Functional characterisation of genetic variants influencing human food preferences using bioinformatics and animal models

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Background: Nutrition is one of the most important factors influencing human health. Unhealthy food choices can lead to several disorders such as obesity, metabolic syndrome, cardiovascular disease and some cancers. Although many studies have focused on the genetics of these disorders, geneticists have only recently started looking at food intake, although with very limited results. In countries where food availability is not an issue, food preference is one of the most important factors driving food choice and consumption. Various studies suggest that food-liking measures are better predictors of actual long-term consumption compared to self-reported food frequency questionnaires. Our recent genome-wide association studies have highlighted numerous genes associated with differences in specific food preferences. Despite these successes, none of the genes encode taste or olfactory receptors and so their role in determining food preferences is still poorly understood. The aim of the project is to identify which of the known taste preference genes have the potential to lead to altered metabolic states and to characterise these genes functionally in cell and animal models. The project has three elements: 1) Definition of the impact of the genetic variants associated with food preferences on reported food consumption - Using the food frequency questionnaires for all 500,000 participants to UK Biobank, and their genotype data (available in early 2017), we shall assess the impact of the 16 previously described food preference genetic variants and functional variants in taste receptor genes on reported food consumption. Although it is known that these types of questionnaires are subject to reporting biases, the very large numbers provide such statistical power that even very small effects can be detected. This step will be important to select which genetic variants are carried forward. 2) Bioinformatic characterisation of the selected genetic variants - The variants selected in the previous steps will be characterized using bioinformatics tools to formulate hypotheses regarding which genes they affect, how they influence the genes and in which tissues. This is important because in many cases genetic variants do not regulate the closest gene but rather have longer range effects. The identification of the actual target, its tissue expression, and potential for interaction with known appetite- and metabolism-related pathways is of fundamental importance for formulating mechanistic hypotheses. 3) Functional characterisation of the genetic variants through the use of cell culture and animal models - The most relevant genetic variants and genes will be studied functionally in animal models and cell cultures, in 3 main stages. (a) Mapping of expression of the candidate genes in brain circuits involved in food preference (e.g. hypothalamic pathways involving the melanocortin MC4 receptor or oxytocin signalling with known links to fat and sugar preference).(b) Modelling the effects of CRISPR-mediated gene knockouts on neuronal function and canonical signalling after CRISPR-targeted knockout or variant-associated regulatory deletion/modification of the gene in a relevant neuronal cell model in vitro (e.g. human SH-SY5Y). (c) CRISPR-targeting of the most promising and/or tractable gene in embryos to rapidly generate a novel transgenic model for full in vivo characterization of the impact of gene deletion on behavioural food preferences (Leng and Menzies, CIP) and on in vivo metabolism (Morton, CVS).Training Outcomes General epidemiology Handling and analysis of very large-scale food frequency questionnaire data. Integrative bioinformatics characterisation of genetic variants Data mining of large publicly available resources In vitro and in vivo modelling of novel genes/biology of animal model, including cell culture, cell-based assays, confocal imaging, cutting-edge transgenic tools, in vivo behavioural and metabolic assays