Evidence map›Paper›PMID 41265447›Full record

ArticleCell genomics2026

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

Seon-Kyeong Jang, Zitian Wang, Richard Border, Dinh Tuan, Angela Wei, Ulzee An, Sriram Sankararaman, Vasilis Ntranos, Jonathan Flint, Noah Zaitlen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Seon-Kyeong JangDepartment of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Zitian WangDepartment of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Richard BorderDepartment of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.
Dinh TuanDepartment of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Angela WeiDepartment of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Ulzee AnDepartment of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.
Sriram SankararamanDepartment of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Vasilis NtranosDepartment of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Jonathan FlintDepartment of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA. Electronic address: jflint@mednet.ucla.edu.
Noah ZaitlenDepartment of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. Electronic address: nzaitlen@g.ucla.edu.

Funding

Methods for Genome-wide Association Studies in Admixed PopulationsR01HG006399 · NHGRI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI PRICE, ALKES L · 2011 to 2024
$6.3M
NHGRI NIH HHS R01 HG006399
6 · The paper itself

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.

Indexed as

Multifactorial InheritanceAllelesBone DensityExomeGene FrequencyGenetic VariationGenome-Wide Association StudyHumansModels, Geneticcomplex traitsexome sequencegene-based testmissense variantsprotein language modelrare-variant association

Identifiers

PMID41265447
PMCPMC12903449

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.