Administrative Component

行政部分

• 批准号: 10616711
• 负责人: Michael A. Province
• 金额: $ 184.43万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10616711
• 关键词: Aging; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Automobile Driving; Big Data; Biological; Biological Markers; Blood Pressure; Budgets; Cardiovascular Diseases; Cognition; Cohort Studies; Collaborations; Communication; Communities; Complex; Data; Data Analyses; Data Collection; Data Set; Dementia; Denmark; Diabetes Mellitus; Diagnosis; Disease; Disease Resistance; Educational workshop; Enrollment; Family; Family Study; Foundations; Generations; Genes; Goals; Hand Strength; Home visitation; Institutional Review Boards; Life; Logistics; Longevity; Malignant Neoplasms; Measures; Medical; Methods; Monitor; Observational Study; Participant; Pathway interactions; Pattern; Persons; Phenotype; Process; Proteomics; Publications; Quality Control; Registries; Research; Research Personnel; Running; Sampling Studies; Site; Specificity; Systems Biology; Training; Universities; Untranslated RNA; Variant; Visit; Washington; cognitive performance; cohort; data cleaning; data integration; data sharing; deep sequencing; electronic data; experience; follow-up; genetic linkage; genetic linkage analysis; genetic pedigree; genetic signature; genetic variant; genome sequencing; genome wide association study; grandchild; healthy aging; improved; meetings; metabolomics; methylomics; novel; novel marker; offspring; prevent; proband; protective allele; pulmonary function; quality assurance; rate of change; success; symposium; transcriptomics; web site; whole genome; Aging; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Automobile Driving; Big Data; Biological; Biological Markers; Blood Pressure; Budgets; Cardiovascular Diseases; Cognition; Cohort Studies; Collaborations; Communication; Communities; Complex; Data; Data Analyses; Data Collection; Data Set; Dementia; Denmark; Diabetes Mellitus; Diagnosis; Disease; Disease Resistance; Educational workshop; Enrollment; Family; Family Study; Foundations; Generations; Genes; Goals; Hand Strength; Home visitation; Institutional Review Boards; Life; Logistics; Longevity; Malignant Neoplasms; Measures; Medical; Methods; Monitor; Observational Study; Participant; Pathway interactions; Pattern; Persons; Phenotype; Process; Proteomics; Publications; Quality Control; Registries; Research; Research Personnel; Running; Sampling Studies; Site; Specificity; Systems Biology; Training; Universities; Untranslated RNA; Variant; Visit; Washington; cognitive performance; cohort; data cleaning; data integration; data sharing; deep sequencing; electronic data; experience; follow-up; genetic linkage; genetic linkage analysis; genetic pedigree; genetic signature; genetic variant; genome sequencing; genome wide association study; grandchild; healthy aging; improved; meetings; metabolomics; methylomics; novel; novel marker; offspring; prevent; proband; protective allele; pulmonary function; quality assurance; rate of change; success; symposium; transcriptomics; web site; whole genome

The Long Life Family Study (LLFS) has enrolled 4,953 participants in 539 pedigrees in the USA and Denmark that are enriched for exceptional longevity, and has measured them longitudinally in two extensive in-home visits measuring key healthy aging phenotypes in all of the major domains of the aging process. We have demonstrated through many publications that selecting on longevity in the first (proband) generation, results in the second (offspring) generation being much healthier than average in many key phenotypes. However, the pedigrees are heterogeneous by phenotype, with different families showing familial clustering of protection in cognition, grip strength, pulmonary function, blood pressure, etc. Further, comprehensive linkage analysis of the LLFS sample identifies extremely strong genetic linkage peaks for cross-sectional as well as longitudinal trajectory rates of change phenotypes for a wide variety of healthy aging domains such as exceptional cognitive performance and lack of Alzheimer’s disease. These peaks are NOT explained by GWAS SNPs (or those that can be imputed by GWAS). Pedigree specific LODs and preliminary deep sequencing suggests that these peaks are driven by rare, protective variants running in selected pedigrees. We propose to do Whole Genome Sequencing on this unique cohort, to identify the rare protective variants driving these strong linkage peaks. We propose to continue longitudinal assessment of the cohort with a third in-person visit, which will allow us to assess potential non-linear patterns of aging, and adding formal assessment of dementia diagnosis for Alzheimer’s Disease and other dementia types, which will increase specificity and power to discover and follow- up on protective variants against Alzheimer’s Disease and other dementia diagnoses. For pedigrees driving multiple strong linkage peaks, we also propose to phenotypically measure the third generation (grandchildren), as these are likely to carry more copies of the rare protective alleles running in these families, which will exponentially increase our power to resolve them. Preliminary evidence from the Danish Medical Registry suggests that, at least in Denmark, the protection persists into this third generation, with significantly lower rates of medical conditions across the disease spectrum. We also propose to do extensive transcriptomics, methylomics, and proteomics on these selected high linkage pedigrees, to begin to move from “statistically associated variants/loci” to the biological genes of action, since we expect most of the driving variants will be regulatory and non-coding. It is critical to find the modes of action of these rare protective variants. We also propose to do metabolomics on the entire LLFS cohort, longitudinally, with the goal of identifying novel biomarkers of healthy aging and resistance to diseases such as Alzheimer’s in this unusually heathy cohort. Combined with a systems biology/network approach to data integration of the proposed “Big Data”, such biomarkers would improve our power to detect even more novel protective genetic variants and identify the genetic signatures and pathways of genes conferring protection in this unique cohort to prevent onset of major diseases such as diabetes, cardiovascular disease, cancer and Alzheimer’s Disease and other dementia types.

Long Life Family研究（LLFS）已在美国和丹麦的539个谱系中招募了4,953名参与者 富含特殊寿命的寿命，并在两次广泛的家庭访问中纵向测量 在衰老过程的所有主要领域中测量关键的健康老化表型。我们有 通过许多出版物证明了在第一（概率）一代中选择长寿的出版物，从而导致 在许多关键表型中，第二代（后代）一代比平均水平要健康得多。但是， 谱系是通过表型异质性的，不同的家庭显示了家庭的家庭聚类 认知，抓地力，肺功能，血压等 LLFS样本确定了横截面和纵向的极强遗传连锁峰 多种健康衰老领域的变化表型的轨迹速率，例如特殊认知 表现和缺乏阿尔茨海默氏病。 GWAS SNP（或那些 可以由GWAS归咎于）。谱系特定的LOD和初步深度测序表明这些峰 由在选定的血统中运行的稀有，受保护的变体驱动。我们建议做整个基因组 在这个独特的队列上进行测序，以确定驱动这些强连接峰的稀有保护变体。我们 提议通过第三次面对面的访问继续对队列进行纵向评估，这将使我们能够 评估潜在的非线性衰老模式，并为痴呆诊断的正式评估 阿尔茨海默氏病和其他痴呆症类型，这将增加发现和跟随的特异性和力量 - 对抗阿尔茨海默氏病和其他痴呆症诊断的保护性变体。用于驾驶血统 多个强大的连锁峰，我们还建议表型测量第三代（孙子）， 由于这些可能会携带更多在这些家庭中运行的稀有保护等位基因的副本，这将 指数增强我们解决它们的力量。丹麦医学注册表的初步证据 表明，至少在丹麦，保护持续到第三代，速率明显降低 疾病范围内的医疗状况。我们还建议进行广泛的转录组学， 这些精选的高连接谱系上的甲基组学和蛋白质组学，开始从统计上移动 相关的变体/基因座”与作用的生物学基因，因为我们预计大多数驾驶变体将是 监管和非编码。找到这些罕见受保护变体的作用方式至关重要。我们也是 在整个LLFS队列上进行代谢组学的建议，纵向，目的是识别新颖 健康衰老和对诸如阿尔茨海默氏病等疾病的健康衰老的生物标志物在这种异常健康的同类中。 结合系统的生物学/网络方法，用于拟议的“大数据”的数据集成 生物标志物将提高我们检测更新颖的保护性遗传变异的能力，并确定 在这种独特的队列中，基因的遗传特征和基因途径，以防止主要发作 糖尿病，心血管疾病，癌症和阿尔茨海默氏病和其他痴呆症类型等疾病。