The Biological Age of a Bloodstain Donor

• NCJ Number: 251894
• Author(s): Jack Ballantyne
• Date Published: May 2014
• Length: 73 pages
• Grant Number(s): 2009-DN-BX-K179
• Sponsoring Agency: National Institute of Justice (NIJ)
• Corporate Author: University of Central Florida
• Sale Source: US Dept of Justice NIJ Pub
• Publication Format: Document; Document (Online)
• Publication Type: Research (Applied/Empirical); Report (Study/Research); Report (Grant Sponsored); Program/Project Description
• Language: English
• Country: United States of America

Annotation

This report presents findings and methodology for a research project whose goal was to identify age-specific biomarkers, so as to design prototype assays that may be used to predict the biological age of the donor of a bloodstain sample.

Abstract

Specifically, the research evaluated the use of RNA profiling methods for age determination by discovering markers for all major stages of life and developing a biomarker panel that could be used by forensic examiners to predict the biological/chronological age of a bloodstain donor. The researchers report a successful identification of novel nine mRNA candidates that - when used in combination with four previously identified genes (HBG1n, HBG2n, IGFBP3, and COL1A2) - enable classification of bloodstain donors into five age classifications. These are 1) newborn, 2) infant-toddler, 3) child-adolescent 4) adult-mature adult, and 5) elderly. Depending on further validation of one of the assays, the five age classifications could be 1) newborn, 2) infant-toddler, 3) child, 4) adolescent-adult-mature adult, and 5) elderly. This panel of ~15 biomarkers, including two housekeeping genes for normalization, is achieved by using four real time PCR assays, three of which are SYBR green based; therefore, they do not require the use of more costly fluorescent labeled probes. In addition, researchers used a novel statistical approach for the analysis of the normalized express data that involve the use of logistic regression (LogR) models. The project used a two-pronged approach to biomarkers identification. The first approach involved analyzing the entire transcriptomes of selected samples of different ages, using deep sequencing RNA-Seq technology, so as to identify candidate genes that show different expression regard donor age. The second approach involved targeting candidate genes that are likely to be involved in the regulation of age-related processes, based on a priori understanding of the physiology and biochemistry of human development. 10 figures, 6 tables, and 135 references