Year 2018, Volume 3, Issue 2
| Year : | 2018 |
| Volume : | 3 |
| Issue : | 2 |
| Authors : | Veronika KUKUČKOVÁ, Radovan KASARDA, Július ŽITNÝ, Nina MORAVČÍKOVÁ |
| Title : | GENETIC MARKERS AND BIOSTATISTICAL METHODS AS APPROPRIATE TOOLS TO PRESERVE GENETIC RESOURCES |
| Abstract : | The aim of presented study was to assess the most suitable way how to distinguish different breeds based on molecular markers. One of the most difficult aspects of quality assurance schemes is their reliability. The verification of fraud needs great efforts in control strategies. The use of DNA markers has been shown to be a useful tool for individual identification. It is necessary to use modern statistical method based on data mining and supervised learning. Supervised pattern recognition techniques use the information about the class membership of the samples to a certain group (class or category) in order to classify new unknown samples in one of the known classes on the basis of its pattern of measurements. Large scale of supervised learning oriented method was used for traceability and identification on individual level. A result of provided study shows the possibility to classify unknown samples according to genetic data. Model is also useful for classification on many logical levels as brand, region and many others. If we take in the account only Slovak and Austrian Pinzgau cattle, based on SNP chip data, it is not possible to separate them using Bayesian approach. Once we considered with the admixture of breeds involved in the historical development as well as inbreeding, selection signatures and migration, we were able to separate even genetically similar breeds. It is possible distinguish between closely related populations based on different markers. We just need to select the appropriate type of analysis. |
| For citation : | Kukučková, V., Kasarda, R., Žitný, J., Moravčíková, N. (2018). Genetic markers and biostatistical methods as appropriate tools to preserve genetic resources. AGROFOR International Journal, Volume 3. Issue No. 2. pp. 41-48. DOI: 10.7251/AGRENG1802041K |
| Keywords : | cattle, markers, supervised learning, structure assessment |
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ISSN 2490-3434 (Printed)
ISSN 2490-3442 (Online)
ISSN 2490-3442 (Online)
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