ArticleBMC genetics2010
An application of Random Forests to a genome-wide association dataset: methodological considerations & new findings.
Article in BMC genetics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
101 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evidence for CRHR1 in multiple sclerosis using supervised machine learning and meta-analysis in 12,566 individuals.Human molecular genetics · 2010Pooled it
- Machine Learning on a Genome-wide Association Study to Predict Late Genitourinary Toxicity After Prostate Radiation Therapy.International journal of radiation oncology, biology, physics · 2018Trial
- A machine learning approach to genome-wide association mapping of disease resistance and geographic origin in sorghum.BMC plant biology · 2026Article
- Leveraging Subjective Parameters and Biomarkers in Machine Learning Models: The Feasibility ofDiagnostics (Basel, Switzerland) · 2025Article
- Spatiotemporal trends and drivers of forest cover change in Metekel Zone forest areas, Northwest Ethiopia.Environmental monitoring and assessment · 2024Article
- Predicting Survival Status in COVID-19 Patients: Machine Learning Models Development with Ventilator-Related and Biochemical Parameters from Early Stages: A Pilot Study.Journal of clinical medicine · 2024Article
- Proteomic Profiles of Maternal Plasma Extracellular Vesicles for Prediction of Preeclampsia.American journal of reproductive immunology (New York, N.Y. : 1989) · 2024Article
- Source Attribution of Antibiotic Resistance Genes in Estuarine Aquaculture: A Machine Learning Approach.Antibiotics (Basel, Switzerland) · 2024Article
- Genome-wide association analysis of the resistance to infectious hematopoietic necrosis virus in two rainbow trout aquaculture lines confirms oligogenic architecture with several moderate effect quantitative trait loci.Frontiers in genetics · 2024Article
- Proteomic profile of extracellular vesicles in maternal plasma of women with fetal death.The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians · 2023Article
- Targeted Metabolomics Analysis Suggests That Tacrolimus Alters Protection against Oxidative Stress.Antioxidants (Basel, Switzerland) · 2023Article
- Downscaling and validating SMAP soil moisture using a machine learning algorithm over the Awash River basin, Ethiopia.PloS one · 2023Article
- Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.PloS one · 2023Article
- Genetic risk factors for ME/CFS identified using combinatorial analysis.Journal of translational medicine · 2022Article
- MicroRNA Expression Profile in TSC Cell Lines and the Impact of mTOR Inhibitor.International journal of molecular sciences · 2022Article
- An Enhanced Multiple Sclerosis Disease Diagnosis via an Ensemble Approach.Diagnostics (Basel, Switzerland) · 2022Article
- KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis.Briefings in bioinformatics · 2022Article
- Article
- The application of machine learning to predict genetic relatedness using human mtDNA hypervariable region I sequences.PloS one · 2022Article
- A feature selection-based framework to identify biomarkers for cancer diagnosis: A focus on lung adenocarcinoma.PloS one · 2022Article
41 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundAs computational power improves, the application of more advanced machine learning techniques to the analysis of large genome-wide association (GWA) datasets becomes possible. While most traditional statistical methods can only elucidate main effects of genetic variants on risk for disease, certain machine learning approaches are particularly suited to discover higher order and non-linear effects. One such approach is the Random Forests (RF) algorithm. The use of RF for SNP discovery related to human disease has grown in recent years; however, most work has focused on small datasets or simulation studies which are limited.
resultsUsing a multiple sclerosis (MS) case-control dataset comprised of 300 K SNP genotypes across the genome, we outline an approach and some considerations for optimally tuning the RF algorithm based on the empirical dataset. Importantly, results show that typical default parameter values are not appropriate for large GWA datasets. Furthermore, gains can be made by sub-sampling the data, pruning based on linkage disequilibrium (LD), and removing strong effects from RF analyses. The new RF results are compared to findings from the original MS GWA study and demonstrate overlap. In addition, four new interesting candidate MS genes are identified, MPHOSPH9, CTNNA3, PHACTR2 and IL7, by RF analysis and warrant further follow-up in independent studies.
conclusionsThis study presents one of the first illustrations of successfully analyzing GWA data with a machine learning algorithm. It is shown that RF is computationally feasible for GWA data and the results obtained make biologic sense based on previous studies. More importantly, new genes were identified as potentially being associated with MS, suggesting new avenues of investigation for this complex disease.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.