A0766
Title: A spatial-correlated multitask linear mixed-effects model for imaging genetics
Authors: Shufei Ge - ShanghaiTech University (China) [presenting]
Abstract: Imaging genetics aims to uncover the hidden relationship between imaging quantitative traits (QTs) and genetic markers and brings valuable insights into the pathogenesis of complex diseases, such as cancers and cognitive disorders. However, most linear models in imaging genetics didn't explicitly model the inner relationship among QTs, which might miss some potential efficiency gains from information borrowing across brain regions. A novel Bayesian regression framework is developed for identifying significant associations between QTs and genetic markers while explicitly modeling spatial dependency between QTs. Firstly, a spatially correlated multitask linear mixed-effects model (LMM) is developed to account for dependencies between QTs by incorporating a population-level mixed-effects term into the model, taking full advantage of the dependent structure of brain imaging-derived QTs. Secondly, the model is implemented in the Bayesian framework, and a Markov chain Monte Carlo (MCMC) algorithm is derived to achieve the model inference. Further, the MCMC samples are incorporated with the Cauchy combination test (CCT) to examine the association between genetic markers and QTs, which avoids computationally intractable multi-test issues. The simulation studies indicated improved power of the proposed model compared to competitors. The new spatial model is also applied to an imaging dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
