ArticleEuropean journal of human genetics : EJHG2010
A screening methodology based on Random Forests to improve the detection of gene-gene interactions.
Article in European journal of human genetics : EJHG, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Detecting genetic interactions with visible neural networks.Communications biology · 2025Article
- RGIE: A Gene Selection Method Related to Radiotherapy Resistance in Head and Neck Squamous Cell Carcinoma.Current radiopharmaceuticals · 2024Article
- Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment.Circulation research · 2023Review
- Effective Macrosomia Prediction Using Random Forest Algorithm.International journal of environmental research and public health · 2022Article
- Maximal Information Coefficient-Based Testing to Identify Epistasis in Case-Control Association Studies.Computational and mathematical methods in medicine · 2022Article
- Effective Analysis of Inpatient Satisfaction: The Random Forest Algorithm.Patient preference and adherence · 2021Article
- Discovery of cell-type specific DNA motif grammar in cis-regulatory elements using random Forest.BMC genomics · 2018Article
- Machine Learning and Radiogenomics: Lessons Learned and Future Directions.Frontiers in oncology · 2018Review
- Identification of immune correlates of protection in Shigella infection by application of machine learning.Journal of biomedical informatics · 2017Article
- Genotypes Associated withFrontiers in microbiology · 2017Article
- A roadmap to multifactor dimensionality reduction methods.Briefings in bioinformatics · 2016Review
- Towards the identification of the loci of adaptive evolution.Methods in ecology and evolution · 2015Review
- Detecting epistasis in human complex traits.Nature reviews. Genetics · 2014Review
- Epistatic effects of multiple receptor genes on pathophysiology of asthma - its limits and potential for clinical application.Medical science monitor : international medical journal of experimental and clinical research · 2014Review
- Exploiting SNP correlations within random forest for genome-wide association studies.PloS one · 2014Article
- Explaining microbial phenotypes on a genomic scale: GWAS for microbes.Briefings in functional genomics · 2013Review
- Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?Briefings in bioinformatics · 2013Article
- Trees Assembling Mann-Whitney approach for detecting genome-wide joint association among low-marginal-effect loci.Genetic epidemiology · 2013Article
- Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes.BMC bioinformatics · 2013Article
- Genetic polymorphisms and associated susceptibility to asthma.International journal of general medicine · 2013Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The search for susceptibility loci in gene-gene interactions imposes a methodological and computational challenge for statisticians because of the large dimensionality inherent to the modelling of gene-gene interactions or epistasis. In an era in which genome-wide scans have become relatively common, new powerful methods are required to handle the huge amount of feasible gene-gene interactions and to weed out false positives and negatives from these results. One solution to the dimensionality problem is to reduce data by preliminary screening of markers to select the best candidates for further analysis. Ideally, this screening step is statistically independent of the testing phase. Initially developed for small numbers of markers, the Multifactor Dimensionality Reduction (MDR) method is a nonparametric, model-free data reduction technique to associate sets of markers with optimal predictive properties to disease. In this study, we examine the power of MDR in larger data sets and compare it with other approaches that are able to identify gene-gene interactions. Under various interaction models (purely and not purely epistatic), we use a Random Forest (RF)-based prescreening method, before executing MDR, to improve its performance. We find that the power of MDR increases when noisy SNPs are first removed, by creating a collection of candidate markers with RFs. We validate our technique by extensive simulation studies and by application to asthma data from the European Committee of Respiratory Health Study II.
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