Project Details
Projekt
Integration of genetic, metabolic and phenotypic variation related to thyroid function using large-scale monogenic thyroid disorder and population-based datasets
Applicant
Dr. Maik Pietzner
Subject Area
Endocrinology, Diabetology, Metabolism
Term
from 2018 to 2019
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 407063889
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.
DFG Programme
Research Fellowships
International Connection
United Kingdom
