Title

Binary Logistic Regression Models Enable Mirna Profiling To Provide Accurate Identification Of Forensically Relevant Body Fluids And Tissues

Keywords

Body fluid identification; Forensic science; Logistic regression analysis; MicroRNA (miRNA); RNA profiling

Abstract

Numerous studies have demonstrated the ability to identify the body fluid of origin of forensic biological stains using messenger (mRNA) profiling. However, the size of the amplification product used in these assays (100-400 bases) may not be ideal for use with environmentally degraded samples. MiRNA profiling represents a potential alternative to mRNA profiling, since the small size of the miRNAs (~22 bases) might still permit their detection in degraded stains. Previously, we reported the first study involving the forensic use of microRNA (miRNA) profiling, which required screening of 452 candidates. Since our initial screening, hundreds of novel miRNAs have been identified. We have therefore evaluated additional miRNA candidates to further improve the sensitivity and specificity of the body fluid assays. Consequently we have expanded our body fluid identification panel to include 18 miRNAs (comprising 5 original and 13 novel miRNAs). This panel permits the identification of all forensically relevant body fluids and, uniquely, includes miRNAs for the identification of skin. Using normalized miRNA expression data, we constructed body fluid specific binary logistic regression models to permit an accurate identification of the body fluid of interest. Using the developed models, we have obtained 100% accuracy in predicting the body fluid of interest. © 2013 Elsevier Ireland Ltd.

Publication Date

10-28-2013

Publication Title

Forensic Science International: Genetics Supplement Series

Volume

4

Issue

1

Number of Pages

-

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.fsigss.2013.10.065

Socpus ID

84889878750 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84889878750

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