Networks and pathways to guide genomic selection 1 − 3

Many traits affecting profi tability and sustainability of meat, milk, and fi ber production are polygenic, with no single gene having an overwhelming infl uence on observed variation. No knowledge of the specifi c genes controlling these traits has been needed to make substantial improvement through selection. Signifi cant gains have been made through phenotypic selection enhanced by pedigree relationships and continually improving statistical methodology. Genomic selection, recently enabled by assays for dense SNP located throughout the genome, promises to increase selection accuracy and accelerate genetic improvement by emphasizing the SNP most strongly correlated to phenotype although the genes and sequence variants affecting phenotype remain largely unknown. These genomic predictions theoretically rely on linkage disequilibrium (LD) between genotyped SNP and unknown functional variants, but familial linkage may increase effectiveness when predicting individuals related to those in the training data. Genomic selection with functional SNP genotypes should be less reliant on LD patterns shared by training and target populations, possibly allowing robust prediction across unrelated populations. Although the specifi c variants causing polygenic variation may never be known with certainty, a number of tools and resources can be used to identify those most likely to affect phenotype. Associations of dense SNP genotypes with phenotype provide a 1-dimensional approach for identifying genes affecting specifi c traits; in contrast, associations with multiple traits allow defi ning networks of genes interacting to affect correlated traits. Such networks are especially compelling when corroborated by existing functional annotation and established molecular pathways. The SNP occurring within network genes, obtained from public databases or derived from genome and transcriptome sequences, may be classifi ed according to expected effects on gene products. As illustrated by functionally informed genomic predictions being more accurate than naive whole-genome predictions of beef tenderness, coupling evidence from livestock genotypes, phenotypes, gene expression, and genomic variants with existing knowledge of gene functions and interactions may provide greater insight into the genes and genomic mechanisms affecting polygenic traits and facilitate functional genomic selection for economically important traits. 1Based on a presentation at the symposium titled “Systems Biology in Animal Breeding: Identifying relationships among markers, genes, and phenotypes” at the Joint Annual Meeting, July 15–19, 2012, Phoenix, AZ. The symposium was sponsored, in part, by Monsanto (St. Louis, MO), with publication sponsored by the American Society of Animal Science and the Journal of Animal Science. 2Mention of a trade name, proprietary product, or specifi c equipment does not constitute a guarantee or warranty by the USDA and does not imply approval to the exclusion of other products that may be suitable. 3The authors thank S. Shackelford, A. King, T. Wheeler G. Hayes, and the USMARC MSQRU and cattle operations staff for animal care and data recording. 4Corresponding author: warren.snelling@ars.usda. gov 5USDA is an equal opportunity provider and employer. Received August 23, 2012. Accepted Octobe r 9, 2012.