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Abstract:
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Although genome wide association studies (GWAS) have been successful at identifying many disease- and trait-associated genetic variants, these studies are hampered by two obstacles. First, despite ever-increasing sample sizes, these studies are still underpowered for variants with weak effect sizes. Second, and more importantly, a large percentage of identified variants reside in non-coding regions, making them difficult to interpret. To address these two challenges, we develop annotation regression of GWAS for multiple phenotypes (multiARoG). MultiARoG jointly considers multiple phenotypes by taking their correlation structure into account, thus, inherently incorporates similarity over phenotypes to improve power of detection. It links GWAS association measures to functional annotation information with penalized finite mixture of linear regression models. Our simulation experiments and analysis of multiple psychiatric disorders GWAS indicate that multiARoG both improves detection power and identifies regulatory mechanisms that might elucidate the roles of non-coding variants.
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