ArticlePLoS genetics2013
Gene-based testing of interactions in association studies of quantitative traits.
Article in PLoS genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
57 citing papers in PubMed, 2 syntheses or guidelines pooled it, 93 citations in OpenAlex.
- Large-scale multi-omics analyses in Hispanic/Latino populations identify genes for cardiometabolic traits.Nature communications · 2025Pooled it
- Pairwise genetic meta-analyses between schizophrenia and substance dependence phenotypes reveals novel association signals with pharmacological significance.Translational psychiatry · 2022Pooled it
- Leveraging hierarchical structures for genetic block interaction studies using the hierarchical transformer.medRxiv : the preprint server for health sciences · 2025Article
- BridGE: a pathway-based analysis tool for detecting genetic interactions from GWAS.Nature protocols · 2024Review
- Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies.G3 (Bethesda, Md.) · 2023Article
- Interpretable network-guided epistasis detection.GigaScience · 2022Article
- Leveraging phenotypic variability to identify genetic interactions in human phenotypes.American journal of human genetics · 2021Article
- Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies.Frontiers in cell and developmental biology · 2021Article
- Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies.Frontiers in genetics · 2021Article
- Gene-Based Tests of a Genome-Wide Association Study Dataset Highlight Novel Multiple Sclerosis Risk Genes.Frontiers in neuroscience · 2021Article
- HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene-Environment Interactions.International journal of molecular sciences · 2020Article
- Pre-selected class-level testing of longitudinal biomarkers reduces required multiple testing corrections to yield novel insights in longitudinal small sample human studies.Statistical communications in infectious diseases · 2020Article
- Gene-Based Association Mapping for Dental Caries in The GENEVA Consortium.Journal of dentistry and dental medicine · 2020Article
- GenEpi: gene-based epistasis discovery using machine learning.BMC bioinformatics · 2020Article
- Integrate Molecular Phenome and Polygenic Interaction to Detect the Genetic Risk of Ischemic Stroke.Frontiers in cell and developmental biology · 2020Article
- Region-based interaction detection in genome-wide case-control studies.BMC medical genomics · 2019Article
- Discovering genetic interactions bridging pathways in genome-wide association studies.Nature communications · 2019Article
- Identifying and exploiting gene-pathway interactions from RNA-seq data for binary phenotype.BMC genetics · 2019Article
- Article
- Confounding of linkage disequilibrium patterns in large scale DNA based gene-gene interaction studies.BioData mining · 2019Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Various methods have been developed for identifying gene-gene interactions in genome-wide association studies (GWAS). However, most methods focus on individual markers as the testing unit, and the large number of such tests drastically erodes statistical power. In this study, we propose novel interaction tests of quantitative traits that are gene-based and that confer advantage in both statistical power and biological interpretation. The framework of gene-based gene-gene interaction (GGG) tests combine marker-based interaction tests between all pairs of markers in two genes to produce a gene-level test for interaction between the two. The tests are based on an analytical formula we derive for the correlation between marker-based interaction tests due to linkage disequilibrium. We propose four GGG tests that extend the following P value combining methods: minimum P value, extended Simes procedure, truncated tail strength, and truncated P value product. Extensive simulations point to correct type I error rates of all tests and show that the two truncated tests are more powerful than the other tests in cases of markers involved in the underlying interaction not being directly genotyped and in cases of multiple underlying interactions. We applied our tests to pairs of genes that exhibit a protein-protein interaction to test for gene-level interactions underlying lipid levels using genotype data from the Atherosclerosis Risk in Communities study. We identified five novel interactions that are not evident from marker-based interaction testing and successfully replicated one of these interactions, between SMAD3 and NEDD9, in an independent sample from the Multi-Ethnic Study of Atherosclerosis. We conclude that our GGG tests show improved power to identify gene-level interactions in existing, as well as emerging, association studies.
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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.