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.

整体项目总结

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