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

Yuan Jiang,Heping Zhang,Rose Calixte,Jennifer S Brennan,Yunxiao He,Epiphanie Nyirabahizi

doi:10.1186/1753-6561-5-s9-s102

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.

Highlights

Recent work supports the involvement of rare variants in complex disease etiology [1,2,3]; despite a low frequency of occurrence, rare variants may be functionally important and may account for a detectable increase in the relative risk of developing the outcome
Several methods have been proposed to handle rare variants in association analyses [4,5,6,7]. The aim of these methods is to construct a set of markers from the original single-nucleotide polymorphisms (SNPs), using predefined groups, such as genes or nearby genomic regions
These candidate markers are considered in an association analysis

Summary

Introduction

Recent work supports the involvement of rare variants in complex disease etiology [1,2,3]; despite a low frequency of occurrence, rare variants may be functionally important and may account for a detectable increase in the relative risk of developing the outcome. Next-generation sequencing has great potential for important applications in human genetics, including the detection of rare variants. Several methods have been proposed to handle rare variants in association analyses [4,5,6,7]. The aim of these methods is to construct a set of markers from the original single-nucleotide polymorphisms (SNPs), using predefined groups, such as genes or nearby genomic regions. These candidate markers are considered in an association analysis. Li and Leal [5]

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Conclusion