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Inference about complex relationships using peak height data from DNA mixtures

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  • Peter J. Green
  • Julia Mortera

Abstract

In both criminal cases and civil cases, there is an increasing demand for the analysis of DNA mixtures involving relationships. The goal might be, for example, to identify the contributors to a DNA mixture where the donors may be related, or to infer the relationship between individuals based on a mixture. This paper introduces an approach to modelling and computation for DNA mixtures involving contributors with arbitrarily complex relationships. It builds on an extension of Jacquard's condensed coefficients of identity, to specify and compute with joint relationships, not only pairwise ones, including the possibility of inbreeding. The methodology developed is applied to two casework examples involving a missing person, and simulation studies of performance, in which the ability of the methodology to recover complex relationship information from synthetic data with known ‘true’ family structure is examined. The methods used to analyse the examples are implemented in the new KinMix R package that extends the DNAmixtures package to allow for modelling DNA mixtures with related contributors.

Suggested Citation

  • Peter J. Green & Julia Mortera, 2021. "Inference about complex relationships using peak height data from DNA mixtures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1049-1082, August.
    • Handle: RePEc:bla:jorssc:v:70:y:2021:i:4:p:1049-1082
    DOI: 10.1111/rssc.12498
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    References listed on IDEAS

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    1. Tvedebrink, Torben & Eriksen, Poul Svante & Morling, Niels, 2015. "The multivariate Dirichlet-multinomial distribution and its application in forensic genetics to adjust for subpopulation effects using the θ-correction," Theoretical Population Biology, Elsevier, vol. 105(C), pages 24-32.
    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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