Biogeographical informativeness of Y-STR haplotypes

Analysis of Y-STR diversity and DNA methylation variation among Black and Indian males from KwaZulu-Natal, South Africa

Y-chromosome short tandem repeats (Y-STRs) are essential in understanding genetic structure and diversity of human populations and, most importantly, in identification of male perpetrators in criminal investigations. DNA methylation differences have been reported in human populations and methylation pattern at the CpG sites found within or flanking the Y-STR sites could also aid in human identification. Studies based on DNA methylation (DNAm) at Y-STRs are currently limited. The current study aimed to analyze the Y-STR diversity in South African Black and Indian individuals living in KwaZulu-Natal, Durban, South Africa, with the Yfiler™ Plus Kit and to analyze DNAm patterns in Y-STR markers CpG sites. DNA from 247 stored saliva samples were isolated and quantified. Across the 27 Y-STR loci in the Yfiler™ Plus Kit, 253 alleles were observed in 113 South African Black and Indian males, 112 unique haplotypes were observed, and one haplotype appeared twice (two Black individuals). No statistically significant differences were observed in the genetic diversity between the two population groups (Fst = 0.028, p-value ≥ 0.05). The kit showed a high discrimination capacity (DC) of 0.9912 and an overall haplotype diversity (HD) = 0.9995 among the sampled population groups. DYS438 and DYS448 markers displayed 2 and 3 CpG sites, respectively. Based on the two-tailed Fisher's Exact test, there were no statistically significant differences in the DNAm levels at DYS438 CpGs of Black and Indian males (p > 0.05). The Yfiler™ Plus Kit can be considered highly discriminatory among South African Black and Indian males. Studies on the South African population using Yfiler™ Plus Kit are scarce. Hence, accumulating Y-STR data on the diverse South African population will enhance the representation of South Africa in STR databases. Knowing which Y-STR markers are significantly informative for South Africa is essential for developing Y-STR kits better suited for the different ethnic groups. And to the best of our knowledge, DNA methylation analysis in Y-STR for different ethnic groups has never been done before. Complementing Y-STR data with methylation knowledge could provide population-specific information for forensic identification.

Predicting haplogroups using a versatile machine learning program (PredYMaLe) on a new mutationally balanced 32 Y-STR multiplex (CombYplex): Unlocking the full potential of the human STR mutation rate spectrum to estimate forensic parameters

We developed a new mutationally well-balanced 32 Y-STR multiplex (CombYplex) together with a machine learning (ML) program PredYMaLe to assess the impact of STR mutability on haplogourp prediction, while respecting forensic community criteria (high DC/HD). We designed CombYplex around two sub-panels M1 and M2 characterized by average and high-mutation STR panels. Using these two sub-panels, we tested how our program PredYmale reacts to mutability when considering basal branches and, moving down, terminal branches. We tested first the discrimination capacity of CombYplex on 996 human samples using various forensic and statistical parameters and showed that its resolution is sufficient to separate haplogroup classes. In parallel, PredYMaLe was designed and used to test whether a ML approach can predict haplogroup classes from Y-STR profiles. Applied to our kit, SVM and Random Forest classifiers perform very well (average 97 %), better than Neural Network (average 91 %) and Bayesian methods (< 90 %). We observe heterogeneity in haplogroup assignation accuracy among classes, with most haplogroups having high prediction scores (99-100 %) and two (E1b1b and G) having lower scores (67 %). The small sample sizes of these classes explain the high tendency to misclassify the Y-profiles of these haplogroups; results were measurably improved as soon as more training data were added. We provide evidence that our ML approach is a robust method to accurately predict haplogroups when it is combined with a sufficient number of markers, well-balanced mutation rate Y-STR panels, and large ML training sets. Further research on confounding factors (such as CNV-STR or gene conversion) and ideal STR panels in regard to the branches analysed can be developed to help classifiers further optimize prediction scores.