56 microsatellite marker map (cfr. Fig. 2B). Results are expressed as F-values (right axis). Dark dots (labeled "SP-T"): Single-point linkage + LD analysis of live weight in the HFxJ intercross using Dualphase (Druet & Georges, 2009) and a 925 SNP + 56 microsatellite marker map. Results are expressed as a likelihood ratio (LR) test (21nLR)(left axis). (B) Black line (labeled "MP-T"): Haplotype-based linkage + LD analysis of live weight in the HFxJ intercross using Dualphase and a 925 SNP + 56 microsatellite marker map. Results are expressed as a likelihood ratio (LR) test (21nLR)(left axis). (C) Black line (labeled "OUTBRED"): Haplotype-based linkage + LD analysis of breeding value for live weight in 2,700 progeny-tested sires from the New Zealand outbred dairy cattle population using Dualphase and 293 SNP markers from the USDA 50K SNP chip (Van tassel et al., 2008) spanning a 15 Mb BTA14 segment spanning the QTL location. Results are expressed as a likelihood ratio (LR) test (21nLR)(left axis). (D) Dark dots (labeled "TAQ"): Single-point linkage + LD analysis of live weight in the HFxJ intercross using Dualphase and 11 candidate QTN identified by resequencing the 750 Kb critical interval in the six Fl sires. (A-D) The X-axis corresponds to the chromosomal position in base pairs. The lower horizontal line corresponds to the QTL critical defined by Mizoshita et al. (2005), the higher horizontal line to the 750 Kb segment sequenced in the present study. Every graph shows the results of all analysis in gray watermarks to facilitate cross comparison.

Figure 4: (A) Dark gray line: Single QTL haplotype-based combined L+LD analysis of live weight in the HFxJ intercross (cfr. Fig. 3B). Black line: Two QTL haplotype-based combined L+LD analysis of live weight conducted with the same mixed model augmented with a random effect corresponding to the hidden state at the most likely QTL position obtained in the single QTL analysis (black arrow). The black line measures the increase in LRT (above that of the single-QTL analysis at the position of the black arrow) by adding a second QTL at the corresponding position. (B) Dark gray line: Single QTL haplotype- based combined L+LD analysis of breeding value for live weight in the outbred New Zealand dairy cattle population (cfr. Fig. 3C). Black line: Two QTL haplotype-based combined L+LD analysis of breeding value for live weight in the outbred New Zealand dairy cattle population with the same mixed model augmented with a random effect corresponding to the hidden state at the most likely QTL position obtained in the single QTL analysis (black arrow). The black line measures the increase in LRT (above that of the single-QTL analysis at the position of the arrow) by adding a second QTL at the corresponding position. Modest evidence (LRT: 20 = lod score: 4.3) for an additional QTL within the interval defined by Mizoshita et al. (2005) is observed (gray arrow).

Figure 5: Effect on live weight (in Kgs; X-axis) and frequency (number of observations in the studied population; Y-axis) for the 20 hidden haplotype states modeled with Dualphase (Druet and Georges, 2009) in the New Zealand outbred dairy cattle population. Shades of gray distinguish the breed origin of the corresponding animals: Holstein-Friesian (black), Jersey (gray), crossbred (white). The number of haplotype states in each class is given above the corresponding bars. Figure 6: Upper track (labeled "Genes"): A. Organization of the eight genes mapping to the 750Kb critical region LYN, RPS20, MOS, PLAG1, CHCHD7, RDHE2, SDR16C6 and PENK). Middle track (labeled "SNP"): Positions of the 14 candidate Quantitative Trait Nucleotides (QTN), plus splice site variant detected in CHCHD7 (half height). Brown track (labeled "Phastcons conserved elements, 5-way Vertebrate Multiz Alignment"): Location of "Phastcons" multispecies conserved elements. (B) Orthologous locus in zebrafish (D. rerio)

Figure 7: Combined linkage + LD analysis of weight at birth (A), weight at 6 months (B), weight at 12 months (C), weight at 18 months (D) and height at 18 months (E) using 925 SNPs + 56 microsatellites in single point analysis (gray dots), 925 SNPs + 56 microsatellites in haplotype-based analysis (grey line), and 10 of the 14 candidate QTN (black circled dots, labeled). The higher and lower horizontal lines respectively mark (i) the 750 Kb critical region sequenced in this work, and (ii) the interval defined by Mizoshita et al. (2005).

Figure 8: (A) Identification of the Q (grey), q (darker grey), and "recombinant" (grey + darker grey) haplotypes in a bovine diversity panel. Breeds and corresponding numbers of genotyped animals are given. Breed-specific haplotype frequencies are given and highlighted with a black box on white text when > 0.03. (B) Haplotype effect on birth weight in Simmental, assuming that the "R" haplotype (cfr. A) is q (left panel) or Q (right panel). Animals are sorted by predicted QTL genotype as shown. Smaller dots (with a shadow) correspond to individual observation. Larger dots correspond to genotype means. Arrows correspond to the 95% confidence intervals of the genotype mean (mean + 1.96*SEM). The regression of birth weight on QTL genotype is near significant (p=0.06) when assuming that R=q (left panel) and non-significant under the alternative hypothesis (R=Q). The difference in birth weight between animals predicted to be of qq and QQ genotype assuming R=q is significant (t-test; p=0.02). Figure 9: Exploring the integrity of the PLAG1 (A-D), CHCHD7 (E-F) and MOS (G-H), ORFs by RT-PCR performed with cDNA prepared from bone (Bo), muscle (M), brain (Br) and liver (L) mRNA extracted from fetuses that were homozygous QQ or qq for the pQTN. Animals that carried the FJX_303486_1:1 CHCHD7 splice site variant (cfr. SUppl. Fig. 11) are indicated with "sp". (1) Negative controls. Figure 10: Effect of pQTN genotype on the expression level of the eight genes mapping to 750-kb CI for the QTL in fetal liver, bone, muscle and brain, estimated by QRTPCR (mid-darkness bars), an allelic imbalance test using 3'UTR SNPs (darkest bars) or an allelic imbalance test using an intronic SNP (lightest bars). The X-axis measures the slope of the regression (QRTPCR) or the ratio of Q over q allele (allelic imbalance tests) on a log₂-scale. The vertical black lines correspond to the absence of an effect of pQTN genotype on expression. #: pO.10; *:0.01<p<0.05; **p<0.01. ND: not done. NE: no detectable expression.

Figure 11: (A) QRT-PCR results for LYN, RPS20, MOS, PLAG1, CHCHD7, RDHE2, SDR16C6 and PENK and MOS in fetal bone, muscle, brain and liver samples. Small grey circles correspond to the expression level in a given animal relative to the mean expression level across all animals. Relative expression levels are expressed on a log₂-scale, and correspond to the mean relative expression level estimated with one or more amplicons (the number of tested amplicons is given in the upper left corner of each graph). Animals were sorted by pQTN genotype as indicated at the top of the figure (qq, Qq and QQ). Large darker circles correspond to the mean relative expression of the corresponding pQTN genotype, "a" corresponds to the slope of the regression line (in log₂-units). p corresponds to the statistical significance of the regression. (B) Results of the allelic imbalance test in heterozygous Qq animals for PLAGl and CHCHD7. The X-axis corresponds to the ratio of peak heights corresponding to the Q and q allele as measured by direct sequencing and Peakpicker (Ge et al., 2005). Small gray circles correspond to the allelic, ratio for individual animals, large darker circles correspond to the overall mean. Numbers on the right of the graphs correspond to the mean value +_ 1.96 x SEM, and (for cDNAs) to the p-value of the difference with genomic DNA (gDNA) evaluated by a t-test. Fetuses that were heterozygous for the CHCHD7 FJX_303486_ 1 :1 splice site variant were excluded from both QRT-PCR and allelic imbalance analyses of PLAGl and CHCHD7. Figure 12: Testing the effect of pQTN FJX_PLAPROTRI_l:l and FJX_PLAPROSNP_l:l on bidirectional promoter strength using a luciferase reporter assay. (A) Schematic representation of the supposedly bidirectional promoter driving expression of the head-to-head oriented PLAGl and CHCHD7 genes (dark line at top of figure), with corresponding Phastcons conservation scores (lighter bars), and multispecies sequence alignment of a segment encompassing the FJX_PLAPROTRI_l :l and FJX_PLAPROSNP_l:l candidate pQTN. The black arrows mark the positions of the "long" and "short" fragments cloned in the pGL4 luciferase reporter vector in the "forward" (towards PLAGl) and "reverse" (towards CHCHD7) orientation. (B) Ratios of firefly to renila luminescence obtained after transfection of Cosl cells with (i) a promoterless pGL4 vector, (ii) two distinct, sequence-verified preparations of the pGL4 vector endowed with the TK promoter, (iii) pairs of sequence-verified preparations of the vector endowed with the q or Q version, of the long or short fragment, cloned either (as denoted under figure) in forward or reverse orientation. Error bars correspond to standard errors of the mean computed from replicates. (C) Schematic representation of the "recombinant" "Q-q" and "q-Q" promoter fragments that were generated by swapping the Q and q residues at the FJX_PLAPROTRI_l : 1 and FJX_PLAPROSNP_l : 1 sites as shown. (D) Firefly/Renilla luminescence obtained with the non-recombinant « Q-Q » promoter as well as recombinant « Q-q » ane « q-Q » promoters cloned in forward and reverse orientation, relative to the cognate non-recombinant « q-q » promoter. Error bars correspond to standard errors of the mean.

Figure 13: Representative results of EMS A experiments conducted with radiolabeled 29- mer (sQ and sq) and 74/80-mer probes (LQ and Lq) spanning the FJX_PLAPROTRI_l : 1 and FJX_PLAPROSNP_l:l pQTN. "sCtrl" corresponds to an umelated 25-mer control duplex used as control competitor. Results shown were obtained with nuclear extracts derived from fetal bone (A) and C2C12 cells (B&C). omplexes with differential affinity for the Q and q allele are marked by the arrows on the left of the gels (short probe; Q<q), second arrow down on the 3^rd column on gel C (long probe; Q<q) and first and third arrows on the 3^rd gel column on gel C (long probe; Q>q). An abundant complex with equal affinity for the Q and q allele detected with the long probe is marked by the green arrow. Free probes are labeled "SQ/q", "LQ" and "Lq". The bar graphs at the bottom of the figure quantify the abundance of two corresponding complexes (longer probe -darker bars, shorter probe - lighter bars) relative to the Q probe (in the absence of cold competitor) determined by densitometry.

Figure 14: (A) Mapping eQTL influencing CHCHD7 expression levels in liver (lighter and upper line) and adipose (darker and lower line line) using a 56 microsatellite BTA14 map and HSQM (Coppieters et al., 1998). Location scores correspond to F-values. The black dotted line corresponds to the 5% chromosome- wide significance threshold determined with a permutation test (Churchill & Doerge, 1994). Inlet: Results of the analysis conducted within each of the six sire families separately. The p-values obtained on BTA14 for each of the six families is indicated by the height of the first (liver) or second (adipose) bars, and indicate segregation of the eQTL in pedigree "1" only (arrow). (B) Haplotype cluster-based association analysis conducted as described in M&M for CHCHD7 expression in liver (lighter and upper line) and adipose (darker and lower line). Markers considered are the 925 BTA14 SNPs + 56 BTA14 microsatellites. Location scores correspond to log(l/p), where p corresponds to the p-value of the corresponding F- test. (C) Single SNP association analysis conducted as described in M&M for CHCHD7 expression in liver (lighter dots) and adipose (darker dots). Markers considered were the 925 BTA14 SNPs + 56 BTA14 microsatellites. The arrows point towards the p-values obtained for the CHCHD7 FJX_303486_1:1 splice site variant. (D) Two SNP SNP association analysis conducted as described in M&M for CHCHD7 expression in liver (lighter dots) and adipose (darker dots). Markers considered were the 925 BTA14 SNPs + 56 BTA14 microsatellites. Log(l/p) values correspond to the p-value of adding the corresponding SNP in a model already including the CHCHD7 FJX_303486_1 :1 splice site variant. (A-D) The position of the sequenced 750 Kb interval containing CHCHD7 is marked by the black horizontal line.

Figure 15: (A) Mapping eQTL influencing RPS20 expression levels in liver (lighter and upper line) and adipose (darker and lower line) using a 56 microsatellite BTA14 map and HSQM (Coppieters et al., 1998b). Location scores correspond to F- values. The black dotted line corresponds to the 5% chromosome-wide significance threshold determined with a permutation test (Churchill & Doerge, 1994). Inlet: Results of the analysis ^" conducted within each of the six sire families separately. The p- values obtained on BTA14 for each of the six families is indicated by the height of the first (liver) or second (adipose) bars, and indicate segregation of the eQTL in pedigree "1", "5" and "6" (arrows). (B) Haplotype cluster-based association analysis conducted as described in M&M for RPS20 expression in liver (upper and darker line) and adipose (lower and lighter line). Markers considered are the 925 BTA14 SNPs + 56 BTA14 microsatellites. Location scores correspond to log(l/p), where p corresponds to the p-value of the corresponding F-test. The position of the sequenced 750 Kb interval containing CHCHD7 is marked by the black horizontal line. Figure 16: (A) Agarose gel of RT-PCR products obtained from AA, AT and TT individuals for the CHCHD7 FJX_303486_1 :1 splice site variant. The homozygous mutant {AA) animal shows three bands shown (by sequencing) to correspond to mRNAs with structures D and E as depicted, i.e. having lost exon 3. The largest band correspond to a D/E heteroduplex. The homozygous wild-type (TT) animals show three bands corresponding to mRNAs with structures A, B and C as depicted. The largest band is a mixture of B/C heteroduplex plus band A. Heterozygous (AT) animals show all fragments (A-E) plus additional heteroduplexes. The darker portions of the different mRNA species correspond to predicted ORF, while the lighter segments correspond to 5' and 3' UTRs. (B) Relative CHCHD7 transcript levels determined by QRT-PCR in muscle, brain and liver samples of heterozygous AT and homozygous wild-type TT fetuses. Smaller dots correspond to - individual expression levels, while the larger dots correspond to the genotypic average. Relative expression levels are expressed as log₂ = (χ,-, /χ , where Xy /Xj is the expression level of individual i measured with amplicon j relative to the other individuals, "a" is the slope of the regression (in log₂ units), and p the significance of its deviation from 0.

PREFERRED EMBODIMENT(S)

The following is a description of the present invention, including preferred embodiments thereof, given in general terms. The invention is further elucidated from the disclosure given under the heading "Examples" herein below, which provides experimental data supporting the invention.

The inventors have identified that particular alleles of a number of genetic markers in a region of chromosome 14 in Bos taurus correlate with the weight and/or height of an animal. The inventors note a particular correlation to weight at birth, at 6 months of age, 12 months of age, 18 months of age and 24 months of age. The genotype of one or more of these particular genetic markers may therefore be used to infer or predict the size potential of an animal and/or its offspring. The size of an animal may contribute to its value to a particular industry. For example, typically a larger animal or an animal with a high growth rate is preferable for beef farming and efficiencies can be gained in dairy farming by using smaller animals which require less feed.

The inventors also note that there is a correlation between increases in the expression of certain genes and the size of an animal. Thus, observing the level of expression of one or more of these genes may also be used to infer or predict the size potential of an animal and/or its offspring.

Thus, analysis of the genetic markers of the invention, or the level of expression of particular genes, may assist in: predicting phentotypic performance; identifying animals more or less likely to have a desired trait; the selection or rejection of animals for breeding purposes; managing animals in order to maximise their individual potential performance and value; estimating the worth or economic value of an animal; improving profits related to selling animals and/or products produced from the animals; improving the genetics of a population of animals by selecting and breeding desirable animals; cloning animals likely to have a specific trait; predicting the suitability of an animal and/or its offspring to use in different industries. The term "genetic marker" as used herein refers to nucleic acids or specific genetic loci (including specific nucleotide positions) that are polymorphic or contain sequence variations within a population, the alleles of which can be detected and distinguished by one or more analytic methods. The term "genetic marker" further includes within its scope_ a plurality of genetic markers co-segregating, in the form of a "haplotype". In this context, the term "haplotype" refers to a plurality of genetic markers that are generally inherited together. Typically, genetic markers within a haplotype are in linkage disequilibrium. Reference herein to analysing a nucleic acid to determine the nucleotide sequence of a "genetic marker" (for example the genetic markers of Table 1) or at a particular genetic position should be taken to include analysing and determining the nucleotide sequence on either strand of the nucleic acid.

The term "single nucleotide polymorphism" (SNP) refers to nucleic acid sequence variations that occur when a single nucleotide in the genome sequence is altered. A single nucleotide polymorphism may also be a single nucleotide insertion or deletion. The different nucleotides within a SNP are referred to as an allele.

"VNTR", or "variable number tandem repeat", refers to a tandem repeat of a nucleic acid sequence at a genetic locus in which the number of repeated DNA segments varies between individuals in a population. The different repeat numbers within a VNTR at a particular locus may be referred to as an allele.

The term "genotype" as used herein means the genetic constitution or nucleotide sequence at one or more genetic locus, in particular the nucleotide sequence of an allele of a genetic locus.

As used herein, "isoform(s)" should be taken broadly and includes those generated by posttranslational modification (for example, glycosylation), and proteolytic processing. The term "precursor(s)" should also be interpreted broadly and includes for example preproteins, prepeptides, preproproteins and prepropeptides including an N-termihal leader sequence.

Reference herein to the term "size" should be taken broadly, and includes total size, weight (including liveweight), height, and/or growth rate. "Growth rate" as used herein is intended to mean the average daily weight gain of an animal. The phrase "infer size potential", and like phrases, should be taken broadly to mean that the presence of a particular allele of a genetic marker indicates an increased likelihood that an animal and/or its offspring has the capacity to have a certain size characteristic (at one or more time points in its life; for example, at birth, or at an age at which it will be put to productive use in a particular industry such as the beef or dairy industry) compared to if it had a different allele of the genetic marker. The phrase should not be taken to imply that the actual size of an animal is predicted. In a preferred embodiment, the presence of a particular allele of a genetic marker of the invention (or a particular level of expression of a specified protein) provides an indication that the animal and/or its offspring is more likely than not to have a particular size characteristic relative to if it had a different allele. In one embodiment of the invention, an animal with one copy of a Q allele of a genetic marker (as outlined herein) will have from an approximately 60% to approximately 70% probability of having a larger size than if it had a different allele of that genetic marker. In one particular embodiment, an animal with one copy of a Q allele of a genetic marker (as outlined herein) will have-an approximately 65% probability of having a larger size than if it had a different allele of that genetic marker. In one embodiment of the invention, an animal with two copies of a Q allele of a genetic marker (as outlined herein) will have from an approximately 60% to approximately 70% probability of having a larger size than if it only had a single copy of the Q allele of that genetic marker. In one particular embodiment, an animal with two copies of a Q allele of a genetic marker (as outlined herein) will have an approximately 65% probability of having a larger size than if it only had a single copy of the Q allele of that genetic marker. In one embodiment, an animal with two copies of a Q allele of a genetic marker (as outlined herein) will have from an approximately 73% to approximately 83% probability of having a larger size than if it had two copies of a different allele of that genetic marker. In one particular embodiment, an animal having two copies of a Q allele of a genetic marker (as outline herein) will have an approximately 78% probability of having a larger size than if it had two copies of a different allele of that genetic marker. The terms "low(er)", "high(er)", "small(er)", and "large(er)" when used herein in relation to size, including weight, height, and/or growth rate, are intended to mean that an animal will have a size that is "low(er)", "high(er)", "small(er)", "large(er)" than if it had a different allele of a genetic marker (or a different level of expression of a specified protein). In one embodiment, the terms indicate that the animal may have a size that is "low(er)", "high(er)", "small(er)", "large(er)" than another animal or the average in a population. In one particular embodiment, the following values are used as reference points: a height average at 24 months of approximately 1.25 metres with a standard deviation (SD) of approximately 3.7cm; weight average at 24 months of approximately 437 kg with a SD of approximately 40kg; average growth rate from birth to 24 months of approximately 0.58 kg/day with a SD of approximately 0.05 kg/day; wherein the population is a population of Holstein Fresian/Jersey crossbreed cattle.

The term "animal" is used herein primarily in reference to mammals within the Bovidae family. In particular embodiments, the animal is a bovine animal. More particularly the animal is Bos taurus or Bos indicus. In one particular embodiment the animal is a beef or dairy breed. By way of further example, the animal may be chosen from the group of animals including, but not limited to, Jersey, Holstein, Friesian, Ayrshire, crossbred dairy cattle, Angus, Hereford, Simmental and crossbred beef cattle.

As used herein the "worth" of an animal refers to an index used to evaluate the value of an animal, for breeding purposes or herd management, for example. The "worth" is the sum of the estimated value of one or more characteristics which may be associated with the animal, typically weighted by an economic value. Exemplary characteristics include milk fat, protein, milk volume, liveweight, fertility, and milk somatic cells. The term "worth" should be taken to encompass "breeding worth" and other known indexes used to assess the value of an animal. Persons skilled in the art to which the invention relates will readily appreciate methods and formulae suitable for estimating breeding worth on the basis of any number of different characteristics. Further details of parameters and methods for calculating the worth of an animal are provided in the "Examples" section herein after.

In certain embodiments, the methods of the invention allow for "selection" or "rejection" of an animal based on the genetic marker present or the level of a specified protein (including precursors, fragments and/or isoforms thereof) and/or nucleic acids encoding said protein, precursor, fragment and/or isoform. Typically an animal is selected for purposes relating to the beef industry or rejected for purposes relating to the dairy industry where the genetic marker or level of protein (including precursors, fragments and/or isoforms thereof) and/or nucleic acid encoding same infers a larger size potential for the animal. Similarly, an animal is selected for purposes relating to the dairy industry or rejected for purposes relating to the beef industry where the genetic marker or level of protein (including precursors, fragments and/or isoforms thereof) arid/or nucleic acid encoding same infers a smaller size potential for the animal. The methods of the invention relate generally to inferring the size potential of an animal and/or its offspring. In one particular embodiment, the methods relate to inferring the liveweight potential of an animal and/or its offspring. In another particular embodiment, the methods relate to inferring the growth rate of an animal and/or its offspring. As mentioned hereinbefore, such information can be used, for example, to select animals for breeding purposes and/or estimate an animal's worth. The invention may also be used to predict bull or cow phenotype performance and as such can be used in production management systems known as Marker Assisted Selection. Animals more or less suitable for a particular production system can be screened early at birth or as embryo's to predict life time performance and segregated or managed to suit their genotype and therefore predicted phenotype.

In one embodiment of the invention, the methods involve the analysis of a nucleic acid from the animal to determine the nucleotide sequence of at least one genetic marker as described herein, wherein the sequence infers the size potential of the animal and/or its offspring. In another embodiment, the methods involve the analysis of the level of a specified protein or nucleic acid encoding it, wherein the level infers the size potential of the animal and/or its offspring.

Typically, the methods involve taking a sample from an animal to be tested. The sample may be any appropriate tissue or body fluid sample. In one embodiment, the sample is blood, muscle, bone, somatic cell(s), saliva, or semen. In other embodiments, the sample is taken from the liver or brain. Such samples can be taken from the animal using standard techniques known in the art. It should be appreciated that a sample may be taken from an animal at any stage of life, including prior to birth; for example, an embryo, feotus, blastocyst. Individual gametes could also be tested using the methods of the invention. This may assist in breeding and/or cloning programmes. A sample may also be taken after the death of an animal. The samples are analysed using techniques which allow for the observation or analysis of levels of nucleic acid and/or the sequence of a particular nucleic acid, as will be described further herein after.

The methods of the invention may be combined with one or more other methods of use in assessing genotype, predicting phenotype, selecting an animal based on certain characteristics, estimating breeding values or estimating worth and the like. Accordingly, the methods of the invention may include, in addition to analysis of the markers identified herein, analysis of additional genetic markers, and/or the level of expression of certain genes/proteins, and/or one or more phenotypic traits, for example.

Genetic Markers

In one embodiment, methods of the invention involve analysing one or more genetic markers to identify the sequence or genotype of the genetic marker(s).

Genetic markers of use in the invention are listed in Table 1 below, along with the genotypes which the inventors have identified to be indicative of a smaller size (q allele), or a larger size (Q allele). The sequence and position provided for each genetic marker is based on the genomic sequence of chromosome 14 in bovine build Btau 4.0

(gi|158137890|gb|CM000190.3| in the GenBank database http://www.ncbi.nlm.nih.gov/) The position of each of the genetic markers should be read in accordance with base position being the start site of the polymorphism, given that the first nucleotide in the sequence (gi| 158137890|gb|CM000190.31) is denoted as position one. Further sequence information in the region of the genetic markers of Table 1 is provided in Table 10 herein after. Table 1

Whilst the nucleotide variations listed in Table 1 provide specific examples of markers of use in the methods of the invention, the inventors contemplate that any variation in a nucleotide or the nucleotide sequence at any one or more the genetic loci referred to may be indicative of size potential.

- In one particular embodiment, the marker(s) are chosen from the group outlined in Table 8 herein after. In another particular embodiment, the marker(s) are chosen from

FJX_PLAPROTRI_l : 1 and FJX PLAPROSNP 1 : 1.

It should also be appreciated that one could analyse the nucleic acid sequence of either strand of the nucleic acid to identify the sequence at a particular genetic loci or position; ie instead of analysing the strand associated with the sequence variants listed in Table 1, the nucleotide sequence of the opposite or complementary strand of DNA could be analysed. Persons of skill in the art will readily appreciate nucleic acid sequence variations on such opposite strand which correlate with the genotypes of Table 1, having regard to the information contained herein and nucleic acid base pairing principles (ie, A pairs with T and C pairs with G).

5 It should be appreciated that the invention also encompasses use of one or more genetic markers which are in linkage disequilibrium with one or more of the markers mentioned herein before. Such markers may be analysed instead of or in addition to the genetic markers mentioned herein before.

10 "Linkage disequilibrium" should be taken broadly to refer to the tendency of the presence of an allele at one genetic loci (for example a genetic marker of Table 1) to predict the presence of an allele at one or more other genetic loci (for example a distinct genetic marker). The genetic loci need not necessarily be on the same chromosome. However, in a preferred embodiment, the genetic loci are located on the same chromosome.

-15

One measure of linkage disequilibrium is DELTA , which is calculated using the formula described by Devlin et al (Genomics 29 (2):311-22 (1995)), and is a measure of how well an allele X at a first genetic loci predicts the occurrence of an allele Y at a second genetic loci. A DELTA value of 1.0 indicates the prediction is perfect (for example, if Y is 20 present then X is present). It should be appreciated that reference to linkage disequilibrium herein should not be taken to imply a DELTA value of 1.0. In particular embodiments, the linkage disequilibrium between an allele at one genetic locus and an allele at a second genetic locus, has a DELTA² value of at least 0.75, at least 0.80, at least 0.85, at least 0.90, at least 0.95, and most preferably 1.0.

Skilled persons will readily appreciate methods for determining whether any two alleles are in linkage disequilibrium. However, by way of example, see Genetic Data Analysis II, Weir, Sinauer Associates, Inc. Publishers, Sunderland, Mass., 1996.

30 Nucleic acids can be analysed to determine the genotype/sequence of the genetic markers described herein according to any appropriate technique. Such techniques include for example polymerase chain reaction (PCR), including allele-specific PCR, gel electrophoresis, the use of oligonucleotide probe hybridisation, Southern blotting, direct sequencing, restriction digestion, restriction fragment length polymorphism (RFLP), single-strand confirmation polymorphism (SSCP), LCR (ligase chain reaction), denaturing gradient gel electrophoresis (DGGE), the use of allele-specific oligonucleotides (ASOs), the use of proteins which recognize nucleic acid mismatches, such as E.coli mutS protein, RNAse protection assays, oligonucleotide array hybridisation (for example microarray), denaturing HPLC (dHPLC), fluorescence quenching PCR (TaqMan™, Applied Biosystems, CA 94404, USA), High Resolution Melting (HRM), and matrix-assisted laser desorption/ionisation time-of-flight mass spectroscopy (MALDI-TOF MS). Combinations of two or more of such techniques may be used. Such combination may increase the sensitivity of the analysis being conducted.

The technique(s) used will depend on the nature of the marker to be detected as will be appreciated by skilled persons. For example, single nucleotide polymorphisms (SNPs), may be analysed using those techniques capable of resolving a single nucleotide difference between sequences; for example, direct sequencing or LCR, allele-specific PCR, RFLP, SSCP, DGGE, using allele-specific oligonucleotides (ASOs), or proteins which recognize nucleic acid mismatches, oligonucleotide array hybridisation, dHPLC, fluorescence quenching PCR and matrix MALDI-TOF MS.

Any one or more of the techniques mentioned hereinbefore (including for example, SSCP, RFLP, DGGE, dHPLC and direct sequencing) may be used to analyse the genetic markers which may include insertion or deletion of one or more nucleotide.

It should be appreciated that certain of the techniques of use in analysing the genetic markers of the invention will utilise one or more oligonucleotides which hybridise to a genetic region encompassing the marker, adjacent to the marker, or flanking the marker. Such oligonucleotides may be DNA, RNA or derivatised forms thereof and include nucleic acid primers, such as PCR and LCR primers, and nucleic acid probes.

Persons of ordinary skill in the art to which the invention relates will readily appreciate appropriate oligonucleotides of use in the invention having regard to the nucleic acid sequence of chromosome 14 (as detailed herein before), particularly in the genetic regions proximal to a particular marker, the nature of the genetic markers to be analysed, and the general principles of nucleic acid hybridisation. The nucleic acids will be capable of hybridising in a specific manner to a target nucleic acid and in the case of primers they will be capable of priming a PCR or like reaction. While such nucleic acids will preferably have 100% complementarity to their target region of the mRNA or cDNA of the protein of interest, they may contain one or more non-complementary nucleotides at a particular position while still substantially retaining specificity for the target nucleic acid to which they are designed to bind. By way of example, the nucleic acids may have approximately 80%, approximately 90%, approximately 95%, or approximately 99% complementarity or homology to its target. By way of further example, in certain cases, the oligonucleotides may be designed such that a mismatch at a particular nucleotide position is indicative of the nature of the genetic marker being analysed (for example, a SNP). By way of example, a mismatch in the nucleotide present at the 3 ' end of an LCR primer will inhibit the reaction providing an indication of the nature of the nucleotide at that position. Mismatches may similarly be utilised in techniques including RNAse protection assays and allele-specific PCR, as well as in fluorescence quenching PCR, for example. Typically, the nucleic acids will hybridise to their target nucleic acid under stringent hybridisation conditions (see for example, Sambrook and Russell, Molecular Cloning: A Laboratory Manual, 2001, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York).

The oligonucleotide probes or primers may be of any length as is appropriate for a particular application, having regard to the sequence of the genetic region to which they are designed to bind. A probe or primer will typically be capable of forming a stable hybrid with the complementary sequence to which it is designed to hybridise. Accordingly, the length is dependent on the nucleic acid composition and percent homology between the oligonucleotide and its complementary sequence, as well as the hybridisation conditions which are utilised (for example, temperature and salt concentrations). Such hybridisation factors are well known in the art to which the invention relates. By way of example, oligonucleotides of use in the present invention may be from 2 to 500 nucleotides in length. In one embodiment, particularly where they are used as primers, the oligonucleotides may be of approximately 15 nucleotides to 30 nucleotides in length.

Oligonucleotide probes and primers of use in the invention may be prepared by any number of conventional DNA synthesis methods including recombinant techniques and chemical synthesis, or they may be purchase commercially. It will be appreciated that the usefulness of any probe or primer may be evaluated, at least notionally, using appropriate software and sequence information for the nucleic acid encoding the protein of interest. For example, software packages such as Primer3 (http://primer3.sourceforge.net/), PC 01igo5 (National Bioscience Inc), Amplify (University of Wisconsin), and the PrimerSelect program (DNAStar Inc) may be used to design and evaluate primers.

Where amplification techniques (for example PCR) are used in methods of the invention amplification may be conducted according to conventional procedures in the art to which this invention relates, such as described in US Patent No 4,683,202. By way of example PCR reactions will generally include 0.1μΜ-1μΜ of each primer, 200μΜ each dNTP, 3- 7mM MgCk, and 1U Taq DNA polymerase. Further, exemplary PCR cycling conditions include: denaturation at a temperature of approximately 94°C for 30 to 60 seconds, annealing at a temperature calculated on the basis of the sequence and length of the primer (as herein after discussed) for 30 to 60 seconds, and extension at a temperature of approximately 70°C to 72°C for 30 to 60 seconds. By way of example, between 25 and 45 cycles are run.

It will be appreciated by those of ordinary skill in the art that the amplification conditions provided herein are merely exemplary and may be varied so as to optimise conditions where, for example, alternative PCR cyclers or DNA polymerases are used, where the quality of the template DNA differs, or where variations of the primers not specifically exemplified herein are used, without departing from the scope of the present invention. The PCR conditions may be altered or optimised by changing the concentration of the various constituents within the reaction and/or changing the constituents of the reaction, altering the number of amplification cycles, the denaturation, annealing or extension times or temperatures, or the quantity of template DNA, for example. Those of skill in the art will appreciate there are a number of other ways in which PCR conditions may be optimised to overcome variability between reactions.

It will be understood that whilst not specifically exemplified herein, appropriate annealing temperatures for any primer within the scope of the present invention may be derived from the calculated melting temperature of that primer. Such melting temperatures may be calculated using standard formulas, such as that described in Sambrook and Russell, 2001. As will be understood by those of ordinary skill in the art to which this invention relates annealing temperatures may be above or below the melting temperature but generally an annealing temperature of approximately 5°C below the calculated melting temperature of the primer is suitable.

Oligonucleotides used for detection and/or analysis of the genetic markers of the invention may be modified to facilitate such detection. Similarly, nucleic acid products obtained using techniques such as PCR may be modified to facilitate detection and/or analysis. For example, the nucleic acid molecules may be labelled to facilitate visual identification using techniques standard in the art. By way of example nucleic acids may be radio-labelled using P³² as may be described in Sambrook and Russell, 2001. Further, nucleic acids may be appropriately labelled for use in colorigenic, fluorogenic or chemiluminescence procedures. Specific examples are provided herein after, in the "Examples" section.

It will be appreciated that the methods of this embodiment of the invention may employ one or more control samples. Such control samples may be positive or negative controls for a particular genetic marker. The type of control samples used may vary depending on such factors as the nature of the genetic marker being analysed and the specific technique being used for such detection and analysis. Positive controls may include samples having known nucleic acid sequences. Negative controls may include samples having no nucleic acid present. By way of general example, in analysing a SNP positive control samples could include nucleic acids known to have a particular nucleotide at the relevant position.

In order to facilitate detection of a genetic marker in accordance with the invention, a sample may be processed prior to analysis. For example, the sample may be processed to isolate nucleic acid from the sample to be analysed or to amplify a specific genetic region to be analysed.

In one embodiment, nucleic acid is isolated or extracted from the sample prior to analysis. In one embodiment, genomic DNA is isolated or extracted from the sample. In an alternative embodiment, mRNA may be isolated or extracted from the sample. In such a case, the mRNA may be converted to cDNA using reverse transcription techniques known in the art. Techniques for isolating nucleic acids from samples will be readily appreciated by skilled persons. By way of Example, methods of use in isolating nucleic acids are described in Sambrook and Russell, 2001. In an alternative form of this embodiment of the invention analysis of the nucleic acid may occur in situ obviating the need to extract nucleic acid from the sample. This may be done using PCR for example. Skilled persons will readily appreciate appropriate techniques and methodology to this end (see for example, Sambrook and Russell, 2001).

Expression levels

In another embodiment, the methods of the invention involve observing the level of one or more of the proteins RPS20, MOS, PLAG1, CHCHD7, RDHE2, SDR16C6 and PENK, including any one or more isoforms, precursors and fragments thereof, and/or any nucleic acids encoding one or more of the foregoing.

The proteins RPS20, MOS, PLAG1, CHCHD7, RDHE2, SDR16C6 and PENK are described further in the "Examples" section herein after. Exemplary amino acid and gene sequences for each protein are described in http://www.ensembl.org/. By way of example see: RPS20 - ENSBTAT00000025484; MOS - ENSBTAT00000025483; PLAG1 - ENSBTAT00000005251; CHCHD7 - ENSBTAT00000047298; RDHE2 - ENSBTAT00000052250; SDR16C6 - ENSBTAT00000052250; and, PENK - ENSBTAT00000006478. The methods of this embodiment of the invention will typically involve taking a sample from an animal, observing the level of expression of one or more of the proteins (including isoforms, precursors or fragments thereof) or nucleic acids encoding same, and comparing the level detected against one or more standard. Any difference in the level of expression between the sample and the standard infers the size potential of an animal and/or its offspring, including growth rate potential and/or liveweight potential.

In one particular embodiment, the sample is a muscle, bone, brain or liver sample.

In one embodiment, at least an approximately 1.2-fold difference in the level of one or more of the proteins, a precursor, fragment, or isoform thereof and/or nucleic acid encoding them compared to the standard is indicative of size potential. In one

embodiment, there is at least an approximately 1.2-fold increase in the level of one or more of. the proteins, a precursor, fragment, or isoform thereof and/or nucleic acid encoding any one thereof. In accordance with this embodiment of the invention, a standard is a control sample having a known level of one or more of the proteins (including isoforms, precursors and fragments thereof) or a nucleic acid encoding same, which is tested concurrently with the sample from an animal to be tested. However, in another embodiment, the standard could be a printed chart or electronic information or the like containing previously generated data considered to provide an appropriate standard and which the test samples could be compared to on the basis of colour, fluorescence levels, or numerical values, for example. In accordance with this embodiment of the invention, the standard is preferably a level of one or more of the proteins (including isoforms, precursors or fragments thereof) or a nucleic acid encoding same, which is associated with an expression level in an animal or animals having a particular size (including growth rate and liveweight). As mentioned herein before, the inventors have identified that an increase in expression of one or more of the above proteins correlates with a larger size. Whilst any increase in the level of expression one or more of these proteins (including isoforms, precursors and fragments thereof or nucleic acids encoding same) may be considered to infer size potential of an animal, in a one embodiment, an increase in the level of expression of at least approximately 1.2 fold infers an increase in the size potential of an animal and/or its offspring.

A decrease or substantially no increase in the level of expression compared to a standard may also infer a lower size (including growth rate and/or liveweight) potential for the animal and/or its offspring.

The one or more proteins (including precursors, fragments and isoforms thereof) and nucleic acids encoding same may be detected and the levels thereof compared to a standard using any one or a combination of techniques which are of use in identifying, quantifying and/or highlighting differential levels or expression of one or more proteins. Such techniques will be readily appreciated by persons of ordinary skill in the art to which the invention relates. However, by way of example protein purification methods, immunological techniques, separation of proteins based on characteristics such as molecular weight and isoelectric point including gel electrophoresis and microfluidics- based technologies as for example in gel-free protein separation techniques, and mass spectroscopy (MS) utilizing isobaric label based MS such as iTRAQ or label-free approaches such as multiple reaction monitoring (MRM) may be employed. Appropriate immunological techniques include enzyme linked immunosorbent assay (ELISA) (sandwich ELISA, double sandwich ELISA, direct ELISA, microparticle ELISA), radioimmunoassay (RIA), immunoprecipitation, Western blotting, immunohistochemical staining, antibody arrays, or agglutination assays. Protocols for carrying out such techniques are readily available; for example, see "Antibodies a Laboratory Manual", Cold Spring Harbor Laboratory Press (1988).

Antibodies of use in such immunological techniques may be purchased commercially or produced according to standard methodology in the art having regard to the nature of the proteins to be tested. For example, polyclonal antibodies and monoclonal antibodies may be produced in accordance with the procedures described in the text "Antibodies a

Laboratory Manual" (Cold Spring Harbor Laboratory Press, 1988) using one or more of the proteins or a fragment thereof as antigen. Preferably monoclonal antibodies are used.

Nucleic acid-based techniques of use in the invention include differential display procedures, Northern Blotting, and competitive PCR. Persons skilled in the art to which the invention relates will readily appreciate methodology for performing these techniques.

Nucleic acids, such as oligonucleotide probes and primers, of use in detecting expression levels of proteins in accordance with the invention (for example using Northern blotting or competitive PCR) will be readily appreciated by skilled persons having regard to the information contained herein and any published amino acid and/or nucleic acid sequence information for RPS20, MOS, PLAG1, CHCHD7, RDHE2, SDR16C6 and PENK. The nucleic acids will be capable of hybridising in a specific manner to a mRNA or cDNA associated with RPS20, MOS, PLAG1 , CHCHD7, RDHE2, SDR16C6 and PENK and in the case of primers they will be capable of priming a PCR or like reaction.

Mass spectroscopy techniques of use in the invention are described for example in "Proteins and proteomics-A laboratory manual" (RJ Simpson, Cold Spring Harbour Laboratory Press (2002). The difference in the levels RPS20, MOS, PLAG1, CHCHD7, PvDHE2, SDR16C6 and PENK or nucleic acids encoding RPS20, MOS, PLAG1, CHCHD7, RDHE2, SDR16C6 and PENK in a sample versus a standard may be compared using standard technology having regard to the method employed to detect the protein or nucleic acid. For example, colorimetric and fluorometric techniques may be used in which a detection molecule (such as an antibody or nucleic acid probe or primer) is labelled with a molecule which can be visualised by the naked eye or otherwise detected using a spectrophotometer, or fluorometer for example. Alternatively, detection molecules could be labelled with radio- isotopes. Incorporating labels into nucleic acids during PCR amplification where it is employed (as opposed to labelling a detection molecule such as a probe or primer), is also contemplated.

Methods for labelling molecules and subsequently measuring the intensity of signals generated will be known to those of skill in the art to which the invention relates.

It should be appreciated that in addition to analysing samples and standards, the methods of the invention may include the testing of one or more positive or negative control samples to ensure the integrity of the results. For example, one could include a sample containing no protein/nucleic acid and one or more samples containing a known level of protein/nucleic acid so that results can be calibrated across different runs of the method.

The sample may be processed prior to analysing one or more of RPS20, MOS, PLAG1, CHCHD7, RDHE2, SDR16C6 and PENK and/or nucleic acids encoding same to facilitate analysis of the proteins or nucleic acids. Skilled persons will readily appreciate appropriate processing steps and techniques suitable for performing them.

In one embodiment, high abundance proteins which have the potential to make it difficult to analyse, such as detect and/or measure the level of RPS20, MOS, PLAG1, CHCHD7, RDHE2, SDR16C6 and/or PENK, may be removed from the sample. For example, Top6 or Top7 depletion may be used (as described herein after under the heading "Multi affinity removal system (MARS) for major abundance proteins"). The sample may also be subject to proteolytic digestion. As such detection of a protein or isoform in accordance with the invention should be taken to include detection of any one or more fragments thereof. Fragments should be of a length sufficient to ensure specificity to one or more of RPS20, MOS, PLAG1, CHCHD7, RDHE2, SDR16C6 and PENK. Such fragments will for example be at least 8 amino acids in length, more preferably at least 10, 15 or 20 amino acids in length. Processing steps for preparing the sample for analysis of nucleic acids encoding one or more of RPS20, MOS, PLAG1, CHCHD7, RDHE2, SDR16C6 and PENK, may include lysing cells, isolating mRNA, and generating cDNA using standard procedures such as reverse transcription-PCR as will be known in the art to which the invention relates. In one embodiment, mRNA may be observed in situ.

Skilled persons may readily appreciate other means by which the sample may be processed for use in the invention.

Kits

The invention also relates to kits which are of use in a method of the invention.

In one embodiment, the kit comprises at least one or more reagents suitable for analysis of the sequence of one or more of the genetic markers referred to herein. Reagents suitable for analysis of one or more of the variants include one or more nucleic acid probes and/or primers as herein before described.

In another embodiment, the kit comprises at least one or more reagents suitable for detection of one or more of RPS20, MOS, PLAG1, CHCHD7, RDHE2, SDR16C6 and PENK (including precursors, isoforms and fragments thereof), and/or nucleic acids encoding same.

By way of example, where an immunological procedure is used the kit may comprise one or more antibody specific to one or more of the proteins (including precursors, isoforms and fragments thereof). In a particular embodiment, ELISA is used and the kit comprises one or more capture and/or detection antibody for one or more of the proteins (including precursors, isoforms and fragments thereof).

By way of further example, where a method of the invention involves detection of the level of nucleic acids encoding one or more of the proteins (including precursors, isoforms and fragments thereof), it may comprise one or more nucleic acid probes and/or primers which have specificity for the target nucleic acids.

In another embodiment, the kit comprises at least one reagent suitable for analysis of the sequence of the one or more genetic markers and at least one reagent suitable for detection of one or more of RPS20, MOS, PLAG1, CHCHD7, RDHE2, SDR16C6 and PENK (including precursors, isoforms and fragments thereof), and/or nucleic acids encoding same. Reagents of use in processing samples for analysis may also be contained in the kits of the invention. The kits may also comprise one or more standard and/or other controls including nucleic acids whose sequence or genotype at a particular position is known, or containing known levels of one or more of RPS20, MOS, PLAG1, CHCHD7, RDHE2, SDR16C6 and PENK (including precursors, isoforms and fragments thereof) or nucleic acids encoding same. Further, kits of the invention can also comprise instructions for the use the components of the kit as well as printed charts or the like that could be used as standards against which results obtained from test samples could be compared. Reagents may be held in any suitable container. Breeding

The invention also relates to methods of breeding animals to produce offspring.

In one embodiment the method involves selecting at least a first animal that has one or more of the following genotypes 22986260T, 23186380T, 23186648A, 23187238A, 23205280A, 23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11, 23264854G, 23272034A, and 2327347 IT and mating said first animal with a second animal to produce offspring, wherein the offspring has a smaller size potential. In one embodiment, the second animal is selected on the basis that it also has one or more of the following genotypes 22986260T, 23186380T, 23186648A, 23187238A, 23205280A,

23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11, 23264854G, 23272034A, and 23273471T. In another embodiment, the method involves selecting at least a first animal that has one or more of the following genotypes 22986260T, 23186380T, 23186648A, 23187238A, 23205280A, 23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11, 23264854G,

23272034A, and 2327347 IT and mating said first animal with a second animal to produce offspring, wherein the offspring has a larger size potential. In one embodiment, the second animal is selected on the basis that it also has one or more of the following genotypes 22986260T, 23186380T, 23186648 A, 23187238 A, 23205280A, 23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11, 23264854G, 23272034A, and 23273471T

In one embodiment, the method involves observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; in at least a first animal, selecting the first animal where there is an increase in the expression of one or more of said proteins, precursors, isoforms, fragments and/or nucleic acids encoding same compared to a standard, and mating said first animal with a second animal to produce offspring. In one embodiment, the method further comprises observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; in the second animal, and selecting the second animal where there is an increase in the expression of one or more of said proteins, precursors, isoforms, fragments and/or nucleic acids encoding same compared to a standard. Preferably the first and/or second animals are selected for breeding for beef farming where the level of expression is increased compared to a standard.

In another embodiment the method involves observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; in at least a first animal, selecting the first animal where there is substantially no increase in the expression of one or more of said proteins, precursors, isoforms, fragments and/or nucleic acids encoding same compared to a standard, and mating said first animal with a second animal to produce offspring. In one embodiment, the method further comprises observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; in the second animal, and selecting the second animal where there is substantially no increase in the expression of one or more of said proteins, precursors, isoforms, fragments and/or nucleic acids encoding same compared to a standard. Preferably the first and/or second animals are selected for breeding for dairy farming where there is no substantial increase in the level of expression compared to a standard.

In one embodiment, an animal is rejected for breeding for the purposes of dairy farming where there is an increase in the level of expression compared to the standard. In one embodiment, the animal is rejected for breeding for the purposes of beef farming wherein there is substantially no increase in the level of expression compared to the standard.

It should be appreciated that any appropriate breeding methods may be utilised in this embodiment of the invention including natural insemination, artificial insemination and in vitro fertilisation. Accordingly, the word "mating" should be construed broadly and not limited to the physical pairing of two animals.

Preferably, the methods herein before described may be used to determine the genotype of one or both potential parents and/or the level of expression of one or more specified protein.

The invention is now further elucidated by the following non-limiting examples. EXAMPLES

Example 1

A QTL with major effect on body size maps to bovine chromosome 14.

To identify QTL affecting traits of importance for the dairy sector, a Holstein-Friesian (HF) x Jersey (J) line-cross comprising 864 F2 cows (Fig. 1) was generated. To that end, 36 HF bulls were mated with 430 J dams, and 24 J bulls with 366 HF dams, yielding six Fl sires (three HFxJ and three JxHF) and 796 Fl dams (430 HFxJ and 366 JxHF). Fl animals were mated in order to obtain approximately equal proportions of the four possible genotypes ((HfxJ)x(HFxJ); (HfxJ)x(JxHF); (JxHF)x(HFxJ); (JxHF)x(JxHF)). This experimental design primarily targets QTL segregating between HF and J, but - if analyzed appropriately - also allows the identification of QTL segregating within breeds. More than 500-hundred traits were measured on the F2 animals (Spelman et al, 2007). More than twenty of these pertained to body size, including weight at birth, 6, 8, 12, 18 and > 24 (- 'live weight") months, as well as height at withers at 18 months. All animals of the pedigree were genotyped for 294 microsatellite markers spanning the bovine genome. When applying a line-cross model (i.e. assuming fixation of alternate QTL alleles in HF and J)(Haley et al, 1994) to weight and height, the genome-wide location scores shown in Fig. 2A were obtained. The strongest signal, exceeding genome-wide significance of P-value O.00001, was on the proximal half of chromosome BTA14.

To increase the information content of BTA14, increased the microsatellite density was increased from eight to 56. The data was re-analyzed using the linear regression option of the HSQM software (Coppieters et al., 1998), treating the data as six independent paternal half-sib pedigrees. Across-family analysis yielded the location scores shown in Fig. 2B, confirming the presence of a genome- wide significant QTL affecting body size on proximal BTA14. The effect on weight was clearly detectable at birth, but its significance increased with age. It was accompanied by a highly significant, co-localized effect on height. Bootstrap analysis (Visscher et al., 1996) of live weight (the most significant trait) defmed a 18.4 Mb confidence interval (CI) spanning positions 9.0 Mb to 27.4 Mb on BTA14. Within-family analyses yielded significant evidence for QTL segregation in four of the six sire families, suggesting (under a biallelic QTL model) that sires 1, 2, 3 and 5 were of "Qq" QTL genotype, while the two remaining sires (4 and 6) were either of "QQ" or "qq" genotype (Fig. 2C). QTL position and allele substitution effects were consistent across the four "Qq" sires (Fig. 2D): -20 Kgs for live weight and ~2 cms for height, corresponding to predicted differences between full-grown QQ and qq animals of -40 Kgs and -4 cms.

Analysis of the scientific literature indicates that the same QTL is likely to be segregating in other populations, thus increasing the interest of identifying the corresponding causative mutation(s). Mizoshita et al. (2004) and Takasuga et al. (2007) reported QTL affecting weight of Japanese Black Cattle on BTA14. Buchanan et al. (2005) reported association between polymorphisms in the CRH gene (located on chromosome 14) and growth in Charolais-cross cattle. Kneeland et al. (2004) reported QTL influencing birth weight as well as average daily gain pre- and post-weaning in commercial beef cattle. Nkrumah et al. (2007) reported QTL affecting average daily gain in offspring of Angus, Charolais and Alberta Hybrid bulls. Using haplotype-sharing methods, Mizoshita et al. (2005) fine- mapped one of their growth QTL to a 1.1 Mb interval (flanked by microsatellites BMS1914 and INRA094) encompassed by the CI defined in this study (Fig. 2B).

-5

Combined linkage and linkage disequilibrium (LD) analysis in the HFxJ intercross and outbred dairy cattle population positions the QTL within a ~750 Kb chromosome segment

To refine the map position of the QTL the BTA14 marker density was increased focusing0 on the region spanning the QTL peak. The entire HFxJ cross was genotyped for 925 SNP markers corresponding to (i) 629 public domain SNPs, and (ii) 296 novel SNPs. The latter were identified by resequencing coding exons as well as highly conserved sequence elements in the six Fl sires. Combined linkage and linkage disequilibrium analyses (e.g. Blott et al., 2003; Druet & Georges, 2009) of live weight ( i.e. the most significant trait)5 were performed. Highly significant location scores were obtained, maximizing at position 23,716,140 (single marker analysis; lodscore: 31.4; Fig. 3A) or 23,221,130 (haplotype- based analyses; lodscore: 34.1; Fig. 3B), i.e. ~1 Mb proximal from the 1.1 Mb interval defined by Mizoshita et al. (2005). Fitting the most likely QTL position in the model and rescanning the chromosome for residual QTL effects didn't reveal any evidence for a0 second QTL in the vicinity (Fig. 4). It is noteworthy that at this stage not a single SNP nor haplotype matched the QTL genotypes of the six Fl sires (1-2-3-5: Qq; 4-6: QQ/qq) (data not shown).

The effect of chromosome 14 on body size was then evaluated in the New-Zealand outbred5 dairy cattle population. A dataset corresponding to 2,700 progeny-tested bulls genotyped for the USDA 50K Illumina SNP chip (Van Tassell et al., 2008) was used. Phenotypes were breeding values for live weight. The data were analyzed using the same mixed model as above (extracting both linkage and linkage disequilibrium information) with addition of a regression on percentage Jersey blood given available pedigree information. The0 obtained location scores are shown in Fig. 3C. A maximum LRT of 156 (corresponding to a lod score of 34.0) was obtained at position 23.425.845, i.e. exactly the same location as in the HFxJ cross despite the use of a distinct SNP panel. These results considerably strengthened the confidence in the location of the QTL. Figure 5 shows the effect on breeding value for live weight of the different haplotype clusters as well as their respective frequencies in the New Zealand dairy cattle population. The bimodal distribution of haplotype effects supports a bi-allelic QTL. As expected, the q allele associated with lower weight/height is virtually fixed in the Jersey population, while the Q allele associated with higher weight/height predominates in the Holstein Friesian population. It is noteworthy that, in the outbred population, fitting the most likely QTL position in the model and scanning BTA14 for additional QTL effects did reveal modest evidence (lod score 4.3) for the existence of a second QTL in the interval defined by Mizoshita et al. (2005)(Fig. 4). From there on, further analyses focused on a ~750 Kb segment spanning all most. likely QTL positions obtained in the different analyses (22,967,680 - 23,718,980).

Massive parallel resequencing of the -750Kb critical region identifies a cluster of 16 candidate QTN.

A 103 long-range PCR products spanning the entire 750 Kb interval was generated. PCR products of the predicted size were obtained from HF and J genomic DNA thereby (i) confirming the accuracy of the local sequence assembly, and (ii) indicating that both HF and J alleles could be amplified with comparable efficacy. The same 103 long-range PCR products were then amplified from genomic DNA of the six Fl sires, pooled, size- fractionated by nebulization, and appended with adaptors allowing for massive parallel resequencing on a Roche FLX instrument. The adaptors included individual specific multiplex identifiers (MIDs) allowing pooling and simultaneous sequencing of the eight libraries (HF, J and six Fl sires). The obtained sequence traces were analyzed using the GS Reference Mapper software from Roche. The average sequence depth of non repetitive sequences was~20-fold per individual. Mining of DNA sequence polymorphisms using GS Reference Mapper revealed a total of 8,851 putative DNA sequence polymorphisms (DSP) corresponding to an average nucleotide diversity of 0.3%. Assuming that the QTL is indeed bi-allelic (as suggested by the bimodal distribution of haplotype effects), the causative "Quantitative Trait Nucleotide" (hereafter referred to as pQTN for phenotypic QTN) has to be heterozygous in the four Fl sires that segregate for the QTL, homozygous in the two non-segregating Fl sires, and homozygous for alternate alleles in the HF and J samples. Applying this filter to the 8,851 DSP yielded only 17 candidate pQTN: 16 SNPs and one VNTR marker (Table 2). Interestingly, all but one of these clustered in a 87 Kb region spanning MOS, PLAG1 and CHCHD7 (Fig. 6). Genotyping assays could be developed for 9 of the 16 SNPs and the VNTR and were used to genotype the HFxJ intercross. All markers in the 87 Kb window proved to be in perfect LD with each other (D'=l; r > 0.985), but not with the more distant one (0.859<D'<0.870;0.728<r²<0.746). Single-marker association, conducted using the same mixed model as before, revealed a LRT of 174.7 for the marker cluster, i.e. 17.9 LRT (or 3.9 lodscore) units above the best signal obtained before. The signal for the isolated SNP, on the contrary, was below that obtained in the previous haplotype^-based analyses (Fig. 3D). These results strongly suggested that the 16 clustered polymorphisms include the pQTN. The same markers also yielded the strongest signals for the other body size phenotypes (weight at birth, 6, 12 and 18 months, height), supporting the fact that the same QTN accounts for all observed QTL effects (Fig. 7 A to E).

An orthologous region was shown in two independent genome wide association studies (GWAS) to control height in humans (Gudbjartsson et al., 2008; Lettre et al., 2008).

Table 2

Marker Chromosome Position Q AM q All Phastcons

FJX_21374_1:1 14 22986260 T C 0.625

FJX_221494 14 23186380 T C 0.571

FJX_221762 14 23186648 A G 0.500

FJX_222352_1:1 14 23187238 A G 0.002

FJX_240394 14 23205280 A G 0.002

FJX_247103 14 23211989 - G 0.002

FJX_250879_1:1 14 23215765 C G 0.905

FJX 253666 14 23218552 T C 0.102 ,

FJX 263178 1:1 14 23228064 G T 0.015

FJX_267438_2:2 14 23232324 A G 0.239

FJX_270353_1:1 14 23235239 C G 0.002

FJX_278702_1:1 14 23243588 - TT 0.004

FJX_278848 14 23243734 A G 0.004

(CCG) (CCG)

FJX_PLAPROT I_l:l 14 23264810 11 9 0.999

FJX PLAPROSNP 1:1 14 23264854 G A 0.996

FJX 307148 1:1 14 23272034 A G 0.033

FJX_308585_1:1 14 23273471 T C NA Exploiting haplotype diversity in other breeds excludes 5 of 16 candidate pQTN as causative pQTN.

The 16 QTN being in complete LD within the HFxJ F2 population precluded further genetic differentiation of causative from "passenger" variants in this pedigree material. To overcome this limitation, a panel of 159 animals representing 12 breeds for 12 of the 16 candidate pQTN were genotyped. While the two haplotypes ("g" and "q") observed in the HfxJ cross accounted for 93.8% of the chromosomes in this diversity panel 10 additional "recombinant" haplotypes were observed. One of these ("i?"), carrying the Q allele at 5/12 positions and the q allele at 7/12 positions, reached a frequency of 37% in the Simmental population (Fig. 8 A). Therefore DNA samples from 44 unrelated Simmental animals with birth weight information were collected and genotyped for the 12 polymorphisms. The phase of the 44 animals was manually determined. The frequency of the Q, q and R haplotypes in this sample was 22%, 35% and 34%, respectively. The remaining 8% of the chromosomes corresponded to four minor haplotypes. The birth weight of the 44 animals on QTL genotype was regressed assuming (i) that the R haplotype was Q, or (ii) that it was q. While no effect on birth weight was observed under the first hypothesis, a near significant (p=0.06) substitution effect on birth weight with the predicted sign was observed under the second. The difference in birth weight between predicted qq and Qq animals (representing 93% of the sample) was significant (p = 0.02) (Fig. 8B). These results strongly suggest that the 5 Q positions of the R haplotype can be excluded as causative pQTN . However, this does not preclude these 5 Q positions being useful as markers in a method of the present invention.

Table 8: Top 8 SNPs

Marker Chromosome Position Q AII q All Phastcons

FJX_21374_1:1 14 22986260 T C 0.625

FJX_221494 14 23186380 T C 0.571

FJX_221762 14 23186648 A G 0.500

FJX_222352_1:1 14 23187238 A G 0.002

FJX_250879_1:1 14 23215765 C G 0.905

FJX_253666 14 23218552 T C 0.102

FJX_263178_1:1 14 23228064 G T 0.015

(CCG) (CCG)

FJX_PLAPROTRI_l:l 14 23264810 11 9 0.999

FJX_PLAPROSNP_1 :1 14 23264854 G A 0.996

Unaffected MOS, PLAGl and CHCHD7 open reading frames support regulatory nature ofpQTK

The candidate pQTN span 87 Kb encompassing three genes {MOS, PLAGl and CHCHD) (Fig. 6A). None of the 16 candidate pQTN resides in the open reading frame (ORF) of either MOS, PLAGl or CHCHD7. Nevertheless, the pQTN could affect the splicing reaction thereby altering coding capacity. To examine this possibility, tissue samples (muscle, bone, brain and liver) were collected from 79 outbred fetuses (Table 9). Fetuses were genotyped for the candidate pQTN, two homozygous "QQ" and two homozygous "qq" fetus were selected, and RT-PCR reactions using overlapping amplicons that would jointly span the complete MOS, PLAGl and CHCHD7 were performed. No evidence for genotype-specific RT-PCR products (Fig. 9) was obtained. These findings indicate that the pQTN affect the expression profile of the causative gene(s) rather than their structure, i.e. they are regulatory pQTN.

The pQTN affect expression of a regulon including RPS20, MOS, PLAGl, CHCHD7, RDHE2, SDR16C6 andPENK.

Being regulatory, the pQTN could not only affect the expression of the spanned MOS, PLAGl and CHCHD7, but also of more distant genes. Thus the effect of pQTN genotype on the expression level of all eight genes in the 750 Kb interval in the fetal tissue samples described above was examined. First expression by QRT-PCR, using up to three amplicons per gene was assayed. Data were normalized using from two to five housekeeping control genes selected from eight candidates using geNorm (Vandesompele et al. 2002). Normalized expression levels were expressed on a log₂-scale, relative to the mean of all animals. For a given gene, the individual's average relative expression across amplicons was computed. The average relative expression on the number of Q alleles in pQTN genotype was regressed. In addition to QRTPCR, allelic imbalance assays for PLAGl and CHCHD7 were performed, taking advantage of pQTN located in the 3'UTRs (FJX_250879_1 :1 and FLX_307148_1 :1). Comparing the allelic ratio in transcripts of heterozygous individuals is potentially more sensitive (as the comparison of the allelic output is performed within the same sample thereby minimizing the effect of confounding factors), and will also detect differences in promoter strength for genes whose transcription levels are under negative feedback regulation (i.e. rendering steady state transcriptional levels independent of promoter strength). Amplicons encompassing the respective SNPs were amplified from genomic and cDNA and allelic ratios compared by direct sequencing and Peakpicker . (Ge B et al. 2005).

Fig. 10 and Fig. 11 summarize the obtained results. Except for LYN, a significant effect of pQTN genotype on the transcript level of each gene in at least one tissue was observed. The Q allele was associated with increased expression (average 1.2-fold) in 26 of the 29 assays performed (where 14.5 are expected if pQTN genotype does not affect expression; p = 2E-12). Significant effects were observed for all four examined tissues. The pQTN effect possibly extends over a longer chromosome segment in brain (from RPS20 to PENK) than in the other tissues (from PLAG1 to PENK).

These findings suggest that the pQTN affect the function of (a) long-range cis-acting element(s) that regulate(s) the expression of RPS20, MOS, PLAG1, CHCHD7, RDHE2, SDR16C6 and PENK, but apparently not LYN, in multiple tissues. It is noteworthy in this regard that synteny is conserved in zebrafish (chromosome 7) for RPS20, PLAG1, CHCHD7, RDHE2 (=SDRC16C5) and PENK but not for LYN which lies at the proximal end of the cluster in mammals (Fig. 6). The coregulation of multiple genes implies a transcriptional effect. In agreement with this hypothesis, the inventor(s) observed the same degree of pQTN-dependent allelic imbalance when assaying PLAG1 pre-mRNAs by means of the FJX_267438_2:2 intronic pQTN candidate (Fig. 10 and Fig. 1 IB).

Reporter and electrophoretic mobility shifty assays support the causality of two pQTN in the PL A G 1-CHCHD 7 bidirectional promoter

Which one(s) of the eight candidate pQTN causes the observed effect on transcription rate? It was noted that two of the eight candidate pQTN (FJX_PLAPROTRI_l :l and FJX_PLAPROSNP_l :l) affect an extremely conserved sequence segment located in the intergenic region separating PLAG1 and CHCHD7 (Fig. 5 A). FJX_PLAPROTRI_l :l (phastcons score: 0.999) is a (CCG)_n trinucleotide repeat with either 9 (q) or 11 (Q) copies located immediately upstream of the presumed human PLAG1 transcriptional start site, while FJX_PLAPROSNP_l :l (phastcons score: 0.996) is a A (q) to G (g) SNP 12-bp upstream from FJX_PLAPROTRI_l : 1. Of note, PLAGl and CHCHD7 are positioned head-to-head, separated by only ~500-bp supposed to encompass a bidirectional promoter (Trinklein ND et al. 2004). Of the six remaining candidate pQTN, only FJX_250879_1 :1 also affects a conserved element (phastcons score: 0.905) located in the 3'UTR of PLAGl (Table 8).

The effect of the FJX PLAPROTRIJ : 1 and FJX_PLAPROSNP_l :l DSP on the presumed bidirectional promoter activity of the PLAG1-CHCHD7 intergenic region was tested. Both allelic forms (Q and q) of a 378-bp and 659-bp fragment centered around the two pQTN were cloned in both orientations in the pGL4 luciferase vector (Fig. 12 A). Cosl cells were transfected and measured luciferase activity after 24 hours. When compared to the promoter-less reference vector, both fragments indeed increased luciferase activity: ~9-fold (short) and ~20-fold (long) in the PLAG1 direction, and ~90-fold (short) and 44-fold (long) in the CHCHD7 direction (Fig. 12B). Most importantly, the level of luciferase activity was systematically higher with the Q constructs than with the q constructs, the difference being ~1.5-fold, i.e. a magnitude comparable to that observed in vivo (Fig. 12B).

To determine the relative contribution of the FJX_PLAPROTRI_l : 1 and FJX_PLAPROSNP_l :l variants to the observed effect, recombinant constructs (i.e. being Q for FJX_PLAPPvOTRI_l:l and q for FJX_PLAPROSNP_l:l or vice versa) were generated, for both the long and short fragments (Fig. 12C). While both variants appeared to be necessary to recapitulate the full eQTL effect in the PLAG1 orientation, the FJX_PLAPROSNP_l:l SNP appeared to play a predominant effect in the CHCHD7 orientation (Fig. 12D).

To examine whether the effect of the FJX_PLAPROTRI_l : 1 and FJX_PLAPROSNP_l : 1 variants might reflect differential binding of trans-act g factors, electrophoretic mobility shift assays (EMSA) were performed. First, radiolabeled double-stranded 29-mers centered around the FJX PLAPROSNP lrl and nuclear extracts from Cosl, C2C12 and ATDC5 cells, as well as from fetal bone, brain, muscle and liver were used. With all extracts, a number of complexes were observed, some with the same mobility across tissues and some specific, that were systematically 1.2 to 3 x more abundant with the q than with the Q duplex. Moreover, cold q duplex tended to be more proficient than cold Q duplex in displacing either of the radio labelled probes (Fig. 13). Then 74/80-mer probes encompassing both the FJX_PLAPROTRI_l:l and FJX_PLAPROSNP_l:l variants (Cosl and C2C12 cells) were used. As with the 29-mer oligonucleotide, large complexes that were ~2.5 x more abundant with the q than with the Q probe were observed. As these would be displaced by cold 29-mers (q more effective than Q), they were assumed to be related to the complexes detected with the 29-mer sequence alone. In addition, with C2C12 extracts we detected a g-specific complex that was not displaced by the 29-mer (Fig. 13). Taken together, these findings suggest that distinct regulatory complexes assemble on the interrogated Q and q sequences, and that this is likely to explain their different promoter strength in reporter assays, and to contribute to the observed differential transcription rate of the Q and q alleles in vivo. Complex assembly in the PLAG1-CHCHD7 intergenic segment is likely to be mediated by broadly expressed and abundant trans-acting factors, as the complexes were readily detected in all examined tissues/cell types.

Exploiting a naturally occurring null allele to exclude the causality of CHCHD7.

Which one(s) of the seven genes affected by the pQTN is (are) controlling stature? RPS20 encodes ribosomal protein S20. RPS20 mutations cause p53-mediated skin darkening with pleiotropic effects including reduced body size (McGowan et al. 2008). MOS encodes a protein kinase that is specifically expressed in oocytes where it is involved in the control of meiotic maturation. However, ectopic expression of MOS in somatic cells induces oncogenic transformation (Sagata 1997). PLAG1 (Pleomorphic Adenoma Gene 1) was discovered as an oncogene whose ectopic expression due to translocation-induced promoter swapping with ubiquitously expressed genes (such as β-catenin), causes pleomorphic adenomas especially of the salivary gland (Van Dyck et al. 2007). PLAG1 encodes a transcription factor that is strongly and broadly expressed during foetal development (particularly in the anterior pituitary), yet severely downregulated after birth. PLAG1 has been shown to regulate transcription of several growth factors including IGF2, a key regulator of body size (Van Dyck et al. (2007) Voz et al. (2004)). Interestingly, the most striking phenotype of PLAG1 knock-out mice is growth retardation (Hensen et al. 2004). CHCHD7 encodes a widely expressed protein of unknown function, named for the coiled-coil-helix-coiled-coil-helix (CHCH) domain it contains. It was identified as a PLAG1 fusion partner in tumors of the salivary gland (Van Dyck et al. 2007). RDHE2 (alias SDR16C5) and SDR16C6 encode members 5 and 6 of the 16C family of short-chain alcohol dehydogenases/reductases. RDHE2 catalyzes the first and rate-limiting step generating retinal from retinol. Imbalances in endogenous retinoids profoundly perturb development and affect growth (Ross et al. 2000). PENK encodes the precursor of met- and leu-enkephalins, which play a role in pain perception and response to stress (Kiefer et al. 2002). While the candidacy of PLAG1 seem strongest, supported by GWAS signals maximizing on top of the same gene in human, available evidence neither proves PLAGVs causality, nor disproves a contribution of one or more of the other genes. To gain additional insights in the causality of the positional candidate genes, genome-wide expression data generated for 429 of the HFxJ F2 animals was used by hybridizing liver and adipose cDNA (sampled at 60 or 70 months of age) on Affymetrix Bovine 24K expression arrays. The array included probes interrogating LYN, RPS20, MOS, CHCHD7 and RDHE2 but not PLA Gl and SDR16C6. QTL mapping using HSQM (Coppieters, et al. 1998) and the 56 microsatellite BTA14 map revealed chromosome-wide significant eQTL effects for CHCHD7 in liver and adipose, and for RPS20 in liver. Fine-mapping using the 925 SNP map positioned both eQTL in the resequenced 750-Kb segment, strongly suggesting cis-acting eQTL (Fig. 14 & 15).

Within-family analysis of the CHCHD7 data showed that only the first Fl sire ("1") was segregating for the eQTL, i.e. a segregation pattern in Fl sires distinct from that of the QTL on height/weight. The resequencing data was examined and a SNP matching the eQTL segregation pattern of the six Fl sires that was predicted to affect the donor splice site of CHCHD7 exon 3 (FJX_303486_1:1) was identified. The HFxJ pedigree was genotyped for the corresponding SNP and single-marker association analysis was performed. FJX_303486_1:1 had a highly significant effect on CHCHD7 expression in both liver and fat. The effect was superior to that of all other markers, thus strongly suggesting that FJX_303486_1:1 is the causative "eQTN" (Fig. 14C). Of note, including FJX_303486_1:1 in the association model caused marked drops in the significance of all other markers. Nevertheless, several of them remained significant, including the pQTN (Fig. 14D).

To confirm that FJX_303486_1:1 affects splicing, the 79 fetuses were genotyped: 61 were homozygous TT, 17 were heterozygous AT and one was homozygous AA (without obvious phenotype) (Table 9). RT-PC experiments were conducted on RNA samples from fetuses of the three genotypes using primers spanning the entire CHCHD7 transcript. RT-PCR products from AA individuals were shortened by 70 bp when compared to TT individuals, which was shown by direct sequencing to result from the skipping of exon 3. The ensuing mutant transcripts have truncated ORF closing within exon four (of five) (Fig. 16 A), and are therefore predicted to undergo nonsense mediated RNA decay which may at least in part account for the observed eQTN effect. To confirm the effect of the FJX_303486_1:1 splice variant on CHCHD7 transcript levels, the available QRT-PCR data was exploited and compared CHCHD7 expression levels between qq (pQTN) fetuses that were homozygous wild-type versus heterozygous for the FJX_303486_1 :1 splice site variant. CHCHD7 transcript levels were indeed found to be reduced ~1.3-fold in AT vs TT animals in all examined tissues, corresponding to mutant transcripts levels ~ ½ those of wild-type transcripts (Fig. 16B). Of note, the effect of pQTN genotype on the transcript levels of the eight positional candidate genes is independent of the FJX_303486_1 :1 splice site variant (Fig. 10 and Fig. 11).

Assuming that variation in CHCHD7 expression levels influences body size, the allele substitution effects on height/growth should be larger in the offspring of sire 1 (heterozygous AT at FJX_303486_1:1) than in any of the other sire-families (sires -being homozygous TT). Note that as FJX_303486_1:1 perturbs splicing, it must affect CHCHD7 levels in all tissues. As can be seen from Fig. 2D the QTL allele substitution effects in sire-family 1 did not differ significantly from those in the other sire-families (2, 3 and 5) segregating for the body size QTL. Moreover, and under the same assumption, the FJX_303486_1:1 should have an independent effect on body size. This hypothesis was tested by estimating the effect of the FJX_303486_1 : 1 SNP on height/weight in the HFxJ line-cross using a mixed model including the pQTN and an individual animal effect. There was no evidence for the slightest residual effect of FJX_303486_1:1 on body size when including the pQTN in the model (data not shown). Finally, assuming that FJX_303486_1:1 creates a CHCHD7 null allele and if CHCHD7 is causally involved in the stature QTL, the [qT/qT] vs [qT/QT] phenotypic contrast should differ from the [qA/qT\ vs [qA/QT] contrast. This "quantitative complementation assay" (QCA) (Georges 2007), was performed using a mixed linear model including an individual animal effect. There was no evidence for a significant difference between the two contrasts. Taken together, these results allow formal exclusion of CHCHD7 as being causally involved in the determinism of the stature QTL.

Within family analysis of the RPS20 liver data indicated that sires 1, 5 and 6 were segregating for this eQTL, i.e. a segregation pattern distinct from both the stature QTL and CHCHD7 eQTL (Suppl. Fig. 10). Contrary to CHCHD7 however, the apparent tissue- specificity of the eQTL does not allow exclusion RPS20 on that basis. Analysis of the sequence traces did not reveal obvious candidate eQTN in the immediate vicinity of the RPS20 gene. Table 10 - Nucleic acid sequence in region of genetic markers

Q - nucleic acid sequence Q allele q - nucleic acid sequence q allele

Materials and methods

Genotyping. Genotyping the 56 microsatellite markers on the HFxJ F2 pedigree was performed using standard procedures labeling the PCR products with fluorescent primers and size fractionating them on a ABI36730 capillary sequencer (e.g. Coppieters et al., 1998a). Genotyping the 925 BTA14 SNP markers on the HFxJ F2 pedigree was performed with a custom-made Golden Gate assay on an Illumina Beadstation500 using standard procedures. Genotyping the pQTN was done using Taqman assays-by-design using standard procedures (ABI, Foster City, CA). The primers used to analyse the genetic markers are listed in Table 7. below.

QTL mapping and fine-mapping. Weight/Height: (i) QTL mapping in the HFxJ F2 population under a line-cross model was conducted with a regression based statistical approach (Haley et al 1995). (ii) QTL mapping in the HFxJ population under a half-sib pedigree model was conducted with HSQM (Coppieters et al., 1998b). Significance thresholds were determined by permutation testing following Churchill & Doerge (1994). Confidence intervals for the QTL were determined by bootstrapping following Visscher et al. (1996). (iii) QTL fine-mapping was achieved by simultaneously exploiting linkage and LD using a haplotype-based approach described recently by Druet & Georges (2009). The mixed model includes a mean (fixed), a haplotype effect (random), an animal effect (random) and an error term (random). The haplotype effect corresponds to haplotype clusters defined using a Hidden Markov Model (HMM). The covariance between distinct clusters is assumed to be zero. Variance components and individual effects are estimated using AI-REML. Results are reported as LRT=21nLR, where LR corresponds to the ratio between the likelihood of the data under HI (model with haplotype effect) and likelihood of the data under HO (model without haplotype effect). LRT is asymptotically distributed as a chi-squared variable with ~ one degree of freedom. In some instances, two QTL positions were fitted simultaneously. Evidence in favor of a QTL at position 2 (conditional on the presence of a QTL at position 1) was then evaluated from the increase in the LRT achieved by adding this second QTL in the model, (iv) Association analysis was conducted using a mixed model including a mean, a regression on the number of alleles "1" for the considered SNPs (fixed), an animal effect (random) and an error (random). Variance components and effects were estimated by REML. The significance of the SNP effect was estimated using an F-test. In some instances, association analysis only considered the marker allele inherited from the mother. Transcriptomic data: (i) eQTL mapping in the HFxJ population under a half-sib pedigree model was conducted with HSQM (Coppieters et al., 1998b) as for the phenotypic data, (ii) Association analysis was conducted using a linear model including a mean, an experiment (1 or 2) effect (fixed), a regression on the number of alleles "1" for the considered SNPs (fixed), and an error term. The significance of the SNP effect was estimated using an F-test. In some instances two distinct SNP effects were fitted simultaneously. The significance of the second SNP effect conditional on the first one was estimated by an F-test. Haplotype-based association analysis was conducted by multiple regression against the number of copies of each of the 20 HMM-defined haplotype clusters (Druet & Georges, 2009)

Sequencing. Resequencing the ~750 Kb critical region was achieved by amplifying the entire interval as 103 long range PCR products, equimolar pooling of amplification products, size-fractionation by nebulization, ligation of adapters including multiplex identifiers, and sequencing on a Roche FLX system using standard procedures. The flow grams were analyzed using the GS Sequencer and Reference Mapper software modules from Roche.

Table 7A

Genotyping Forward Primer Reverse Primer Name

Assay ID Forward Primer Seq. Reverse Primer Seq.

method Name (SEQ ID No) (SEQ ID No)

Taqman FJX_21374-161 F

FJX 21374 1 :1 (35) CCTTCAAGAAGGTGGAGAAGAAAACA FJX_21374-161 R (36) GACTGAACTGAACTGAACTGAACTG

Taqman FJX_221494-FJX2F

FJX 221494 (37) AAACCAGCAATCGCAAAACACTT FJX_221494-FJX2R (38) CAGAAGGGAGAAGCAGAAACCT

Taqman FJX_221762-2195F

FJX 221762 (39) AGTCATGTATGGGTGTAAGAGTTGGA FJX_221762-2195R (40) CAACACCACAGTTCAAAAGCATCAA

Taqman FJX__L222352-FJX3F GCTTTCCTCCATCCCAATGTTAAATTTTAC

FJX 222352 1 :1 (41) TA FJX_222352-FJX3R (42) GTCCCTTTTCCAGTTATCTGCATGA

FJX 240394 RFLP-PCR FJX_240394-1 F

(Haelll) (87) tattcattggaaggactgatgcta FJX_240394-1 R (88) AACAAGGAAACATGCTGAAATACA

Re- FJX_247103-1 F

FJX 247103 sequencing (89) CATTAGGCACTGTGCTGTTTATCT FJX_247103-1 R (90) cgctatataccacatggagctaga

Taqman FJX_250879-2531 F

FJX 250879 1 :1 (43) GATCTCAGTAGGCTGGAAGCA FJX_250879-2531R (44) CACACTGTCTTCCCAGTATGTAAGT

Taqman FJX_253666-FJX9F

FJX 253666 (45) AAAACACTGAATGAGAAAGATACCACAGT FJX_253666-FJX9R (46) CACCAAAAGCAGAAAACAGAACAAC

Taqman FJX_263178-FJX4F GCTAATTAAGGGCAAAAGCTATCATAATC ATGTTGACATTTTCTTAATTGTTTACTAATGAGT

FJX 263178 1 :1 (47) AA FJX_263178-FJX4R (48) TTTATGA

Taqman FJX_267438-RPTF

FJX 267438 2:2 (49) CTGTTCCACCAGGATCCTTATTCT FJX_267438-RPTR (50) GGAAGGGTCCGGGTACCT

Taqman FJX_270353-FJX5F TTAAATCTTTATTTCTTGG I I I I I CTCCTTACAT

FJX 270353 1 :1 (51) TGTTGTAGAAATAGGTACTCAGAGCCT FJX_270353-FJX5R (52) TTTTAGAA

Taqman FJX_278702-FJX6F CAAAAAACCCCAAAAAATCAATTCTTCAAG

FJX 278702 1 :1 (53) AAAA FJX_278702-FJX6R (54) CACTGAACTGACTGACTGACTGATA

FJX 278848 RFLP-PCR

(Taq I) FJ^_278848 (91) atccaacagagatgcagttaacaa FJX_278848 (92) Taattgatgcttttgaactgtggt

FJX_PLAPRO i icrosat Fragment length I

TRI 1 :1 (analysis 5UTR .SNP3F (55) AGAACTCACCGCGGGGCTTTAACAT 5UTR_SNP2R (56) GGAGGAGCGCGCGGGGAAGG

Re ^"

sequencing

(Sanger

FJX_PLAPROS method

NP 1 :1 ABI3730) 5UTR_SNP3F (85) AGAACTCACCGCGGGGCTTTAACAT 5UTR_SNP2R (86) GGAGGAGCGCGCGGGGAAGG

Taqman FJX_307148-3031 F

FJX 307148 1 :1 (57) CAGATAGTTTGTGTCCCTTCTCTTCAT FJX_307148-3031 R (58) ACGAGCTGGACAGTTTGTGT

Taqman FJX_308585-FJX7F

FJX 308585 1 :1 (59) AGTCCAAAGGTTAACATCTGTGTTTCT FJX_308585-FJX7R (60) TGGTGGGCTGCCATCTATG

Table 7B

Reporter 1 Name Reporter 2 Name (SEQ

Assay ID Reporter 1 Dye Reporter 1 Sequence Reporter 2 Dye Reporter 2 Sequence Design Str

(SEQ ID No) ID No)

FJX 21374-

FJX_21374_1 :1 161 2 (61) IC TCTGCCAGACTCTCC FJX 21374-161 M2 (62) FAM TGCCAGGCTCTCC Reverse

FJX 221494- FJX 221494-FJX2 1

FJX_221494 FJX2V1 (63) IC TGTGCATCTCACCCCCT (64) FA CTGTGCATCTTACCCCCT Forward

FJX 221762- FJX 221762-2195M1

FJX_221762 2195V1 (65) VIC AAGAACACTAAGCACCAAAG (66) FAM AACACTAAGCGCCAAAG Forward

FJX 222352- FJX 222352-FJX3M1

FJX_222352_1 :1 FJX3V1 (67) VIC CTTCTCACATCATGTAATC (68) FAM CTCACATCGTGTAATC Forward

FJX_240394

FJX_247103

FJX 250879- FJX 250879-2531 M1

FJX_250879_1 :1 2531 V1 (69) VIC ACTTGGGTCAATATTT (70) FA CTTGGGTGAATATTT Forward

FJX 253666- FJX 253666-FJX9 2

FJX_253666 FJX9V2 (71) VIC CCGGCTCACCAACCA (72) FAM CGGCTCGCCAACCA Reverse

FJX 263178- FJX 263178-FJX4M1

FJX_263178_1 :1 FJX4V1 (73) VIC rrTCAGTCTCCTGTATGCTA (74) FAM TTTCAGTCTCCTTTATGCTA Forward

FJX 267438- FJX 267438-RPT 1

FJX_267438_2:2 RPTV1 (75) VIC CATCTGCCACATCCCA (76) FAM TCTGCCGCATCCCA Forward

FJX 270353- FJX 270353-FJX5M2

FJX_270353_1 :1 FJX5V2 (77) VIC CAGCATAAATGGAGACTTAA (78) FAM AGCATAAATGGACACTTAA Reverse

FJX 278702- FJX 278702-FJX6M2

FJX_278702_1 :1 FJX6V2 (79) VIC TGACATATCTCTCAAGTCTATT (80) FAM TGACATATCTCTCGTCTATT Reverse

FJX_278848

FJX_PLAPR0TRI_1 :1 - - -

FJX_PLAPR0SNP_1 :1 - - - - - -

FJX 307148- FJX 307148-3031 2

FJX_307148_1 :1 3031 2 (81) VIC CAGCAGCCGTTGTAAT (82) FAM CAGCAGCCGTCGTAAT Reverse

FJX 308585- FJX 308585-FJX7M2

FJX_308585_1 :1 FJX7V2 (83) VIC ACTGAAGCAACTTAGC (84) FAM CTGAAGCGACTTAGC Reverse

Example 2

SNP Effect and Breeding Value Calculations

The effect of the C allele for the FJX_250879_1:1 SNP has been demonstrated to increase live weight in mature dairy cattle by approximately 10-20 kg in comparison to the G allele. The inventors have demonstrated this effect in 3 breeds of cattle (Holstein- Friesian, Jersey and Ayrshire). Live weight has a negative relative economic value in the New Zealand dairy breeding index; Breeding Worth (BW). This reflects that a larger cow requires more energy for maintenance. Animals that are smaller but produce the same output of milk solids are desirable.

Analysis of the genotype of the FJX_250879_1:1 SNP (and other genetic markers identified herein) may assist in calculations of the BW of an animal. The effect of the genotype of the FJX_250879_1 :1 SNP for 34 dairy traits that are of economic or farmer importance was studied.

Two thousand one hundred and fifty Holstein-Friesian (HF) progeny tested sires were genotyped for FJX_250879_1:1 and FJX_307148jl:l. There was total concordance between the genotypes for the two SNPs, i.e. all animals that had a C allele for FJX_250879_1:1 had a corresponding A allele for FJX_307148_1 :1. Therefore, the results that are presented here are applicable to both SNPs.

Genotype results of the 2150 HF sires identified that the A allele frequency for FJX_307148_1:1 is 0.87. There were only 38 sires that have the GG genotype for the FJX_307148_1:1 marker. For the statistical analysis the allelic effects were estimated between animals with the AA genotype (1678 animals) and AC genotype (479 animals).

The model fitted included the genotype as a fixed effect and also fitted year of birth and percent of North American genetics as covariates.

The effect of the genotype of the FJX_307148_1 :1 SNP for 34 dairy traits is shown in Table 3 below. There are a number of traits that are significant at the 5% threshold level. For example, live weight has a P-value of <0.0001 with the effect of the G allele being 10.9 kg less than the A allele. Other traits that are significant at the 5% level are; body condition score, calving difficulty, capacity, dairy conformation, fat percent, fat persistency, fore udder, gestation length, legs, milk volume, overall opinion, protein yield, protein persistency, rear teat, rump angle and width, somatic cell, stature and temperament.

Table 3: Allelic effect of the G allele compared to the A allele for marker FJX_307148_1:1 for 34 dairy traits.

31 Temperament -0.05 0.0007

32 Total Longevity -3.56 0.72

33 Udder Overall -0.004 0.83

34 Udder Support 0.001 0.95

Materials and Methods

Genotvping

As mentioned above, two thousand and fifty Holstein-Friesian progeny tested sires were genotyped for the SNPs FJX_250879_1:1 and FJX_307148_1:1. Genotyping was conducted using Taqman assays-by-design using standard procedures (ABI, Foster City, CA). The assays were performed using the relevant primers herein before described in Example 1 (Table 7). The statistical model fitted for each of the phenotypes (breeding values) was a linear model with genotype as a fixed effect and year of birth and percent of North American genetics fitted as covariates.

Breeding Values

Estimated breeding values were calculated for a number of traits in progeny tested sires, in accordance with the following information.

Calculation of Breeding Values

Estimated Breeding Values (EBVs) are an estimate of a bull or cow's genetic merit for any given trait. Such genetic merit may also reflect somatic cell and/or germ cell characteristics, depending on the trait(s) being examined. EBVs can therefore not only predict phenotype performance and herd lifetime productivity (that is, provide a genetic diagnostic of phenotype) but also provide predictive insight into the ability of an animal to pass on superior genetic material (such as increased protein yield) to its offspring.

Breeding values are calculated/estimated for production traits (expressed in units of measurements, that is, litres of milk and kilograms of fat and protein), fertility traits, conformation traits, as well as other health, management and survival traits; for example, individual type traits, liveweight, somatic cell count, daughter fertility, calving difficulty and gestation length. In reference to BLUP: 'Best' is a reference to the method which gives the best estimates for the breeding value, or to put it more precisely, this minimizes the variance of difference between the estimates and the true breeding values. 'Linear' means that there is a linear relation between parameters in the statistical model. 'Unbiased' means that the breeding value estimates are central, that they are expected to be normally distributed with the true breeding value as mean value. 'Prediction' normally refers to the future. But prediction also refers to the estimation of realized values of a random variable drawn from a population with known variance- and co-variance-structure. A simpler and less precise formulation: 'prediction' is estimation of a given value of a random variable. It is better to use the expression 'prediction of breeding values', but 'breeding value estimates' are commonly used. With BLUP the breeding values are calculated for all animals simultaneously, and at the same time environmental effects can be corrected for. Animal Evaluation

Animals were evaluated according to New Zealand's current national genetic evaluation system to dairy cattle, known as "Animal Evaluation" (www.aeu.org.nz). The genetic evaluation system is conducted with a common base for all breeds and crosses. The genetic base for all evaluations apart from Calving Difficulty is a group of 1985-born cows which had all traits recorded in 1987. These cows were of all breeds and crosses. The New

Zealand national breeding objective is to identify animals whose progeny will be the most efficient converters of feed into farmer profit. The evaluation system is designed to identify the most profitable and efficient sires regardless of breed. Trait Evaluation

Production Traits - The current models for the national genetic evaluation of milk volume, fat yield and protein yield are multiple lactation (ML) random regression (RR) test-day (TD) models where lactations 1, 2 and 3 are modelled as individual lactations and lactations 4, 5 and 6 together are modelled as one lactation under the same genetic control. Each lactation (1-6) is considered a different trait for modelling the permanent

environmental effects. These statistical models include effects for:

- herd-year-season-test-day contemporary group, with season referring to spring or autumn calving period; - age at calving in days (nested within breed);

- induced lactation;

- heterosis class;

- stage of lactation fixed effects of days in milk at test-day (standardised Legendre polynomials of order 2 to 5);

- random additive genetic effect of the animal at each lactation day (four lactations and standardised Legendre polynomials of order 1 to 3);

- random non-additive genetic and permanent environment effect of the animal at each lactation day (six lactations and standardised Legendre polynomials of order lto 3).

The BVs reported from these models are calculated as the simple average of the four 270- day lactation BVs. For the national evaluation of the production traits, genetic groups were assigned by breed, sex of missing parent, birth year and country of origin. Four breed classes were assigned genetic grouping, namely, Holstein-Friesian, Jersey, Ayrshire-Red, and other breeds. Genetic groups were assigned in five year intervals from 1960 to 1980 then yearly, with the first birth year group being prior to 1960. Country of origin was - defined as New Zealand, North American and Other. Missing male parent of New Zealand origin had two categories: (i) unknown male but known from mating records to be an artificial insemination proven sire, or (ii) completely unknown male. If genetic groups had less than 200 animals per group, birth years were clustered. No clustering occurred across breed, origin or sex genetic groups. Foreign information for sires for is blended with New Zealand information for calculating the breeding values for the dairy production traits (and for the female fertility, udder health, longevity and conformation traits). The blending procedure uses data from the International Genetic Evaluation Service provided by

Interbull (http://www.interbull.slu.se).

, Liveweight - The statistical model for liveweight is a repeated record, single trait, additive effects repeatability model. It includes effects for herd-year-season-age contemporary group, age at calving in months (nested within breed), stage of lactation when weighed (nested within age), heterosis, genetic merit of the animal, and random non-additive genetic and permanent environment effects. Cow Fertility - The objective for herd reproductive performance in most New Zealand herds is to achieve high pregnancy rates in a short time period following the planned start of mating, and to maintain calving intervals very close to 365 days. In this system successful reproduction depends on two factors which display genetic variation. The first factor is the ability of the cow to resume cycling soon after calving, and to be mated early in the herd's mating period. The second factor is the cow's ability to conceive, sustain a pregnancy and calve early in the herd's subsequent calving period. Animal Evaluation has developed a genetic evaluation of cow fertility that incorporates both these aspects of successful reproductive performance in seasonal dairying. Mating records - Being presented for mating in the first 21 days of the herd's mating period (PM21) is scored 1 for the cows that are mated in this period, and 0 for cows which fail to be mated. Calving records - In the New Zealand circumstances, successfully bearing a calf in the first 42 days of the herd's calving period is the best indicator of successful reproduction (CR42, standing for Calving Rate in the first 42 days of the herd's calving period). CR42 is scored 1 for cows that successfully re-calve in the first 42 days, and 0 for cows that fail to re-calve in this period. CR42 is coded "missing" for cows which leave the herd prior to the re- calving period for reasons other than low fertility, or which have not yet had the opportunity to re-calve to the recorded mating. The analysis includes eight traits: PM21 in the first three parities, and the associated CR42 in the second, third and fourth lactations— together with Body Condition Score at day 60 of the first lactation, and first lactation milk yield relative to contemporaries. These records are evaluated using a multiple trait animal model (www.aeu.org.nz/page.cfm?id=59&nid=35). The reported BV relates to the comparative percentage likelihood of a bull's daughters to re-calve for their second lactation in the first 42 days of the herd's calving period.

Somatic Cells in milk - Test day records for somatic cell counts are transformed into somatic cell scores (SCS) by taking the log (base 2) of test day SCC/1000. SCS is analysed in a multiple trait random regression animal model. The two traits are first lactation SCS — and second and third lactation SCS analysed as repeated observations of a single trait. The statistical model for analysis of a cow with SCS records includes effects for herdyear- season-age-testday contemporary groups, induced lactation, heterosis, age at calving (in months nested within breed class), stage of lactation, genetic group, genetic merit of the animal, random non-additive genetic and permanent environment effects, and random residual effects. Results for breeding values averaged over the two traits are reported.

Herd-life - Herd-life is defined as the interval from the date a cow has her first calf to the date when she has her last herd test, and is recorded in days. It is evaluated using a single- trait animal model. The multiple-trait (MT) animal model used for the national genetic evaluation of survival contains 4 survival traits (SV12, SV13, SV14, SV15) and 9 predictor traits. The model can cope with missing data on any combination of traits. SV15 is the trait reported by the AE system (converted into days of herd life). Each individual trait record was modelled as: yijkl = hysij + htiswnshs=\ 6∑+ aik + eijkl where

yyki is the record for ith trait,

hysij is the jth herd-year-season fixed effect for a cow's first lactation for trait i, with season referring to spring or autumn calving period,

htis is the linear regression coefficient for the sth heterosis effect for trait i,

Whns is the sth heterosis covariate for animal n,

ai_k is the random additive genetic effect of animal k for trait i,

eyki is the random residual associated with record yijkl.

Genetic correlations between two survival traits and 9 predictor traits (all significantly different from zero) are summarised in Table 4.

Table 4:

SV12 SV15

Protein yield 0.424 0.418

SCC -0.156 -0.040

CR42 0.841 0.478

BCS 0.370 0.240

Milking speed 0.072 0.220

Overall opinion 0.330 0.379 Legs -0.119 -0.074

Udder overall 0.113 0.113

Dairy conformation 0.235 0.183

Herd-life breeding values are reported as Total Longevity.

Residual Survival - For inclusion in the total economic merit index called Breeding Worth (BW), Residual Survival has been defined in a way that ensures that herd-life is counted only once in the index. Residual Survival is defined as "Herd-life after accounting for the genetic effects of production, liveweight, fertility and milk somatic cells on herd life." The Residual Survival trait included in BW is calculated from the following equation, where EBV stands for Estimated Breeding Value.

EBV(Total Long'ty) = 5.434*EBV(Milkfat) + 4.408 *EBV(Protein) + 0.03815*EBV(Milk) - 0.489*EBV(Liveweight) + 27.847*EBV(Fertility) - 65,13 l*EBV(Somatic Cells) + Residual Survival The estimated Residual Survival Breeding Values calculated by this method are uncorrelated with the production, liveweight and fertility traits included in the BW index. This property is important in order to ensure that effects on herd life are counted only once in the BW index. Management and Conformation Traits - The models for the linear management and conformation traits (apart from Udder overall) are single record, multiple trait, additive genetic effects models. The statistical model for analysis of a cow with linear type scores includes effects for herd-year-season contemporary group, stage of lactation class when scored and age at first calving class (in months nested within breed), heterosis, genetic group, animal genetic merit and the random residual. The four farmer-scored management traits (Adaptability to milking, Shed temperament, Milking speed, Overall opinion) are evaluated together in a multiple trait evaluation that takes the genetic correlations amongst the four traits into account. The inspector-scored traits associated with body conformation (Stature, Capacity, Rump angle, Rump width, Legs, Dairy conformation) are evaluated together in a multiple trait evaluation that takes the genetic correlations amongst these six traits into account. The inspector-scored traits associated with udder conformation (but excluding Udder overall) are analysed together in a multiple trait evaluation. These traits are Udder support, Front udder, Rear udder, Front teat placement, and Rear teat placement. They are evaluated together in a multiple trait evaluation that takes the genetic correlations amongst the five traits into account. Udder overall is evaluated in a single trait model. Breeding Values for individual traits are expressed in the units in which the trait is measured.

Calving Difficulty - Calving Difficulty Breeding Values are supplied to help Artificial Breeding organisations assess the suitability of bulls for mating with yearling heifers; and to give farmers knowledge about bulls which cause higher than usual rates of calving assistance when mated to their cows and heifers. A sire's Calving Difficulty Breeding Value (BV) predicts the percentage of assisted calvings expected when he is mated to yearling heifers. The higher the BV, the higher the expected percentage of assisted calvings. The average BV of sires born in 1985 is set to zero. All breeds have been evaluated together for the calculation of Calving Difficulty BVs. Although the Calving Difficulty BV is expressed in terms of assisted births in first-calving heifers, the BV can also be used to identify bulls that are expected to increase rates of calving assistance for cows carrying the bulls' calves. The records used for the analysis comprise over 5 million records of calving assistance collected from New Zealand herds since 1994. The records were extracted in January 2006. The BV for Calving Difficulty was estimated with a multiple-trait sire model using BLUP methodology (best linear unbiased prediction). The two traits were: - assistance for calves born to heifers, and - assistance for calves born to cows. The statistical model used in the estimation was:

Yi_jkin, = HERD-YEAR-AGEj + CALF-SEX_j + CALF-BREED_mk+ HETEROSIS_m + SIRE, + Residual^ in which Yj_jkim is the observation of calving assistance (unassisted = 0, assisted = 1) of CALF_m, in HERD-YEAR-AGE GROUP;, of SEX_j, with CALF_m's BREED GROUPS_k, BREED

HETEROZYGOSITY of CALF_m, and with SIREi Residuali_jWm is the variation that is not explained by the model. Body Condition Score Breeding Values - Body condition score (BCS) is commonly used as a method to assess body energy reserves. In recent years there has been considerable interest in assessing the potential use of BCS in dairy cattle breeding programmes. In particular, breeders are interested in knowing about sires that transmit lower or higher BCS for their daughters in early lactation when cows' body reserves are being mobilised for lactation at the same time as the cows are expected to get back in calf. Consequently Estimated Breeding Values for day 60 of first lactation heifers have been calculated for all AE Enrolled sires.

* The Dexcel BCS scale of 1 to 10 is used in NZ ,

* A cow with a BCS of less than 3.0 is considered emaciated

* A cow with a BCS greater than 7.0 is considered obese

(Kevin Macdonald and John Roche, Condition Scoring Made Easy, Dexcel, 2004.)

Persistency Breeding Values - Persistency Breeding Values have been calculated for milkfat, protein, and volume. The Persistency BVs are estimates of the genetic ability of cows to maintain production subsequent to peak of lactation. They are reported on a scale where higher values correspond to greater persistency of lactation after the peak.

Describing the mathematical technique used is beyond the scope of this Introduction. Two explanatory points are relevant. Cows with persistent lactation curves can have high total lactation yields, average total lactation yields or low total lactation yields. The persistency measure is designed to be independent of total yield - so farmers ought not to use high Persistency Breeding Values in an attempt to select for higher yields. Persistency Breeding Values are reported in the same units as the dairy production traits - kilograms of milkfat, kilograms of protein, and litres of volume. To a close approximation, a Persistency BV of +5 kg can be interpreted as the genetic predisposition to yield 5 kg more in the lactation period after the 115th day of lactation compared to the earlier lactation period. A

Persistency BV of -5 kg can be interpreted as the genetic pre-disposition to yield 5 kg less in the lactation period after the 115th day of lactation compared to the earlier lactation period. The total lactation period used for the evaluation is 270 days.

Gestation length - Gestation length is the number of days from date of insemination to the date of parturition. The average number of days for Gestation length in dairy bovine is 282 days. A breeding value of -10 can be interpreted as an animal that will leave progeny on average 5 days shorter than the dairy bovine average.

Economic Evaluation

To compare individual animals on net farm profitability for breeding replacements, the Breeding Worth (BW) has been developed. The BW is the sum of the Breeding Values for milkfat, protein, milk volume, liveweight, fertility, milk somatic cells and residual survival each weighted by an economic value. The BW economic values for each trait represent the expected net income per unit of genetic change (per unit of feed) from breeding

replacements. The economic values are calculated using a bio-economic devised to value technological change. The model includes income streams from milk production, cull cows and bobby calf sales and cost streams associated with maintaining and growing cows and replacements, the feed required for production and dairy cash expenses. Predictions of future milk component prices are taken into account. The prices and costs used in the farm model are reviewed annually. The unit of feed adopted for reporting economic values is 4.5 tonnes of dry matter of feed containing 10.5 megajoules of metabolisable energy per kilogram. The economic values used in the BW are given in the following table.

aBreeding animals for pro fit and efficiency

Selecting animals on Breeding Worth identifies the animals expected to be the most profitable and efficient for breeding replacements. Breeding Worth BW ($) - The expected ability of an animal to breed replacements which are efficient converters of feed into profit. A Breeding Worth of 206 indicates the bull is expected to generate an extra $206 profit per year per unit of feed, through breeding replacements, compared with using a bull with a BW of zero. Reliability - Associated with the BW, the reliability figure is the amount of confidence we can place in the figure. The more information included in the evaluation, the greater the reliability and less likely it is to change with additional records. Reliability ranges from 0%, meaning we know nothing about the animal or any of its ancestors, to 99%. If a sire has a Milkfat Breeding Value of +25 with 80% reliability it is expected that his future Milkfat Breeding Value would be in the interval of +15 to +35 in the great majority of cases. If a sire has a Milkfat Breeding Value of +25 with 95% reliability it is expected that his future Milkfat Breeding Value would be in the interval of +20 to +30 in the great majority of cases. The reliability reflects the amount of information used in the calculation of the Breeding Value. Breeding Value - The genetic merit of an animal for individual traits relative to a base zero (1985 born cow). A Breeding Value of +10 kg protein indicates the bull will produce daughters, which on average are genetically superior by 5kg protein per lactation above the base cow (a bull can pass 1/2 of its genetic merit on average onto offspring). Fat %, Protein % - The Fat % and Protein % are Breeding Value estimates for the sire. The Breeding Values for Fat % and Protein % are not expressed as deviations but include the base averages (1985 cows)

The base averages are:

Fat % = 4.79

Protein % = 3.69

The Protein % and Fat % Breeding Values are across breed values.

Traits Other than Production

The Traits Other than Production (TOP) Evaluation System is a national scheme for assessment of non-productive characteristics of dairy cattle (bulls and cows) based on linear assessment. Its main objective is to provide bull and herd owners with accurate and easy-to-use information for decision making. Daughters are evaluated for 16 traits using a linear assessment on a scale from 1 to 9 where 1 and 9 represent the biological extremes. The traits are scored across breed and are defined as follows: Traits scored by the herd owner:

Adaptability to milking - describes how soon the animal settled into the milking routine after calving.

Slowly 1 5— ~ 9 Quickly

Shed Temperament - describes the temperament of the animal in the shed while being handled and milked. It is a different trait to adaptability to milking and should be assessed once animals have settled into the milking routine.

Vicious 1 5 9 Placid

Milking Speed - describes the milking speed of the animal, i.e. the time from putting cups on to the time milk flow stops or the cups are taken off.

Slow 1 -— 5 -— 9 Fast Overall Opinion - describes the farmer's overall acceptance of the animal as a herd member.

Undesirable 1 5 9 Desirable

Traits scored by the Inspector:

Stature - describes the height at the shoulders of the animal. Each score represents 5 cm height at the withers.

Under 105cm 1 5 9 Over 140cm

Capacity - describes the capacity of the animal as a combination of strength and depth of chest and body as viewed from side, rear and front in relation to the physical size of the animal.

Frail 1 5 9 Capacious

Rump Angle - describes the angle of a line between the centre of the hips and the top of the pins.

Pins high 1 5 9 Pins low/Sloping Rump Width - describes the width of the pins, hips and thurls relative to the size of the animal.

Narrow 1 -— 5 -— 9 Wide Legs - describes the straightness or curvature of the back legs from an imaginary line between the thurls and the mid hoof while the animal is walking.

Straight 1 -— 5 -— 9 Sickled/Curved

Udder Support - describes the strength of the suspensory ligament as viewed from the rear. It also includes udder depth relative to the hocks.

Weak 1 5 9 Strong

Front Udder - describes how well the front udder is attached to the body wall.

Loose 1 5 9 Strong

Rear Udder - describes the height and width of the rear udder attachment, as distinct from udder support.

Low 1 ----- 5 -— 9 High Front Teat Placement - describes the placement of the front teats (at the point of attachment to the udder) relative to the centre of the quarter as viewed from the rear.

Wide 1 -— 5 -— 9 Close

Rear Teat Placement - describes the placement of the rear teats (at the point of attachment to the udder) relative to the centre of the quarter as viewed from the rear.

Wide 1 -— 5 -— 9 Close

Udder Overall - All traits pertaining to the udder including those traits that have been linearly scored.

Undesirable 1 5 -— 9 Desirable

Dairy Conformation - All traits pertaining to dairy conformation including those body traits that have been linearly scored, but excluding all the udder traits. Undesirable 1 5 9 Desirable

All two-year-old daughters in the herd are inspected to avoid bias through selection of daughters and to allow valid comparisons.

Example 3

Further analysis of the FJX_250879_1 : 1 SNP in the Ayrshire and Jersey populations was undertaken for live weight using breeding values. One hundred and eighteen Ayrshire sires were genotyped with an allele frequency of 71% for the G allele. In this population, significant differences (P <0.05) were found between the 3 genotypic groups for live weight. The average allelic effect for the C allele in the Ayrshire population is 17.3 kg compared to the G allele.

Table 5: Genotyic effects on live weight in the Ayrshire population for marker FJX_307148_1 : 1 relative to the CC genotype.

In the Jersey population 1309 sires were genotyped for the FJX 250879 1 :r SNP with an allele frequency of 98.5% for the G allele. Only 2 sires were CC genotype, 35 CG and 1272 GG. The live weight effect between the GG and GC genotypic groups was statistically significant for live weight (P <0.05). The allelic effect for the C allele in the Jersey population is 17.9 kg compared to the G allele.

Table 6: Genotyic effects on live weight in the Jersey population for marker

FJX_307148_1:1 relative to the GC genotype.

Genotypic state Live weight (kg) Number genotyped

CG 0.0 35

GG -17.9 1272 The results from these two breeds demonstrate the SNP effects are consistent across breeds.

Materials and methods

Genotyping was conducted using Taqman assays-by-design using standard procedures (ABI, Foster City, CA). The assays were performed using the relevant primers referred to herein before in Example 1 (Table 7).

The statistical model fitted for each of the phenotypes (breeding values) was a linear model with genotype as a fixed effect and year of birth fitted as a covariate.

The invention has been described herein, with reference to certain preferred embodiments, in order to enable the reader to practice the invention without undue experimentation. However, a person having ordinary skill in the art will readily recognise that many of the components and parameters may be varied or modified to a certain extent or substituted for known equivalents without departing from the scope of the invention. Accordingly, as noted herein before, it should be appreciated that such modifications and equivalents are herein incorporated as if individually set forth. Similarly, the invention also includes all of the steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations of any two or more of said steps or features.

Furthermore, titles, headings * or the like are provided to enhance the reader's

comprehension of this document, and should not be read as limiting the scope of the present invention.

The entire disclosures of all applications, patents and publications, cited above and below, if any, are hereby incorporated by reference. However, the reference to any applications, patents and publications in this specification is not, and should not be taken as, an acknowledgment or any form of suggestion that they constitute valid prior art or form part of the common general knowledge in any country in the world. Throughout this specification and any claims which follow, unless the context requires otherwise, the words "comprise", "comprising" and the like, are to be construed in an inclusive sense as opposed to an exclusive sense, that is to say, in the sense of "including, but not limited to".

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Claims

CLAIMS:

1. A method for inferring the size potential of an animal and/or its offspring, the method comprising at least the step of analysing a nucleic acid from said animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471, or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof, wherein the nucleotide present at the one or more positions infers the size potential of the animal and/or its offspring.

2. A method for inferring the growth rate potential and/or liveweight potential of an animal and/or its offspring, the method comprising at least the step of analysing a nucleic acid from said animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471, or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof, wherein the nucleotide present at the one or more positions infers the growth rate potential and/or the liveweight potential of the animal and/or its offspring.

3. A method for selecting or rejecting an animal, the method comprising at least the step of analysing a nucleic acid from said animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471, or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof, wherein the nucleotide present at the one or more positions infers the size potential of the animal and/or its offspring.

4. A method as claimed in claim 3 wherein the method comprises at least the steps of: a) analysing a nucleic acid from the animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471 or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof; and,

5. A method for estimating the worth of an animal and/or its offspring, the method comprising at least the step of analysing a nucleic acid from the animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471 or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof, wherein the nucleotide present infers of the size potential of the animal and/or its offspring.

6. A method for estimating the worth of an animal and/or its offspring, the method comprising at least the step of analysing a nucleic acid from the animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765,

23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854,' 23272034, and 23273471 or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof, wherein the nucleotide present infers of the growth rate potential and/or the liveweight potential of the animal and/or its offspring.

7. A method as claimed in any one of claims 1 to 6 wherein the presence of one or more of22986260T, 23186380T, 23186648A, 23187238A, 23205280A,

23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11, 23264854G, 23272034A, and 2327347 IT infers a larger size, a higher growth rate and/or a higher liveweight for the animal and/or its offspring.

8. A method as claimed in any one of claims 1 to 6 wherein the presence of one or ' more of 22986260C, 23186380C, 23186648G, 23187238G, 23205280G, 23211989G, 23215765G, 23218552C, 23228064T, 23232324G, 23235239G, 23243588TT,

23243734G, 23264810(CCG)9, 23264854A, 23272034G, and 23273471C infers a smaller size, a lower growth rate and/or a lower liveweight for the animal and/or its offspring.

9. A method of identifying animals which are more likely or less likely to produce one or more desirable trait the method comprising at least the step of analysing a nucleic acid from the animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471 or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof.

10. A method as claimed in claim 9 wherein the method comprises at least the steps of: a) analysing a nucleic acid from the animal to determine the nucleotide present at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471 or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof; and,

11. A method as claimed in claim 10 wherein the one or more desirable trait is size, growth rate or liveweight.

12. A method as claimed in any one of claims 9 to 11 wherein animals which are more likely to have a smaller size, lower growth rate and/or lower liveweight are identified on the basis of the presence of one or more of 22986260C, 23186380C, 23186648G,

23187238G, 23205280G, 23211989G, 23215765G, 23218552C, 23228064T, 23232324G, 23235239G, 23243588TT, 23243734G, 23264810(CCG)9, 23264854A, 23272034G, and 23273471C

13. A method as claimed in any one of claims 9 to 11 wherein animals which are more likely to have a larger size, a higher growth rate and/or a higher liveweight are identified ^~ on the basis of the presence of one or more 22986260T, 23186380T, 23186648A,

23187238A, 23205280A, 23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11,

23264854G, 23272034A, and 2327347 IT.

14. A method for identifying an animal having a higher growth rate potential and/or higher liveweight potential, the method comprising at least the step of analysing a nucleic acid from said animal to determine the nucleotide sequence at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471, or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof, wherein the presence of one or more of 22986260T, 23186380T, 23186648 A, 23187238A, 23205280A, 23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11,

23264854G, 23272034A, and 2327347 IT infers a higher growth rate and/or higher liveweight potential.

15. A method for identifying an animal having a lower liveweight potential and/or a lower growth rate potential, the method comprising at least the step of analysing a nucleic acid from said animal to determine the nucleotide sequence at one or more of the following positions on chromosome 14 of Bos taurus 22986260, 23186380, 23186648, 23187238, 23205280, 23211989, 23215765, 23218552, 23228064, 23232324, 23235239, 23243588, 23243734, 23264810, 23264854, 23272034, and 23273471, or to determine the nucleotide sequence of one or more genetic marker in linkage disequilibrium with one or more thereof, wherein the presence of one or more of 22986260C, 23186380C, 23186648G, 23187238G, 23205280G, 23211989G, 23215765G, 23218552C, 23228064T, 23232324G, 23235239G, 23243588TT, 23243734G, 23264810(CCG)9, 23264854A, 23272034G, and 23273471C infers a lower liveweight and/or lower growth rate potential.

16. A method for breeding animals to produce offspring, which comprises selecting at least a first animal that has one or more of the following genotypes 22986260T,

23186380T, 23186648A, 23187238A, 23205280A, 23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11, 23264854G, 23272034A, and 23273471T (read in relation to chromosome 14 of Bos Taurus) and mating said first animal with a second animal to produce offspring.

17.. A method as claimed in claim 16 wherein the second animal is selected on the basis it has one or more of the following genotypes 22986260T, 23186380T, 23186648 A, 23187238A, 23205280A, 23211989(deletion), 23215765C, 23218552T, 23228064G, 23232324A, 23235239C, 23243588 (deletion), 23243734A, 23264810(CCG)11,

23264854G, 23272034A, and 2327347 IT.

18. A method for breeding animals to produce offspring, which comprises selecting at least a first animal that has one or more of the following genotypes 22986260C,

23186380C, 23186648G, 23187238G, 23205280G, 23211989G, 23215765G, 23218552C, 23228064T, 23232324G, 23235239G, 23243588TT, 23243734G, 23264810(CCG)9, 23264854A, 23272034G, and 23273471C (read in relation to chromosome 14 of Bos Taurus) and mating said first animal with a second animal to produce offspring.

19. A method as claimed in claim 18 wherein the second animal is selected on the basis it has one or more of the following genotypes 22986260C, 23186380C, 23186648G, 23187238G, 23205280G, 23211989G, 23215765G, 23218552C, 23228064T, 23232324G, 23235239G, 23243588TT, 23243734G, 23264810(CCG)9, 23264854A, 23272034G, and 23273471C.

20. A method of any one of claims 1 to 19 wherein analysis of the nucleotide sequence of the one or more genetic markers occurs using one or more of: polymerase chain reaction (PCR); gel electrophoresis; Southern blotting; nucleic acid sequencing; restriction fragment length polymorphism (RFLP); single-strand confirmation polymphism (SSCP); LCR (ligase chain reaction); denaturing gradient gel electrophoresis (DGGE); allele- specific oligonucleotides (ASOs); proteins which recognize nucleic acid mismatches; RNAse protection; oligonucleotide array hybridisation; denaturing HPLC (dHPLC); high resolution melting (HRM); and, matrix-assisted laser desorption/ionisation time-of-flight mass spectroscopy (MALDI-TOF MS).

21. A method for inferring the size potential of an animal and/or its offspring, the method comprising at least the step of observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; wherein the level infers the size potential of the animal and/or its offspring.

22. A method for inferring the growth rate and/or the liveweight potential of an animal and/or its offspring, the method comprising at least the step of observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; wherein the level infers the growth rate potential and/or the liveweight potential of the animal and/or its offspring.

23. A method for selecting or rejecting an animal the method comprising at least the step of observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; and selecting or rejecting an animal based on the level of one or more of said proteins, precursors, isoforms or fragments thereof, and/or nucleic acids encoding same.

24. The method of claim 23 wherein the method comprises comparing the level of expression of one or more of said proteins, precursor thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding said proteins against a standard, wherein a difference in the level of expression in the sample compared to the standard infers the size potential, growth rate potential and/or liveweight potential of the animal and/or its offspring.

25. A method for estimating the worth of an animal and/or its offspring, the method comprising at least the step of observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAGl; CHCHD7; RDHE2; SDR16C6; and, PENK; wherein the level infers the size potential of the animal and/or its offspring.

26. A method for estimating the worth of an animal and/or its offspring, the method comprising at least the step of observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAGl; CHCHD7; RDHE2; SDR16C6; and, PENK; wherein the level infers the growth rate potential and/or the liveweight potential of the animal and/or its offspring.

27. A method of identifying animals which are more or less likely to produce one or more desirable trait the method comprising at least the step of observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAGl; CHCHD7; RDHE2; SDR16C6; and, PENK.

28. A method as claimed in claim 27 wherein the method comprises at least the steps of:

a) observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAGl; CHCHD7; RDHE2; SDR16C6; and, PENK.; and,

b) identifying the animals which are more likely or less likely to produce one or more desirable trait on the basis of the level of the one or more protein, isoform, precursor, fragment or nucleic acid encoding any one thereof.

29. A method as claimed in claim 28 wherein the trait is size, growth rate or

liveweight.

30. A method for identifying an animal having a higher growth rate potential and/or a higher liveweight potential, the method comprising at least the step of observing the level of expression of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAGl; CHCHD7; RDHE2; SDR16C6; and, PENK, wherein an increase in the level of expression compared to a standard infers a higher growth rate potential and/or higher liveweight potential.

31. A method for identifying an animal having a lower growth rate potential and/or a lower liveweight potential, the method comprising at least the step of observing the level of expression of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAGl; CHCHD7; RDHE2; SDR16C6; and, PENK, wherein a decrease or substantially no increase in the level of expression compared to a standard infers a lower growth rate potential and/or lower liveweight potential.

32. A method for breeding animals to produce offspring, the method comprising observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAGl; CHCHD7; RDHE2; SDR16C6; and, PENK; in at least a first animal, selecting the first animal where there is an increase in the expression of one or more of said proteins, precursors, isoforms, fragments and/or nucleic acids encoding same compared to a standard, and mating said first animal with a second animal to produce offspring.

33. A method as claimed in claim 32 wherein the method further comprises observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAGl; CHCHD7; RDHE2; SDR16C6; and, PENK; in the second animal, and selecting the second animal where there is an increase in the expression of one or more of said proteins, precursors, isoforms, fragments and/or nucleic acids encoding same compared to a standard.

34. A method for breeding animals to produce offspring, the method comprising observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; in at least a first animal, selecting the first animal where there is a decrease or substantially no increase in the expression of one or more of said proteins, precursors, isoforms, fragments and/or nucleic acids encoding same compared to a standard, and mating said first animal with a second animal to produce offspring.

35. A method as claimed in claim 34 wherein the method further comprises observing the level of one or more of the following proteins, precursors thereof, isoforms thereof, fragments thereof, and/or nucleic acids encoding the proteins: RPS20; MOS; PLAG1; CHCHD7; RDHE2; SDR16C6; and, PENK; in the second animal, and selecting the second animal where there is a decrease or substantially no increase in the expression of one or more of said proteins, precursors, isoforms, fragments and/or nucleic acids encoding same compared to a standard.

36. A method as claimed in any one of claims 21 to 36 comprising at least the steps of: a) taking a sample from an animal;

b) detecting one or more of the proteins RPS20, MOS, PLAG1, CHCHD7, RDHE2, SDR16C6, and, PENK, a precursor thereof, an isoform thereof, a fragment thereof, and a nucleic acid encoding any one or more thereof in the sample; and,

37. A method as claimed in any one of claims 21 to 36 wherein at least an

approximately 1.2 fold increase in the level of one or more of the proteins, a precursor thereof, an isoform thereof, a fragment thereof, and/or a nucleic acid encoding same, is indicative of a higher growth rate potential and/or a higher liveweight potential of the animal and/or its offspring.

38. A method as claimed in any one of claims 21 to 37 wherein the level of the one or more proteins, precursors, fragments, isoforms and nucleic acids encoding same is determined using an immunoassay, separation based on characteristics such as molecular weight and isoelectric point, gel electrophoresis, Western Blotting or mass spectroscopy.

39. A method as claimed in any one of claims 1 to 38 wherein the method further comprises analysis of one or more additional biological markers.

40. A method of any one of claims 1 to 39 wherein the animal is bovine.

41. A method as claimed in claim 40 wherein the bovine animal is Bos taurus or Bos indicus.

42. A method as claimed in claim 41 wherein the animal is chosen from the group consisting Jersey, Holstein, Friesian, Simmental or crossbred dairy cattle.