Integration of genetic, metabolic and phenotypic variation related to thyroid function using large-scale monogenic thyroid disorder and population-based datasets

使用大规模单基因甲状腺疾病和基于人群的数据集整合与甲状腺功能相关的遗传、代谢和表型变异

• 批准号: 407063889
• 负责人: Dr. Maik Pietzner
• 金额: --
• 依托单位: BIH Zentrum Digitale Gesundheit
• 依托单位国家: 德国
• 项目类别: Research Fellowships
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2018-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/407063889?language=en
• 关键词: Integration; genetic; metabolic; phenotypic; variation

Thyroid hormones are critical for normal human development and metabolic processes. Despite recent progress in identifying the genetic architecture of a) rare monogenic thyroid disease and b) the set point of the thyroid axis in the general population, changes in circulating metabolites that may reflect and mediate the effects of genetic susceptibility (or genetic influences on thyroid function) have not been systematically investigated. Comprehensive measurement of circulating bioactive substances (small molecules or proteins) reflecting diverse physiological and biochemical processes – so called multi-OMICs profiling – is now possible. This enables systematic investigation of genotype-pathway-phenotype associations if performed at scale in well characterized studies of monogenic thyroid disease patients on the one hand and large representative population based cohorts (with densely-imputed genotypic information and assessment of thyroid function) on the other. Through collaboration with the MRC Epidemiology Unit and Wellcome-MRC Metabolic Research Laboratories at the University of Cambridge, my proposal brings together complementary clinical and epidemiological studies to harness these opportunities, supported by international experts in thyroid function and molecular epidemiology. Investigating the spectrum of allele frequencies determining thyroid function, I propose a synergistic set of analyses that systematically characterizes the metabolic impact of such variation using non-targeted, mass spectrometry-derived metabolomics undertaken in patients with monogenic disease [Resistance to Thyroid Hormone α and β (RTHα and RTHβ)] and in the general population (EPIC-Norfolk and Fenland cohorts). In population cohorts changes in candidate metabolites will be tested for association with comorbidities. Network approaches will enable extrapolation of patterns of metabolite changes to the pathway level. Direction of causality of observations will be tested by genetic prediction using instruments from the largest discovery study on metabolome-traits (prepublication access provided by the MRC Epidemiology Unit) done to date in the publicly available data from UK Biobank. My proposal builds on and expands my previous work on the metabolic characterization of thyroid function by integrating large-scale data on genetics, metabolomics and diseases using unique patient and population cohorts. In brief, I aim to discover the biochemical and metabolic consequences of both rare and common human genetic variants that control thyroid status. Furthermore, on my return, the statistical methodologies described in this project can be transferred to other disease contexts.

甲状腺激素对正常的人体发育和代谢过程至关重要。尽管最近在确定a）罕见单基因甲状腺疾病和B）一般人群甲状腺轴设定点的遗传结构方面取得了进展，但尚未系统研究可能反映和介导遗传易感性（或遗传对甲状腺功能的影响）的循环代谢物变化。现在可以全面测量反映不同生理和生化过程的循环生物活性物质（小分子或蛋白质）-所谓的多OMIC分析。如果一方面在单基因甲状腺疾病患者的充分表征的研究中进行大规模研究，另一方面在基于大代表性人群的队列（具有密集插补的基因型信息和甲状腺功能评估）中进行，则这使得能够系统地研究基因型-途径-表型关联。通过与剑桥大学MRC流行病学单位和Wellcome-MRC代谢研究实验室的合作，我的建议汇集了互补的临床和流行病学研究，以利用这些机会，并得到甲状腺功能和分子流行病学国际专家的支持。通过调查决定甲状腺功能的等位基因频率谱，我提出了一组协同分析，该分析使用非靶向的、质谱衍生的代谢组学，系统地表征了这种变异对单基因疾病[甲状腺激素α和β抵抗（RTHα和RTHβ）]患者和普通人群（EPIC-诺福克和芬兰队列）的代谢影响。在人群队列中，将检测候选代谢物的变化与合并症的相关性。网络方法将能够将代谢物变化的模式外推到途径水平。将使用迄今为止在UK Biobank公开可用数据中完成的最大的代谢组特征发现研究（由MRC流行病学部门提供的出版前访问）的工具，通过遗传预测来检验观察结果的因果关系方向。我的建议建立在并扩展了我以前关于甲状腺功能代谢特征的工作基础上，通过使用独特的患者和人群队列整合遗传学，代谢组学和疾病的大规模数据。简而言之，我的目标是发现控制甲状腺状态的罕见和常见人类遗传变异的生化和代谢后果。此外，在我回来后，这个项目中描述的统计方法可以转移到其他疾病的情况下。