Analysis Methods Core

分析方法核心

• 批准号: 10388282
• 负责人: PAOLA SEBASTIANI
• 金额: $ 49.51万
• 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 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10388282
• 关键词: Address; Aging; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Automobile Driving; Big Data; Biological; Biological Markers; Blood Pressure; Cardiovascular Diseases; Code; Cognition; Cohort Studies; Collaborations; Computer software; Custom; DNA; Data; Data Analyses; Dementia; Denmark; Diabetes Mellitus; Diagnosis; Disease; Disease Resistance; Documentation; Enrollment; Ensure; Face; Family; Family Study; Funding; Generations; Genes; Genetic; Goals; Grant; Hand Strength; Home visitation; Infrastructure; Joints; Life; Longevity; Longitudinal Studies; Malignant Neoplasms; Measures; Medical; Methodology; Methods; Mission; Modeling; Molecular; Participant; Pathway interactions; Pattern; Persons; Phase; Phenotype; Procedures; Process; Proteomics; Publications; RNA methylation; Recommendation; Registries; Reproducibility; Reproducibility of Results; Research; Research Personnel; Resources; Role; Running; Sampling Studies; Site; Specificity; Standardization; Statistical Methods; Structure; Systems Biology; Techniques; Testing; Training; Untranslated RNA; Validation; Variant; Visit; Work; analytical method; base; cognitive performance; cohort; complex data; data harmonization; data integration; deep sequencing; follow-up; genetic analysis; genetic linkage; genetic linkage analysis; genetic pedigree; genetic signature; genetic variant; genome sequencing; genome wide association study; genomic data; healthy aging; improved; machine learning method; meetings; metabolomics; methylomics; multidisciplinary; novel; novel marker; offspring; phenotypic data; prevent; proband; protective allele; pulmonary function; rate of change; research and development; software development; tool; transcriptomics; virtual; 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.

长寿家族研究（LLFS）在美国和丹麦的539个家系中招募了4,953名参与者 他们在两次广泛的家庭访问中纵向测量了他们， 在衰老过程的所有主要领域测量关键的健康衰老表型。我们有 通过许多出版物证明，在第一代（先证者）中选择长寿，导致 第二代（后代）在许多关键表型上比平均水平健康得多。但 家系的表型是异质性的，不同的家庭表现出保护性的家族聚集性， 认知、握力、肺功能、血压等。进一步， LLFS样本确定了横向和纵向极强遗传连锁峰 各种健康老龄化领域的表型变化率轨迹，如异常认知能力、 表现和缺乏阿尔茨海默病。这些峰不能用GWAS SNP（或那些 可由GWAS估算）。谱系特异性LOD和初步深度测序表明，这些峰 都是由在特定谱系中运行的罕见的保护性变体驱动的。我们建议做全基因组 对这个独特的队列进行测序，以鉴定驱动这些强连锁峰的罕见保护性变体。我们 建议通过第三次面对面访问继续对队列进行纵向评估，这将使我们能够 评估潜在的非线性老化模式，并增加痴呆症诊断的正式评估， 阿尔茨海默病和其他痴呆类型，这将增加特异性和发现和跟踪的能力- 研究针对阿尔茨海默病和其他痴呆症诊断的保护性变体。对于血统驾驶 多个强连锁峰，我们还建议表型测量第三代（孙子）， 因为这些人可能携带更多的稀有保护等位基因拷贝， 以指数方式增加我们解决问题的能力。丹麦医学登记处的初步证据 表明，至少在丹麦，这种保护持续到第三代，比率明显降低 疾病谱中的医疗条件。我们还建议进行广泛的转录组学研究， 甲基化组学和蛋白质组学在这些选定的高连锁谱系上的应用，开始从“统计学上的 相关的变异/基因座”的生物基因的作用，因为我们预计大多数驱动变异将是 监管和非编码。找到这些罕见的保护性变体的作用模式至关重要。我们也 我建议对整个LLFS队列纵向进行代谢组学研究，目的是确定新的 在这个异常健康的队列中，健康衰老和对阿尔茨海默氏症等疾病的抵抗力的生物标志物。 结合系统生物学/网络方法，对拟议的“大数据”进行数据集成， 生物标志物将提高我们检测更多新的保护性遗传变异的能力，并识别出 在这一独特的队列中赋予保护作用的基因的遗传特征和途径，以防止重大 糖尿病、心血管疾病、癌症、阿尔茨海默病和其他痴呆症。