Paper Info
Title
Proper joint analysis of summary association statistics requires the adjustment of heterogeneity in SNP coverage pattern.
Abstract
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.
Year
DOI
Venue
2018
10.1093/bib/bbx072
BRIEFINGS IN BIOINFORMATICS
Keywords
Field
DocType
genome-wide association study,summary data reporting,multi-locus analyses,false positive
Data mining,Biology,Correlation and dependence,Bioinformatics,Statistics,SNP
Journal
Volume
Issue
ISSN
19
6
1467-5463
Citations 
PageRank 
References 
0
0.34
10
Authors
4
Name
Order
Citations
PageRank
Han Zhang101.01
William Wheeler200.68
Lei Song300.34
Yu, Kai44799255.21