A cross-population compendium of gene–environment interactions is a dataset published in Nature (2026). On theSindex it has a DataRank of 0.269, placing it in the top 53.9% of the data-sharing corpus. It has been cited 5 times, with 3 citing works in its 1-hop citation network. Its calibrated FAIR score is 50/100.
Environmental differences in genetic effect sizes, namely, gene-environment interactions, may uncover the genetic encoding of phenotypic plasticity1-3. We provide a cross-population atlas of gene-environment interactions comprising 440,210 individuals from European and Japanese populations, with replication in 539,794 individuals from diverse populations. By decomposing the contributions from age, sex and lifestyles, we delineate the aetiology of these gene-environment interactions, including a reverse-causality from a disease-related dietary change. Genome-wide analyses uncovered missing heritability and trait-trait relationships connected by the synergistic effects of genome and environments, which systematically affected polygenic prediction accuracy and cross-population portability. Single-cell projection revealed aging shift of pathways and cell types responsible for genetic regulation. Omics-level gene-environment analyses identified multiple sex-discordant genetic effects in lipid metabolism, informing clinical trial failures for genetically supported drug development. Our comprehensive gene-environment study decodes the dynamics of genetic associations, offering insights into complex trait biology, personalized medicine and drug development.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
0.269
From this paper's citation signal
Citation Network Contribution
0
From 0 citing papers with measurable signal
This paper's DataRank is currently driven only by its base citation score. None of the citing papers had measurable citation signal.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.