Approved Research

Detecting genetic effects on the variability of cardiometabolic risk profiles and clinical outcomes

Lay summary

Aims: This project aims to detect the genetic effects on the variability of cardiometabolic risk profiles and related clinical outcomes. The project will also explore whether these signals can capture gene-gene (GxG) and gene-environment (GxE) interactions underlying cardiometabolic disease risk.

Scientific rationale: Obesity is a major public health concern and a key risk factor for cardiometabolic disease. Obesity is influenced by multiple genetic variants, environmental factors, and their interactions. Previous studies have identified evidence for both GxG and GxE interactions underlying obesity. Identifying such interactions is challenging, but the results better characterise the genetic architecture of obesity and its associated disease risk. Furthermore, the presence of GxE interactions suggests potential for targeted lifestyle interventions in individuals of particular genotypes to reduce obesity levels and susceptibility to cardiometabolic disease.

The presence of GxG or GxE interactions leads to an increase of phenotypic variability. Therefore, detecting the genetic effects on trait variability (vQTLs) is an alternative approach to capture GxG and GxE interactions. Previous vQTL studies of cardiometabolic traits identified several GxE interactions in UK Biobank (Wang et al. 2019 Science Advances, Westerman et al. 2022 Nature Communication, Lu et al. 2022 Genetics). These studies focused on anthropometric measures, such as BMI, and circulating cardiometabolic risk biomarkers. Here, we expand this work by considering additional measures of adiposity including body fat distribution measured by MRI and DXA. This is of particular relevance to cardiometabolic health because central adiposity has stronger association with metabolic disease than whole-body fat. Overall, this project seeks to detect vQTLs for multiple measures of adiposity, body fat distribution, cardiometabolic risk biomarkers, and related clinical outcomes. If vQTLs are detected, follow up analyses will explore evidence for GxG and GxE interactions at selected signals. GxG analyses will consider interactions with previously detected GWAS signals. GxE interaction analysis will be conducted with multiple environmental factors, including smoking, exercise, dietary intakes, and others. Lastly, the project will explore whether the vQTLs, and GxG and GxE interaction effects can be incorporated in polygenic risk scores to improve the prediction of cardiometabolic traits and disease.

Project Duration:  The project forms part of a PhD thesis and should be completed within 3 years.

Public Health Impact: Our findings could provide novel insights into gene-environment interactions of cardiometabolic risk and clinical outcomes. The current study may also help to develop personalized prevention and prediction strategies for obesity and cardiometabolic disease risk.