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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.A.* ; Wray, N.R.* ; GIANT Consortium (Albrecht, E. ; Grallert, H. ; Lichtner, P. ; Müller-Nurasyid, M. ; Ried, J.S. ; Thorand, B. ; Gieger, C. ; Illig, T. ; Peters, A. ; Strauch, K. ; Wichmann, H.-E. ; Heid, I.M.)

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

Eur. J. Hum. Genet. 25, 137-146 (2017)
Publ. Version/Full Text Research data DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
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.
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Publication type Article: Journal article
Document type Scientific Article
Language english
Publication Year 2017
Prepublished in Year 2016
HGF-reported in Year 2017
ISSN (print) / ISBN 1018-4813
e-ISSN 1476-5438
Journal European Journal of Human Genetics
Quellenangaben Volume: 25, Issue: 1, Pages: 137-146 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Reviewing status Peer reviewed
Institute(s) Institute of Genetic Epidemiology (IGE)
Institute of Epidemiology (EPI)
CF Genomics (CF-GEN)
Institute of Human Genetics (IHG)
POF-Topic(s) 30501 - Systemic Analysis of Genetic and Environmental Factors that Impact Health
30202 - Environmental Health
Research field(s) Genetics and Epidemiology
PSP Element(s) G-504100-001
G-504091-002
A-632700-001
G-504000-002
G-504091-004
G-504091-001
G-504000-010
G-504000-009
G-504000-007
G-500700-001
DOI 10.1038/ejhg.2016.106
WOS ID WOS:000394116100021
Scopus ID 84983451946
PubMed ID 27552965
Erfassungsdatum 2018-02-09
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