Optimizing the sensitivity and specificity of genetic disease detection / by Travis E. Marion.

Abstract

The molecular diagnosis of genetic disease is of great importance to the field of medicine and medical research. Though many rare inherited diseases have been described in the medical literature, the inheritance pattern of more common diseases has yet to be established. Until recently evolutionary disease research has been conducted either by molecular diagnosis of those living with the disease or by archaeological investigations examining the morphological pathologies of tissue remains. It was the purpose of this study to design and optimize a PCR based multi-stepped multiplexed SNE methodology to detect -haemoglobinopathy biomarkers that could be applied to degraded medical and archaeological specimens and expand upon previous work. In order to apply the methodology to degraded tissue samples it was hypothesized that the necessary increase specificity and sensitivity could be obtained by increasing the number of primers used to amplify target sequences and by using the products of previous PCR reactions in subsequent PCR reactions respectively. Furthermore by multiplexing and incorporating an SBE SNP detection methodology the amount o f time, cost, involved in genetic disease detection could be reduced. The optimal methodology to be followed in future applications consists o f three sequential steps: multiplex PCR, hemi-nested PCR, and single base primer extension SNP detection. It was through this methodology that multiple gene amplifications were produced and sequenced from dilution extracts o f 1:1000000 that exhibited 100% homology to the reference sequence. With the increase in detection sensitivity and specificity, multiplex capabilities and the involvement o f SNP detection this methodology can be expanded to detect multiple genetic mutations/variants and be applied to medical screening, association studies, population mapping, identification o f individuals, evolutionary disease studies, and validate pre-existing research.