Mapping Complex Traits Using Random Forests

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dc.contributor.author Bureau, Alexandre en_US
dc.contributor.author Dupuis, Josée en_US
dc.contributor.author Hayward, Brooke en_US
dc.contributor.author Falls, Kathleen en_US
dc.contributor.author Van Eerdewegh, Paul en_US
dc.date.accessioned 2012-01-11T15:51:12Z
dc.date.available 2012-01-11T15:51:12Z
dc.date.copyright 2003 en_US
dc.date.issued 2003-12-31 en_US
dc.identifier.citation Bureau, 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.issn 1471-2156 en_US
dc.identifier.uri http://hdl.handle.net/2144/3073
dc.description.abstract Random 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.iso en en_US
dc.publisher BioMed Central en_US
dc.rights 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. en_US
dc.rights.uri http://creativecommons.org/licenses/by/2.0 en_US
dc.title Mapping Complex Traits Using Random Forests en_US
dc.type article en_US
dc.identifier.doi 10.1186/1471-2156-4-S1-S64 en_US
dc.identifier.pubmedid 14975132 en_US
dc.identifier.pmcid 1866502 en_US

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