Genetics Core

遗传学核心

• 批准号: 10495157
• 负责人: ANDREW J SAYKIN
• 金额: $ 96.35万
• 依托单位: NORTHERN CALIFORNIA INSTITUTE/RES/EDU
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-30 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10495157
• 关键词: Address; Adverse effects; Age of Onset; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease related dementia; Amyloid; Artificial Intelligence; Asian; Autophagocytosis; Bioinformatics; Biological; Biological Assay; Biological Markers; Biological Process; Black race; Blood Vessels; Candidate Disease Gene; Clinical; Clinical Trials; Clinical Trials Design; Cognition; Cognitive; Collaborations; Collection; Communities; Complex; DNA; DNA methylation profiling; Data; Development; Diagnostic; Disease; Endocytosis; Enrollment; Ensure; Fostering; Foundations; Funding; Future; Generations; Genes; Genetic; Genetic Markers; Genetic Models; Genetic Transcription; Genomics; Genotype; Goals; Grant; Heritability; Immune; Individual; Industry; Investigation; Knowledge; Koreans; Latinx; Machine Learning; Measures; Methods; Modeling; Molecular; Multimodal Imaging; Nerve Degeneration; Outcome; Pathology; Pathway interactions; Peripheral Blood Mononuclear Cell; Phase; Play; Predisposition; Quality Control; RNA; Reproducibility; Resources; Risk; Risk Assessment; Role; Sample Size; Sampling; Science; Stem Cell Development; Stratification; Systems Biology; Therapeutic Agents; Underrepresented Populations; Validation; biobank; cohort; cost; deep learning; drug development; drug discovery; endophenotype; genetic analysis; genetic variant; genome sequencing; genome wide association study; genome-wide; genomic locus; health disparity; improved; induced pluripotent stem cell; industry partner; knowledge base; lipid metabolism; lipidomics; low socioeconomic status; machine learning model; metabolomics; multi-ethnic; multiple omics; neuroimaging; new therapeutic target; phenotypic biomarker; polygenic risk score; power analysis; pre-clinical; precision medicine; predictive marker; programs; recruit; repository; sample collection; screening; synergism; tau Proteins; trial design; whole genome; working group

Genetics Core: Summary/Abstract The overarching goal of the ADNI Genetics Core is to support the identification and validation of genetic markers for use in clinical trials and drug discovery, in alignment with Project 1 and the broader NIA and NAPA goals of effective precision medicine for AD/ADRD. AD is a complex and highly heritable disease, with genetics playing an important role in identifying novel targets for therapeutics, diagnostics, and risk assessment. APOE remains an attractive target and is widely employed for stratification and enrichment of clinical trials, as it influences onset age, amyloid accumulation, and susceptibility to adverse effects of anti-amyloid treatment. Large scale genome- wide association studies (GWAS), most including ADNI data, have identified multiple genes and pathways related to amyloid and tau, immune dysregulation, lipid metabolism, vascular integrity, endocytosis/autophagy, and other biological mechanisms. The core will continue its focus on collaboratively advancing genetics and related omics to discover, validate, and implement markers that can improve the precision and power of AD clinical trials. Specific Aims for ADNI4: (1) Continue longitudinal sample collection, processing, banking, curation, and dissemination in partnership with NCRAD, including baseline PBMC collection to support future iPSCs development or other NIA-approved uses by the scientific community; (2) Continue providing genome-wide genotyping data, including APOE, with new emphasis on diverse ancestry; (3) Support the ADNI4 multiethnic screening strategy (n=4000); (4) Continue to perform and facilitate bioinformatic analyses of genetic and quantitative endophenotype data related to the goals of ADNI, which include improvement of clinical trial design through enrichment using genetic markers. With Project 1, we will assess genetic models including APOE and polygenic risk scores (PRS) as predictors of biomarker and cognitive outcomes. The Core will continue to explore the benefit of adding transcriptional risk scores (TRS) and DNA methylation profiling in candidate gene loci to models. Using a systems biology framework, genetic data will be used to predict trajectories of amyloid, tau, neurodegeneration, and vascular pathways (A/T/N/V) as measured by ADNI biomarkers, as well as clinical course. The Core will also continue to explore how advanced AI methods (machine learning and deep learning) can contribute to AD genetic analyses, working with affiliated NIA programs including ADSP, AMP-AD and AI4AD. With the AD Metabolomics Consortium, we will continue to assess genes related to longitudinal metabolomics/lipidomics changes in AD; and (5) Continue as a “hub” providing organization, collaboration, and support for genomic studies of quantitative biomarker phenotypes and outcomes in ADNI, including fostering new knowledge through team science working groups. Funded or pending collaborations will support sequencing of previously collected ADNI DNA and RNA samples, the application of AI and systems biology, and analysis of diverse cohorts, adding new collaborations with Asian AD consortia including the ADNI-like KBASE2 cohort.

遗传学核心：摘要/摘要 ADNI遗传学核心的首要目标是支持遗传标记的鉴定和验证 用于临床试验和药物发现，与项目1和更广泛的国家评估和国家适应行动方案目标保持一致， 有效精准治疗AD/ADRD。AD是一种复杂且高度遗传的疾病，遗传学起着重要作用。 在确定治疗、诊断和风险评估的新靶点方面发挥重要作用。APOE仍然存在 一个有吸引力的目标，并广泛用于分层和丰富的临床试验，因为它影响发病 年龄、淀粉样蛋白积聚和对抗淀粉样蛋白治疗的副作用的易感性。大规模基因组- 广泛关联研究（GWAS），大多数包括ADNI数据，已经确定了多个基因和途径 与淀粉样蛋白和tau、免疫失调、脂质代谢、血管完整性、内吞作用/自噬， 和其他生物机制。核心将继续专注于合作推进遗传学， 相关的组学，以发现，验证和实施标记，可以提高AD的精度和功率 临床试验ADNI 4的具体目标：（1）继续纵向样本收集、处理、储存、管理， 与NCRAD合作进行传播，包括基线PBMC收集，以支持未来的iPSC 开发或科学界批准的其他NIA用途;（2）继续提供全基因组 基因分型数据，包括载脂蛋白E，新的重点是不同的祖先;（3）支持ADNI 4多种族 筛选策略（n=4000）;（4）继续进行和促进遗传和 与ADNI目标相关的定量内表型数据，包括改进临床试验设计 通过使用遗传标记进行富集。在项目1中，我们将评估遗传模型，包括APOE和 多基因风险评分（PRS）作为生物标志物和认知结果的预测因子。核心将继续探索 在候选基因座中增加转录风险评分（TRS）和DNA甲基化谱的好处是， 模型使用系统生物学框架，遗传数据将用于预测淀粉样蛋白，tau蛋白， 通过ADNI生物标志物测量的神经变性和血管通路（A/T/N/V），以及临床 当然了Core还将继续探索先进的AI方法（机器学习和深度学习） 可以为AD遗传分析做出贡献，与附属的NIA项目合作，包括ADSP，AMP-AD和 AI4AD通过AD代谢组学联盟，我们将继续评估与纵向代谢相关的基因。 AD的代谢组学/脂质组学变化;以及（5）继续作为提供组织、协作和 支持ADNI中定量生物标志物表型和结局的基因组研究，包括促进 通过团队科学工作组获得新知识。已获资助或待定的合作将支持排序 先前收集的ADNI DNA和RNA样品，人工智能和系统生物学的应用， 多样化的队列，增加了与亚洲AD联盟的新合作，包括ADNI样KBASE 2队列。