ArticleNucleic acids research2011
Ranking causal variants and associated regions in genome-wide association studies by the support vector machine and random forest.
Article in Nucleic acids research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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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.
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Who cites it
25 citing papers in PubMed.
- Functional Validation of Noncoding Variants Associated With Nonsyndromic Orofacial Cleft.Human mutation · 2025Review
- Multi-trait modeling and machine learning discover new markers associated with stem traits in alfalfa.Frontiers in plant science · 2024Article
- Review
- Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.Sensors (Basel, Switzerland) · 2023Article
- A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci.Scientific reports · 2022Article
- DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies.NAR genomics and bioinformatics · 2021Article
- Genomics models in radiotherapy: From mechanistic to machine learning.Medical physics · 2020Review
- Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci.Frontiers in genetics · 2020Review
- A method combining a random forest-based technique with the modeling of linkage disequilibrium through latent variables, to run multilocus genome-wide association studies.BMC bioinformatics · 2018Article
- Machine Learning and Radiogenomics: Lessons Learned and Future Directions.Frontiers in oncology · 2018Review
- Article
- Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies.Scientific reports · 2016Article
- Genotype distribution-based inference of collective effects in genome-wide association studies: insights to age-related macular degeneration disease mechanism.BMC genomics · 2016Article
- Chi8: a GPU program for detecting significant interacting SNPs with the Chi-square 8-df test.BMC research notes · 2015Article
- Genome wide association scan for chronic periodontitis implicates novel locus.BMC oral health · 2014Article
- Explaining microbial phenotypes on a genomic scale: GWAS for microbes.Briefings in functional genomics · 2013Review
- Impact of natural genetic variation on gene expression dynamics.PLoS genetics · 2013Article
- Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?Briefings in bioinformatics · 2013Article
- Predicting disease risk using bootstrap ranking and classification algorithms.PLoS computational biology · 2013Article
- Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data.BMC medical informatics and decision making · 2013Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We study the number of causal variants and associated regions identified by top SNPs in rankings given by the popular 1 df chi-squared statistic, support vector machine (SVM) and the random forest (RF) on simulated and real data. If we apply the SVM and RF to the top 2r chi-square-ranked SNPs, where r is the number of SNPs with P-values within the Bonferroni correction, we find that both improve the ranks of causal variants and associated regions and achieve higher power on simulated data. These improvements, however, as well as stability of the SVM and RF rankings, progressively decrease as the cutoff increases to 5r and 10r. As applications we compare the ranks of previously replicated SNPs in real data, associated regions in type 1 diabetes, as provided by the Type 1 Diabetes Consortium, and disease risk prediction accuracies as given by top ranked SNPs by the three methods. Software and webserver are available at http://svmsnps.njit.edu.
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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.