|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. Methods for meta-analysis of Genome-wide association studies [abstract]. Revista Argentina de Producción Animal. 34 (Supplement 1):83 (Abstract # GM 7).
Technical Abstract: A limitation of many genome-wide association studies (GWA) in animal breeding is that there are many loci with small effect sizes; thus, larger sample sizes (N) are required to guarantee suitable power of detection. For increasing N, results from different GWA can be combined in a meta-analysis (MA-GWA). The goal of this research was to describe methods for implementation of MA-GWA, combining results from multiple genomic evaluations, using two alternatives for weighting SNP effects. The methodology is exemplified with an application to real data, comparing results with those obtained from a joint-analysis. Measures of chop redness (CIE a*) 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 PorcineGGP-LD and imputed with high accuracy. In population GWA, peaks were observed on SSC6 but none of them reached the genome-wide significance threshold. Joint-analysis detected one significant genomic region on SSC6, which can be explained by the high LD observed on this chromosome in our populations. This association was confirmed by MA-GWA, which also identified a significant QTL on SSC5. Thus, MA-GWA increased power of detection, confirming and identifying associations for CIE a*. In contrast to joint-analysis, MA-GWA is an attractive alternative to summarize results from multiple genomic evaluations, avoiding problems of combination of different datasets (definition of fixed effects, heterogeneous variances, population structure and different scales of measurement).