Analysis of likelihood ratios for 3, 4, and 5-person mixtures from the PROVEDIt database using DNA-View Mixture Solution
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Abstract
Through advancements in the sensitivity of DNA typing methodology, increasingly smaller amounts of DNA as well as damaged DNA have been able to be detected. As a result, there has been an increase in complex DNA mixtures. Many of these mixtures are considered indistinguishable by traditional manual interpretation methods as a result of their number of contributors or contributor ratios. The interpretation of these mixtures becomes further complicated by stochastic effects such as stutter, drop-out, drop-in, peak height imbalance, and further by the effect of allele sharing which becomes increasingly prevalent as the number of contributors to a mixture increases. Therefore, over the last several years probabilistic genotyping softwares which combat and target these issues have been rising in popularity. These systems use models and algorithms to analyze DNA profiles and produce likelihood ratios which have become attractive for their ability to approach to mixture interpretation with much less subjectivity. One such software is DNA-View Mixture Solution, which makes use of stochastic modeling to analyze mixtures and directly compute likelihood ratios. Many laboratories choose not to analyze complex mixtures with more than four contributors due to the challenges that these mixtures present. However, these mixtures may contain useful information and produce LR’s with substantial evidentiary weight. For this reason, this study has analyzed and produced likelihood ratios for 3, 4, and 5-person mixtures from the PROVEDIt database using DNA-View Mixture Solution. The goal being to relate variable effects on LR by using a population database in which, at each locus, the frequency of all alleles are equal. This study additionally considered estimates of number of contributors as well as proportions, and how these aspects may be benefited by defining a known contributor within the hypothesis.
All hypotheses considering contributors as the sole POI with no assumed contributors produced LRs with strong support for the prosecution hypotheses. It was determined that as the NoC to the mixture increased, LRs generally decreased and estimates for NoC as well as proportion estimates became less accurate. However, when the program was assisted, especially with a known minor contributor, estimates for NoC and proportions improved and LRs increased drastically. Lastly, as the ratio of major to minor contributors became larger, LRs of major contributors generally increased, while LRs of minor contributors stayed consistent or decreased.
Description
2024