|BERNAL RUBIO, Y - Michigan State University|
|GUALDRON DUARTE, J - University Of Buenos Aires|
|BATES, R - Michigan State University|
|ERNST, C - Michigan State University|
|Nonneman, Danny - Dan|
|King, David - Andy|
|CANTER, R - University Of Buenos Aires|
|STEIBEL, J - Michigan State University|
Submitted to: World Congress of Genetics Applied in Livestock Production
Publication Type: Proceedings
Publication Acceptance Date: 8/17/2014
Publication Date: 8/17/2014
Citation: Bernal Rubio, Y.L., Gualdrón Duarte, J.L., Bates, R.O., Ernst, C.W., Nonneman, D., Rohrer, G.A., King, A., Shackelford, S.D., Wheeler, T.L., Canter, R.J.C., Steibel, J.P. 2014. Meta-analysis of genome wide association studies for pork quality traits. In: Proceedings of 10th World Congress of Genetics Applied to Livestock Production, August 17-22, 2014, Vancouver, British Columbia, Canada. 3 pp.
Interpretive Summary: Pork quality is an important trait for the swine industry and many studies have been conducted to identify genetic markers associated with measures of quality. In an attempt to identify additional genetic markers and to refine the location of genes affecting pork quality, statistical methods to combine data from distinctly different populations were developed. The developed methods were used to analyze the combined pork quality data from three swine populations. In total, nearly 4,500 animals were included in the analyses and over 40,000 SNP markers were evaluated. The results indicated that the developed method was able to refine the location of previously known QTL, by reducing the confidence interval by at least 50% for most QTL. In addition, the increased power to detect QTL enabled the identification of four additional QTL important for pork quality.
Technical Abstract: Given the importance of pork quality in the meat processing industry, genome-wide association studies were performed for eight meat quality traits and also, a meta-analysis (MA) of GWA was implemented combining independent results from pig populations. Data from three pig datasets (USMARC, Commercial and MSUPRP) were used. MA was implemented by combining z-scores derived for each SNP in every population, and then, weighting them using the inverse of estimated variance of SNP effects. In population specific GWA, several regions were identified as significantly associated with most of the traits. MA-GWA permitted detection of peaks where no significant associations at population level were reported. Thus, MA-GWA methodology is an attractive alternative to integrate results for economically relevant traits from multiple GWA, when several populations are available.