NG00119 – Health and Retirement Study Genotype Data 2006-2012

We will use the HRS data to pursue two complementary strategies. One is the discovery of particular genetic polymorphisms associated with social-science outcomes. Because the effect of an individual genetic polymorphism on the outcome is likely to be very small, the HRS sample is too small, taken alone, to be used to discover new associations. Hence, we will pursue this strategy with HRS data in conjunction with other datasets that we have organized in the Social Science Genetic Association Consortium (SSGAC; www.thessgac.org). Our second strategy focuses on exploiting the uniquely rich social-science data in the HRS. We will conduct analyses that will shed light on the genetic architecture of a range of social-science outcomes. We will apply statistical methods that use the information contained in the dense SNP data taken as a whole and are thus well-powered in a sample size such as that of the HRS. Our specific aims are: 1. To incorporate data from the HRS into ongoing meta-GWAS efforts from the SSGAC for a range of social-science outcomes, such as educational attainment, and personality. 2. To continue to include HRS in the future releases of the Polygenic Index (PGI) Repository. PGIs (aka polygenic scores) are summaries of a person's genetic predisposition to a particular trait. HRS was included in the first release of the Repository, for which we created PGIs for 47 phenotypes in 11 datasets, which were returned to the datasets to be shared with users according to the datasets’ own data sharing procedures. We will regularly update the existing PGIs and add new phenotypes as larger GWAS and better methodologies become available. Details on the Repository can be found in Becker et al. (2021, Resource profile and user guide of the Polygenic Index Repository. Nat. Hum. Behav.). 3. To use the HRS genotype data to conduct polygenic prediction analyses for a range of social-science traits. Besides the direct interest in assessing the degree of predictive power in PGIs, we will examine how these PGIs interact with environmental factors to influence life outcomes. 4. To estimate heritability and genetic correlations for social science traits in an older population.

We will use HRS data to explore the genetic architecture of social-science outcomes. To do so, we will either use HRS data together with other datasets to identify specific genetic variants associated with these outcomes, or analyze the aggregate effect of all genetic variants in HRS alone using heritability analyses and polygenic indexes (PGIs). PGIs are summaries of a person's known genetic predisposition to a particular trait. We will use PGIs to examine the pathways underlying the relationship between genetic variants and outcomes of interest, including analyses of how genes and environment interact. We will also include HRS in future releases of the PGI Repository, an initiative that makes PGIs for a wide range of traits available in a number of datasets that may be useful to social scientists (https://www.thessgac.org/pgi-repository ). HRS was included in the first release of the Repository, and we wish to continue to update the HRS PGIs and add PGIs for new phenotypes as more data or better methodologies become available.

Polygenic risk scores (PRS) for dementia and aging-related conditions are known to be associated with cognitive outcomes in older age, but little isknown about their relationship to mid-life cognitive decline. We plan to use raw genetic data to derive novel PRS scores for each of three proposed GWAS sources (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.

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.

Alzheimer's disease(AD) is the most common cause of dementia, accounting for 60% to 80% of cases that affect over six million people in the United States. The disease gradually progresses from mild cognitive impairment(MCI) to dementia, which takes more than a decade. Identifying individuals who have a high risk of AD earlier is essential for AD prevention and intervention. As the heritability of AD is high(up to 79%), genetic data should be powerful to identify individuals at high risk. Indeed, polygenic risk score (PRS), designed to estimate individual genetic liability by integrating large GWAS summary statistics and individual genotype data, has been shown to be promising for AD risk prediction(AUCs up to 84%). However, the prediction accuracy using a single PRS is still not sufficient for MCI and AD classification in clinical practice. We hypothesize that convolution neural network(CNN) models can improve the classification of AD and MCI by multiple integrating PRSs from multiple traits, multi-omics data (genotyping data, scRNA-seq), clinical data, and imaging data. The objective is to develop advanced AI algorithms and build data-driven models for disease risk assessment, earlier identifying individuals with high risk for MCI and AD. Our long-term goal is to develop and validate a prediction model that can be translated into clinical practice. Our CNN model has recently shown an improved performance for AD with PRSs from multiple traits(AUC 92.4%). We want to extend our approach to predicting AD and MCI in different ethnic groups and validate the results with independent datasets. To this end, we would like to apply for multi-omics data in NG00067.v9 from https://dss.niagads.org/datasets/ng00067/. With an extensive experience in genetic studies on complex disorders and disease modeling, we are confident that we will achieve the specified goals and promote the integration of genetic data with AI algorithms, facilitating data-driven, personalized care of AD. We expect to finish this study within 2 years with publication and grant application. We have IRB approval and will follow the rules for data sharing and acknowledgment.

Alzheimer’s disease (AD), the most common form of dementia, that usually develops from mild cognitive impairment to dementia. There is currently no treatment to slow the progression of this disorder. But earlier identification of the individuals with higher risk maybe critical to prevent the disease. We propose a new approach to create models for classification of AD and MCI with artificial intelligence and genetic data. This study will have a significant value in personalized medicine for AD risk assessment, classification, and earlier intervention.We don’t have the planned collaboration with researchers outside Cleveland Clinic in the current analytic plans.

Research Use Statement: Our project will rely on phenotype and genotype data from the Health and Retirement Study (HRS), a nationally representative longitudinal study of the older adult population in the U.S. This is an on-going study. Data we have been using beginning in 2016 are from an approved application through dbGaP, from 15,507 HRS participants and include single nucleotide polymorphism (SNP) data on just under 2.5 million markers, imputed data on approximately 21 million DNA variants, and phenotype data on disease incidence and prevalence, functioning, biomarkers, mortality, and environmental and behavioral covariates. Our request from NIAGADS would provide us with an additional genetic sample to what we have been using, for the additional data on 3,409 participations (yielding N=18,916 total with harmonized genetic data through NIAGADS). Data usage will not create additional risk to participants. Aims of the project are to (1) Identify genetic networks and pathways that influence human aging, disease, functioning, and longevity; (2) Develop predictive models of aging-related health outcomes using information from gene networks; and (3) Examine how social and environmental conditions interact with genes within these aging-related gene networks. We will implement statistical models to test for associations between genetic variants and the same phenotype data. In moving forward with the additional samples, we will use the HRS genome-wide data to examine genetic signatures of healthspan, lifespan, and cognitive aging. Using these genetic signatures, we plan to (i) run pathway enrichment analysis to identify influential biological pathways, (ii) use them for predictive modeling of morbidity/mortality risk and cognitive aging, and (iii) incorporate information from social and behavioral data to examine GxE interactions. The overall goal of the project is to identify mechanistic gene and environment networks that contribute to aging acceleration or deceleration.

Non-Technical Summary: Aging is the largest risk factor for morbidity and mortality. Previous research using animal models or case-control studies of centenarians have suggested that variations in the pace of aging may be partially explained by genetic and genomic differences. However, few genetic regulators of human lifespan and healthspan have been identified. Furthermore, there is reason to suggest that the pace of aging may be a polygenic trait, for which multiple genes form complex networks that collectively influence aging and longevity phenotypes. These complex genetic networks may further interact with exogenous factors causing variation to arise in health outcomes under diverse environments. The goal of this project is to use advantaged statistical modeling techniques to understand how gene-gene and gene-environment interactions influence longevity and aging-related conditions.

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.

As part of the Collaborative for Alzheimer's Disease genetics REsearch (CADRE: NIA grant U01-AG058654), we plan to analyze whole exome and whole genome sequence data generated from subjects with Alzheimer's disease (AD) and elderly normal controls. These data will be generated by the National Human Genome Institute Large-Scale Sequence Program. The goal of the planned analyses is to identify genes that have alleles that protect against or increase susceptibility to AD. We will evaluate variants detected in the sequence data for association with AD to identify protective and susceptibility alleles using the whole exome case-control data. We will also evaluate sequence data from multiplex AD families to identify variants associated with AD risk and protection, and evaluate variant co-segregation with AD. The family data will be whole genome data. The family-based data will be used to inform the cases control analysis and visa versa. We also will focus on structural variants (insertion-deletions, copy number variants, and chromosomal rearrangements). Evaluation of structural variants will involve both whole genome and whole exome data. Structural variants will be analyzed with single nucelotide variants detected and analyzed in the case-control and family-based data.

We are attempting to identify all the inherited elements that contribute to Alzheimer's disease risk. To do this we will analyze DNA sequence data from subjects with Alzheimer's disease and elderly subjects who are cognitively normal. The sequence data from these 2 groups will be compared to identify differences that contribute to the risk of developing Alzheimer's disease of that protect against Alzheimer's disease. These DNA differences can be at a single site in the genetic code, or can span multiple sites, changing the copy number of DNA sequences. Both types of genetic variants will be examined.

Objectives of the proposed Research: 1) To test for and measure secular trends for a range of traits in humans, with a focus on behavioral and health traits.2) To test whether the strength and direction of these trends changes between generations.3) To test hypothesized mediators, including age at first birth and SES.Study design and Analysis Plan: Using cohorts that have completed their fertility we will run regressions with the fertility rate as the dependent variable and polygenic scores as the independent variable. From this we will calculate the selection differential and the strength of selection, after adjusting for the missing variance of the polygenic scores. We will test the role of different moderators by splitting the sample according to the moderators. Analyses will be done and reported with and without the use of sampling weights. We intend to study selection of a range of behavioral and health related traits including the Big Five personality traits, occupational status, ADHD, BMI, educational attainment, cognitive performance, smoking cessation, smoking initiation, height, schizophrenia, depression and autism. We will derive our own polygenic scores from available summary statistics, not limiting ourselves to what is available in the Polygenic Score Data provided by HRS.Secondary analyses will include: 1) measuring change in the polygenic scores between cohorts, with a focus on the difference between those born before, during and after the Second World War. 2) Estimate genotypic change using phenotypes available in the HRS that are closest to our genotypic traits, which include the Big Five personality traits, occupational status, ADHD, BMI, educational attainment, cognitive performance, smoking cessation, smoking initiation, height, schizophrenia, depression and autism.We will not collaborate with researchers from other institutions.

Many traits affect and are associated with the number of children we have. Illnesses and education can get in the way of reproduction, for example. This results in our culture, society and environment selecting for certain traits in future generations. Although the speed of this process is extremely slow, its direction and exact strength is unclear for many traits. We would like to measure this effect.

Genome-wide association studies (GWAS) have identified tens of thousands of associations for numerous complex traits. However, despite the identifications of associated genetic variants, interpretation of GWAS findings remains challenging. The complex structure of linkage disequilibrium in the human genome, coupled with weak effect sizes of common genetic variants, hinder our ability to identify biologically functional genetic variants and understand their functional mechanism. Recent advances in epigenetic and transcriptomic functional annotations have accelerated discoveries in a variety of human genetics applications including GWAS downstream analysis. In this project, we leverage integrative genomic functional annotations in GWAS data to dissect the genetic architecture of complex traits. Specifically, we will integrate the requested GWAS data with epigenetic and transcriptomic annotation data in public repositories (e.g. Epigenomics Roadmap Project, ECNODE, and GTEx) to explore the underlying genetic architecture of various sociogenomics traits available in the HRS, examine shared genetic components among these traits, leverage pleiotropy and functional annotation information to prioritize genetic variants affecting these phenotypes, and robust and interpretable produce genetic prediction models. We think that integrating functional annotation information can effectively reduce noises and spurious associations in the non-functional regions in the human genome. More importantly, the tissue-specific nature of epigenetic and transcriptomic data would provide novel insights into the genetic basis and functional pathways of sociogenomic phenotypes. Finally, using better prioritized variants and annotation-informed effect size estimates can improve the prediction accuracy of polygenic risk score, which enhances the statistical power in studying the genetic relationship among multiple phenotypes.

Overwhelming evidence indicates that common genetic variants account for a substantial proportion of phenotypic variance in many complex behavioral phenotypes. As a systematic and robust approach, GWAS can effectively identify genetic variants associated with human traits. In this study, we employ genetic data from HRS to identify genetic variants associated with a variety of sociological phenotypes. Then, we will apply state-of-the-art statistical and computational methods to help interpret our findings. Specifically, we will integrate external annotation information of the human genome to fine-map causal variants at identified genetic loci, identify related tissue and cell types for sociological traits, and identify candidate risk genes. Further, by jointly modeling multiple traits, we dissect the shared and distinct genetic architecture among related sociological traits.

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.

Our study will investigate the impact of skin color-based discrimination (colorism) on socioeconomic and health outcomes. We will do so by measuring how genetic variants that increase melanin production are associated with surveyed outcomes in the Health and Retirement Survey (HRS), including employment, income, education, and medical history. The results will add quantitative evidence to the large body of qualitative and critical literature on colorism and enhance our understanding of how colorism contributes to structural inequality.To measure the causal impact of colorism on life outcomes, we will use genetic variant data from the Genotype Data Version 3 (2006-2012 Samples) available via NAGADS and outcomes from HRS survey questions, such as income and years of education. We will assemble a list of SNPs from prior studies that have been shown to cause darker skin, and study the impacts of these SNPs on individuals in the HRS. The specific SNPS of interest include rs16891982, rs1426654, and rs1800404, which according to correspondence with Amanda Kuzma are available in the Genotype Data (at least via HRC imputation). To estimate effects, we will fit regression models that relate life outcomes to the presence of SNPs while controlling for any confounding variables, including genetic principal components. If colorism leads to worse life outcomes, we would expect to see a negative slope between the effect size of the SNP and the predicted outcomes. Our analysis can be considered a version of Mendelian randomization (MR). We will perform the work in Python and R.Our study will bring new statistical evidence to a large body of work demonstrating that colorism is a widespread form of discrimination in America. Using Mendelian randomization with variants that modulate genetically predisposed skin color, we can directly isolate the causal effect of colorism on social inequalities. Our findings have the potential to support the lived experiences of people who experience skin color-based discrimination and improve public knowledge that colorism is an important contributor to inequality in our society, and one that we can better address through policy.

Our study will investigate the impact of skin color-based discrimination (colorism) on socioeconomic and health outcomes. We will do so by measuring how genetic variants that increase melanin production are associated with surveyed outcomes in the Health and Retirement Survey (HRS), including employment, income, education, and medical history. The results will add quantitative evidence to the large body of qualitative and critical literature on colorism and enhance our understanding of how colorism contributes to structural inequality. Our findings have the potential to support the lived experiences of people who experience skin color-based discrimination and improve public knowledge that colorism is an important contributor to inequality in our society, and one that we can better address through policy.

Alzheimer's disease (AD) is the most common form of dementia and is characterized by cognitive impairment and progressive neurodegeneration. Genome-wide association studies of AD have identified more than 70 risk loci; however, a major challenge in the field is that the majority of these risk factors are harbored within non-coding regions where their impact on AD pathogenesis has been difficult to establish. Therefore, the molecular basis of AD development and progression remains elusive and, so far, reliable treatments have not been found. The overarching goal of this proposal is to examine and validate AD-related changes on chromatin accessibility and the 3D genome at the single cell level. Based on recent data from our group and others, we hypothesize that genotype-phenotype associations in AD are causally mediated by cell type-specific alterations in the regulatory mechanisms of gene expression. To test our hypothesis, we propose the following Specific Aims: (1) perform multimodal (i.e., within cell) profiling of the chromatin accessibility and transcriptome at the single cell level to identify cell type-specific AD-related changes on the 3D genome; (2) fine-map AD risk loci to identify causal variants, regulatory regions and genes; (3) functionally validate putative causal variants and regulatory sequences using novel approaches that combine massively parallel reporter assays, CRISPR and single cell assays in neurons and microglia derived from induced pluripotent stem cells; and (4) develop and maintain a community workspace that provides for the rapid dissemination and open evaluation of data, analyses, and outcomes. Overall, our multidisciplinary computational and experimental approach will provide a compendium of functionally and causally validated AD risk loci that has the potential to lead to new insights and avenues for therapeutic development.

Alzheimer’s disease (AD) affects half the US population over the age of 85 and despite decades of research, reliable treatments for AD have not been found. The overarching goal of our proposal is to generate multiscale genomics (gene expression and epigenome regulation) data at the single cell level and perform fine mapping to detect and validate causal variants, transcripts and regulatory sequences in AD. The proposed work will bridge the gap in understanding the link among the effects of risk variants on enhancer activity and transcript expression, thus illuminating AD molecular mechanisms and providing new targets for future therapeutic development.

AD is the most common cause of dementia and presents a substantial and increasing economic and social burden. Our ability to diagnose and classify AD from cognitive normals (CN), or discriminate among individuals with AD, early mild cognitive impairment [EMCI], or late mild cognitive impairment (LMCI), is essential for the prevention, diagnosis, and treatment of AD. Since individuals with MCI have a high chance of converting to AD, effectively discriminating between those who convert to AD (MCI-C) from those who do not convert (MCINC) is important for early diagnosis of AD. The heterogeneity of AD has motivated attempts to classify distinct subgroups of AD to better inform the underlying physiology. There is evidence to suggest that using data across multiple modalities (e.g. genetics, imaging, metabolomics) has potential to classify AD subgroups better than using single modality. We will apply machine and deep learning methods to gain deeper insight into AD and ADRD pathobiology. We will use datasets that include genomics, genetics, metabolomics, and phenotypic data for this purpose. Data will be divided into discovery and validation sets. On the discovery set, state-of-the-art ML and DL methods for integrative analysis that we and others have developed will be coupled with resampling techniques to determine candidate molecular signatures and pathways discriminating the AD groups considered. Molecular scores will be developed from these candidate biomarkers. The clinical utility of the scores beyond well-known clinical risk factors for AD will be ascertained. We will validate our findings using the validation data. We will visually and quantitatively compare the risk scores across several clinical variables and outcomes. We will use (un)supervised clustering methods to identify molecular clusters, and we will investigate molecular clusters differentiating MCI to AD converters from non-converters. We may explore differences across ethnic subgroups. We will also innovatively apply our multimodal molecular subtyping methods to discover, reproduce, and characterize novel molecular subgroups of AD– this will allow for better risk stratification.

We have been developing novel machine learning (ML) and deep learning (DL) methods that leverage genomics, other omics (including proteomics and metabolomics), clinical and epidemiology data to better understand the pathogenesis of complex diseases. By integrating data from different sources, we have identified molecular signatures contributing to the risk of the development of complex diseases beyond established risk factors. We are proposing to innovatively apply these, and other existing, methods, to data pertaining to Alzheimer’s disease (AD) and Alzheimer’s disease related dementias (ADRD). A deeper understanding of the genes, genetic pathways, and other molecular signatures of AD is essential and could facilitate the identification of potential therapeutic targets for the disease.

The goal of this project is to utilize standard genetics tools and ensemble/deep learning methods to predict/classify the etiological aspects of Alzheimer's disease and other neurodegenerative diseases based on genetic data and genomic data (including individual level data e.g. genotype and sequencing data, transcriptomic, and epigenomics data, and also by the use of summary statistics). Our primary phenotypes of interest include case:control status, age at onset, survival time (in terms of disease duration from diagnosis to loss to follow-up) and related biomarker data, although there may be other phenotypes of interest that are derived later based on available data.

We are attempting to identify and predict risk of Alzheimer's disease and other neurodegenerative diseases based on genetic and genomic data using standard tools and advanced machine-learning methods.

Alzheimer’s disease (AD) dementia is a heterogeneous neurodegenerative disease among older adults. Early detection of AD dementia remains challenging due to heterogeneity in disease onset and progression. Our goal is to develop a genetic variants-based VAE model to predict AD dementia. We will use GWAS data collected in Health and Retirement Study (HRS). HRS has genotyped saliva DNA samples collected since 2006 at multiple time points during field visits yielding a total of 19004 unique participants. We will use the most recent version of genotype data from 2006 to 2015 for those samples that have both epigenetic and transcriptomic data available. The genotyping was performed by NIH Center for Inherited Disease Research, using the Illumina HumanOmni2.5-4v1/8v1 array, and genotyping QC analysis was performed at the University of Michigan using HumanOmni2.5-4v1 H for SNP annotation. We will use the quality and minor allele frequency (MAF) filters specified in the HRS QC report for genotypic data to filter out poor quality SNPs. We will use cognition measures collected in HRS 2016 survey to classify participants into 'Dementia' and 'Normal' using the Langa Weir Classification algorithm.We will employ two main feature selection processes: 1. Based on the association with dementia, the top 50% of associated SNPs will be selected to input to the VAE model and filter out low-frequency SNPs. 2. We will also train a VAE model with a more comprehensive list of SNPs.We will employ the model regularization by incorporating biological knowledge as constraints in the model using the gene-gene interaction network from REACTOME/ STRING. We will also evaluate the biological interpretability of latent features that are representative of input genetic variants. We will evaluate the distribution of weights of all encoded features to select the positive high and negative high features based on 2 SD above or below the mean weight. These selected features will be input for the pathway analysis to identify pathways associated with AD. The candidate genes identified can be used to develop blood-based biomarkers for early identification of AD

We will develop a genetic variants-based VAE model to predict dementia. We will employ various feature selection processes based on the complexity of data to evaluate the VAE model performance and to identify a representative list of genes. In addition, we will evaluate the biological interpretability of latent features obtained from the VAE encoder layer by extracting their decoder weights that captures the input feature contribution to the learned latent feature. This will also allow us to evaluate if the VAE model has learned novel features known to be associated with AD dementia. We will then utilize the learned features to identify the biological pathways associated with AD dementia.

Our goal is to investigate the roles of cumulative genetic variation, disparity-related factors, and their interactions on late-onset Alzheimer’s disease (LOAD) and dementia phenotypes, extending precision public health to environmental susceptibility across ancestries. LOAD is the leading terminal form of dementia affecting a growing number of aging U.S. adults. As LOAD risk is disproportionately high among minorities, women, rural inhabitants, and people with lower education, disparities in LOAD risk represent a critical knowledge gap. Novel approaches characterizing the multifaceted etiology of LOAD disparities are needed to identify the genetic underpinnings, biological pathways, and potentially modifiable environmental factors that lead to sustained LOAD disparities. We propose whole genome estimations of polygenic risk of cognition, LOAD, and LOAD risk factors to be examined for their effect on dementia phenotypes among individuals >70, independently and in concert; potential interactions between PGS and factors with disparities in LOAD; and application of our methods in European and African ancestry groups (Fig. 1). AIM 1. Determine the cumulative genetic risk of LOAD by estimating the effect of cognitive polygenic scores on dementia phenotypes in individuals of European and African ancestry. AIM 2. Determine the association between polygenic scores for a) behavioral, b) physiological, and c) social/psychosocial domains and dementia phenotypes in individuals of European and African ancestry. We will consider Mendelian Randomization approaches for this aim. AIM 3. For the relationships between polygenic scores and dementia phenotypes (AIMS 1 and 2), test for effect modification by LOAD disparity-related factors (sex, educational attainment, urban/rural), in individuals of European and African ancestry.

The overall purpose of this proposal is to establish the relevance of polygenic risk in susceptibility to dementia, particularly among groups at increased risk of disease, including women, minorities, rural inhabitants, and those with low educational attainment. Because an individual’s susceptibility to dementia is likely a combination of genetics and environmental risk factors, we will jointly test the effects of cumulative genetic risk and dementia risk factors in our analysis. The proposal provides an opportunity to identify a genetic etiologic component in vulnerable groups that could lead to mechanistic understanding or targeted interventions to substantially benefit public health in the US.

We aim to uncover new genetic risk variants for Alzheimer’s disease (AD) by analysis of an integrated analysis of proteomics and genetic sequencing performed at Emory University. Results of these analyses will be used to weight analysis of whole-genome sequencing (WGS), whole-genome genotyping (WGG), and whole-exome sequencing (WES) data from dbGaP and ADSP. We plan to publish our findings, so they are shared with the scientific community.Outcomes that will be tested include: (1) clinical disease status, (2) pathologic characterization (e.g., measures of beta-amylodi, tau, etc.), and (3) cognitive decline. For sequencing data, we will perform joint calling from samples previously mapped by ADSP using PECaller using default settings. Variant annotation will be performed using Bystro and quality control will follow Wingo et al., 2017. For rare variants, we will use burden- and variance-based tests to estimate association between genetic variants and each outcome for every gene in the genome. External weights from proteomic analyses will be optionally used, as well as measures of genomic conservation for each site. For common variants, we plan to test for differences in allele frequencies using maximum likelihood tests. For all analyses, we plan to control for population structure deriving principal components from the underlying sequencing or genotyping data.

Our aim is to identify genetic variants that are associated with Alzheimer's Disease (AD) either using genomic data (from dbGap or from Emory University) or brain protein sequencing data (from Emory University) as a starting point. Each center’s data will be analyzed separately, and we will determine whether the findings are consistent among the centers. Additionally, we will use protein data from brain or cerebrospinal fluid of individuals with or without AD to guide the analysis of the genomic data to identify genetic variants that influence AD risk. Our overarching aim is to use genetic discoveries to identify mechanisms of AD pathogenesis and creation of more meaningful models of the disease.

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.

Alzheimer’s disease (AD) affects 5.6 million Americans over the age of 65 and exacts tremendous and increasing demands on patients, caregivers, and healthcare resources. Our current understanding of the biology and pathophysiology of AD is still limited, hindering advances in the development of therapeutic and preventive strategies. Existing genetic studies of AD have some success but these explain only a fraction of the overall disease risk, suggesting opportunities for additional discoveries. The proposed project will leverage existing neuroimaging and genetic data resources from the UK Biobank, the Alzheimer’s Disease Sequencing Project (ADSP), the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, and will be conducted by a multidisciplinary team of investigators. We will derive AD endophenotypes from neuroimaging data in the UK Biobank using deep learning (DL). We will identify novel genetic loci associated with DL-derived imaging endophenotypes and optimize the co-heritability of these endophenotypes with AD-related phenotypes using UK Biobank genetic data. We will leverage resources and collaborations with AD Consortia and the power of DL-derived neuroimaging endophenotypes to identify novel genes for Alzheimer’s Disease and AD-related traits. Also, we will develop DL-based neuroimaging harmonization and imputation methods and distribute implementation software to the research community. We expect to discover new genes relevant to AD which may leads to understanding of molecular basis of AD and potential new treatment.

Alzheimer’s disease (AD) exacts a tremendous burden on patients, caregivers, and healthcare resources. Our current understanding of the biology of AD is still limited, hindering advances in the development of treatment and prevention. Existing genetic studies of AD have some success but more studies are needed. The proposed project will leverage existing neuroimaging and genetic data resources from the UK Biobank, the Alzheimer’s Disease Sequencing Project (ADSP) and other consortia and will be conducted by a multidisciplinary team of investigators. We will derive new AD relevant intermediate phenotypes from neuroimaging data using deep learning (DL), an AI approach. We will identify novel genetic loci associated with these phenotypes. Also, we will develop imaging harmonization and imputation methods and distribute implementation software to the research community. We expect to discover new genes relevant to AD which may leads to understanding of molecular basis of AD and potential new treatment.