EconPapers    
Economics at your fingertips  
 

An improved robust association test for GWAS with multiple diseases

Zhongxue Chen, Hanwen Huang and Hon Keung Tony Ng

Statistics & Probability Letters, 2014, vol. 91, issue C, 153-161

Abstract: In a previous study, we proposed a new design and analysis strategy for Genome-wide Association Studies (GWAS) with multiple diseases but no controls. We have proposed to use an overall chi-square test to test for the association between an SNP with any one of the diseases. The overall chi-square test is not sensitive to the underlying model assumption; however, it does not use the information about the trend among the relative risks of the three genotypes. In this study, we propose a new overall test based on the chi-square partition method. The overall p-value of the proposed approach can be estimated by combining independent p-values from the more powerful one-sided tests which incorporate the trend among the relative risks. Simulation study and real data application show that the proposed test is more powerful and robust.

Keywords: Chi-square partition; Genetic model; Robust test; Trend test (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715214001461
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:91:y:2014:i:c:p:153-161

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2014.04.015

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).

 
Page updated 2024-12-28
Handle: RePEc:eee:stapro:v:91:y:2014:i:c:p:153-161