Inference about complex relationships using peak height data from DNA mixtures

TL;DR

This paper presents a new statistical approach and software for analyzing complex DNA mixtures involving related individuals, enhancing forensic and civil investigations by accurately inferring relationships.

Contribution

It introduces an extension of identity coefficients to model joint relationships, including inbreeding, and implements this in the KinMix R package for practical analysis.

Findings

Successfully applied to casework examples involving a missing person.

Demonstrated ability to recover complex relationship information from synthetic data.

Extended DNAmixtures package to handle related contributors efficiently.

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. KinMix inherits from DNAmixtures the capacity to deal with mixtures with many contributors, in a time- and space-efficient way.