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Within the application, add this dataset (accession NG00106) in the “Choose a Dataset” section.
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In 2018, 960 respondents from LASI-DAD who consented to the blood sample collection have been genotyped using Illumina Infinium genotyping platforms. The datasets being submitted include the original genotype assayed by the genotyping platforms, imputed data to the 1000G reference panel, as well as imputed data to the TOPMed reference panel. The first dataset is the genotype data assayed by the Illumina Infinium Global Screening Array-24 v2.0 BeadChip. It contains 1008 scans derived from 993 unique subjects (including 960 LASI-DAD subjects and 33 1000G control subjects) and is in PLINK format. The second dataset contains the imputed data to the 1000G reference panel (phase 3 v5) and is in vcf format. It contains 960 unique LASI-DAD subjects. The third dataset contains the imputed data to the TOPMed reference panel (r2) and is in vcf format. It contains 960 unique LASI-DAD subjects.
Sample Summary per Data Type
|Sample Set||Accession||Data Type||Number of Samples|
|LASI-DAD GWAS GSA||snd10023||GWAS||n = 1008|
|LASI-DAD – GWAS Data||fsa000011||NG00106.v1||GWAS Data|
|LASI-DAD - 1000G Imputed Data||fsa000012||NG00106.v1||1000G Imputed Data|
|LASI-DAD - TOPMed Imputed Data||fsa000013||NG00106.v1||TopMed Imputed Data|
|LASI-DAD Association Results, Phenotypes, etc.||fsa000014||NG00106.v1||Association Results, Phenotypes, etc.|
View the File Manifest for a full list of files released in this dataset.
The dataset contains 1008 scans derived from 993 unique subjects (including 960 LASI-DAD subjects and 33 1000G control subjects) and is in PLINK format. The DNA samples were genotyped at MedGenome Inc. using the Illumina Infinium Global Screening Array-24 v2.0 BeadChip.
|Sample Set||Accession Number||Number of Subjects|
|LASI-DAD GWAS GSA||snd10023||n = 993|
|Consent Level||Number of Subjects|
|GRU-IRB-PUB||n = 993|
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: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: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:Yang, JingjingInstitution:Emory UniversityProject Title:Novel Bayesian methods for integrating transcriptomic data in GWASDate of Approval:February 16, 2022Request 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:Yokoyama, JenniferInstitution:University of California, San FranciscoProject Title:Trans-ethnic meta-analysis and fine-mapping of Alzheimer’s Disease lociDate of Approval:August 11, 2022Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:Alzheimer’s disease (AD) is a leading cause of death and disability worldwide. Despite the tremendous burden of the disease and arduous research efforts in the field, there are currently no disease-modifying treatments. Better elucidation of the genetic etiology of AD is needed to drive further drug discovery. Although the largest genome-wide association study (GWAS) of AD included over one million individuals of European ancestry and identified 38 associated loci, previous studies have highlighted the disproportionate disease risk and differing genetic architecture for AD across global populations. We hypothesize that a trans-ancestry approach would improve the generalizability of GWAS results, increase statistical power for locus discovery, and improve fine-mapping resolution to identify putative causal variants. Using a method that accounts for ancestral heterogeneity, we plan to leverage published and de-novo GWAS from individuals of European, East Asian, African American, South Asian, and Caribbean Hispanic ancestry and performed the largest trans-ancestry meta-analysis of AD to date. This method has allowed us to identify a novel AD locus near the Lymphocyte Cytosolic Protein 1 (LCP1) gene and prioritize an intronic SNP, rs2146890, as likely driving the observed association. Our findings further support the involvement of immune-related pathways in AD pathogenesis and highlight the importance of multi-ancestry representation in genetic studies. We propose utilizing the LASI-DAD GWAS and Imputation dataset to further extend these results by completing additional trans-ancestry meta-analyses.Non-Technical Research Use Statement:Alzheimer’s disease (AD) is a leading cause of death and disability worldwide. Although the largest genome-wide association study (GWAS) of AD included over one million individuals of European ancestry and identified 38 associated loci, previous studies have highlighted the disproportionate disease risk and differing genetic architecture for AD across global populations. Our findings have supported the involvement of immune-related pathways in AD pathogenesis and highlight the importance of multi-ancestry representation in genetic studies. We will use the LASI-DAD GWAS and Imputation data in conjunction with existing open datasets from individuals of European, East Asian, African American, South Asian, and Caribbean Hispanic ancestry to perform the largest trans-ancestry meta-analysis of Alzheimer’s disease to date.
- 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 NG00106.
For investigators using LASI-DAD (sa000019) data:
In text: "The Longitudinal Aging Study in India, Diagnostic Assessment of Dementia data is sponsored by the National Institute on Aging (grant numbers R01AG051125 and U01AG065958) and is conducted by the University of Southern California."
In references: "The Longitudinal Aging Study in India, Diagnostic Assessment of Dementia Study. Produced and distributed by the University of Southern California with funding from the National Institute on Aging (grant numbers R01AG051125 and U01AG065958), Los Angles, CA."
For investigators using HRS (sa000021) data:
HRS is supported by the National Institute on Aging (NIA U01AG009740). The genotyping was partially funded by separate awards from NIA (RC2 AG036495 and RC4 AG039029). Our genotyping was conducted by the NIH Center for Inherited Disease Research (CIDR) at Johns Hopkins University. Genotyping quality control and final preparation were performed by the Genetics Coordinating Center at University of Washington (Phases 1-3) and the University of Michigan (Phase 4).
- Lee J, Dey AB. Introduction to LASI-DAD: The Longitudinal Aging Study in India-Diagnostic Assessment of Dementia. J Am Geriatr Soc. 2020 Aug;68 Suppl 3(Suppl 3):S3-S4. doi: 10.1111/jgs.16740. PMID: 32815600; PMCID: PMC7513796. PubMed link
- Lee J, Khobragade PY, Banerjee J, Chien S, Angrisani M, Perianayagam A, Bloom DE, Dey AB. Design and Methodology of the Longitudinal Aging Study in India-Diagnostic Assessment of Dementia (LASI-DAD). J Am Geriatr Soc. 2020 Aug;68 Suppl 3(Suppl 3):S5-S10. doi: 10.1111/jgs.16737. PMID: 32815602; PMCID: PMC7503220. PubMed link
- Smith JA, Zhao W, Yu M, Rumfelt KE, Moorjani P, Ganna A, Dey AB, Lee J, Kardia SLR. Association Between Episodic Memory and Genetic Risk Factors for Alzheimer’s Disease in South Asians from the Longitudinal Aging Study in India-Diagnostic Assessment of Dementia (LASI-DAD). J Am Geriatr Soc. 2020 Aug;68 Suppl 3(Suppl 3):S45-S53. doi: 10.1111/jgs.16735. PMID: 32815605; PMCID: PMC7507858. PubMed link