Evidence map›Paper›PMID 41889932›Full record

ArticlebioRxiv : the preprint server for biology2026

A biobank-scale method for learning modulators of gene-environment interaction underlying human complex traits from multiple environmental exposures.

Zhengtong Liu, Arush Ramteke, Aakarsh Anand, Aditya Gorla, Moonseong Jeong, Sriram Sankararaman

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

5 · Who and what money

Authors and funding

6 authors.

Zhengtong LiuDepartment of Computer Science, UCLA, Los Angeles, CA, USA.
Arush RamtekeDepartment of Computer Science, Courant Institute of Mathematical Sciences, NYU, NY, USA.
Aakarsh AnandDepartment of Computer Science, UCLA, Los Angeles, CA, USA.
Aditya GorlaBioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
Moonseong JeongDepartment of Computer Science, UCLA, Los Angeles, CA, USA.
Sriram SankararamanDepartment of Computer Science, UCLA, Los Angeles, CA, USA.

Funding

Expressive and scalable statistical models for genomic and biomedical dataR35GM153406 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Sriram Sankararaman · 2024 to 2026
$1.0M
Subtyping complex phenotypes via constrastive learning by leveraging electronic health recordsR21HG013393 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI RAHMANI, ELIOR · 2023 to 2023
$428k
NHGRI NIH HHS R21 HG013393NIGMS NIH HHS R35 GM153406
6 · The paper itself

Abstract

It is increasingly recognized that genetic effects on complex traits and diseases are shaped by environmental context. Biobanks that measure diverse environmental exposures alongside genotypes and phenotypes at scale enable systematic study of gene-environment (

Identifiers

PMID41889932
PMCPMC13015455

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.