ArticlePloS one2014
Exploiting SNP correlations within random forest for genome-wide association studies.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Machine Learning-Based Genome-Wide Association Study Reveals Genetic Loci Associated with Body Measurement Traits in Yili Horses.Animals : an open access journal from MDPI · 2026Article
- Immune cells and rheumatic heart disease: A 2-sample and Bayesian weighted Mendelian randomization study.Medicine · 2026Article
- A machine learning approach to genome-wide association mapping of disease resistance and geographic origin in sorghum.BMC plant biology · 2026Article
- Exploring Random Forest in Genetic Risk Score Construction.Genetic epidemiology · 2025Article
- EnSCAN: ENsemble Scoring for prioritizing CAusative variaNts across multiplatform GWASs for late-onset alzheimer's disease.BioData mining · 2025Article
- Considerations in the search for epistasis.Genome biology · 2024Review
- Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES).BMC bioinformatics · 2024Article
- Screening the Best Risk Model and Susceptibility SNPs for Chronic Obstructive Pulmonary Disease (COPD) Based on Machine Learning Algorithms.International journal of chronic obstructive pulmonary disease · 2024Article
- Searching for gene-gene interactions through variance quantitative trait loci of 29 continuous Taiwan Biobank phenotypes.Frontiers in genetics · 2024Article
- Genetic risk assessment based on association and prediction studies.Scientific reports · 2023Article
- Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.Sensors (Basel, Switzerland) · 2023Article
- Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests.Nucleic acids research · 2022Article
- A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci.Scientific reports · 2022Article
- Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components.International journal of molecular sciences · 2022Article
- Evaluation of tree-based statistical learning methods for constructing genetic risk scores.BMC bioinformatics · 2022Article
- Pharmacogenetics of Praziquantel Metabolism: Evaluating the Cytochrome P450 Genes of Zimbabwean Patients During a Schistosomiasis Treatment.Frontiers in genetics · 2022Article
- A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction.Frontiers in bioinformatics · 2022Review
- From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients.EBioMedicine · 2022Article
- DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies.NAR genomics and bioinformatics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The primary goal of genome-wide association studies (GWAS) is to discover variants that could lead, in isolation or in combination, to a particular trait or disease. Standard approaches to GWAS, however, are usually based on univariate hypothesis tests and therefore can account neither for correlations due to linkage disequilibrium nor for combinations of several markers. To discover and leverage such potential multivariate interactions, we propose in this work an extension of the Random Forest algorithm tailored for structured GWAS data. In terms of risk prediction, we show empirically on several GWAS datasets that the proposed T-Trees method significantly outperforms both the original Random Forest algorithm and standard linear models, thereby suggesting the actual existence of multivariate non-linear effects due to the combinations of several SNPs. We also demonstrate that variable importances as derived from our method can help identify relevant loci. Finally, we highlight the strong impact that quality control procedures may have, both in terms of predictive power and loci identification. Variable importance results and T-Trees source code are all available at www.montefiore.ulg.ac.be/~botta/ttrees/ and github.com/0asa/TTree-source respectively.
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