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dc.contributor.authorBureau, Alexandreen_US
dc.contributor.authorDupuis, Joséeen_US
dc.contributor.authorHayward, Brookeen_US
dc.contributor.authorFalls, Kathleenen_US
dc.contributor.authorVan Eerdewegh, Paulen_US
dc.date.accessioned2012-01-11T15:51:12Z
dc.date.available2012-01-11T15:51:12Z
dc.date.copyright2003en_US
dc.date.issued2003-12-31en_US
dc.identifier.citationBureau, Alexandre, Josée Dupuis, Brooke Hayward, Kathleen Falls, Paul Van Eerdewegh. "Mapping complex traits using Random Forests" BMC Genetics 4(Suppl 1):S64. (2003)en_US
dc.identifier.issn1471-2156en_US
dc.identifier.urihttp://hdl.handle.net/2144/3073
dc.description.abstractRandom Forest is a prediction technique based on growing trees on bootstrap samples of data, in conjunction with a random selection of explanatory variables to define the best split at each node. In the case of a quantitative outcome, the tree predictor takes on a numerical value. We applied Random Forest to the first replicate of the Genetic Analysis Workshop 13 simulated data set, with the sibling pairs as our units of analysis and identity by descent (IBD) at selected loci as our explanatory variables. With the knowledge of the true model, we performed two sets of analyses on three phenotypes: HDL, triglycerides, and glucose. The goal was to approach the mapping of complex traits from a multivariate perspective. The first set of analyses mimics a candidate gene approach with a high proportion of true genes among the predictors while the second set represents a genome scan analysis using microsatellite markers. Random Forest was able to identify a few of the major genes influencing the phenotypes, such as baseline HDL and triglycerides, but failed to identify the major genes regulating baseline glucose levels.en_US
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.rightsCopyright 2003 Bureau et al; licensee BioMed Central Ltd This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en_US
dc.titleMapping Complex Traits Using Random Forestsen_US
dc.typearticleen_US
dc.identifier.doi10.1186/1471-2156-4-S1-S64en_US
dc.identifier.pubmedid14975132en_US
dc.identifier.pmcid1866502en_US


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Copyright 2003 Bureau et al; licensee BioMed Central Ltd This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as Copyright 2003 Bureau et al; licensee BioMed Central Ltd This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.