Meta-analysis of genome-wide association from genomic prediction models

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.

ANIMAL GENETICS

WILEY-BLACKWELL PUBLISHING, INC

Lugar: Londres; Año: 2015

0268-9146

Genome-wide association (GWA) studies based on GBLUP models are a common practice inanimal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizesto enhance power of detection of rare variants. Because of difficulties in increasing samplesize in animal populations, one alternative is to implement a meta-analysis (MA),combining information and results from independent GWA studies. Although thismethodology has been used widely in human genetics, implementation in animal breedinghas been limited. Thus, we present methods to implement a MA of GWA, describing theproper approach to compute weights derived from multiple genomic evaluations based onanimal-centric GBLUP models. Application to real datasets shows that MA increases powerof detection of associations in comparison with population-level GWA, allowing forpopulation structure and heterogeneity of variance components across populations to beaccounted for. Another advantage of MA is that it does not require access to genotype datathat is required for a joint analysis. Scripts related to the implementation of this approach,which consider the strength of association as well as the sign, are distributed and thusaccount for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA isan attractive alternative to summarizing results from multiple genomic studies, avoidingrestrictions with genotype data sharing, definition of fixed effects and different scales ofmeasurement of evaluated traits.