A hybrid bayesian approach for genome-wide association studies on related individuals.
Both single marker and simultaneous analysis face challenges in GWAS due to the large number of markers genotyped for a small number of subjects. This large p small n problem is particularly challenging when the trait under investigation has low heritability.In this article, we propose a two-stage approach that is a hybrid method of single and simultaneous analysis designed to improve genomic prediction of complex traits. In the first stage, we use a Bayesian independent screening method to select the most promising SNPs. In the second stage, we rely on a hierarchical model to analyze the joint impact of the selected markers. The model is designed to take into account familial dependence in the different subjects, while using local-global shrinkage priors on the marker effects.We evaluate the performance in simulation studies, and consider an application to animal breeding data. The illustrative data analysis reveals an encouraging result in terms of prediction performance and computational cost.
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