An examination of STR nomenclatures, filters and models for MPS mixture interpretation

The increased interest in the use of Massively Parallel Sequencing (MPS) technologies to type traditional autosomal STR markers raises multiple questions regarding interpretation of the results via probabilistic genotyping. To begin to address some of those questions, we examined the effects of using differing degrees of sequence information, pre-filtering, and data modeling to interpret complex MPS-STR mixtures in a probabilistic genotyping software. Sixty ForenSeq typing results for mixtures of from two to four contributors were: 1) represented using three separate formats that captured different degrees of sequence information, and 2) were analyzed using three different filtering approaches prior to probabilistic interpretation. All mixtures for the different format and filtering variants were subsequently interpreted with respect to ten reference profiles, using both qualitative (LRmix) and quantitative (EuroForMix) models to calculate the likelihood ratio (LR). The LR results indicated moderate information gain when the STR nomenclature was based upon the longest uninterrupted stretch (LUS) compared with conventional capillary electrophoresis repeat units (RU), whereas additional gains were very small when the complete sequence information was utilised. Use of a static analytical threshold for data pre-filtering improved LRs compared to a dynamic (percentage-based) threshold, as the static threshold prevented excessive filtering of alleles originating from minor contributors. For interpretations performed using a quantitative model, a small improvement in performance was observed if a stutter model was employed instead of using stutter thresholds to pre-filter the data, whereas - as expected - performance worsened considerably under the qualitative model when stutter was not pre-filtered. Given the empirical and theoretical findings in this study we discuss the value of utilizing sequence-level information and potential paths forward to increase information gain using MPS systems.