CPR2013 Control of Pig Reproduction IX Reproductive Biotechnology (5 abstracts)
Genome-wide selection for reproductive traits in swine
David G McLaren 1 , Matthew A Cleveland 2 , Nader Deeb 2 , Selma Forni 2 , Alan J Mileham 1 , Scott Newman 2 & Olwen I Southwood 3 and Lizhen Wang
1Genus R&D, 1525 River Rd., DeForest, WI 53532, USA; 2Genus R&D, 100 Bluegrass Commons Blvd., Suite 2200, Hendersonville, TN 37075, USA; 3Genus Breeding, Alpha Building, London Road, Nantwich, CW5 7JW, UK
The introduction of high-density SNP arrays in livestock species has enabled genomic evaluations on a scale not possible just a few years ago. Faster genetic gains are realized from application of genomics in pigs by increasing the accuracy of selection. This is especially important for lowly heritable reproductive traits, where female selection candidates have not yet expressed a phenotype at the point of selection and male candidates have no phenotype at all. Litter size was one of the first traits for which the breeding company PIC implemented genome assisted selection, using a 196 SNP panel in 2010. Significant improvements in accuracy for all selection index traits are now achieved using single step genomic evaluation incorporating genomic information into multivariate mixed model evaluations. Genotyping costs have been a barrier to fullscale implementation, but this challenge has been overcome by imputing high density genotypes from low density panels on selection candidates. Next generation sequencing technology is revolutionizing genomics research. The recently published draft pig genome sequence, along with the availability of phenotypes and tissue samples on thousands of animals, often with complete pedigree, facilitate discovery of genomic regions, genes and causative mutations for traits such as disease resistance. In the future, genome editing has the potential to introgress beneficial alleles from rare or indigenous breeds not present in improved commercial lines, as well as increasing the expression of beneficial genes through directed gene duplication. Additionally, nonlinear and nonparametric methods may be applied to further improve statistical predictions of genetic merit and performance.
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