NIJ and Rutgers University: Advancing Mixture Interpretation Analysis with NOCIt
Published September 2020
DNA samples recovered from crime scenes often contain at least two contributors. Complex forensic DNA mixture interpretation can be challenging and requires computational advancements that support its use. Using forensic probabilistic tools to identify a DNA sample’s number of contributors (NOC) is crucial to accurately computing the weight of evidence for a person of interest. Drs. Catherine Grgicak and Desmond Lun at Rutgers University developed and validated a probabilistic system, “NOCIt”, that determines a probability distribution on the NOC given an STR electropherogram. NOCIt incorporates models of peak height (including degradation and differential degradation), forward and reverse stutter, and noise and allelic drop-out—in addition to accounting for the number of alleles, and thus is considered a fully continuous system. Dr. Grgicak and colleagues determined that NOCIt calculates accurate, repeatable, and reliable inferences about the NOC—significantly outperforming manual methods that rely on filtering the signal.
“One could argue that a better approach than opting for the minimum number of contributors to a mixture might be to determine the number of contributors best supported by the data.”
—Jaheida Perez, et al. Croat Med J. 2011; 52: 314–26
The New York City Office of Chief Medical Examiner (OCME)