Specific aim

To reveal the pathways leading to diabetes by combining advanced genomic analyses with functional studies in large human populations

Active PIs

Genomics

The genetic spectrum of diabetes

An important aim of this Initiative is to provide a gene atlas of T2D. Together with co-workers at the Broad Institute (MIT/Harvard) and Novartis we will perform a SNP genome wide association study using 500,000 SNPs on an Affymetrix chip in 1500 Scandinavian patients with T2D and 1500 non-diabetic control subjects. We expect at least 1500 SNPs (1.5%) to show nominally significant association with T2D. Fig. 2 illustrates how we will narrow down the number of putative T2D genes from 1500 to a more workable <50. Alongside these efforts, the genetics of T1D will be mapped in a similar way.

As a next step we will analyse our unique diabetic populations spanning from < 1 years to 75 yrs of age (including > 100,000 individuals with DNA available) for the above panel of ≈ 50 T2D SNPs as well as T1D-associated HLA haplotypes and autoimmune markers. This should allow us to describe the genetic spectrum of diabetes across all age groups.

The centre will also house the European Type 1 diabetes Genetics Network (ET1DGN) which is part of the international effort of the Type 1 Diabetes Genetics Consortium (T1DGC) to identify and collect families in which at least 2 siblings suffer from T1D to search for genetic markers of the disease.

We have also collected a large number of families with early-onset diabetes of unknown cause but showing Mendelian inheritance. In one such family, 6 children had onset of diabetes below the age of 2 yrs. There was no linkage to the HLA region and no detectable autoantibodies and these patients are therefore likely to represent novel neonatal forms of diabetes rather than T2D.

Polymorphisms in the gene(s) can be expected to contribute to the common forms of T1D and T2D. We expect to be able to identify the mutation underlying the disease and, using the islet cell physiology platform, determine the functional consequences.

Genetic prediction of outcome

Our data base contains information on mortality and cause of mortality, development of diabetic complications, response to treatment in a large number of patients (>10,000) etc. Accordingly, we shall use SNPs to identify individuals at high risk of developing complications but also to predict responsiveness to therapy (pharmacological substances, diet etc.).

Gene-environment interactions

Under most circumstances environmental triggers (like unhealthy diet and lack of exercise) are required to unmask the effect of the polymorphisms. Such gene-environment interaction studies have not been possible since neither have the polymorphisms been known, nor have there been any data on environmental risk factors. We are in a unique position to address these issues thanks to the Malmö Nutrition Cancer study (part of the EPIC study in Europe) with 30,000 individuals with DNA and a comprehensive data base on nutrition and food intake and through the Botnia study with 5000 individuals with information on exercise capacity, food intake and presence of other risk factors.

Last updated: March 20, 2009
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