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BMC Proc. 2011 Nov 29;5 Suppl 9:S102. doi: 10.1186/1753-6561-5-S9-S102.

Novel tree-based method to generate markers from rare variant data.

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  • 1Department of Epidemiology and Public Health, Yale School of Public Health, School of Medicine, Yale University, 60 College Street, PO Box 208034, New Haven, CT 06520-8034, USA. heping.zhang@yale.edu.

Abstract

Existing methods for analyzing rare variant data focus on collapsing a group of rare variants into a single common variant; collapsing is based on an intuitive function of the rare variant genotype information, such as an indicator function or a weighted sum. It is more natural, however, to take into account the single-nucleotide polymorphism (SNP) interactions informed directly by the data. We propose a novel tree-based method that automatically detects SNP interactions and generates candidate markers from the original pool of rare variants. In addition, we utilize the advantage of having 200 phenotype replications in the Genetic Analysis Workshop 17 data to assess the candidate markers by means of repeated logistic regressions. This new approach shows potential in the rare variant analysis. We correctly identify the association between gene FLT1 and phenotype Affect, although there exist other false positives in our results. Our analyses are performed without knowledge of the underlying simulating model.

PMID:
22373418
[PubMed]
PMCID:
PMC3287825
Free PMC Article
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