Gary W. Beecham, Eder Lucio Fonseca, Jiji Thulaseedhara Kurup, Margaret A. Pericak‐Vance, Eden R. Martin, Gerard D. Schellenberg, Victoria Fernandez, Carlos Cruchaga, Christiane Reitz

Genomic studies of Alzheimer disease (AD) have primarily focused on non-Hispanic White (NHW) participants affected by the late-onset form (LOAD; onset age: >65), or the study of early onset AD (EOAD; onset age <=65) cases 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 this unexplained category accounting for ∼90% of EOAD cases. To address this, we aim to identify additional EOAD-associated variants, genes and pathways through a large-scale whole-genome sequencing (WGS) study of unexplained EOAD. We will include cases from several 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 5,400 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 Hispanic families and singletons allows the examination of EOAD risk in a significantly understudied 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. When completed this study will point to novel genetic contributors to EOAD, shed light on the mechanisms of AD and facilitate the development of novel prediction models and 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.