Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles

• NCJ Number: 308364
• Journal: Bmc Bioinformatics Volume: 16 Issue: 298 Dated: 2015
• Author(s): Keith Inman; Norah Rudin; Ken Cheng; Chris Robinson; Adam Kirschner; Luke Inman-Semerau; Kirk E. Lohmueller
• Date Published: 2015
• Length: 10 pages
• Grant Number(s): 2013-DN-BX-K029
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)

Annotation

This paper evaluates Lab Retriever, a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles.

Abstract

This paper concluded that the application Lab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems. Technological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists. To address this need, researchers developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. The authors redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer. (Published Abstract Provided)