Abstract
In addition to methods that can identify common variants associated with susceptibility to common diseases, there has been increasing interest in approaches that can identify rare genetic variants. We use the simulated data provided to the participants of Genetic Analysis Workshop 17 (GAW17) to identify both rare and common single-nucleotide polymorphisms and pathways associated with disease status. We apply a rare variant collapsing approach and the usual association tests for common variants to identify candidates for further analysis using pathway-based and tree-based ensemble approaches. We use the mean log p-value approach to identify a top set of pathways and compare it to those used in simulation of GAW17 dataset. We conclude that the mean log p-value approach is able to identify those pathways in the top list and also related pathways. We also use the stochastic gradient boosting approach for the selected subset of single-nucleotide polymorphisms. When compared the result of this tree-based method with the list of single-nucleotide polymorphisms used in dataset simulation, in addition to correct SNPs we observe number of false positives.
Highlights
Many genome-wide association studies (GWAS) have been conducted in the search for specific genetic variants associated with common diseases
Data Our analyses focus on the case-control data provided to the participants of Genetic Analysis Workshop 17 [11]
The mean log p-value (MLP) method is an effort in this direction, where both common and rare variants are considered on the basis of their functional implication in disease etiology
Summary
Many genome-wide association studies (GWAS) have been conducted in the search for specific genetic variants associated with common diseases. In testing for association with common polymorphisms, those variants that were identified were able to explain a modest proportion of disease heritability. This led to the hypothesis that multiple rare variants may play a role in complex disease etiology [1][2][3]. It is likely that both common and rare genetic variants contribute to disease risk. Approaches targeted at uncovering associations between common polymorphisms and disease are generally underpowered for detecting the influence of rare
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