Identifying the most likely contributors to a Y-STR mixture using the discrete Laplace method

Area of Science:

• Forensic Genetics
• Computational Biology
• Statistical Genetics

Background:

• Y-STR markers are crucial for analyzing male DNA in forensic cases, especially in sexual assault investigations where male DNA may be scarce.
• Mixtures of Y-STR profiles, often from multiple offenders, necessitate advanced deconvolution techniques for accurate identification.

Purpose of the Study:

• To evaluate the discrete Laplace method for separating two-person Y-STR mixtures.
• To assess the method's performance when true contributor profiles are absent from reference datasets.
• To investigate the impact of the number of Y-STR loci on mixture separation accuracy.

Main Methods:

• Simulation study using three Y-STR datasets (Denmark, Somalia, Germany) with varying loci (7-21).
• Composition of 550 two-person Y-STR mixtures by random haplotype sampling.
• Application of the discrete Laplace method for mixture deconvolution and ranking of contributor pairs based on haplotype frequencies.

Main Results:

• Successful separation of true contributor pairs was achieved in 42-52% of cases with 21 loci.
• Separation accuracy significantly improved with fewer loci, reaching 92-99% for 10 or fewer loci.
• The discrete Laplace method demonstrated effectiveness even when reference profiles were not directly available.

Conclusions:

• The discrete Laplace method is a viable tool for Y-STR mixture deconvolution in forensic science.
• Reducing the number of Y-STR loci analyzed can enhance the success rate of mixture separation.
• The method shows promise for real-world casework involving complex Y-STR mixtures.