Comparison of the performance of different models for the interpretation of low level mixed DNA profiles
Michael D. Coble, Todd Bille, Steven Weitz, John Buckleton, Jo Bright
DNA analyses from casework samples commonly result in complex DNA profiles. Often, these profiles consist of multiple contributors and display multiple stochastic events such as peak height imbalance, allelic or locus drop-out, allelic drop-in, and excessive or indistinguishable stutter. This increased complexity has established a need for more sophisticated methods of DNA mixture interpretation. This study compares the effectiveness of statistical models in the interpretation of artificially created low template two person mixed DNA profiles at varying proportions and template quantities. Two binary models (Combined Probability of Inclusion and Random Match Probability), a semi-continuous (Lab Retriever), and continuous model (STRmix) were compared. Generally, as the sophistication of the models increase, the power of discrimination increases. Differences in discrimination often correlate to each model's ability to use observed data effectively. Binary models require static thresholds resulting in wasted data and outliers which may lead to difficult or incorrect interpretation. Semi-continuous and continuous models eliminate the stochastic threshold however, Lab Retriever does not account for stochastic events beyond drop- out and drop-in leading to possible wasted data. STRmix incorporates all stochastic events into the calculation making the most efficient use of the observed data and providing the greatest assistance to the end user.
, Bille, T.
, Weitz, S.
, Buckleton, J.
and Bright, J.
Comparison of the performance of different models for the interpretation of low level mixed DNA profiles, Electrophoresis, [online], https://doi.org/10.1002/elps.201400110
(Accessed October 4, 2023)