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

甲状腺激素对人体正常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的遗传结构和b)一般人群中甲状腺轴的设定点方面取得了进展，但循环代谢物的变化可能反映和介导遗传易感性（或遗传对甲状腺功能的影响）的影响尚未得到系统的研究。综合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）——即所谓的多组学分析——现在是可能的。这使得基因型-途径-表型关联的系统调查成为可能，如果在单基因甲状腺疾病患者和大型代表性人群为基础的队列（密集输入的基因型信息和甲状腺功能评估）中进行大规模的研究。通过与剑桥大学MRC流行病学部门和威康-MRC代谢研究实验室的合作，我的提议汇集了互补的临床和流行病学研究来利用这些机会，并得到了甲状腺功能和分子流行病学方面的国际专家的支持。为了研究决定甲状腺功能的等位基因频率谱，我提出了一套协同分析，系统地描述了这种变异的代谢影响，使用非靶向、质谱衍生的代谢组学对单基因疾病患者[对甲状腺激素α和β （RTHα和RTHβ）的抵抗]和一般人群（EPIC-Norfolk和Fenland队列）进行了分析。在人群队列中，候选代谢物的变化将被检测与合并症的关联。网络方法将使代谢物变化的模式外推到途径水平。观察结果的因果关系方向将通过遗传预测进行测试，使用的仪器来自迄今为止英国生物银行（UK Biobank）公开数据中最大的代谢组特征发现研究（MRC流行病学部门提供的预发表访问）。我的建议建立并扩展了我以前的工作，通过整合遗传学，代谢组学和疾病的大规模数据，使用独特的患者和人群队列，研究甲状腺功能的代谢特征。简而言之，我的目标是发现控制甲状腺状态的罕见和常见的人类遗传变异的生化和代谢后果。此外，在我回国后，这个项目中描述的统计方法可以转移到其他疾病背景。

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.