Amanda Kuzma

Apolipoprotein E Genotype and Sex Risk Factors for Alzheimer Disease: A Meta-analysis

Importance It is unclear whether female carriers of the apolipoprotein E (APOE) &egr;4 allele are at greater risk of developing Alzheimer disease (AD) than men, and the sex-dependent association of mild cognitive impairment (MCI) and APOE has not been established. Objective To determine how sex and APOE genotype affect the risks for developing MCI and AD. Data Sources Twenty-seven independent research studies in the Global Alzheimer’s Association Interactive Network with data on nearly 58 000 participants. Study Selection Non-Hispanic white individuals with clinical diagnostic and APOE genotype data. Data Extraction and Synthesis Homogeneous data sets were pooled in case-control analyses, and logistic regression models were used to compute risks. Main Outcomes and Measures Age-adjusted odds ratios (ORs) and 95% confidence intervals for developing MCI and AD were calculated for men and women across APOE genotypes. Results Participants were men and women between ages 55 and 85 years. Across data sets most participants were white, and for many participants, racial/ethnic information was either not collected or not known. Men (OR, 3.09; 95% CI, 2.79-3.42) and women (OR, 3.31; CI, 3.03-3.61) with the APOE &egr;3/&egr;4 genotype from ages 55 to 85 years did not show a difference in AD risk; however, women had an increased risk compared with men between the ages of 65 and 75 years (women, OR, 4.37; 95% CI, 3.82-5.00; men, OR, 3.14; 95% CI, 2.68-3.67; P = .002). Men with APOE &egr;3/&egr;4 had an increased risk of AD compared with men with APOE &egr;3/&egr;3. The APOE &egr;2/&egr;3 genotype conferred a protective effect on women (OR, 0.51; 95% CI, 0.43-0.61) decreasing their risk of AD more (P value = .01) than men (OR, 0.71; 95% CI, 0.60-0.85). There was no difference between men with APOE &egr;3/&egr;4 (OR, 1.55; 95% CI, 1.36-1.76) and women (OR, 1.60; 95% CI, 1.43-1.81) in their risk of developing MCI between the ages of 55 and 85 years, but women had an increased risk between 55 and 70 years (women, OR, 1.43; 95% CI, 1.19-1.73; men, OR, 1.07; 95% CI, 0.87-1.30; P = .05). There were no significant differences between men and women in their risks for converting from MCI to AD between the ages of 55 and 85 years. Individuals with APOE &egr;4/&egr;4 showed increased risks vs individuals with &egr;3/&egr;4, but no significant differences between men and women with &egr;4/&egr;4 were seen. Conclusions and Relevance Contrary to long-standing views, men and women with the APOE &egr;3/&egr;4 genotype have nearly the same odds of developing AD from age 55 to 85 years, but women have an increased risk at younger ages.

Whole exome sequencing study identifies novel rare and common Alzheimer’s-Associated variants involved in immune response and transcriptional regulation

The Alzheimer’s Disease Sequencing Project (ADSP) undertook whole exome sequencing in 5,740 late-onset Alzheimer disease (AD) cases and 5,096 cognitively normal controls primarily of European ancestry (EA), among whom 218 cases and 177 controls were Caribbean Hispanic (CH). An age-, sex- and APOE based risk score and family history were used to select cases most likely to harbor novel AD risk variants and controls least likely to develop AD by age 85 years. We tested ~1.5 million single nucleotide variants (SNVs) and 50,000 insertion-deletion polymorphisms (indels) for association to AD, using multiple models considering individual variants as well as gene-based tests aggregating rare, predicted functional, and loss of function variants. Sixteen single variants and 19 genes that met criteria for significant or suggestive associations after multiple-testing correction were evaluated for replication in four independent samples; three with whole exome sequencing (2,778 cases, 7,262 controls) and one with genome-wide genotyping imputed to the Haplotype Reference Consortium panel (9,343 cases, 11,527 controls). The top findings in the discovery sample were also followed-up in the ADSP whole-genome sequenced family-based dataset (197 members of 42 EA families and 501 members of 157 CH families). We identified novel and predicted functional genetic variants in genes previously associated with AD. We also detected associations in three novel genes: IGHG3 (p = 9.8 × 10−7), an immunoglobulin gene whose antibodies interact with β-amyloid, a long non-coding RNA AC099552.4 (p = 1.2 × 10−7), and a zinc-finger protein ZNF655 (gene-based p = 5.0 × 10−6). The latter two suggest an important role for transcriptional regulation in AD pathogenesis.

Genetic Variation in Genes Underlying Diverse Dementias May Explain a Small Proportion of Cases in the Alzheimer’s Disease Sequencing Project

Background/Aims: The Alzheimer’s Disease Sequencing Project (ADSP) aims to identify novel genes influencing Alzheimer’s disease (AD). Variants within genes known to cause dementias other than AD have previously been associated with AD risk. We describe evidence of co-segregation and associations between variants in dementia genes and clinically diagnosed AD within the ADSP. Methods: We summarize the properties of known pathogenic variants within dementia genes, describe the co-segregation of variants annotated as “pathogenic” in ClinVar and new candidates observed in ADSP families, and test for associations between rare variants in dementia genes in the ADSP case-control study. The participants were clinically evaluated for AD, and they represent European, Caribbean Hispanic, and isolate Dutch populations. Results/Conclusions: Pathogenic variants in dementia genes were predominantly rare and conserved coding changes. Pathogenic variants within ARSA, CSF1R, and GRN were observed, and candidate variants in GRN and CHMP2B were nominated in ADSP families. An independent case-control study provided evidence of an association between variants in TREM2, APOE, ARSA, CSF1R, PSEN1, and MAPT and risk of AD. Variants in genes which cause dementing disorders may influence the clinical diagnosis of AD in a small proportion of cases within the ADSP.

Functional annotation of genomic variants in studies of late-onset Alzheimer’s disease

Abstract Motivation Annotation of genomic variants is an increasingly important and complex part of the analysis of sequence-based genomic analyses. Computational predictions of variant function are routinely incorporated into gene-based analyses of rare-variants, though to date most studies use limited information for assessing variant function that is often agnostic of the disease being studied. Results In this work, we outline an annotation process motivated by the Alzheimer’s Disease Sequencing Project, illustrate the impact of including tissue-specific transcript sets and sources of gene regulatory information and assess the potential impact of changing genomic builds on the annotation process. While these factors only impact a small proportion of total variant annotations (∼5%), they influence the potential analysis of a large fraction of genes (∼25%). Availability and implementation Individual variant annotations are available via the NIAGADS GenomicsDB, at https://www.niagads.org/genomics/ tools-and-software/databases/genomics-database. Annotations are also available for bulk download at https://www.niagads.org/datasets. Annotation processing software is available at http://www.icompbio.net/resources/software-and-downloads/. Supplementary information Supplementary data are available at Bioinformatics online.

A statistical framework for cross-tissue transcriptome-wide association analysis

Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to predict (impute) gene expression levels from genotypes from samples with matched genotypes and expression levels in a specific tissue. However, it is challenging to develop robust and accurate imputation models with limited sample sizes for any single tissue. Here, we first introduce a multi-task learning approach to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average 39% improvement in imputation accuracy and generated effective imputation models for an average 120% (range 13%-339%) more genes in each tissue. We then describe a summary statistic-based testing framework that combines multiple single-tissue associations into a single powerful metric to quantify overall gene-trait association at the organism level. When our method, called UTMOST, was applied to analyze genome wide association results for 50 complex traits (Ntotal=4.5 million), we were able to identify considerably more genes in tissues enriched for trait heritability, and cross-tissue analysis significantly outperformed single-tissue strategies (p=1.7e-8). Finally, we performed a cross-tissue genome-wide association study for late-onset Alzheimer’s disease (LOAD) and replicated our findings in two independent datasets (Ntotal=175,776). In total, we identified 69 significant genes, many of which are novel, leading to novel insights on LOAD etiologies.

Inferring the molecular mechanisms of noncoding Alzheimer's disease-associated genetic variants

INTRODUCTION We set out to characterize the causal variants, regulatory mechanisms, tissue contexts, and target genes underlying noncoding late-onset Alzheimer’s Disease (LOAD)-associated genetic signals. METHODS We applied our INFERNO method to the IGAP genome-wide association study (GWAS) data, annotating all potentially causal variants with tissue-specific regulatory activity. Bayesian co-localization analysis of GWAS summary statistics and eQTL data was performed to identify tissue-specific target genes. RESULTS INFERNO identified enhancer dysregulation in all 19 tag regions analyzed, significant enrichments of enhancer overlaps in the immune-related blood category, and co-localized eQTL signals overlapping enhancers from the matching tissue class in ten regions (ABCA7, BIN1, CASS4, CD2AP, CD33, CELF1, CLU, EPHA1, FERMT2, ZCWPW1). We validated the allele-specific effects of several variants on enhancer function using luciferase expression assays. DISCUSSION Integrating functional genomics with GWAS signals yielded insights into the regulatory mechanisms, tissue contexts, and genes affected by noncoding genetic variation associated with LOAD risk.

VCPA: genomic Variant Calling pipeline and data management tool for Alzheimer’s Disease Sequencing Project

Abstract Summary We 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 implementation VCPA 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.

THE GCAD CLOUD-BASED WORKFLOW FOR PROCESSING WHOLE EXOME AND WHOLE GENOME DATA FROM THE ALZHEIMER’S DISEASE SEQUENCING PROJECT

membership models to the transcriptome data for clustering genes and identified ones that were characterized in each cluster. We performed Gene Ontology analysis on the characteristic genes in each cluster to examine their biological functions. Results:We found 673 genes that were differentially expressed among the different Braak stages (10% false discovery rate).Among them, genes in clusters related to neurons were downregulated in Braak stage V–VI as expected. Their downregulation appears to be consistent with the timing of tau-related neuropathology spreading into frontal cortex. Interestingly, genes in a cluster related to myelination were greatly altered in the Braak stages III–IV or more. Conclusions: The transcriptome analysis of autopsy brains in this study revealed aberrant expression in genes associated with myelination, which may precede the appearance of neuropathological changes associatedwithAD.Our resultsmay imply thatmyelin alteration could be an early event linking with AD pathogenesis.

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

Functional Expression Regulating N-terminal domain of Kv4.2. The p.F11 residue plays a crucial role in the binding to the potassium channel-Interacting protein (KChIP). Mutations in this residue are known to disrupt KChIP binding, trafficking, and functional modulation of the Kv4.2 channel (Kunjilwar et al., 2013). Conclusions:The genetic data suggest that Kv4.2, the molecular partner of DPP6, is intolerant to mutations. Together, our results as well as the specific protein function, warrant further investigation of this multimeric protein complex in the pathogenesis of neurodegenerative brain diseases.

NIA GENETICS OF ALZHEIMER’S DISEASE DATA STORAGE SITE (NIAGADS): UPDATE 2018

Ellen Wijsman, Margaret A. Pericak-Vance, Richard Mayeux and Alzheimer’s Disease Sequencing Project, Columbia University, New York, NY, USA; John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA; University of Washington, Seattle, WA, USA; Mother and Teacher Pontifical Catholic University, Santiago, Dominican Republic; University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Boston University, Boston, MA, USA; University of Miami, Miami, FL, USA; Case Western Reserve University, Cleveland, OH, USA; Erasmus University Medical Center, Rotterdam, Netherlands; Washington University, Saint Louis,MO,USA; BostonUniversity Alzheimer’s Disease Center, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA. Contact e-mail: bnv2103@cumc.columbia.edu