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EM-random forest and new measures of variable importance for multi-locus quantitative trait linkage analysis.
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MedLine Citation:
PMID:  18499695     Owner:  NLM     Status:  MEDLINE    
MOTIVATION: We developed an EM-random forest (EMRF) for Haseman-Elston quantitative trait linkage analysis that accounts for marker ambiguity and weighs each sib-pair according to the posterior identical by descent (IBD) distribution. The usual random forest (RF) variable importance (VI) index used to rank markers for variable selection is not optimal when applied to linkage data because of correlation between markers. We define new VI indices that borrow information from linked markers using the correlation structure inherent in IBD linkage data.
RESULTS: Using simulations, we find that the new VI indices in EMRF performed better than the original RF VI index and performed similarly or better than EM-Haseman-Elston regression LOD score for various genetic models. Moreover, tree size and markers subset size evaluated at each node are important considerations in RFs.
AVAILABILITY: The source code for EMRF written in C is available at
Sophia S F Lee; Lei Sun; Rafal Kustra; Shelley B Bull
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't     Date:  2008-05-21
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  24     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2008 Jul 
Date Detail:
Created Date:  2008-07-08     Completed Date:  2008-09-09     Revised Date:  2013-06-05    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  England    
Other Details:
Languages:  eng     Pagination:  1603-10     Citation Subset:  IM    
Department of Public Health Sciences, University of Toronto, Toronto M5T3M7, Canada.
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MeSH Terms
Chromosome Mapping
Computational Biology / methods*
Data Interpretation, Statistical
Genetic Linkage*
Lod Score
Models, Genetic*
Models, Statistical
Programming Languages
Quantitative Trait, Heritable
Random Allocation
Regression Analysis
Grant Support

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Full Text
Journal Information
Journal ID (nlm-ta): Bioinformatics
Journal ID (publisher-id): bioinformatics
Journal ID (hwp): bioinfo
ISSN: 1367-4803
ISSN: 1460-2059
Publisher: Oxford University Press
Article Information
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© 2008 The Author(s)
creative-commons: This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received Day: 15 Month: 11 Year: 2007
Revision Received Day: 16 Month: 5 Year: 2008
Accepted Day: 17 Month: 5 Year: 2008
Print publication date: Day: 15 Month: 7 Year: 2008
Electronic publication date: Day: 21 Month: 5 Year: 2008
pmc-release publication date: Day: 21 Month: 5 Year: 2008
Volume: 24 Issue: 14
First Page: 1603 Last Page: 1610
ID: 2638262
DOI: 10.1093/bioinformatics/btn239
Publisher Id: btn239
PubMed Id: 18499695

EM-random forest and new measures of variable importance for multi-locus quantitative trait linkage analysis
Sophia S. F. Lee12
Lei Sun13
Rafal Kustra1
Shelley B. Bull12*
1Department of Public Health Sciences, University of Toronto, Toronto M5T 3M7, 2Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto M5G 1X5 and 3Genetics and Genomic Biology, The Hospital for Sick Children Research Institute, Toronto M5G 1L7, Canada
Associate Editor: Martin Bishop
Correspondence: *To whom correspondence should be addressed.

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