RT Journal Article
SR Electronic
T1 Across-cohort QC analyses of genome-wide association study summary statistics from complex traits
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 033787
DO 10.1101/033787
A1 Guo-Bo Chen
A1 Sang Hong Lee
A1 Matthew R Robinson
A1 Maciej Trzaskowski
A1 Zhi-Xiang Zhu
A1 Thomas W Winkler
A1 Felix R Day
A1 Damien C Croteau-Chonka
A1 Andrew R Wood
A1 Adam E Locke
A1 Zoltán Kutalik
A1 Ruth J F Loos
A1 Timothy M Frayling
A1 Joel N Hirschhorn
A1 Jian Yang
A1 Naomi R Wray
A1 The Genetic Investigation of Anthropometric Traits (GIANT) Consortium
A1 Peter M Visscher
YR 2015
UL http://biorxiv.org/content/early/2015/12/06/033787.abstract
AB 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.