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Application of a 24-SNP Multiplex Genotyping Assay System for Phenotypic Identification of Fujian Han Population.

Authors :
Hong Yu
Lili Han
Dian Chen
Yaocheng Wang
Shanglong Liu
Suimei Wu
Tongtong Zheng
Li Lai
Source :
Clinical Laboratory; 2024, Vol. 70 Issue 10, p1937-1946, 10p
Publication Year :
2024

Abstract

Background: This study aimed to evaluate the utility of 24 single nucleotide polymorphism (SNP) loci associated with iris color and hair color in phenotypic identification of the Han Chinese population in Fujian Province. The selected SNPs, known for their strong correlation with specific human phenotypic features, provide valuable reference data for developing a molecular phenotypic identification system. Methods: A multiplex genotyping assay system was established with primers for the 24 SNPs linked to iris color and hair color synthesized based on existing literature. In total, 235 unrelated individuals of Han Chinese ethnicity in Fujian Province were included in this study. PowerStats v12 was employed to calculate forensic parameters associated with the 24 SNP loci, including gene frequencies, genotype frequencies, minor allele frequencies, discrimination power (DP), polymorphism information content (PIC), and observed heterozygosity (Ho). Hardy- Weinberg equilibrium tests were conducted for each locus. The SNP genotyping results were uploaded to the HIrisPlex model (https://HIrisPlex.erasmusmc.nl/) to predict iris and hair colors, and the inferred results were compared with manually assessed images. The accuracy of pigment phenotype inference was evaluated by using ROC curves in SPSS 26.0 software. Results: The accuracy rates of inferring brown iris and black hair phenotypes were 99.6% and 99.5%. The area under the curve (AUC) values were 0.923 and 0.980, respectively. Conclusions: The 24 SNP loci demonstrated high accuracy in inferring iris color and hair color; it seems to be a useful tool for forensic phenotypic identification and anthropological or evolutionary applications. Establishment of suitable pigment classification criteria and optimized prediction models is based on revealing more phenotypic genetic markers. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14336510
Volume :
70
Issue :
10
Database :
Complementary Index
Journal :
Clinical Laboratory
Publication Type :
Academic Journal
Accession number :
180290255
Full Text :
https://doi.org/10.7754/Clin.Lab.2024.240425