|BERNAL RUBIO, YENI - 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|
|CANTET, R - University Of Buenos Aires|
|STEIBEL, J - Michigan State University|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 10/20/2014
Publication Date: 10/20/2014
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., Cantet, R.J.C., Steibel, J.P. 2014. Implementing Meta-analysis for genome-wide association studies of pork quality traits [abstract]. Revista Argentina de Producción Animal. 34 (Supplement 1):82 (Abstract # GM 6).
Technical Abstract: Pork quality is a critical concern in the meat industry. Implementation of genome-wide association studies (GWA) allows identification of genomic regions that explain a substantial portion of the variation of relevant traits. It is also important to determine the consistency of results of GWA across populations. However, animal datasets show stratification that can result in spurious associations. In order to decrease false associations, results from different GWA can be combined in a meta-analysis of GWA (MA-GWA). The goal of this research was to implement a MA-GWA for pork quality traits, combining results from multiple independent GBLUP evaluations. Records from eight pork quality traits from three populations were analyzed: 1) F2 generation (n=928) of the Michigan State University Pig Resource Population (MSUPRP), 2) Meat Animal Research Center Population (MARC, n=1237) and 3) a commercial population (n=2001). Animals were genotyped using the PorcineSNP60 BeadChip or the Neogen Porcine GGP LD and imputed with high accuracy. Population-specific GWA identified significant peaks on SSC2 for shear force (SF), on SSC4 for purge loss (PRL), and on SSC15 for PRL, pHu, SF, cooking loss (CKL) and CIE L*. All significant SNP found on SSC15 were located near the gene PRKAG3, which has been shown to affect meat quality. Comparing these results with MA-GWA, significant SNP for PRL, pHu and CKL on SSC15 and on SSC2 for SF were confirmed. Although MA-GWA did not detect significant associations for PRL on SSC4 or for CIE L* on SSC15, this methodology allowed identification of a significant QTL on SSC5 for CKL that was not detected in population-specific GWA. Combining datasets in a MA-GWA allowed identification of additional significant regions for CKL. A combined analysis or MA-GWA using GBLUP is a suitable approach to increase power to identify variants with small effects, which are consistent across populations.