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Enhancing Mixture Interpretation With Highly Informative STRs

NCJ Number
253067
Author(s)
Bruce Budowle
Date Published
June 2018
Length
14 pages
Annotation
This Final Technical Report presents the basic features and findings of a research project with the broad goal of improving mixture interpretation with highly informative STRs.
Abstract
The objectives in achieving this goal were to 1) develop software to identify and characterize all the potential variants within a STR amplicon; 2) determine and characterize allele sequence variation of those STRs of the CODIS core set and typically used Y STRs in several major population groups; 3) identify novel autonomic STRs and Y STRs by mining from public data sets that may be better suited for analysis of complex samples; and 4) model simple and compound locus stutter, such that stutter may be better defined and provide data to contribute to the development of probabilistic genotyping of allelic sequence data. Overall, the project was successful in all these objectives. A suite of software tools collectively named STRait Razor was developed. It is freely available and provides users the capability to characterize sequence variation from data generated by MPS (massively parallel sequencing). The software is used by several laboratories worldwide. The download package for STRait Razor v2s can be found at https://www.unthse.edu/graduate-school-of-biomedical-sciences/molecular-and-medical-genetics/laboratory-faculty-and-staff/strait-razor. The website is also provided for STRait Razor v3.0, which is open source and freely available. In addition, the project developed a description of the underlying genetic variation for the commonly used autosomal and Y STRs. Publicly available sequence databases were mined to identify STRs that may be better suited for forensic casework, particularly mixture analyses. Although sample data are limited, modeling of stutter from simple and two example compound STRs was performed. The overall data support that MPS will provide substantial improvements in data analysis and interpretation. 30 references

Date Created: July 8, 2019