Chen, Guo-Bo and Lee, Sang Hong and Robinson, Matthew R. and Trzaskowski, Maciej and Zhu, Zhi-Xiang and Winkler, Thomas W. and Day, Felix R. and Croteau-Chonka, Damien C. and Wood, Andrew R. and Locke, Adam E. and Kutalik, Zoltan and Loos, Ruth J. F. and Frayling, Timothy M. and Hirschhorn, Joel N. and Yang, Jian and Wray, Naomi R. and Visscher, Peter M. (2017) Across-cohort QC analyses of GWAS summary statistics from complex traits. EUROPEAN JOURNAL OF HUMAN GENETICS, 25 (1). pp. 137-146. ISSN 1018-4813, 1476-5438
Full text not available from this repository. (Request a copy)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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | GENOME-WIDE ASSOCIATION; METAANALYSIS; INSIGHTS; |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Psychiatrie und Psychotherapie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 14 Dec 2018 12:57 |
| Last Modified: | 22 Feb 2019 09:45 |
| URI: | https://pred.uni-regensburg.de/id/eprint/81 |
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