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This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

Open Access Proceedings

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

Liping Tong12*, Bamidele Tayo2, Jie Yang3 and Richard S Cooper2

Author Affiliations

1 Department of Mathematics and Statistics, Loyola University, Chicago, IL 60626, USA

2 Department of Preventive Medicine and Epidemiology, Loyola University Medical School, Maywood, IL 60153, USA

3 Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA

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BMC Proceedings 2011, 5(Suppl 9):S41  doi:10.1186/1753-6561-5-S9-S41

Published: 29 November 2011

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.