Location: Reproduction ResearchTitle: Meta-analysis genomewide association of pork quality traits: ultimate pH and shear force Author
|Bernal Rubio, Yeni - Michigan State University|
|Gualdron Duarte, Jose - University Of Buenos Aires|
|Bates, Ronald - Michigan State University|
|Ernst, Catherine - Michigan State University|
|Nonneman, Danny - Dan|
|King, David - Andy|
|Cantet, Rodolfo - University Of Buenos Aires|
|Steibel, Juan - Michigan State University|
Submitted to: Midwestern Section of the American Society of Animal Science
Publication Type: Abstract Only
Publication Acceptance Date: 3/1/2014
Publication Date: 3/1/2014
Citation: Bernal Rubio, Y.L., Gualdron Duarte, J.L., Bates, R.0., Ernst, C.W., Nonneman, D., Rohrer, G.A., King, A., Shackelford, S.D., Wheeler, T.L., Cantet, R.J.C., Steibel, J.P. 2014. Meta-analysis genomewide association of pork quality traits: ultimate pH and shear force [abstract]. Journal of Animal Science. 92(Supplement 2):11-12 (Abstract #26).
Technical Abstract: It is common practice to perform genome-wide association analysis (GWA) using a genomic evaluation model of a single population. Joint analysis of several populations is more difficult. An alternative to joint analysis could be the meta-analysis (MA) of several GWA from independent genomic evaluations. The MA of GWA allows combining results from individual studies, so as to account for population substructure. The objectives of this research were: (a) to produce GWA from genomic evaluations for pork quality traits in three populations; and (b) to implement a MA searching for significant associations across pig populations. Data from two U.S. Meat Animal Research Center populations (Commercial and MARC) and one Michigan State University population (MSU) were used. Population-specific GWA were performed by fitting genomic evaluation models to each population for ultimate pH (n = 1857 Commercial, n = 530 MARC and n = 904 MSU) and shear force (SF; n = 1234 Commercial, n = 1892 MARC and n = 911 MSU). A MA was implemented by combining z-scores derived for each SNP in every population using two different weighting schemes: a) sample size (N) and b) estimated variance of SNP effects. One peak at SSC15 was identified for pH in MSU and in the Commercial populations (135Mb, p-value<1.21e-11 for MSU and 134MB, p-value<9.26e-11 for Commercial). In the N-weighted MA, a peak was detected on SSC15 at position 134Mb (p-value < 2.13e-13). A virtually identical result was obtained using variance-weighted MA: a peak on SSC15, at 135Mb, p-value<5.18e-11. For SF, GWA for MSU showed one peak on SSC15 (135Mb, p-value<1.48e-8) and another peak on SSC2 (2.9Mb, p-value< 2.88e-8). For Commercial and MARC populations, a peak on SSC2 was identified at positions 109Mb and 5.5Mb (p-value<1.83e-7 and 1.64e-7 respectively). The variance-weighted MA detected one peak on SSC2 (6.3Mb, p-value<7.82e-11) and another one on SSC15 (135Mb, p-value<2.14e-7). In contrast, N-weight MA, yielded two peaks on SSC2, at 5.9Mb and at 105Mb (p-value<6.73e-12 and 1.23e-6 respectively). Based on our results, selecting a weighting scheme for MA-GWA is very important because it may influence the results. Regardless of the approach used, MA-GWA revealed peaks that were present in at least two populations. Thus, MA-GWA methodology is an attractive alternative to synthesize results from multiple GWA derived from genomic evaluations and it can be used to elucidate the genetic architecture of economically relevant traits, when several populations are available.