Computer-assisted study of regulatory gene variants expression implicated in type 2 diabetes and various comorbid diseases in the European populations of Russia.
Approved Research ID: 59397
Approval date: July 27th 2020
The International Diabetes Federation estimated that 415 million adults had diabetes mellitus in 2015. Major risk factors in the development of this disease and its complications are obesity, lifestyle factors, genetic predispositions, epigenetics and early developmental factors. Notwithstanding huge amount of papers demonstrating the influence of many genetic variants on type 2 diabetes development, there are numerous conflicting results that cast doubt on the molecular-genetic basis determining the development of the disease.
The aim of the proposed study is to elucidate the genetic and epigenetic causes of type 2 diabetes associated with regulatory gene variants, that resides in enchancers and promoters, worldwide and in particular, in Russia. In order to do this, we should answer the numerous questions. The main one is: What is the molecular basis of phenotypical penetration and expression of heterozygous regulatory and coding gene variants implicated in type 2 diabetes and comorbid diseases?
On the one hand, Onuchic et al. (doi: 10.1126/science.aar3146) revealed sequence-dependent CpG methylation imbalances at thousands of heterozygous regulatory loci in human cells that are enriched for random transitions between fully methylated and unmethylated states of DNA, which in turn could be the main disease-associated factors. On the other hand, a recent breakthrough in identification biomarkers of aging based on DNA methylation data (doi: 10.1038/s41576-018-0004-3; doi: 10.1093/nar/gkx1139) links organism development and cell identity maintenance to biological aging and various age-dependent diseases. Hence, it is tempting to analyze the bulk of data of this CpG methylation in-depth in order to calculate scores (such as polygenic risk scores) that help physicians to give an early diagnosis and treat diseases based on in-depth knowledge on epigenetic dynamics in heterozogous loci.
In our proposed work for analyzing the phenotypical expression of heterozygous regulatory gene variants implicated in type 2 diabetes we will construct various genetic context-driven, phenotypic-awared and cohort-informed prioritized or weighted datasets. After this, we will analyze and annotate these datasets by various available statistical procedures and with all known databases depositing genome-wide data to uncover heterozygous regions that characterizes by high cumulative association between the ranks of risk (and/or negative prognosis) of type 2 diabetes (and/or comorbid diseases) and special signatures in genetic context.
We plan to complete the project within one and half year. After this time period, we will construct screening panels composed of loci guiding physicians to reach an early diagnosis and treat type 2 diabetes and comorbid diseases in a more effective way.