Author
Listed:
- Benjamin B. Sun
(Biogen Inc.
University of Cambridge)
- Mitja I. Kurki
(Massachusetts General Hospital
The Broad Institute of MIT and Harvard
University of Helsinki
Massachusetts General Hospital)
- Christopher N. Foley
(University of Cambridge
Optima Partners)
- Asma Mechakra
(Université de Lyon 1, Université Lyon 1, INSERM, CNRS, INMG)
- Chia-Yen Chen
(Biogen Inc.)
- Eric Marshall
(Biogen Inc.)
- Jemma B. Wilk
(Biogen Inc.)
- Mohamed Chahine
(Université Laval)
- Philippe Chevalier
(Université de Lyon 1, Université Lyon 1, INSERM, CNRS, INMG)
- Georges Christé
(Université de Lyon 1, Université Lyon 1, INSERM, CNRS, INMG)
- Aarno Palotie
(Massachusetts General Hospital
The Broad Institute of MIT and Harvard
University of Helsinki
Massachusetts General Hospital)
- Mark J. Daly
(Massachusetts General Hospital
The Broad Institute of MIT and Harvard
University of Helsinki
Massachusetts General Hospital)
- Heiko Runz
(Biogen Inc.)
Abstract
Genome-wide association studies (GWAS) have identified thousands of genetic variants linked to the risk of human disease. However, GWAS have so far remained largely underpowered in relation to identifying associations in the rare and low-frequency allelic spectrum and have lacked the resolution to trace causal mechanisms to underlying genes1. Here we combined whole-exome sequencing in 392,814 UK Biobank participants with imputed genotypes from 260,405 FinnGen participants (653,219 total individuals) to conduct association meta-analyses for 744 disease endpoints across the protein-coding allelic frequency spectrum, bridging the gap between common and rare variant studies. We identified 975 associations, with more than one-third being previously unreported. We demonstrate population-level relevance for mutations previously ascribed to causing single-gene disorders, map GWAS associations to likely causal genes, explain disease mechanisms, and systematically relate disease associations to levels of 117 biomarkers and clinical-stage drug targets. Combining sequencing and genotyping in two population biobanks enabled us to benefit from increased power to detect and explain disease associations, validate findings through replication and propose medical actionability for rare genetic variants. Our study provides a compendium of protein-coding variant associations for future insights into disease biology and drug discovery.
Suggested Citation
Benjamin B. Sun & Mitja I. Kurki & Christopher N. Foley & Asma Mechakra & Chia-Yen Chen & Eric Marshall & Jemma B. Wilk & Mohamed Chahine & Philippe Chevalier & Georges Christé & Aarno Palotie & Mark , 2022.
"Genetic associations of protein-coding variants in human disease,"
Nature, Nature, vol. 603(7899), pages 95-102, March.
Handle:
RePEc:nat:nature:v:603:y:2022:i:7899:d:10.1038_s41586-022-04394-w
DOI: 10.1038/s41586-022-04394-w
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Cited by:
- Matthew Tegtmeyer & Jatin Arora & Samira Asgari & Beth A. Cimini & Ajay Nadig & Emily Peirent & Dhara Liyanage & Gregory P. Way & Erin Weisbart & Aparna Nathan & Tiffany Amariuta & Kevin Eggan & Marzi, 2024.
"High-dimensional phenotyping to define the genetic basis of cellular morphology,"
Nature Communications, Nature, vol. 15(1), pages 1-12, December.
- Atlas Khan & Ning Shang & Jordan G. Nestor & Chunhua Weng & George Hripcsak & Peter C. Harris & Ali G. Gharavi & Krzysztof Kiryluk, 2023.
"Polygenic risk alters the penetrance of monogenic kidney disease,"
Nature Communications, Nature, vol. 14(1), pages 1-10, December.
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