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Analysis of Secondary Phenotype Involving the Interactive Effect of the Secondary Phenotype and Genetic Variants on the Primary Disease

Analysis of Secondary Phenotype Involving the Interactive Effect of the Secondary Phenotype and... A genome‐wide association (GWA) study is usually designed as a case‐control study, where the presence and absence of the primary disease define the cases and controls, respectively. Using the existing data from GWA studies, investigators are also trying to identify the association between genetic variants and secondary phenotypes, which are defined as traits associated with the primary disease. However, recent studies have shown that bias arises in the estimation of marker‐secondary phenotype association using originally collected data. We recently proposed a bias correction approach to accurately estimate the odds ratio (OR) for marker‐secondary phenotype association. In this communication, we further investigated whether our bias correction approach is robust for a scenario involving the interactive effect of the secondary phenotype and genetic variants on the primary disease. We found that in such a scenario, our bias correction approach also provides an accurate estimation of OR for marker‐secondary phenotype association. We investigated accuracy of our approach using simulation studies and showed that the approach better controlled for type I errors than the existing approaches. We also applied our bias correction approach to the real data analysis of association between an N‐acetyltransferase gene, NAT2, and smoking on the basis of colorectal adenoma data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Human Genetics Wiley

Analysis of Secondary Phenotype Involving the Interactive Effect of the Secondary Phenotype and Genetic Variants on the Primary Disease

Wang, Jian; Shete, Sanjay
Annals of Human Genetics , Volume 76 (6) – Jan 1, 2012
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References (19)

Publisher
Wiley
Copyright
Copyright © 2012 Wiley Subscription Services
ISSN
0003-4800
eISSN
1469-1809
DOI
10.1111/j.1469-1809.2012.00725.x
pmid
22881407
Publisher site
See Article on Publisher Site

Abstract

A genome‐wide association (GWA) study is usually designed as a case‐control study, where the presence and absence of the primary disease define the cases and controls, respectively. Using the existing data from GWA studies, investigators are also trying to identify the association between genetic variants and secondary phenotypes, which are defined as traits associated with the primary disease. However, recent studies have shown that bias arises in the estimation of marker‐secondary phenotype association using originally collected data. We recently proposed a bias correction approach to accurately estimate the odds ratio (OR) for marker‐secondary phenotype association. In this communication, we further investigated whether our bias correction approach is robust for a scenario involving the interactive effect of the secondary phenotype and genetic variants on the primary disease. We found that in such a scenario, our bias correction approach also provides an accurate estimation of OR for marker‐secondary phenotype association. We investigated accuracy of our approach using simulation studies and showed that the approach better controlled for type I errors than the existing approaches. We also applied our bias correction approach to the real data analysis of association between an N‐acetyltransferase gene, NAT2, and smoking on the basis of colorectal adenoma data.

Journal

Annals of Human Genetics – Wiley

Published: Jan 1, 2012

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