Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12864-018-4859-7
Title: A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework
Authors: LeBlanc, M
Zuber, V
Thompson, W.K
Keywords: article
genome-wide association study
human
joint
phenotype
pleiotropy
quantitative analysis
simulation
statistics
case control study
computer simulation
genetics
genome-wide association study
genotype
phenotype
procedures
single nucleotide polymorphism
Case-Control Studies
Computer Simulation
Genome-Wide Association Study
Genotype
Humans
Phenotype
Polymorphism, Single Nucleotide
Issue Date: 2018
Citation: LeBlanc, M, Zuber, V, Thompson, W.K (2018). A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework. BMC Genomics 19 (1) : 494. ScholarBank@NUS Repository. https://doi.org/10.1186/s12864-018-4859-7
Rights: Attribution 4.0 International
Abstract: Background: There is considerable evidence that many complex traits have a partially shared genetic basis, termed pleiotropy. It is therefore useful to consider integrating genome-wide association study (GWAS) data across several traits, usually at the summary statistic level. A major practical challenge arises when these GWAS have overlapping subjects. This is particularly an issue when estimating pleiotropy using methods that condition the significance of one trait on the signficance of a second, such as the covariate-modulated false discovery rate (cmfdr). Results: We propose a method for correcting for sample overlap at the summary statistic level. We quantify the expected amount of spurious correlation between the summary statistics from two GWAS due to sample overlap, and use this estimated correlation in a simple linear correction that adjusts the joint distribution of test statistics from the two GWAS. The correction is appropriate for GWAS with case-control or quantitative outcomes. Our simulations and data example show that without correcting for sample overlap, the cmfdr is not properly controlled, leading to an excessive number of false discoveries and an excessive false discovery proportion. Our correction for sample overlap is effective in that it restores proper control of the false discovery rate, at very little loss in power. Conclusions: With our proposed correction, it is possible to integrate GWAS summary statistics with overlapping samples in a statistical framework that is dependent on the joint distribution of the two GWAS. © 2018 The Author(s).
Source Title: BMC Genomics
URI: https://scholarbank.nus.edu.sg/handle/10635/181195
ISSN: 14712164
DOI: 10.1186/s12864-018-4859-7
Rights: Attribution 4.0 International
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