ArticleGenetic epidemiology2016
A perspective on interaction effects in genetic association studies.
Article in Genetic epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 4 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
78 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Better together against genetic heterogeneity: A sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank.PLoS genetics · 2024Pooled it
- Canadian COVID-19 host genetics cohort replicates known severity associations.PLoS genetics · 2024Pooled it
- Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes.International journal of molecular sciences · 2020Pooled it
- Quality of dietary fat and genetic risk of type 2 diabetes: individual participant data meta-analysis.BMJ (Clinical research ed.) · 2019Pooled it
- A gene-diet interaction-based score predicts response to dietary fat in the Women's Health Initiative.The American journal of clinical nutrition · 2020Trial
- Interactions Between Genetic Risk and Macronutrient Intake on Obesity-Related Traits in Sri Lankan Adults.Nutrients · 2026Article
- Partitioned blood pressure polygenic risk reveals differential genetic effects of tissue-specific enhancers and their interactions on cardiovascular disease.Research square · 2026Article
- Article
- Regular exercise modifies the effects of 12 LDL-C SNPs on cardiovascular disease risk: KoGES.Lipids in health and disease · 2026Article
- Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship.Cell genomics · 2026Article
- Mapping the Analytical Landscape of Gene-Diet Interactions in Epidemiology: From Classical Models to Causal and Multi-Omics Frameworks.Nutrients · 2026Review
- Interactions between genetic predisposition to obesity, insulin resistance and type 2 diabetes risk, and food or beverage intake for incident type 2 diabetes: European Prospective Investigation into Cancer and Nutrition (EPIC) InterAct case-cohort study.The American journal of clinical nutrition · 2026Article
- Article
- A Narrative Review of Gene-Environment Interactions in Pediatric Pulmonology.Pediatric pulmonology · 2026Review
- Pathway-specific polygenic scores substantially increase the discovery of gene-adiposity interactions impacting liver biomarkers.HGG advances · 2026Article
- Diagnosing migraine from genome-wide genotype data: a machine learning analysis.Brain : a journal of neurology · 2026Article
- Low diet quality increases the genetic risk of high blood cholesterol levels in Asian Indian adults [CURES - 165].PloS one · 2026Article
- Interactions with polygenic background impact quantitative traits in the UK Biobank.medRxiv : the preprint server for health sciences · 2025Article
- Hypothesis test of specific parametric structure in a generalized additive model.medRxiv : the preprint server for health sciences · 2025Article
- Uncovering causal gene-tissue pairs and variants through a multivariate TWAS controlling for infinitesimal effects.Nature communications · 2025Article
18 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
1 author.
Funding
Abstract
The identification of gene-gene and gene-environment interaction in human traits and diseases is an active area of research that generates high expectation, and most often lead to high disappointment. This is partly explained by a misunderstanding of the inherent characteristics of standard regression-based interaction analyses. Here, I revisit and untangle major theoretical aspects of interaction tests in the special case of linear regression; in particular, I discuss variables coding scheme, interpretation of effect estimate, statistical power, and estimation of variance explained in regard of various hypothetical interaction patterns. Linking this components it appears first that the simplest biological interaction models-in which the magnitude of a genetic effect depends on a common exposure-are among the most difficult to identify. Second, I highlight the demerit of the current strategy to evaluate the contribution of interaction effects to the variance of quantitative outcomes and argue for the use of new approaches to overcome this issue. Finally, I explore the advantages and limitations of multivariate interaction models, when testing for interaction between multiple SNPs and/or multiple exposures, over univariate approaches. Together, these new insights can be leveraged for future method development and to improve our understanding of the genetic architecture of multifactorial traits.
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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.