ArticleAnnals of human genetics2011
Model-based multifactor dimensionality reduction for detecting epistasis in case-control data in the presence of noise.
Article in Annals of human genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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
40 citing papers in PubMed.
- From Serum to Genome: γ-Glutamyltransferase Gene Family Variants Shape Ischemic Stroke Risk via Sex-Specific Gene-Environment Interactions.Life (Basel, Switzerland) · 2026Article
- 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
- Single nucleotide polymorphisms of Interleukin - 4, Interleukin-18, FCRL3 and sPLA2IIa genes and their association in pathogenesis of endometriosis.Molecular biology reports · 2023Article
- Missing Causality and Heritability of Autoimmune Hepatitis.Digestive diseases and sciences · 2023Article
- NSPA: characterizing the disease association of multiple genetic interactions at single-subject resolution.Bioinformatics advances · 2023Article
- Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests.Nucleic acids research · 2022Article
- Detecting gene-gene interactions from GWAS using diffusion kernel principal components.BMC bioinformatics · 2022Article
- Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure.BioData mining · 2021Article
- Article
- Gene-gene interaction: the curse of dimensionality.Annals of translational medicine · 2019Review
- How to increase our belief in discovered statistical interactions via large-scale association studies?Human genetics · 2019Review
- Confounding of linkage disequilibrium patterns in large scale DNA based gene-gene interaction studies.BioData mining · 2019Article
- Male-specific epistasis between WWC1 and TLN2 genes is associated with Alzheimer's disease.Neurobiology of aging · 2018Article
- Lifestyle chemical carcinogens associated with mutations in cell cycle regulatory genes increases the susceptibility to gastric cancer risk.Environmental science and pollution research international · 2018Article
- Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification.BMC bioinformatics · 2018Article
- Fuzzy set-based generalized multifactor dimensionality reduction analysis of gene-gene interactions.BMC medical genomics · 2018Article
- The search for gene-gene interactions in genome-wide association studies: challenges in abundance of methods, practical considerations, and biological interpretation.Annals of translational medicine · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Analyzing the combined effects of genes and/or environmental factors on the development of complex diseases is a great challenge from both the statistical and computational perspective, even using a relatively small number of genetic and nongenetic exposures. Several data-mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR) has proven its utility in a variety of theoretical and practical settings. Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new MDR-based technique that is able to unify the best of both nonparametric and parametric worlds, was developed to address some of the remaining concerns that go along with an MDR analysis. These include the restriction to univariate, dichotomous traits, the absence of flexible ways to adjust for lower order effects and important confounders, and the difficulty in highlighting epistatic effects when too many multilocus genotype cells are pooled into two new genotype groups. We investigate the empirical power of MB-MDR to detect gene-gene interactions in the absence of any noise and in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Power is generally higher for MB-MDR than for MDR, in particular in the presence of genetic heterogeneity, phenocopy, or low minor allele frequencies.
Indexed as
Identifiers
What OpenQuestion holds
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.