Systems genetic and reverse phenotypic analysis of age and retirement

年龄和退休的系统遗传和反向表型分析

• 批准号: 8882214
• 负责人: Steve Horvath
• 金额: $ 30.62万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2016-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8882214
• 关键词: Affect; Age; Aging; American; Americas; Behavioral; Biological; Birth; Cognitive; Cohort Studies; Computer software; Coupled; DNA; DNA Methylation; Data; Data Analyses; Data Set; Databases; Development; Elderly; Epidemiologist; Expenditure; Gene Expression; Genes; Genetic; Genetic Markers; Genome Scan; Genomics; Genotype; Goals; Health; Heterogeneity; Human; Impairment; Income; Individual; Lead; Link; Literature; Longevity; Machine Learning; Measures; Mental Health; Methodology; Methods; Methylation; Mining; Modeling; Molecular Genetics; Pathway Analysis; Pathway interactions; Personal Satisfaction; Phenotype; Physical Function; Population; Process; Psychologist; Psychosocial Factor; Research Personnel; Resources; Retirement; Sampling; Scanning; Single Nucleotide Polymorphism; Specificity; Statistical Models; Subgroup; Surveys; System; Techniques; Testing; Time; Variant; Weight; age effect; age related; base; bead chip; behavioral economics; child depression; cognitive function; cohort; design; economic implication; endophenotype; exome; gene environment interaction; genetic analysis; genetic variant; genome wide association study; health data; improved; interest; novel; novel strategies; psychosocial; repository; reverse genetics; social implication; sociologist; statistics; text searching

DESCRIPTION (provided by applicant): High throughput genomic platforms have revealed a profound effect of age on gene expression and gene methylation levels. For example, our integromic analysis of multiple publicly available data sets have identified hundreds of age related genes that can be organized into network modules. These gene sets along with genes and pathways known from the vast literature on aging and longevity provide valuable candidates in allelic association studies of the health and well-being of older Americans. The rich longitudinal data set and the genome wide scans of DNA samples from the Health and Retirement Study (HRS) provide a unique resource for evaluating the phenotypic effects of genetic variants underlying aging related genes, gene sets and for developing multivariable models that include behavioral, psychosocial, and genetic factors. This proposal aims to apply systems biologic and systems genetic methods for identifying gene sets based on existing gene expression, gene methylation, and multiple large scale genome wide association studies (GWAS). Single nucleotide polymorphisms (SNPs) underlying these genes will then be related to health, cognitive, behavioral, and economic phenotypes using multivariable regression-, machine learning-, and weighted network analysis approaches. An iterative process between SNP and phenotype selection facilitates the definition of clinically and economically important phenotypes under genetic control (reverse phenotyping). The proposal will not only elucidate the genetic and molecular underpinnings of retirement relevant phenotypes (cognitive functioning, psychosocial factors, and health related expenditures) but also lead to a phenotypic annotation of aging related genes and pathways.

描述（由申请人提供）：高通量基因组平台已经揭示了年龄对基因表达和基因甲基化水平的深刻影响。例如，我们对多个公开数据集的整合分析已经确定了数百个与年龄相关的基因，这些基因可以组织成网络模块。这些基因集沿着从大量关于衰老和长寿的文献中已知的基因和途径，为美国老年人健康和福祉的等位基因关联研究提供了有价值的候选者。来自健康与退休研究（HRS）的DNA样本的丰富纵向数据集和全基因组扫描为评估衰老相关基因、基因集的遗传变异的表型效应以及开发包括行为、心理和遗传因素的多变量模型提供了独特的资源。该提案旨在应用系统生物学和系统遗传学方法，基于现有的基因表达，基因甲基化和多个大规模全基因组关联研究（GWAS）来识别基因集。这些基因背后的单核苷酸多态性（SNP）将与健康，认知，行为和经济表型相关，使用多变量回归，机器学习和加权网络分析方法。SNP和表型选择之间的迭代过程有助于在遗传控制下定义临床和经济上重要的表型（反向表型）。该提案不仅将阐明退休相关表型（认知功能，心理社会因素和健康相关支出）的遗传和分子基础，还将导致衰老相关基因和途径的表型注释。