부산대학교 도서관

Chronic diseases place a huge burden on society and are the leading cause of death worldwide. Obesity, which is currently a worldwide epidemic, is a risk factor for chronic disease. Understanding drivers of food choices may help to address the escalating obesity problem. Many people are at an increased risk of obesity as they carry genetic variants linked to particular eating behaviours. Identification of causal variants can help to understand which genes and biological mechanisms underlie food preference. Previous work used UK Biobank data from ~500,000 individuals to identify 302 loci associated with food consumption. This research project aimed to understand which genes were responsible for these genetic associations, prioritise genes for functional validation study, and understand the function of these genes through in vivo food preference studies. It is well-known that there can be intake-related bias in food frequency questionnaire responses due to participants over- or under-reporting dietary intake and this makes nutritional research difficult. Mendelian randomization was used to statistically identify reporting bias in the UK Biobank food frequency questionnaire responses and these were subsequently corrected by the group using a bespoke method. A 6-stage gene prioritisation strategy was developed to identify a candidate gene for functional validation in vivo. Genes were allocated points based on three main components: gene expression in humans and mice, conservation of gene product across species using pairwise protein alignments, and knockout phenotype details from available mouse models. Genes were ranked in order of preference for functional follow-up study. The top two genes identified were IER5L (associated with meat and fat) and DCAF12 (associated with salt). It was difficult to determine a coherent role for IER5L in food preference. The top SNP mapped to IER5L was found to be in an eQTL containing two other genes: CRAT and PPP2R4. It was not possible to exclude any of these genes from the meat/fat association, but there were clear links in the literature between CRAT and food intake. CRAT was selected for functional follow up, along with the DCAF12 gene associated with adding salt to food. However, it was not possible to follow up the CRAT gene due to logistical reasons. The project instead progressed to the in vivo stage using the well-known MC4R appetite regulatory gene to develop a model of food preference that could be used with Crat-/- mice in the future. A novel 3-food preference model was developed using Mc4r-/- mice and WT mice. Mice were given a choice of three diets: protein-enriched diet (to model the CRAT meat association), fat-enriched diet (to model the CRAT fat association) and standard chow. Mice preferred to eat a completely fat diet or a mixed choice diet regardless of genotype. Mc4r-/- mice were heavier than WT mice but were not hyperphagic as was expected from previous studies. As expected, Mc4r-/- mice demonstrated hyperinsulinemia compared to WT mice but there were no differences in glucose or triglyceride levels found between Mc4r-/- and WT mice. In vivo modelling of the DCAF12 salt association was carried out using Dcaf12-/- and WT mice to investigate salt preference. Mice were given a choice of two bottles of water followed by a choice of 0.4% (75mM) salt solution and water. This was repeated using a higher concentration 0.8% (150mM) salt solution. Dcaf12-/- mice did not show a preference for the salt solution as expected, and preference reduced as salt concentration increased. Unexpectedly, mice demonstrated a right-hand side preference during the water bottle choice period. Furthermore, mice consumed less food as salt concentration increased. This salt-preference study highlighted a number of complexities that can be associated with translating human effects into in vivo models. Research into the genetics of eating behaviour can help to identify and understand the impact of genetic variants on dietary pathways. If we can understand biological drivers of individual food choices, this information may be used to guide people towards a healthier lifestyle and reduce levels of obesity.