ArticlePLoS genetics2016
Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models.
Article in PLoS genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Evaluation of the Genetic Variance of Alzheimer's Disease Explained by the Disease-Associated Chromosomal Regions.Journal of Alzheimer's disease : JAD · 2019Pooled it
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- Context-specific genetic effects inform endotypes and treatment in asthma.The Journal of allergy and clinical immunology · 2026Article
- A fast method for breeding by design via G × E interactions detected in large-scale climatic, phenomic and genomic data.National science review · 2026Article
- Genome-wide association study revealed genomic regions associated with tuber quality traits in water yam (Dioscorea alata L.).PloS one · 2026Article
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- Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks.Nature communications · 2025Article
- The integration of quantile regression with 3VmrMLM identifies more QTNs and QTN-by-environment interactions using SNP- and haplotype-based markers.Plant communications · 2025Article
- Hierarchical selection of genetic and gene by environment interaction effects in high-dimensional mixed models.Statistical methods in medical research · 2025Review
- IIIVmrMLM.QEI: An effective tool for indirect detection of QTN-by-environment interactions in genome-wide association studies.Computational and structural biotechnology journal · 2024Article
- Genome-Wide Association Study as an Efficacious Approach to Discover Candidate Genes Associated with Body Linear Type Traits in Dairy Cattle.Animals : an open access journal from MDPI · 2024Review
- A scalable and robust variance components method reveals insights into the architecture of gene-environment interactions underlying complex traits.American journal of human genetics · 2024Article
- Genome-wide SNP discovery and genotyping delineates potential QTLs underlying major yield-attributing traits in buckwheat.The plant genome · 2024Article
- Genome-wide association study as a powerful tool for dissecting competitive traits in legumes.Frontiers in plant science · 2023Review
- Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS.Plants (Basel, Switzerland) · 2022Review
- Genome-Wide Association Study for Agro-Morphological Traits in Eggplant Core Collection.Plants (Basel, Switzerland) · 2022Article
- Article
- Rare and low-frequency exonic variants and gene-by-smoking interactions in pulmonary function.Scientific reports · 2021Article
- Genome-wide association analyses identify genotype-by-environment interactions of growth traits in Simmental cattle.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Although genome-wide association studies (GWASs) have discovered numerous novel genetic variants associated with many complex traits and diseases, those genetic variants typically explain only a small fraction of phenotypic variance. Factors that account for phenotypic variance include environmental factors and gene-by-environment interactions (GEIs). Recently, several studies have conducted genome-wide gene-by-environment association analyses and demonstrated important roles of GEIs in complex traits. One of the main challenges in these association studies is to control effects of population structure that may cause spurious associations. Many studies have analyzed how population structure influences statistics of genetic variants and developed several statistical approaches to correct for population structure. However, the impact of population structure on GEI statistics in GWASs has not been extensively studied and nor have there been methods designed to correct for population structure on GEI statistics. In this paper, we show both analytically and empirically that population structure may cause spurious GEIs and use both simulation and two GWAS datasets to support our finding. We propose a statistical approach based on mixed models to account for population structure on GEI statistics. We find that our approach effectively controls population structure on statistics for GEIs as well as for genetic variants.
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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.