Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle
2019
Aguilar, Ignacio | Legarra, Andres | Cardoso, Fernando | Masuda, Yutaka | Lourenco, Daniela | Misztal, Ignacy | Instituto Nacional de Investigación Agropecuaria (INIA) | Génétique Physiologie et Systèmes d'Elevage (GenPhySE) ; Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-École nationale supérieure agronomique de Toulouse (ENSAT) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT) | Universidade Federal de Pelotas = Federal University of Pelotas (UFPel) | Embrapa Pecuária Sul ; Brazilian Agricultural Research Corporation = Empresa Brasileira de Pesquisa Agropecuária (Embrapa) | University of Georgia [USA] | American Angus Association | Agriculture and Food Research Initiative Competitive Grants from the US Department of Agriculture's National Institute of Food and Agriculture
International audience
Mostrar más [+] Menos [-]Inglés. Single-step genomic best linear unbiased prediction (SSGBLUP) is a comprehensive method for genomic prediction. Point estimates of marker effects from SSGBLUP are often used for genome-wide association studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for single-marker GWAS studies within the single-step GWAS (SSGWAS) framework by deriving computational algorithms and procedures, and by applying these to a large beef cattle population.MethodsP-values were obtained based on the prediction error (co)variances for single nucleotide polymorphisms (SNPs), which were obtained from the prediction error (co)variances of genomic predictions based on the inverse of the coefficient matrix and formulas to estimate SNP effects.ResultsComputation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree and 1424 genotyped sires, and no inflation of statistics was observed. The SNPs that passed the Bonferroni threshold of 10−5.9 were the same as those that explained the highest proportion of additive genetic variance, but even at the same significance levels and effects, some of them explained less genetic variance due to lower allele frequency.ConclusionsThe use of a p-value for SSGWAS is a very general and efficient strategy to identify quantitative trait loci (QTL). It can be used for complex datasets such as those used in animal breeding, where only a proportion of the pedigreed animals are genotyped.
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