Genetic associations of protein-coding variants in human disease View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-02-23

AUTHORS

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 J. Daly, Heiko Runz

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. More... »

PAGES

95-102

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41586-022-04394-w

    DOI

    http://dx.doi.org/10.1038/s41586-022-04394-w

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1145780842

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/35197637


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