Leveraging protein language models to identify complex trait associations with previously inaccessible classes of functional rare variants.

ABSTRACT: Protein language models (PLMs) improve variant effect predictions, but their role in gene discovery for complex traits remains unclear. We introduce an allelic series-based regression test that uses PLM-derived variant effect predictions as proxies for effect sizes, identifying ∼46% more associations than standard burden tests. Extending this to isoform-level analysis, we find 26 gene-trait pairs with stronger associations in non-canonical versus canonical transcripts, highlighting isoform-specific effects. Finally, we identify evolutionary plausible variants (EPVs), missense variants assigned higher likelihoods than the wild-type alleles by PLMs, representing 0.45% of missense variants. EPVs show higher allele frequencies than synonymous variants, consistent with differential selection pr

SUBMITTER: Jang SK 

PROVIDER: S-EPMC12903449 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature