Skip to navigation Skip to main content Skip to footer

Approved research

Identification of novel risk variants associated with metabolism and type 2 diabetes

Principal Investigator: Professor Jian Yan
Approved Research ID: 52238
Approval date: September 24th 2019

Lay summary

Diabetes is a chronic, metabolic disease characterized by elevated levels of blood glucose, which leads over time to serious damage to the heart, blood vessels, eyes, kidneys, and nerves. According to World Health Organization, an estimated 1.6 million deaths were directly caused by diabetes in 2016. Type 2 diabetes, which accounts for 90-95% of the total number of diabetic patients, is a complex, heritable disease. It means that in addition to environmental factors such as obesity and lack of exercise, genetic factor also plays an important role in aetiology of type 2 diabetes. Genetics studies have shown that type 2 diabetes (T2D) is not caused by a single gene variation, but instead multiple risk mutations added up significantly increase the susceptibility. Therefore, traditional methods cannot identify these genes. In the past ten years, genome-wide association study has identified about 240 risk loci that may predispose type 2 diabetes. However, the association between single-nucleotide polymorphisms (SNPs) and disease risk is merely based on statistics. The causal single-nucleotide polymorphisms are not necessarily the genome-wide association study hits due to the low minor allele frequency in tested population. So, it is important to narrow down the causal SNPs list and find the real risk variant. In our previous study, we found that over 11,000 single-nucleotide polymorphisms displayed a significant impact on the binding of transcription factors which can regulate gene expression. In this study, we have systematically evaluated the impact of these SNPs on protein binding and regulatory activity, which narrows down the candidate of causal variants in T2D. We will then test the enrichment of these variants in the diabetic patients compared with non-T2D controls from UK biobank and determine the final list of causal candidates that will be used as biomarkers to assess the risk of T2D. Furthermore, we can build a machine learning model to assess the risk of T2D by using the collection of genotypes of these causal candidates. The whole project is expected to take two years. The results will improve accuracy of early clinical diagnosis of type 2 diabetes, and be of great significance for development of therapeutic strategy.