Highlights
- •Y-SNP Pedigree Tagging System combined with the 41Y panel are utilized to build a regional Y-DNA database.
- •Y-SNP haplogroups show ability in discriminating some near-matching Y-STR haplotype pairs under the 41Y panel.
- •Machine-learning based approaches are employed to classify Y-STR haplotypes from different Y-SNP haplogroups.
- •Haplogroup prediction and Y-SNP-based discrimination of Y-STR haplotype pair could be utilized to improve Y-STR databases.
Abstract
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.
Keywords
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Article info
Publication history
Published online: December 29, 2021
Accepted:
December 27,
2021
Received in revised form:
December 14,
2021
Received:
April 14,
2021
Identification
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© 2021 Elsevier B.V. All rights reserved.
