Analysis of Genome-Wide Data in the Health and Retirement Study

健康与退休研究中的全基因组数据分析

• 批准号: 9134583
• 负责人: Daniel J Benjamin
• 金额: $ 32万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9134583
• 关键词: Accounting; Aging; American; Architecture; Bayesian Method; Behavioral; Behavioral Mechanisms; Biological; Characteristics; Cognitive; Collection; Computer software; Data; Data Set; Dementia; Development; Economics; Environment; Feedback; Fostering; Gene Frequency; Genetic; Genetic Polymorphism; Genetic study; Genomics; Genotype; Goals; Health; Individual; Joints; Lead; Light; Measures; Mediating; Medical; Memory; Meta-Analysis; Methods; Molecular; Pathway interactions; Personal Satisfaction; Personality Traits; Phenotype; Provider; Proxy; Request for Proposals; Research; Research Infrastructure; Research Personnel; Resources; Respondent; Retirement; Risk; Sample Size; Sampling; Science of genetics; Scientist; Social Sciences; Statistical Methods; Surveys; Testing; Time; Trust; Variant; Weight; Work; Yang; behavior measurement; behavioral genomics; behavioral outcome; economic behavior; economic outcome; endophenotype; gene discovery; gene environment interaction; genetic analysis; genetic association; genome wide association study; genome-wide analysis; genomic data; health data; improved; indexing; learning strategy; meetings; multidisciplinary; preference; psychologic; response; social; social genomics; tool

 DESCRIPTION (provided by applicant): The Health and Retirement Study (HRS) has the potential to become a critical resource in social-science genetics in general, and the behavioral genomics of aging in particular, due to its relatively large sample size, its rich longitudinal behavioral measures, and the availability of dense genomic data for approximately 13,000 older Americans, with data on 7,000 more on the way. The proposed research will use the HRS's rich phenotypic, genetic, and environmental data to pursue two complementary strategies. One is to incorporate HRS data into large consortium meta-analyses of behavioral phenotypes conducted under the auspices of the Social Science Genetic Association Consortium (SSGAC), which the applicants organize. The second strategy is to use the HRS data to test specific hypotheses arising from the consortium's findings and to shed light on the genetic architecture-i.e., the joint distribution of genetic effect sizes and allele frequencies-of the rich set of behavioral phenotypes measured in the HRS. Our general aim is to use the phenotypic, genetic, and environmental data from the HRS to significantly advance understanding of behavioral genomics in general, and of the economic behavior, health, and well- being of older Americans in particular. Among the goals of our proposal are: (a) discoveries of specific genetic polymorphisms that are associated with important behavioral outcomes, psychological characteristics, and economic preferences; (b) analysis of biological pathways that underlie these associations; (c) development of polygenic scores (indexes of many polymorphisms) that, when constructed with weights estimated in large samples, will have substantial predictive power for behavioral phenotypes; (d) identification of behavioral mechanisms (i.e., endophenotypes) that mediate associations with specific polymorphisms and polygenic scores; (e) analysis of the genetic architecture of a range of phenotypes measured in the HRS; and (f) examination of gene-environment interactions.

 描述（由适用提供）：健康和退休研究（HRS）有可能成为一般社会科学遗传学的关键资源，尤其是衰老的行为基因组学，由于其相对较大的样本量，其丰富的纵向行为测量值，其较丰富的纵向行为测量值，以及大约13,000名在7,000名数据上的大约13,000名美国人的密集基因组数据的可用性以及大约13,000名。拟议的研究将使用HRS丰富的表型，遗传和环境数据来购买两种完整的策略。一种是将人力资源数据纳入申请人组织的社会科学遗传联盟（SSGAC）的主持下进行的行为表型的大型联盟荟萃分析。第二个策略是使用HRS数据来测试由财团的发现引起的特定假设，并阐明遗传体系结构-I.E。，遗传效应大小的联合分布以及等位基因在HRS中测得的丰富行为表型的频率集。我们的总体目的是使用从人力资源管理的表型，遗传和环境数据，以显着提高人们对行为基因组学的理解，以及尤其是老年人的经济行为，健康和良好的经济行为，健康和良好。我们提案的目标之一是：（a）与重要的行为结果，心理特征和经济偏好相关的特定遗传多态性的发现； （b）分析这些关联基础的生物学途径； （c）开发多基因评分（许多多态性的索引），当用大型样品中估计的权重构建时，将对行为表型具有实质性的预测能力； （d）鉴定介导与特定多态性和多基因分数相关的行为机制（即内型型）； （e）分析在HRS中测量的一系列表型的遗传结构； （f）检查基因环境相互作用。