Exact p-values for large-scale single step genome-wide association, with an application for birth weight in American Angus

Abstract

BACKGROUNDSingle Step GBLUP (SSGBLUP) is the most 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 GWAS studies in the ssGBLUP framework, showing algorithms, computational procedures, and an application to a large beef cattle population. METHODSP-values were obtained based on the prediction error (co)variance for SNP, which uses the inverse of the coefficient matrix and formulas to compute 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 was observed. The SNP passing the Bonferroni threshold of 5.9 in the -log10 scale were the same as those that explained the highest proportion of additive genetic variance, but the latter was penalized (as GWAS signal) by low allele frequency. CONCLUSIONThe exact p-value for SSGWAS is a very general and efficient strategy for QTL detection and testing. It can be used in complex data sets such as used in animal breeding, where only a proportion of pedigreed animals are genotyped.