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• W4309410723 abstract "Human ear morphology prediction with SNP-based genotypes is growing in forensic DNA phenotyping and is scarcely explored in Pakistan as a part of EVCs (externally visible characteristics). The ear morphology prediction assays with 21 SNPs were assessed for their potential utility in forensic identification of population. The SNaPshot™ multiplex chemistries, capillary electrophoresis methods and GeneMapper™ software were used for obtaining genotypic data. A total of 33 ear phenotypes were categorized with digital photographs of 300 volunteers. SHEsis software was applied to make LD plot. Ordinal and multinomial logistic regression was implemented for association testing. Multinomial logistic regression was executed to construct the prediction model in 90% training and 10% testing subjects. Several influential SNPs for ear phenotypic variation were found in association testing. The model based on genetic markers predicted ear phenotypes with moderate to good predictive accuracies demonstrated with the area under curve (AUC), sensitivity and specificity of predicted phenotypes. As an additional EVC, the estimated ear phenotypic profiles have the possibility of determining the human ear morphology differences in unknown biological samples found in crimes that do not result in a criminal database hit. Furthermore, this can help in facial reconstruction and act as an investigational lead." @default.
• W4309410723 created "2022-11-27" @default.
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• W4309410723 date "2022-11-19" @default.
• W4309410723 modified "2023-10-12" @default.
• W4309410723 title "Evaluation of loci to predict ear morphology using two SNaPshot assays" @default.
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• W4309410723 doi "https://doi.org/10.1007/s12024-022-00545-7" @default.
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• W4309410723 hasPublicationYear "2022" @default.
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