• DocumentCode
    1499934
  • Title

    Inferring the Number of Contributors to Mixed DNA Profiles

  • Author

    Paoletti, D.R. ; Krane, D.E. ; Raymer, M.L. ; Doom, T.E.

  • Author_Institution
    Dept. of Comput. Sci., Pennsylvania State Univ. Beaver, Monaca, PA, USA
  • Volume
    9
  • Issue
    1
  • fYear
    2012
  • Firstpage
    113
  • Lastpage
    122
  • Abstract
    Forensic samples containing DNA from two or more individuals can be difficult to interpret. Even ascertaining the number of contributors to the sample can be challenging. These uncertainties can dramatically reduce the statistical weight attached to evidentiary samples. A probabilistic mixture algorithm that takes into account not just the number and magnitude of the alleles at a locus, but also their frequency of occurrence allows the determination of likelihood ratios of different hypotheses concerning the number of contributors to a specific mixture. This probabilistic mixture algorithm can compute the probability of the alleles in a sample being present in a 2-person mixture, 3-person mixture, etc. The ratio of any two of these probabilities then constitutes a likelihood ratio pertaining to the number of contributors to such a mixture.
  • Keywords
    DNA; biochemistry; biology computing; mixtures; molecular biophysics; probability; statistical analysis; evidentiary samples; mixed DNA profiles; probabilistic mixture algorithm; statistical weight; Bioinformatics; Biological cells; Computational biology; Computational complexity; DNA; Humans; Probabilistic logic; DNA; bioinformatics.; mixture; optimization; probabilistic computation; Algorithms; Computational Biology; DNA; Forensic Genetics; Humans; Models, Statistical; Sequence Analysis, DNA;
  • fLanguage
    English
  • Journal_Title
    Computational Biology and Bioinformatics, IEEE/ACM Transactions on
  • Publisher
    ieee
  • ISSN
    1545-5963
  • Type

    jour

  • DOI
    10.1109/TCBB.2011.76
  • Filename
    5753885