Search

Background: The ADSP is a National Institute on Aging (NIA) initiative focused on identifying genetic risk and protective variants for Alzheimer Disease (AD). Initial phases (Discovery and Discovery Extension) were predominantly non‐Hispanic Whites of European Ancestry (NHW‐EA). The ADSP expanded the population diversity in the Follow Up Study (ADSP‐FUS), and the current phase, ADSP‐FUS 2.0: The Diverse Population Initiative, focusing on whole genome sequencing (WGS) of non‐European populations including Hispanic/Latino (HL), non‐Hispanic Black with African Ancestry (NHB‐AA) and Asian populations. Support for these efforts include newly funded initiatives such as The DAWN Project, focused on recruitment of African, African‐American and Hispanic American populations, and the Asian Cohort for Alzheimer's Disease (ACAD). Methods: ADSP cohorts consist of studies of AD, dementia, and age‐related conditions. Clinical classifications are assigned based on standard criteria and derived from clinical measures and history, as well as additional neuropathologic data. In addition to production of WGS, APOE genotyping is available for all ADSP samples. Results: The ADSP currently consists of 40 cohorts comprised of ∼36,300 individuals, with plans to sequence >110,000 individuals from diverse race/ethnicity. Genotyping, sequencing, and clinical adjudication has been performed on 36,361 participants (cases N = 12,133, median age = 72; cognitively‐unimpaired(CU) individuals N = 17,116, median age = 74; ADRD N = 7,112, median age = 71). Mean ages for cases and controls vary across cohorts, 57.0+5.6 to 86.5+4.2 cases and 63.3+7.8 to 90.0+0 controls. 61% participants female, distributed as follows: cases(60.3%), CU(63.7%), and ADRD(55.8%). APOE genotype proportions differ considerably across reported race/ethnicity, for example highest for APOE ε4/ε4 carriers observed in Non‐Hispanic whites participants (7.4%) and the lowest in Asians (1.7%) Conclusion: The results provide an overview of clinical features in ADSP cohorts. The growth of the ADSP‐FUS 2.0 is central to the ADSP and expanding the size and diversity of this genomic resource available via NIAGADS. WGS data will be integrated with ADSP programs focused on phenotype harmonization, association analyses, functional genomics, and machine learning. In concert with these programs, the ADSP‐FUS 2.0 will accelerate the identification and understanding of potential genetic risk and protective variants for AD across all populations with the target of developing new treatments that are globally effective. [ABSTRACT FROM AUTHOR]

Background: 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. Method: 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. Result: 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. Conclusion: 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. [ABSTRACT FROM AUTHOR]

Background: 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. Methods: 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. Results: 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. Conclusion: 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. [ABSTRACT FROM AUTHOR]

Background: 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. Methods: 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. Results: 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. Conclusion: 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. [ABSTRACT FROM AUTHOR]