A new research study led by researchers at Oxford and Liverpool Universities identified specific DNA sequence variants that correlate with the individual risk of type 2 diabetes and help explain the biological processes involved in disease development. The research paper, entitled “Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci” was published in the journal Nature Genetics.
Type 2 diabetes (T2D) is a chronic metabolic disease characterized by the inappropriate use of insulin by the body, whose pathogenesis is attributed to several factors. According to previous research, currently there are 56 established susceptibility loci (specific position or location of a gene) that contribute to the onset of T2D. However, researchers believe that this identification, mostly through large-scale genome-wide association studies, only covers a small percentage of the true contribution of genetic factors for disease development.
In order to further investigate how some of these known variants affect disease risk and increase knowledge of how genetics play a biological role in T2D, scientists studied 39 of the highlighted genome regions in 27,206 cases and 57,574 controls with European ancestry.
The analysis of the genetic data led to the identification of 49 distinct association signals in these loci, including strong signals in locations linked to interaction with FOXA2, a regulatory protein of gene expression. Furthermore, there were strong enrichment signals near the MTNR1B gene, which codes for one of the receptors of melatonin, a hormone that works as a biological clock involved in the sleep cycle and other physiological functions. In this specific region, researchers were able to implicate a single variant in the driving of the T2D association, confirming that the risk allele for this variant leads to changes in melatonin receptor expression and activity in insulin-producing pancreatic cells.
Wellcome Trust Senior Investigator Professor Mark McCarthy from the University of Oxford, and the study’s co-senior author, commented the research results in a press release: “For most regions of the genome associated with type 2 diabetes, it has not been clear how genetic variants affect disease risk. By getting closer to many of the specific genetic changes that influence diabetes risk, we could for the first time detect signals that point to molecules that are key to type 2 diabetes development.”
The study demonstrated that integration of genomic and genetic information can help identify the molecular mechanisms by which certain gene variants contribute to disease biology in key organs and translate this information to patient risk evaluation for T2D in a clinical setting.
