Type 1 diabetes (T1D) is an autoimmune form of diabetes where the patient's own immune system attacks the insulin producing islets of Langerhans in the pancreas. The long-term complications of diabetes reduce quality of life, lead to premature deaths and place a burden on the health care system. Diabetic kidney disease, known as diabetic nephropathy, is a major diabetic complication affecting one third of the patients with T1D. In some cases, diabetic nephropathy may lead to end stage renal disease (ESRD), a condition characterized by the inability of the kidneys to function at the level needed for day-to-day life. Patients with ESRD require regular dialysis treatment or kidney transplantation to survive. While the pathogenesis of diabetic nephropathy is poorly understood, it is known that diabetic nephropathy clusters in families, suggesting that genetic risk factors affect the susceptibility to this complex disease. However, the genetic risk factors are not well known. Identification of the genetic risk factors would help to understand the biological processes causing the disease, paving the way for novel pharmacological target molecules and better biochemical risk markers. The aim of this dissertation was to identify genetic risk factors for diabetic nephropathy by applying a range of computational methods to high-throughput genetic data. This dissertation is mainly based on genome-wide data of ~550,000 single nucleotide polymorphisms (SNPs) genotyped in 3,650 Finnish patients with T1D. Similar genetic data were available for two other studies. Using computational methods and a European reference population, the number of SNPs for each patient was increased to 2.4 million. With this large genomic data set, we first reassessed the previously suggested genetic risk factors for diabetic nephropathy. We then performed genome-wide association studies (GWASs) in the three cohorts. Combining our results with other studies, the resulting analysis included data from over 12,000 patients with T1D. In this larger cohort, we identified variants in the AFF3 gene and between the RGMA and MCTP2 genes associated with ESRD. Additionally, we identified variants that were only associated with the risk of ESRD in women with T1D. Furthermore, we identified risk variants for increased urinary albumin excretion, an important marker of diabetic kidney disease. Finally, using data mining methods, we identified the previously reported RGMA – MCTP2 locus and two novel putative genetic risk factors for ESRD. All in all, this thesis reports the first genetic risk factors for diabetic nephropathy in T1D with strong statistical evidence of association.
|Translated title of the contribution||Diabeettisen nefropatian geneettisten riskitekijöiden genominlaajuinen etsintä laskennallisin menetelmin|
|Publication status||Published - 2014|
|MoE publication type||G5 Doctoral dissertation (article)|
- computational genetics
- genome-wide association study
- diabetic nephropathy