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Title: Genetic epidemiology studies of aspects of diabetic complications
Author: Deshmukh, Harshal
ISNI:       0000 0004 5347 7822
Awarding Body: University of Dundee
Current Institution: University of Dundee
Date of Award: 2014
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Introduction Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD), present in approximately 25%-40% of patients with long-standing diabetes and conferring additional risk of cardiovascular disease and mortality. Variations in the clinical presentations of DKD, heritability estimates from family-based studies and, more recently, the results from Genome-wide Association Studies (GWAS) demonstrate a heritable component of DKD. However, as is the case with the most of complex disorders, identifying causal genetic variants contributing to DKD has proven difficult. An important step in identifying variants associated with DKD in diabetes will involve integration of patient populations across multiple DKD cohorts, investigating rarer variants and by addressing the heterogeneity in DKD disease phenotypes in diabetes. Methods In this thesis, I reviewed the existing literature in genetic epidemiology in diabetic kidney disease. I then estimate chip-based heritability of DKD sub-phenotypes and replicated the association of known SNPS associated with renal function and upstream risk factors for diabetic kidney disease (BP, HbA1c) in patients with Type 2 Diabetes. I performed first GWAS for soluble receptor for advanced glycation products (sRAGE) a biomarker implicated in the pathogenesis of DKD. Finally, I performed GWAS for various DKD phenotypes on Type 1 Diabetes cohort (EURODIAB) and Type 2 Diabetes cohort (Go-DARTS) and helped with joint metaanalysis with DKD cohorts in SUMMIT consortium investigating genetic determinants of DKD. Results First, I showed that some DKD sub-phenotypes (like macro-albuminuria and ESRD) might be more heritable than others are and demonstrate that usefulness of estimation of chip-based heritability for complex trait by GCTA can be limited in the absence of large sample sizes. Second, I investigated the known genes for renal function (eGFR) and upstream risk factors for diabetic kidney disease (BP, HbA1c) in patients with Type 2 diabetes and showed that cumulative genetic risk for BP and HbA1c is associated with DKD. Third, I replicated the association of known loci associated with eGFR (UMOD GCKR and SHROOM3) in patients with Type 2 diabetes and showed that albuminuria affects the association of these variants with renal function. Fourth, I conducted a GWAS for sRAGE, an important biomarker associated with DKD, and identified novel variants in ITGA1 and HLA-DQA1 associated with circulating sRAGE levels. Finally, I performed GWAS for various DKD sub-phenotypes, and assisted in GWAS meta-analysis with SUMMIT consortium and identified potential novel genetic determinants for diabetic kidney diseases. Conclusion In conclusion this thesis has shown that a) estimation of chip based heritability of various DKD sub-phenotypes using GCTA has limited utility and requires GWAS studies with extremely large sample sizes b) the genetic determinants of renal function (eGFR) can interact with albuminuria in patients with T2D c) there are yet unidentified genetic markers associated with DKD and have identified potentially novel genetic markers associated with sRAGE (an important biomarker for DKD) and DKD itself which can be investigated in future studies for their reproducibility and functional consequences.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available