NG00142-Plasma Cell-Free RNA Transcriptomics for AD and Related Dementia

The objective of this study is to identify genetic variants that influence plasma levels of transcripts and proteins that could serve as diagnostic blood-based biomarkers of Alzheimer’s disease in African Americans. Plasma samples were collected from the Florida Consortium of African Americans Alzheimer’s disease studies (FCA3DS). Targeted DNA sequencing was performed across ten Alzheimer’s disease-associated loci to assess genetic variation. Plasma transcript levels and total tau protein concentrations were previously measured in this cohort using a custom nanoString panel and Simoa assays. Plasma proteome data were generated using the SomaScan 7k platform. Phased haplotypes generated with SHAPEIT4 were used to infer local ancestry via RFMix v2, referencing five superpopulations from the 1000 Genomes Project. Using ancestry-specific allelic dosages estimated with Tractor, association testing was conducted between genetic variants and molecular endophenotypes. Receiver operating characteristic analysis was performed to evaluate the additive classification accuracy of age, sex, and the most significant expression/protein QTLs in distinguishing Alzheimer’s disease cases from cognitively unimpaired controls. We plan to utilize the requested dataset to validate our findings as these datasets include plasma proteome measurements, pQTL summary statistics, and plasma cell free RNA transcriptomics from healthy control, presymptomatic AD, early symptomatic AD, and symptomatic AD participants.

African Americans are historically underrepresented in Alzheimer’s disease research and may have disproportionately limited access to neurology clinics where Alzheimer’s disease is often diagnosed and treated. Blood-based biomarkers could offer a means for patients to obtain accessible and accurate diagnostic information related to Alzheimer’s disease. This study seeks to discover novel blood-based biomarkers to aid in diagnosis of Alzheimer’s disease specifically for African Americans.

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 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.

We have been developing more powerful statistical methods to detect common variant (CV)- or rare variant (RV)-complex trait associations and/or putative causal relationships for GWAS and DNA sequencing data. Here we propose applying our new methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data provided by NIA, hence requesting approval for accessing the ADSP sequencing and other related GWAS/genetic data. We have the following two specific Aims: Aim1. Association testing under genetic heterogeneity: For complex traits, genetic heterogeneity, especially of RVs, is ubiquitous as well acknowledged in the literature, however there is barely any existing methodology to explicitly account for genetic heterogeneity in association analysis of RVs based on a single sample/cohort. We propose using secondary and other omic data, such as transcriptomic or metabolomic data, to stratify the given sample, then apply a weighted test to the resulting strata, explicitly accounting for genetic heterogeneity that causal RVs may be different (with varying effect sizes) across unknown and hidden subpopulations. Some preliminary analyses have confirmed power gains of the proposed approach over the standard analysis. Aim 2. Meta analysis of RV tests: Although it has been well appreciated that it is necessary to account for varying association effect sizes and directions in meta analysis of RVs for multi-ethnic cohorts, existing tests are not highly adaptive to varying association patterns across the cohorts and across the RVs, leading to power loss. We propose a highly adaptive test based on a family of SPU tests, which cover many existing meta-analysis tests as special cases. Our preliminary results demonstrated possibly substantial power gains.

We propose applying our newly developed statistical analysis methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data to detect common or rare genetic variants associated with Alzheimer’s disease (AD). The novelty and power of our new methods are in two aspects: first, we consider and account for possible genetic heterogeneity with several subcategories of AD; second, we apply powerful meta-analysis methods to combine the association analyses across multiple subcategories of AD. The proposed research is feasible, promising and potentially significant to AD research. In addition, our proposed analyses of the existing large amount of ADSP sequencing data and other AD GWAS data with our developed new methods are novel, powerful and cost-effective.

Objective: Utilize ADSP data sets to identify genes & specific genetic variants that confer risk for or protection from Alzheimer disease. Aim 1: Using combined WGS/WES across the ADSP Discovery, Disc-Ext, and FUS Phases, including single nucleotide variants, small insertion/deletions, and structural variants. We will: Aim 1a. Perform whole genome single variant and rare variant case/control association analyses of AD using ADSP and other available data; Aim 1b. Target protective variant identification via association analysis using selected controls within the ADSP data and performing meta analysis across association results based on selected controls from non-ADSP data sets. Aim 1c. Perform endophenotype analyses including cognitive function measures, hippocampal volume and circulation beta-amyloid ADSP data in subjects for which these measures are available. Meta analysis will be conducted across ADSP and non-ADSP analysis results. Aim 2: To leverage ethnically-diverse and admixed populations to identify AD variants we will: Aim 2a. Estimate and account for global and local ancestry in all analyses; Aim 2b. Perform admixture mapping in samples of admixed ancestry; and Aim 2c. Perform ethnicity-specific and trans-ethnic meta-analyses. Aim 3: To identify putative therapeutic targets through functional characterization of genes and networks via bioinformatics, integrative ‘omics analyses. We will: Aim 3a. Annotate variants with their functional consequences using bioinformatic tools and publicly available “omics” data. Aim 3b. Prioritize results, group variants with shared function, and identify key genes functionally related to AD via weighted association analyses and network approaches. Analyses will be performed in coordination with the following PIs. Coordination will involve sharing expertise, analysis plans or analysis results. No individual level data will be shared across institutions. Philip De Jager, Columbia University; Eric Boerwinkle & Myriam Fornage, U of Texas Health Science Center, Houston; Sudha Seshadri, U of Texas, San Antonio; Ellen Wijsman, U of Washington. William Salerno, Baylor College of Medicine

This proposal seeks to analyze existing genetic sequencing data generated as part of the Alzheimer’s Disease Sequencing Project (ADSP) including the ADSP Follow-up Study (FUS) with the goal of identifying genes and specific changes within those genes that either confer risk for Alzheimer’s Disease or provide protection from Alzheimer’s Disease. Analytic challenges include analysis of whole genome sequencing data, appropriately accounting for population structure across European ancestry, Hispanic, and African American participants, and interpreting results in the context of other genomic data available.

Recently approved Alzheimer’s disease (AD) therapies, such as lecanemab (Leqembi) and donanemab (Kisunla), represent a significant advancement toward disease-modifying treatment. However, their impact on cognitive decline remains modest, and both are associated with potentially serious adverse events, including amyloid-related imaging abnormalities (ARIA). These limitations underscore the urgent need for additional therapeutic strategies to reduce disease burden. Genetic approaches offer a powerful avenue for drug target discovery, with evidence suggesting that genetically supported targets are at least twice as likely to progress successfully through clinical development to FDA approval (Nelson et al., 2015, Nat Genet; King et al., 2019, PLoS Genet; Minikel et al., 2024, Nature). To date, most genetic studies in AD have focused on identifying loci associated with disease risk. Large-scale genome-wide association studies (GWAS) have uncovered approximately 75 risk loci (Bellenguez et al., 2022, Nat Genet), providing valuable insights into disease etiology. However, therapeutic interventions are typically aimed at individuals already diagnosed with AD, making the genetics of disease progression a critical—yet underexplored—complementary approach for target discovery. Progression-focused genetic studies face challenges due to limited availability of longitudinal phenotypic data. To address this, meta-analysis of multiple GWAS datasets offers a practical strategy to increase statistical power and detect robust associations. We propose to incorporate summary statistics from the Knight Alzheimer Disease Research Center (Knight-ADRC) AD progression GWAS into a meta-analysis alongside several publicly available and proprietary datasets. Our objective is to identify novel genetic drivers of AD progression, prioritize new therapeutic targets, and assess the impact of existing pipeline candidates on disease trajectory.

New Alzheimer’s treatments like lecanemab (Leqembi) and donanemab (Kisunla) are an important step forward in the search for ways to help patients, but these drugs have only moderate benefits and can come with serious side effects. Better therapies are still needed to reduce the impact of the disease. Genetics offers a powerful way to discover new drugs—studies show that treatments based on genetic findings are more likely to succeed. So far most genetic research has focused on the genes which increase the risk of developing Alzheimer’s, but understanding genes that drive how the disease progresses in Alzheimer’s patients may be even more beneficial. However this type of data, which involves following participants over time, is limited, combining results from multiple smaller studies (a meta-analysis) can help uncover important patterns. We plan to add data from the Knight Alzheimer Disease Research Center to a larger analysis to find new genetic clues, identify better treatment targets, and evaluate how current and future drugs may slow disease progression.