| Title: | Leveraging protein language models to identify complex trait associations with previously inaccessible classes of functional rare variants |
| Journal: | Cell Genomics |
| Published: | 19 Nov 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41265447/ |
| DOI: | https://doi.org/10.1016/j.xgen.2025.101068 |
Publication 18882
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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 pressures, and are linked to nine traits, including protective associations with low-density lipoprotein (LDL) and bone mineral density. Together, our results demonstrate how PLMs can enhance rare-variant interpretation and gene-trait association discovery in exome data.</p>
9 Keywords
- Alleles
- Bone Density
- Exome
- Gene Frequency
- Genetic Variation
- Genome-Wide Association Study
- Humans
- Models, Genetic
- Multifactorial Inheritance
10 Authors
- Seon-Kyeong Jang
- Zitian Wang
- Richard Border
- Dinh Tuan
- Angela Wei
- Ulzee An
- Sriram Sankararaman
- Vasilis Ntranos
- Jonathan Flint
- Noah Zaitlen
1 Application
| Application ID | Title |
|---|---|
| 33127 | Methods for large-scale medical and population genetic data |
Enabling scientific discoveries that improve human health