Constraints on eQTL Fine Mapping in the Presence of Multisite Local Regulation of Gene Expression

• Type of Material: Article
• Authors: Biao Zeng, Georgia Institute of Technology; Luke R. Lloyd-Jones, University of Queensland; Alexander Holloway, University of Queensland; Urko M Marigorta, Georgia Institute of Technology; Andres Metspalu, University of Tartu; Grant W. Montgomery, University of Queensland; Tonu Esko, University of Tartu; Kenneth Brigham, Emory University; Arshed Quyyumi, Emory University; Youssef Idaghdour, New York University Abu Dhabi; Jian Yang, University of Queensland; Peter M Visscher, University of Queensland; Joseph E. Powell, University of Queensland; Greg Gibson, Georgia Institute of Technology
• Language: English
• Date: 2017-08-01
• Publisher: Genetics Society of America
• Publication Version: Final Published Version
• Copyright Statement: © 2017 Zeng et al.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1534/g3.117.043752
• Title of Journal or Parent Work: G3
• ISSN: 2160-1836
• Volume: 7
• Issue: 8
• Start Page: 2532
• End Page: 2544
• Grant/Funding Information: Assembly of the CAGE cohort was supported by Australian NH&MRC Grant 1046880 to P.V.; G.G. is partially supported by US NIH Grants P01-GM099568 and R01-HG008146, and B.Z. was the recipient of a Chinese Scholarship Council pre-doctoral fellowship.
• Keywords: GENOME-WIDE ASSOCIATION; HUMAN HEIGHT; CELL-TYPES; multivariable regression; PERIPHERAL-BLOOD; Life Sciences & Biomedicine; PARTITIONING HERITABILITY; DISEASE; fine mapping; gene regulation; Genetics & Heredity; linkage disequilibrium; CAUSAL VARIANTS; COMPLEX TRAITS; colocalization; Science & Technology; ARCHITECTURE; IDENTIFICATION
• Research Categories: Engineering, Biomedical; Biology, Genetics

Abstract

Expression quantitative trait locus (eQTL) detection has emerged as an important tool for unraveling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and the magnitude of effects. We describe a series of simulation studies designed to evaluate the impact of linkage disequilibrium (LD) on the fine mapping of causal variants with typical eQTL effect sizes. In the presence of multisite regulation, even though between 80 and 90% of modeled eSNPs associate with normally distributed traits, up to 10% of all secondary signals could be statistical artifacts, and at least 5% but up to one-quarter of credible intervals of SNPs within r 2 > 0.8 of the peak may not even include a causal site. The Bayesian methods eCAVIAR and DAP (Deterministic Approximation of Posteriors) provide only modest improvement in resolution. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine mapping of causal variants needs to be adjusted for multisite influences, as conditional estimates can be highly biased by interference among linked sites, but ultimately experimental verification of individual effects is needed. Presumably similar conclusions apply not just to eQTL mapping, but to multisite influences on fine mapping of most types of quantitative trait.