Family-based approaches led to the identification of disease-causing Alzheimer?s Disease (AD) variants in the genes encoding Amyloid-beta Precursor Protein (APP), Presenilin 1 (PSEN1) and Presenilin 2 (PSEN2). Subsequently, the identification of these genes led to the A?-cascade hypothesis and recently to the development of drugs that target that pathway. In this proposal, we will identify rare risk and protective alleles. In recent studies, we have identified a rare coding variant in TREM2, ABCA7, PLD3 and SORL1 with large effect sizes for risk for AD, confirming that rare coding variants play a role in the etiology of AD. We will use sequence data from families densely affected by AD, because we hypothesize that these families are enriched for genetic risk factors. We have generated to sequence data from 285 families (1,235 individuals), that combined with the Alzheimer's Disease Sequencing Project (ADSP) data and the families form the National Institute of Mental Health (NIMH) will lead to a very large family-based dataset, totalizing more than 1,042 families and 4,684 participants. Our preliminary results support the flexibility of this approach and strongly suggest that protective and risk variants with large effect size will be found. The identification of those variants and genes will lead to a better understanding of the biology of the disease.
Aging populations worldwide, particularly in developed countries, face an increasing burden of neurodegenerative diseases, especially Alzheimer's disease (AD). Recent studies indicate that there are rare variants with large effect size for risk for Alzheimer's disease, that are not identified by genome-wide association analysis. Family-bases studies have been instrumental to identify Mendelian and risk genes for AD and other complex traits. In this study, we propose to analyze a very large family-based dataset to identify both risk and protective variants and genes.
