A systems based approach to integrating genetic and longitudinal omics data to support diagnosis and prediction of common chronic disease

新技术正在以许多新的方式提供衡量健康和疾病的机会。产生的数据很复杂，即使是临床医生和卫生工作者也很难破译。这项提案将研究如何使用现代分子技术来测量血液中基因的活性和表达以及细胞中化学反应(代谢物)的特征，以帮助预测早期疾病。要做到这一点，我们需要探索全球基因表达和代谢物如何随着时间的推移而变化，以及这些纵向变化如何与其他新的分子和基因技术(称为组学)一起用于探索老龄化人口的疾病机制和易感性。为了探索“基因组”变异的生物学，并为遗传和基因组数据的临床整合奠定基础，我们将调查700对双胞胎在三个时间段的细胞和基因组表型的纵向关系，包括全球基因表达和代谢物，超过7年。研究对象来自TwinsUK队列，他们已经有了广泛的临床信息以及横截面的遗传和基因组数据。在现有数据的基础上，利用双胞胎设计提供的特定方法学机会和优势，我们将探索这些基因组特征如何跟踪和随时间变化，确定这种变化如何与潜在的遗传变异相关，并探索遗传和基因组数据对疾病风险和发病的共同贡献。我们还将探讨在一个综合的个性化医疗框架内监测这些情况和其他情况变化的潜在价值。我们将使用和开发新的分析方法来整合这些复杂的数据集，并建议哪些变化可能在临床相关试验和疾病本身中发挥作用。这项研究将为疾病理解提供新的见解，并刺激更大规模的努力，将现代基因和基因组数据结合起来，在未来临床上受益。这些研究将为个体化医学铺平道路。

New technologies are providing opportunities to measure health and disease in many novel ways. The data produced is complex and hard to decipher even by clinicians and health workers. This proposal will investigate how we can use modern molecular techniques which measure in blood the activity and expression of genes and the signatures of chemical reactions (metabolites) in the cell to help predict early disease. To do this we need to explore how global gene expression and metabolites alter over time and how these longitudinal changes along with other new molecular and genetic techniques (called omics) can be used to explore disease mechanisms and susceptibility in ageing populations. To explore the biology of "omic" variability, and to lay the foundation for the clinical integration of genetic and genomic data, we will investigate the longitudinal relationships of cellular and genomic phenotypes, including global gene expression and metabolites, in 700 twins over 7 years, measured at three time-periods. The study subjects derive from the TwinsUK cohort on whom there is already extensive clinical information and cross-sectional genetic and genomic data. Building on these existing data, and making use of the specific methodological opportunities and advantages afforded by the twin design, we will explore how these genomic traits track and vary over time, determine how such variation relates to underlying genetic variation, and explore the joint contribution of genetic and genomic data to disease risk and onset. We will also explore the potential value of monitoring changes in these and other situations within an integrated personalised medicine framework. We will use and develop new analysis approaches to integrate these complex data sets and suggest which changes might play a role in clinically relevant tratis and disease itself. This study will provide novel insights into disease understanding and stimulate larger-scale efforts to combine modern genetic and genomic data for clinical benefit in the future. These studies will pave the way for individualised medicine.