Two large-scale gene association projects have identified a large number of new loci that influence glycemic traits such as blood glucose and insulin levels, and impact on the risk of type 2 diabetes. The two research consortia report their data independently in Nature Genetics, and hope the results will provide new insights into the genetic networks and mechanisms that control glucose and insulin, and the genetic interplay that impacts on susceptibility to type 2 diabetes.

Meta-analyses of genome-wide association studies (GWAS) previously carried out by the Meta-Analysis of Glucose and Insulin-related traits Consortium (MAGIC) had identified multiple loci associated with glycemic traits including fasting glucose, fasting insulin, and post-challenge glucose concentrations. However, while this work had highlighted important biological pathways, the costs associated with de novo genotyping meant only a limited number of loci from discovery analyses were taken forward in to follow-up analyses.

To progress their studies further, the team turned to the Illumina CardioMetabochip (Metabochip), a custom iSELECT array of 196,725 SNPs that has been developed to support large-scale follow-up studies of SNPs potentially involved in a range of cardiovascular and metabolic traits, and fine map established loci. The chip includes 66,000 SNPs for cardiovascular and metabolic traits, and the MAGIC consortium had contributed about 5,000 of the top-ranking SNPs for fasting glucose, and 1,000 SNPs each for fasting insulin concentration and post-challenge glucose.

Their new work comprised a genome-wide association meta-analysis of some 133,000 nondiabetic individuals of European descent, including individuals from the previous meta-analyses, individuals from new genome-wide association studies, and individuals newly genotyped on the Metabochip array. The results identified 41 glycemic associations that hadn’t previously been described, including 20 for fasting glucose concentration, 17 for fasting insulin concentration, and four for post-challenge glucose concentration (2-hour glucose, or 2hGlu). Most notably, of the 53 nonoverlapping loci, 33 were also associated with type 2 diabetes.

The MAGIC researchers hope that functional analyses of the newly identified loci will aid in the drive to more fully understand biological mechanisms involved in controlling glucose and insulin levels. “Consideration of such loci in future studies will better exploit data from GWAS and complimentary approaches and further improve our biological understanding of glycemic control and the etiology of diabetes,” they state. Their published paper is titled “Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.”

Importantly, they add, data from large-scale association analyses will help map overlapping pathways that are almost certainly involved in the interplay between the regulation of glucose, insulin, and the development of type 2 diabetes. “Our research is beginning to allow us to look at the overlap between genomic regions that influence insulin levels and other metabolic traits,” comments Inga Prokopenko, co-lead author at the University of Oxford. “We observed some overlap between the regions we identified and genetic regions associated with abdominal obesity and various lipid levels, which are a hallmark of insulin resistance. We hope that these studies will help to find gene networks with potential key modifiers for important metabolic processes and related diseases, such as type 2 diabetes.”

The work also hinted at the relevance of many more, but less significant, genetic regions. What’s needed now, the investigators point out, is more data to definitively establish them as significant. “There is statistical evidence that many other regions that appear to be biologically plausible also influence these traits, but what’s limiting is that we don’t have large enough sample sizes to have the power to validate them,” explains Ines Barroso, M.D., co-lead author from the Wellcome Trust Sanger Institute. “Nevertheless, studying these functionally would be extremely beneficial if we want to fully understand the biology of blood glucose levels and the origin of diabetes.”

In a separate paper published in parallel in Nature Genetics, a consortium led by investigators at the Wellcome Trust Centre for Human Genetics at the University of Oxford, reports on a meta-analysis of type 2 diabetes-associated genetic variants including 34,840 cases and 114,981 controls, primarily of European descent. The analysis combined data from the GWAS meta-analysis conducted by the  DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) consortium, with a stage 2 meta-analysis comprising 22,669 cases and 58,119 controls genotyped with Metabochip.

The results identified 10 new loci harboring SNPs that could reliably be linked with the risk of type 2 diabetes. One of these loci was associated specifically with disease risk in men, and another was linked specifically with disease risk in women.

The researchers say their findings take to 60 the number of genes and gene regions now linked with type 2 diabetes. Notably, the enlarged set of loci implicate processes including CREBBP-related transcription, adipocytokine signaling and cell cycle regulation, in development of the disease. Results from the studies are published in a paper titled “Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes”.

To increase the bank of genetic data even further, lead researcher professor Mark McCarthy at the Wellcome Trust Centre for Human Genetics has headed a University of Oxford team working with collaborators in the U.S. and Europe to sequence the entire genomes of 1,400 type 2 diabetic and non-diabetic individuals. First results will be available next year.

“Now we have the ability to do a complete job, capturing all genetic variation lined to type 2 diabetes,” professor McCarthy states. “Not only will we be able to look for signals we’ve so far missed, but we will also be able to pinpoint which individual DNA change is responsible. These genome sequencing studies will really help us to push forward toward a more complete biological understanding of diabetes.” 

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