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

A haplogroup-based methodology for assigning individuals to geographical regions using Y-STR data

Y chromosome markers are essential tools in forensic genetics, offering valuable insights for genetic identification. This study seeks to develop a forensic prediction model using machine learning techniques to improve the efficiency of genetic identification processes. Specifically, the model aims to predict an individual's nearest geographical area of residence based on Y chromosome marker analysis. The methodology involved four key steps: haplogroup determination, primary branch identification, geographical region assignment, model stratification, and fine-tuning. Once developed, the model can be integrated into decision support systems, providing forensic geneticists with a reliable knowledge source to enhance decision-making during investigations.

YHP: Y-chromosome Haplogroup Predictor for predicting male lineages based on Y-STRs

Human Y chromosome reflects the evolutionary process of males. Male lineage tracing by Y chromosome is of great use in evolutionary, forensic, and anthropological studies. Identifying the male lineage based on the specific distribution of Y haplogroups narrows down the investigation scope, which has been used in forensic scenarios. However, existing software aids in familial searching using Y-STRs (Y-chromosome short tandem repeats) to predict Y-SNP (Y-chromosome single nucleotide polymorphism) haplogroups, they often lack resolution. In this study, we developed YHP (Y Haplogroup Predictor), a novel software offering high-resolution haplogroup inference without requiring extensive Y-SNP sequencing. Leveraging existing datasets (219 haplogroups, 4064 samples in total), YHP predicts haplogroups with 0.923 accuracy under the highest haplogroup resolution, employing a random forest algorithm. YHP, available on Github (https://github.com/cissy123/YHP-Y-Haplogroup-Predictor-), facilitates high-resolution haplogroup prediction, haplotype mismatch analysis, and haplotype similarity comparison. Notably, it demonstrates efficacy in East Asian populations, benefiting from training data from eight distinct East Asian ethnic populations. Moreover, it enables seamless integration of additional training sets, extending its utility to diverse populations.

Machine learning applications in forensic DNA profiling: A critical review

Machine learning (ML) is a range of powerful computational algorithms capable of generating predictive models via intelligent autonomous analysis of relatively large and often unstructured data. ML has become an integral part of our daily lives with a plethora of applications, including web, business, automotive industry, clinical diagnostics, scientific research, and more recently, forensic science. In the field of forensic DNA, the manual analysis of complex data can be challenging, time-consuming, and error-prone. The integration of novel ML-based methods may aid in streamlining this process while maintaining the high accuracy and reproducibility required for forensic tools. Due to the relative novelty of such applications, the forensic community is largely unaware of ML capabilities and limitations. Furthermore, computer science and ML professionals are often unfamiliar with the forensic science field and its specific requirements. This manuscript offers a brief introduction to the capabilities of machine learning methods and their applications in the context of forensic DNA analysis and offers a critical review of the current literature in this rapidly developing field.

Improving the regional Y-STR haplotype resolution utilizing haplogroup-determining Y-SNPs and the application of machine learning in Y-SNP haplogroup prediction in a forensic Y-STR database: A pilot study on male Chinese Yunnan Zhaoyang Han population

Improving the resolution of the current widely used Y-chromosomal short tandem repeat (Y-STR) dataset is of great importance for forensic investigators, and the current approach is limited, except for the addition of more Y-STR loci. In this research, a regional Y-DNA database was investigated to improve the Y-STR haplotype resolution utilizing a Y-SNP Pedigree Tagging System that includes 24 Y-chromosomal single nucleotide polymorphism (Y-SNP) loci. This pilot study was conducted in the Chinese Yunnan Zhaoyang Han population, and 3473 unrelated male individuals were enrolled. Based on data on the male haplogroups under different panels, the matched or near-matching (NM) Y-STR haplotype pairs from different haplogroups indicated the critical roles of haplogroups in improving the regional Y-STR haplotype resolution. A classic median-joining network analysis was performed using Y-STR or Y-STR/Y-SNP data to reconstruct population substructures, which revealed the ability of Y-SNPs to correct misclassifications from Y-STRs. Additionally, population substructures were reconstructed using multiple unsupervised or supervised dimensionality reduction methods, which indicated the potential of Y-STR haplotypes in predicting Y-SNP haplogroups. Haplogroup prediction models were built based on nine publicly accessible machine-learning (ML) approaches. The results showed that the best prediction accuracy score could reach 99.71% for major haplogroups and 98.54% for detailed haplogroups. Potential influences on prediction accuracy were assessed by adjusting the Y-STR locus numbers, selecting Y-STR loci with various mutabilities, and performing data processing. ML-based predictors generally presented a better prediction accuracy than two available predictors (Nevgen and EA-YPredictor). Three tree models were developed based on the Yfiler Plus panel with unprocessed input data, which showed their strong generalization ability in classifying various Chinese Han subgroups (validation dataset). In conclusion, this study revealed the significance and application prospects of Y-SNP haplogroups in improving regional Y-STR databases. Y-SNP haplogroups can be used to discriminate NM Y-STR haplotype pairs, and it is important for forensic Y-STR databases to develop haplogroup prediction tools to improve the accuracy of biogeographic ancestry inferences.