[Pubmed] Small-cohort GWAS discovery with AI over massive functional genomics knowledge graph

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medRxiv [Preprint]. 2024 Dec 5:2024.12.03.24318375. doi: 10.1101/2024.12.03.24318375.

ABSTRACT

Genome-wide association studies (GWASs) have identified tens of thousands of disease associated variants and provided critical insights into developing effective treatments. However, limited sample sizes have hindered the discovery of variants for uncommon and rare diseases. Here, we introduce KGWAS, a novel geometric deep learning method that leverages a massive functional knowledge graph across variants and genes to improve detection power in small-cohort GWASs significantly. KGWAS assesses the strength of a variant's association to disease based on the aggregate GWAS evidence across molecular elements interacting with the variant within the knowledge graph. Comprehensive simulations and replication experiments showed that, for small sample sizes ( N =1-10K), KGWAS identified up to 100% more statistically significant associations than state-of-the-art GWAS methods and achieved the same statistical power with up to 2.67× fewer samples. We applied KGWAS to 554 uncommon UK Biobank diseases ( N case

Source: https://pubmed.ncbi.nlm.nih.gov/3967747 ... t9+e462414