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. 2023 Oct 26;20(2):1123–1136. doi: 10.1002/alz.13509

NIAGADS Alzheimer's GenomicsDB: A resource for exploring Alzheimer's disease genetic and genomic knowledge

Emily Greenfest‐Allen 1,2,3,✉, Otto Valladares 1,2,3, Pavel P Kuksa 1,2,3, Prabhakaran Gangadharan 1,2,3, Wan‐Ping Lee 1,2,3, Jeffrey Cifello 1,3, Zivadin Katanic 1,2,3, Amanda B Kuzma 1,2,3, Nicholas Wheeler 4, William S Bush 4, Yuk Yee Leung 1,2,3, Gerard Schellenberg 1,2,3, Christian J Stoeckert Jr 2,5, Li‐San Wang 1,2,3,✉
PMCID: PMC10916966  NIHMSID: NIHMS1934930  PMID: 37881831

Abstract

INTRODUCTION

The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site Alzheimer's Genomics Database (GenomicsDB) is a public knowledge base of Alzheimer's disease (AD) genetic datasets and genomic annotations.

METHODS

GenomicsDB uses a custom systems architecture to adopt and enforce rigorous standards that facilitate harmonization of AD‐relevant genome‐wide association study summary statistics datasets with functional annotations, including over 230 million annotated variants from the AD Sequencing Project.

RESULTS

GenomicsDB generates interactive reports compiled from the harmonized datasets and annotations. These reports contextualize AD‐risk associations in a broader functional genomic setting and summarize them in the context of functionally annotated genes and variants.

DISCUSSION

Created to make AD‐genetics knowledge more accessible to AD researchers, the GenomicsDB is designed to guide users unfamiliar with genetic data in not only exploring but also interpreting this ever‐growing volume of data. Scalable and interoperable with other genomics resources using data technology standards, the GenomicsDB can serve as a central hub for research and data analysis on AD and related dementias.

Highlights

  • The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) offers to the public a unique, disease‐centric collection of AD‐relevant GWAS summary statistics datasets.

  • Interpreting these data is challenging and requires significant bioinformatics expertise to standardize datasets and harmonize them with functional annotations on genome‐wide scales.

  • The NIAGADS Alzheimer's GenomicsDB helps overcome these challenges by providing a user‐friendly public knowledge base for AD‐relevant genetics that shares harmonized, annotated summary statistics datasets from the NIAGADS repository in an interpretable, easily searchable format.

Keywords: Alzheimer's Disease Sequencing Project, ADSP, Alzheimer's disease, data sharing, genetic associations, genetics, genome‐wide association study, GWAS, knowledge base, National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site, NIAGADS

1. BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that currently affects approximately 6.7 million Americans aged 65 or older. 1 Our current understanding of genetic risk for AD comes mainly from ongoing massive genotyping and sequencing efforts carried out by the Alzheimer's Disease Genetics Consortium (ADGC), the International Genomics of Alzheimer's Project (IGAP), and the Alzheimer's Disease Sequencing Project (ADSP). Large‐scale genome‐wide association studies (GWASs) and GWAS‐derived meta‐analyses have been performed by each of these groups. 2 , 3 , 4 , 5 The results of these and other recent AD‐GWASs have greatly expanded the known list of AD risk‐associated variants, identifying more than 75 AD risk‐associated loci to date. 6 Most of these loci lie within non‐coding regions, necessitating downstream analyses that annotate variants by known pathogenicity and weigh association strength against proximal genomic features to elucidate causal variants and potential therapeutic targets for mitigating AD and related dementias (ADRD) pathologies. Such analyses typically integrate GWAS summary statistics with a variety of genomic annotations, ranging from genomic features (eg, Ensembl genes and transcripts 7 ) to regulatory elements and epigenomic or transcriptomic annotations in “disease‐relevant” tissues or cell populations (eg, brain, cerebrospinal fluid, microglia, and other neuronal cells) selectively pulled from large‐scale projects such as ENCODE, 8 , 9 the FANTOM5 enhancer atlas, 10 and the Genotype‐Tissue Expression Portal (GTEx). 11 Consequently, characterizing the potential regulatory impact of GWAS loci requires substantial bioinformatics efforts to standardize data and enable accurate mapping of genomic annotations to GWAS results, often on genome‐wide scales.

Results from ADGC, ADSP, IGAP, and other AD‐relevant GWASs, including their summary statistics, are deposited at the National Institute of Aging (NIA) Genetics of Alzheimer's Disease Data Storage Site (NIAGADS). NIAGADS is a NIA‐designated essential national infrastructure, providing a one‐stop access portal for Alzheimer's disease omics datasets. 12 Investigators can gain access to these datasets through a qualified data access request (DAR) process. NIAGADS also makes p values reflecting association strength (see section 2.2.6) from >100 GWAS summary statistics and meta‐analysis datasets (eg, single‐variant and rare‐variant aggregation tests of association) available for public download. Most are aligned to GRCh37/hg19, but newer GRCh38/hg38 datasets are now available, with more incoming.

The NIAGADS collection of GWAS summary statistics datasets is unique in that it provides a disease‐centric resource for AD researchers. However, AD‐relevant datasets are also available from disease‐agnostic compendia such as the Open GWAS Project developed by the Integrative Epidemiology Unit of the UK Medical Research Council (MRC‐IEU) 13 and the GWAS Catalog curated by the National Human Genome Research Institute and European Bioinformatics Institute (NHGRI‐EBI). 14  OpenGWAS currently includes 12 GRCh37/hg19 aligned AD GWAS summary statistics datasets, a quarter of which are from ADGC or IGAP studies deposited at NIAGADS. The GWAS Catalog offers more – listing 15 “Alzheimer's disease” publications with 38 GWAS summary statistics datasets for public download, some of which overlap with OpenGWAS and NIAGADS offerings on both genome builds.

Despite the availability of these GWAS summary statistics datasets, three substantial challenges preclude AD researchers from taking full advantage of this ever‐increasing volume of data. These are (1) the systematic integration and normalization of the summary datasets, which vary in study design (eg, number and selection criteria for samples, statistical methods, and adjustments) and in file format and contents; (2) the computational overhead and bioinformatics expertise involved in downstream analyses necessary to identify causal variants and their potential molecular or functional impact; and (3) the ability to disseminate contextualized association data in a standardized, organized, and searchable format so that they can be a resource for all AD researchers (ie, molecular biologists and clinicians, as well as bioinformaticians).

Here, we describe the NIAGADS Alzheimer's Genomics Database (GenomicsDB), which was developed to help overcome these hurdles and make the summary statistics datasets deposited at NIAGADS more accessible. The GenomicsDB, available at https://www.niagads.org/genomics, is a user‐friendly web knowledge base that disseminates annotated AD‐relevant GWAS summary statistics datasets, facilitates real‐time data mining, and provides summaries of association results in the context of functionally annotated genes and variants.

RESEARCH IN CONTEXT

  1. Systematic review: Genome‐wide association studies (GWASs) investigating genetic risk for Alzheimer's disease (AD) and related dementias (ADRD) have produced a growing body of summary statistics datasets. Many of these datasets are publicly available from NIAGADS, a national genetics repository for AD/ADRD. Mining these data requires substantive bioinformatics expertise to harmonize and annotate the datasets, limiting downloads and data reuse.

  2. Interpretation: We introduce the NIAGADS Alzheimer's Genomics Database (GenomicsDB), which integrates public summary statistics datasets from NIAGADS with functional genomics information and variant annotations from the Alzheimer's Disease Sequencing Project. This establishes an interactive knowledge base for AD genetics that overcomes barriers to data reusability to make it accessible to a broader research community.

  3. Future directions: The GenomicsDB is regularly updated to keep up with advances in AD genetics. It uses the latest web and database technologies to allow for easy integration of new AD/ADRD datasets. Future work will focus on bringing in relevant datasets from other large‐scale curated GWAS data repositories and developing an Application Programming Interface (API) for programmatic access to facilitate integration with data analysis pipelines.

2. METHODS

2.1. Database infrastructure

An overview of the NIAGADS GenomicsDB system architecture is provided in Figure 1. The GenomicsDB is powered by a PostgreSQL relational database system that has been optimized for parallel big data querying, allowing for efficient real‐time data mining. Data are organized using the modular Genomics Unified Schema version 4 (GUS4), designed for scalable integration and dissemination of large‐scale omics datasets. Loading of all data is managed by the GUS4 application layer, 15 which ensures the data integrity and accuracy of data integration.

FIGURE 1.

FIGURE 1

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Overview of GenomicsDB system architecture. Dataset harmonization and mapping to functional genomics tracks and variant or gene annotations are managed by GUS application layer. Data are stored in a big data optimized PostgreSQL relational database and organized using the GUS schema (section 2.1), which was designed for scalable integration of large‐scale omics datasets. The web development kit used to generate the GenomicsDB website provides RESTful services to efficiently query data from the database that are tied to a graphical front end for interactive data exploration. The same services can also be used to programmatically query the database, allowing integration with data analysis pipelines. The GenomicsDB website supplies quick links back to the NIAGADS repository to facilitate formal data‐access requests.

2.2. Data harmonization and annotation

In the GenomicsDB, data harmonization depends on two main elements. First variant and gene features are assigned unique identifiers following established conventions that will persist across future versions of the knowledge base (see sections 2.2.4 and 2.2.5, respectively, for details). Adopting standardized feature identifiers is essential for automatic discovery that facilitates large‐scale data harmonization. Doing so allows NIAGADS to identify and link equivalent features across disparate annotation resources and in turn also lets third‐party resources easily generate permalinks back to GenomicsDB records using templated URLs. Second, indexing genomic locations permits fast retrieval of proximal, overlapping, and co‐located elements.

2.2.1. Indexing of genomic features and annotations

All genomic features (including genes, gene subfeatures, and variants) and annotations in GenomicsDB are sorted and indexed on genomic location using a bin indexing system modeled after that used by the UCSC Genome Browser. 16 , 17 Each chromosome is split into a set of evenly spaced, nested bins of increasingly finer resolution. During loading, features are assigned to the smallest enclosing bins, which are represented in the database as ltree objects, a PostgreSQL data type that stores hierarchical data in string dot‐path notation to reduce storage overhead but leverages tree traversal algorithms for fast data querying and retrieval. 18 , 19 GenomicsDB queries for feature or annotation overlaps with any region of interest assign the queried region to the minimally enclosing bin and then use simple comparison operators to find all annotations within the assigned bin or any parent or nested child bins that overlap the query region.

2.2.2. ADSP variant annotations

As part of their sequencing effort, the ADSP developed an annotation pipeline that builds on Ensembl's Variant Effect Predictor software 20 to efficiently annotate variants and rank potential variant consequences according to the severity of the predicted effects (such as codon changes, loss of function, and potential deleteriousness). 21 , 22 Elements of the ADSP annotation pipeline have been integrated with data preprocessing scripts for GenomicsDB, allowing non‐ADSP variants in the knowledge base to be annotated according to ADSP conventions. ADSP annotations applied to GenomicsDB variants include the ranked variant consequences, allele frequencies, and impacted genes, transcripts, and regulatory elements. Super population allele frequency data are pulled from 1000 Genomes 23 (GRCh37/hg19: phase 3 version 1 [May 11, 2011]; GRCh38/hg38: 1000 Genomes 30x 24 ), ExAC (GRCh37/hg19 only), 25 the Genome Aggregation Database (gnomAD), 26 and the ALFA project (GRCh38/hg38 only). 27 Allele frequencies from NIAGADS GWAS analyses, ADSP variant calling, and ADSP meta‐analyses are restricted and only accessible via formal data access requests to NIAGADS. The pipeline also extracts Combined Annotation Dependent Depletion (CADD) scores 28 , 29 from the CADD version 1.6 release for both genome builds, which quantify and rank potential variant deleteriousness for single nucleotide polymorphisms (SNPs) and short indels.

2.2.3. Linkage disequilibrium

Linkage‐disequilibrium (LD) structures around annotated variants for 1000 Genomes super populations (GRCh37/hg19: phase 3 version 1 [May 11, 2011]; GRCh38/hg38: 1000 Genomes 30x 24 ) were estimated using PLINK version 1.90b2i 64‐bit. 30 Only LD scores meeting a correlation threshold of r 2 ≥ 0.2 are stored in the database. Available in the GRCh38/hg38 GenomicsDB are LD estimates for the European (non‐Hispanic white) subset of the ADSP R3 17k (section 2.2.4) whole genome sequencing (WGS) samples. These were calculated using emerald. 31 Updated LD estimates for ADSP populations will be made available along with future releases of ADSP variants called from WGS. Comparable ADSP WGS results are not available for GRCh37/hg19; no LD for ADSP populations is available for that genome build.

2.2.4. Variant records

SNPs and short indels are uniquely identified in GenomicsDB by chromosomal coordinates and allelic variant (chr:position:ref_allele:alt_allele). This allows accurate mapping of risk‐association statistics to ADSP variants (see below) and to external variant records in annotation resources such as dbSNP, 32 gnomAD, and GTEx (eg, expression quantitative trait loci, or eQTLs).

All standardized and annotated variants are stored in the PostgreSQL database, creating an in‐house reference set against which incoming variants from GWAS summary statistics datasets or third‐party annotations can be compared. Approximately one billion variants from dbSNP have been annotated using the ADSP annotation pipeline (section 2.2.2) and provide the foundation for this reference set. Identifiers for variants called and passing quality control (QC) checks from ADSP sequencing efforts (ADSP variants) are then standardized and mapped against the dbSNP reference. Found records passing ADSP QC are flagged and updated to include the QC status for the ADSP release; unmatched records are run through the integrated ADSP annotation pipeline and added to the reference variant set. For GRCh37/hg19, the ADSP reference variants are SNPs and short indels identified during the ADSP Discovery Phase WGS and whole‐exome sequencing (WES) efforts. 21 For GRCh38/hg38, ADSP reference variants are from a quality checked joint‐genotype calling of 16,906 whole genomes (R3 17k WGS; NIAGADS ACCN: NG00067.v5). Reference variants will be updated or added as additional ADSP joint‐genotype calls are released to the public.

Risk‐associated variants from GWAS summary statistics datasets and trait associations pulled from the NHGRI‐EBI GWAS Catalog are likewise mapped against the set of reference variants. New variant records are annotated and added to the reference when a “novel” (unmatched) variant is found, and a flag is added to the record if the variant has genome‐wide significance for AD or an AD‐related trait in the association analysis (p ≤ 5e−8, adjusted to account for false positives due to testing associations of millions of variants simultaneously).

2.2.5. Gene records

Gene and transcript models are currently pulled from the GENCODE Release 19 (GRCh37/hg19) and GENCODE Release 36 (GRCh38/hg38) reference gene annotation 33 ; gene annotations are updated as needed to keep them current. GenomicsDB gene records are uniquely identified by their Ensembl identifiers, as are any subfeatures (eg, transcripts, exons) and protein products. Alternative gene identifiers (including NCBI Gene IDs, UniProtKB IDs, UCSC Gene IDs) are mapped to Ensembl IDs via the UniProtKB 34 ID mapping file for human genes; additional standard gene nomenclature is imported from the HUGO Gene Nomenclature Committee at the European Bioinformatics Institute (HGNC). 35 This includes both official gene symbols and names, as well as homologs in model organisms and identifiers in clinical knowledge bases such as the Online Mendelian Inheritance in Man (OMIM) database. 36 , 37

These mappings are used to link GenomicsDB records to external resources, as well as harmonize third‐party gene annotations not typically mapped to Ensembl IDs, such as pathway and Gene Ontology (GO) associations. Gene membership in molecular and metabolic pathways is obtained from the Kyoto Encyclopedia of Genes and Genomes 38 and Reactome. 39 Annotations of the functions of genes and gene products are taken from packaged releases of the Gene Ontology and GO‐gene associations 40 and are updated yearly.

2.2.6. GWAS summary statistics dataset records

GWAS summary statistics datasets deposited at NIAGADS are added to GenomicsDB as they become publicly available via publication or permission of the submitting researchers. These include studies that focus specifically on AD, as well as those on AD biomarkers and related neuropathologies. Up‐to‐date listings of the available summary statistics datasets are provided by the site's dataset browser, which is accessed via the site navigation menu (section 3.1).

Prior to loading in the database, metadata are compiled for each dataset to capture provenance, phenotypes (eg, disease state, neuropathology, population, APOE genotype), and relevant elements of the study design (eg, family or case control study, sample size, statistical covariates). Phenotypes are standardized using controlled vocabularies pulled from Open Biomedical Ontolog (OBO Foundry ontologies 41 , 42 to facilitate searches for related datasets and harmonization of trait associations with curated catalogs of GWAS association results (eg, NHGRI‐EBI GWAS Catalog). During the data loading process, variant representations are standardized, indexed, and annotated as described in previous sections. This enables fast lookups and simplifies harmonization with third‐party annotations.

To ensure the privacy of personal health information, the NIAGADS GenomicsDB website only makes p values from the summary statistics available for browsing (on dataset, gene, and variant reports and as genome browser tracks) and analysis. Access to the full summary statistics is restricted as under some conditions (eg, large GWAS sample sizes or family‐based studies), some values (eg, genome‐wide allele frequencies) may be personally identifiable. 43 , 44 The full summary statistics and corresponding GWAS or sequencing results can be obtained via formal data‐access requests made to NIAGADS. All datasets are properly credited to the submitting researchers or sequencing project.

The GRCh38/hg38 version of the GenomicsDB provides summary statistics that have been lifted over from GRCh37/hg19. If the original dataset identifies variants only by dbSNP refSNP IDs, GRCh38/hg38 coordinates are determined by mapping the refSNP identifier against the GenomicsDB variant reference set (described in section 2.2.4), with checks made for deprecated or synonymized rsIDs. In all other cases a two‐stage lift over process is applied. A Browser Extensible Data file is generated containing all variant features and their positional information, which is then run through the UCSC Genome Browser liftOver script. 16 , 45 Any unmapped or uncertain mappings are then submitted to the NCBI coordinate remapping service. 46 Any features left unmapped are dropped, as are long indels, which are not lifted over from GRCh37/hg19 to GRCh38/hg38 as there is lower confidence that the full sequence is conserved or that the allele sequences will still be valid.

2.3. Website design and organization

The NIAGADS GenomicsDB is powered by an open‐source database system and web‐development kit (WDK) 47 developed and successfully deployed by the Eukaryotic Pathogen, Vector and Host Informatics (VEuPathDB) Bioinformatics Resource Center. 48 The VEuPathDB WDK provides a query engine that ties the database system to the website via an easily extensible XML data model. The data model is used to automatically generate and organize searches, search results, and reports, with concepts and data organized by topics from the EMBRACE Data And Methods (EDAM) ontology, which defines a comprehensive set of concepts that are prevalent within bioinformatics. 49 This facilitates updates of third‐party data and rapid integration of new datasets as they become publicly available.

The WDK also provides a framework for lightweight Java/Jersey representational state transfer (REST) services for data querying. The framework allows search results and reports to be returned in multiple file formats (eg, delimited‐text, XML, and JSON) in addition to browsable, interactive web pages. The user interface is powered by a React/JS framework that queries the REST services and dynamically updates website content and displays depending on user choices.

A combination of in‐house JavaScript genomics visualizations and third‐party visualization toolkits are used to provide graphical interfaces for browsing, summarizing, and mining datasets and annotations in gene and variant reports. This includes interactive LocusZoom.js 50 , 51 plots for viewing risk‐associated loci in the context of the local LD structure. Custom data adapters have been written for LocusZoom.js that leverage the GenomicsDB REST services to pull summary statistics results, LD, and gene features directly from the knowledge base.

GenomicsDB also provides a genome browser, powered by IGV.js, which is an embeddable interactive JavaScript genome visualization tool developed by the Interactive Genomics Viewer (IGV) team. 52 For the GenomicsDB project, customizations have been made to allow IGV.js to query track data directly from underlying database, again using the WDK REST services, and from the NIAGADS Functional genomics repository (FILER), which provides harmonized functional genomics datasets that have been GIGGLE indexed for quick lookups. 53 , 54 Additional customizations provide links to NIAGADS gene and variant records in track‐feature popups and coloring of features by NIAGADS annotations (eg, ADSP annotations of variants, risk‐association statistics) in track displays.

The browser is paired with a track selection tool to help users filter this extensive listing to find tracks of interest based on assay types and annotations captured in the track metadata. Like the summary statistics datasets, track metadata have been standardized, with biosample types, in particular, consistently annotated from the Uberon multispecies anatomy ontology (UBERON), 55 , 56 Cell Ontology, 57 and Cell Line Ontology 58 .

3. RESULTS

As of April 2023, the NIAGADS GenomicsDB offers access to summary statistics p values from >80 GWAS and ADSP meta‐analyses on both the GRCh37/hg19 and GRCh38/hg38 reference genome builds. Summary statistics are linked to >150 million ADSP annotated single‐nucleotide variants and indels. Annotated variants in the GRCh37/hg19 version of the NIAGADS GenomicsDB include more than 29 million SNPs and approximately 50,000 short‐indels identified during the ADSP Discovery Phase WGS and WES efforts. 21 The GRCh38/hg38 GenomicsDB builds on this to include additional variants from the ADSP's ongoing efforts, with >232 million SNPs and short indels identified from joint‐genotype calling of 16,906 whole genomes (R3 17k WGS). Of these, approximately 290,000 have significant AD or ADRD‐risk association (p ≤ 5e−8) as reported in at least one NIAGADS GWAS summary statistics dataset to date. ADSP variants are highlighted in variant and dataset reports on the GenomicsDB website. The reference variant database (section 2.2.4 ) also allows lookups and generation of annotated variant reports (section 3.2.2) for variants of interest to the user that are present in dbSNP but currently lack genetic evidence associating them with AD/ADRD pathologies.

The standardization and harmonization effort described in the methods not only facilitates integration of large‐scale annotation resources but also helps optimize the database for big‐data queries. Accordingly, the knowledge base can query across millions of records and return compiled reports in real time. This is most evident in GenomicsDB gene reports, which extract the top risk‐associated variants (p < .001) from the GWAS summary statistics datasets within ± 100 kb of each gene and provide a comprehensive listing of both these variants and relevant annotations needed to make inferences about their potential regulatory impact on the gene.

The NIAGADS Alzheimer's GenomicsDB creates a public forum for sharing, discovery, and analysis of genetic evidence for AD that is made accessible via an interface designed for easy mastery by biological researchers, regardless of background. The knowledge base compiles all available data concerning summary statistics datasets and genetic evidence linking AD/ADRD to genes and variants into organized and easily navigated reports. Bioinformaticians can visit dataset report pages to interactively mine GWAS summary statistics datasets or use LocusZoom views of GWAS loci to weigh association strength of individual variants against that of others in its LD block. Clinicians and molecular scientists can look up their favorite genes or variants to peruse detailed reports that summarize known AD/ADRD associations within a functional genomic context.

3.1. Finding variants, genes, and datasets

The GenomicsDB homepage and navigation menu contain a site search that allows users to quickly find variants, genes, and datasets of interest by identifier or keyword (Figure 2). Also offered is an interactive dataset browser that provides a full listing of all summary statistics datasets currently available in the GenomicsDB (Figure 3). Users can find datasets of interest by manipulating the table in several ways, including searching by keyword (Figure 3A) or applying advanced filters to identify datasets with specific sample or study design characteristics, such as clinical phenotypes, population, genotype, and sequencing center (Figure 3B).

FIGURE 2.

FIGURE 2

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Searching GenomicsDB. Search bars on NIAGADS GenomicsDB navigation menu (1) and home page (2) allow users to quickly find a sequence feature using standard identifiers (eg, genes: Ensembl, NCBI Entrez, official symbol; variants: refSNP identifier, chr:pos:ref:alt) or to perform a keyword search for genes and summary statistics datasets. Illustrated here are results found when searching for the gene “APOE.” Clicking on one of the top suggested search result will take the user to a detailed report. Users can also browse a listing of the full search results, which are reported as ranked lists of matching genes, variants, or datasets (3). A full listing of summary statistics datasets available in the GenomicsDB is accessed by selecting “Browse Datasets” from the main navigation menu (4). More details on the dataset browser are provided in Figure 3.

FIGURE 3.

FIGURE 3

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The GenomicsDB dataset browser provides a complete listing of summary statistics and meta‐analysis results available in the resource. (A) The browser can be searched by keyword (1) or mined using advanced filters that summarize phenotypes associated with sample cohorts (4; see also 3B). Active advanced filters are listed above the table (5). Columns can be added or removed from the table (3) or sorted by contents (8) and the sorted, modified, and/or filtered table can be exported as tab‐delimited text (2). Table contents are paged to improve site performance, but users can adjust the number of displayed rows (6). Help icons provide additional information about table fields (7). All tables in GenomicsDB have similar functionality. (B) The advanced filter interface summarizes the information in the table, providing drop‐down lists or interactive graphics to help guide users in data discovery. Hovering over chart elements or plot legends provides additional descriptive information and counts of table rows that match the filter criteria (inset 1). Selecting chart elements will apply the filter (eg, the red pie slice in inset 1 indicates that the filter for AD‐related neuropathologies was applied). Filter choices are updated dynamically as new filter criteria are applied and alter the table contents (inset 2).

3.2. Browsing and mining reported data

Once users identify a gene, variant, or dataset of interest, they are taken to a detailed report that summarizes all annotations in GenomicsDB related to the search target and provides links to related features or datasets. GenomicsDB reports all have a standard layout, as illustrated in Figures 4 and 5. Reports contain a header that provides identifying and descriptive information and a graphical overview of the AD/ADRD risk associations in the dataset or linked to the genomic feature (Figures 4, 5). Headers also include link outs to the relevant third‐party reference database for genes (Ensembl) and variants (dbSNP) and to the accession in NIAGADS for summary statistics datasets.

FIGURE 4.

FIGURE 4

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Example sections from GenomicsDB dataset report. (A) Dataset report header provides a summary, including an interactive Manhattan plot providing an overview of the dataset, links to related datasets in GenomicsDB and to NIAGADS repository for more information and instructions on making data access requests. (B) The Manhattan plot is paired with a table found further down the page that lists the top hits (p ≤ .001) in the dataset, prefiltered for variants meeting a genome‐wide significance cut‐off (p ≤ 5e−8) (1). The paired table is linked to the “Browse the association evidence” link in the page header to enable quick access. The table of top hits includes links to the variant reports (and impacted gene, when relevant) associated with each hit and is, in turn, paired with LocusZoom (3) to allow users to select a variant (2) and view its association strength in its LD context.

FIGURE 5.

FIGURE 5

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Example sections from a GenomicsDB variant report. (A) Variant report header provides summary information about the variant, including top predicted consequence from applying the ADSP annotation pipeline, whether the variant was included in an ADSP release (and passed quality control filters), quick links to alternative alleles or co‐located variants, and a graphical summary of significant AD/ADRD risk associations found in NIAGADS summary statistics datasets; comprehensive association results for the variant are listed in tables later in the report. (B) Example navigation panel listing the structured information available in a variant report. Users can navigate directly to sections by clicking on the relevant link or toggling section visibility in the report using the checkboxes to the right. Also provided are buttons for exporting and sharing the report and for viewing the feature in the NIAGADS genome browser. (C) Table of association results in NIAGADS AD GWAS summary statistics datasets for variant highlighted in A. All top hits for the variant are reported (p ≤ .001); the table is prefiltered for associations meeting a genome‐wide significance cut‐off (p ≤ 5e−8) (1). Links are provided to GenomicsDB reports for the relevant datasets (2).

All reports also have a navigation panel (Figure 5B) containing at a minimum a table of contents to help navigate the available information contained in the reports. Clicking on a report section in the navigation panel will scroll the page to the selected section. Navigation panels also include action buttons for sharing and exporting the information in the report, as well as one that loads a feature locus in the NIAGADS genome browser. Most data in GenomicsDB are presented in interactive tables that can be modified, exported, and filtered by keyword or more advanced filters (see Figure 3 for details).

3.2.1. Summary statistics dataset reports

A comprehensive report is provided for each of the GWAS summary statistics and ADSP meta‐analysis datasets in the NIAGADS GenomicsDB (Figure 4). These reports allow users to browse the top risk‐associated variants in the dataset and quickly isolate the genetic variants with genome‐wide significance in the dataset (p ≤ 5e−8) via tables and interactive plots. The report header links back to the parent accession in NIAGADS where users can view the study details and publications, download the complete (genome‐wide) p values, or make formal data access requests for the full summary statistics, related GWAS, expression, or sequencing data associated with the accession.

Dataset reports include an interactive Manhattan plot illustrating the distribution of risk‐associated variants across the genome (Figure 4A). Hovering one's mouse over displayed points will display variant information (eg, identifier, p value, predicted impacted gene). Users can zoom into regions of interest and take snapshots of customized views. Static, high‐resolution Manhattan plots are available for download.

The Manhattan plot is supplemented with a table that lists the top hits (p value ≤ .001) in the dataset (Figure 4B). This table is by default filtered for variants with genome‐wide significance (p ≤ 5e−8), but the filter threshold can be adjusted as desired using the advanced filter tool. Also reported are predicted variant consequences that provide insight into the potential functional or regulatory impacts of the top variants (and proximal gene loci); these annotations can be used to filter the table to help elucidate variants more likely to have a causal impact. All genes and variants listed in a dataset report are linked to corresponding feature reports in GenomicsDB that offer detailed information about genetic evidence for AD/ADRD for the feature (see next sections). The table is paired with an interactive LocusZoom plot; the observed span and LD reference variant can be changed by manipulating the plot directly or by selecting a row in the top hits table.

GenomicsDB also generates reports for meta‐analysis summary statistics generated by the ADSP that offer genetic evidence for gene‐level and single‐variant risk associations for AD. These are currently only available for the GRCh37/hg19 reference genome and result from a WES case/control association analysis spanning 24 cohorts provided by the ADGC and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) completed as part of the ADSP discovery phase (NIAGADS ACCN:NG00065) 5 .

3.2.2. Variant reports

Variant reports include a basic summary about the variant (alleles, variant type, flanking sequence, genomic location) and a graphical overview of NIAGADS GWAS summary statistics datasets in which the variant has genome‐wide significance (Figure 5A). All other information in the report is subdivided into multiple sections that can be expanded or hidden at the user's discretion (see navigation panel, Figure 5B). These include selections on genetic variation (eg, allele population frequencies and LD), function prediction determined via the ADSP annotation pipeline (including transcript and regulatory consequences), and comprehensive listings of GWAS inferred disease or trait associations from both NIAGADS summary statistics and the NHGRI‐EBI GWAS Catalog (see Figure 5C for an example). Tables listing summary statistics results can be dynamically filtered by p value, dataset, phenotype, or covariate, and the filtered results are downloadable. Links to the source datasets for each reported statistic are also provided, leading to detailed dataset reports (eg, NIAGADS GWAS summary statistics) or to the source publication (eg, curated variant catalogs). A table is also provided linking the GenomicsDB record to external variant annotation resources such as gnomAD, ClinVar, 59 and GTEx (eQTLs).

3.2.3. Gene reports

GenomicsDB gene reports present the user with basic summary information about the gene (nomenclature, gene type, genomic span) and a graphical overview of NIAGADS GWAS summary statistics‐linked variants proximal to or within the footprint of the gene that is paired with an interactive table listing the top risk‐associated variants from the GWAS summary statistics datasets contained within ± 100 kb of the gene, similar in format to that reporting the top hits for a summary statistics dataset (Figure 4B). Also in the gene report are sections reporting function prediction (GO associations and evidence) and pathway memberships. Links to external gene annotation resources (eg, NCBI Gene, Ensembl, UniProtKB, the UCSC Genome Browser, and OMIM) are also provided.

3.3. Genome browser

The NIAGADS GenomicsDB genome browser enables researchers to visually inspect and browse GenomicsDB data and annotations in a broader genomic context (Figure 6). Tracks uniquely available in the NIAGADS genome browser include annotated ADSP variant tracks and tracks for each NIAGADS GWAS summary statistics dataset in the GenomicsDB, all of which are queried from the underlying database using REST services. Also available are over 5000 functional genomics tracks, queried directly from FILER. To date these include data tracks from ENCODE, FANTOM5, GTEx, and the NIH Roadmap Epigenomics Mapping Consortium (Roadmap Epigenomics). 60

FIGURE 6.

FIGURE 6

Open in a new tab

The NIAGADS Genome Browser is powered by IGV.js and allows users to visually inspect any of the NIAGADS GWAS summary statistics datasets in a broader genomic context. (A) Example genome browser view illustrating a comparison between AD‐relevant GWAS summary statistics datasets (1), annotated ADSP variants (2), and brain‐related functional genomics tracks (3) in the region around the known AD risk‐associated ABCA7 locus. Tracks are interactive; clicking on features will provide additional information (eg, summary statistics or annotations) as well as a link to the selected gene or variant record (insets, 4). (B) The interactive track selector allows users to browse all available tracks and search for tracks of interest by keyword, data source, biotype, and functional genomics. The track listing can be searched by keyword (1) or using the advanced filters (2), with active filters listed above the table for easy removal (3). See Figure 3 for more details on manipulating and filtering GenomicsDB tables. Tracks can be added or removed from the browser by checking the row in the selector table (4).

Variant tracks are annotated by CADD scores and the most severe or deleterious predicted variant consequence as determined by the ADSP annotation pipeline. Annotations for individual variants can be viewed by clicking on the feature in a track or compared by adjusting the track coloring based on annotation value. Annotation track feature pop‐ups also provide links back to the GenomicsDB variant records, as do those for GWAS summary statistics tracks (Figure 6A; highlight 4). The reference gene track is colored by gene type; gene feature pop‐ups provide details about the gene and its subfeatures and links back to the associated GenomicsDB gene record.

Users can discover tracks using the genome browser's track selector (Figure 6B). Tracks can be found via keyword search or by applying advanced filters that pull information from standardized track metadata, such as the original data source, biotype (eg, cell or tissue), feature type (eg, variant, gene, or regulatory element), or type of functional annotation. User tracks can be loaded via URL parameters; details about this functionality are provided in the site documentation.

4. DISCUSSION

The NIAGADS Alzheimer's Genomics Database is a user‐friendly platform for interactive browsing and real‐time in‐depth mining of published genetic evidence and genetic risk factors for AD. It provides unrestricted access to p values from summary statistics of genome‐wide association analysis of ADRD or neuropathologies.

Currently, GenomicsDB only disseminates summary statistics datasets pulled from the NIAGADS repository, with new datasets added as soon as they are approved for public release. Future versions will include selected AD‐relevant GWAS datasets (ie, assessed for analysis strength, quality, relevancy, and study impact) pulled from the curated NHGRI‐EBI GWAS Catalog and IEU OpenGWAS project.

Every entry in GenomicsDB is linked to relevant external resources and functional genomics annotations to supply further information and assist researchers in interpreting the potential functional or regulatory role of risk‐associated variants and susceptibility loci. External, third‐party annotations are updated yearly to help keep the resource relevant. This is facilitated by a rigorous data standardization and harmonization process, which includes assigning genes and variants globally unique and persistent identifiers. This in turn creates opportunities for NIAGADS to facilitate further data sharing as it enables cross‐references with external knowledge bases, as already established with the UniProtKB 61 and the Agora AD Knowledge Portal. 62

GenomicsDB is a powerful research platform that provides a service for the AD genetics research community by hosting comprehensive AD genetic and genomic findings and making them more accessible and creating opportunities for data reuse. It uses the latest web and database technologies to allow integration with new tools, and NIAGADS is constantly improving. The GenomicsDB front end is updated periodically with enhanced features and new data visualizations. In addition, the REST services used to query the database to generate genome browser tracks or dataset and feature reports provide the foundation of an API that allows programmatic access to the database. Future work will focus on further developing this API to allow researchers to integrate standardized and harmonized GenomicsDB annotations in their own analysis pipelines. This work would allow users to retrieve variant annotations singularly or in bulk or pull out all harmonized annotations available for an arbitrary genomic span of interest.

The AD research community is actively encouraged through outreach and collaboration to submit data to NIAGADS to keep this public platform updated and timely. The database was designed to scale, with the rigorous data standardization practices and harmonized database underlying the GenomicsDB making it easy to add new data and annotations to the resource without requiring recalibration or reharmonization of the whole system with each new addition. As more data become available, we envision the NIAGADS Alzheimer's Genomics Database will become a central hub for AD/ADRD research and data analysis.

CONFLICTS OF INTEREST STATEMENT

The authors have no financial interests to disclose. Author disclosures are available in the supporting information.

CONSENT STATEMENT

The consent statement is not necessary for this study.

Supporting information

Supporting Information

ALZ-20-1123-s001.docx (24.9KB, docx)

Supporting Information

ALZ-20-1123-s002.pdf (1.3MB, pdf)

ACKNOWLEDGEMENTS

Acknowledgement statement for the ADSP can be found in the supplement. This work is supported by the National Institutes of Health National Institute on Aging with funding to the NIAGADS (U24AG041689), the Genome Center for Alzheimer's Disease (U54AG052427), and the ADGC (U01AG032984).

Greenfest‐Allen E, Valladares O, Kuksa PP, et al. NIAGADS Alzheimer's GenomicsDB: A resource for exploring Alzheimer's disease genetic and genomic knowledge. Alzheimer's Dement. 2024;20:1123–1136. 10.1002/alz.13509

Contributor Information

Emily Greenfest‐Allen, Email: allenem@pennmedicine.upenn.edu.

Li‐San Wang, Email: lswang@pennmedicine.upenn.edu.

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Supplementary Materials

Supporting Information

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ALZ-20-1123-s002.pdf (1.3MB, pdf)

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