- Title
- A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework
- Creator
- LeBlanc, Marissa; Zuber, Verena; Thompson, Wesley K.; Andreassen, Ole A.; Schizophrenia and Bipolar Disorder Working Groups of the Psychiatric Genomics Consortium; Frigessi, Arnoldo; Andreassen, Bettina Kulle; Loughland, Carmel J.; Schall, Ulrich; Tooney, Paul A.; Michie, Paul T.; Cairns, Murray J.; Henskens, Frans A.; Carr, Vaughan
- Relation
- BMC Genomics Vol. 19, no. 494
- Publisher Link
- http://dx.doi.org/10.1186/s12864-018-4859-7
- Publisher
- BioMed Central
- Resource Type
- journal article
- Date
- 2018
- Description
- 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.
- Subject
- data integration; meta-analysis with shared subjects; covariate-modulated false discovery rate; cross-phenotype association
- Identifier
- http://hdl.handle.net/1959.13/1392365
- Identifier
- uon:33389
- Identifier
- ISSN:1471-2164
- Rights
- © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Language
- eng
- Full Text
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