Target identification and validation in Alzheimer’s Disease with Whole-Genome and Whole-Exome Sequence Data

Aim 1: Identify novel genes and replicate existing gene associations for Alzheimer’s disease (AD).
Aim 1a: Common variant genome-wide association analysis. With this approach, we will leverage existing consortium GWAS summary statistics where makes sense (or request leave-one/N summary association statistics out if we see a need to use a different version of phenotype definition from the same cohort) and augment them with additional datasets available internally.
Aim 1b: Rare variant gene-level genetic burden analysis. Using the ADSP analysis pipeline, we will aim to use the same analysis pipeline (but reserve the option to use an alternative pipeline) to contribute the whole genome sequencing (WGS) data generated from the internal galantamine samples to ADSP-led consortium analysis.
We will perform case-control and/or family-based genetic analyses and/or quantitative trait genetic analyses using AD traits such as diagnosis, age of onset, amyloid positivity, tau positivity, CSF biomarker endophenotypes, disease progression, etc. (where the phenotype is available) as the outcome of interest. Covariates include age, sex, and principal components. ADSP, UKB, and FinnGen will be analyzed separately and combined with a meta-analysis. Biobank cases will be defined using ICD-9/ICD-10 codes, and proxy cases and controls will be carefully defined using questionnaire data on the parental history of AD. Both true and proxy cases will be considered to maximize the number of AD cases.
Aim 2: Prioritize novel gene associations identified in Aim 1. We will perform genetic fine-mapping and leverage tissue and cell-type specific datasets (e.g. GTEx, AD Knowledge Portal including AMP-AD, internal datasets, MiGA, Harari et al snRNA-Seq) to prioritize targets for further functional and analytical interrogation. Furthermore, multi-omics-based network approaches will be used to identify disease-related molecular modules and tissue-specific regulatory circuits.
Aim 3: utilize single-nuclei sequencing data to more fully catalog cell type heterogeneity in the brains of individuals with AD and how this differs from brain from uninjured, cognitively unimpaired individuals.

Alzheimer’s disease (AD) is a common, progressive, neurodegenerative disorder with a strong genetic component with heritability estimates ranging from 58–79% for late-onset AD and over 90% for early-onset AD. To date, there is only one approved treatment option intended to mediate the disease progression of AD, while all others treat symptoms associated with AD. Genetic association studies are important to highlight key biological mechanisms contributing to the etiology of AD and provide insights into potential pathways that can ultimately be targeted for future therapeutic development. The aim of this study is to perform a retrospective analysis of genetic data collected from large-scale population-based and case-control cohorts including the UK Biobank, the Alzheimer’s Disease Sequencing Project (ADSP), FinnGen, and Janssen internal cohorts. We will also integrate them with available multi-modal datasets including but not limited to, Microglia Genomic Atlas, Harari et al snRNA-Seq, and neuroimaging data to identify novel and existing evidence for genetic determinants of AD.