Evidence map›Paper›PMID 42907526›Full record

ArticleNature genetics2026

Genome-wide detection of genotype-environment interactions at biobank scale with fastGxE.

Chao Ning, Xiang Zhou

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In one paragraph

Article in Nature genetics, 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
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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

2 authors.

Chao NingDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-8247-1700
Xiang ZhouDepartment of Statistics and Data Science, Yale University, New Haven, CT, USA. xiang.zhou.xz735@yale.edu.ORCID http://orcid.org/0000-0002-4331-7599

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional genome-wide association studies have primarily focused on detecting main genotype effects, often overlooking genotype-environment interactions (GxE), which are essential for understanding context-specific genetic effects and refining disease etiology. Here we present fast genotype-environment interaction analysis (fastGxE), a scalable and effective genome-wide GxE method designed to identify genetic variants that interact with environmental factors to influence traits of interest. fastGxE controls for polygenic effects, polygenic interaction effects and noise heterogeneity; it remains robust to the number of environmental factors involved in GxE interactions; and it ensures scalability for genome-wide GxE analysis in large biobank studies, achieving speed improvements of 32.98-126.49 times over existing approaches. We illustrate the benefits of fastGxE through extensive simulations and an in-depth analysis of 32 physical traits and 67 blood biomarkers from the UK Biobank. In real-data applications, fastGxE identifies 7 genomic loci associated with physical traits, including 4 novel ones, and 18 genomic loci associated with blood biomarkers, 12 of which are novel. The new discoveries highlight the dynamic interplay between genetics and the environment, uncovering potentially clinically relevant pathways that could inform personalized interventions and treatment strategies.

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Registered trials

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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.