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PLoS Comput Biol. 2017 Feb 17;13(2):e1005388. doi: 10.1371/journal.pcbi.1005388. eCollection 2017 Feb.

graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture.

Chung D1, Kim HJ2, Zhao H3,4,5,6.

Author information

1
Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America.
2
Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio, United States of America.
3
Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America.
4
Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.
5
Department of Genetics, Yale School of Medicine, New Haven, Connecticut, United States of America.
6
VA Cooperative Studies Program Coordinating Center, West Haven, Connecticut, United States of America.

Abstract

Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. However, identification of risk variants associated with complex diseases remains challenging as they are often affected by many genetic variants with small or moderate effects. There has been accumulating evidence suggesting that different complex traits share common risk basis, namely pleiotropy. Recently, several statistical methods have been developed to improve statistical power to identify risk variants for complex traits through a joint analysis of multiple GWAS datasets by leveraging pleiotropy. While these methods were shown to improve statistical power for association mapping compared to separate analyses, they are still limited in the number of phenotypes that can be integrated. In order to address this challenge, in this paper, we propose a novel statistical framework, graph-GPA, to integrate a large number of GWAS datasets for multiple phenotypes using a hidden Markov random field approach. Application of graph-GPA to a joint analysis of GWAS datasets for 12 phenotypes shows that graph-GPA improves statistical power to identify risk variants compared to statistical methods based on smaller number of GWAS datasets. In addition, graph-GPA also promotes better understanding of genetic mechanisms shared among phenotypes, which can potentially be useful for the development of improved diagnosis and therapeutics. The R implementation of graph-GPA is currently available at https://dongjunchung.github.io/GGPA/.

PMID:
28212402
PMCID:
PMC5347371
DOI:
10.1371/journal.pcbi.1005388
[Indexed for MEDLINE]
Free PMC Article
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