Across-cohort QC analyses of GWAS summary statistics from complex traits.
European journal of human genetics : EJHG 2015 ; 25: 137-146.
Chen GB, Lee SH, Robinson MR, Trzaskowski M, Zhu ZX, Winkler TW, Day FR, Croteau-Chonka DC, Wood AR, Locke AE, Kutalik Z, Loos RJF, Frayling TM, Hirschhorn JN, Yang J, Wray NR, Genetic Investigation of ANthropometric Traits (GIANT) Consortium, and Visscher PM
DOI : 10.1038/ejhg.2016.106
PubMed ID : 27552965
PMCID : PMC5159754
URL : https://www.nature.com/articles/ejhg2016106
Genome-wide association studies (GWASs) have been successful in discovering SNP trait associations for many quantitative traits and common diseases. Typically, the effect sizes of SNP alleles are very small and this requires large genome-wide association meta-analyses (GWAMAs) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study, we propose four metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We propose methods to examine the concordance between demographic information, and summary statistics and methods to investigate sample overlap. (I) We use the population genetics F statistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. (II) We conduct principal component analysis based on reported allele frequencies, and are able to recover the ancestral information for each cohort. (III) We propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. (IV) To quantify unknown sample overlap across all pairs of cohorts, we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy.