NG00153 – Health and Retirement Study (HRS) DNA Methylation

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 proposed study aims to investigate cognitive aging disparities among various racial and ethnic groups, focusing on the role of DNA methylation (DNAm) in mediating these differences. This research addresses the public health challenge posed by higher rates of cognitive decline and dementia in African Americans, Hispanics/Latinos, and Native Americans compared to non-Hispanic Whites. The study hypothesizes that socioeconomic factors (SES) such as education, and occupation, influence DNAm patterns, which in turn affect cognitive health. The study design involves a retrospective analysis of longitudinal data from the Health and Retirement Study (HRS), supplemented by DNAm data. The analysis plan includes developing a predictive algorithm for cognitive age using existing DNAm data, examining the association of SES with DNAm markers, and exploring whether these markers mediate racial/ethnic disparities in cognitive decline rates. The algorithm will be developed by regressing cognitive change slopes against DNAm parameters using elastic net linear regression, resulting in a DNAm biomarker indicative of cognitive age (DNAmCogAge). This biomarker's predictive power for future dementia will be evaluated using multilevel linear mixed models, focusing on its correlation with cognitive decline rates and the influence of race/ethnicity. In addition to the quantitative analysis, the study incorporates a qualitative component through the development of a questionnaire aimed at understanding community perspectives, particularly among older racial and ethnic minorities. This questionnaire will assess knowledge, perceived barriers, and attitudes towards epigenetic biomarkers and dementia risk, informing future efforts to develop DNAm biomarkers of cognitive age and enhance health knowledge in these communities. This study aligns with NIAGADS's mission by utilizing genetic analysis data to uncover mechanisms underlying cognitive aging disparities, bridging scientific advancements with community perspectives to foster a more holistic understanding of dementia risk factors.

Our research focuses on understanding why certain racial and ethnic groups experience faster cognitive decline and higher dementia rates. We're exploring how life experiences, particularly socio-economic factors like education and income, might affect brain health through DNA changes. Using data from previous studies, we aim to develop a new way to measure 'cognitive age' based on these DNA changes. This will help us see if and how these changes link to cognitive decline. Additionally, we are creating a survey to understand community views on this topic, especially among older adults from diverse backgrounds. Our goal is to combine scientific findings with community insights, making our research relevant and beneficial for everyone. This study could lead to better ways of predicting and understanding dementia risk, particularly in underrepresented communities

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

Moderate to severe impairments in cognitive functioning are a primary hallmark of Alzheimer's disease (AD) and Alzheimer's Disease Related Dementias (ADRD), a class of disorders affecting ~30% of the population by age 90. Currently, scientists hypothesize that the AD/ADRD disease process begins decades prior to the low functioning observed at the time of diagnosis. The ideal study design to gain insight into liability in prodromal and preclinical stages of AD/ADRD would involve collecting data on a wide range of measures from a large group of participants across the entirety of the life course. However, this data collection strategy includes major pragmatic barriers. Longitudinal study designs that might identify prospective risk factors of later life disease onset carry high participant and financial costs and take decades to produce conclusive results. Because of these limitations, much of the literature has been left to speculate in a piecemeal fashion on what characterizes the AD/ADRD prodromal period. However, research into these prodromal and preclinical periods holds significant promise for improving prevention and intervention efforts by identifying at-risk individuals and those who are at an earlier, and likely more intervenable, stage of disease.We will analyze the links between genotypes and phenotypes to investigate the onset times of risk factors for AD/ADRD across the life course using data from the HRS. Our insights will focus on pinpointing specific periods when these outcomes manifest as individuals age. We hypothesize that genetic data and structural equation modeling can help identify the specific times when Alzheimer's risk factors emerge as individuals age throughout their lives. Our team proposes to leverage the Genomic Structural Equation Modeling (Genomic SEM) framework to identify genetic risk pathways to AD/ADRD across the life course using existing data from large epidemiological studies that index different age ranges. Results from this study will add previously unseen levels of precision to our understanding of when genetic risk for AD/ADRD emerges across the life course and which specific risk factors index its onset.\

We will analyze the links between genotypes and phenotypes to investigate the onset times of risk factors for Alzheimer's and Dementia (AD) across the life course using data from the Health and Retirement Study (HRS). Our insights will focus on pinpointing specific periods when these outcomes manifest as individuals age. We hypothesize that genetic data and structural equation modeling can help identify the specific times when Alzheimer's risk factors emerge as individuals age throughout their lives.