ArticleNature genetics2018
Identifying loci affecting trait variability and detecting interactions in genome-wide association studies.
Article in Nature genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
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Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers.Nature communications · 2022Pooled it
- Genome-wide analysis of social behaviour across social domains, reporters and developmental stages: a meta-regression approach.Nature human behaviour · 2026Article
- Optimizing phenotype scale improves genetic analyses in large-scale biobanks.bioRxiv : the preprint server for biology · 2026Article
- Pitfalls in estimating and interpreting the contribution of ultra-rare genetic variants to the heritability of complex traits.medRxiv : the preprint server for health sciences · 2026Article
- Detecting gene-environment interactions to guide personalized intervention: Boosting distributional regression for polygenic scores.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Integrated single marker scanning and sparse Bayesian learning improves performance of detection for GWAS.Plant methods · 2026Article
- Polygenic modeling of genetic effects on both phenotypic mean and variance: distributional regression for BMI, blood and urine biomarkers in the UK Biobank.Frontiers in bioinformatics · 2026Article
- Interactions with polygenic background impact quantitative traits in the UK Biobank.medRxiv : the preprint server for health sciences · 2025Article
- Mean and variance heterogeneity loci impact kernel compositional traits in maize.The plant genome · 2025Article
- Detecting latent interaction effects when analyzing binary traits.PLoS genetics · 2025Article
- PIGEON: a statistical framework for estimating gene-environment interaction for polygenic traits.Nature human behaviour · 2025Article
- Three Loci Affecting Variance of Body Mass Index in African Americans and Sub-Saharan Africans.Genetic epidemiology · 2025Article
- Distinct explanations underlie gene-environment interactions in the UK Biobank.American journal of human genetics · 2025Article
- Mining for gene-environment and gene-gene interactions: parametric and non-parametric tests for detecting variance quantitative trait loci.Frontiers in genetics · 2025Article
- Genetic Prediction Modeling in Large Cohort Studies via Boosting Targeted Loss Functions.Statistics in medicine · 2024Article
- Task-Agnostic Machine-Learning-Assisted Inference.Advances in neural information processing systems · 2024Article
- Causal interpretations of family GWAS in the presence of heterogeneous effects.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Association mapping and candidate gene identification for yield traits in European hazelnut (Plant direct · 2024Article
- Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank.Nature communications · 2024Article
- 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
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Identification of genetic variants with effects on trait variability can provide insights into the biological mechanisms that control variation and can identify potential interactions. We propose a two-degree-of-freedom test for jointly testing mean and variance effects to identify such variants. We implement the test in a linear mixed model, for which we provide an efficient algorithm and software. To focus on biologically interesting settings, we develop a test for dispersion effects, that is, variance effects not driven solely by mean effects when the trait distribution is non-normal. We apply our approach to body mass index in the subsample of the UK Biobank population with British ancestry (n ~408,000) and show that our approach can increase the power to detect associated loci. We identify and replicate novel associations with significant variance effects that cannot be explained by the non-normality of body mass index, and we provide suggestive evidence for a connection between leptin levels and body mass index variability.
Indexed as
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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.