NG00132 – Health and Retirement Study (HRS) APOE and Serotonin Transporter Alleles

• 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.

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.

We are studying the effects of rare (minor allele frequency < 5%) genetic variants on the risk of developing late-onset Alzheimer’s Disease (AD). We are interested in variants that have a protective effect in subjects who are at an increased genetic risk, or variants that lead to multiple dementias. Our aim is to identify any genetic variants that are present in the “case” group but not the “AD control” groups for both types of variants. The raw data we receive will be annotated to identify SNP locations and frequencies using existing databases such as 1,000 Genomes. We will filter the data based on genetic models such as compounded heterozygosity, recessive and dominant models to identify different types of variants.

Current genetic understanding of Alzheimer’s Disease (AD) does not fully explain its heritability. The APOE4 allele is a well-established risk factor for the development of Alzheimer’s Disease (AD). However, some individuals who carry APOE4 remain cognitively healthy until advanced ages. Additionally, the cause of mixed dementia pathology development in individuals remains largely unexplained. We aim to identify genetic factors associated with these “protected” and mixed pathology phenotypes.

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.

a. Objectives of the Proposed Research This study aims to investigate the relationship between psychiatric traits and age-related cognitive decline, addressing a critical knowledge gap in understanding how mental health influences aging outcomes. b. Study Design The study employs a multi-level investigative approach combining epidemiological, genetic, and molecular methodologies. The design incorporates three complementary components: first, identification of phenotypic associations between psychiatric traits and MCI/AD through comprehensive clinical assessment; second, investigation of genetic architecture through analysis of coding and non-coding variants, genetic correlation assessments, polygenic scoring, and Mendelian randomization for causal inference; and third, examination of molecular mechanisms through genetically regulated epigenetic and proteomic processes. The study design enables stratified analyses by sex and ethnicity while controlling for demographic and lifestyle confounders, providing a comprehensive framework for understanding the psychiatric-cognitive decline relationship across multiple biological levels. c. Analytical Plan The analytical approach will proceed in sequential phases, beginning with statistical modeling to identify psychiatric traits significantly associated with MCI and AD outcomes while adjusting for demographic and lifestyle factors. Genetic analyses will employ polygenic risk scores and Mendelian randomization techniques to establish causal relationships between psychiatric conditions (particularly depression and alcohol use disorder) and cognitive outcomes. Molecular analyses will focus on identifying shared genetic loci between psychiatric and cognitive phenotypes, followed by investigation of genetically regulated methylation and proteomic markers as potential mediators. The analysis plan includes development of molecular weights to aid causal inference analyses and determination of effect directionality, with stratified results reported by sex and ethnicity to identify population-specific risk patterns and potential intervention targets.

This research examines how mental health conditions like depression and anxiety may increase the risk of memory problems and Alzheimer's disease as people age. Using genetic data and biological markers, we'll study whether psychiatric conditions directly cause cognitive decline or if they share common underlying causes. The study will identify which mental health factors pose the greatest risk for dementia, particularly looking at differences between men and women and various ethnic groups. Results could help better predict and prevent cognitive decline by addressing mental health early in life, potentially improving outcomes for millions facing both psychiatric and age-related brain conditions.

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.

Objectives The goal of this study is to perform statistical and bioinformatics analyses and identify DNA methylation changes associated with cognitive data and dementia outcomes collected by the Health and Retirement Study. Our specific aims are: (1) Identify blood DNAm associated with aging, AD diagnosis and longitudinal cognitive outcomes (2) Validate DNAm-based prediction models for identifying subjects with high risk for dementia.Study design. The Health and Retirement Study (HRS) is a longitudinal panel studyAnalysis plan To identify blood DNAm associated with LOAD diagnosis and longitudinal cognitive outcomes, we will fit a mixed effects model with LOAD diagnosis (or longitudinal outcome) as the dependent variable, CpG methylation as the main independent variable, along with age, sex, batch type, estimated blood cell type proportions as covariates. In addition, we will apply our DNAm-based prediction models for LOAD to the Health and Retirement Study data and evaluate the prediction model by computing AUC (area under ROC curve).Phenotypic characteristics. We will evaluate the association of DNA methylation variants with dementia diagnosis and chronological age, adjusting for covariate variables race/ethnicity, APOE genotype, baseline MMSE at time of blood draw, age, sex, education, smoking history.

This study aims to explore how DNA methylation changes are associated dementia outcomes in older adults using data from the Health and Retirement Study. We will identify specific DNA methylation patterns associated with aging and Alzheimer's dementia, while also validating prediction models to identify individuals at high risk for dementia.

Our goal is to determine the joint epigenetic and environmental contributions to ADRD risk that underlie these health disparities. Using existing epigenetic and genetic data, well-characterized dementia phenotypes, and diverse risk factor data, we will analyze a population representative, multi-ethnic aging sample from the Health and Retirement Study (HRS). We aim to (1) test the associations between DNA methylation and dementia phenotypes (prevalent, 8-year incident), stratified by race/ethnicity and test for effect modification by ADRD disparity-related factors (educational attainment, sex, urban/rural); (2) identify associations between longitudinal measures of modifiable risk factors for ADRD and DNA methylation, stratified by race/ethnicity and test for effect modification or mediation by ADRD disparity-related factors; and finally, (3) identify genetic polymorphisms controlling DNA methylation and whether these are enriched in dementia outcomes to evaluate the role of DNA methylation in disease development. This study will likely impact the field of Alzheimer’s research and contribute to public health because it will a) establish the relevance of DNA methylation on ADRD in multiple race/ethnicities; b) elucidate important biological epigenetic mechanisms; c) determine the combined and individual epigenetic-environment interplay contributions to ADRD; and d) consider the effects of sex, educational attainment, race/ethnicity, younger age groups, and urban/rural status in the same study where comparisons of relative contribution to risk can be made. Here, we have the opportunity to simultaneously and substantially improve our understanding of the genetic and environmental etiologic contributions to health disparities in ADRD.

The overall purpose of this proposal is to identify modifiable risk factors for Alzheimer’s disease and related dementias that influence DNA methylation and dementia status among groups at increased risk for dementia including women, minorities, rural inhabitants, and those with low educational attainment. Results from this proposal may provide an opportunity to identify epigenetic components that contribute to the prevalence and risk of dementia that could lead to a mechanistic understanding or targeted interventions that may substantially decrease the burden of Alzheimer’s disease and related dementias in the US population

GWAS, WES and WGS have identified many genes associated with Alzheimer’s Dementia (AD) and its related traits. However, the identified genes thus far collectively explain only a small proportion of disease heritability, suggesting that more genes remained to be identified. Moreover, there is a clear gender and ethnic disparity for AD susceptibility, but little research has been done to identify gender- and ethnic-specific variants associated with AD. Of the many challenges for deciphering AD pathology, lacking of efficient and power statistical methods for genetic association mapping and causal inference represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the multi-omics and clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Specifically, we will (1) validate our novel methods for identifying novel risk and protective genomic variants and multi-omics causal pathways of AD; (2) identify novel ethnicity- and gender-specific genes and molecular causal pathways of AD. We will share our results, statistical methods and computational software with the scientific community.

Although many genes have been associated with Alzheimer’s Dementia (AD), these genes altogether explain only a small fraction of disease etiology, suggesting more genes remained to be identified. Of the many challenges for deciphering AD pathology, lacking of power statistical methods represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the rich genetic and other omic data along with clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Such results will enhance our understanding of AD pathogenesis and may also serve as biomarkers for early diagnosis and therapeutic targets.