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

AprioriGWAS, a new pattern mining strategy for detecting genetic variants associated with disease through interaction effects.

PLoS computational biology - 1 Jun 2014

Zhang Qingrun, Long Quan, Ott Jurg

Abstract excerpt

Identifying gene-gene interaction is a hot topic in genome wide association studies. Two fundamental challenges are: (1) how to smartly identify combinations of variants that may be associated with the trait from astronomical number of all possible combinations; and (2) how to test epistatic interaction when all potential combinations are available. We developed AprioriGWAS, which brings two innovations. (1)...

Read the complete abstract on PubMed

Topics

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

Share to Bipolar Disorder
Share to Complement Factor H
Share to Computer Simulation

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Data Mining

Share to Databases, Genetic

Share to Epistasis, Genetic

Share to Logistic Models

Share to Macular Degeneration

Source

Open in PubMed Open full text

Identifiers

PMID: 24901472
DOI: 10.1371/journal.pcbi.1003627
PMCID: PMC4046917

Genes

CFH
GABRB2
GRIA1
ANGPT1

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

AprioriGWAS, a new pattern mining strategy for detecting genetic variants associated with disease through interaction effects.PMID 24901472Read ↗

Detecting genome-wide epistases based on the clustering of relatively frequent items.Semantic proximity Read ↗

Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic TraitsSemantic proximity Read ↗

Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic Traits.Semantic proximity Read ↗

AA9int: SNP interaction pattern search using non-hierarchical additive model set.Semantic proximity Read ↗

GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data.Semantic proximity Read ↗

A Scoring System to Evaluate the Impact of SNPs in a Path Related Context to Study Behçet’s Disease Aetiology in Japanese PopulationSemantic proximity Read ↗

Learning high-order interactions for polygenic risk prediction.Semantic proximity Read ↗

Learning High-Order Interactions for Polygenic Risk PredictionSemantic proximity Read ↗

Gene-gene interaction of CFH, ARMS2, and ARMS2/HTRA1 on the risk of neovascular age-related macular degeneration and polypoidal choroidal vasculopathy in Chinese population.Semantic proximity Read ↗

FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control StudiesSemantic proximity Read ↗

Clustering and summarising association rules mined from phenotype, genotype and environmental data concerning age-related hearing impairment.Semantic proximity Read ↗

Research on single nucleotide polymorphisms interaction detection from network perspective.Semantic proximity Read ↗

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