Mogensen, H. S. | bioRxiv Science

bioRxiv2025

Probabilities of finding trace profile donors and their paternal relatives in Y-STR reference databases

Forensic investigative genetic genealogy using Y-chromosome short tandem repeat (Y-STR) DNA profiles can give investigative leads in criminal cases by searching for the Y-STR trace profile or similar but not identical Y-STR profiles in relevant Y-STR databases. We conducted a simulation study with Yfiler Plus and PowerPlex(R) Y23 Y-STR profiles to estimate the probabilities of finding matches and near-matches in Y-STR databases. The success rate of finding the trace profile donors or their close relatives was quantified. We used the malan R software package to simulate the populations based on the Wright-Fisher model with the YHRD Y-STR mutation rates where uncertainties were incorporated in a Bayesian manner, a variance in reproductive success of 0.2, and a constant size for 100 generations followed by a 2% growth for 150 generations. Y-STR databases were generated by randomly drawing Y-STR profiles from a Yfiler Plus and PowerPlex(R) Y23 Y-STR population data set, respectively. In a population of 500,066 individuals, a database size of 0.5% of the population resulted in a Y-STR database match probability of ca. 6% and 10% for Yfiler Plus and PowerPlex(R) Y23, respectively. Increasing the database size to 5% of the population resulted in a Y-STR match probability of ca. 41% and 54% for Yfiler Plus and PowerPlex(R) Y23, respectively. When a Y-STR match was found in the database, the probability of one of the individuals with the matching profiles being related within five meioses to the trace donor was ca. 64% and 56% for Yfiler Plus and PowerPlex(R) Y23, respectively, including the cases where the Y-STR profile originated from the donor. In this case, the closest relative in the database was found among the matching individuals with a probability of ca. 91%.

genetics↗

bioRxiv2024

Enhanced SNP Genotyping with Symmetric Multinomial Logistic Regression

In genotyping, determining Single Nucleotide Polymorphisms (SNPs) is standard practice, but it becomes difficult when analysing small quantities of input DNA, as is often required in forensic applications. Existing SNP genotyping methods, such as the HID SNP Genotyper Plugin (HSG) from Thermo Fisher Scientific, perform well with adequate DNA input levels but often produce erroneously called genotypes when DNA quantities are low. To mitigate these errors, genotype quality can be checked with the HSG. However, enforcing the HSGs quality checks decreases the call rate by introducing more no-calls, and it does not eliminate all wrong calls. This study presents and validates a Symmetric Multinomial Logistic Regression (SMLR) model designed to enhance genotyping accuracy and call rate with small amounts of DNA. Comprehensive bootstrap and cross-validation analyses across a wide range of DNA quantities demonstrate the robustness and efficiency of the SMLR model in maintaining high call rates without compromising accuracy compared to the HSG. For DNA amounts as low as 31.25 pg, the SMLR method reduced the rate of no-calls by 50.0% relative to the HSG while maintaining the same rate of wrong calls, resulting in a call rate of 96.0%. Similarly, SMLR reduced the rate of wrong calls by 55.6% while maintaining the same call rate, achieving an accuracy of 99.775%. The no-call and wrong-call rates were significantly reduced at 62.5-250 pg DNA. The results highlight the SMLR models utility in optimising SNP genotyping at suboptimal DNA concentrations, making it a valuable tool for forensic applications where sample quantity and quality may be decreased. This work reinforces the feasibility of statistical approaches in forensic genotyping and provides a framework for implementing the SMLR method in practical forensic settings. The SMLR model applies for genotyping biallelic data with a signal (e.g. reads, counts, or intensity) for each allele. The model can also improve the allele balance quality check.

genetics↗