TY - JOUR
T1 - Constraints on eQTL fine mapping in the presence of multisite local regulation of gene expression
AU - Zeng, Biao
AU - Lloyd-Jones, Luke R.
AU - Holloway, Alexander
AU - Marigorta, Urko M.
AU - Metspalu, Andres
AU - Montgomery, Grant W.
AU - Esko, Tonu
AU - Brigham, Kenneth L.
AU - Quyyumi, Arshed A.
AU - Idaghdour, Youssef
AU - Yang, Jian
AU - Visscher, Peter M.
AU - Powell, Joseph E.
AU - Gibson, Greg
N1 - Publisher Copyright:
© 2017 Zeng et al.
PY - 2017
Y1 - 2017
N2 - 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 r2 > 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.
AB - 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 r2 > 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.
KW - Colocalization
KW - Fine mapping
KW - Gene regulation
KW - Linkage disequilibrium
KW - Multivariable regression
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U2 - 10.1534/g3.117.043752
DO - 10.1534/g3.117.043752
M3 - Article
C2 - 28600440
AN - SCOPUS:85021624557
SN - 2160-1836
VL - 7
SP - 2533
EP - 2544
JO - G3: Genes, Genomes, Genetics
JF - G3: Genes, Genomes, Genetics
IS - 8
ER -