Genomic studies of Alzheimer's disease (AD) have primarily focused on non-Hispanic White (NHW) participants affected by the late-onset form of the disease (LOAD; onset age: >65), or the study of early onset AD (EOAD; onset age <=65) cases from families showing Mendelian inheritance patterns associated with mutations in the APP, PSEN1 and PSEN2 genes. However, mutations in these three genes explain ~10% of EOAD cases. There are no large-scale efforts to collect and study EOAD cases not explained by these genes, despite the fact that this unexplained EOAD category accounts for ~90% of cases. The few smaller studies that have been conducted suggest that the genetic architecture of EOAD overlaps with the late-onset form only partially. Thus, studying EOAD in subjects without APP, PSEN1 and PSEN2 mutations is a critical gap that provides a unique opportunity for discovering novel therapeutic targets and molecular pathways. To address this issue we aim to identify additional EOAD-associated variants through a large-scale whole- genome sequencing (WGS) study of unexplained EOAD. We will include cases from several well-established AD cohorts including the Resource for Early-onset Alzheimer Disease Research (READR), the Knight-ADRC at Washington University, the Alzheimer's Disease Genetics Consortium (ADGC), and others. Generating and harmonizing a dataset of 200 non-Hispanic White (NHW) and Caribbean Hispanic (CH) multiplex EOAD families, over 4,000 EOAD singletons and over 13,000 unrelated, cognitive controls, all with WGS, this project will yield the largest EOAD genomics dataset to-date, improving statistical power for variant identification and allowing us to assess the impact of specific factors such as APOE genotype, vascular risk factors, and neuropsychiatric comorbidities. The inclusion of a large set of CH families and singletons will allow the examination of EOAD risk in a significantly understudied but fast-growing minority population. Analyses will comprise both linkage and association-based approaches, analyses of polygenic and ancestry effects, and a thorough examination of neurocognitive, neuropsychiatric and cardiovascular endophenotypes. We expect that when successfully completed, this study will point to novel genetic contributors to EOAD, shed light on the mechanisms of AD and facilitate the development of novel therapeutics. Sampling, phenotyping and sequencing analysis protocols will be complementary to and compatible with the existing LOAD genomics resources, such as the Alzheimer Disease Sequencing Project (ADSP) and related studies. This phenotypic and genomic consistency, together with the use of existing AD infrastructure (NIAGADS), allows for immediate integration with the leading efforts on LOAD, enabling rapid large-scale investigation of a variety of additional critical AD genomics hypotheses.
Known Mendelian mutations account for only ~10% of early-onset Alzheimer's disease (EOAD) cases; despite this, there are no large-scale efforts to collect and study EOAD without known mutations (i.e., unexplained EOAD). We propose here a large whole genome sequencing experiment of individuals with EOAD, cognitive controls, and families with multiple EOAD members by harmonization and joint analyses of variety of datasets, followed by extensive analyses that will include identifying EOAD genetic risk factors, investigating the role of ancestry in EOAD, and assessing subphenotypes related to EOAD, such as vascular traits, depression, and impairment in specific cognitive domains. This study is expected to contribute vital information on the genetic variation and molecular mechanisms underlying Alzheimer's disease etiology that is not derived from ongoing Alzheimer disease studies, providing a unique opportunity to discover novel drug targets and pathways.
