SNP assays for DVI: cost, time, and performance information for decision-makers

Abstract

In mass disaster events, forensic DNA laboratories may be called upon to quickly pivot their operations toward identifying bodies and reuniting remains with family members. Ideally, laboratories have considered this possibility in advance and have a plan in place. Compared with traditional short tandem repeat (STR) typing, single nucleotide polymorphisms (SNPs) may be better suited to these disaster victim identification (DVI) scenarios due to their small genomic target size, resulting in an improved success rate in degraded DNA samples. As the landscape of technology has shifted toward DNA sequencing, many forensic laboratories now have benchtop instruments available for massively parallel sequencing (MPS), facilitating this operational pivot from routine forensic STR casework to DVI SNP typing. Herein, we review the commercially available SNP sequencing assays amenable to DVI, we use data simulations to explore the potential for kinship prediction from SNP panels of varying size, and we give an example DVI scenario as context for presenting the matrix of considerations: kinship predictive potential, cost, and throughput of current SNP assay options. This information is intended to assist laboratories in choosing a SNP system for disaster preparedness. Highlights3 to 5 bullet points (maximum 100 characters per bullet point, including spaces). Each bullet point should be a full sentence and should outline the key contributions of your manuscript and how it impacts forensic science. O_LISingle nucleotide polymorphisms (SNPs) are useful in disaster victim identification (DVI). C_LIO_LISNP panels amenable to human identification and extended kinship are described. C_LIO_LISimulations demonstrate the potential for kinship prediction from SNP panels of varying size. C_LIO_LIKinship predictive potential, cost, and throughput are presented for an example DVI scenario. C_LIO_LIInformation is intended to assist laboratories in choosing a SNP system for disaster preparedness. C_LI