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Contributors are a nuisance (parameter) for DNA mixture evidence evaluation.
1VU University Amsterdam, Netherlands Forensic Institute (NFI), The Netherlands.
Forensic Science International. Genetics
|August 27, 2018
Summary
The number of contributors in DNA mixture analysis impacts likelihood ratios. This study clarifies how varying contributor numbers under different hypotheses affect weight of evidence calculations in forensic science.
Area of Science:
- Forensic Science
- Genetics
- Probability Theory
Background:
- A debate exists regarding the number of contributors assumed in DNA mixture profile analysis.
- Specifically, whether the number of contributors should be consistent across compared hypotheses for likelihood ratio calculations.
Purpose of the Study:
- To clarify the impact of the number of contributors on weight of evidence calculations in DNA mixture analysis.
- To investigate how assuming different numbers of contributors under competing hypotheses affects likelihood ratios.
Main Methods:
- Treating the number of contributors as a nuisance parameter in statistical models.
- Analyzing the influence of prior distribution assumptions for the nuisance parameter.
- Examining scenarios with independent and dependent prior distributions of contributor numbers relative to hypotheses.
- Illustrating with examples from various kinship analysis cases.
Main Results:
- The overall likelihood ratio is sensitive to the prior distributions of the number of contributors.
- Equivalence and dependence of prior distributions under different hypotheses significantly alter the likelihood ratio.
- The choice of prior distributions for the number of contributors can lead to considerable variation in the weight of evidence.
Conclusions:
- The calculation of the weight of evidence in DNA mixture interpretation is not solely dependent on likelihood ratios for fixed contributor numbers.
- Prior distributions of the number of contributors play a crucial role and can substantially influence the final outcome.
- Forensic practitioners must carefully consider the assumptions made about the number of contributors and their prior probabilities.
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