Highlights
- •This novel Y-SNP panel contains 256 Y-SNPs and allows inferring 253 Y haplogroups.
- •This Y-SNP MPS panel could provide an achievable high-resolution in the Chinese populations.
- •This Y-SNP MPS panel performs highly accurate, is sensitive, reliable, specific and robust.
Abstract
Y-chromosomal haplogroups determined by Y-chromosomal single nucleotide polymorphisms
(Y-SNPs) allow paternal lineage identification and paternal biogeographic ancestry
inference, which has attracted a lot of interest in the forensic community. Recently,
a comprehensive Y-SNP tool with dominant markers targeting haplogroups in R, E and
I branches has been reported, which allows the inference of 640 Y haplogroups. It
had a very good performance and could provide a high level of Y haplogroup resolution
in most populations. However, the predominant haplogroups in the Chinese populations
are O, C and N, suggesting that more Y-SNPs under these clades are needed to achieve
the population-specific high resolution. Herein, aiming at the Chinese population,
we presented a largely improved custom Y-SNP MPS panel that contains 256 carefully
ascertained Y-SNPs based on our previous studies, and evaluated this panel via a series
of tests, including the tests for concordance, repeatability, sensitivity, specificity,
and stability, as well as the mixture, degraded and case-type sample analysis. The
preliminary developmental validation demonstrated that this panel was highly reliable,
sensitive, specific, and robust. In the sensitivity test, even when the DNA input
was reduced to as low as 0.5 ng, the sample could still be assigned to the correct
Y haplogroup. For mixture analysis, even the 1:99 (Male: Female) mixtures had no effects
on the assignation of the Y haplogroup of the male contributor. In summary, this assay
has provided a high-resolution Y-chromosomal haplogrouping workflow to determine a
male’s paternal lineage and/or paternal biogeographic ancestry and could be widely
used for Chinese Y-chromosomal haplogroups dissection.
Keywords
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Article Info
Publication History
Published online: October 08, 2022
Accepted:
October 4,
2022
Received in revised form:
August 25,
2022
Received:
June 19,
2022
Identification
Copyright
© 2022 Elsevier B.V. All rights reserved.
