ArticleBioinformatics (Oxford, England)2010
TEAM: efficient two-locus epistasis tests in human genome-wide association study.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 2 of them syntheses that pooled it.
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Who cites it
63 citing papers in PubMed, 2 syntheses or guidelines pooled it, 169 citations in OpenAlex.
- Association between chemokine CXC ligand 12 gene polymorphism (rs1746048) and coronary heart disease: A MOOSE-compliant meta-analysis.Medicine · 2017Pooled it
- Ultrafast genome-wide scan for SNP-SNP interactions in common complex disease.Genome research · 2012Pooled it
- ACOCMPMI: An Ant Colony Optimization Algorithm Based on Composite Multiscale Part Mutual Information for Detecting Epistatic Interactions.Human mutation · 2025Article
- Review on GPU accelerated methods for genome-wide SNP-SNP interactions.Molecular genetics and genomics : MGG · 2024Review
- Computational Identification of Milk Trait Regulation Through Transcription Factor Cooperation in Murciano-Granadina Goats.Biology · 2024Article
- Interaction models matter: an efficient, flexible computational framework for model-specific investigation of epistasis.BioData mining · 2024Article
- A gene-based association test of interactions for maternal-fetal genotypes identifies genes associated with nonsyndromic congenital heart defects.Genetic epidemiology · 2023Article
- Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies.G3 (Bethesda, Md.) · 2023Article
- networkGWAS: a network-based approach to discover genetic associations.Bioinformatics (Oxford, England) · 2023Article
- Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS.Plants (Basel, Switzerland) · 2022Review
- Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests.Nucleic acids research · 2022Article
- Concurrent outcomes from multiple approaches of epistasis analysis for human body mass index associated loci provide insights into obesity biology.Scientific reports · 2022Article
- A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction.Frontiers in bioinformatics · 2022Review
- Maximal Information Coefficient-Based Testing to Identify Epistasis in Case-Control Association Studies.Computational and mathematical methods in medicine · 2022Article
- GEP-EpiSeeker: a gene expression programming-based method for epistatic interaction detection in genome-wide association studies.BMC genomics · 2021Article
- MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes.Biology · 2021Article
- SAMA: A Fast Self-Adaptive Memetic Algorithm for Detecting SNP-SNP Interactions Associated with Disease.BioMed research international · 2020Article
- Logicome Profiler: Exhaustive detection of statistically significant logic relationships from comparative omics data.PloS one · 2020Article
- Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies.BMC bioinformatics · 2019Article
- VARIABLE PRIORITIZATION IN NONLINEAR BLACK BOX METHODS: A GENETIC ASSOCIATION CASE STUDYThe annals of applied statistics · 2019Article
3 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
As a promising tool for identifying genetic markers underlying phenotypic differences, genome-wide association study (GWAS) has been extensively investigated in recent years. In GWAS, detecting epistasis (or gene-gene interaction) is preferable over single locus study since many diseases are known to be complex traits. A brute force search is infeasible for epistasis detection in the genome-wide scale because of the intensive computational burden. Existing epistasis detection algorithms are designed for dataset consisting of homozygous markers and small sample size. In human study, however, the genotype may be heterozygous, and number of individuals can be up to thousands. Thus, existing methods are not readily applicable to human datasets. In this article, we propose an efficient algorithm, TEAM, which significantly speeds up epistasis detection for human GWAS. Our algorithm is exhaustive, i.e. it does not ignore any epistatic interaction. Utilizing the minimum spanning tree structure, the algorithm incrementally updates the contingency tables for epistatic tests without scanning all individuals. Our algorithm has broader applicability and is more efficient than existing methods for large sample study. It supports any statistical test that is based on contingency tables, and enables both family-wise error rate and false discovery rate controlling. Extensive experiments show that our algorithm only needs to examine a small portion of the individuals to update the contingency tables, and it achieves at least an order of magnitude speed up over the brute force approach.
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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.