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A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework

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  • Marissa LeBlanc
  • Verena Zuber
  • Wesley K. Thompson
  • Ole A. Andreassen
  • Arnoldo Frigessi
  • Bettina Kulle Andreassen
  • Stephan Ripke
  • Benjamin M. Neale
  • Aiden Corvin
  • James T.R. Walters
  • Kai How Farh
  • Phil Lee
  • Brendan Bulik-Sullivan
  • David A. Collier
  • Hailiang Huang
  • Tune H. Pers
  • Ingrid Agartz
  • Esben Agerbo
  • Margot Albus
  • Madeline Alexander
  • Farooq Amin
  • Silviu A. Bacanu
  • Martin Begemann
  • Richard A. Belliveau
  • Judit Bene
  • Elizabeth Bevilacqua
  • Tim B. Bigdeli
  • Donald W. Black
  • Richard Bruggeman
  • Nancy G. Buccola
  • Randy L. Buckner
  • Wiepke Cahn
  • Guiqing Cai
  • Murray J. Cairns
  • Dominique Campion
  • Rita M. Cantor
  • Vaughan J. Carr
  • Noa Carrera
  • Stanley V. Catts
  • Kimberly D. Chambert
  • Raymond C.K. Chan
  • Ronald Y.L. Chen
  • Eric Y.H. Chen
  • Wei Cheng
  • Mark Hansen
  • Thomas Hansen
  • Sandra Meier
  • Line Olsen
  • Henrik B. Rasmussen
  • Thomas Werge
  • Schizophrenia and Bipolar Disorder Working Groups of the Psychiatric Genomics Consortium
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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.

Original languageEnglish
Article number494
JournalBMC Genomics
Volume19
Issue number1
ISSN1471-2164
DOIs
Publication statusPublished - 25 Jun 2018

    Research areas

  • Covariate-modulated false discovery rate, Cross-phenotype association, Data integration, Meta-analysis with shared subjects

ID: 55289573