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

The Genetic Investigation of Anthropometric Traits (GIANT) Consortium, Guo Bo Chen, Sang Hong Lee, Hong Lee, Maciej Trzaskowski, Zhi Xiang Zhu, Thomas W. Winkler, Felix R. Day, Damien C. Croteau-Chonka, Andrew R. Wood, Adam E. Locke, Zoltán Kutalik, Ruth J.F. Loos, Timothy M. Frayling, Joel N. Hirschhorn, Jian Yang, Naomi R. Wray, Peter M. Visscher

Research output: Contribution to journalArticle

6 Citations (Scopus)

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 F st 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.

LanguageEnglish
Pages137-146
Number of pages10
JournalEuropean Journal of Human Genetics
Volume25
Issue number1
DOIs
Publication statusPublished - 1 Jan 2016

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Cite this

The Genetic Investigation of Anthropometric Traits (GIANT) Consortium (2016). Across-cohort QC analyses of GWAS summary statistics from complex traits. European Journal of Human Genetics, 25(1), 137-146. https://doi.org/10.1038/ejhg.2016.106
The Genetic Investigation of Anthropometric Traits (GIANT) Consortium. / Across-cohort QC analyses of GWAS summary statistics from complex traits. In: European Journal of Human Genetics. 2016 ; Vol. 25, No. 1. pp. 137-146.
@article{b958248cfd4e4dd780cb95efb26852ce,
title = "Across-cohort QC analyses of GWAS summary statistics from complex traits",
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 F st 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.",
author = "{The Genetic Investigation of Anthropometric Traits (GIANT) Consortium} and Chen, {Guo Bo} and Lee, {Sang Hong} and Hong Lee and Maciej Trzaskowski 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 Zolt{\'a}n Kutalik and Loos, {Ruth J.F.} and Frayling, {Timothy M.} and Hirschhorn, {Joel N.} and Jian Yang and Wray, {Naomi R.} and Visscher, {Peter M.}",
year = "2016",
month = "1",
day = "1",
doi = "10.1038/ejhg.2016.106",
language = "English",
volume = "25",
pages = "137--146",
journal = "European Journal of Human Genetics",
issn = "1018-4813",
publisher = "Nature Publishing Group",
number = "1",

}

The Genetic Investigation of Anthropometric Traits (GIANT) Consortium 2016, 'Across-cohort QC analyses of GWAS summary statistics from complex traits', European Journal of Human Genetics, vol. 25, no. 1, pp. 137-146. https://doi.org/10.1038/ejhg.2016.106

Across-cohort QC analyses of GWAS summary statistics from complex traits. / The Genetic Investigation of Anthropometric Traits (GIANT) Consortium.

In: European Journal of Human Genetics, Vol. 25, No. 1, 01.01.2016, p. 137-146.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Across-cohort QC analyses of GWAS summary statistics from complex traits

AU - The Genetic Investigation of Anthropometric Traits (GIANT) Consortium

AU - Chen, Guo Bo

AU - Lee, Sang Hong

AU - Lee, Hong

AU - Trzaskowski, Maciej

AU - Zhu, Zhi Xiang

AU - Winkler, Thomas W.

AU - Day, Felix R.

AU - Croteau-Chonka, Damien C.

AU - Wood, Andrew R.

AU - Locke, Adam E.

AU - Kutalik, Zoltán

AU - Loos, Ruth J.F.

AU - Frayling, Timothy M.

AU - Hirschhorn, Joel N.

AU - Yang, Jian

AU - Wray, Naomi R.

AU - Visscher, Peter M.

PY - 2016/1/1

Y1 - 2016/1/1

N2 - 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 F st 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.

AB - 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 F st 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.

UR - http://www.scopus.com/inward/record.url?scp=84983451946&partnerID=8YFLogxK

U2 - 10.1038/ejhg.2016.106

DO - 10.1038/ejhg.2016.106

M3 - Article

VL - 25

SP - 137

EP - 146

JO - European Journal of Human Genetics

T2 - European Journal of Human Genetics

JF - European Journal of Human Genetics

SN - 1018-4813

IS - 1

ER -

The Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Across-cohort QC analyses of GWAS summary statistics from complex traits. European Journal of Human Genetics. 2016 Jan 1;25(1):137-146. https://doi.org/10.1038/ejhg.2016.106