NG00154 – Dementia in Parkinson’s Disease (PDD)

The goal of this study is to identify new genes and mutations that cause or increase risk for Alzheimer disease (AD), as well as protective factors. Individuals and families were selected from the Knight-ADRC (Washington University) and the NIA-LOAD study. Only families with at least three first-degree affected individuals were included. Families with pathogenic variants in the known AD or FTD genes, or in which APOE4 segregated with disease were excluded. At least two cases and one control were selected per family. Cases had an age at onset (AAO) after 65 yo and controls had a larger age at last assessment than the latest AAO within the family. Whole exome (WES) and whole genome sequencing (WGS) was generated for 1,235 individuals (285 families) that together with data from our collaborators and the ADSP family-based cohort (3,449 individuals and 757 families) will provide enough statistical power to identify new genes for AD. Dr. Tanzi (Harvard Medical School) will provide WGS from 400 families from the NIMH Alzheimer disease genetics initiative study. We will perform single variant and gene-based analyses to identify genes and variants that increase risk for disease in AD families. Single variant analysis will consist of a combination of association and segregation analyses. We will run family-based gene-based methods to identify genes that show and overall enrichment of variants in AD cases. We will also look for protective and modifier variants. To do this we will identify families loaded with AD cases, that also include individuals with a high burden of known risk variants but that do not develop the disease (escapees). We will use the sequence data and the family structure to identify variants that segregate with the escapee phenotype. The most promising variants and genes will be replicated in independent datasets (ADSP case-control, ADNI, Knight-ADRC, NIA-LOAD ). We will perform single variant and gene-based analyses to replicate the initial findings, and survival analysis to replicate the protective variants. We will select the most promising variants/genes for functional studies

Family-based approaches led to the identification of disease-causing Alzheimer’s Disease (AD) variants in the genes encoding APP, PSEN1 and PSEN2. The identification of these genes led to the A?-cascade hypothesis and to the development of drugs that target this pathway. Recently, we have identified rare coding variants 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. In this proposal, we will identify rare risk and protective alleles using sequence data from families densely affected by AD. We hypothesize that these families are enriched for genetic risk factors. We already have sequence data from 695 families (2,462 individuals), that combined with the ADSP and the NIMH dataset will lead to a dataset of more than 1,042 families (4,684 individuals). Our preliminary results support the flexibility of this approach and strongly suggest that protective and risk variants with large effect size will be found, which will lead to a better understanding of the biology of the disease.

The goal of analyzing ADSP umbrella cohort data is identifying variants, genes and pathways associated to Alzheimer’s disease (AD), and stratifying patients by genetic risks. Following describes procedure.1) Identification and validation of genetic risks The whole genome and whole exome sequencing data will be analyzed to identify genetic variants or genes associated to phenotypes in case-control cohort, such as AD status and Braak stages. Several latest methods will be applied, such as VEP [William McLaren et al, 2016], LOFTEE [Karczewski, 2015] and PEXT scoring [Beryl B.C et al., 2020] for variant annotation and SAIGE-GENE [Wei Zhou et al., 2020] for the association test. The association will be tested for other endophenotypes such as cognitive scores and brain volumes that available in subset of the cohort. Replication and meta-analysis will be conducted on UK biobank and Tohoku medical megabank organization (ToMMo) cohort data. The ToMMo data consists of Japanese cohort so that we can analyze the effect of the variants among multi ethnic groups.2) Patient stratification in ADSP cohort Leveraging the increased sample size, we will stratify the cohort by genetic risks such as ApoE types, or phenotypes such as Braak stages, and compare the effect size of variants or genes among the patient groups. In addition, the genetic risk score (GRS) will be calculated using LDpred2 [Florian Prive, 2020], RapidoPGS [Guillermo Reales, 2020], and PRSice2 [Choi, S.W., 2020], and validated in independent cohorts and compared to available clinical endophenotypes. Last, we will search the effect of the GRS to extensive phenotypes in UK biobank and ToMMo.3) Identification of variants associated with pathologies and disease progression To further characterize patients by genetic risk, we will conduct GWAS and EWAS on pathology measurements, models of co-pathology, comorbidity with other neurological diseases, and disease progression. NG00127 and NG00154 will be used for this purpose.

The aim of our study is identifying variants or genes potentially causal of the Alzheimer’s disease in whole or subset of patients. To be specific, WES and WGS data will be analyzed to investigate common and rare variants associated with disease status and intermediate phenotypes. In addition, the patients will be stratified and sub-grouped by their genetic or phenotypic characteristics. Last, we will incorporate other large biobanks such as UK biobank or ToMMo to investigate the genetic effects to extensive phenotypes potentially linked to symptoms appearing in sub patient groups.