ArticlePLoS genetics2017
Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits.
Article in PLoS genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed, 129 citations in OpenAlex.
- Multimodal deep learning enhances genomic risk prediction for cardiometabolic diseases.Briefings in bioinformatics · 2026Article
- Inference of fitness landscapes with heterogeneous patterns of epistasis across sites.bioRxiv : the preprint server for biology · 2026Article
- Evaluation of epistasis detection methods for quantitative phenotypes.Bioinformatics (Oxford, England) · 2026Article
- Inference and Visualization of Complex Genotype-Phenotype Maps.Molecular biology and evolution · 2026Article
- Selection for Molecularly Complementary Modules (MCMs) Drives the Origins and Evolution of Pleiofunctional, Epistatic Interactomes (PEIs).Life (Basel, Switzerland) · 2026Review
- Interactions with polygenic background impact quantitative traits in the UK Biobank.medRxiv : the preprint server for health sciences · 2025Article
- Applying gradient tree boosting to QTL mapping with Shapley additive explanations.Breeding science · 2025Article
- A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic interaction effects.Nature genetics · 2025Article
- Powerful one-dimensional scan to detect heterotic quantitative trait loci.Nature communications · 2025Article
- Sparse modeling of interactions enables fast detection of genome-wide epistasis in biobank-scale studies.American journal of human genetics · 2025Article
- On the analysis of genetic association with long-read sequencing data.PLoS genetics · 2025Article
- Inference and visualization of complex genotype-phenotype maps withbioRxiv : the preprint server for biology · 2025Article
- Efficient Mendelian randomization analysis with self-adaptive determination of sample structure and multiple pleiotropic effects.American journal of human genetics · 2025Article
- Investigating the sources of variable impact of pathogenic variants in monogenic metabolic conditions.Nature communications · 2025Article
- Epistasis regulates genetic control of cardiac hypertrophy.Nature cardiovascular research · 2025Article
- Deciphering epistatic genetic regulation of cardiac hypertrophy.medRxiv : the preprint server for health sciences · 2025Article
- The importance of epistasis in the evolution of viral pathogens.Virus evolution · 2025Review
- Scrutinizing neurodegenerative diseases: decoding the complex genetic architectures through a multi-omics lens.Human genomics · 2024Review
- Genome-wide association study between SARS-CoV-2 single nucleotide polymorphisms and virus copies during infections.PLoS computational biology · 2024Article
- Article
7 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Epistasis, commonly defined as the interaction between multiple genes, is an important genetic component underlying phenotypic variation. Many statistical methods have been developed to model and identify epistatic interactions between genetic variants. However, because of the large combinatorial search space of interactions, most epistasis mapping methods face enormous computational challenges and often suffer from low statistical power due to multiple test correction. Here, we present a novel, alternative strategy for mapping epistasis: instead of directly identifying individual pairwise or higher-order interactions, we focus on mapping variants that have non-zero marginal epistatic effects-the combined pairwise interaction effects between a given variant and all other variants. By testing marginal epistatic effects, we can identify candidate variants that are involved in epistasis without the need to identify the exact partners with which the variants interact, thus potentially alleviating much of the statistical and computational burden associated with standard epistatic mapping procedures. Our method is based on a variance component model, and relies on a recently developed variance component estimation method for efficient parameter inference and p-value computation. We refer to our method as the "MArginal ePIstasis Test", or MAPIT. With simulations, we show how MAPIT can be used to estimate and test marginal epistatic effects, produce calibrated test statistics under the null, and facilitate the detection of pairwise epistatic interactions. We further illustrate the benefits of MAPIT in a QTL mapping study by analyzing the gene expression data of over 400 individuals from the GEUVADIS consortium.
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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.