Cost-effective non-additive GWAS across 2329 diseases in 500,349 individuals

Le résumé fourni par la source

Drug candidates supported by genetic evidence are more likely to succeed in clinical trials, with genome-wide association studies (GWAS) providing a key source of such evidence. Standard GWAS approaches assume additive effects of alleles on the phenotype, but non-additive models have also successfully identified novel associations across various traits. Despite their potential, the large-scale application of non-additive GWAS across thousands of phenotypes in biobanks has been limited by high computational costs. To address this challenge, we present a method that leverages the correlation between additive and non-additive p-values to prioritize variants likely to reach genome-wide significance in non-additive analyses. Applied to the FinnGen dataset comprising 500,349 individuals and 2329 phenotypes, this method reduces computational costs by three orders of magnitude while retaining nearly all true non-additive associations, identifying 781 novel loci missed by additive GWAS. We report fine-mapping and colocalization with 571 datasets for novel loci, uncovering likely causal variants and potential insights into biological mechanisms. The authors introduce a cost-efficient, non-additive genome-wide association study pipeline that, when applied to a dominant analysis of 2329 phenotypes in 500,349 individuals, reduced computational costs from $27,000 to $39 and identified 781 new associations.

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