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Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder with many biological processes, and molecular changes. The etiology of AD is complex and not specific to a single genetic factor. Epigenetic changes could help explain the missing heritability not capture in GWAS chips and determine functional variants in genome-wide significant loci. DNA methylation data from 431 parietal lobe of AD and neuropath-free controls was generated from the Knight-ADRC. The Illumina Infinium MethylationEPIC interrogates the methylation over 850,000 CpG and non-CpG sites, open chromatin, enhancers, DNase hypersensitive sites and promoters.
Sample Summary per Data Type
|Sample Set||Accession||Data Type||Number of Samples|
|Harari Methylation||fsa000018||NG00114.v1||Methylation Data, Phenotypes, etc.|
View the File Manifest for a full list of files released in this dataset.
DNA methylation data from 431 parietal lobe of AD and neuropath-free controls was generated from the Knight-ADRC. The Illumina Infinium MethylationEPIC interrogates the methylation over 850,000 CpG and non-CpG sites, open chromatin, enhancers, DNase hypersensitive sites and promoters.
|Sample Set||Accession Number||Number of Subjects|
|Consent Level||Number of Subjects|
Visit the Data Use Limitations page for definitions of the consent levels above.
- Investigator:Chen, JingchunInstitution:University of Nevada, Las VegasProject Title:Classification of Alzheimer’s disease with Genetic Data and Artificial IntelligenceDate of Approval:March 28, 2023Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement: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.Non-Technical Research Use Statement: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.
- Investigator:Cruchaga, CarlosInstitution:Washington University School of MedicineProject Title:The Familial Alzheimer Sequencing (FASe) ProjectDate of Approval:March 28, 2023Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement: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 studiesNon-Technical Research Use Statement: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.
- Investigator:Greicius, MichaelInstitution:Stanford University School of MedicineProject Title:Examining Genetic Associations in Neurodegenerative DiseasesDate of Approval:May 22, 2023Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement: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.Non-Technical Research Use Statement: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.
- Investigator:Hatchwell, EliInstitution:Population BioProject Title:Mutational Spectrum of Causal Genes for Neurological/Neurodegenerative Diseases and Endometriosis Identified via High Resolution Genome Wide Copy Number AnalysisDate of Approval:September 7, 2023Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:While single gene rare variants have been shown to play a significant role in Early-Onset Alzheimer’s Disease (EOAD), their role in Late-Onset (LOAD) has not been emphasised. The gene discovery methodology we have developed at Population Bio allows for unbiased exploration of highly informative genomic variants in any cohort of interest. Our approach is based on ultra-high resolution copy number variant (CNV) analysis. We have invested heavily in such analysis on normal populations. These are used as comparators for cohorts of interest, such as LOAD. In our LOAD work, this analysis generated a list of CNVs which were either absent in the normal populations we studied or else present at significantly higher frequency in the LOAD cohort. Such CNVs are routinely annotated to determine if they overlie known genes and/or regulatory regions. As an example, we have discovered a deletion in 3% of our LOAD cases, which is present in <= 1% of normals. This deletion disrupts a transcription factor binding site in the intron of a gene, which, via GeneHancer, is known to control exon 1 of the gene. The gene in question is novel to LOAD, and is an important metabolic gene, with known biology. It is vital that we validate this finding by analysis of independent LOAD datasets. In addition, we wish to validate other genes discovered in the same manner We have very deep experience of analyzing WGS/WES datasets. Our focus will be to pull out of the available WGS/WES datasets all the variants for the candidate genes of interest. Such variants, including SNVs, indels and CNVs (called using a variety of tools we have experience with) will be analyzed by reference to databases of normal individuals: i.CNVs, by reference to our own internal database but also gnomad (https://gnomad.broadinstitute.org) CNV data and DGV (http://dgv.tcag.ca) ii.SNVs/indels, by reference to gnomad These analyses will allow us to determine whether there exists a mutational burden for our candidate genes of interest in independent LOAD cohorts, and will serve as validation/refutation. The main phenotype of interest will be definitive diagnoses of LOAD, based on neuropathological and clinical cognitive analysesNon-Technical Research Use Statement:Most of the common conditions that affect large numbers of the general population have a genetic basis. While progress has been rapid in the field of cancer, the same cannot be said for common, non-cancer, conditions, such as Late-Onset Alzheimer's Disease (LOAD). It is pretty clear now that not all cases of LOAD represent the same disease, in terms of what is the cause. Our approach has been to consider common diseases as collections of rare subgroups, each of which has a specific cause and which, in due course, will have a specific treatment. We have pioneered and implemented a method to rapidly uncover potentially causal genes in common disorders and will use the data generated from this study to strengthen our discoveries, by validating a set of novel candidate genes we have identified in LOAD Our project will allow us to: 1.Define subsets of disease 2.Work with pharmaceutical companies to develop drugs that will specifically target each subset of disease. In some cases, disease progression may be halted by the therapies developed. In some cases, reversal and/or cure may be possible
- Investigator:Pan, WeiInstitution:University of MinnesotaProject Title:Powerful and novel statistical methods to detect genetic variants associated with or putative causal to Alzheimer’s diseaseDate of Approval:February 16, 2023Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement: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.Non-Technical Research Use Statement: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.
- Investigator:Pendergrass, RionInstitution:GenentechProject Title:Genetic Analyses Using Data from the Alzheimer’s Disease Sequencing Project (ADSP) and related studiesDate of Approval:August 30, 2023Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:The purpose of our study is to identify novel genetic factors associated with Alzheimer’s Disease, corticobasal degeneration (CBD) and progressive supranuclear palsy (PSP). This includes identifying genetic factors associated with the risk of these conditions, as well as genetic risk factors associated with age-at-onset (AAO) for these conditions. We will also evaluate genetic associations with sub-phenotypes individuals have within these broad disease categories, such as their Braak staging results which provide insights into the level of severity of Alzheimer’s. Thus we are requesting access to the set of genomic Whole Exome and Whole Genome Sequences (WES and WGS) have just been released through the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (DSS NIAGADS). The findings from our genetic association testing have the potential for identification of new therapeutic targets for Alzheimer's Disease, CBD, and PSP. The findings from our studies also have the potential for identification of genetic and phenotypic biomarkers that will be beneficial for subsetting patients in new ways. We will use standard genetic epidemiological methods to handle the WGS and WES data. We will also analyze cell type-specific expression differences in AD to identify biomarkers and disease pathways using standard gene expression analysis methods currently in use. We will also use other multi-omic and other genetic data that has now become available to further understand genetic association results we have found in AD.All data will remain anonymized and securely stored, and only those listed on our application and their staff will have access to these data. We will not share any of the individual level data outside of Genentech nor beyond the researchers on our application. We will adhere to all data use agreement stipulations through the DSS NIAGADS. We have a secure computational environment called Rosalind within Genentech where we will use these data. We have IT security staff that constantly monitor all our research computing, assuring safety and privacy of all of our stored data. We will not collaborate with researchers at other institutions.Non-Technical Research Use Statement:Genetic variation and gene expression data allows us to understand more of the genetic contribution to risk and protection from diseases such as Alzheimer’s and dementia. This information also allows us to identify important biological contributors to disease for developing effective treatment strategies, and identifying groups of individuals that would benefit most from new treatments. Our exploration of this relationship between genotype, disease traits, gene expression, and outcomes, through these datasets will allow us to pursue important new findings for disease treatment.
- Investigator:Roussos, PanagiotisInstitution:Icahn School of Medicine at Mount SinaiProject Title:Higher Order Chromatin and Genetic Risk for Alzheimer's DiseaseDate of Approval:August 16, 2023Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement: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.Non-Technical Research Use Statement: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.
- Investigator:Safo, SandraInstitution:University of MinnesotaProject Title:Innovative Machine and Deep Learning Analyses of Alzheimer's Disease Omics and Phenotypic DataDate of Approval:October 27, 2023Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement: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.Non-Technical Research Use Statement: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.
- Investigator:Singleton, AndrewInstitution:National Institute on AgingProject Title:Genetic Characterization of Movement Disorders and DementiasDate of Approval:March 3, 2023Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement: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.Non-Technical Research Use Statement: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.
- Investigator:Zhao, JinyingInstitution:University of FloridaProject Title:Identifying novel biomarkers for human complex diseases using an integrated multi-omics approachDate of Approval:November 21, 2023Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement: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.Non-Technical Research Use Statement: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.
- Investigator:Zhi, DeguiInstitution:University of Texas Health Science Center at HoustonProject Title:Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's DiseaseDate of Approval:October 2, 2023Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement: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.Non-Technical Research Use Statement: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.
Acknowledgment statement for any data distributed by NIAGADS:
Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer’s Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24-AG041689), funded by the National Institute on Aging.
Use the study-specific acknowledgement statements below (as applicable):
For investigators using any data from this dataset:
Please cite/reference the use of NIAGADS data by including the accession NG00114.
For investigators using Charles F. and Joanne Knight Alzheimer’s Disease Research Center (sa000008) data:
This work was supported by grants from the National Institutes of Health (R01AG044546, P01AG003991, RF1AG053303, R01AG058501, U01AG058922, RF1AG058501 and R01AG057777). The recruitment and clinical characterization of research participants at Washington University were supported by NIH P50 AG05681, P01 AG03991, and P01 AG026276. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, and the Departments of Neurology and Psychiatry at Washington University School of Medicine.
We thank the contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, and the Departments of Neurology and Psychiatry at Washington University School of Medicine.
For use of the ADSP-PHC harmonized phenotypes deposited within dataset, ng00067, use the following statement:
The Memory and Aging Project at the Knight-ADRC (Knight-ADRC), supported by NIH grants R01AG064614, R01AG044546, RF1AG053303, RF1AG058501, U01AG058922 and R01AG064877 to Carlos Cruchaga. The recruitment and clinical characterization of research participants at Washington University was supported by NIH grants P30AG066444, P01AG03991, and P01AG026276. Data collection and sharing for this project was supported by NIH grants RF1AG054080, P30AG066462, R01AG064614 and U01AG052410. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the Neurogenomics and Informatics Center (NGI: https://neurogenomics.wustl.edu/) and the Departments of Neurology and Psychiatry at Washington University School of Medicine.