Integration of machine learning models with microsatellite markers: New avenue in world grapevine germplasm characterization

Abstract

Development of efficient analytical techniques is required for effective interpretation of biological data to take novel hypotheses and finding the critical predictive patterns. Machine Learning algorithms provide a novel opportunity for development of low-cost and practical solutions in biology. In this study, we proposed a new integrated analytical approach using supervised machine learning algorithms and microsatellites data of worldwide vitis populations. A total of 1378 wild (V. vinifera spp. sylvestris) and cultivated (V. vinifera spp. sativa) accessions of grapevine were investigated using 20 microsatellite markers. Data cleaning, feature selection, and supervised machine learning classification models vis, Naive Bayes, Support Vector Machine (SVM) and Tree Induction methods were implied to find most indicative and diagnostic alleles to represent wild/cultivated and originated geography of each population. Our combined approaches showed microsatellite markers with the highest differentiating capacity and proved efficiency for our pipeline of classification and prediction of vitis accessions. Moreover, our study proposed the best combination of markers for better distinguishing of populations, which can be exploited in future germplasm conservation and breeding programs.

Keywords: Feature selection; Machine learning; Microsatellites; Vitis.

Fig. 1

Flowchart of the data analysis, which shows the structure of the analytical approach to the investigation of microsatellite (SSR) markers in this study.

Fig. 2

Decision Tree generated model showing separation of wild and cultivated grape populations in the 2-targeted (2-t) experiment.

Fig. 3

Decision Tree generated model showing separation of grape populations in the 9-targeted (9-t) experiment.