ArticleBMC proceedings2009
Detecting significant single-nucleotide polymorphisms in a rheumatoid arthritis study using random forests.
Article in BMC proceedings, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- Alterations in DNA methylation profiles in cancellous bone of postmenopausal women with osteoporosis.FEBS open bio · 2020Article
- Pipeline design to identify key features and classify the chemotherapy response on lung cancer patients using large-scale genetic data.BMC systems biology · 2018Article
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- Article
- Identification of the key genes connected with plasma cells of multiple myeloma using expression profiles.OncoTargets and therapy · 2015Article
- Impact of natural genetic variation on gene expression dynamics.PLoS genetics · 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
- An integrated approach to reduce the impact of minor allele frequency and linkage disequilibrium on variable importance measures for genome-wide data.Bioinformatics (Oxford, England) · 2012Article
- Risk estimation and risk prediction using machine-learning methods.Human genetics · 2012Review
- Germline genetic variation and treatment response on CCG-1891.Pediatric blood & cancer · 2012Article
- Maximal conditional chi-square importance in random forests.Bioinformatics (Oxford, England) · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Random forest is an efficient approach for investigating not only the effects of individual markers on a trait but also the effect of the interactions among the markers in genetic association studies. This approach is especially appealing for the analysis of genome-wide data, such as those obtained from gene expression/single-nucleotide polymorphism (SNP) array experiments in which the number of candidate genes/SNPs is vast. We applied this approach to the Genetic Analysis Workshop 16 Problem 1 data to identify SNPs that contribute to rheumatoid arthritis. The random forest computed a raw importance score for each SNP marker, where higher importance score suggests higher level of association between the marker and the trait. The significance level of the association was determined empirically by repeatedly reapplying the random forest on randomly generated data under the null hypothesis that no association exists between the markers and the trait. Using random forest, we were able to identify 228 significant SNPs (at the genome-wide significant level of 0.05) across the whole genome, over two-thirds of which are located on chromosome 6, especially clustered in the region of 6p21 containing the human leukocyte antigen (HLA) genes, such as gene HLA-DRB1 and HLA-DRA. Further analysis of this region indicates a strong association to the rheumatoid arthritis status.
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.