NG00178 – Multi-brain region mRNA-seq of sporadic ALS patients with pTDP-43 pathological staging

We propose to develop capable and intelligent computer-based toolboxes that enable searching, sharing, visualizing, querying, and analyzing genetics, genomics, multi-omics, and clinical data for AD. The central unifying hypothesis of this project (1U01AG073323-01 [pending for Council meeting at May/2021) is that a genome-wide, multimodal artificial intelligence (AI) framework to identify novel risk genes and networks from human WGS/WES and multi-omics findings will offer drug targets for targeted therapeutic development in AD. Aim 1 will identify rare coding variant-based risk genes using a sequence and structure-based deep learning model. Aim 2 will identify rare non-coding variant-based risk genes using a multiple kernel learning approach. Aim 3 will test whether GWAS common variants linked to AD pathobiology and endophenotypes are enriched in gene regulatory networks in a cell-type specific manner using a Bayesian framework. These analyses will leverage variants from ethnically diverse WGS/WES and clinical data (i.e., imaging, biomarkers, and cognitive measures) from Alzheimer's Disease Sequencing Project (ADSP), and publicly available chromatin interactomic data from NIH RoadMap, FANTOM5, and NIH 4D Nucleome. We will validate our findings using WGS/WES data and protein expression data from our existing cohorts: The Cleveland Clinic Lou Ruvo Center for Brain Health Aging and Neurodegenerative Disease Biobank (CBH-Biobank) and the Cleveland Alzheimer's Disease Research Center (CADRC). We will compile information for clinical data harmonization, including functional imaging, AD biomarkers, and cognitive measures for all integrative analyses. There are no any PHI information will collected or used in the data analysis. We don’t have the planned collaboration with researchers outside Cleveland Clinic in the current analytic plans.

It is estimated that more than 16 million people with AD live in the United States by 2050 and the predisposition to AD involves a complex, polygenic, and pleiotropic genetic architecture. This project will develop intelligent computer-based network medicine and systems biology tools, capable of identifying and validating human genome sequencing findings for novel risk gene discoveries and targeted therapeutic development in AD. The innovative network-based, artificial intelligence toolboxes and novel risk genes and biologically relevant targeted therapeutic approaches developed in this proposal will prove to be novel and effective ways to improve outcomes in long-term brain care for the rapidly growing AD population, an essential goal of AD precision medicine.

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 objective of this study is to contribute to our understanding of neurodegenerative diseases by examining the genetic contributors of major dementia neuropathological hallmarks (amyloid-β deposition, tau pathology, TDP-43, hippocampal sclerosis, Lewy body pathology, and cerebrovascular disease, among others. We will generate the largest Iberian database(N=3500) of neuropathologically curated brains (Aim 1) with a subset of those (N≈350) undergoing deep digital phenotyping (Aim 3). We will generate an associated genetic map (Aim 2) order to elucidate how common and rare genetic variants contribute to specific pathologies. We additionally aim to determine how polygenic risk scores (PRS) and pathway-specific PRS correspond to single and mixed neuropathological profiles, and to clarify the genetic architecture driving co-pathologies that frequently complicate clinical diagnosis. Eventually, we will replicate and fine-map our findings (Aim 4) leveraging available datasets at NIAGADS and other public repositories.Our analysis plan includes genome-wide association testing of ordinal, binary, and quantitative neuropathological traits; rare-variant burden analyses for coding and non-coding regions; PRS and pathway-PRS modeling across multiple dementia-related diseases; unsupervised clustering to identify variant sets defining specific endophenotypes; and pathway and network analyses to interpret significant signals. Colocalization and functional annotation approaches will integrate genomic findings with transcriptomic and proteomic resources.Data obtained from NIAGADS will be used to strengthen replication, broaden meta-analytic power, validate associations across independent neuropathology cohorts, and support functional interpretation using available genetic, expression, and multi-omic datasets. All analyses will use de-identified data in compliance with ethical and data-sharing standards.

Dementia is an immensely challenging and prevalent condition, deeply impacting the lives of over 55 million individuals worldwide. While Alzheimer's disease stands as the most commonly recognized form of dementia, there exist other conditions that present comparable symptoms but distinct underlying pathological characteristics. To provide more effective support to patients and their families, we need to better understand the genetic causes associated to each of these brain pathologies, and to develop advanced tools for early classification and diagnosis. This grant proposal aims to tackle these challenges by establishing the largest Iberian (Spanish and Portuguese) database of dementia neuropathological cases, marked by a modernized and standardized neuropathological classification alongside comprehensive genomic data. Our goal is to delve further into the genetic architecture underpinning these pathological features and to refine existing risk assessment tools for more accurate diagnoses.

The objective of the proposed research is to determine the relationship between Alzheimer’s disease (AD) genetic risk and associated molecular phenotypes. Genotype data will be used to compute a polygenic risk score (PRS) for disease-affected and control (non-disease-affected) participants. Statistical regression and mediation analyses will be used to model variation of molecular phenotypes with respect to PRS and, where available, pathology stage or cognitive impairment. Molecular phenotypes to be analyzed include bulk/single-cell/single-nucleus transcriptome, epigenome, proteome, metabolome, lipidome, amyloid, and tau. Molecular phenotypes of participants, including controls, will be matched with molecular phenotypes of in vitro cellular models, informing the design of in vitro perturbation experiments that recapitulate the genetic drivers of AD risk.

Our goal is to determine the relationship between human genetic profiles associated with Alzheimer’s disease (AD) risk and specific measurable characteristics of human cells. Using multiple statistical analysis methods, we will build quantitative models that describe how those characteristics vary as a function of AD genetic risk. The models we build will help us design in vitro cellular systems that reflect different levels of AD risk, enabling experiments that inform new strategies for treating or preventing AD.