A structured brain‐wide and genome‐wide association study using ADNI PET images

Abstract

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.

A multi-network approach to Alzheimer’s Disease gene prioritization complementscis-regulatory prioritization with molecular quantitative trait loci

“…Late-onset Alzheimer's disease (LOAD) is a genetically complex neurodegenerative disorder characterized by progressive memory loss and cognitive dysfunction (1) involving all major cell types of brain (2). Dozens of genetic risk loci have been associated to LOAD-related disease processes through genome-wide association studies (GWAS) (3)(4)(5)(6)(7)(8)(9) and candidate genes within these loci have been nominated by multiple criteria (9)(10)(11)(12)(13). A subset of these genes has been mechanistically validated (14)(15)(16), but most LOAD risk loci only have putative candidate variants, and the functional role of these loci in LOAD pathogenesis remains unclear.…”