? We, as a society, have made tremendous investments in developing the scientific infrastructure to enable breakthrough discoveries on the primary biological risk factors for common disorders, such as diabetes, and its common complications, including kidney failure. These disorders account for a disproportionate and growing share of public health care expenditures. While it is clear that the relationships between genetic variation and these common disease phenotypes are complex, there is good preliminary evidence that identifying genetic risk factors for common disorders will improve understanding of their etiology and pathogenesis, leading to more specific and effective therapies. Moreover, since virtually all common disease arises as a consequence of the actions and interactions of both genetic and non-genetic risk factors, the identification of genetic risk factors should allow design of epidemiological studies to identify more specific non-genetic risk factors that might be cost-effective targets for prevention of disease. The recent development of high-throughput platforms for very large-scale SNP genotyping makes genome-wide association (GWA) mapping a viable additional tool in the effort to identify genetic risk factors for diabetic complications including kidney failure and related phenotypes. Effective use of this tool, however, requires both investment in methods of genetic analysis and a keen understanding of the phenotypes. We have thus put together a multi-disciplinary team including statistical geneticists, molecular geneticists, and experts on kidney function and failure and propose to apply a variety of approaches for genome-wide association studies on kidney failure and related quantitative phenotypes using the GoKinD samples. Specifically, we propose to 1) genotype all probands and parents in the GoKinD collection (2,807 individuals) using the Illumina HapMap 300 beadchip platform; 2) conduct both standard and novel genetic analyses of the data to map genes associated with diabetic nephropathy and related phenotypes; 3) verify genotyping and carry out fine-mapping studies in genes or regions showing association with diabetic nephropathy, related quantitative phenotypes, and other measures of diabetic complications measured in the GoKinD samples. Identification of the genes and the specific genetic variants that contribute to the development of diabetic nephropathy will lead to new approaches for preventing and treating this common long-term complication of diabetes. ? ? ?
| Porta, Massimo; Toppila, Iiro; Sandholm, Niina et al. (2016) Variation in SLC19A3 and Protection From Microvascular Damage in Type 1 Diabetes. Diabetes 65:1022-30 |
| Eny, Karen M; Orchard, Trevor J; Miller, Rachel Grace et al. (2015) Caffeine Consumption Contributes to Skin Intrinsic Fluorescence in Type 1 Diabetes. Diabetes Technol Ther 17:726-34 |
| Eny, Karen M; Lutgers, Helen L; Maynard, John et al. (2014) GWAS identifies an NAT2 acetylator status tag single nucleotide polymorphism to be a major locus for skin fluorescence. Diabetologia 57:1623-34 |
| Grassi, Michael A; Tikhomirov, Anna; Ramalingam, Sudha et al. (2012) Replication analysis for severe diabetic retinopathy. Invest Ophthalmol Vis Sci 53:2377-81 |
| Grassi, Michael A; Tikhomirov, Anna; Ramalingam, Sudha et al. (2011) Genome-wide meta-analysis for severe diabetic retinopathy. Hum Mol Genet 20:2472-81 |
| Pluzhnikov, Anna; Below, Jennifer E; Konkashbaev, Anuar et al. (2010) Spoiling the whole bunch: quality control aimed at preserving the integrity of high-throughput genotyping. Am J Hum Genet 87:123-8 |
| Paterson, Andrew D; Waggott, Daryl; Boright, Andrew P et al. (2010) A genome-wide association study identifies a novel major locus for glycemic control in type 1 diabetes, as measured by both A1C and glucose. Diabetes 59:539-49 |
| De la Cruz, Omar; Wen, Xiaoquan; Ke, Baoguan et al. (2010) Gene, region and pathway level analyses in whole-genome studies. Genet Epidemiol 34:222-231 |
| Song, Minsun; Nicolae, Dan L (2009) Restricted parameter space models for testing gene-gene interaction. Genet Epidemiol 33:386-93 |
| Wen, Xiaoquan; Nicolae, Dan L (2008) Association studies for untyped markers with TUNA. Bioinformatics 24:435-7 |
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