Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

A comparison of methods sensitive to interactions with small main effects.

Genetic epidemiology - 1 May 2012

Culverhouse Robert C

Abstract excerpt

Numerous genetic variants have been successfully identified for complex traits, yet these genetic factors only account for a modest portion of the predicted variance due to genetic factors. This has led to increased interest in other approaches to account for the "missing" genetic contributions to phenotype, including joint gene-gene or gene-environment analysis. A variety of methods for such analysis have been...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Computer Simulation
Share to Epistasis, Genetic

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Vectors

Share to Multifactor Dimensionality Reduction

Share to Reproducibility of Results

Share to Support Vector Machine

Source

Open in PubMed Open full text

Identifiers

PMID: 22460684
DOI: 10.1002/gepi.21622
PMCID: PMC3357917

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A comparison of methods sensitive to interactions with small main effects.PMID 22460684Read ↗

Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity.Semantic proximity Read ↗

Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data.Semantic proximity Read ↗

The restricted partition method.Semantic proximity Read ↗

Dimensionality reduction approach for many-objective epistasis analysis.Semantic proximity Read ↗

Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions.Semantic proximity Read ↗

Class Balanced Multifactor Dimensionality Reduction to Detect Gene-Gene Interactions.Semantic proximity Read ↗

Shrinkage methods for variable selection and prediction with applications to genetic dataSemantic proximity Read ↗

Exploring the performance of Multifactor Dimensionality Reduction in large scale SNP studies and in the presence of genetic heterogeneity among epistatic disease models.Semantic proximity Read ↗

Multiobjective multifactor dimensionality reduction to detect SNP-SNP interactions.Semantic proximity Read ↗

Weighted risk score-based multifactor dimensionality reduction to detect gene-gene interactions in nasopharyngeal carcinoma.Semantic proximity Read ↗

Log-linear model-based multifactor dimensionality reduction method to detect gene gene interactions.Semantic proximity Read ↗

Model-Based Multifactor Dimensionality Reduction for Rare Variant Association Analysis.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.