Document Type

Article

Publication Date

5-31-2013

Department

Mathematics, Statistics, and Computer Science

Keywords

genetic testing

Abstract

Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situations. In particular, we focus on the impact of non-causal SNPs and a variety of LD structures on the behavior of these tests. Ultimately, we find that non-causal SNPs can significantly impact the power of all gene-based tests. On average, we find that the “noise” from 6–12 non-causal SNPs will cancel out the “signal” of one causal SNP across five popular gene-based tests. Furthermore, we find complex and differing behavior of the methods in the presence of LD within and between non-causal and causal SNPs. Ultimately, better approaches for a prioriprioritization of potentially causal SNPs (e.g., predicting functionality of non-synonymous SNPs), application of these methods to sequenced or fully imputed datasets, and limited use of window-based methods for assigning inter-genic SNPs to genes will improve power. However, significant power loss from non-causal SNPs may remain unless alternative statistical approaches robust to the inclusion of non-causal SNPs are developed.

Comments

Copyright © 2013 Petersen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Source Publication Title

PLoS ONE

Volume

8

Issue

5

First Page

e62161

DOI

10.1371/journal.pone.0062161

Download

Share

COinS