Genome Center for Alzheimer's Disease (GCAD)

阿尔茨海默病基因组中心 (GCAD)

• 批准号: 10604370
• 负责人: GERARD DAVID SCHELLENBERG
• 金额: $ 397.33万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10604370
• 关键词: ATAC-seq; Abbreviations; Affect; African American population; Age; Alleles; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Artificial Intelligence; Asian population; Award; Benchmarking; Calibration; Caregivers; Caribbean Hispanic; Cells; ChIP-seq; Clinical; Cognitive; Collection; Communities; Computer software; Copy Number Polymorphism; Data; Data Analyses; Data Collection; Data Set; Disease; Documentation; Drug Targeting; Elderly; Environment; Epigenetic Process; Equilibrium; Ethnic Population; European; Fostering; Frontotemporal Dementia; Funding; Genes; Genetic; Genetic Annotation; Genetic Diseases; Genome; Genomics; Goals; Grant; Hi-C; Individual; Infrastructure; International; LGR5 gene; Lewy Body Dementia; Machine Learning; Maps; Methods; Methylation; Molecular Conformation; National Heart, Lung, and Blood Institute; National Human Genome Research Institute; National Institute on Aging; Not Hispanic or Latino; Parkinson' s Dementia; Pathway interactions; Patients; Persons; Phase; Precision Medicine Initiative; Predisposition; Prevention; Process; Progressive Supranuclear Palsy; Proteomics; Public Health; Quality Control; Research; Research Personnel; Resources; Risk Factors; Sampling; Scientist; Sequence Alignment; Single Nucleotide Polymorphism; Source; Systems Analysis; Trans-Omics for Precision Medicine; United States; Variant; Work; candidate identification; cognitive system; collaborative environment; computer code; computing resources; corticobasal degeneration; data repository; deep learning; deep neural network; drug candidate; exome; exome sequencing; file format; functional genomics; gene network; genetic variant; genome analysis; genome wide association study; genome-wide linkage; genomic data; insertion/deletion mutation; learning network; metabolomics; neural network; novel therapeutic intervention; phenotypic data; prevent; programs; quality assurance; reference genome; therapeutic candidate; therapeutic target; trait; transcriptome sequencing; whole genome

Overall Project Summary Alzheimer’s disease (AD) affects 5.8 million people in the United States, and is an immense burden on patients, caregivers and on the economy. No disease-modifying treatments or preventions exist, and we need better understanding of the disease and new therapeutic strategies. Genetic discoveries are one source of candidate therapeutic targets. One source of genetic targets is the Alzheimer’s Disease Sequencing Project (ADSP), a National Institute on Aging (NIA) initiative since 2012 to sequence genomes and exomes of AD subjects and cognitively normal elderly controls. The Genome Center for Alzheimer’s Disease (GCAD) is the analysis coordinating center for the ADSP. In the previous grant cycle, GCAD processed all AD-relevant sequencing data producing harmonized genetic data for AD research. This renewal responds to the increase in the amount and complexity of ADSP sequence data, the collection of new types of data, and an expansion of the types of analysis being performed. In addition to sequence data, GCAD will harmonize functional genomics data. GCAD will provide fully quality-controlled and annotated genetic and functional genomics data that is analysis ready. In addition to AD, GCAD will also work with data for AD related disorders (ADRD). These include frontotemporal dementias (FTDs), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), Lewy body dementia (LBD), and Parkinson’s disease with dementia (PD-d). In the Y6-10 funding period, GCAD will assemble and harmonize whole-genome/whole-exome sequencing data, and provide it to ADSP investigators and the general scientific community. GCAD will work with US and non-US groups to analyze data by fostering a collaborative environment, and providing infrastructure support. GCAD will also assemble and harmonize functional genomics data which will be integral to identifying genes as candidate drug targets. The research plan will lead to a high quality, comprehensive, harmonized collection of genetic and functional data with detailed supporting resources including documentation and optimized computer codes. This resource will be invaluable for the entire AD research community.

项目总体概要 阿尔茨海默病 (AD) 影响着美国 580 万人，是一种巨大的疾病 患者、护理人员和经济的负担。没有缓解疾病的治疗或 预防措施是存在的，我们需要更好地了解这种疾病和新的治疗方法 策略。基因发现是候选治疗靶点的来源之一。来源之一 遗传目标是阿尔茨海默病测序项目 (ADSP)，这是一个国家研究所 老龄化 (NIA) 计划自 2012 年起对 AD 受试者的基因组和外显子组进行测序， 认知正常的老年人对照。 阿尔茨海默病基因组中心 (GCAD) 是该疾病的分析协调中心 ADSP。在上一个资助周期中，GCAD 处理了所有与 AD 相关的测序数据，生成 协调 AD 研究的遗传数据。此次续订是为了响应金额的增加 ADSP序列数据的复杂性、新型数据的收集和扩展 正在执行的分析类型。除了序列数据外，GCAD 还将协调 功能基因组学数据。 GCAD 将提供完全质量控制和注释的遗传和 可供分析的功能基因组学数据。除了 AD 之外，GCAD 还将与 AD 相关疾病 (ADRD) 的数据。这些包括额颞叶痴呆（FTD）， 进行性核上性麻痹 (PSP)、皮质基底节变性 (CBD)、路易体痴呆 (LBD) 和帕金森病伴痴呆 (PD-d)。 在 Y6-10 资助期间，GCAD 将组装和协调全基因组/全外显子组 测序数据，并将其提供给 ADSP 研究人员和一般科学界。 GCAD 将与美国和非美国团体合作，通过促进协作来分析数据 环境，并提供基础设施支持。 GCAD 还将组装和协调 功能基因组学数据对于识别候选药物靶点的基因至关重要。 该研究计划将导致高质量、全面、协调的遗传基因收集 和功能数据以及详细的支持资源，包括文档和优化 计算机代码。该资源对于整个 AD 研究界来说都是无价的。

项目成果

Admixture mapping implicates 13q33.3 as ancestry-of-origin locus for Alzheimer disease in Hispanic and Latino populations.

• DOI: 10.1016/j.xhgg.2023.100207
• 发表时间: 2023-07-13
• 期刊: HUMAN GENETICS AND GENOMICS ADVANCES
• 作者: Horimoto, Andrea R. V. R.;Boyken, Lisa A.;Blue, Elizabeth E.;Grinde, Kelsey E.;Nafikov, Rafael A.;Sohi, Harkirat K.;Nato, Alejandro Q.;Bis, Joshua C.;Brusco, Luis I.;Morelli, Laura;Ramirez, Alfredo;Dalmasso, Maria Carolina;Temple, Seth;Satizabal, Claudia;Browning, Sharon R.;Seshadri, Sudha;Wijsman, Ellen M.;Thornton, Timothy A.
• 通讯作者: Thornton, Timothy A.

Packaging Biocomputing Software to Maximize Distribution and Reuse.

打包生物计算软件以最大化分发和再利用。

• 发表时间: 2020
• 期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
• 作者: Bush,WilliamS;Wheeler,Nicholas;Beaulieu-Jones,Brett;Darabos,Christian
• 通讯作者: Darabos,Christian

SparkINFERNO: a scalable high-throughput pipeline for inferring molecular mechanisms of non-coding genetic variants.

SparkINFERNO：一个可扩展的高通量管道，用于推断非编码遗传变异的分子机制。

• DOI: 10.1093/bioinformatics/btaa246
• 发表时间: 2020
• 期刊: Bioinformatics (Oxford, England)
• 作者: Kuksa,PavelP;Lee,Chien-Yueh;Amlie-Wolf,Alexandre;Gangadharan,Prabhakaran;Mlynarski,ElizabethE;Chou,Yi-Fan;Lin,Han-Jen;Issen,Heather;Greenfest-Allen,Emily;Valladares,Otto;Leung,YukYee;Wang,Li-San
• 通讯作者: Wang,Li-San

FILER: a framework for harmonizing and querying large-scale functional genomics knowledge.

• DOI: 10.1093/nargab/lqab123
• 发表时间: 2022-03
• 期刊: NAR genomics and bioinformatics
• 影响因子: 4.6
• 作者: Kuksa PP;Leung YY;Gangadharan P;Katanic Z;Kleidermacher L;Amlie-Wolf A;Lee CY;Qu L;Greenfest-Allen E;Valladares O;Wang LS
• 通讯作者: Wang LS

ODACH: a one-shot distributed algorithm for Cox model with heterogeneous multi-center data.

• DOI: 10.1038/s41598-022-09069-0
• 发表时间: 2022-04-22
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Luo, Chongliang;Duan, Rui;Naj, Adam C.;Kranzler, Henry R.;Bian, Jiang;Chen, Yong
• 通讯作者: Chen, Yong