Comparison of SNP-based and gene-based association studies in detecting rare variants using unrelated individuals

Liping Tong,Richard S Cooper,Bamidele Tayo,Jie Yang

doi:10.1186/1753-6561-5-s9-s41

Liping Tong, Richard S Cooper + Show 2 more

Open Access

https://doi.org/10.1186/1753-6561-5-s9-s41

Copy DOI

Abstract

We compare the SNP-based and gene-based association studies using 697 unrelated individuals. The Benjamini-Hochberg procedure was applied to control the false discovery rate for all the multiple comparisons. We use a linear model for the single-nucleotide polymorphism (SNP) based association study. For the gene-based study, we consider three methods. The first one is based on a linear model, the second is similarity based, and the third is a new two-step procedure. The results of power using a subset of SNPs show that the SNP-based association test is more powerful than the gene-based ones. However, in some situations, a gene-based study is able to detect the associated variants that were neglected in a SNP-based study. Finally, we apply these methods to a replicate of the quantitative trait Q1 and the binary trait D (D = 1, affected; D = 0, unaffected) for a genome-wide gene search.

Highlights

Our aims are (1) to compare single-nucleotide polymorphism (SNP) based and gene-based association studies and (2) to apply both methods to a genome-wide search for associated genes
In the SNP-based association study, we delete alias SNPs and use a linear or logistic model to find the p-value for each SNP
We propose a two-step procedure that first classifies and collapses multilocus genotypes and uses the classic T and Pearson chi-square statistics to perform an association test

Summary

Introduction

Our aims are (1) to compare single-nucleotide polymorphism (SNP) based and gene-based association studies and (2) to apply both methods to a genome-wide search for associated genes. To check the effect of covariates, we use linear models for the numerical traits Q1, Q2, and Q4 and a logistic model for the binary trait D. We modify the original trait value by adjusting for significant covariate effects. In the SNP-based association study, we delete alias SNPs and use a linear or logistic model to find the p-value for each SNP. In the gene-based association study, we consider three methods. The second method is similarity based and is useful for binary traits only [2]. The p-values are calculated using the cumulant-based estimation procedure [3]. These two multilocus association study methods can have reduced

Objectives

Methods

Results