title: Inferring the Number of Contributors to Complex DNA Mixtures Using Three Methods: Exploring the Limits of Low-Template DNA Interpretation.
abstract: In forensic DNA casework, the interpretation of an evidentiary profile may be dependent upon the assumption on the number of individuals from whom the evidence arose. Three methods of inferring the number of contributors-NOCIt, maximum likelihood estimator, and maximum allele count, were evaluated using 100 test samples consisting of one to five contributors and 0.5-0.016 ng template DNA amplified with Identifiler®Plus and PowerPlex®16 HS. Results indicate that NOCIt was the most accurate method of the three, requiring 0.07 ng template DNA from any one contributor to consistently estimate the true number of contributors. Additionally, NOCIt returned repeatable results for 91% of samples analyzed in quintuplicate, while 50 single-source standards proved sufficient to calibrate the software. The data indicate that computational methods that employ a quantitative, probabilistic approach provide improved accuracy and additional pertinent information such as the uncertainty associated with the inferred number of contributors.
authors.affiliation: Biomedical Forensic Sciences, Boston University School of Medicine, Boston, MA.
authors.forename: Lauren E
authors.initials: LE
authors.lastname: Alfonse
authors.affiliation: Biomedical Forensic Sciences, Boston University School of Medicine, Boston, MA.
authors.forename: Genesis
authors.initials: G
authors.lastname: Tejada
authors.affiliation: Center for Computational and Integrative Biology, Rutgers University, Camden, NJ.
authors.forename: Harish
authors.initials: H
authors.lastname: Swaminathan
authors.affiliation: Center for Computational and Integrative Biology, Rutgers University, Camden, NJ.|School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA, Australia.
authors.forename: Desmond S
authors.initials: DS
authors.lastname: Lun
authors.affiliation: Biomedical Forensic Sciences, Boston University School of Medicine, Boston, MA.
authors.forename: Catherine M
authors.initials: CM
authors.lastname: Grgicak
mesh_terms: D000483:Alleles; D004247:DNA / Q000032:analysis / Q000235:genetics*; D016172:DNA Fingerprinting / Q000379:methods*; D005787:Gene Frequency; D006801:Humans; D016013:Likelihood Functions; D018895:Microsatellite Repeats; D009010:Monte Carlo Method; D016133:Polymerase Chain Reaction; D015203:Reproducibility of Results
chemical_list: D004247:DNA
keywords: DNA mixture interpretation; NOCIt; forensic science; maximum allele count; maximum likelihood estimator; number of contributors