|Cushman, Robert - Bob|
|MALTECCA, CHRISTIAN - North Carolina State University|
|THOMAS, MILTON - Colorado State University|
|FORTES, MARINA - University Of Queensland|
|REVERTER, ANTONIO - Commonwealth Scientific And Industrial Research Organisation (CSIRO)|
Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/9/2012
Publication Date: 2/1/2013
Citation: Snelling, W.M., Cushman, R.A., Keele, J.W., Maltecca, C., Thomas, M.G., Fortes, M.R., Reverter, A. 2013. Networks and pathways to guide genomic selection. Journal of Animal Science. 91(2):537-552.
Interpretive Summary: Genomic selection, enabled by recently developed dense (approximately 50,000 single nucleotide polymorphism [SNP]) genotyping assays for livestock, allows genomic breeding value predictions of new-born animals that can be as accurate as breeding values predicted from parents of 5 to 10 progeny, more or less depending on trait. These higher accuracy predictions of young animals promise to further accelerate the already dramatic genetic improvement that has occurred within well-recorded livestock populations. Currently implemented genomic selection schemes, however, are limited to selection for commonly measured traits within breeds and breeding programs that amassed substantial pedigree and performance records prior to recently obtained whole-genome genotypes. The opportunity to extend genomic selection from intensely studied herds to broader industry populations, especially for traits that are too difficult or expensive to measure routinely, appears to be limited by the lack of consistent associations between dense SNP genotypes and unknown causal mutations across populations. The body of knowledge about gene functions and interactions in biological pathways, primarily developed from studying humans, mice, and simpler model organisms can be applied to focus livestock genomic selection on groups of genes most likely to have functional effects on performance regardless of breed or genetic makeup. An analysis of beef tenderness measured in crosses of different breeds illustrates that functional genomic selection, incorporating existing functional annotation with genotype-phenotype associations, can be more accurate across populations than genomic predictions based only on genotype-phenotype associations assessed in one population. Identifying and genotyping specific mutations that can affect gene function and regulation may enable further increases in accuracy above that achievable with currently available dense SNP arrays.
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. Substantial gains have been made through phenotypic selection, enhanced by pedigree relationships and continually improving statistical methodology. Genomic selection, recently enabled by assays for dense single nucleotide polymorphism (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. 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. Associations of dense SNP genotypes with phenotype provide a one-dimensional approach for identifying genes affecting specific traits; in contrast, 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, obtained from public databases or derived from genome and transcriptome sequences, may be classified 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.