EM-random Forest and New Measures of Variable Importance for Multi-locus Quantitative Trait Linkage Analysis

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2008 May 24

PMID 18499695

Citations 9

Authors

Sophia S F Lee

Lei Sun

Rafal Kustra

Shelley B Bull

Affiliations

Soon will be listed here.

Abstract

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 www.infornomics.utoronto.ca/downloads/EMRF.

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