Imaging genetics aims to uncover the hidden relationship between imaging quantitative traits (QTs) and genetic markers (e.g. single nucleotide polymorphism (SNP)), and brings valuable insights into the pathogenesis of complex diseases, such as cancers and cognitive disorders (e.g. the Alzheimer's Disease). 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. In this work, we developed a novel Bayesian regression framework for identifying significant associations between QTs and genetic markers while explicitly modeling spatial dependency between QTs, with the main contributions as follows. Firstly, we developed a spatial-correlated multitask linear mixed-effects model (LMM) to account for dependencies between QTs. We incorporated a population-level mixed effects term into the model, taking full advantage of the dependent structure of brain imaging-derived QTs. Secondly, we implemented the model in the Bayesian framework and derived a Markov chain Monte Carlo (MCMC) algorithm to achieve the model inference. Further, we incorporated the MCMC samples with the Cauchy combination test (CCT) to examine the association between SNPs and QTs, which avoided computationally intractable multi-test issues. The simulation studies indicated improved power of our proposed model compared to classic models where inner dependencies of QTs were not modeled. We also applied the new spatial model to an imaging dataset obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

翻译：影像遗传学旨在揭示影像定量性状（QTs）与遗传标记（如单核苷酸多态性（SNP））之间的潜在关联，并为癌症、认知障碍（如阿尔茨海默病）等复杂疾病的发病机制提供有价值的见解。然而，现有影像遗传学中的大多数线性模型未能显式建模QTs之间的内在关联，这可能错失跨脑区信息借用所带来的潜在效率提升。本研究开发了一种新颖的贝叶斯回归框架，在显式建模QTs间空间依赖性的同时识别QTs与遗传标记之间的显著关联，主要贡献如下：首先，我们构建了一种空间相关的多任务线性混合效应模型（LMM）以刻画QTs间的依赖关系。该模型引入了群体水平的混合效应项，充分利用了脑影像衍生QTs的依赖结构。其次，我们在贝叶斯框架下实现了该模型，并推导出马尔可夫链蒙特卡罗（MCMC）算法进行模型推断。进一步地，我们将MCMC样本与柯西组合检验（CCT）相结合来检测SNPs与QTs之间的关联，从而避免了计算上难以处理的多重检验问题。仿真研究表明，相较于未建模QTs内部依赖性的经典模型，我们提出的模型具有更高的统计功效。我们还将该新型空间模型应用于阿尔茨海默病神经影像学倡议（ADNI）数据库的影像数据集进行实证分析。