Discovery of novel genetic networks associated with 19 economically important traits in beef cattle

Authors: JIANG, ZHIHUA - Washington State University; MICHAL, JENNIFER - Washington State University; CHEN, JIE - Washington State University; DANIELS, TYLER - Washington State University; KUNEJ, TANJA - Washington State University; GARCIA, MATTHEW - Washington State University; GASKINS, CHARLES - Washington State University; BUSBOOM, JAN - Washington State University; Alexander, Leeson; WRIGHT, RAYMOND - Washington State University; Macneil, Michael

Submitted to: International Journal of Biological Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/16/2009
Publication Date: 7/29/2009
Citation: Jiang, Z., Michal, J.J., Chen, J., Daniels, T.F., Kunej, T., Garcia, M.D., Gaskins, C.T., Busboom, J.R., Alexander, L.J., Wright, R.W., Macneil, M.D. 2009. Discovery of novel genetic networks associated with 19 economically important traits in beef cattle. International Journal of Biological Sciences. 5: 528-542.

Interpretive Summary: Identification and use of candidate genes for economically important traits is one of the most important long-term goals to improve production efficiency, product quality and animal health in the United States livestock industry. Our objective was to take advantage of bovine gene/genome annotations and various candidate gene selection approaches to discover genetic networks associated with carcass traits, eating quality and fatty acid composition in beef cattle. A Wagyu x Limousin reference population was assessed for 5 carcass, 6 eating quality and 8 fatty acid composition traits. A total of 144 significant associations (P < 0.05) were identified, but 50 of them were discounted due to limited sample size. The remaining 94 single-trait associations were then placed into three groups with potential additive, dominant and overdominant effects. Subsequent analysis the single-gene associations for 4 traits, but revealed two-gene networks for 8 traits and three-gene networks for 5 traits. High correlations between predicted and actual values of performance provided evidence that the classical Mendelian principals of inheritance can be applied in understanding genetic complexity of complex phenotypes. Our present study also indicated that carcass, eating quality and fatty acid composition traits rarely share genetic networks. Therefore, marker-assisted selection for improvement of one category of traits would not interfere with improvement of another.

Technical Abstract: Quantitative or complex traits are determined by the combined effects of many loci, and are affected by gene-gene interactions, genetic networks or molecular pathways. In the present study, we genotyped a total of 138 mutations, mainly single nucleotide polymorphisms derived from 71 functional genes on a Wagyu x Limousin reference population. The F2 animals were measured for 5 carcass, 6 eating quality and 8 fatty acid composition traits. A total of 2,280 single marker-trait association runs with 120 tagged mutations selected based on the HAPLOVIEW analysis revealed 144 significant associations (P < 0.05), but 50 of them were removed from the analysis due to the small number of animals (< 9) in one genotype group or absence of one genotype among three genotypes. The remaining 94 single-trait associations were then placed into three groups of quantitative trait modes (QTMs) with additive, dominant and overdominant effects. All significant markers and their QTMs associated with each of these 19 traits were involved in a linear regression model analysis, which confirmed single-gene associations for 4 traits, but revealed two-gene networks for 8 traits and three-gene networks for 5 traits. Such genetic networks involving both genotypes and QTMs resulted in high correlations between predicted and actual values of performance, thus providing evidence that the classical Mendelian principals of inheritance can be applied in understanding genetic complexity of complex phenotypes. Our present study also indicated that carcass, eating quality and fatty acid composition traits rarely share genetic networks. Thus, marker-assisted selection for improvement of one category of traits would not interfere with improvement of another.