Across-cohort QC analyses of genome-wide association study summary statistics from complex traits
Sang Hong Lee,
Matthew R. Robinson,
Thomas W Winkler,
Felix R. Day,
Damien C Croteau-Chonka,
Andrew R. Wood,
Adam E Locke,
Ruth J F Loos,
Timothy M Frayling,
Joel N. Hirschhorn,
Naomi R. Wray,
The Genetic Investigation of Anthropometric Traits (GIANT) Consortium,
Peter M Visscher
Posted 06 Dec 2015
bioRxiv DOI: 10.1101/033787
Posted 06 Dec 2015
Genome-wide association studies (GWASs) have been successful in discovering replicable SNP-trait associations for many quantitative traits and common diseases in humans. Typically the effect sizes of SNP alleles are very small and this has led to large genome-wide association meta-analyses (GWAMA) 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 a new set of metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We proposed a pair of methods in examining the concordance between demographic information and summary statistics. In method I, we use the population genetics Fst 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. In method II, we conduct principal component analysis based on reported allele frequencies, and is able to recover the ancestral information for each cohort. In addition, 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. Finally, 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.
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