ArticleNature genetics2019
A linear mixed-model approach to study multivariate gene-environment interactions.
Article in Nature genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
100 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Genetic susceptibility to caffeine intake and metabolism: a systematic review.Journal of translational medicine · 2024Pooled it
- Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies.Bioinformatics (Oxford, England) · 2023Pooled it
- Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers.Nature communications · 2022Pooled it
- Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model.Scientific reports · 2021Trial
- From QTL to candidate genes: a data-driven approach to unravel the genetic architecture of yellow rust resistance in central European wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Decoding Immune Regulation: From Genetic Variation to Mechanism Through Single-Cell Genomics.Immune network · 2026Review
- A fast method for breeding by design via G × E interactions detected in large-scale climatic, phenomic and genomic data.National science review · 2026Article
- Incorporating polygenic risk scores and social determinants of health across populations: Considerations and best practices in research.American journal of human genetics · 2026Review
- A genome-wide association study to elucidate the genetic basis of the cottonseed protein content in Upland cotton (Breeding science · 2026Article
- HistoGWAS: an AI-enabled framework for automated genetic analysis of tissue phenotypes in histology cohorts.Genome biology · 2026Article
- A biobank-scale method for learning modulators of gene-environment interaction underlying human complex traits from multiple environmental exposures.bioRxiv : the preprint server for biology · 2026Article
- Mapping the Analytical Landscape of Gene-Diet Interactions in Epidemiology: From Classical Models to Causal and Multi-Omics Frameworks.Nutrients · 2026Review
- Article
- Inferring genotype-phenotype maps using attention models.PNAS nexus · 2026Article
- Leveraging polygenic risk scores to infer causal directions in genotype-by-environment interactions between complex traits.Human genetics · 2026Article
- Genome-Wide by Lifetime Environment Interaction Studies of Brain Imaging Phenotypes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Posterior estimation of longitudinal variance components from nonlongitudinal data using Bayesian Gaussian process model.Genetics · 2026Article
- Multi-omics Data Integration.Advances in experimental medicine and biology · 2026Review
- ΦX174 bacteriophage viability predicted by protein biophysical modeling.bioRxiv : the preprint server for biology · 2025Article
- Decades' progress and prospects on maize functional genomics and molecular breeding.Science China. Life sciences · 2025Review
40 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Different exposures, including diet, physical activity, or external conditions can contribute to genotype-environment interactions (G×E). Although high-dimensional environmental data are increasingly available and multiple exposures have been implicated with G×E at the same loci, multi-environment tests for G×E are not established. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to identify and characterize loci that interact with one or more environments. After validating our model using simulations, we applied StructLMM to body mass index in the UK Biobank, where our model yields previously known and novel G×E signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.