(258d) Detection of Rare Variants of Type 2 Diabetes by CE-SSCP | AIChE

(258d) Detection of Rare Variants of Type 2 Diabetes by CE-SSCP

Authors 

Jernigan, A. C. - Presenter, University of Arkansas
Lintag, H. - Presenter, University of Arkansas
Hestekin, C. - Presenter, University of Arkansas


Type 2 diabetes is estimated to impact 17.7 million people in the United States (US) and about 171 million people world-wide. The prevalence and seriousness of this disease demonstrates the importance of developing better diagnostic tools and therapeutics. Recent genome-wide association studies of type 2 diabetes have identified at least 15 common variants that increase the risk of type 2 diabetes in populations of European descent. However, these variants individually have small effect, and together account for fewer than 10% of the genetic risk. Therefore, the idea of genetic risk for a disease being conferred by a large number of rarely occurring mutations seems increasingly plausible. These rare variants are characterized by amino acid changes, stop codons, or splice mutations and have large effects. The exploration of the role of rare variants has been hampered by technological challenges. At least several hundred genes could contribute to pathways of insulin action, insulin secretion, or hepatic glucose production, and even exploration of only coding sequences of each gene is a daunting and expensive challenge using current generation sequencing technology. Capillary electrophoresis (CE) single strand conformational polymorphism (SSCP) has the ability to separate DNA not only by size, but by nucleic acid sequence. It can sensitively detect mutations as small as a single base pair alteration. However, CE-SSCP must be optimized and multiplexed in order to be a low cost, high-throughput, and rapid screening method. Currently, we are examining the ability to multiplex CE-SSCP by looking at the simultaneous analysis of multiple DNA fragment sizes (80 ? 800 bp) with multiple fluorescent dyes. In order to maintain sensitive mutation detection, three mutation types (substitution, insertion, and deletions) are being studied, along with the ability to resolve them as fragment size increases. Translation to a microfluidic chip is also being explored.