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

Chen, Guo-Bo, Lee, Sang Hong, Robinson, Matthew R., Trzaskowski, Maciej, Zhu, Zhi-Xiang, Winkler, Thomas W., Day, Felix R., Croteau-Chonka, Damien C., Wood, Andrew R., Locke, Adam E., Kutalik, Zoltán, Loos, Ruth J. F., Frayling, Timothy M., Hirschhorn, Joel N., Yang, Jian, Wray, Naomi R. and Visscher, Peter M. (2016) Across-cohort QC analyses of GWAS summary statistics from complex traits. European Journal of Human Genetics, 25 1: 137-146. doi:10.1038/ejhg.2016.106


Author Chen, Guo-Bo
Lee, Sang Hong
Robinson, Matthew R.
Trzaskowski, Maciej
Zhu, Zhi-Xiang
Winkler, Thomas W.
Day, Felix R.
Croteau-Chonka, Damien C.
Wood, Andrew R.
Locke, Adam E.
Kutalik, Zoltán
Loos, Ruth J. F.
Frayling, Timothy M.
Hirschhorn, Joel N.
Yang, Jian
Wray, Naomi R.
Visscher, Peter M.
Title Across-cohort QC analyses of GWAS summary statistics from complex traits
Journal name European Journal of Human Genetics   Check publisher's open access policy
ISSN 1476-5438
1018-4813
Publication date 2016-08-24
Year available 2017
Sub-type Article (original research)
DOI 10.1038/ejhg.2016.106
Open Access Status DOI
Volume 25
Issue 1
Start page 137
End page 146
Total pages 10
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Language eng
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.
Formatted 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.
Keyword Biochemistry & Molecular Biology
Genetics & Heredity
Biochemistry & Molecular Biology
Genetics & Heredity
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID 1011506
GM 099568
Institutional Status UQ

 
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