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Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits

Authors
  • Gleason, Kevin J.1
  • Yang, Fan2
  • Pierce, Brandon L.1, 3
  • He, Xin3
  • Chen, Lin S.1
  • 1 Department of Public Health Sciences, University of Chicago, 5841 South Maryland Ave MC2000, Chicago, IL, 60637, USA , Chicago (United States)
  • 2 Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 E. 17th Place, Aurora, CO, 80045, USA , Aurora (United States)
  • 3 Department of Human Genetics, University of Chicago, 920 E 58th St, Chicago, IL, 60637, USA , Chicago (United States)
Type
Published Article
Publication Date
Sep 11, 2020
Volume
21
Issue
1
Identifiers
DOI: 10.1186/s13059-020-02125-w
Source
Springer Nature
Keywords
License
Green

Abstract

To provide a comprehensive mechanistic interpretation of how known trait-associated SNPs affect complex traits, we propose a method, Primo, for integrative analysis of GWAS summary statistics with multiple sets of omics QTL summary statistics from different cellular conditions or studies. Primo examines association patterns of SNPs to complex and omics traits. In gene regions harboring known susceptibility loci, Primo performs conditional association analysis to account for linkage disequilibrium. Primo allows for unknown study heterogeneity and sample correlations. We show two applications using Primo to examine the molecular mechanisms of known susceptibility loci and to detect and interpret pleiotropic effects.

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