Title: A robust association test for detecting genetic variants with heterogeneous effects.
Authors: Yu, Kai; Zhang, Han; Wheeler, William; Horne, Hisani N; Chen, Jinbo; Figueroa, Jonine D
Published In Biostatistics, (2015 Jan)
Abstract: One common strategy for detecting disease-associated genetic markers is to compare the genotype distributions between cases and controls, where cases have been diagnosed as having the disease condition. In a study of a complex disease with a heterogeneous etiology, the sampled case group most likely consists of people having different disease subtypes. If we conduct an association test by treating all cases as a single group, we maximize our chance of finding genetic risk factors with a homogeneous effect, regardless of the underlying disease etiology. However, this strategy might diminish the power for detecting risk factors whose effect size varies by disease subtype. We propose a robust statistical procedure to identify genetic risk factors that have either a uniform effect for all disease subtypes or heterogeneous effects across different subtypes, in situations where the subtypes are not predefined but can be characterized roughly by a set of clinical and/or pathologic markers. We demonstrate the advantage of the new procedure through numeric simulation studies and an application to a breast cancer study.
PubMed ID:
25057183
MeSH Terms: Breast Neoplasms/genetics; Data Interpretation, Statistical*; Female; Genetic Markers; Genetic Variation/genetics*; Genome-Wide Association Study/methods*; Humans; Models, Genetic*; Risk Factors