NG00106 - LASI-DAD GWAS and Imputation

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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 Set	Accession	Data Type	Number of Samples
LASI-DAD GWAS GSA	snd10023	GWAS	n = 1008

Available Filesets

Name	Accession	Latest Release	Description
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	Number of Samples
LASI-DAD GWAS GSA	snd10023	993	1,008

Consent Level	Number of Subjects
GRU-IRB-PUB	n = 993

Total number of approved DARs: 15

Investigator:
Belloy, Michael
Institution:
Washington University in St Louis
Project Title:
Elucidating sex-specific risk for Alzheimer's disease through state-of-the-art genetics and multi-omics
Date of Approval:
January 6, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
• Objectives: In this project, we seek to holistically investigate the genetic and molecular drivers of sex dimorphism in Alzheimer’s disease across ancestries. • Study design: This study integrates large-scale population genetics with multi-omics and endophenotype analyses. We are integrating all data available from ADGC and ADSP, together with other data from AMP-AD and biobanks such as UKB, FinnGen, and MVP to conduct large-scale multi-ancestry GWAS, rare-variant gene aggregation analyses, QTL studies, PWAS, TWAS, etc. We also particularly focus on X chromosome association studies. The study design also interrogates interactions with ancestry, hormone exposures, and with APOE*4, as well as comparisons to non-stratified GWAS/XWAS of Alzheimer’s disease. Further, we will also employ genetic correlation analyses, mendelian randomization, colocalization, and pleiotropy analyses, to interrogate overlap with other complex traits to better understand the mechanisms underlying sex dimorphism in Alzheimer’s disease. • Analysis plan, including the phenotypic characteristics that will be evaluated in association with genetic variants: Our phenotypes will include Alzheimer’s disease risk, conversion risk, various endophenotypes (including amyloid/tau biomarkers, brain imaging metrics, etc.) as well as molecular traits. As noted above, we will conduct large-scale multi-ancestry GWAS, XWAS, rare-variant gene aggregation analyses, QTL studies, PWAS, TWAS, etc. Specific aims include interrogating these question and analyses on (1) the autosomes, (2) the X chromosome, and (3) leveraging sex stratified QTL studies to drive discovery of risk genes.
Non-Technical Research Use Statement:
Alzheimer’s disease (AD) manifests itself differently across men and women, but the genetic and molecular factors that drive this remain elusive. AD is the most common cause of dementia and till today remains largely untreatable. It is thus crucial to study the genetics of AD in a sex-specific manner, as this will help the field gain important insights into disease pathophysiology, identify novel sex-specific risk factors relevant to personalized genetic medicine, and uncover potential new AD drug targets that may benefit both sexes. This project uses large-scale genomics and multi-omics to elucidate novel sex agnostic and sex-specific AD risk genes. We will interrogate sex dimorphism for AD risk on the autosomes and the sex chromosomes. We similarly interrogate sex dimorphism in the genetic regulation of gene expression and protein levels, which we will integrate with genetic risk for Alzheimer’s disease to further discovery risk genes. Throughout, we will also interrogate how sex-specific risk for AD interactions with hormone exposures, ancestry, and the APOE*4 risk allele.
Investigator:
Chen, Jingchun
Institution:
University of Nevada, Las Vegas
Project Title:
Classification of Alzheimer’s disease with Genetic Data and Artificial Intelligence
Date of Approval:
May 8, 2024
Request status:
Approved
Research use statements:
Show statements
Technical 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:
Crimmins, Eileen
Institution:
University of Southern California
Project Title:
GWAS and Systems Biology Analyses for Aging-Related Conditions: Longevity and Disease
Date of Approval:
November 26, 2024
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
Our project will rely on phenotype and genotype data from the Health and Retirement Study (HRS), a nationally representative longitudinal study of the older adult population in the U.S. This is an on-going study. Data we have been using beginning in 2016 are from an approved application through dbGaP, from 15,507 HRS participants and include single nucleotide polymorphism (SNP) data on just under 2.5 million markers, imputed data on approximately 21 million DNA variants, and phenotype data on disease incidence and prevalence, functioning, biomarkers, mortality, and environmental and behavioral covariates. Our request from NIAGADS would provide us with an additional genetic sample to what we have been using, for the additional data on 3,409 participations (yielding N=18,916 total with harmonized genetic data through NIAGADS) as well as the DNA methylation data for the sub-sample (N=4,018). We will also seek validation of genetic findings in a study from the HRS International Family of Studies cohort, the LASI-DAD. Data usage will not create additional risk to participants. Aims of the project are to (1) Identify genetic networks and pathways that influence human aging, disease, functioning, and longevity; (2) Develop predictive models of aging-related health outcomes using information from gene networks; and (3) Examine how social and environmental conditions interact with genes within these aging-related gene networks. We will implement statistical models to test for associations between genetic variants and the same phenotype data. In moving forward with the additional samples, we will use the HRS genome-wide data to examine genetic signatures of healthspan, lifespan, and cognitive aging. Using these genetic signatures, we plan to (i) run pathway enrichment analysis to identify influential biological pathways, (ii) use them for predictive modeling of morbidity/mortality risk and cognitive aging, and (iii) incorporate information from social and behavioral data to examine GxE interactions. The overall goal of the project is to identify mechanistic gene and environment networks that contribute to aging acceleration or deceleration.
Non-Technical Research Use Statement:
Non-Technical Summary: Aging is the largest risk factor for morbidity and mortality. Previous research using animal models or case-control studies of centenarians have suggested that variations in the pace of aging may be partially explained by genetic and genomic differences. However, few genetic regulators of human lifespan and healthspan have been identified. Furthermore, there is reason to suggest that the pace of aging may be a polygenic trait, for which multiple genes form complex networks that collectively influence aging and longevity phenotypes. These complex genetic networks may further interact with exogenous factors causing variation to arise in health outcomes under diverse environments. The goal of this project is to use advantaged statistical modeling techniques to understand how gene-gene and gene-environment interactions influence longevity and aging-related conditions.
Investigator:
Cruchaga, Carlos
Institution:
Washington University School of Medicine
Project Title:
The Familial Alzheimer Sequencing (FASe) Project
Date of Approval:
May 9, 2024
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:
Farrer, Lindsay
Institution:
Boston University
Project Title:
ADSP Data Analysis
Date of Approval:
January 22, 2024
Request status:
Approved
Research use statements:
Show statements
Technical 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, Michael
Institution:
Stanford University School of Medicine
Project Title:
Examining Genetic Associations in Neurodegenerative Diseases
Date of Approval:
December 19, 2024
Request status:
Approved
Research use statements:
Show statements
Technical 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:
Masters, Colin
Institution:
The Florey Institute, The University of Melbourne
Project Title:
The Australian Imaging Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing: Detecting and Preventing Alzheimer’s disease: Towards Lifestyle Interventions-Somatic mutation in Alzheimer's Disease
Date of Approval:
May 15, 2024
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
Project Title: The Australian Imaging Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing: Detecting and Preventing Alzheimer’s disease: Towards Lifestyle Interventions - Somatic mutation in Alzheimer's Disease (sub-project)Objectives -- Somatic Mutation in AD is a project to identify non-congenitally acquired genetic risks associated with disease onset of sporadic Alzheimer’s disease (AD). Somatic mutation can be any form of alteration in DNA that occur after conception. As opposed to congenital, it’s generally not hereditary unless the germ cells are involved. These alterations can (but do not always) cause disease. We aim to identify somatic variants that contribute to sporadic AD. We believe that the detection of somatic mutations can overcome the flaws of the large genome-wide multiple testing and increase the signal-to-noise ratio to pinpoint the rare genetic determinants that were largely neglected by current genetic association studies.Study design -- We have collected 20 paired human brain microglial DNAs (treated as “tumour”) and whole blood DNAs (treated as “normal”) to call somatic mutations by a tumour-normal mode using a software, MuTect2 (Broad Institute). The sequence has been obtained from the whole genome. Hundreds of rare genetic variants have been identified to connect with AD.Analysis plan -- We’d like to validate our results using datasets like NG00067, NG00105 and NG00106. However, it’s ideal if we could access the alignment data (i.e., BAM files) as well. Because technically somatic calling is not simply a difference between normal (germline) and reference; but also calls for tumour against normal (germline) alongside alignment. MuTect2 is developed to identify somatic mutations. It works with or without matching normal. Once we get access to the alignment data, we will reprocess all samples using the MuTect2 without matching the normal pipeline. We'll call somatic mutations using those datasets and validate the rare genetic determinants that contribute to sporadic AD.
Non-Technical Research Use Statement:
Somatic Mutation in Alzheimer's disease is a project to identify non-congenitally acquired genetic risks associated disease onset of a sporadic Alzheimer’s disease (AD). We believe that detection of somatic mutations can pinpoint the rare genetic determinants that were largely neglected by current genetic association studies. In our pilot study, we have identified hundreds of rare genetic mutations that are strongly associated with AD. We'd like to validate our results using an independent cohort. We plan to reprocess NIH datasets using our own pipeline. But we would need to access the raw data rather than the processed data. This research will greatly accelerate the research on the molecular genetics of AD.
Investigator:
Pendergrass, Rion
Institution:
Genentech
Project Title:
Genetic Analyses Using Data from the Alzheimer’s Disease Sequencing Project (ADSP) and related studies
Date of Approval:
October 3, 2024
Request status:
Approved
Research use statements:
Show statements
Technical 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:
Pericak-Vance, Margaret
Institution:
University of Miami
Project Title:
Collaboration on Alzheimer Disease Research
Date of Approval:
December 9, 2024
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
We plan to analyze GWAS, whole exome and whole genome sequence data generated from subjects with Alzheimer's disease (AD) and elderly normal controls. 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 and whole genome 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:
Wainberg, Michael
Institution:
Sinai Health System
Project Title:
Uncovering the causal genetic variants, genes and cell types underlying brain disorders
Date of Approval:
September 5, 2024
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
We propose a multifaceted approach to elucidate and interpret genetic risk factors for Alzheimer's disease. First, we propose to perform a whole-genome sequencing meta-analysis of the Alzheimer's Disease Sequencing Project with the UK Biobank and All of Us to associate rare coding and non-coding variants with Alzheimer's disease and related dementias. We will explore a variety of case definitions in the UK Biobank and All of Us, including those based on ICD codes from electronic medical records (inpatient, primary care and/or death), self-report of Alzheimer's disease or Alzheimer's disease and related dementias, and/or family history of Alzheimer's disease or Alzheimer's disease and related dementias. We will perform single-variant, coding-variant burden, and non-coding variant burden tests using the REGENIE genome-wide association study toolkit.Second, we propose to develop statistical and machine learning models that can effectively infer (“fine-map”) the causal gene(s), variant(s), and cell type(s) underlying each association we find, as well as associations from existing genome-wide association studies and other Alzheimer's- and aging-related cohorts found in NIAGADS. In particular, we propose to improve causal gene identification by incorporating knowledge of gene function as a complement to functional genomics. For instance, we plan to develop improved methods for inferring biological networks, particularly from single-cell data, and integrate these networks with the results of the non-coding associations from our first aim to fine-map causal genes. To fine-map causal variants and cell types, we plan to integrate the associations from our first aim with single-nucleus chromatin accessibility data from postmortem brain cohorts to simultaneously infer which variant(s) are causal for each discovered locus and which cell type(s) they act through.
Non-Technical Research Use Statement:
We have a comprehensive plan to understand and explain the genetic factors that contribute to Alzheimer's disease. Our approach involves two main steps.First, we'll analyze genetic information from large research databases to identify rare genetic changes associated with Alzheimer's and related memory disorders. We'll look at both specific changes in genes and other parts of the genetic code. We'll use data from different studies and combine them to get a clearer picture.Second, we'll create advanced computer models that can help us figure out which specific genes, genetic changes, and cell types are responsible for these associations. This will help us pinpoint the most important factors contributing to Alzheimer's disease. We'll also analyze data from previous studies to build a more complete understanding of these genetic links.
Investigator:
Xiao, Peng
Institution:
University of Nebrask Medical Center
Project Title:
Uncovering the genetic basis of Alzheimer's Diseases by integrating GWAS with multiomics approaches across different ethnicities
Date of Approval:
August 9, 2024
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
ObjectivesIn this study we aim to understand the system level understanding of Alzheimer's disease (AD) by integrating GWAS with robust multiomics datasets across diverse ethnic groups and harmonization of the results to include associated genes and pathways to understand underlying disease mechanisms and to inform our understanding of biological continuum of the diseases. Investigating AD associated variants as quantitative trait loci for epigenetic, transcriptomic, and proteomic layers to explore how variants in genes perturb pathways leading to AD.Study designWe will comprehensively examine the genetic architecture of Alzheimer's diseases based on different races (Caucasians, Latinos, Asians and Africans) GWAS data from publicly available datasets. We request access to as many datasets available in NIAGADS and other repositories like EADB-consortium, IGAP and we also have requested access to datasets through our literature search from corresponding authors for Asian cohorts. We will perform meta-analysis at two levels for GWAS datasets and find genome wide significant loci (GWS). Mendelian randomization analyses will be adapted to multi-omics setting through analyzing QTLs and GWS. We will perform correlation and enrichment analysis for significant findings from different omics layers.Analysis PlanGenome wide meta-analysis across different races to identify new loci and functional pathways influencing AD. Find genes most likely to be responsible for association signal with AD at each loci by applying mendelian randomization (MR) method. We plan to use MR method to combine multiomics (GWAS, eqtl, mqtl, aqtl and pqtl).
Non-Technical Research Use Statement:
To our knowledge our findings reveal crosstalk between epigenetic, genomic, and transcriptomic determinants of AD pathogenesis and define catalogues of candidate genes. In addition, rare or population specific common variants can be identified thus genes with underlying genetic support for an association with AD are likely to encode successful drug targets in clinical development. This should further lead to patient stratification.
Investigator:
Yang, Jingjing
Institution:
Emory University
Project Title:
Novel Bayesian methods for integrating transcriptomic data in GWAS
Date of Approval:
June 12, 2024
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
The proposed project aims to derive novel statistical methods to integrate multi-omics data with genome-wide association studies (GWAS) summary data for studying complex phenotypes. First, we will develop novel statistical methods to integrate transcriptomic, proteomic, and DNA methylation molecular trait data with GWAS data, for the goal of identifying risk genes with interpretable biological functions. We will develop novel statistical methods to learn molecular QTL information by using external individual-level transcriptomics data sets such as GTEx V8 and summary-level molecular QTL datasets, and then integrate this information with the whole genome sequence data from ADSP to prioritize risk genes associated with AD-related phenotypes. We are interested in studying all AD-related phenotypes 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 study power.Second, we will investigate integrating multi-omics data and polygenic risk scores, with clinical variables to derive biomarkers for Alzheimer's disease. We will also use both ROS/MAP and ADSP data to improve the study power.We plan to use ADSP data as a validation data set to test our derived methods. 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 statistical methods to integrate multi-omics data such as summary-level molecular QTL data with GWAS summary data to study complex phenotypes, prioritizing potential causal genes. i) We will develop novel statistical methods to leverage summary-level molecular QTL data of multiple cohorts and ancestries. ii) We will apply our developed methods to study AD-related phenotypes. iii) We will derive risk prediction models using multiple modalities of omics, clinical, and image data, for predicting AD dementia. We propose to apply our proposed methods to the applied genomic analysis data and ROS/MAP multi-omics data to study AD-related phenotypes that are profiled for both ADSP and ROS/MAP samples, including AD, AD-related pathology traits, and related psychological disorders.
Investigator:
Yokoyama, Jennifer
Institution:
University of California, San Francisco
Project Title:
Trans-ethnic meta-analysis and fine-mapping of Alzheimer’s Disease loci
Date of Approval:
August 14, 2023
Request status:
Closed
Research use statements:
Show statements
Technical 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:
Zhao, Jinying
Institution:
University of Florida
Project Title:
Identifying novel biomarkers for human complex diseases using an integrated multi-omics approach
Date of Approval:
November 21, 2023
Request status:
Expired
Research use statements:
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Technical 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, Degui
Institution:
University of Texas Health Science Center at Houston
Project Title:
Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease
Date of Approval:
October 2, 2023
Request status:
Expired
Research use statements:
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Technical 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.

NG00106 – LASI-DAD GWAS and Imputation

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For investigators using The Diagnostic Assessment of Dementia for the Longitudinal Aging Study of India (LASI-DAD) (sa000019) data:

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Total number of samples: 993