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Exploring Genetics of Type 2 Diabetes

A global study combining the efforts of more than 300 scientists is revealing the genetic roots of Type 2 Diabetes through the use of genomic big data.

Type 2 Diabetes - EHN

A global study combining the efforts of more than 300 scientists from 22 different countries around the world is revealing the genetic roots of Type 2 Diabetes through the use of genomic big data.

The Study, which was published last month in Nature, combined the genomic sequencing data obtained from two collaborative research projects on Type 2 Diabetes, as well as integrating the data from further 111,000 individuals to form this large scale study.

The first collaborative research project, called the Genetics of Type 2 Diabetes (GoT2D) project, provided 2,600 genetic samples from the United Kingdom, Sweden, Finland and Germany. Half of these individuals had been diagnosed with Type 2 Diabetes, while the remainder did not have Type 2 Diabetes.

The second study, called the Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) project, sequences the genomes of nearly 13,000 individuals who could trace their ancestry to regions all around the world.

National Institutes of Health Director, Dr Francis Collins, described the trials as being, “unprecedented in its investigative scale and scope,” with the inclusion of the “largest-ever inventory of DNA sequence changes involved in Type 2 Diabetes (T2D).”

He further explained: “While diet and exercise are critical contributory factors to this potentially devastating disease, genetic factors are also important. In fact, over the last decade alone, studies have turned up more than 80 genetic regions that contribute to T2D risk, with much more still to be discovered.”

Collins further stated that the study’s Big Data was already yielding new and important insights into the biology underlying the disease. He believes that some of these insights may reveal novel approaches to treatment and prevention.

The study has revealed over a dozen genes that contain diabetes-related variants that changed the structure and function of proteins and could influence the presence of Type 2 Diabetes.

“Several of these new coding-region discoveries offer intriguing clues about the mechanisms underlying T2D,” said Collins.

“For example, the data uncovered a single coding variant in a gene called PAX4 that was powerfully associated with T2D, but only in people from East Asian countries, including Korea, China and Singapore.”

“The study also implicated another gene, called TM6SF2, already known for its role in the development of fatty liver disease, a chronic liver condition that is very common in people with T2D and often makes their diabetes harder to control,” continued Collins.

The study takes a much deeper look into genetics than the standard genome-wide association studies (GWAS), which provide scientists with a broad view of genetic variants that may be implicated in the likelihood of developing certain diseases. These GWAS studies lack a sufficiently detailed view of genetics which would allow for personalised medicine.

“By doing direct sequencing in GoT2D and T2D-GENES, it was possible to search directly for rare changes in the coding region of protein-coding genes that had been invisible before,” Collins explained.

The study has stirred new conversation among Type 2 Diabetes researchers, with some advocating that common genetic variants are behind predispositions, whilst others argue that rarer inherited genetic traits are responsible.

“Clearly from this extensive analysis, the first option is right: it is common variants that provide the majority of the genetic risk for this disorder,” said Collins.

“Taken together, the findings suggest that everyone carries an assortment of genetic variants related to T2D, including some that may offer protection and others that may place us at greater risk for disease,” concluded Collins.

Significant progress has been made in understanding the disease, but further research will be required to understand the risk factors and thus predict a patient’s likelihood to develop Type 2 Diabetes within their lifetime.

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