Journal
BRIEFINGS IN BIOINFORMATICS
Volume 19, Issue 6, Pages 1337-1343Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbx072
Keywords
genome-wide association study; summary data reporting; multi-locus analyses; false positive
Funding
- Intramural Research Program of the National Institute of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics
- Intramural Research Program of the National Cancer Institute, USA
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As meta-analysis results published by consortia of genome-wide association studies (GWASs) become increasingly available, many association summary statistics-based multi-locus tests have been developed to jointly evaluate multiple single-nucleotide polymorphisms (SNPs) to reveal novel genetic architectures of various complex traits. The validity of these approaches relies on the accurate estimate of z-score correlations at considered SNPs, which in turn requires knowledge on the set of SNPs assessed by each study participating in the meta-analysis. However, this exact SNP coverage information is usually unavailable from the meta-analysis results published by GWAS consortia. In the absence of the coverage information, researchers typically estimate the z-score correlations by making oversimplified coverage assumptions. We show through real studies that such a practice can generate highly inflated type I errors, and we demonstrate the proper way to incorporate correct coverage information into multi-locus analyses. We advocate that consortia should make SNP coverage information available when posting their meta-analysis results, and that investigators who develop analytic tools for joint analyses based on summary data should pay attention to the variation in SNP coverage and adjust for it appropriately.
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