### Abstract

Language | English |
---|---|

Pages | 1-16 |

Number of pages | 16 |

Journal | Forensic Science International: Genetics |

Volume | 27 |

DOIs | |

State | Published - 1 Mar 2017 |

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### Keywords

- Allelic dropout
- DNA mixtures
- Deconvolution
- Likelihood ratios
- Low template DNA
- Semi-continuous model
- Weight of evidence

### Cite this

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**Accurate assessment of the weight of evidence for DNA mixtures by integrating the likelihood ratio.** / Slooten, K.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Accurate assessment of the weight of evidence for DNA mixtures by integrating the likelihood ratio

AU - Slooten,K.

PY - 2017/3/1

Y1 - 2017/3/1

N2 - Several methods exist for weight of evidence calculations on DNA mixtures. Especially if dropout is a possibility, it may be difficult to estimate mixture specific parameters needed for the evaluation. For semi-continuous models, the LR for a person to have contributed to a mixture depends on the specified number of contributors and the probability of dropout for each. We show here that, for the semi-continuous model that we consider, the weight of evidence can be accurately obtained by applying the standard statistical technique of integrating the likelihood ratio against the parameter likelihoods obtained from the mixture data. This method takes into account all likelihood ratios belonging to every choice of parameters, but LR's belonging to parameters that provide a better explanation to the mixture data put in more weight into the final result. We therefore avoid having to estimate the number of contributors or their probabilities of dropout, and let the whole evaluation depend on the mixture data and the allele frequencies, which is a practical advantage as well as a gain in objectivity. Using simulated mixtures, we compare the LR obtained in this way with the best informed LR, i.e., the LR using the parameters that were used to generate the data, and show that results obtained by integration of the LR approximate closely these ideal values. We investigate both contributors and non-contributors for mixtures with various numbers of contributors. For contributors we always obtain a result close to the best informed LR whereas non-contributors are excluded more strongly if a smaller dropout probability is imposed for them. The results therefore naturally lead us to reconsider what we mean by a contributor, or by the number of contributors.

AB - Several methods exist for weight of evidence calculations on DNA mixtures. Especially if dropout is a possibility, it may be difficult to estimate mixture specific parameters needed for the evaluation. For semi-continuous models, the LR for a person to have contributed to a mixture depends on the specified number of contributors and the probability of dropout for each. We show here that, for the semi-continuous model that we consider, the weight of evidence can be accurately obtained by applying the standard statistical technique of integrating the likelihood ratio against the parameter likelihoods obtained from the mixture data. This method takes into account all likelihood ratios belonging to every choice of parameters, but LR's belonging to parameters that provide a better explanation to the mixture data put in more weight into the final result. We therefore avoid having to estimate the number of contributors or their probabilities of dropout, and let the whole evaluation depend on the mixture data and the allele frequencies, which is a practical advantage as well as a gain in objectivity. Using simulated mixtures, we compare the LR obtained in this way with the best informed LR, i.e., the LR using the parameters that were used to generate the data, and show that results obtained by integration of the LR approximate closely these ideal values. We investigate both contributors and non-contributors for mixtures with various numbers of contributors. For contributors we always obtain a result close to the best informed LR whereas non-contributors are excluded more strongly if a smaller dropout probability is imposed for them. The results therefore naturally lead us to reconsider what we mean by a contributor, or by the number of contributors.

KW - Allelic dropout

KW - DNA mixtures

KW - Deconvolution

KW - Likelihood ratios

KW - Low template DNA

KW - Semi-continuous model

KW - Weight of evidence

U2 - 10.1016/j.fsigen.2016.11.001

DO - 10.1016/j.fsigen.2016.11.001

M3 - Article

VL - 27

SP - 1

EP - 16

JO - Forensic Science International: Genetics

T2 - Forensic Science International: Genetics

JF - Forensic Science International: Genetics

SN - 1878-0326

ER -