- •We built a computational tool to infer the number of contributors to a DNA sample.
- •The tool (NOCIt) uses peak heights and accounts for dropout and stutter.
- •NOCIt was tested on 278 samples containing between 1 and 5 contributors.
- •NOCIt correctly identified the number of contributors in 83% of the samples.
- •Using the quantitative information in the signal enhances mixture interpretation.
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 0.25 ng. 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.
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Published online: November 15, 2014
Accepted: November 9, 2014
Received in revised form: September 24, 2014
Received: April 29, 2014
© 2014 Elsevier Ireland Ltd. Published by Elsevier Inc. All rights reserved.