Analysis Methods Core

分析方法核心

• 批准号: 10616721
• 负责人: PAOLA SEBASTIANI
• 金额: $ 42.44万
• 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/10616721
• 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; Development; Diabetes Mellitus; Diagnosis; Dimensions; 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; Methylation; Mission; Modeling; Molecular; Participant; Pathway interactions; Pattern; Persons; Phase; Phenotype; Procedures; Process; Proteomics; Publications; RNA; 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; 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; grandchild; 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.

长寿家庭研究 (LLFS) 已在美国和丹麦招募了 539 个血统的 4,953 名参与者 这些产品具有超长的使用寿命，并在两次广泛的家访中对其进行了纵向测量 测量衰老过程所有主要领域的关键健康衰老表型。我们有 通过许多出版物证明，选择第一代（先证者）的长寿会导致 第二代（后代）在许多关键表型上都比平均水平健康得多。然而， 谱系在表型上是异质的，不同的家族表现出家族聚集性的保护 认知、握力、肺功能、血压等，进一步综合联动分析 LLFS 样本识别出横截面和纵向的极强遗传连锁峰 各种健康老龄化领域（例如异常认知）的表型变化轨迹率 表现和缺乏阿尔茨海默病。这些峰不能用 GWAS SNP（或那些 可以通过 GWAS 进行估算）。谱系特定 LOD 和初步深度测序表明这些峰 是由在选定的谱系中运行的稀有保护性变体驱动的。我们建议做全基因组 对这个独特的队列进行测序，以确定驱动这些强连锁峰的罕见保护性变异。我们 建议通过第三次亲自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，并添加痴呆症诊断的正式评估 阿尔茨海默氏病和其他痴呆症类型，这将增加发现和跟踪的特异性和能力- 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，我们还建议对第三代（孙辈）进行表型测量， 因为它们可能携带更多在这些家族中运行的罕见保护性等位基因的副本，这将 成倍增加我们解决这些问题的能力。来自丹麦医疗登记处的初步证据 表明，至少在丹麦，这种保护一直持续到第三代，而且比率要低得多 涵盖整个疾病谱系的医疗状况。我们还建议进行广泛的转录组学， 对这些选定的高连锁谱系的甲基组学和蛋白质组学，开始从“统计上 相关变体/基因座”与作用的生物基因，因为我们预计大多数驱动变体将是 监管和非编码。找到这些罕见保护性变体的作用模式至关重要。我们也 建议对整个 LLFS 队列进行纵向代谢组学研究，目标是识别新的 在这个异常健康的人群中，健康衰老和抵抗阿尔茨海默病等疾病的生物标志物。 结合系统生物学/网络方法对拟议的“大数据”进行数据集成，例如 生物标志物将提高我们检测更多新颖的保护性遗传变异并识别 基因特征和基因通路在这个独特的群体中提供保护，以防止重大疾病的发生 糖尿病、心血管疾病、癌症、阿尔茨海默病和其他痴呆类型等疾病。