The reliability and reporting of DNA match strength for uncertain genotype evidence

Informative DNA evidence often yields uninformative results. Most crime laboratories apply binary threshold criteria that discard DNA data and inhibit reporting. The information loss is more common with complex evidence having multiple contributors or little DNA. Probative inculpatory and exculpatory evidence is lost to criminal justice.Probability provides a non-binary way to accurately represent genotype uncertainty. Bayesian models scrutinize evidence data to transform prior to posterior genotype probability. A likelihood ratio (LR) gives the evidential change for a reference. An LR distribution charts the LR values at every reference, for Noncontributor or Contributor categories. Our previous Heliyon paper efficiently constructed dense LR distributions directly from an evidence genotype, without computing any LRs. A distribution can determine LR error rates.This paper applies LR distributions and error rates to reliably report LR results from uncertain genotypes. We present the symmetry between inclusionary and exclusionary match statistics, which have equal mathematical standing. We dissect two low-LR cases, one a criminal conviction and the other an exoneration, illustrating LR reporting, validation, and error rate. We show how thresholds reduce DNA information and when they are unnecessary. We explain why casework field data can be used for genotyping validation.A novel application of receiver operating characteristic (ROC) analysis to a genotype's Noncontributor and Contributor LR score distributions generates a continuous ROC curve. The area under the curve (AUC) is an objective measure of discrimination accuracy. We use the AUC to assess genotype accuracy, compare genotyping methods, and understand how data thresholds reduce DNA match information.Probabilistic genotyping examines DNA data to form an uncertain genotype. This single genotype outcome contains everything needed to build its Noncontributor and Contributor LR distributions, calculate error rates, conduct ROC analysis, and measure accuracy. The genotype provides a complete statistical framework, before ever comparing with a reference.