GBAT: a gene-based association test for robust detection of trans- gene regulation
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GBAT: a gene-based association test for robust detection of trans-gene regulation Xuanyao Liu1,2*†, Joel A. Mefford3†, Andrew Dahl3, Yuan He4, Meena Subramaniam3, Alexis Battle4, Alkes L. Price1 and Noah Zaitlen3* * Correspondence: xuanyao@ uchicago.edu; NZaitlen@mednet. ucla.edu † Xuanyao Liu and Joel A. Mefford contributed equally to this work. 1 Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA 3 Departments of Neurology and Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA Full list of author information is available at the end of the article
Abstract The observation that disease-associated genetic variants typically reside outside of exons has inspired widespread investigation into the genetic basis of transcriptional regulation. While associations between the mRNA abundance of a gene and its proximal SNPs (cis-eQTLs) are now readily identified, identification of high-quality distal associations (trans-eQTLs) has been limited by a heavy multiple testing burden and the proneness to false-positive signals. To address these issues, we develop GBAT, a powerful gene-based pipeline that allows robust detection of high-quality trans-gene regulation signal. Keywords: Gene expression, eQTLs, trans-eQTLs, trans gene regulation
Introduction The vast majority of genetic variants associated with complex traits are found in noncoding regions of the genome [1], leading to a natural hypothesis that their effects are mediated through changes in transcriptional regulation. For computational and statistical reasons, efforts to date have focused on mapping cis-genetic effects on gene expression despite the fact that trans-effects explain more than twice the variability in gene expression than cis-effects [2, 3]. Furthermore, while cis-genetic effects are widely shared across cell types [4, 5], disease outcomes frequently result from dysregulation of genes in specific cell types [6–10]. In contrast, trans-genetic effects are more cell-typespecific [5, 11] and may therefore harbor disease-causing variants not captured in cis analyses [12]. It was recently estimated that trans-genetic effects to core disease genes could explain 70–100% of the complex trait heritability, and the widespread trans effects also underlie the highly polygenic architecture of complex traits [13]. Therefore, detecting and understanding trans-genetic effects is a key step towards a complete understanding of complex trait genetics. However, robust discovery of trans-eQTLs is very challenging for several reasons. First, trans-effects are typically much smaller than cis-effects and thus hard to detect [14]. Second, genome-wide scans for trans-eQTLs have heavy burden of multiple testing [3, 14]: a genome-wide trans-eQTL test of over twenty thousand genes and one © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or form
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