MOTIVATION:statistics from genome-wide association studies (GWAS) are widely used in fine-mapping and colocalization analyses to identify causal variants and their enrichment in functional contexts, such as affected cell types and genomic features. With the expansion of functional genomic (FG) datasets, which now include hundreds of thousands of tracks across various cell and tissue types, it is critical to establish scalable algorithms integrating thousands of diverse FG annotations with GWAS results. RESULTS:We propose BTS (Bayesian Tissue Score), a novel, highly efficient algorithm uniquely designed for (i) identifying affected cell types and functional elements (context-mapping) and (ii) fine-mapping potentially causal variants in a context-specific manner using large collections of cell type-specific FG annotation tracks. BTS leverages GWAS summary statistics and annotation-specific Bayesian models to analyze genome-wide annotation tracks, including enhancers, open chromatin, and histone marks. We evaluated BTS on GWAS summary statistics for immune and cardiovascular traits, such as Inflammatory Bowel Disease (IBD), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Coronary Artery Disease (CAD). Our results demonstrate that BTS is over 100× more efficient in estimating functional annotation effects and context-specific variant fine-mapping compared to existing methods. Importantly, this large-scale Bayesian approach prioritizes both known and novel annotations, cell types, genomic regions, and variants and provides valuable biological insights into the functional contexts of these diseases. AVAILABILITY AND IMPLEMENTATION:Docker image is available at https://hub.docker.com/r/wanglab/bts with preinstalled BTS R package (https://bitbucket.org/wanglab-upenn/BTS-R) and BTS GWAS summary statistics analysis pipeline (https://bitbucket.org/wanglab-upenn/bts-pipeline).

The Genome Center for Alzheimer’s Disease (GCAD) coordinates the integration and meta-analysis of all available Alzheimer’s disease (AD) relevant whole genome sequencing (WGS) data to facilitate the goal of identifying AD risk or protective genetic variants and eventual therapeutic targets. The WGS datasets are generated via the collaboration of scientists from the Alzheimer’s Disease Sequencing Project (ADSP) and GCAD. To minimize data heterogeneity introduced by different sequencing protocols and machines, GCAD processes all samples using identical pipelines. The raw sequencing data are first mapped to GRCh38/hg38 and variants (SNVs and indels) are called using GATK. Additionally, compact VCF and GDS formatted files are generated to facilitate researchers who want to use smaller pVCFs. SNVs and indels are annotated using the ADSP annotation pipeline. Lastly, structural variants (SV) are called using Smoove and Manta and joint genotyped using GraphTyper2. The dataset (ADSP Release 5, R5, 2024) includes ∼60,000 genomes from >50 diverse cohorts with 4 major ancestries: 47% Non-Hispanic White, 29% Hispanic or Latino, 16% Black or African American and 8% Asian. Data are deeply sequenced (average genome coverage: >30x). CRAMs, gVCFs from GATK, and SV VCFs of a subset of the R5 samples (n = 36,361) were deposited into NIAGADS Data Sharing Service (DSS) ( https://dss.niagads.org/ ) for public distribution in 2022, and similarly, the new samples in R5 will be released after the joint call is complete. In addition, joint-genotype VCFs on SNVs, indels, and SVs will be available. These will undergo full quality control and annotation process. The ADSP and GCAD generate high quality genotype and SV calls. Currently the project is processing ∼60,000 WGS samples sequenced primarily through the ADSP Follow-Up Study, which will contain a more ancestrally diverse set of populations. We anticipate this 2024 release will continue to benefit the research community studying AD genetics.

The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) is the National Institute on Aging-designated national data repository for human genetics research on Alzheimer's disease and related dementias (ADRD). NIAGADS maintains a high-quality data collection for ADRD genetic/genomic research and supports genetics data production and analysis, including whole genome and exome sequence data from the Alzheimer's Disease Sequencing Project and other genotype/phenotype data, encompassing 211,000 samples. NIAGADS shares these data with hundreds of research groups around the world via the Data Sharing Service, a Federal Information Security Modernization Act moderate compliant cloud-based platform that fully supports the National Institutes of Health Genomic Data Sharing Policy. NIAGADS Open Access consists of multiple knowledge bases with genome-wide association summary statistics and rich annotations on the biological significance of genetic variants and genes across the human genome. As a one-stop access portal for Alzheimer's disease (AD) genetics, NIAGADS stands as a keystone in promoting collaborations to advance the understanding and treatment of AD. HIGHLIGHTS: The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) is a data repository for the storage of genetics and genomics data. NIAGADS houses data for Alzheimer's disease, related dementias, and healthy aging. NIAGADS offers open and qualified access data and knowledgebases to explore open access data. The Alzheimer's Disease Sequencing Project dataset is the largest Alzheimer's disease and related dementias joint called whole genome sequencing dataset (≈ 58,000 whole genomes).

The Alzheimer's Disease Sequencing Project (ADSP) is a national initiative to understand the genetic architecture of Alzheimer's Disease and Related Dementias (AD/ADRD) by sequencing whole genomes of affected participants and age-matched cognitive controls from diverse populations. The Genome Center for Alzheimer's Disease (GCAD) processed whole-genome sequencing data from 36,361 ADSP participants, including 35,014 genetically unique participants of which 45% are from non-European ancestry, across 17 cohorts in 14 countries in this fourth release (R4). This sequencing effort identified 387 million bi-allelic variants, 42 million short insertions/deletions, and 2.2 million structural variants. Annotations and quality control data are available for all variants and samples. Additionally, detailed phenotypes from 15,927 participants across 10 domains are also provided. A linkage disequilibrium panel was created using unrelated AD cases and controls. Researchers can access and analyze the genetic data via NIAGADS Data Sharing Service, the VariXam tool, or NIAGADS GenomicsDB.

NIAGADS is a national data repository that offers qualified investigators access to genomic data for Alzheimer’s disease (AD) and related dementia. In addition, NIAGADS has made substantial effort to curate, harmonize, standardize, and disseminate AD-relevant variant, gene, and sequence annotations from publications, functional genomics datasets, and summary statistics deposited at NIAGADS. These results are made available to the public in a collection of interactive knowledgebases (AD Variant Portal, FILER Functional Genomics Repository, VariXam, Alzheimer’s GenomicsDB & Genome Browser), all of which are accessible programmatically via the NIAGADS API. However, as these offerings grow, navigating them can be challenging. Here, we introduce AI-based enhancements to NIAGADS sites to help guide researchers and facilitate data discovery. We leverage OpenAI’s generative AI to build and train three large language models (LLMs) based on NIAGADS documentation, step-by-step recipes for data-access requests, subject-specific vocabularies, and the OpenAPI specification defining the NIAGADS API that allows programmatic access to the NIAGADS knowledgebases. For users of the API and to enhance search interfaces, we build on the LLMs to construct a framework for handling complex natural language instructions that decomposes an inquiry into tasks and subtasks and then plans, selects, and optionally executes API calls and parses the results. Developing these LLMs allows NIAGADS to improve user experiences by integrating topic-specific chatbots and generative AI search tools into NIAGADS sites. Rule-based chatbots that leverage conversational AI on the NIAGADS portal and Data Sharing Service will respond to inquiries with answers inferred from the LLMs, with responses improving with user feedback. These bots will also supplement help requests, suggesting solutions to common inquiries. Planner-enhanced generative AI based on the API-specification trained LLMs will be tied to knowledgebase searches and filters in resources such as the GenomicsDB and FILER to allow users to leverage natural language processing to ask sophisticated questions that require multiple API calls to resolve the answer. Introducing AI-enhanced search creates an interactive opportunity for NIAGADS users to learn new information or discover resources and tools they can use to supplement their research, which, in turn, improves NIAGADS ability to support AD genetics research.

NIAGADS is a national genomics data repository that facilitates access of genotypic and sequencing data to qualified investigators for the study of the genetics of Alzheimer’s disease (AD) and related neurological diseases. Collaborations with large consortia and centers such as the Alzheimer’s Disease Genetics Consortium (ADGC), Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, the Alzheimer’s Disease Sequencing Project (ADSP), and the Genome Center for Alzheimer’s Disease (GCAD) allow NIAGADS to lead the effort in managing large AD datasets that can be easily accessed and fully utilized by the research community. NIAGADS is supported by the National Institute on Aging (NIA) under a cooperative agreement. All data derived from NIA funded AD genetics studies are expected to be deposited in NIAGADS or another NIA approved site. NIAGADS manages a Data Sharing Service (DSS) that facilitates the deposition and sharing of genomic data and association results with approved users in the neurodegenerative research community. In addition, researchers are able to freely use the NIAGADS Alzheimer’s Genomics Database ( www.niagads.org/genomics/ ) to search annotation resources that link published AD studies to AD-relevant sequence features and genome-wide annotations. As of January 2024, NIAGADS houses 105 datasets comprised of >183,000 samples including array data, sequencing, gene expression, annotations, deep phenotypes, summary statistics, among others. Qualified investigators can retrieve ADSP sequencing data with ease and flexibility through the NIAGADS DSS. To date, the ADSP and other contributing studies have completed whole exome sequencing (WES) of 20,499 samples and whole genome sequencing (WGS) of 36,352 samples. Raw WES and WGS files, quality controlled VCF files, and phenotype data files are available via qualified access. The next round of sequencing currently underway will generate around 30,000 additional genomes to be released in mid-2024. NIAGADS is a rich resource for AD researchers, with the goal of facilitating advances in Alzheimer’s genetics research. By housing datasets from many projects and institutions, NIAGADS enables AD researchers to meet their research goals more efficiently. Datasets, guidelines, and features are available on our website at https://www.niagads.org .

NIAGADS is the National Institute on Aging (NIA) designated national data repository for human genetics research on Alzheimers Disease and related dementia (ADRD). NIAGADS maintains a high-quality data collection for ADRD genetic/genomic research and supports genetics data production and analysis. NIAGADS hosts whole genome and exome sequence data from the Alzheimers Disease Sequencing Project (ADSP) and other genotype/phenotype data, encompassing 209,000 samples. NIAGADS shares these data with hundreds of research groups around the world via the Data Sharing Service, a FISMA moderate compliant cloud-based platform that fully supports the NIH Genome Data Sharing Policy. NIAGADS Open Access consists of multiple knowledge bases with genome-wide association summary statistics and rich annotations on the biological significance of genetic variants and genes across the human genome. NIAGADS stands as a keystone in promoting collaborations to advance the understanding and treatment of Alzheimers disease.

The heterogeneity of the whole-exome sequencing (WES) data generation methods present a challenge to a joint analysis. Here we present a bioinformatics strategy for joint-calling 20,504 WES samples collected across nine studies and sequenced using ten capture kits in fourteen sequencing centers in the Alzheimer’s Disease Sequencing Project. The joint-genotype called variant-called format (VCF) file contains only positions within the union of capture kits. The VCF was then processed specifically to account for the batch effects arising from the use of different capture kits from different studies. We identified 8.2 million autosomal variants. 96.82% of the variants are high-quality, and are located in 28,579 Ensembl transcripts. 41% of the variants are intronic and 1.8% of the variants are with CADD > 30, indicating they are of high predicted pathogenicity. Here we show our new strategy can generate high-quality data from processing these diversely generated WES samples. The improved ability to combine data sequenced in different batches benefits the whole genomics research community.

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.

The Genome Center for Alzheimer’s Disease (GCAD) coordinates the integration of all available Alzheimer’s disease (AD) relevant whole genome sequencing (WGS) data with the goal of identifying AD risk or protective genetic variants and eventual therapeutic targets. The WGS datasets are generated through collaboration between investigators from the Alzheimer’s Disease Sequencing Project (ADSP) and GCAD. With the goal of minimizing data heterogeneity, introduced by different sequencing protocols and assays, GCAD processes all samples using standardized pipelines and performs quality control (QC)/quality assurance (QA) checks. Raw sequencing data (FASTQs or BAMs) were aligned to GRCh38/hg38 by BWA, and variant calling and joint genotyping on single nucleotide variants (SNVs), insertions and deletions (indels), were done by GATK. Structural variants (SVs) were called per sample using the Smoove, Manta, and Strelka packages. Preliminary QA checks including sex check, contamination, and genotype concordance were performed followed by QC per ADSP protocol to evaluate the quality of samples and variants. To facilitate access and usage of massive joint-genotype called VCF files, a compact version for storing variant info and sample genotypes only was released first. We dropped 275 (0.7%) samples of poor coverage (<20×), and we flagged 219 (0.6%) samples that were of borderline quality. As a result, the dataset (ADSP Release 4, 2022) includes 36,361 genomes from 40 diverse cohorts with 4 major ancestries: 16,573 Non-Hispanic Whites, 11,358 Hispanics; 5,422 African Americans; and 2,802 Asians. Data are deeply sequenced (average genome coverage: 40x). All samples’ CRAMs and gVCFs from GATK were deposited into NIAGADS Data Sharing Service (DSS) ( https://dss.niagads.org/ ) for public distribution. Joint-genotyped called VCFs are undergoing a full QC/annotation process and will be made available. This joint-genotyped called VCF contains >362M bi-allelic variants, >58M multi-allelic variants, with 95% of variants remaining after QC. SV calling is ongoing and data will be ready prior to the conference. The ADSP and GCAD generate high quality SNVs, indels and SV calls. Currently GCAD is preparing the next release of ∼60,000 more ancestrally-diverse WGS samples sequenced primarily through the ADSP Follow-Up Study, which we anticipate will be released in 2023 to greatly benefit the AD genetics community.

NIAGADS is a national genomics data repository that facilitates access of genotypic and sequencing data to qualified investigators for the study of the genetics of Alzheimer’s disease (AD) and related neurological diseases. Collaborations with large consortia and centers such as the Alzheimer’s Disease Genetics Consortium (ADGC), Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, the Alzheimer’s Disease Sequencing Project (ADSP), and the Genome Center for Alzheimer’s Disease (GCAD) allow NIAGADS to lead the effort in managing large AD datasets that can be easily accessed and fully utilized by the research community. NIAGADS is supported by National Institute on Aging (NIA) under a cooperative agreement. All data derived from NIA funded AD genetics studies are expected to be deposited in NIAGADS or another NIA approved site. NIAGADS manages a Data Sharing Service (DSS) that facilitates the deposition and sharing of genomic data and association results with approved users in the neurodegenerative research community. In addition, researchers are able to freely use the NIAGADS Alzheimer’s Genomics Database (www.niagads.org/genomics/) to search annotation resources that link published AD studies to AD-relevant sequence features and genome-wide annotations. As of January 2023, NIAGADS houses 93 datasets comprised of >149,000 samples including GWAS, sequencing, gene expression, annotations, deep phenotypes, and summary statistics. Qualified investigators can retrieve ADSP sequencing data with ease and flexibility through the NIAGADS DSS. To date, the ADSP and other contributing studies have completed whole exome sequencing (WES) of 20,503 samples and whole genome sequencing (WGS) of 36,361 samples. Raw WES and WGS files, quality controlled VCF files, and phenotype data files are available via qualified access. The next round of sequencing currently underway will generate around 30,000 additional genomes to be released in late 2023. NIAGADS is a rich resource for AD researchers, with the goal of facilitating advances in Alzheimer’s genetics research. By housing datasets from many projects and institutions, NIAGADS enables AD researchers to meet their research goals more efficiently. Datasets, guidelines, and features are available on our website at https://www.niagads.org .

The Genome Center for Alzheimer’s Disease (GCAD) coordinates the integration and meta-analysis of all available Alzheimer’s disease (AD) relevant whole genome sequencing (WGS) data with the goal of identifying AD risk or protective genetic variants and eventual therapeutic targets. The WGS datasets are generated via the collaboration of scientists from the Alzheimer’s Disease Sequencing Project (ADSP) and GCAD. With the vision to minimize data heterogeneity, introduced by different sequencing protocols and machines, GCAD processes all samples using identical pipelines and performs quality assurance (QA) checks. Raw sequencing data (FASTQs or BAMs) were aligned to GRCh38/hg38 by BWA, and variant calling and joint genotyping were done by GATK. Furthermore, Smoove, Manta and Streka were applied to generate structural variant (SV) calls per sample. QA checks including sex, contamination and genotype concordance as well as the ADSP QC protocol were performed to evaluate the quality of samples and variants. To facilitate the access and usage of the big joint-genotyped VCF files, we introduced a compact version for storing variant info and sample genotypes only. We dropped 235 (1.3%) samples of poor coverage (<20x) or that failed QA checks, and we flagged 173 (1.0%) samples that were of borderline quality. As a result, the dataset (ADSP Release 3, 2021) includes 16,905 genomes from 17 diverse cohorts with 3 major ethnicities: 10,651 Non-Hispanic Whites, 3,212 Hispanics and 2,874 African Americans. Data are deeply sequenced (average genome coverage: >30x). All samples’ CRAMs, gVCFs from GATK, and VCFs from the three SV callers were deposited into NIAGADS Data Sharing Service (DSS) ( https://dss.niagads.org/ ) for public distribution. In addition, joint-genotype VCFs are available in both compact and QC versions. This joint-genotype VCF contains >206M bi-allelic single-nucleotide variants, 16M bi-allelic indels and 28M multi-allelic variants, with 96% of variants remaining after stringent QC. The ADSP and GCAD generate high quality genotype calls and SV calls. Currently the project is processing ∼37,000 WGS samples sequenced primarily through the ADSP Follow-Up Study, which will contain a more ancestrally diverse set of populations. We anticipate this 2022 release will continue to benefit the research community studying AD genetics.

Bigger sample size can help to identify new genetic variants contributing to an increased risk of developing Alzheimer’s disease. However, the heterogeneity of the whole-exome sequencing (WES) data generation methods presents a challenge to a joint analysis. Here we present a bioinformatics strategy for joint calling 20,504 WES samples collected across nine studies and sequenced using ten different capture kits in fourteen sequencing centers in the Alzheimer’s Disease Sequencing Project. gVCFs of samples were joint-called by the Genome Center for Alzheimer’s Disease into a single VCF, containing only positions within the union of capture kits. The VCF was then processed using specific strategies to account for the batch effects arising from the use of different capture kits from different studies. We identified 8.2 million autosomal variants. 96.82% of the variants are high-quality, and are located in 28,579 Ensembl transcripts. 41% of the variants are intronic and 15% are missense variants. 1.8% of the variants are with CADD>30. Our new strategy for processing these diversely generated WES samples has shown to generate high-quality data. The improved ability to combine data sequenced in different batches benefits the whole genomics research community. The WES data are accessible to the scientific community via https://dss.niagads.org/ .

ABSTRACT Querying massive functional genomic and annotation data collections, linking and summarizing the query results across data sources/data types are important steps in high-throughput genomic and genetic analytical workflows. However, these steps are made difficult by the heterogeneity and breadth of data sources, experimental assays, biological conditions/tissues/cell types and file formats. FILER (FunctIonaL gEnomics Repository) is a framework for querying large-scale genomics knowledge with a large, curated integrated catalog of harmonized functional genomic and annotation data coupled with a scalable genomic search and querying interface. FILER uniquely provides: (i) streamlined access to >50 000 harmonized, annotated genomic datasets across >20 integrated data sources, >1100 tissues/cell types and >20 experimental assays; (ii) a scalable genomic querying interface; and (iii) ability to analyze and annotate user’s experimental data. This rich resource spans >17 billion GRCh37/hg19 and GRCh38/hg38 genomic records. Our benchmark querying 7 × 109 hg19 FILER records shows FILER is highly scalable, with a sub-linear 32-fold increase in querying time when increasing the number of queries 1000-fold from 1000 to 1 000 000 intervals. Together, these features facilitate reproducible research and streamline integrating/querying large-scale genomic data within analyses/workflows. FILER can be deployed on cloud or local servers (https://bitbucket.org/wanglab-upenn/FILER) for integration with custom pipelines and is freely available (https://lisanwanglab.org/FILER).

NIAGADS is a national genomics data repository that facilitates access of genotypic and sequencing data to qualified investigators for the study of the genetics of Alzheimer’s disease (AD) and related neurological diseases. Collaborations with large consortia and centers such as the Alzheimer’s Disease Genetics Consortium (ADGC), Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, the Alzheimer’s Disease Sequencing Project (ADSP), and the Genome Center for Alzheimer’s Disease (GCAD) allow NIAGADS to lead the effort in managing large AD datasets that can be easily accessed and fully utilized by the research community. NIAGADS is supported by National Institute on Aging (NIA) under a cooperative agreement. All data derived from NIA funded AD genetics studies are expected to be deposited in NIAGADS or another NIA approved site. NIAGADS manages a Data Sharing Service (DSS) that facilitates the deposition and sharing of genomic data and association results with approved users in the neurodegenerative research community. In addition, researchers are able to freely use the NIAGADS Alzheimer’s Genomics Database ( www.niagads.org/genomics/ ) to search annotation resources that link published AD studies to AD-relevant sequence features and genome-wide annotations. As of January 2022, NIAGADS houses 82 datasets comprised of >93,000 samples including GWAS, sequencing, gene expression, annotations, deep phenotypes, and summary statistics. Qualified investigators can retrieve ADSP sequencing data with ease and flexibility through the NIAGADS DSS. As of January 2022, the ADSP and other contributing studies have completed whole exome sequencing (WES) of 20,503 samples and whole-genome sequencing (WGS) of 16,905 samples. Raw WES and WGS files, quality controlled VCF files, and phenotype data files are available via qualified access. The next round of sequencing currently underway will generate around 18,000 additional genomes to be released at the middle of 2022. NIAGADS is a rich resource for AD researchers, with the goal of facilitating advances in Alzheimer’s genetics research. By housing datasets from many projects and institutions, NIAGADS enables AD researchers to meet their research goals more efficiently. Datasets, guidelines, and new features are available on our website at https://www.niagads.org .

Motivation Querying massive collections of functional genomic and annotation data, linking and summarizing the query results across data sources and data types are important steps in high-throughput genomic and genetic analytical workflows. However, accomplishing these steps is difficult because of the heterogeneity and breadth of data sources, experimental assays, biological conditions (e.g., tissues, cell types), data types, and file formats. Results FunctIonaL gEnomics Repository (FILER) is a large-scale, harmonized functional genomics data catalog uniquely providing: 1) streamlined access to >50,000 harmonized, annotated functional genomic and annotation datasets across >20 integrated data sources, >1,100 biological conditions/tissues/cell types, and >20 experimental assays; 2) a scalable, indexing-based genomic querying interface; 3) ability for users to analyze and annotate their own experimental data against reference datasets. This rich resource spans >17 Billion genomic records for both GRCh37/hg19 and GRCh38/hg38 genome builds. FILER scales well with the experimental (query) data size and the number of reference datasets and data sources. When evaluated on large-scale analysis tasks, FILER demonstrated great efficiency as the observed running time for querying 1000x more genomic intervals (10 6 vs. 10 3 ) against all 7×10 9 hg19 FILER records increased sub-linearly by only a factor of 15x. Together, these features facilitate reproducible research and streamline querying, integrating, and utilizing large-scale functional genomics and annotation data. Availability and implementation FILER can be 1) freely accessed at https://lisanwanglab.org/FILER , 2) deployed on cloud or local servers ( https://bitbucket.org/wanglab-upenn/FILER ), and 3) integrated with other pipelines using provided scripts. Contact lswang@pennmedicine.upenn.edu

NIAGADS is a national genomics data repository that facilitates access of genotypic and sequencing data to qualified investigators for the study of the genetics of Alzheimer’s disease (AD) and related neurological diseases. Collaborations with large consortia and centers such as the Alzheimer’s Disease Genetics Consortium (ADGC), Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, the Alzheimer’s Disease Sequencing Project (ADSP), and the Genome Center for Alzheimer’s Disease (GCAD) allow NIAGADS to lead the effort in managing large AD datasets that can be easily accessed and fully utilized by the research community. NIAGADS is supported by National Institute on Aging (NIA) under a cooperative agreement. All data derived from NIA funded AD genetics studies are expected to be deposited in NIAGADS or another NIA approved site. NIAGADS manages a Data Sharing Service (DSS) that facilitates the deposition and sharing of genomic data and association results with approved users in the neurodegenerative research community. In addition, researchers are able to freely use the NIAGADS Alzheimer’s Genomics Database (www.niagads.org/genomics/) to search annotation resources that link published AD studies to AD-relevant sequence features and genome-wide annotations. As of January 2021, NIAGADS houses 74 datasets comprised of >90,000 samples including GWAS, sequencing, gene expression, annotations, deep phenotypes, and summary statistics. Qualified investigators can retrieve ADSP sequencing data with ease and flexibility through the NIAGADS DSS. As of February 2021, the ADSP and other contributing studies have completed whole exome sequencing (WES) of 20,504 samples and whole-genome sequencing (WGS) of 16,908 samples. Raw WES and WGS files, quality controlled VCF files, and phenotype data files are available via qualified access. The next round of sequencing currently underway will generate around 18,000 additional genomes to be released at the end of 2021. NIAGADS is a rich resource for AD researchers, with the goal of facilitating advances in Alzheimer’s genetics research. By housing datasets from many projects and institutions, NIAGADS enables AD researchers to meet their research goals more efficiently. Datasets, guidelines, and new features are available on our website at https://www.niagads.org.

The Alzheimer's Genomics Database (NIAGADS GenomicsDB) provides public access to GWAS summary statistics datasets deposited at the NIA Genetics of Alzheimer's Disease Data Storage Site (NIAGADS). The NIAGADS GenomicsDB makes available 69 summary statistics datasets and reports >150 million annotated variants from 23 GWAS studies of Alzheimer's disease and related dementias (AD/ADRD). Programmatic access to the database is essential for many bioinformatics applications dealing with AD/ADRD genetics.The NIAGADS GenomicsDB is powered by a big data optimized relational database system. It uses ontologies to consistently annotate datasets, facilitating data harmonization and efficient real-time data mining. AD/ADRD-risk associated variants from GWAS datasets are annotated using the Alzheimer's Disease Sequencing Project's (ADSP) annotation pipeline (Butkiewicz et al. 2018). Variants are also mapped to proximal and co-located genes and regulatory regions using the NIAGADS FILER functional genomics data repository (https://lisanwanglab.org/FILER). We have created a RESTful Application Programming Interface (API) that facilitates the integration of NIAGADS GenomicsDB datasets and annotations into software applications. The web service supports queries that return annotated GenomicsDB entries (genes, variants, genomic loci) and GWAS summary statistics results in the form of JSON documents. Results may contain functional annotations, genetic evidence for AD/ADRD risk, and/or sequence information associated with an entry. Calls to the API are based on simple URLs that specify the entry type and data required. The API also provides endpoints for querying variant linkage disequilibrium blocks, variant tracks (annotations and summary statistics) compatible with the IGV.js genome browser (https://github.com/igvteam/igv.js/), and LocusZoom.js (https://github.com/statgen/locuszoom/) renderings of the GWAS summary statistics datasets.The NIAGADS GenomicsDB API establishes a simple framework that enables sharing and integration of annotated NIAGADS GenomicsDB entries and datasets. Use of the API requires only a HTTPS library and JSON parser, permitting large-scale programmatic access and analysis of AD/ADRD-linked variants independent of any specific programming language.The NIAGADS GenomicsDB API enables the easy retrieval of a wide range of genetic evidence for AD/ADRD, promoting data sharing and reuse among the AD/ADRD research community. Information about accessing the REST API as it becomes available can be found on the NIAGADS GenomicsDB website (https://www.niagads.org/genomics).

Summary We report SparkINFERNO (Spark-based INFERence of the molecular mechanisms of NOn-coding genetic variants), a scalable bioinformatics pipeline characterizing noncoding GWAS association findings. SparkINFERNO prioritizes causal variants underlying GWAS association signals and reports relevant regulatory elements, tissue contexts, and plausible target genes they affect. To achieve this, the SparkINFERNO algorithm integrates GWAS summary statistics with large-scale collection of functional genomics datasets spanning enhancer activity, transcription factor binding, expression quantitative trait loci, and other functional datasets across more than 400 tissues and cell types. Scalability is achieved by an underlying API implemented using Apache Spark and Giggle-based genomic indexing. We evaluated SparkINFERNO on large GWAS studies and show that SparkINFERNO is more than 60-times efficient and scales with data size and amount of computational resources. Availability SparkINFERNO runs on clusters or a single server with Apache Spark environment, and is available at https://bitbucket.org/wanglab-upenn/SparkINFERNO or https://hub.docker.com/r/wanglab/spark-inferno . Contact lswang@pennmedicine.upenn.edu

The Alzheimer's Genomics Database (https://ww.niagads.org/genomics) provides public access to GWAS summary statistics datasets deposited at NIAGADS, a national genetics data repository for AD. This resource makes available 54 comprehensive summary statistics datasets from 14 GWAS studies for real-time mining with a flexible search interface and exploration via detailed reports and a genome browser. The GenomicsDB is powered by a big-data optimized relational database system. It uses OBO Foundry ontologies to consistently annotate study designs and phenotypes, facilitating data harmonization and efficient real-time data analysis. AD-risk associated variants from GWAS datasets are mapped to up-to-date annotations from dbSNP, ExAC, and similar resources. Variants are also mapped against the GRCh37.p13 assembly and linked to proximal and colocated genes and regulatory regions from functional genomics data repositories (e.g., ENCODE, Roadmap, FANTOM5). The Alzheimer's GenomicsDB efficiently compiles detailed variant and gene reports to assist researchers in interpreting their potential functional or regulatory role in the context of AD neurodegeneration. Updated variant reports highlight those discovered and QC'd by the Alzheimer's Disease Sequencing Project (ADSP) and include ranked predicted variant consequences generated with the ADSP's Annotation Pipeline (Butkiewicz et al. 2018). Other new features include linkage disequilibrium associations, which can be explored via interactive LocusZoom plots. Gene reports have also been updated and now include meta-analysis results from aggregate association tests performed by the ADSP (Bis et al. 2018), allowing us to flag those with genetic-evidence for AD. The system powering the GenomicsDB allows researchers to not only mine, but also analyze and annotate search results or uploaded data. In addition to functional and pathway enrichment analyses of gene lists, researchers can now run INFERNO (Amlie-Wolf et al. 2018) to compare variant lists against transcription factor binding from ENCODE, histone modification sites from Roadmap, FANTOM5 expressed enhancer regions, and GTex eQTL sites to identify potential regulatory elements, tissue contexts, and target genes. Providing access to genetic evidence for AD, the publicly available and user-friendly Alzheimer's GenomicsDB is a rich resource for the AD research community.