Testing for Association with Multiple Traits in Generalized Estimation Equation, with Application to Neuroimaging Data

• Activity Number: 645
• Type: Contributed
• Date/Time: Thursday, August 7, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Yiwei Zhang*+ and Zhiyuan Xu and Wei Pan and Xiaotong Shen
• Companies: Novartis and University of Minnesota and University of Minnesota and University of Minnesota
• Keywords: Generalized estimation equation ; data-adaptive ; neuroimaging ; multiple traits

Abstract

There is an increasing need to develop and apply powerful statistical tests to detect multiple traits-single locus associations, as arising from neuroimaging genetics and other studies. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI), thousands of neuroimaging and neuropsychological phenotypes as intermediate phenotypes for Alzheimer's disease, have been collected. Although some classic methods like MANOVA and newly proposed methods may be applied, they have their own limitations. In this paper we propose a class of data-adaptive weights and the corresponding weighted tests in the general framework of generalized estimation equations (GEE). A highly adaptive test is proposed to select the most powerful one from this class of the weighted tests so that it can maintain high power across a wide range of situations. We also analytically show relationships among some existing and our proposed tests, indicating that many existing tests are special cases of our proposed tests. Finally, we applied the methods to an ADNI dataset. We recommend the use of the GEE-based Score test and our proposed adaptive test for their high and complementary performance.