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Wang S, Chen W, Chen X, et al. Double genomic control is not effective to correct for population stratification in meta-analysis for genome-wide association studies. Front Genet. 2012;3:300doi: 10.3389/fgene.2012.00300.
Wang, S., Chen, W., Chen, X., Hu, F., Archer, K. J., Liu, H. N., Sun, S., & Gao, G. (2012). Double genomic control is not effective to correct for population stratification in meta-analysis for genome-wide association studies. Frontiers in genetics, 3300. https://doi.org/10.3389/fgene.2012.00300
Wang, Shudong, et al. "Double genomic control is not effective to correct for population stratification in meta-analysis for genome-wide association studies." Frontiers in genetics vol. 3 (2012): 300. doi: https://doi.org/10.3389/fgene.2012.00300
Wang S, Chen W, Chen X, Hu F, Archer KJ, Liu HN, Sun S, Gao G. Double genomic control is not effective to correct for population stratification in meta-analysis for genome-wide association studies. Front Genet. 2012 Dec 24;3:300. doi: 10.3389/fgene.2012.00300. eCollection 2012. PMID: 23269928; PMCID: PMC3529452.
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Front Genet. 2012 Dec 24;3:300. doi: 10.3389/fgene.2012.00300. eCollection 2012.

Double genomic control is not effective to correct for population stratification in meta-analysis for genome-wide association studies.

Frontiers in genetics

Shudong Wang, Wenan Chen, Xiangning Chen, Fengjiao Hu, Kellie J Archer, Hb Nianjun Liu, Shumei Sun, Guimin Gao

Affiliations

  1. Department of Biostatistics, School of Medicine, Virginia Commonwealth University Richmond, VA, USA ; College of Information Science and Engineering, Shandong University of Science and Technology Qingdao, China.

PMID: 23269928 PMCID: PMC3529452 DOI: 10.3389/fgene.2012.00300

Abstract

Meta-analysis of genome-wide association studies (GWAS) has become a useful tool to identify genetic variants that are associated with complex human diseases. To control spurious associations between genetic variants and disease that are caused by population stratification, double genomic control (GC) correction for population stratification in meta-analysis for GWAS has been implemented in the software METAL and GWAMA and is widely used by investigators. In this research, we conducted extensive simulation studies to evaluate the double GC correction method in meta-analysis and compared the performance of the double GC correction with that of a principal components analysis (PCA) correction method in meta-analysis. Results show that when the data consist of population stratification, using double GC correction method can have inflated type I error rates at a marker with significant allele frequency differentiation in the subpopulations (such as caused by recent strong selection). On the other hand, the PCA correction method can control type I error rates well and has much higher power in meta-analysis compared to the double GC correction method, even though in the situation that the casual marker does not have significant allele frequency difference between the subpopulations. We applied the double GC correction and PCA correction to meta-analysis of GWAS for two real datasets from the Atherosclerosis Risk in Communities (ARIC) project and the Multi-Ethnic Study of Atherosclerosis (MESA) project. The results also suggest that PCA correction is more effective than the double GC correction in meta-analysis.

Keywords: double genomic control correction; genome-wide association studies; meta-analysis; population stratification; principal components analysis

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