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Description

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 SetAccessionData TypeNumber of Samples
LASI-DAD GWAS GSA snd10023GWASn = 1008

Available Filesets

NameAccessionLatest ReleaseDescription
LASI-DAD – GWAS Datafsa000011NG00106.v1GWAS Data
LASI-DAD - 1000G Imputed Datafsa000012NG00106.v11000G Imputed Data
LASI-DAD - TOPMed Imputed Datafsa000013NG00106.v1TopMed Imputed Data
LASI-DAD Association Results, Phenotypes, etc.fsa000014NG00106.v1Association 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 SetAccession NumberNumber of Subjects
LASI-DAD GWAS GSAsnd10023n = 993
Consent LevelNumber of Subjects
GRU-IRB-PUBn = 993

Visit the Data Use Limitations page for definitions of the consent levels above.

Total number of approved DARs: 2
  • Investigator:
    Cruchaga, Carlos
    Institution:
    Washington University School of Medicine
    Project Title:
    The Familial Alzheimer Sequencing (FASe) Project
    Date of Approval:
    March 2, 2022
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical 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 studies
    Non-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:
    Yang, Jingjing
    Institution:
    Emory University
    Project Title:
    Novel Bayesian methods for integrating transcriptomic data in GWAS
    Date of Approval:
    February 16, 2022
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical 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.

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 number R01AG051125, RF1AG055273, 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 number R01AG051125, RF1AG055273, 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