Prioritization of candidate causal genes for asthma in susceptibility loci derived from UK Biobank

Kim Valette,Catherine Labbé,Andréanne Côté,Ma’En Obeidat,Sébastien Thériault,Yohan Bossé,Louis‐Philippe Boulet ,Valentin Bon-Baret,Nathalie Gaudreault,Patrick Mathieu,Zhonglin Li,Wim Timens,Arnaud Chignon,Ke Hao,Krystelle Godbout,Don D Sin,Jennifer Lamothe,Michel Laviolette,Ali Eslami ,Maarten Van Den Berge,David C Nickle,Jean‐Christophe Bérubé ,Philippe Joubert

doi:10.1038/s42003-021-02227-6

Abstract

To identify candidate causal genes of asthma, we performed a genome-wide association study (GWAS) in UK Biobank on a broad asthma definition (n = 56,167 asthma cases and 352,255 controls). We then carried out functional mapping through transcriptome-wide association studies (TWAS) and Mendelian randomization in lung (n = 1,038) and blood (n = 31,684) tissues. The GWAS reveals 72 asthma-associated loci from 116 independent significant variants (PGWAS < 5.0E-8). The most significant lung TWAS gene on 17q12-q21 is GSDMB (PTWAS = 1.42E-54). Other TWAS genes include TSLP on 5q22, RERE on 1p36, CLEC16A on 16p13, and IL4R on 16p12, which all replicated in GTEx lung (n = 515). We demonstrate that the largest fold enrichment of regulatory and functional annotations among asthma-associated variants is in the blood. We map 485 blood eQTL-regulated genes associated with asthma and 50 of them are causal by Mendelian randomization. Prioritization of druggable genes reveals known (IL4R, TSLP, IL6, TNFSF4) and potentially new therapeutic targets for asthma.

Highlights

To identify candidate causal genes of asthma, we performed a genome-wide association study (GWAS) in UK Biobank on a broad asthma definition (n = 56,167 asthma cases and 352,255 controls)
GWAS and expression quantitative trait loci (eQTL) results can be integrated at the genome-wide scale to (1) find shared association signals using colocalization[14], (2) identify genes whose genetically-predicted gene expression levels are associated with asthma using a transcriptome-wide association study (TWAS)[15], and (3) infer causal association between genetically-determined gene expression and asthma using Mendelian randomization
We hypothesized that existing omics datasets coupled with new bioinformatics tools will prioritize candidate causal genes underlying asthma susceptibility loci revealed by GWAS

Summary

Introduction

In parallel to GWAS, large expression quantitative trait loci (eQTL) datasets have been generated in asthma-relevant tissues, such as the lung and blood[8,9,10] By leveraging these eQTL datasets, previous studies have identified genes whose expression levels were associated with asthma genetic variants[11,12,13]. GWAS and eQTL results can be integrated at the genome-wide scale to (1) find shared association signals using colocalization[14], (2) identify genes whose genetically-predicted gene expression levels are associated with asthma using a transcriptome-wide association study (TWAS)[15], and (3) infer causal association between genetically-determined gene expression and asthma using Mendelian randomization. We describe 72 physically-defined asthma susceptibility loci in UK Biobank, identify 55 significant lung TWAS genes as well as 50 blood genes causally associated with asthma by Mendelian randomization, and prioritize 40 druggable genes as therapeutic targets for asthma

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