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 Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.

Genetic epidemiology - 1 Sept 2015

Marceau Rachel, Lu Wenbin, Holloway Shannon, Sale Michèle M, Worrall Bradford B, Williams Stephen R, Hsu Fang-Chi, Tzeng Jung-Ying

Abstract excerpt

Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene-gene or...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Gene-Environment Interaction

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Homocysteine

Share to Quantitative Trait Loci

Share to Regression Analysis

Share to Software

Share to Stroke

Share to Vitamins

Source

Open in PubMed Open full text

Identifiers

PMID: 26139508
DOI: 10.1002/gepi.21909
PMCID: PMC4544636

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 Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.PMID 26139508Read ↗

Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors.Semantic proximity Read ↗

A unified method for rare variant analysis of gene-environment interactions.Semantic proximity Read ↗

Kernel Approach for Modeling Interaction Effects in Genetic Association Studies of Complex Quantitative Traits.Semantic proximity Read ↗

Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method.Semantic proximity Read ↗

A kernel machine method for detecting effects of interaction between multidimensional variable sets: an imaging genetics application.Semantic proximity Read ↗

A kernel regression approach to gene-gene interaction detection for case-control studies.Semantic proximity Read ↗

Multivariate phenotype association analysis by marker-set kernel machine regression.Semantic proximity Read ↗

Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples.Semantic proximity Read ↗

Rare-variant association testing for sequencing data with the sequence kernel association test.Semantic proximity Read ↗

An optimal kernel-based multivariate U-statistic to test for associations with multiple phenotypes.Semantic proximity Read ↗

General Kernel Machine Methods for Multi-Omics Integration and Genome-Wide Association Testing With Related Individuals.Semantic proximity Read ↗

Marginal interaction test for detecting interactions between genetic marker sets and environment in genome-wide studies.Semantic proximity Read ↗

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