Alzheimer’s disease (AD) is the most prevalent cause of dementia. While there is no effective treatment for AD, a growing body of evidence points to passive immunotherapy with monoclonal antibodies against amyloid beta (Aβ) as a promising therapeutic strategy. Meningeal lymphatic drainage plays an important role in Aβ accumulation in the brain, yet it is unknown if or how modulating meningeal lymphatic function can influence the outcome of anti-Aβ immunotherapy in AD. Analysis of the meninges of middle-aged AD transgenic 5xFAD mice revealed an accelerated deterioration of lymphatic vasculature. Ablation of meningeal lymphatic vessels in adult 5xFAD mice exacerbated Aβ deposition, microgliosis and affected neurovascular activation, increasing the amount of Aβ load and aggravating behavioral deficits following passive immunotherapy. On the contrary, therapeutic delivery of vascular endothelial growth factor C improved Aβ clearance by monoclonal antibodies and tuned hippocampal function in aged AD transgenic mice. Furthermore, we present a set of AD-associated genes that are highly expressed by meningeal lymphatic endothelial cells and correlate with altered levels of Aβ42 in the cerebrospinal fluid as well. We sought to investigate the microglial pathways affected in AD in human brains. To do so, we generated single-nuclei RNA-seq (snRNA-seq) from the parietal cortex from the Knight ADRC. We generated snRNAseq (10X chemistry v3) for AD, presymptomatic (ATN+ with cognition intact) and controls with none or neglectable AD pathology. After data cleaning and quality control,11,166 microglia nuclei remained for clustering and downstream analyses. A significant overlap was found between the gene signature of microglia from 5xFAD mice with dysfunctional meningeal lymphatics and the transcriptional profile of activated microglia from the human AD brain. Overall, our data demonstrates that impaired meningeal lymphatic drainage impacts the microglial inflammatory response in AD and that enhancement of meningeal lymphatics, alone or combined with potential passive immunotherapies, could lead to better clinical outcomes.
Single nuclei RNA-seq (10X chemistry v3) was generated from the parietal lobe as described in https://pubmed.ncbi.nlm.nih.gov/33911285/. Brain autopsied samples from Knight-ADRC. After data cleaning and quality control, 11,166 microglia nuclei remained for downstream analyses.
The study is published in the manuscript “Meningeal lymphatics affect microglia responses and anti-Aβ immunotherapy”, By Da Mesquita et al, Nature 2021 (https://pubmed.ncbi.nlm.nih.gov/33911285/).
1. The first release (June 1, 2021) includes sample meta data, study-wide unique barcodes of nuclei, gene expression data, and sample consent information.
2. The second release (November 23, 2021) has added single-nuclei RNA-seq sequence files, code files, genotypes for EQTL analysis, and phenotype files.
Sample Summary per Data Type
|Sample Set||Accession||Data Type||Number of Samples|
|Microglia expression profiles in AD||snd10021||Single Cell RNA Sequencing||n = 44|
|Microglia expression profiles in AD||fsa000007||NG00108.v2||Microglia profile, subject, and phenotype data|
|Microglia expression profiles in AD - GWAS||fsa000015||NG00108.v2||GWAS data|
|Microglia expression profiles in AD - Single-nuclei RNA-seq Sequence||fsa000016||NG00108.v2||Single-nuclei RNA-seq Sequence data|
View the File Manifest for a full list of files released in this dataset.
The microglia profile of the Parietal lobe of 44 donors from the Knight ADRC were sequenced using 10X genomics 3’ Chemistry v3 (10,000 nuclei per donor, 50,000 reads per nuclei), and aligned to the precursor mRNA reference (build GRCh38).
|Sample Set||Accession||Number of Subjects|
|Microglia expression profiles in AD||snd10021||n = 44|
|Consent Level||Number of Subjects|
|DS-ADRD-IRB-PUB||n = 44|
Visit the Data Use Limitations page for definitions of the consent levels above.
- Investigator:Cruchaga, CarlosInstitution:Washington University School of MedicineProject Title:The Familial Alzheimer Sequencing (FASe) ProjectDate of Approval:March 2, 2022Request status:ExpiredResearch 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:Farrer, LindsayInstitution:Boston UniversityProject Title:ADSP Data AnalysisDate of Approval:January 24, 2023Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement: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.Non-Technical Research Use Statement: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.
- Investigator:Greicius, MichaelInstitution:Stanford University School of MedicineProject Title:Examining Genetic Associations in Neurodegenerative DiseasesDate of Approval:October 17, 2022Request status:ExpiredResearch 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:Hohman, TimothyInstitution:Vanderbilt University Medical CenterProject Title:Genetic Drivers of Resilience to Alzheimer's DiseaseDate of Approval:October 17, 2022Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:“Asymptomatic” Alzheimer’s disease (AD) is a phenomenon in which 30% of individuals over age 65 meet criteria for autopsy-confirmed pathological AD (beta-amyloid plaques and tau aggregation) but do not clinically manifest cognitive impairment.1-3 The resilience that underlies asymptomatic AD is marked by both protection from neurodegeneration (brain resilience)4 and preserved cognition (cognitive resilience).Our central hypothesis is that genetic effects allow a subset of individuals to endure extensive AD neuropathology without marked brain atrophy or cognitive impairment. We are uniquely positioned to identify resilience genes by leveraging the Resilience from Alzheimer’s Disease (RAD) database, a local resource in which we have harmonized a validated quantitative phenotype of resilience across 8 large AD cohort studies.Our strong interdisciplinary team represents international leaders in genetics, neuroscience, neuropsychology, neuropathology, and psychometrics who will leverage the infrastructure and rich resources of the AD Genetics Consortium, IGAP, ADSP, and our recently established and harmonized continuous metric of resilience to fulfill the following aims:Aim 1. Identify and replicate common genetic variants that predict cognitive resilience (preserved cognition) and brain resilience (protection from brain atrophy) in the presence of AD pathology. We hypothesize that common genetic variation will explain variance in resilience above and beyond known predictors like education. Replication analyses will leverage age of onset data from IGAP to demonstrate that resilience loci predict a later age of AD onset.Aim 2. Identify and replicate rare and low-frequency genetic variants that predict cognitive and brain resilience. Rare and low-frequency variants with large effects have been identified in AD case/control studies, providing new insight into the genetic architecture of AD.Aim 3: Identify sex-specific genetic drivers of cognitive and brain resilience to AD pathology. Our preliminary results highlight sex differences in the downstream consequences of AD neuropathology, including sex-specific genetic markers of resilience.Non-Technical Research Use Statement:As the population ages, late-onset Alzheimer’s disease (AD) is becoming an increasingly important public health issue. Clinical trials targeted a reducing AD progression have demonstrated that patients continue to decline despite therapeutic intervention. Thus, there is a pressing need for new treatments aimed at novel therapeutic targets. A shift in focus from risk to resilience has tremendous potential to have a major public health impact by highlighting mechanisms that naturally counteract the damaging effects of AD neuropathology. The goal of the present project is to characterize genetic factors that protect the brain from the downstream consequences of AD neuropathology. We will identify both rare and common genetic variants using a robust metric of resilience developed and validated by our research team. The identification of such genetic effects will provide novel targets for therapeutic intervention in AD.
- Investigator:Jaffe, AndrewInstitution:Neumora TherapeuticsProject Title:Comparisons of pre- and post-mortem microglial populationsDate of Approval:July 21, 2022Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:In the study, we propose to directly compare and analyze pre-mortem microglial cells obtained during surgical resection from Young et al [PMID: 34083789] with post-mortem microglia from Lopes et al [PMID: 34992268, Dataset NG00105] to better define the transcriptional landscape of human microglia and the effects of tissue processing. We have previously re-processed and re-analyzed bulk and single cell data from Young et al. to identify expression quantitative trait loci (eQTLs) and develop RNA deconvolution models to partition bulk microglia profiles (like those measured by Dataset NG00105) into cell fractions of 7 important microglial subpopulations/cell states including “homeostatic”, “stress”, and “chemokine/cytokine” using the single cell RNA-seq (scRNA-seq) data from Young et al. We propose to perform this RNA deconvolution in Lopes et al, and test whether any of these cell populations – particularly related to neuroinflammation – are more prevalent in neurodegenerative disorders like Alzheimer’s (AD) or Parkinson’s Diseases (PD). We will also test whether these cell subtype fractions identified in pre-mortem tissue are consistent in postmortem tissue. As validation, we will perform supervised clustering of the NG00108 snRNA-seq data (in mouse) and test whether any AD-associated microglial cell subtypes were enriched in the 5xFAD genotype. Lastly, we propose to combine genotype and RNA data from Lopes et al (NG00105) and Young et al and perform eQTL mega-analysis to double the discovery sample size of microglial eQTLs. We hypothesize that this mega-analysis will produce a much larger number of significant eQTLs, as the GTEx project [PMID: 32913098] found approximately ~3000 eGenes in 100 subject discovery datasets (which was the approximate sample sizes of Young et al and Lopes et al) and ~7000 eGenes in 200 subjects (the combined sample size in this proposal). We will also assess clinical relevance by performing colocalization analysis of this larger eQTL map with genome-wide association studies (GWAS) of neurodegenerative disorders. Overall, this proposal will compare and contrast two recently large-scale genomic efforts profiling human microglia.Non-Technical Research Use Statement:Non-technical: This proposal will compare and contrast two recently large-scale genomic efforts profiling human microglia, including from premortem human brain tissue (Young et al, PMID: 34083789) and from postmortem brain tissue (Lopes et al, PMID: 34992268, Dataset: NG00105). We will specifically assess the distribution of various microglial cell states – derived from single cell RNA-seq data – and determine if all of these states are represented in microglia from postmortem tissue. We will perform validation analyses of these cellular states in a mouse model of AD (Dataset: NG00108). Assuming the pre- and post-mortem datasets are comparable, we will combine these datasets and perform joint analysis of genotype and phenotype to better understand variation in microglia gene expression.
- 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 1, 2022Request 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 1, 2022Request 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: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:Yang, JingjingInstitution:Emory UniversityProject Title:Novel Bayesian methods for integrating transcriptomic data in GWASDate of Approval:February 24, 2023Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:The objective of the proposed project is to derive novel Bayesian methods to integrate multi-omics data in genome-wide association studies (GWAS) for studying complex phenotypes, with the goal of prioritizing genetic variants and identifying causal genes. First, we will model the expression quantitative trait loci (eQTL) and other molecular QTL information in GWAS by an adapted Bayesian variable selection model, such that the model can quantify the enrichment of associated genetic variants with respect to each annotation such as eQTL and prioritize genetic variants that are of the enriched annotation. Second, we will be conducting transcriptome-wide association studies (TWAS) by a Bayesian approach to identify potentially causal genes. Third, we will use our Bayesian GWAS results to evaluate a Bayesian polygenic risk score for the complex phenotype of interest.We will first learn molecular QTL information by using external transcriptomics data set such as GTEx V8 and external molecular QTL from TCGA, and then integrate this information with the whole genome sequence data from ADSP to prioritize genetic variants associated with complex phenotypes of interest and conduct TWAS to identify risk genes. We are interested in studying all complex phenotypes that were profiled for the ADSP samples, especially Alzheimer’s disease (AD) and AD-related complex phenotypes. Especially, our lab has access to the ROS/MAP multi-omics data shared by the Rush Alzheimer’s disease center (http://www.radc.rush.edu/). All samples in the ROS/MAP study are well-characterized with extensive complex phenotypes profiled, including clinical diagnosis of AD, AD-related complex phenotypes, and psychological phenotypes. We will combine the whole genome sequence data from both ADSP and ROS/MAP samples to increase the total sample size in our study, thus improving the mapping power.The purpose of using ADSP data is to increase the sample size for testing our derived methods for functional genetic association studies of complex phenotypes. We are not limited to studying AD only. We are flexible to study any complex phenotypes that are profiled for both ADSP and ROS/MAP samples.Non-Technical Research Use Statement:This proposed project is to develop novel Bayesian methods to integrate multi-omics data such as transcriptomic in genome-wide association studies (GWAS) of complex phenotypes, with the goal of prioritizing genetic variants and identifying causal genes. i) We will model molecular quantitative trait loci information in GWAS, such that the model can quantify the enrichment for associated genetic variants with respect to each annotation and prioritize genetic variants that are of the enriched annotation. ii) We will derive a novel Bayesian model to use the eQTL effect-sizes as weights to conduct gene-based association tests. iii) We will use the Bayesian results from the proposed two methods to calculate Bayesian polygenic risk scores. We propose to test our proposed methods on the applied genomic analysis data and ROS/MAP multi-omics data to study complex phenotypes that are profiled for both ADSP and ROS/MAP samples, including AD, AD-related pathology traits, and related psychological disorders.
- Investigator:Zhao, JinyingInstitution:University of FloridaProject Title:Identifying novel biomarkers for human complex diseases using an integrated multi-omics approachDate of Approval:October 18, 2022Request 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:July 14, 2022Request 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 NG00108.
For investigators using KnightADRC (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.
Da Mesquita, Sandro et al. “Meningeal lymphatics affect microglia responses and anti-Aβ immunotherapy.” Nature vol. 593,7858 (2021): 255-260. doi:10.1038/s41586-021-03489-0
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