Annotation Regression for Genome-Wide Association Studies with an Application to Psychiatric Genomic Consortium Data.

ABSTRACT: Although genome-wide association studies (GWAS) have been successful at finding thousands of disease-associated genetic variants (GVs), identifying causal variants and elucidating the mechanisms by which genotypes influence phenotypes are critical open questions. A key challenge is that a large percentage of disease-associated GVs are potential regulatory variants located in noncoding regions, making them difficult to interpret. Recent research efforts focus on going beyond annotating GVs by integrating functional annotation data with GWAS to prioritize GVs. However, applicability of these approaches is challenged by high dimensionality and heterogeneity of functional annotation data. Furthermore, existing methods often assume global associations of GVs with annotation data. This strong as

SUBMITTER: Shin S 

PROVIDER: S-EPMC5542423 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature