Estimating the Number of Contributors to Forensic DNA Mixtures: Does Maximum Likelihood Perform Better Than Maximum Allele Count?

Authors

  • Hinda Haned M.S.,

    1. CNRS UMR 5558—Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, 43 bd du 11 novembre 1918, 69622 Villeurbanne, Cedex, France.
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  • Laurent Pène M.S.,

    1. Institut National de Police Scientifique, Laboratoire de Police Scientifique de Lyon, Lyon, France.
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  • Jean R. Lobry Ph.D.,

    1. CNRS UMR 5558—Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, 43 bd du 11 novembre 1918, 69622 Villeurbanne, Cedex, France.
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  • Anne B. Dufour Ph.D.,

    1. CNRS UMR 5558—Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, 43 bd du 11 novembre 1918, 69622 Villeurbanne, Cedex, France.
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  • Dominique Pontier Ph.D.

    1. CNRS UMR 5558—Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, 43 bd du 11 novembre 1918, 69622 Villeurbanne, Cedex, France.
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Additional information and reprint requests:
Hinda Haned, M.S.
CNRS UMR 5558—Laboratoire de Biométrie et Biologie Evolutive
Université Lyon 1
43 bd du 11 novembre 1918
69622 Villeurbanne, Cedex
France
E-mail: Hinda.Haned@univ-lyon1.fr

Abstract

Abstract:  Determining the number of contributors to a forensic DNA mixture using maximum allele count is a common practice in many forensic laboratories. In this paper, we compare this method to a maximum likelihood estimator, previously proposed by Egeland et al., that we extend to the cases of multiallelic loci and population subdivision. We compared both methods’ efficiency for identifying mixtures of two to five individuals in the case of uncertainty about the population allele frequencies and partial profiles. The proportion of correctly resolved mixtures was >90% for both estimators for two- and three-person mixtures, while likelihood maximization yielded success rates 2- to 15-fold higher for four- and five-person mixtures. Comparable results were obtained in the cases of uncertain allele frequencies and partial profiles. Our results support the use of the maximum likelihood estimator to report the number of contributors when dealing with complex DNA mixtures.

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