Disease context and longevity genes

摘要：疾病背景和长寿基因项目。许多遗传因素 除了一些基本的机制外， 老化和/或衰老。我们认为，了解两者之间可能存在多少重叠， 影响疾病易感性以及疾病相关过程的遗传变异，以及遗传变异 可以通过对大数据集进行遗传关联研究来获得对寿命有独特影响的基因 由患有不同疾病的人和在一个特别长的健康的环境中生活的人组成 生活我们将从资源中获得尽可能多的具有基因型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的 分析。这将导致前所未有的大的组合数据集，具有出色的统计能力， 到兆（即，组合的原始数据）和Meta（即，仅使用汇总统计）数据分析。相关 分析可能涉及直接关联检验或孟德尔随机化（MR）检验， 用于因果关系分析的中间表型，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 表型的协调与发展和应用，将分析方法处理分层 和异质性。事实上，开发和实施分析方法， 异质性将是拟议研究的主要特征。我们强调，其他LC的所有结果 研究者将在拟议的分析中接受测试，或者直接测试遗传变异（例如，产生 Perls-Centenarians项目），通过对米勒小鼠/细胞项目产生的基因的直系同源性， 隐藏有趣的人类遗传变异，或通过插补，在可能的情况下，作为中间表型 (e.g.,作为Orwoll-蛋白质组学产生的蛋白质或Fiehn-Metabolomics项目的代谢物） 进行MR测试。此外，从拟议分析中发现的所有相关因素也将 提供给其他研究者以及Price-Systems Biology和Girke-Chemoinformatics 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 各种方法来产生以前没有记录的见解。例如，如果糖尿病和肥胖症有相同的遗传基因， 决定因素，然后将患有糖尿病和肥胖症的个体与患有糖尿病和肥胖症的个体进行比较， 没有糖尿病或肥胖的长寿者应该揭示介导一般脆弱性的遗传因素 对所有或部分个体的代谢性疾病具有前所未有的影响力。

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.