ArticleEuropean journal of human genetics : EJHG2011
Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data.
Article in European journal of human genetics : EJHG, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Machine learning in Alzheimer's disease genetics.Nature communications · 2025Article
- Detecting genetic interactions with visible neural networks.Communications biology · 2025Article
- Lack of epistatic interaction ofBrain communications · 2025Article
- Article
- Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure.BioData mining · 2021Article
- Empowering individual trait prediction using interactions for precision medicine.BMC bioinformatics · 2021Article
- Article
- Human collectin-11 (COLEC11) and its synergic genetic interaction with MASP2 are associated with the pathophysiology of Chagas Disease.PLoS neglected tropical diseases · 2019Article
- How to increase our belief in discovered statistical interactions via large-scale association studies?Human genetics · 2019Review
- KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies.BMC bioinformatics · 2017Article
- Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.PLoS genetics · 2016Article
- A roadmap to multifactor dimensionality reduction methods.Briefings in bioinformatics · 2016Review
- A cautionary note on the impact of protocol changes for genome-wide association SNP × SNP interaction studies: an example on ankylosing spondylitis.Human genetics · 2015Article
- Filter-free exhaustive odds ratio-based genome-wide interaction approach pinpoints evidence for interaction in the HLA region in psoriasis.BMC genetics · 2015Article
- gammaMAXT: a fast multiple-testing correction algorithm.BioData mining · 2015Article
- Evaluating methods for modeling epistasis networks with application to head and neck cancer.Cancer informatics · 2015Article
- Detecting epistasis in human complex traits.Nature reviews. Genetics · 2014Review
- Pathway-guided identification of gene-gene interactions.Annals of human genetics · 2014Article
- Host genetics and viral load in primary HIV-1 infection: clear evidence for gene by sex interactions.Human genetics · 2014Article
- A robustness study of parametric and non-parametric tests in model-based multifactor dimensionality reduction for epistasis detection.BioData mining · 2013Article
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3 authors.
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Abstract
Detecting gene-gene interactions or epistasis in studies of human complex diseases is a big challenge in the area of epidemiology. To address this problem, several methods have been developed, mainly in the context of data dimensionality reduction. One of these methods, Model-Based Multifactor Dimensionality Reduction, has so far mainly been applied to case-control studies. In this study, we evaluate the power of Model-Based Multifactor Dimensionality Reduction for quantitative traits to detect gene-gene interactions (epistasis) in the presence of error-free and noisy data. Considered sources of error are genotyping errors, missing genotypes, phenotypic mixtures and genetic heterogeneity. Our simulation study encompasses a variety of settings with varying minor allele frequencies and genetic variance for different epistasis models. On each simulated data, we have performed Model-Based Multifactor Dimensionality Reduction in two ways: with and without adjustment for main effects of (known) functional SNPs. In line with binary trait counterparts, our simulations show that the power is lowest in the presence of phenotypic mixtures or genetic heterogeneity compared to scenarios with missing genotypes or genotyping errors. In addition, empirical power estimates reduce even further with main effects corrections, but at the same time, false-positive percentages are reduced as well. In conclusion, phenotypic mixtures and genetic heterogeneity remain challenging for epistasis detection, and careful thought must be given to the way important lower-order effects are accounted for in the analysis.
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