title: NOCIt: a computational method to infer the number of contributors to DNA samples analyzed by STR genotyping.
abstract: Repetitive sequences in the human genome called short tandem repeats (STRs) are used in human identification for forensic purposes. Interpretation of DNA profiles generated using STRs is often problematic because of uncertainty in the number of contributors to the sample. Existing methods to identify the number of contributors work on the number of peaks observed and/or allele frequencies. We have developed a computational method called NOCIt that calculates the a posteriori probability (APP) on the number of contributors. NOCIt works on single source calibration data consisting of known genotypes to compute the APP for an unknown sample. The method takes into account signal peak heights, population allele frequencies, allele dropout and stutter-a commonly occurring PCR artifact. We tested the performance of NOCIt using 278 experimental and 40 simulated DNA mixtures consisting of one to five contributors with total DNA mass from 0.016 to 0.25ng. NOCIt correctly identified the number of contributors in 83% of the experimental samples and in 85% of the simulated mixtures, while the accuracy of the best pre-existing method to determine the number of contributors was 72% for the experimental samples and 73% for the simulated mixtures. Moreover, NOCIt calculated the APP for the true number of contributors to be at least 1% in 95% of the experimental samples and in all the simulated mixtures.
authors.affiliation: Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA.
authors.forename: Harish
authors.initials: H
authors.lastname: Swaminathan
authors.affiliation: Biomedical Forensic Sciences Program, Boston University School of Medicine, Boston, MA 02118, USA.
authors.forename: Catherine M
authors.initials: CM
authors.lastname: Grgicak
authors.affiliation: Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
authors.forename: Muriel
authors.initials: M
authors.lastname: Medard
authors.affiliation: Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA; School of Mathematics and Statistics, University of South Australia, Mawson Lakes, SA 5095, Australia. Electronic address: dslun@rutgers.edu.
authors.forename: Desmond S
authors.initials: DS
authors.lastname: Lun
mesh_terms: D000465:Algorithms; D005838:Genotype; D006801:Humans; D018895:Microsatellite Repeats / Q000235:genetics*; D035501:Uncertainty
keywords: DNA; Mixture interpretation; Number of contributors; Short tandem repeats