NG00075 – IGAP Rare Variant Summary Statistics – Kunkle et al. (2019)

• Objectives: In this project, we seek to holistically investigate the genetic and molecular drivers of sex dimorphism in Alzheimer’s disease across ancestries. • Study design: This study integrates large-scale population genetics with multi-omics and endophenotype analyses. We are integrating all data available from ADGC and ADSP, together with other data from AMP-AD and biobanks such as UKB, FinnGen, and MVP to conduct large-scale multi-ancestry GWAS, rare-variant gene aggregation analyses, QTL studies, PWAS, TWAS, etc. We also particularly focus on X chromosome association studies. The study design also interrogates interactions with ancestry, hormone exposures, and with APOE*4, as well as comparisons to non-stratified GWAS/XWAS of Alzheimer’s disease. Further, we will also employ genetic correlation analyses, mendelian randomization, colocalization, and pleiotropy analyses, to interrogate overlap with other complex traits to better understand the mechanisms underlying sex dimorphism in Alzheimer’s disease. • Analysis plan, including the phenotypic characteristics that will be evaluated in association with genetic variants: Our phenotypes will include Alzheimer’s disease risk, conversion risk, various endophenotypes (including amyloid/tau biomarkers, brain imaging metrics, etc.) as well as molecular traits. As noted above, we will conduct large-scale multi-ancestry GWAS, XWAS, rare-variant gene aggregation analyses, QTL studies, PWAS, TWAS, etc. Specific aims include interrogating these question and analyses on (1) the autosomes, (2) the X chromosome, and (3) leveraging sex stratified QTL studies to drive discovery of risk genes.

Alzheimer’s disease (AD) manifests itself differently across men and women, but the genetic and molecular factors that drive this remain elusive. AD is the most common cause of dementia and till today remains largely untreatable. It is thus crucial to study the genetics of AD in a sex-specific manner, as this will help the field gain important insights into disease pathophysiology, identify novel sex-specific risk factors relevant to personalized genetic medicine, and uncover potential new AD drug targets that may benefit both sexes. This project uses large-scale genomics and multi-omics to elucidate novel sex agnostic and sex-specific AD risk genes. We will interrogate sex dimorphism for AD risk on the autosomes and the sex chromosomes. We similarly interrogate sex dimorphism in the genetic regulation of gene expression and protein levels, which we will integrate with genetic risk for Alzheimer’s disease to further discovery risk genes. Throughout, we will also interrogate how sex-specific risk for AD interactions with hormone exposures, ancestry, and the APOE*4 risk allele.

Polygenic risk scores (PRS) for dementia and aging-related conditions are known to be associated with cognitive outcomes in older age, but little is known about their relationship to mid-life cognitive decline. We plan to use raw genetic data to derive novel PRS from GWAS sources (including Lambert Alzheimer’s disease PRS, with and without APOE; aPRS for coronary artery disease; a longevity PRS) and evaluate their predictive accuracy for cognitive outcomes in middle age relative to existing PRS. Specifically, we want to create a measure of genetic risk associated with three outcomes: age-related cognition; telomere shortening; and methylation/epigenetic clocks. To achieve this, we will combine the HRS Genotype data with other HRS datasets (Harmonized Cognitive Assessment Protocol (HCAP) (2016 Early V1.0); 2008 Telomere Data; Epigenetic Clocks; 2016 Venous Blood Study (VBS)) to which we already have access. Once we have approved NIAGADS genomics data access, we will additionally request access to the HRS-NIAGADS Cross-Reference File (Genotype Data v3,2006-2012) to link the genomics and HRS datasets. In our ongoing analyses, we would like to update our PRS models by incorporating the most recent GWAS summary statistics. For Alzheimer's disease, this requires access to the full summary statistics from the Kunkle et al., 2019 GWAS. We also would like access to the full summary statistics from the Farrell et al., 2024 GWAS and the Rajabli et al., 2025 GWAS to identify genetic modifiers of tauopathy by comparing progressive supranuclear palsy GWAS results with cross-ancestry Alzheimer’s disease GWAS results.

There is evidence to suggest that differences in people’s genetic code might contribute to differences in age-associated cognitive changes. For example, some people develop memory problems in middle age, and other people experience no changes in memory. Researchers think this may be partially explained by differences in people’s genetic code. We might be able to predict who could experience age-related cognitive changes based on their DNA sequence. If we know which people have experienced memory problems, we can see what their DNA has in common compared to the DNA of people who don’t have any memory problems. Then, we can test this by looking at the DNA of a different group of people; evaluating if their DNA has the same things in common as the group of people with memory problems (vs. no memory problems); and predicting whether they will develop memory problems. The long-term goal of this work is to help identify people who might be at risk for developing memory problems and help them access preventative care or interventions to minimize future cognitive impairment.

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.

Aging encompasses a range of phenotypes, including lifespan, healthspan, aging-related diseases, and other aging-related outcomes. Aging-related phenotypes are correlated with each other but not perfectly. We will develop a multivariate genomic model of aging-related outcomes and diseases in order to 1) understand whether aging-related phenotypes are best described individually, clustered in small groups, or as a single group and 2) identify genetic variants that underly processes shared across multiple aging-related phenotypes. First, we will examine genetic correlations among aging-related phenotypes such as Alzheimer's disease, longevity, epigenetic aging, telomere length, parental lifespan, healthspan, frailty, and other aging-related diseases (e.g. Parkinson's disease). We will then examine patterns of genomic covariation among aging-related phenotypes using Genomic Structural Equation Modeling. Finally, we will perform multivariate genome-wide association studies and derive polygenic scores from these GWAS.

A wide range of characteristics and behaviors are related to aging, including lifespan, aging-related diseases such as Alzheimer's disease and Parkinson's disease, and frailty. Aging-related characteristics are genetically correlated, meaning that some genetic variants affect more than one aging-related phenotype (e.g. some genetic variants may confer risk for Alzheimer's disease and may be related to shorter lifespan). We aim to identify how different aspects of aging are genetically related to each other, and whether we can use broader groups of correlated phenotypes to identify genetic variants related to certain aspects of aging.

Objectives: We are requesting access to the NIAGADS datasets to augment our ongoing studies on the genetics of Alzheimer’s disease (AD) and AD-related endophenotypes being carried out by Kamboh and his group since 1995. We are doing GWAS using array genotypes, whole-exome sequencing and whole-genome sequencing on datasets derived from University of Pittsburgh ADRC and ancillary population-based longitudinal studies on dementia and biomarkers. Different available phenotypes include AD and non-AD dementia, age-at-set, disease progression and survival, neuroimaging, cognitive decline, plasma biomarkers for the core ATN and non-ATN pathologies. We also plan to expand on gene-gene interaction and sex-stratified analyses which require the actual genotype data. The NIAGADS datasets will be used for replication and meta-analysis, and for gene-gene interaction and sex-stratified analyses. Study Design: A case-control design will incorporate a diverse cohort of individuals with AD and age-matched controls. For quantitative traits (neuroimaging and plasma biomarkers, cognitive performance measures, indicators of disease progression), linear regression analyses will be performed to identify genetic loci. To ensure the findings are robust and inclusive, participants from diverse demographic backgrounds will be included, enabling the exploration of potential genetic variations across populations. Analysis Plan: We will conduct GWAS and targeted analyses on candidate genes on different AD and AD-related phenotypes. Primary phenotypic variables include AD disease status, age-at-onset, last age for controls, APOE genotype, cognitive decline trajectories, sex, and race. Analyses will evaluate the influence of specific genetic variants on disease risk, cognitive performance, and biomarker levels, considering both individual and interactive effects of the APOE genotype. Results will be adjusted for potential confounders, such as demographic factors, to ensure valid associations. Detail analytical methods are described in our published papers for case-control (PMID: 32651314;35694926), quantitative traits (PMID: 30361487;37666928), and cognitive decline (PMID: 37089073; 30954325).

Our research group at the University of Pittsburgh (Pitt), has been working on the genetics of Alzheimer’s disease (AD) and AD-related endophenotypes for almost three decades, on data derived largely from the University of Pittsburgh Alzheimer’s Disease Research Center and ancillary dementia studies. We are requesting access to the NIAGADS genotype and phenotype datasets to augment our sample size to increase power to detect novel genetic associations with AD and related endophenotypes.

Objectives: This study aims to improve understanding of the genetic and molecular mechanisms underlying Alzheimer’s disease (AD) by applying advanced computational and deep learning models to existing genomic and transcriptomic datasets. Specifically, we seek to identify and characterize genetic variants associated with AD risk, progression, and related phenotypes, contributing to precision medicine approaches for neurodegenerative disorders. Study Design: This project involves secondary analysis of de-identified, controlled-access datasets from NIAGADS (NG00067, NG00116, NG00174, NG00027, NG00075). No new data will be collected. The data will be securely downloaded and analyzed on institutional servers at New Mexico Tech under an approved IRB and Data Use Agreement. Analysis Plan: We will integrate genomic, transcriptomic, and phenotypic data to develop and evaluate machine learning models—such as large language model–based architectures and disease-specific neural networks—to predict variant pathogenicity and gene-level associations. Phenotypic characteristics evaluated will include Alzheimer’s disease diagnosis, cognitive performance measures, neuropathological burden, and biomarker profiles (e.g., amyloid and tau levels). Statistical and model-based analyses will assess associations between genetic variants and these phenotypes, with results reported in aggregate, non-identifiable form. Collaborations (if applicable): N/A

This project uses advanced artificial intelligence and statistical tools to study the genetic and molecular factors that contribute to Alzheimer’s disease. By analyzing existing, de-identified research data from the National Institute on Aging’s NIAGADS repository, we aim to identify genetic variants and biological pathways linked to disease risk and progression. The study will combine information from DNA and gene-expression data to build computer models that can better predict how certain genetic changes affect brain health. Our ultimate goal is to improve scientific understanding of Alzheimer’s disease and support future efforts in early detection and personalized treatment.