Systems genetic and reverse phenotypic analysis of age and retirement

描述（由申请人提供）：高通量基因组平台揭示了年龄对基因表达和基因甲基化水平的深远影响。例如，我们对多个公开可用数据集的整体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。这些基因集以及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）的丰富的纵向数据集和DNA样本的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、社会心理和遗传因素的多变量模型提供了独特的资源。本提案旨在应用系统生物学和系统遗传学方法来识别基于现有基因表达、基因甲基化和多个大规模基因组全关联研究（GWAS）的基因集。然后，利用多变量回归、机器学习和加权网络分析方法，将这些基因背后的单核苷酸多态性（snp）与健康、认知、行为和经济表型联系起来。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上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、社会心理因素和健康相关支出）的遗传和分子基础，而且还将导致衰老相关基因和途径的表型注释。

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.