Abstract

Multi-locus effect modeling is a powerful approach for detection of genes influencing a complex disease. Especially for rare variants, we need to analyze multiple variants together to achieve adequate power for detection. In this paper, we propose several parsimonious branching model techniques to assess the joint effect of a group of rare variants in a case-control study. These models implement a data reduction strategy within a likelihood framework and use a weighted score test to assess the statistical significance of the effect of the group of variants on the disease. The primary advantage of the proposed approach is that it performs model-averaging over a substantially smaller set of models supported by the data and thus gains power to detect multi-locus effects. We illustrate these proposed approaches on simulated and real data and study their performance compared to several existing rare variant detection approaches. The primary goal of this paper is to assess if there is any gain in power to detect association by averaging over a number of models instead of selecting the best model. Extensive simulations and real data application demonstrate the advantage the proposed approach in presence of causal variants with opposite directional effects along with a moderate number of null variants in linkage disequilibrium.

Highlights

  • Genome-wide association studies (GWASs) have successfully identified many common genetic variants that are associated with a given outcome, but little risk can be explained by these identified single nucleotide polymorphisms (SNPs)

  • For the purpose of this simulation, we have considered pairwise correlation of ρ = 0 and ρ = 0.9 which implies linkage equilibrium among the variants and strong linkage disequilibrium (LD) among the variants, respectively

  • Through our simulations and the real data analysis we found that the Seq-aSum-VS and Branching under Ratios (BUR) approaches maintain reasonable power in almost all situations and never suffer huge power loss unlike the other methods, when we have both common variants (CVs) and rare variants (RVs) in the analysis and the CVs strongly contribute to disease risk

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Summary

Introduction

Genome-wide association studies (GWASs) have successfully identified many common genetic variants that are associated with a given outcome, but little risk can be explained by these identified single nucleotide polymorphisms (SNPs). There are several hypotheses for genetic factors contributing to disease risk [1,2,3,4]. One such hypothesis is that rare variants (RVs) measured in sequencing studies with large effect sizes contribute to the disease risk. The existing approaches for rare variant detection can be broadly classified into three separate categories: (1) Collapsing methods based on pooling multiple RVs such as the Sum test

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