Risk haplotype pattern discovery for gene mapping by recursive partitioning method based on weighted classification trees

Abstract

In this paper, we present a combinatorial approach based on recursive weighted longest prefix trees (RWLPT) for mining a massive genetic marker data. Given a case and a control chromosome dataset, we develop a fast recursive permutation algorithm for building an RWLPT. This algorithm provides an optimal solution to find a privileged order of classifiers for constructing optimum haplotype clusters and biclusters. Using RWLPT model, extracting haplotype clusters and haplotype patterns will be performed in linear time on the number of markers. Employing the measure of linkage disequilibrium and the statistical test, we examine the case-control association analysis for identifying risk haplotype patterns associated with the disease permitting likely location of disease susceptibility genes. We demonstrate that the case-control association test based on haplotype patterns reduces degrees of freedom and number of tests while increasing the power of disease-haplotype association analysis. The experimental results through the real published SNP dataset show that our approach tends to be more effective and powerful than the sliding window approach, classification and regression trees and single marker analysis.