Across-cohort QC analyses of GWAS summary statistics from complex traits.

Details

Serval ID
serval:BIB_E56034AE514C
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Across-cohort QC analyses of GWAS summary statistics from complex traits.
Journal
European journal of human genetics
Author(s)
Chen G.B., Lee S.H., Robinson M.R., Trzaskowski M., Zhu Z.X., Winkler T.W., Day F.R., Croteau-Chonka D.C., Wood A.R., Locke A.E., Kutalik Z., Loos R.J., Frayling T.M., Hirschhorn J.N., Yang J., Wray N.R., Visscher P.M.
Working group(s)
Genetic Investigation of Anthropometric Traits (GIANT) Consortium
ISSN
1476-5438 (Electronic)
ISSN-L
1018-4813
Publication state
Published
Issued date
01/2016
Peer-reviewed
Oui
Volume
25
Number
1
Pages
137-146
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
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 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. (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.

Keywords
Alleles, Cohort Studies, Genetic Heterogeneity, Genome-Wide Association Study/statistics & numerical data, Genotype, Humans, Meta-Analysis as Topic, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci/genetics, Software
Pubmed
Web of science
Open Access
Yes
Create date
09/09/2016 15:24
Last modification date
20/08/2019 16:08
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