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Machine Learning for Detecting Potato Populations Using SSR Markers

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Iranian Journal of Science and Technology, Transactions A: Science Aims and scope Submit manuscript

Abstract

A study was performed to determine how bioinformatics algorithms could be employed to classify and estimate 50 potato commercial and wild populations using microsatellite (SSR) markers. In this study, 40 SSR primers were used for estimating the genetic structure of all 50 potato populations. The data were generated in two different experiments including 32 wild and 18 commercial accessions. The results indicate that 31 SSR primers were polymorphic, and also these primers with 334 alleles were analyzed. Analyses through data cleaning, attribute weighting, and machine learning classified the populations into different categories. The machine learning methods used here for classifying the potato accessions discovered that the SS110 was the best SSR primer for the potato population analysis, and also, it can be emphasized that the machine learning analyses are the best tool for the classification of potato populations based on populations specific genetic architecture.

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Authors and Affiliations

  1. Department of Horticultural Science, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

    Mousa Torabi-Giglou

  2. Crop and Horticultural Science Research Department, Ardabil Agricultural and Natural Resources Research and Education Center, AREEO, Moghan, Iran

    Sajjad Moharramnejad

  3. Department of Horticultural Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

    Jaber Panahandeh

  4. Moghan College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

    Asghar Ebadi-Segherloo

  5. Institute of Biotechnology, College of Agriculture, Shiraz University, Shiraz, Iran

    Elham Ghasemi

Authors
  1. Mousa Torabi-Giglou
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  2. Sajjad Moharramnejad
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  3. Jaber Panahandeh
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  4. Asghar Ebadi-Segherloo
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  5. Elham Ghasemi
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Contributions

MTG performed and designed the experiments; SM wrote the article and analyzed the data. JP and AE designed the experiments. EG helped to analyze the data.

Corresponding author

Correspondence to Sajjad Moharramnejad.

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The authors declare that there is no conflict of interest.

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Torabi-Giglou, M., Moharramnejad, S., Panahandeh, J. et al. Machine Learning for Detecting Potato Populations Using SSR Markers. Iran J Sci Technol Trans Sci 44, 911–918 (2020). https://doi.org/10.1007/s40995-020-00896-2

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  • DOI: https://doi.org/10.1007/s40995-020-00896-2

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