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Evaluation of low-template DNA profiles using peak heights

Author

Listed:
  • Steele Christopher D.

    (University College London – UGI, Darwin Building Gower Street, London WC1E 6BT, United Kingdom of Great Britain and Northern Ireland)

  • Greenhalgh Matthew

    (Orchid Cellmark Ltd., Abingdon Business Park, Blacklands Way, Abingdon OX14 1YX, United Kingdom of Great Britain and Northern Ireland)

  • Balding David J.

    (University of Melbourne – Centre for Systems Genomics, School of BioSciences and School of Mathematics and Statistics, Melbourne, Victoria, Australia)

Abstract

In recent years statistical models for the analysis of complex (low-template and/or mixed) DNA profiles have moved from using only presence/absence information about allelic peaks in an electropherogram, to quantitative use of peak heights. This is challenging because peak heights are very variable and affected by a number of factors. We present a new peak-height model with important novel features, including over- and double-stutter, and a new approach to dropin. Our model is incorporated in open-source R code likeLTD. We apply it to 108 laboratory-generated crime-scene profiles and demonstrate techniques of model validation that are novel in the field. We use the results to explore the benefits of modeling peak heights, finding that it is not always advantageous, and to assess the merits of pre-extraction replication. We also introduce an approximation that can reduce computational complexity when there are multiple low-level contributors who are not of interest to the investigation, and we present a simple approximate adjustment for linkage between loci, making it possible to accommodate linkage when evaluating complex DNA profiles.

Suggested Citation

  • Steele Christopher D. & Greenhalgh Matthew & Balding David J., 2016. "Evaluation of low-template DNA profiles using peak heights," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(5), pages 431-445, October.
    • Handle: RePEc:bpj:sagmbi:v:15:y:2016:i:5:p:431-445:n:5
    DOI: 10.1515/sagmb-2016-0038
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    References listed on IDEAS

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    1. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2011. "DEoptim: An R Package for Global Optimization by Differential Evolution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i06).
    2. R. G. Cowell & T. Graversen & S. L. Lauritzen & J. Mortera, 2015. "Analysis of forensic DNA mixtures with artefacts," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(1), pages 1-48, January.
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