Apr 25, 2016

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

BioRxiv : the Preprint Server for Biology
Sunyoung Shin, Sündüz Keleş


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 assumption is susceptible to violations for GVs involved in many complex diseases. To address these issues, we develop a general regression framework, named Annotation Regression for GWAS (ARoG). ARoG is based on finite mixture of linear regression models where GWAS association measures are viewed as responses and functional annotations as predictors. This mixture framework addresses heterogeneity of effects of GVs by grouping them into clusters...Continue Reading

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Mentioned in this Paper

Genome-Wide Association Study
Laboratory Procedures
Transcription, Genetic
Autistic Disorder
mullerian-inhibiting hormone

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