4.5 Article

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

期刊

ANIMAL GENETICS
卷 47, 期 1, 页码 36-48

出版社

WILEY
DOI: 10.1111/age.12378

关键词

GBLUP; genome-wide association studies; multiple populations

资金

  1. Agriculture and Food Research Initiative Competitive Grant from the USDA National Institute of Food and Agriculture [2010-65205-20342]
  2. National Pork Board Grant [11-042]
  3. Pork Checkoff (National Pork Board)
  4. US Pig Genome Coordination Funds
  5. MSU Animal Agriculture Initiative
  6. Administrative Department of Science, Technology and Innovation COLCIENCIAS (Colombia)
  7. NIFA [581200, 2010-65205-20342] Funding Source: Federal RePORTER

向作者/读者索取更多资源

Genome-wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta-analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal-centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population-level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that 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 thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.

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