Analysis of secondary phenotype involving the interactive effect of the secondary phenotype and genetic variants on the primary disease.

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.