Alzheimer's Disease Sequencing Project Research Articles

An association test of the spatial distribution of rare missense variants within protein structures identifies Alzheimer's disease-related patterns.

More than 90% of genetic variants are rare in most modern sequencing studies, such as the Alzheimer's Disease Sequencing Project (ADSP) whole-exome sequencing (WES) data. Furthermore, 54% of the rare variants in ADSP WES are singletons. However, both single variant and unit-based tests are limited in their statistical power to detect an association between rare variants and phenotypes. To best use missense rare variants and investigate their biological effect, we examine their association with phenotypes in the context of protein structures. We developed a protein structure–based approach, protein optimized kernel evaluation of missense nucleotides (POKEMON), which evaluates rare missense variants based on their spatial distribution within a protein rather than their allele frequency. The hypothesis behind this test is that the three-dimensional spatial distribution of variants within a protein structure provides functional context to power an association test. POKEMON identified three candidate genes (TREM2, SORL1, and EXOC3L4) and another suggestive gene from the ADSP WES data. For TREM2 and SORL1, two known Alzheimer's disease (AD) genes, the signal from the spatial cluster is stable even if we exclude known AD risk variants, indicating the presence of additional low-frequency risk variants within these genes. EXOC3L4 is a novel AD risk gene that has a cluster of variants primarily shared by case subjects around the Sec6 domain. This cluster is also validated in an independent replication data set and a validation data set with a larger sample size.

Copy number variation (CNV) identification and association study on 3,928 Alzheimer's disease whole genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP).

Estimation of heritability of Alzheimer's Disease (AD) ranges from 49-79%, but the conventional single nucleotide variants (SNVs) identified to date account for <50% of the AD heritability. The involvement of genetic variants, such as SNVs and copy number variations (CNVs), has been proposed to explain part of the missing heritability. To accelerate AD genetic discovery, the Alzheimer's Disease Sequencing Project (ADSP) sequenced cases and cognitively normal elder controls from multiple ethnic groups. Leveraging this large-scale Whole Genome Sequence (WGS) collection, we could detect and conduct AD genetic analyses in full-spectrum of CNVs (small or large, common or rare, and coding or non-coding in genomic regions). We developed a bioinformatics pipeline consisting of four steps. First, we applied three calling algorithms, CNVnator, JAX-CNV, and Smoove, on each sample. Results of each tool were then merged by Svimmer. Second, we employed GraphTyper for genotyping. GraphTyper is a graph-pangenome-based method that may mitigate bias of human references. Finally, we resolved issues of multiple CNVs overlapping in a region by bedtools. Once the CNV callset was obtained, we conducted an association study to identify CNV regions that contribute to AD. We consider conventional association methods as well as a new developed CNV-curve-centric strategy which models CNVs as piecewise constant curves to assess the CNV effect on AD. For each sample, we identified an average of 7,959 CNVs by three algorithms. After merging CNVs of all samples, we obtained 56,316 deletions and 16,390 duplications for GraphTyper joint genotyping. The preliminary results of PLINK permutation-based test on the chromosome 19 indicated significant CNVs (p-value < 0.05 after adjusting for multiple tests) on genes UNC13A, IGSF23, KMT2B, LIN37, PSENEN, IGFLR1, and U2AF1L4. UNC13A, KMT2B, and PSENEN have been discovered to be associated in frontotemporal dementia, dystonia, and AD. Further validations of those CNVs are necessary. We developed a scalable CNV detection pipeline and applied it to 3,928 ADSP WGS samples. The preliminary results of the association study validate some known AD, dementia, and dystonia genes. To the best of our knowledge, this is the first large-scale CNV investigation of AD using WGS data.

NIA genetics of Alzheimer's disease data storage site (NIAGADS): 2021 update.

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.

ADSP follow‐up study: NCRAD biospecimens

The National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD) is collaborating with the Alzheimer's Disease Sequencing Project (ADSP) to ensure that subjects with sequencing have available biospecimens. The latest phase of the ADSP, the Follow-Up Study (ADSP-FUS), is generating sequencing on additional subjects from diverse populations and ethnic groups; many of these subjects have samples available at NCRAD for validation and extension studies.NCRAD collects, stores, and distributes biospecimens from subjects in studies included in the ADSP-FUS, such as the National Institute on Aging Late Onset Alzheimer's Disease (NIA-LOAD) Family Based Study, the Alzheimer's Disease Neuroimaging Initiative (ADNI), and subjects accrued by Alzheimer's Disease Research Centers (ADRCs). DNA for subjects in these studies is shipped to The American Genome Center (TAGC), under the direction of Dr. Clifton Dalgard, at the Uniformed Services University of the Health Sciences (USUHS), for whole genome sequencing. NCRAD has online catalogs for many of these cohorts, including information on biospecimens as well as demographic and clinical data, to help investigators identify and request biospecimens.NCRAD has shipped >6,000 DNA samples to USUHS for sequencing and inclusion in ADSP-FUS from subjects with additional biospecimens housed at NCRAD. Many of these subjects have unique data sets or unique phenotypes, including the >1,000 early onset Alzheimer's disease (AD) samples, 773 samples from ADNI, and >3,000 AD cases/controls with clinical and/or autopsy data. The remainder were samples from cohorts focused on diverse populations and ethnic groups. For ADNI, genomic and cell line DNA, RNA, and lymphoblastoid cell lines (LCLs) are available to approved investigators; for NIA-LOAD, genomic and cell line DNA and LCLs are available, with a subset also having peripheral blood mononuclear cells (PBMCs). For ADRC subjects, in addition to genomic or cell line DNA, RNA, plasma, serum, and PBMCs are available for select subjects. Currently, there are also iPSCs or fibroblasts available for 18 ADSP-FUS subjects. NCRAD catalogs are currently available for the ADRCs and for NIA-LOAD.NCRAD has extensive biospecimens available for investigators interested in validation or extension studies leveraging the whole genome sequencing data available through the ADSP-FUS.

Introducing the NIAGADS Alzheimer's GenomicsDB API: A toolkit for remote exploration of Alzheimer's disease genetics.

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).

The Alzheimer's Disease Sequencing Project - Follow Up Study (ADSP-FUS): Increasing ethnic diversity in Alzheimer's genetics research with the addition of potential new cohorts.

The ADSP-FUS is a National Institute on Aging (NIA) initiative focused on identifying genetic risk and protective variants for late-onset Alzheimer Disease (LOAD). A concern in AD genetic studies is a lack of racial-ethnic diversity. The ADSP-FUS collects and sequences existing ethnically diverse and unique cohorts with clinical data to expand the utility of new discoveries for individuals from all populations. Additional multi-ethnic cohorts are presently being recruited (e.g., Amerindian, Asian and Indian). The cohorts consist of participants from studies of AD, dementia, and aging-related conditions. Clinical classification (i.e., AD, dementia, and non-affected) is implemented using algorithms based on a minimal set of criteria derived from standard measures (e.g., global cognitive screeners, dementia rating scales, etc.) and clinical history. Data dictionaries are curated for each cohort by the FUS clinical staff. In total, the ADSP-FUS intends to sequence over 60,000 individuals. Biospecimens are processed and DNA is prepared and allocated through the National Centralized Repository for Alzheimer's (NCRAD). The DNA is delivered to the Uniformed Services University of the Health Sciences (USUHS) for whole genome sequencing (WGS). The resulting raw sequence data is conveyed to the Genome Center for Alzheimer's Disease (GCAD) for processing and harmonization followed QC analysis at University of Pennsylvania and University of Miami resulting in analysis-ready genotype data. Lastly, all clinical, genotype and sequence data are sent to the NIA Genetics of Alzheimer Disease Data Storage Site (NIAGADS), serving as the ASDP-FUS data storage, management and sharing center. Over 60,000 samples have been ascertained and are distributed by ancestry as follows: 7,686 African; 9,652 Hispanic; 40,575 non-Hispanic white (1,400 EOAD and 3,745 autopsy); 89 Amerindian; 4,003 Asian and 2,000 Indian. To date, we have sequenced 8,208 NHW; 5,528 African; 6,149 Hispanic & 89 Amerindian. The ADSP-FUS will enhance ongoing efforts to identify shared and novel genetic risk factors for LOAD among different populations. This project will expand our knowledge of potential genetic risk and protective variants for LOAD across all populations with the hope of developing new treatments that work for everyone.

A large-scale, whole genome sequencing study of unexplained early-onset Alzheimer disease.

Genomic studies of Alzheimer disease (AD) have primarily focused on non-Hispanic White (NHW) participants affected by the late-onset form (LOAD; onset age: >65), or the study of early onset AD (EOAD; onset age <=65) cases showing Mendelian inheritance patterns associated with mutations in the APP, PSEN1 and PSEN2 genes. However, mutations in these three genes explain ∼10% of EOAD cases. There are no large-scale efforts to collect and study EOAD cases not explained by these genes, despite this unexplained category accounting for ∼90% of EOAD cases. To address this, we aim to identify additional EOAD-associated variants, genes and pathways through a large-scale whole-genome sequencing (WGS) study of unexplained EOAD. We will include cases from several AD cohorts, including the Resource for Early-onset Alzheimer Disease Research (READR), the Knight-ADRC at Washington University, the Alzheimer's Disease Genetics Consortium (ADGC), and others. Generating and harmonizing a dataset of 200 non-Hispanic White (NHW) and Caribbean Hispanic (CH) multiplex EOAD families, over 5,400 EOAD singletons and over 13,000 unrelated, cognitive controls, all with WGS, this project will yield the largest EOAD genomics dataset to-date, improving statistical power for variant identification and allowing us to assess the impact of specific factors such as APOE genotype, vascular risk factors, and neuropsychiatric comorbidities. The inclusion of a large set of Hispanic families and singletons allows the examination of EOAD risk in a significantly understudied population. Analyses will comprise both linkage and association-based approaches, analyses of polygenic and ancestry effects, and a thorough examination of neurocognitive, neuropsychiatric and cardiovascular endophenotypes. When completed this study will point to novel genetic contributors to EOAD, shed light on the mechanisms of AD and facilitate the development of novel prediction models and therapeutics. Sampling, phenotyping and sequencing analysis protocols will be complementary to and compatible with the existing LOAD genomics resources, such as the Alzheimer Disease Sequencing Project (ADSP) and related studies. This phenotypic and genomic consistency, together with the use of existing AD infrastructure (NIAGADS), allows for immediate integration with the leading efforts on LOAD, enabling rapid large-scale investigation of a variety of additional critical AD genomics hypotheses.

Clonal Hematopoiesis is Associated with Reduced Risk of Alzheimer's Disease

▪Clonal hematopoiesis of indeterminate potential (CHIP) occurs when hematopoietic stem cells (HSCs) acquire a mutation, most commonly a null variant in TET2 or DNMT3A, that confers a selective advantage. Blood cancers may result if additional cooperating mutations are acquired. However, CHIP may also cause atherosclerosis and other inflammatory diseases because these mutations alter the function or development of effector immune cells derived from the HSCs. Genome-wide association studies have implicated microglia, the resident myeloid cells in the brain, as key players in the biology of Alzheimer's disease (AD). Here, we asked whether CHIP associated with AD dementia or neuropathologic change, and whether mutant marrow-derived cells could be found in the brains of CHIP carriers.To test for an association, we used data from the Trans-omics for Precision Medicine project (TOPMed) and the Alzheimer's Disease Sequencing Project (ADSP), where whole genome or exome sequencing data as well as AD phenotype data was available on 5,730 persons. TOPMed contained population-based cohorts unselected for AD, while ADSP was a case-control study for AD. We surprisingly discovered that the presence of CHIP was associated with a reduced risk of AD dementia in both projects (fixed-effects meta-analysis odds ratio 0.64, p = 3.0 x 10-5, adjusted for age, sex and APOE genotype) (Figure 1). The protective effect of CHIP was strongest in those with APOE e3 or e4 alleles, but not seen in those with APOE e2 allele. No substantial differences in AD risk were seen based on mutated driver gene. In addition, the presence of CHIP was associated with a reduced burden of amyloid plaques and neurofibrillary tangles in the brains of those without dementia. In sum, our human genetic analyses indicated that CHIP was robustly associated with protection from AD dementia and AD-related neuropathologic changes.A causal link between CHIP and AD would be strengthened by finding the mutated cells infiltrating the brain. However, it is presumed that bone marrow progenitors have minimal contribution to the adult microglial pool. To determine if the mutations seen in the blood of CHIP carriers could also be found in the brain, we obtained 8 occipital cortex samples from autopsy of donors with CHIP, 6 of whom were cognitively normal at the time of death. The 8 CHIP carriers had mutations in DNMT3A, TET2, ASXL1, SF3B1, and GNB1 with the highest frequency in DNMT3A and TET2, which is representative of the relative proportion of these mutations in the general population. We detected the CHIP somatic variants in the microglia enriched (NeuN- c-Maf+) fraction of brain in 7 out of 8 CHIP carriers, with a VAF ranging from 0.02 to 0.28 (representing 4% to 56% of nuclei) (Figure 2), but at low levels or absent in the other fractions of brain. We then performed single-cell ATAC-sequencing on brain samples from 2 CHIP carriers and 1 control to specify the cellular population harboring CHIP mutations. This revealed that hematopoietic cells in the 3 samples formed a single myeloid cluster that had accessible chromatin at the microglia marker genes TMEM119, P2RY12, and SALL1, but not in genes specific to monocytes or dendritic cells. We further determined that the proportion of cells in this cluster bearing the CHIP mutations ranged from ~40-80% in these two samples, indicating widespread replacement of the endogenous microglial pool by mutant cells.We show here that, unexpectedly, the presence of CHIP is associated with protection from AD dementia. CHIP is also associated with lower levels of neuritic plaques and neurofibrillary tangles in those without dementia, indicating a possible modulating effect of CHIP on the underlying pathophysiology of AD. Consistent with this hypothesis, we also detect substantial infiltration of brain by marrow-derived mutant cells which adopt a microglial-like phenotype. We speculate that the mutations associated with CHIP confer circulating precursor cells with an enhanced ability to engraft in the brain, to differentiate into microglia once engrafted, and/or to clonally expand relative to unmutated cells in the brain microenvironment. These non-mutually exclusive possibilities could provide protection from AD by supplementing the phagocytic capacity of the endogenous microglial system during aging. [Display omitted] DisclosuresJaiswal: Novartis: Consultancy, Honoraria; Foresite Labs: Consultancy; Genentech: Consultancy, Honoraria; AVRO Bio: Consultancy, Honoraria; Caylo: Current holder of stock options in a privately-held company.

Copy Number Variation Identification on 3,800 Alzheimer's Disease Whole Genome Sequencing Data from the Alzheimer's Disease Sequencing Project.

Alzheimer’s Disease (AD) is a progressive neurologic disease and the most common form of dementia. While the causes of AD are not completely understood, genetics plays a key role in the etiology of AD, and thus finding genetic factors holds the potential to uncover novel AD mechanisms. For this study, we focus on copy number variation (CNV) detection and burden analysis. Leveraging whole-genome sequence (WGS) data released by Alzheimer’s Disease Sequencing Project (ADSP), we developed a scalable bioinformatics pipeline to identify CNVs. This pipeline was applied to 1,737 AD cases and 2,063 cognitively normal controls. As a result, we observed 237,306 and 42,767 deletions and duplications, respectively, with an average of 2,255 deletions and 1,820 duplications per subject. The burden tests show that Non-Hispanic-White cases on average have 16 more duplications than controls do (p-value 2e-6), and Hispanic cases have larger deletions than controls do (p-value 6.8e-5).

Association of mitochondrial variants and haplogroups identified by whole exome sequencing with Alzheimer's disease.

IntroductionFindings regarding the association between mitochondrial DNA (mtDNA) variants and Alzheimer's disease (AD) are inconsistent.MethodsWe developed a pipeline for accurate assembly and variant calling in mitochondrial genomes embedded within whole exome sequences (WES) from 10,831 participants from the Alzheimer's Disease Sequencing Project (ADSP). Association of AD risk was evaluated with each mtDNA variant and variants located in 1158 nuclear genes related to mitochondrial function using the SCORE test. Gene‐based tests were performed using SKAT‐O.ResultsAnalysis of 4220 mtDNA variants revealed study‐wide significant association of AD with a rare MT‐ND4L variant (rs28709356 C>T; minor allele frequency = 0.002; P = 7.3 × 10−5) as well as with MT‐ND4L in a gene‐based test (P = 6.71 × 10−5). Significant association was also observed with a MT‐related nuclear gene, TAMM41, in a gene‐based test (P = 2.7 × 10−5). The expression of TAMM41 was lower in AD cases than controls (P = .00046) or mild cognitive impairment cases (P = .03).DiscussionSignificant findings in MT‐ND4L and TAMM41 provide evidence for a role of mitochondria in AD.

Large-scale sequencing studies expand the known genetic architecture of 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.

Late-onset vs nonmendelian early-onset Alzheimer disease: A distinction without a difference?

There is mounting evidence that only a small fraction of early-onset Alzheimer disease cases (onset <65 years) are explained by known mutations. Even multiplex families with early onset often also have late-onset cases, suggesting that the commonly applied categorization of Alzheimer disease into early- and late-onset forms may not reflect distinct underlying etiology. Nevertheless, this categorization continues to govern today's research and the design of clinical trials. The aim of this review is to evaluate this categorization by providing a comprehensive, critical review of reported clinical, neuropathologic, and genomic characteristics of both onset-based subtypes and explore potential overlap between both categories. The article will lay out the need to comprehensively assess the phenotypic, neuropathologic, and molecular variability in Alzheimer disease and identify factors explaining the observed significant variation in onset age in persons with and without known mutations. The article will critically review ongoing large-scale genomic efforts in Alzheimer disease research (e.g., Alzheimer Disease Sequencing Project, Dominantly Inherited Alzheimer Network, Alzheimer Disease Neuroimaging Initiative) and their shortcomings to disentangle the delineation of unexplained nonmendelian early-onset from late-onset and mendelian forms of Alzheimer disease. In addition, it will outline specific approaches including epigenetic research through which a comprehensive characterization of this delineation can be achieved.

Whole-Exome Sequencing Analysis of Alzheimer's Disease in Non-APOE*4 Carriers.

The genetics of late-onset Alzheimer's disease (AD) is complex due to the heterogeneous nature of the disorder. APOE*4 is the strongest genetic risk factor for AD. Genome-wide association studies have identified more than 30 additional loci, each having relatively small effect size. Known AD loci explain only about 30% of the genetic variance, and thus much of the genetic variance remains unexplained. To identify some of the missing heritability of AD, we analyzed whole-exome sequencing (WES) data focusing on non-APOE*4 carriers from two WES datasets: 720 cases and controls from the University of Pittsburgh and 7,252 cases and controls from the Alzheimer's Disease Sequencing Project. Following separate WES analyses in each dataset, we performed meta-analysis for overlapping markers present in both datasets. Among the four variants reaching the exome-wide significance threshold, three were from known AD loci: APOE/rs7412 (odds ratio (OR) = 0.40; p = 5.46E-24), TOMM40/rs157581 (OR = 1.49; p = 4.04E-07), and TREM2/rs75932628 (OR = 4.00; p = 1.15E-07). The fourth significant variant, rs199533, was from a novel locus on chromosome 17 in the NSF gene (OR = 0.78; p = 2.88E-07). NSF was also significant in the gene-based analysis (p = 1.20E-05). In the GTEx data, NSF/rs199533 is a cis-eQTL for multiple genes in the brain and blood, including NSF that is highly expressed across all brain tissues, including regions that typically show amyloid-β accumulation. Further characterization of genes that are affected by NSF/rs199533 may help to shed light on the roles of these genes in AD etiology.

Weighted burden analysis of exome-sequenced late-onset Alzheimer's cases and controls provides further evidence for a role for PSEN1 and suggests involvement of the PI3K/Akt/GSK-3β and WNT signalling pathways.

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.

NIA GENETICS OF ALZHEIMER’S DISEASE DATA STORAGE SITE (NIAGADS): ALZHEIMER’S GENOMICS DATABASE – 2019 UPDATE

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.

The Puerto Rico Alzheimer Disease Initiative (PRADI): A Multisource Ascertainment Approach.

IntroductionPuerto Ricans, the second largest Latino group in the continental US, are underrepresented in genomic studies of Alzheimer disease (AD). To increase representation of this group in genomic studies of AD, we developed a multisource ascertainment approach to enroll AD patients, and their family members living in Puerto Rico (PR) as part of the Alzheimer’s Disease Sequencing Project (ADSP), an international effort to advance broader personalized/precision medicine initiatives for AD across all populations.MethodsThe Puerto Rico Alzheimer Disease Initiative (PRADI) multisource ascertainment approach was developed to recruit and enroll Puerto Rican adults aged 50 years and older for a genetic research study of AD, including individuals with cognitive decline (AD, mild cognitive impairment), their similarly, aged family members, and cognitively healthy unrelated individuals age 50 and up. Emphasizing identification and relationship building with key stakeholders, we conducted ascertainment across the island. In addition to reporting on PRADI ascertainment, we detail admixture analysis for our cohort by region, group differences in age of onset, cognitive level by region, and ascertainment source.ResultsWe report on 674 individuals who met standard eligibility criteria [282 AD-affected participants (42% of the sample), 115 individuals with mild cognitive impairment (MCI) (17% of the sample), and 277 cognitively healthy individuals (41% of the sample)]. There are 43 possible multiplex families (10 families with 4 or more AD-affected members and 3 families with 3 AD-affected members). Most individuals in our cohort were ascertained from the Metro, Bayamón, and Caguas health regions. Across health regions, we found differences in ancestral backgrounds, and select clinical traits.DiscussionThe multisource ascertainment approach used in the PRADI study highlights the importance of enlisting a broad range of community resources and providers. Preliminary results provide important information about our cohort that will be useful as we move forward with ascertainment. We expect that results from the PRADI study will lead to a better understanding of genetic risk for AD among this population.

Analysis of Whole-Exome Sequencing Data for Alzheimer Disease Stratified by APOE Genotype

Previous genome-wide association studies of common variants identified associations for Alzheimer disease (AD) loci evident only among individuals with particular APOE alleles. To identify APOE genotype-dependent associations with infrequent and rare variants using whole-exome sequencing. The discovery stage included 10 441 non-Hispanic white participants in the Alzheimer Disease Sequencing Project. Replication was sought in 2 independent, whole-exome sequencing data sets (1766 patients with AD, 2906 without AD [controls]) and a chip-based genotype imputation data set (8728 patients with AD, 9808 controls). Bioinformatics and functional analyses were conducted using clinical, cognitive, neuropathologic, whole-exome sequencing, and gene expression data obtained from a longitudinal cohort sample including 402 patients with AD and 647 controls. Data were analyzed between March 2017 and September 2018. Score, Firth, and sequence kernel association tests were used to test the association of AD risk with individual variants and genes in subgroups of APOE ε4 carriers and noncarriers. Results with P ≤ 1 × 10-5 were further evaluated in the replication data sets and combined by meta-analysis. Among 3145 patients with AD and 4213 controls lacking ε4 (mean [SD] age, 83.4 [7.6] years; 4363 [59.3.%] women), novel genome-wide significant associations were obtained in the discovery sample with rs536940594 in AC099552 (odds ratio [OR], 88.0; 95% CI, 9.08-852.0; P = 2.22 × 10-7) and rs138412600 in GPAA1 (OR, 1.78; 95% CI, 1.44-2.2; meta-P = 7.81 × 10-8). GPAA1 was also associated with expression in the brain of GPAA1 (β = -0.08; P = .03) and its repressive transcription factor, FOXG1 (β = 0.13; P = .003), and global cognition function (β = -0.53; P = .009). Significant gene-wide associations (threshold P ≤ 6.35 × 10-7) were observed for OR8G5 (P = 4.67 × 10-7), IGHV3-7 (P = 9.75 × 10-16), and SLC24A3 (P = 2.67 × 10-12) in 2377 patients with AD and 706 controls with ε4 (mean [SD] age, 75.2 [9.6] years; 1668 [54.1%] women). The study identified multiple possible novel associations for AD with individual and aggregated rare variants in groups of individuals with and without APOE ε4 alleles that reinforce known and suggest additional pathways leading to AD.

Rare genetic variation implicated in non-Hispanic white families with Alzheimer disease.

ObjectiveTo identify genetic variation influencing late-onset Alzheimer disease (LOAD), we used a large data set of non-Hispanic white (NHW) extended families multiply-affected by LOAD by performing whole genome sequencing (WGS).MethodsAs part of the Alzheimer Disease Sequencing Project, WGS data were generated for 197 NHW participants from 42 families (affected individuals and unaffected, elderly relatives). A two-pronged approach was taken. First, variants were prioritized using heterogeneity logarithm of the odds (HLOD) and family-specific LOD scores as well as annotations based on function, frequency, and segregation with disease. Second, known Alzheimer disease (AD) candidate genes were assessed for rare variation using a family-based association test.ResultsWe identified 41 rare, predicted-damaging variants that segregated with disease in the families that contributed to the HLOD or family-specific LOD regions. These included a variant in nitric oxide synthase 1 adaptor protein that segregates with disease in a family with 7 individuals with AD, as well as variants in RP11-433J8, ABCA1, and FISP2. Rare-variant association identified 2 LOAD candidate genes associated with disease in these families: FERMT2 (p-values = 0.001) and SLC24A4 (p-value = 0.009). These genes still showed association while controlling for common index variants, indicating the rare-variant signal is distinct from common variation that initially identified the genes as candidates.ConclusionsWe identified multiple genes with putative damaging rare variants that segregate with disease in multiplex AD families and showed that rare variation may influence AD risk at AD candidate genes. These results identify novel AD candidate genes and show a role for rare variation in LOAD etiology, even at genes previously identified by common variation.

VCPA: genomic variant calling pipeline and data management tool for Alzheimer's Disease Sequencing Project.

SummaryWe report VCPA, our SNP/Indel Variant Calling Pipeline and data management tool used for the analysis of whole genome and exome sequencing (WGS/WES) for the Alzheimer’s Disease Sequencing Project. VCPA consists of two independent but linkable components: pipeline and tracking database. The pipeline, implemented using the Workflow Description Language and fully optimized for the Amazon elastic compute cloud environment, includes steps from aligning raw sequence reads to variant calling using GATK. The tracking database allows users to view job running status in real time and visualize >100 quality metrics per genome. VCPA is functionally equivalent to the CCDG/TOPMed pipeline. Users can use the pipeline and the dockerized database to process large WGS/WES datasets on Amazon cloud with minimal configuration.Availability and implementationVCPA is released under the MIT license and is available for academic and nonprofit use for free. The pipeline source code and step-by-step instructions are available from the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (http://www.niagads.org/VCPA).Supplementary information Supplementary data are available at Bioinformatics online.

Analysis of pedigree data in populations with multiple ancestries: Strategies for dealing with admixture in Caribbean Hispanic families from the ADSP.

Multipoint linkage analysis is an important approach for localizing disease-associated loci in pedigrees. Linkage analysis, however, is sensitive to misspecification of marker allele frequencies. Pedigrees from recently admixed populations are particularly susceptible to this problem because of the challenge of accurately accounting for population structure. Therefore, increasing emphasis on use of multiethnic samples in genetic studies requires reevaluation of best practices, given data currently available. Typical strategies have been to compute allele frequencies from the sample, or to use marker allele frequencies determined by admixture proportions averaged over the entire sample. However, admixture proportions vary among pedigrees and throughout the genome in a family-specific manner. Here, we evaluate several approaches to model admixture in linkage analysis, providing different levels of detail about ancestral origin. To perform our evaluations, for specification of marker allele frequencies, we used data on 67 Caribbean Hispanic admixed families from the Alzheimer's Disease Sequencing Project. Our results show that choice of admixture model has an effect on the linkage analysis results. Variant-specific admixture proportions, computed for individual families, provide the most detailed regional admixture estimates, and, as such, are the most appropriate allele frequencies for linkage analysis. This likely decreases the number of false-positive results, and is straightforward to implement.