Basic Science and Pathogenesis.

Introduction:Structural variants (SV), genomic alterations spanning more than 50 base pairs, can significantly impact gene expression and protein function. However, their contribution to Alzheimer’s Disease (AD) remains poorly understood. Leveraging a novel SV calling pipeline, we identified SVs with high accuracy in a diverse sample of the Alzheimer’s Disease Sequencing Project (ADSP) and investigated the role of SVs in AD.Results:We analyzed SVs in 16,841 individuals from ADSP whole genome sequencing data using BioGraph, a semi-assembly-based method that employs graph-based representation for accurate SV detection. We identified 456,644 high-quality SVs, 65% of which were novel. Of these, 272,728 SVs directly impact genes, including 86 AD-related genes. Association analyses were performed within three ancestry groups, including 3,371 African (AFR), 6,327 European (EUR), and 2,126 Latin (LAT). Multiple deletions and insertions were observed in moderate to high linkage disequilibrium with known AD loci, including TPCN1 and TMEM106B. In EUR, genome-wide association analysis identified two significant low-frequency deletions associated with AD, located in introns of CCDC12 and CCDC88B, both encoding coiled-coil domain-containing proteins. Gene-based analyses further identified rare pathogenic SVs in several known AD genes, including PSEN1 in LAT and ABCA7 in AFR.Conclusions:Using a novel graph-based SV calling pipeline, we identified high-quality SVs across a large and ancestrally diverse cohort. Our analyses revealed both common and rare SVs associated with AD. These findings provide valuable insights into the genetic architecture of AD, emphasizing the value of including diverse populations in AD genomic studies.

Quality control and integration of genotypes from two calling pipelines for whole genome sequence data in the Alzheimer's disease sequencing project

The Recruitment and Retention of Alzheimer's Disease Diversity Genetic Cohorts in the Alzheimer's Disease Sequencing Project (READD-ADSP) was established to bridge thegap inAlzheimer's disease and related dementias (ADRD) research in Africa. We examined the role of Community Advisory Boards (CABs) in improving participant recruitment and retention in ADRD research in Africa, by exploring how culturally sensitive engagement, community trust-building, and stakeholder involvement influence research participation. Amulti-site, multi-phased qualitative study approachthat involved establishment of CABs, initial planning meetings and community engagement cum recruitment activities. Twenty-one CABs (141 members) were established across 14 study sites in 9 African Dementia Consortium (AfDC)member countries. Co-creation planning meetings, culturally sensitive outreaches for brain health, dementia awareness creation, and recruitment were held. Establishing CABs within the READD-ADSP project has proven instrumental in co-creating culturally appropriate and community-centered strategies for recruitment and retention in ADRD research in Africa. The Recruitment and Retention of Alzheimer's Disease Diversity Genetic Cohorts in the Alzheimer's Disease Sequencing Project (READD-ADSP) project utilizes a community-engaged research (CER) framework to develop a community engagement program and establish Community Advisory Boards (CABs) to enhance participant recruitment and retention. There is a need to standardize CER practices and promote context-sensitive and culturally appropriate CE activities across African Dementia Consortium (AfDC) sites to ensure cultural sensitivity and enhance recruitment and retention. The project seeks to empower CABs to promote national dementia policies by collaborating with policy-makers and advancing equitable dementia scienced and care in Africa.

BackgroundTelomere length (TL), a marker of biological aging, has been implicated in multiple age‐related diseases, but its association with Alzheimer's disease (AD) remains unclear. We analyzed whole genome sequence data from the Alzheimer's Disease Sequencing Project (release 4) to identify genetic variants associated with both AD and TL.MethodWe estimated TL for 35,014 participants using TelSeq, which after quality control yielded a dataset including 6,973 persons of European ancestry (EA; 3,701 AD cases, 3,273 controls), 4,188 African Americans (AA; 1,329 AD cases, 2,859 controls), 4,008 Caribbean Hispanics (CH; 1,588 AD cases, 2,420 controls), and 4,170 Native American Hispanics (NAH; 754 AD cases, 3,416 controls). The association of AD with TL was tested using a linear regression model including covariates for age, sex, and dosage of the APOE e2 and e4 alleles. The interaction of TL with SNPs having a minor allele count >20 was evaluated for its association with AD in each ancestry group by logistic regression models using GENESIS including SNP, TL, an interaction term for SNPxTL, and covariates for age, sex, sequencing center, genetic relationship matrix, and principal components of ancestry. Results were combined across groups using METASOFT.ResultAD (p = 5.32x10‐12), age (p = 2.00x10‐16), sex (p = 2.00x10‐16), and APOE e2 (p = 5.35x10‐6) were significantly associated with TL. In the EA sample, interaction of TL with CRTC3‐AS1 intronic SNP rs533691286 was associated with AD at the genome‐wide significance (GWS) level (p = 1.13x10‐8). Suggestive associations were found with rs537146885 located between RP5‐884C9.2 and LINC01343 (p = 8.95x10‐8). In the NAH group, GWS associations were observed for interactions of TL with MIR548XHG intronic variants rs117856971 (p = 2.06x10‐9) and rs18578882 (p = 3.70x10‐8). No TLxSNP interactions were GWS in the CH and AA groups. In the total sample, GWS associations were identified for interactions of TL with intronic variants in SLC23A2 (rs184956772, p = 3.19x10‐9) and CFAP61 (rs769676169, p = 3.64x10‐9), rs1037982496 upstream of DEFB123 (p = 2.96x10‐9), and several intergenic variants including DEFB115‐DKKL1P1 (rs968020553, p = 2.96X10‐9), RP5‐884C9.2‐LINC01343 (rs537146885, p = 4.98x10‐9), MIR3193‐COX4I2 (rs921951239, p = 2.70x10‐9), AC010091.1 (rs552422184, p = 5.86x10‐9), and CTA‐109P11.1‐RP11‐328K15.1 (rs77699417, p = 6.51x10‐9).ConclusionWe identified variants that significantly impact AD risk through their interaction with TL, suggesting that TL maintenance pathways may be central to AD pathogenesis.

Previous studies have implicated common and rare genetic variants as risk factors for late-onset Alzheimer's disease (LOAD). Here, weighted burden analysis was applied to over 10,000 exome-sequenced subjects from the Alzheimer's Disease Sequencing Project. Analyses were carried out to investigate whether rare variants predicted to have a functional effect within a gene were more commonly seen in cases or in controls. Confirmatory results were obtained for TREM2, ABCA7, and SORL1. Additional support was provided for PSEN1 (p=0.0002), which previously had been only weakly implicated in LOAD. There was suggestive evidence that functional variants in PIK3R1, WNT7A, C1R, and EXOC5 might increase risk and that variants in TIAF1 and/or NDRG2 might have a protective effect. Overall, there was strong evidence (p=5×10-6 ) that variants in tyrosine phosphatase genes reduce the risk of developing LOAD. Because PIK3R1 variants are expected to impair PI3K/Akt/GSK-3β signalling while variants in tyrosine phosphatase genes would enhance it, these findings are in line with those from animal models, suggesting that this pathway is protective against Alzheimer's disease.

IntroductionGenes implicated by genome‐wide association studies and family‐based studies of Alzheimer's disease (AD) are largely discordant. We hypothesized that genes identified by sequencing studies like the Alzheimer's Disease Sequencing Project (ADSP) may bridge this gap and highlight shared biological mechanisms.MethodsWe performed structured literature review of genes prioritized by ADSP studies, genes underlying familial dementias, and genes nominated by genome‐wide association studies. Gene set enrichment analyses of each list identified enriched pathways.ResultsThe genes prioritized by the ADSP, familial dementia studies, and genome‐wide association studies minimally overlapped. Each gene set identified dozens of enriched pathways, several of which were shared (e.g., regulation of amyloid beta clearance).DiscussionAlternative study designs provide unique insights into AD genetics. Shared pathways enriched by different genes highlight their relevance to AD pathogenesis, while the patterns of pathway enrichment unique to each gene set provide additional targets for functional studies.

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.

INTRODUCTION:Genome-wide association studies (GWAS) have identified loci associated with Alzheimer’s disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci.METHODS:We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases=2,184, N controls=2,383) and targeted analyses in sub-populations using WGS data from the Alzheimer’s Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants.RESULTS:Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses.DISCUSSION:This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.

Mitochondrial DNA (mtDNA) is a double-stranded circular DNA and has multiple copies in each cell. Excess heteroplasmy, the coexistence of distinct variants in copies of mtDNA within a cell, may lead to mitochondrial impairments. Accurate determination of heteroplasmy in whole-genome sequencing (WGS) data has posed a significant challenge because mitochondria carrying heteroplasmic variants cannot be distinguished during library preparation. Moreover, sequencing errors, contamination, and nuclear mtDNA segments can reduce the accuracy of heteroplasmic variant calling. To efficiently and accurately call mtDNA homoplasmic and heteroplasmic variants from the large-scale WGS data generated from the Alzheimer's Disease Sequencing Project (ADSP), and test their association with Alzheimer's disease (AD). In this study, we present MitoH3-a comprehensive computational pipeline for calling mtDNA homoplasmic and heteroplasmic variants and inferring haplogroups in the ADSP WGS data. We first applied MitoH3 to 45 technical replicates from 6 subjects to define a threshold for detecting heteroplasmic variants. Then using the threshold of 5% ≤variant allele fraction≤95%, we further applied MitoH3 to call heteroplasmic variants from a total of 16,113 DNA samples with 6,742 samples from cognitively normal controls and 6,183 from AD cases. This pipeline is available through the Singularity container engine. For 4,311 heteroplasmic variants identified from 16,113 samples, no significant variant count difference was observed between AD cases and controls. Our streamlined pipeline, MitoH3, enables computationally efficient and accurate analysis of a large number of samples.

Neuropsychiatric Symptoms (NPS) are highly prevalent in Alzheimer's Disease patients and are associated with accelerated decline and a detrimental impact on quality of life of both patients and caregivers. There are no effective pharmaceutical interventions targeting neuropsychiatric symptoms in AD, making a better understanding of their underlying etiologic mechanisms critical to develop improved treatments. To facilitate identification of genetic loci and mechanistic pathways underlying NPS in AD, we have initiated an effort (NIH: U01AG079850) to collate and harmonize NPS data in over 70 cohorts (>80,000 samples) of diverse ancestries with whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP; ADSP-FUS), and analyze these data to identify genetic loci and mechanistic pathways associated with NPS in AD. Across ADSP-FUS cohorts, in addition to the 12 individual symptom scores on the NPI-Q, three symptom clusters are being defined and calculated based on the scientific literature: Early psychosis/agitation (EPA), late psychosis/agitation (LPA), and affective symptoms (AS), accounting in the data harmonization process for stage of illness, pre-existing psychiatric illness, treatment of NPS and other relevant covariates RESULT: To date, neuropsychiatric data on 13 ADSP cohorts of diverse ancestry (over 30,000 individuals) has been harmonized, and harmonization of several additional cohorts is currently in process, including extensive datasets on individuals of African and Hispanic/LatinX ancestry. Additional ADSP-FUS datasets are continuously being added. Analysis of whole-genome sequencing data on the harmonized cohorts is ongoing. Primary core analyses will (1) identify novel genetic risk factors associated with neuropsychiatric symptoms in AD, (2) characterize the shared genetic architecture of neuropsychiatric symptoms in AD and primary psychiatric disorders, and (3) assess the role of ancestry effects in the etiology of neuropsychiatric symptoms in AD. Expansion of the ADSP-FUS to harmonized and refined NPS phenotypes coupled with the proposed core analyses will lay the foundation to disentangle the molecular mechanisms underlying these detrimental symptoms in AD in diverse populations.

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).

INTRODUCTIONAlzheimer's disease (AD) is a common disorder of the elderly that is both highly heritable and genetically heterogeneous.METHODSWe investigated the association of AD with both common variants and aggregates of rare coding and non‐coding variants in 13,371 individuals of diverse ancestry with whole genome sequencing (WGS) data.RESULTSPooled‐population analyses of all individuals identified genetic variants at apolipoprotein E (APOE) and BIN1 associated with AD (p < 5 × 10−8). Subgroup‐specific analyses identified a haplotype on chromosome 14 including PSEN1 associated with AD in Hispanics, further supported by aggregate testing of rare coding and non‐coding variants in the region. Common variants in LINC00320 were observed associated with AD in Black individuals (p = 1.9 × 10−9). Finally, we observed rare non‐coding variants in the promoter of TOMM40 distinct of APOE in pooled‐population analyses (p = 7.2 × 10−8).DISCUSSIONWe observed that complementary pooled‐population and subgroup‐specific analyses offered unique insights into the genetic architecture of AD.HighlightsWe determine the association of genetic variants with Alzheimer's disease (AD) using 13,371 individuals of diverse ancestry with whole genome sequencing (WGS) data.We identified genetic variants at apolipoprotein E (APOE), BIN1, PSEN1, and LINC00320 associated with AD.We observed rare non‐coding variants in the promoter of TOMM40 distinct of APOE.

Genetic-based risk prediction is becoming increasingly available for a wide range of common diseases thanks to the growth of large-scale biobanks and population-scale genetic studies. However, despite widespread availability, genetic risk prediction typically offers only modest predictive power, as complex biological processes that mediate disease manifestation are often not fully captured by genetic variation alone. In contrast, imaging-based prediction can achieve substantially higher predictive accuracy for many diseases but remains costly and unavailable for the majority of populations. Here, we present G2DBridge, a predictive machine learning framework that links genetic variation to disease risk by modeling imaging-derived phenotypes as intermediate traits. Using Alzheimer's Disease Neuroimaging Initiative (ADNI) for training and Alzheimer's Disease Sequencing Project (ADSP) for external validation, G2DBridge outperforms state-of-the-art polygenic risk scores in Alzheimer's Disease (AD) prediction while relying on the same genetic data in the target data, providing additional predictive power without requiring new data modalities. The framework generalizes across cohorts, enables risk stratification with clear separation between cases and controls, and highlights biologically meaningful regions consistent with AD pathology. This approach offers a scalable strategy for applying genetically inferred imaging features in precision medicine.

ObjectiveThe genetic bases of Alzheimer's disease remain uncertain. An international effort to fully articulate genetic risks and protective factors is underway with the hope of identifying potential therapeutic targets and preventive strategies. The goal here was to identify and characterize the frequency and impact of rare and ultra‐rare variants in Alzheimer's disease, using whole‐exome sequencing in 20,197 individuals.MethodsWe used a gene‐based collapsing analysis of loss‐of‐function ultra‐rare variants in a case–control study design with data from the Washington Heights‐Inwood Columbia Aging Project, the Alzheimer's Disease Sequencing Project and unrelated individuals from the Institute of Genomic Medicine at Columbia University.ResultsWe identified 19 cases carrying extremely rare SORL1 loss‐of‐function variants among a collection of 6,965 cases and a single loss‐of‐function variant among 13,252 controls (P = 2.17 × 10−8; OR: 36.2 [95% CI: 5.8–1493.0]). Age‐at‐onset was 7 years earlier for patients with SORL1 qualifying variant compared with noncarriers. No other gene attained a study‐wide level of statistical significance, but multiple top‐ranked genes, including GRID2IP,WDR76 and GRN, were among candidates for follow‐up studies.InterpretationThis study implicates ultra‐rare, loss‐of‐function variants in SORL1 as a significant genetic risk factor for Alzheimer's disease and provides a comprehensive dataset comparing the burden of rare variation in nearly all human genes in Alzheimer's disease cases and controls. This is the first investigation to establish a genome‐wide statistically significant association between multiple extremely rare loss‐of‐function variants in SORL1 and Alzheimer's disease in a large whole‐exome study of unrelated cases and controls.

BackgroundPrevious whole exome and whole genome sequencing (WES/WGS) studies identified genome-wide significant associations for late-onset Alzheimer's disease (AD) with rare variants but highlighted the need for larger samples.ObjectiveIdentify associations of rare coding variants with AD risk in a large-scale, multi-ancestry exome-wide.MethodsWe combined non-overlapping portions of the Alzheimer's Disease Sequencing Project (ADSP) WES (n = 18 717) and WGS (n = 35 014) datasets obtaining a sample (n = 34 202) including participants ages ≥ 60 from four genomic similarity clusters consistent with European ancestry (EA, 9 744 AD cases and 9 095 controls), African American (AA, 1 944 AD cases and 4 215 controls), Caribbean Hispanic (CH 2 344 AD cases and 3 465 controls), and Native American Hispanic (NAH 743 AD cases and 2 652 AD controls) populations. Association of AD with 253,421 bi-allelic variants with minor allele count ≥ 20 in the total sample and each population group was evaluated using GENESIS. Gene-based tests comprising predicted moderate and high-impact variants were performed using SAIGE.ResultsNovel study-wide significant associations (p < 1.97 × 10-7) were identified with variants enriched among the Amish in BTBD8 (rs927193859, p = 1.58 x10-10), LINGO1 (rs150289554, p = 8.60 × 10-8), and KCNG2 (rs140218057, p = 1.77 × 10-7) in the total sample. Population-specific analyses confirmed significant associations with APOE in all groups and detected an association in CH individuals with the PSEN1 missense mutation G206A (p = 3.07 × 10-7), a variant that was previously linked to early-onset AD in Hispanics. Gene-based tests highlighted significant associations with LINGO1 (p = 2.75 × 10-8), DYNLT4 (p = 2.80 × 10-7), and ADCY4 (p = 7.43 × 10-7).ConclusionsWe identified rare variants in novel genes which provide new insights into AD pathogenesis.