Networks and pathways to guide genomic selection

Snelling, W. M., Cushman, R. A., Keele, J. W., Maltecca, C., Thomas, M. G., Fortes, M. R. S. and Reverter, A. (2013) Networks and pathways to guide genomic selection. Journal of Animal Science, 91 2: 537-552. doi:10.2527/jas.2012-5784

Author Snelling, W. M.
Cushman, R. A.
Keele, J. W.
Maltecca, C.
Thomas, M. G.
Fortes, M. R. S.
Reverter, A.
Title Networks and pathways to guide genomic selection
Journal name Journal of Animal Science   Check publisher's open access policy
ISSN 0021-8812
Publication date 2013-02
Year available 2012
Sub-type Article (original research)
DOI 10.2527/jas.2012-5784
Open Access Status
Volume 91
Issue 2
Start page 537
End page 552
Total pages 16
Place of publication Savoy, IL, United States
Publisher American Society of Animal Science
Collection year 2013
Language eng
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 substantial improvement through selection. Significant 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. 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.
Keyword Beef
Gene function
Gene network
Genomic selection
Systems biologytenderness
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online: 24 October 2012.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Alliance for Agriculture and Food Innovation
Official 2013 Collection
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Citation counts: TR Web of Science Citation Count  Cited 12 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 14 times in Scopus Article | Citations
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Created: Thu, 07 Feb 2013, 10:02:17 EST by Marina Fortes on behalf of School of Veterinary Science