Phenome-wide analysis of copy number variants in 470,727 UK Biobank genomes
Abstract
Copy number variants (CNVs) are key drivers of human diversity and disease risk1. Here we evaluate the role of CNVs across a broad range of human phenotypes and diseases by analysing CNVs from 470,727 UK Biobank whole-genome sequences and conducting a variant- and gene-level phenome-wide association study (PheWAS) with 2,941 plasma protein abundance measurements, 13,336 binary clinical phenotypes and 1,911 quantitative traits.
Proteomic analyses validated functional associations of CNVs with nearby genes (cis-protein quantitative trait loci; cis-pQTLs)—with deletions and duplications typically associated with reduced and increased protein levels, respectively—and uncovered previously unknown protein–protein interactions (trans-pQTLs). Our PheWAS recapitulated known associations and uncovered associations in both coding and non-coding regions.
Notably, we identified a rare deletion in ZNF451 associated with increased leukocyte telomere length and a non-coding deletion of a SLC2A9 enhancer associated with reduced gout risk. In addition, by combining CNVs with protein-coding single nucleotide variants and indels, we enhanced the power of our study to detect gene–disease associations. Finally, we leveraged this multiomics dataset to identify several pQTLs that constitute candidate biomarkers, including TMPRSS5 for Charcot–Marie–Tooth disease type 1A.
This multiancestry whole-genome-sequence CNV PheWAS offers insights into the roles of CNVs in human health outcomes and could serve as a valuable resource for therapeutic development.
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Zou, Xueqing Zoe; Hu, Fengyuan; Lou, Haiyi; Burren, Oliver S.; Li, Xiaoyin; Megy, Karyn; Wheeler, Eleanor; Wu, Qiang; Atanur, Santosh S.; Karpinski, Marcin; Loesch, Douglas; Fairhurst-Hunter, Zammy; Deevi, Sri V. V.; Oerton, Erin; Wen, Sean; Jiang, Xiao; Salvoro, Cecilia; Mitchell, Jonathan; Nag, Abhishek; Hollis, Ben; O’Neill, Amanda; Anderson-Dring, Lauren; Bohlooly-Y, Mohammad; Buvall, Lisa; Cameron-Christie, Sophia; Prins, Bram; Cohen, Suzanne; Danielson, Regina F.; Davis, Andrew; Ding, Wei; Dougherty, Brian; Garg, Manik; Georgi, Benjamin; Harper, Andrew; Haefliger, Carolina; Hammar, Mårten; Hanna, Richard N.; Henry, Ian; Kundu, Kousik; Lai, Zhongwu; Lal, Mark; Lassi, Glenda; Liang, Yupu; Lopes, Margarida; Lythgow, Kieren; Maisuria-Armer, Meeta; March, Ruth; Matelska, Dorota; Menzies, Rob; Michaëlsson, Erik; Mowrey, Bill; Muthas, Daniel; Ohne, Yoichiro; Pullman, Benjamin; Hess, Sonja; Raies, Arwa; Reznichenko, Anna; Romero Ros, Xavier; Stevens, Helen; Tachmazidou, Ioanna; Viollet, Coralie; Vitsios, Dimitrios; Walentinsson, Anna; Wang, Lily; Wang, Qing-Dong; Cuomo, Anna; Martin Herranz, Daniel Elias; O’Connell, Jared; Del-Aguila, Jorge L.; Konkar, Anish; Challis, Benjamin; Platt, Adam; Ort, Tatiana; Garnett, James; Peng, Xiao-Rong; Baumberg, Gabrielle; Frydrych, Natalia; Stefanucci, Luca; Szymaniak, Anna; Tsakiroglou, Anna Maria; Sharma, Rahul; Harrow, Jen; MacArthur, Stewart; Wasilewski, Sebastian; O’Dell, Sean; Tian, Lifeng; Smith, Katherine R.; del Angel, Guillermo; Fabre, Margarete; Dhindsa, Ryan S.; Wang, Quanli; Petrovski, Slavé; Carss, Keren
Abstract
Copy number variants (CNVs) are key drivers of human diversity and disease risk1. Here we evaluate the role of CNVs across a broad range of human phenotypes and diseases by analysing CNVs from 470,727 UK Biobank whole-genome sequences and conducting a variant- and gene-level phenome-wide association study (PheWAS) with 2,941 plasma protein abundance measurements, 13,336 binary clinical phenotypes and 1,911 quantitative traits.
Proteomic analyses validated functional associations of CNVs with nearby genes (cis-protein quantitative trait loci; cis-pQTLs)—with deletions and duplications typically associated with reduced and increased protein levels, respectively—and uncovered previously unknown protein–protein interactions (trans-pQTLs). Our PheWAS recapitulated known associations and uncovered associations in both coding and non-coding regions.
Notably, we identified a rare deletion in ZNF451 associated with increased leukocyte telomere length and a non-coding deletion of a SLC2A9 enhancer associated with reduced gout risk. In addition, by combining CNVs with protein-coding single nucleotide variants and indels, we enhanced the power of our study to detect gene–disease associations. Finally, we leveraged this multiomics dataset to identify several pQTLs that constitute candidate biomarkers, including TMPRSS5 for Charcot–Marie–Tooth disease type 1A.
This multiancestry whole-genome-sequence CNV PheWAS offers insights into the roles of CNVs in human health outcomes and could serve as a valuable resource for therapeutic development.
Web | DOI | PMC | PDF | Nature | Open Access
