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The LASSIE MPS panel: Predicting externally visible traits in dogs for forensic purposes.
- Source :
- Forensic Science International: Genetics; Sep2023, Vol. 66, pN.PAG-N.PAG, 1p
- Publication Year :
- 2023
-
Abstract
- Predicting the outward appearance of dogs via their DNA, also known as Canine DNA Phenotyping, is a young, emerging field of research in forensic genetics. The few previous studies published in this respect were restricted to the consecutive analysis of single DNA markers, a process that is time- and sample-consuming and therefore not a viable option for limited forensic specimens. Here, we report on the development and evaluation of a Massively Parallel Sequencing (MPS) based molecular genetic assay, the LASSIE MPS Panel. This panel aims to predict externally visible as well as skeletal traits, which include coat color, coat pattern, coat structure, tail morphology, skull shape, ear shape, eye color and body size from DNA using 44 genetic markers in a single molecular genetic assay. A biostatistical naïve Bayes classification approach was applied to identify the most informative marker combinations for predicting phenotypes. Overall, the predictive performance was characterized by a very high classification success for some of the trait categories, and high to moderate success for others. The performance of the developed predictive framework was further evaluated using blind samples from three randomly selected dog individuals, whose appearance was well predicted. [Display omitted] • First MPS-based molecular genetic assay to predict the canine outward appearance for forensic purposes. • Combination of 44 genetic markers describing eight trait categories to draw a comprehensive picture of the canine phenotype. • Application of naïve Bayes likelihood-based approaches for selecting best genotype-phenotype relationships. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18724973
- Volume :
- 66
- Database :
- Supplemental Index
- Journal :
- Forensic Science International: Genetics
- Publication Type :
- Academic Journal
- Accession number :
- 169786592
- Full Text :
- https://doi.org/10.1016/j.fsigen.2023.102893