A multistage variable selection method is introduced for detecting association signals in structured brain‐wide and genome‐wide association studies (brain‐GWAS). Compared to conventional methods that link one voxel to one single nucleotide polymorphism (SNP), our approach is more efficient and powerful in selecting the important signals by integrating anatomic and gene grouping structures in the brain and the genome, respectively. It avoids resorting to a large number of multiple comparisons while effectively controlling the false discoveries. Validity of the proposed approach is demonstrated by both theoretical investigation and numerical simulations. We apply our proposed method to a brain‐GWAS using Alzheimer's Disease Neuroimaging Initiative positron emission tomography (ADNI PET) imaging and genomic data. We confirm previously reported association signals and also uncover several novel SNPs and genes that are either associated with brain glucose metabolism or have their association significantly modified by Alzheimer's disease status.

中文翻译：

---

使用 ADNI PET 图像的结构化全脑和全基因组关联研究

引入了一种多阶段变量选择方法，用于检测结构化全脑和全基因组关联研究（brain-GWAS）中的关联信号。与将一个体素与一个单核苷酸多态性 (SNP) 联系起来的传统方法相比，我们的方法通过分别整合大脑和基因组中的解剖结构和基因分组结构，在选择重要信号方面更加有效和强大。在有效控制错误发现的同时，避免了大量的多重比较。理论研究和数值模拟都证明了所提出方法的有效性。我们使用阿尔茨海默病神经成像倡议正电子发射断层扫描 (ADNI PET) 成像和基因组数据将我们提出的方法应用于大脑-GWAS。