Diabetic nephropathy (DN) is a devastating complication of diabetes, and the leading cause of end-stage renal disease in the United States. Current treatment strategies only slow progression of, rather than prevent or reverse, this disease. Although tight control of blood glucose reduces the rate of diabetic complications including DN, there is variability in susceptibility to DN that is not explained by glycemic contro alone. Studies of families of siblings with diabetes strongly suggest a role for heritable factors n the development of DN;however, the specific genetic factors influencing DN risk remain largely unknown. Uncovering the genetic basis of DN risk holds the promise of better understanding the biology of the pathogenesis of DN, and identifying novel targets for prevention and treatment. Advances in genetic research have enabled genome-wide analysis studies (GWAS), which offer an unbiased scan of the entire genome, powered to detect effects of modest size. Initial GWAS of DN identified several potential risk loci, but these studies were hindered by low variant densities or sample sizes too small to detect modest effects. The largest GWAS of DN to date, an international consortium of T1D cohorts, was unable to replicate most of the previously associated genetic associations for DN;a subsequent meta-analysis identified several GWAS associations for ESRD. A notable finding of this analysis was that stronger genetic associations were apparent using the more stringent ESRD phenotype compared to the traditional proteinuria-based definition of DN. This project will build on a current expanded GWAS effort, totaling ~20,000 cases and controls, funded by the Juvenile Diabetes Research Foundation, to explore signals uncovered by using alternate phenotype definitions, evaluating quantitative DN- associated traits, and restricting analysis to extreme phenotypes. Additionally, uncommon and rare variants will be evaluated using the most current gene-burden tests, which group all variants within a gene collectively. Finally, the results from our cross-sectional analysis will be integrated with longitudinal data from our collaborators to further explore suggestive signals;thi data will be further leveraged to explore the causality of heritable risk factors (e.g. obesity or hypertension) with epidemiologic association with DN. The advanced analytic approaches outlined in this proposal have the promise to uncover new genetic variants associated with development of renal disease in patients with T1D.
Diabetes is the leading cause of kidney failure in the United States;the risk of this complication is predicted not only by blood sugar control but also by inherited (genetic) factors. Understanding these genetic factors is key to developing preventative measures and treatments, yet few of the specific genes involved in kidney disease risk are known. This project proposes to look at genetic information in new ways, using different statistical approaches, to better search for the inherited factors that affect the risk of diabetic kidney disease.