Functional role of candidate genes emerging from GWAS in diabetic nephropathy
Diabetic nephropathy (DN) is the leading cause of end-stage renal disease (ESRD) worldwide. Although tight control of blood sugar can decrease the risk of diabetic kidney disease, many patients with adequate control develop DN, while others remain complication-free despite poor glycaemic control. Family studies point to a strong role for inherited factors in the development of DN, but the relevant genetic variants remain unknown, limiting our understanding of this devastating complication. Our long term goal is to discover the genetic factors that influence the risk of DN, with the hope that improved understanding of the disease will identify novel biological targets for therapeutic and preventive intervention. The Genetics of Nephropathy – an International Effort (GENIE) Consortium have recently completed the largest genome wide association study (GWAS) to date and meta-analysis of type 1 DN and ESRD. This Pilot application has two aims: Aim1 - To investigate the functional relevance of candidate genes from the GENIE GWAS: ERBB4 - encoding an epidermal growth factor receptor subfamily member; and AFF3 - an AFF transcription factor and type 1 diabetes candidate gene. Our preliminary data from renal cell models and expression analysis suggest a potentially important role for both of these genes in DN. We will investigate the putative role of these genes by either overexpressing or repressing levels in cell models that we have previously shown to mimic important aspects of the pathology of DN. These data will inform further investigations of differential gene expression and regulatory netwoks which may be awry in DN. Aim 2 – To investigate the FRMD3/rs1888747 variant previously reported to be associated with DN. Recent data from our collaborators suggest that FRMD3 gene expression is dysregulated in DN, potentially due to the rs1888747 promoter variant. We will investigate the impact of this variant on FRMD3 function in our established cellular models of DN. In summary, we aim to interrogate the emerging GWAS data, and investigate the underlying biological signature.

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