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Open Access Methodology article

A method to incorporate prior information into score test for genetic association studies

Sergii Zakharov12*, Garrett HK Teoh1, Agus Salim3 and Anbupalam Thalamuthu4*

Author Affiliations

1 Human Genetics, Genome Institute of Singapore, 60 Biopolis Street, #02-01 Genome, Singapore 138672, Singapore

2 Saw Swee Hock School of Public Health, National University of Singapore, 16 Medical Drive, Singapore 117597, Singapore

3 Department of Mathematics and Statistics, La Trobe University, Bundoora, VIC 3086, Australia

4 Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia

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BMC Bioinformatics 2014, 15:24  doi:10.1186/1471-2105-15-24

Published: 22 January 2014

Abstract

Background

The interest of the scientific community in investigating the impact of rare variants on complex traits has stimulated the development of novel statistical methodologies for association studies. The fact that many of the recently proposed methods for association studies suffer from low power to identify a genetic association motivates the incorporation of prior knowledge into statistical tests.

Results

In this article we propose a methodology to incorporate prior information into the region-based score test. Within our framework prior information is used to partition variants within a region into several groups, following which asymptotically independent group statistics are constructed and then combined into a global test statistic. Under the null hypothesis the distribution of our test statistic has lower degrees of freedom compared with those of the region-based score statistic. Theoretical power comparison, population genetics simulations and results from analysis of the GAW17 sequencing data set suggest that under some scenarios our method may perform as well as or outperform the score test and other competing methods.

Conclusions

An approach which uses prior information to improve the power of the region-based score test is proposed. Theoretical power comparison, population genetics simulations and the results of GAW17 data analysis showed that for some scenarios power of our method is on the level with or higher than those of the score test and other methods.

Keywords:
Prior information; Association analysis; Score test; Rare variants