Enhancements in sensitivity now allow DNA profiles to be obtained from only tens of picograms of DNA, corresponding to a few cells, even for samples subject to degradation from environmental exposure. However, low-template DNA (LTDNA) profiles are subject to stochastic effects, such as "dropout" and "dropin" of alleles, and highly variable stutter peak heights. Although the sensitivity of the newly developed methods is highly appealing to crime investigators, courts are concerned about the reliability of the underlying science. High-profile cases relying on LTDNA evidence have collapsed amid controversy, including the case of Hoey in the United Kingdom and the case of Knox and Sollecito in Italy. I argue that rather than the reliability of the science, courts and commentators should focus on the validity of the statistical methods of evaluation of the evidence. Even noisy DNA evidence can be more powerful than many traditional types of evidence, and it can be helpful to a court as long as its strength is not overstated. There have been serious shortcomings in statistical methods for the evaluation of LTDNA profile evidence, however. Here, I propose a method that allows for multiple replicates with different rates of dropout, sporadic dropins, different amounts of DNA from different contributors, relatedness of suspected and alternate contributors, "uncertain" allele designations, and degradation. R code implementing the method is open source, facilitating wide scrutiny. I illustrate its good performance using real cases and simulated crime scene profiles.
Keywords: forensic genetics; forensic identification; statistical genetics; weight of evidence.