Considerations on the application of a mutation model for Y-STR interpretation

Abstract

If Y-STR profiling is to be more effective in criminal casework, the methods used to evaluate evidential weight require improvement. Many forensic scientists assign an evidential weight by estimating the number of times a Y-STR profile obtained from a questioned sample has been observed in YHRD datasets. More sophisticated models have been suggested but not yet implemented into routine casework, e.g. Andersen & Balding [1]. Mutation is inherent to STR meiosis (or inheritance) and is encountered in practice. We evaluated a mutation model that can be incorporated into a method for assigning evidential weight to Y-STR profiles, an essential part of bringing any method into practice. Since an important part of implementation to casework is communication, the article is written in an accessible format for practitioners as well as statisticians.The mutation component within the MUTEA model by Willems et al. [2] incorporates the potential for multistep mutations and a tendency for alleles to revert towards a central length, reflecting observed mutation data, e.g. [3]. We have estimated the parameters in this model and in a simplified symmetric version of this model, using sequence data from father/son pairs [4] and deep-rooted pedigrees [5]. Both datasets contain multistep mutations, which may have an effect on models based on simulations [1].We introduce Beta-Binomial and Beta-Geometric conjugate analyses for estimating rate and step parameters for the mutation models presented here, which require only summations and multiplications. We proved mathematically that the parameters can be estimated independently. We show the importance of reporting the variability of the parameters and not only a point estimate. The parameters can be easily incorporated into statistical models, and updated sequentially as more data becomes available. We recommend fuller publication of data to enable the development and evaluation of a wider range of mutation models.