Phenome-wide analysis of copy number variants in 470,727 UK Biobank genomes

Copy number variants (CNVs) are key drivers of human diversity and disease risk<sup>1</sup>. 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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Nature
Published
2026-02-04
DOI
https://doi.org/10.1038/s41586-025-10087-x
Primary Topic
Genomic variations and chromosomal abnormalities
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article
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article

Phenome-wide analysis of copy number variants in 470,727 UK Biobank genomes

Erin Oerton, C. Haefliger, Satbhai Santosh, Benjamin Challis et al.
Nature
Genomic variations and chromosomal abnormalities
article

Phenome-wide analysis of copy number variants in 470,727 UK Biobank genomes

Erin Oerton, C. Haefliger, Satbhai Santosh, Benjamin Challis, Kieren Lythgow, Dorota Matelska, Xavier Romero Ros, Q. Wang, Mark Lal, Stewart MacArthur, Marcin Karpiński, Haiyi Lou, James D. Garnett, Ken Smith, Xiao Jiang, Cecilia Salvoro, Ryan S. Dhindsa, Arwa Bin Raies, Andrew Harper, Margarida Lopes, Anna Maria Tsakiroglou, Anna S. E. Cuomo, Tatiana Ort, Guillermo del Angel, Ruth Karen March, Sebastian Wasilewski, Ben Hollis, Anish Konkar, Richard N. Hanna, Brian Dougherty, Gabrielle Cass Baumberg, Xiaoyin Li, Wei Ding, Abhishek Nag, Zhongwu Lai, Sean Wen, Andrew Davis, Anna Reznichenko, Xueqing Zoe Zou, Adam Platt, Fengyuan Hu, Oliver S. Burren, Kousik Kundu, Zammy Fairhurst-Hunter, Glenda Lassi, Eleanor Wheeler, Jen Harrow, Yoichiro Ohne, Douglas Loesch, Slavé Petrovski, Sonja Hess, Ioanna Tachmazidou, Lily Wang, Amanda O’Neill, Regina F. Danielson, Sophia Cameron-Christie, Karyn Megy, Margarete Fabre, Mohammad Bohlooly-Y, Jonathan Mitchell, Meeta Maisuria-Armer, Rob Menzies, Xiao-Rong Peng, Bram Prins, Anna Szymaniak, Daniel Elias Martin Herranz, AstraZeneca Genomics Initiative, Lisa Buvall, Qiang Wu, Manik Garg, Sri V. V. Deevi, Coralie Viollet, Lauren Anderson-Dring, Lifeng Tian, Jorge L. Del-Aguila, Keren Carss, Ian Henry, Sean O’Dell, Natalia Frydrych, Mårten Hammar, Helen Stevens, Bill Mowrey, Rahul Sharma, Daniel Muthas, Suzanne Cohen, Benjamin Pullman, Benjamin Georgi
article en

Abstract

Copy number variants (CNVs) are key drivers of human diversity and disease risk<sup>1</sup>. 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.

Nature
AstraZeneca (United Kingdom) (GB), Baylor College of Medicine (US), University of Cambridge (GB), Cambridge University Hospitals NHS Foundation Trust (GB), AstraZeneca (Australia) (AU), AstraZeneca (Singapore) (SG), Neurological Research Institute (US), Alexion Pharmaceuticals (United States) (US), AstraZeneca (Switzerland) (CH), AstraZeneca (Japan) (JP), AstraZeneca (Poland) (PL), AstraZeneca (Finland) (FI), AstraZeneca (Sweden) (SE), Texas Children's Hospital (US), AstraZeneca (Spain) (ES), AstraZeneca (United States) (US), Austin Health (AU), University of Tennessee at Knoxville (US)
Openalex Percentile: Top 63%
Genomic variations and chromosomal abnormalities
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