title: EM-random forest and new measures of variable importance for multi-locus quantitative trait linkage analysis.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  The source code for EMRF written in C is available at www.infornomics.utoronto.ca/downloads/EMRF.
authors.affiliation: Department of Public Health Sciences, University of Toronto, Toronto M5T3M7, Canada.
authors.forename: Sophia S F
authors.initials: SS
authors.lastname: Lee
authors.forename: Lei
authors.initials: L
authors.lastname: Sun
authors.forename: Rafal
authors.initials: R
authors.lastname: Kustra
authors.forename: Shelley B
authors.initials: SB
authors.lastname: Bull
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D008040:Genetic Linkage; D005838:Genotype; D006801:Humans; D008126:Lod Score; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D011381:Programming Languages; D019655:Quantitative Trait, Heritable; D011897:Random Allocation; D012044:Regression Analysis