title: Genomic prediction of breeding values in the New Zealand sheep industry using a 50K SNP chip.
abstract: The aim of genomic prediction is to predict breeding value from genomic data. We describe the development of genomic prediction equations and accuracies for molecular breeding values (MBV) for industry use, focusing on the methodology used to deal with predictions for the New Zealand sheep population structure. This is made up of a mixture of pure and crossbred animals, but principally Romney based. In particular, we used pedigree-based EBV for 8 traits (weaning weight as a direct effect, weaning weight as a maternal effect, live weight at 8 mo, live weight at 12 mo, greasy fleece weight at 12 mo, lamb fleece weight, adult fleece weight, and number of lambs born) and Illumina OvineSNP50 BeadChip genotypes from 13,420 animals to investigate BLUP with different genomic relationship matrices (GRM) based on SNP markers and to investigate varying sets of older animals (training sets) to predict the MBV of younger animals (validation sets). The GRM tested included modifications to account for allele frequency differences between breeds, rescaling so that the mean GRM is equal to the mean of the traditional pedigree numerator relationship matrix A: , and combining of the GRM with A: using a convex combination with a weight estimated by maximizing a conditional restricted likelihood. We found that these modifications were beneficial and recommend using a breed-adjusted GRM combined with A: . Training data sets with Romney, Coopworth, and Perendale animals all together usually predicted better than using just a pure breed training data set for all traits. But predictions for the breed Perendale were more accurate with a Perendale training set for 3 of the 8 traits. We concluded that using a mixed-breed training set for all combinations of traits and breeds was best but advise that increasing the number of Perendale animals genotyped should be a priority to increase the MBV accuracies obtained for that breed.
authors.affiliation: Animal Productivity Group, AgResearch Limited, Mosgiel 9053, New Zealand benoit.auvray@agresearch.co.nz.
authors.forename: B
authors.initials: B
authors.lastname: Auvray
authors.affiliation: Animal Productivity Group, AgResearch Limited, Mosgiel 9053, New Zealand.
authors.forename: J C
authors.initials: JC
authors.lastname: McEwan
authors.affiliation: Animal Productivity Group, AgResearch Limited, Mosgiel 9053, New Zealand.
authors.forename: S-A N
authors.initials: SA
authors.lastname: Newman
authors.affiliation: Animal Productivity Group, AgResearch Limited, Mosgiel 9053, New Zealand.
authors.forename: M
authors.initials: M
authors.lastname: Lee
authors.affiliation: Animal Productivity Group, AgResearch Limited, Mosgiel 9053, New Zealand.
authors.forename: K G
authors.initials: KG
authors.lastname: Dodds
mesh_terms: D000818:Animals; D001947:Breeding / Q000379:methods / Q000706:statistics & numerical data*; D005787:Gene Frequency; D016678:Genome / Q000235:genetics*; D023281:Genomics / Q000379:methods*; D005838:Genotype; D016013:Likelihood Functions; D008957:Models, Genetic; D009520:New Zealand; D020411:Oligonucleotide Array Sequence Analysis / Q000662:veterinary; D010375:Pedigree; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012756:Sheep; D034561:Sheep, Domestic / Q000235:genetics*
keywords: New Zealand; genomic prediction; genomic selection; sheep