Asaghiar, Fisal E. (2019) Application of forensic RNA analysis as a method for body fluid stain age prediction. Doctoral thesis, University of Huddersfield.

The basic questions that must be answered during the crime investigation is who left the biological evidence and when. DNA profiling can, in most cases, successfully identify the person who deposited the sample, leaving, therefore, as the main concern question about the length of time between deposition of the stain and its subsequent recovery. The research in the present thesis is concerned with development of the mRNA and miRNA analysis for correct assessment of the age of blood, saliva, and semen samples. It is widely accepted that the level of RNA in the sample decreases over time. Therefore, reverse transcription quantitative polymerase chain reaction (RT-qPCR) method was performed to quantity the selected markers and investigate how they degrade as a function of time. Single and multiple regression analysis were employed in data analysis, which suggested that some of tested markers could be used to predict the stain age. Human specific markers for blood, saliva and semen, as well as oxygen regulated factors such as vascular endothelial growth factor A (VEGFA) and hypoxia inducible factor 1A (HIF1A) were therefore investigated using TaqMan and SYBR Green chemistries. The predictive equations were derived to determine the age of an unknown sample. Linear regression analysis using relative quantification (RQ) and cycle quantification (Cq) of the primers revealed the strongest linearity for HIF1A and VEGFA in saliva samples. MicroRNA markers were also explored by targeting miRNA 451, 205, and 891a for blood, saliva and semen samples, respectively, and it was shown that the targets were successfully detected in the samples that were up to 28 days old. Finally, upon development of the predictive models, blind testing was carried out. In blind blood samples, Cq of selected primers decreased over time and gave accurate prediction of samples’ age.

Asaghiar thesis.pdf - Accepted Version
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