A Penalized Regression Approach for Integrative Analysis in Gen ome- Wide Association Studies
- *Corresponding Author:
- Liu J
Centre of Quantitative Medicine
Duke-NUS Graduate Medical School, Singapore
Tel: +65 6516 7666
E-mail: [email protected]
Received date: February 13, 2015; Accepted date: May 22, 2015; Published date: May 29, 2015
Citation: Liu J, Wang F, Gao X, Zhang H, Wan X, et al. (2015) A Penalized Regression Approach for Integrative Analysis in Genome-Wide Association Studies. J Biomet Biostat 6: 228. doi: 10.4172/2155-6180.1000228
Copyright: © 2015 Liu J, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are are credited.
Over one thousand genome-wide association studies (GWAS) have been conducted in the past decade. Increasing biological evidence suggests the polygenic genetic architecture of complex traits: a complex trait is affected by many risk variants with small or moderate effects jointly. Meanwhile, recent progress in GWAS suggests that complex human traits may share common genetic bases, which is known as “pleiotropy”. To further improve statistical power of detecting risk genetic variants in GWAS, we propose a penalized regression method to analyze the GWAS dataset of primary interest by incorporating information from other related GWAS. The proposed method does not require the individual-level of genotype and phenotype data from other related GWAS, making it useful when only summary statistics are available. The key idea of the proposed approach is that related traits may share common genetic basis. Specifically, we propose a linear model for integrative analysis of multiple GWAS, in which risk genetic variants can be detected via identification of nonzero coefficients. Due to the pleiotropy effect, there exist genetic variants affecting multiple traits, which correspond to a consistent nonzero pattern of coefficients across multiple GWAS. To achieve this, we use a group Lasso penalty to identify this nonzero pattern in our model, and then develop an efficient algorithm based on the proximal gradient method. Simulation studies showed that the proposed approach had satisfactory performance. We applied the proposed method to analyze a body mass index (BMI) GWAS dataset from a European American (EA) population and achieved improvement over single GWAS analysis.