Disease context and longevity genes

摘要：疾病背景与长寿基因计划。许多基因介导的因素 除了一些基本的长寿机制外，可能还会影响疾病的进程 衰老和/或衰老。我们认为，对两者之间可能存在多大重叠的理解 影响疾病易感性的遗传变异，以及与疾病相关的过程，以及遗传变异 通过对大数据集进行遗传关联研究，可以获得对寿命有独特影响的数据 由患有不同疾病的人和活得特别长和健康的人组成 生活。我们将从资源中获得尽可能多的带有基因和测序数据的相关数据集 例如DBGaP，并将它们与针对不同类型数据集唯一寿命联盟(LC)数据集相结合 分析。这将导致空前庞大的组合数据集，具有出色的统计能力， 对于大量(即组合的原始数据)和元(即仅使用汇总统计数据)数据分析。相关 分析可以包括直接关联测试或孟德尔随机化(MR)测试，利用 用于因果分析的中间表型，但必须适应表型的协调， 控制种群分层，以及遗传效应的潜在异质性。处理以下问题的工具 将开发和应用表型协调，以及处理分层的分析方法 和异质性。事实上，分析方法的开发和实施符合 异质性将是拟议研究的一个主要特征。我们强调，所有来自其他信用证的调查结果 调查人员将在拟议的分析中接受测试，如果基因变异(例如，由 Perls-百岁老人计划)，通过对米勒-老鼠/细胞计划产生的基因进行正交学，这可能 发现有趣的人类遗传变异，或在可能的情况下，通过推定，作为中间表型 (例如，作为奥沃尔蛋白质组学产生的蛋白质或费恩代谢组学项目的代谢物) 可以接受MR测试。此外，从拟议分析中发现的所有感兴趣的因素也将 提供给其他调查人员以及价格系统生物学和吉克化学信息学 用于进一步和综合分析的核心。我们强调，拟议的分析可以在广泛的范围内进行 提供以前未记录在案的见解的各种方法。例如，如果糖尿病和肥胖症具有相同的基因 决定因素，然后将患有糖尿病和肥胖症的人结合起来，并将他们与 在没有糖尿病或肥胖的情况下长寿应该会揭示基因因素导致的普遍易感性 以前所未有的力量在所有或部分个人中引发代谢性疾病。

ABSTRACT: Disease Context and Longevity Genes Project. Many genetically-mediated factors contributing to longevity are likely to impact disease processes in addition to some fundamental mechanism of aging and/or senescence. We believe that an understanding of how much overlap there might be between genetic variants that impact disease susceptibility, as well as disease-related processes, and genetic variants that uniquely impact longevity can be obtained by pursuing genetic association studies with large data sets made up of individuals with different diseases and individuals that have lived an exceptionally long and healthy life. We will obtain as many relevant data sets with genotype and sequencing data as possible from resources such as dbGAP and combine them with unique Longevity Consortium (LC) data sets for different types of analyses. This will lead to unprecedentedly large combined data sets with excellent statistical power, amenable to either mega (i.e., combined raw data) and meta (i.e., only using summary statistics) data analyses. Relevant analyses can involve direct association testing or Mendelian Randomization (MR) testing leveraging imputed intermediate phenotypes for causality analysis, but will have to accommodate a harmonization of phenotypes, control for population stratification, as well as potential heterogeneity in genetic effects. Tools for dealing with phenotypic harmonization with be developed and applied, as will analytical methods for handling stratification and heterogeneity. In fact, the development and implementation of analytical methods that accommodate heterogeneity will be a main feature of the proposed research. We emphasize that all findings from other LC investigators will be tested in the proposed analyses, either directly if a genetic variant (e.g., arising from the Perls-Centenarians project), via orthology for genes arising from the Miller-Mice/Cells project that may harbor interesting human genetic variants, or via imputation, where possible, as an intermediate phenotype (e.g., as a protein arising from the Orwoll-Proteomics or metabolite from the Fiehn-Metabolomics projects) amenable to MR tests. In addition, all factors found to be of interest from the proposed analyses will also be provided to the other investigators as well as the Price-Systems Biology and Girke-Chemoinformatics cores for further and integrated analyses. We emphasize that the proposed analyses can be pursued in a wide variety of ways to yield previously undocumented insights. For example, if diabetes and obesity share genetic determinants, then combining individuals with diabetes and obesity and comparing them to individuals who have lived a long life without diabetes or obesity should reveal genetic factors mediating general vulnerabilities to metabolic diseases in all or a subset of individuals with unprecedented power.