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ARS Home » Plains Area » Clay Center, Nebraska » U.S. Meat Animal Research Center » Genetics and Animal Breeding » Research » Publications at this Location » Publication #278284

Title: Networks and pathways to guide genomic selection

item Snelling, Warren
item Cushman, Robert - Bob
item Keele, John
item MALTECCA, CHRISTIAN - North Carolina State University
item THOMAS, MILTON - Colorado State University
item FORTES, MARINA - Commonwealth Scientific And Industrial Research Organisation (CSIRO)
item REVERTER, ANTONIO - Commonwealth Scientific And Industrial Research Organisation (CSIRO)

Submitted to: Journal of Animal Science Supplement
Publication Type: Abstract Only
Publication Acceptance Date: 3/5/2012
Publication Date: 7/1/2012
Citation: Snelling, W.M., Cushman, R.A., Keele, J.W., Maltecca, C., Thomas, M.G., Fortes, M.R., Reverter, A. 2012. Networks and pathways to guide genomic selection [abstract]. Journal of Animal Science Supplement. 90(Supplement 3):162. (Abstract No. 57).

Interpretive Summary:

Technical Abstract: Many traits affecting profitability and sustainability of meat, milk, and fiber production are polygenic, with no single gene having an overwhelming influence on observed variation. No knowledge of the specific genes controlling these traits has been needed to make dramatic improvement through selection. Gains have been made through phenotypic selection, enhanced by pedigree relationships and continually improved 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 for predicting individuals related to those in the training data. Genomic selection with biologically relevant SNP genotypes should be less reliant on LD patterns shared by training and target populations, possibly allowing robust prediction across unrelated populations. While the specific variants causing polygenic variation may never be known with certainty, a number of tools and resources can be employed to identify those most likely to affect phenotype. Dense SNP associated with phenotype provide a one-dimensional approach to identify genes affecting specific traits, while associations with multiple traits allow defining 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, mined from public databases or derived from genome and transcriptome sequences, may be classified according to expected effects on gene products. 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 enable functional genomic selection for economically important traits.