Abstract
We tested the ability of the self-organizing map (SOM), a type of artificial neural network, in revealing genetic patterns within the autotetraploid potato (Solanum tuberosum L.). A total of 591 potato varieties, originating from various main European breeder collections and released into different market segments between 1815 and 2015, were examined using a set of 21 informative microsatellite markers. The consistency of this artificial intelligence approach in detecting genetic stratifications in such a homogeneous population was evaluated through the comparison with three other multivariate methods that are widely used for this purpose. Results showed that the SOM was equally suitable for classifying varieties into main detected groups and visualizing inter-group genetic dissimilarities. When it came to reveal the organization of the population structure at the intra-group level, traditional multivariate methods lost in resolution. Contrariwise, the SOM provided additional information on the intra-group diversity by highlighting a multitude of consistent subgroups, which seemed to be mainly related to their common heritage, spatio-temporal features and certain agronomic traits. Relations between computed SOM subgroups and the market segments were subject to certain elucidations. The relevance of using more flexible multivariate statistical approaches for mapping population structures of crop species is considered throughout this paper in terms of current and future prospects for breeding programs.
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Acknowledgments
This research was fully supported by the Haute Ecole Provinciale de Hainaut-CONDORCET, 17 Chemin du Champ de Mars, 7000 Mons, Wallonia, Belgium and by the non-profit organization of agricultural services of the Province of Hainaut (CARAH), 11 rue Paul Pastur, 7800 Ath, Wallonia, Belgium. The authors gratefully acknowledged Maarten Vossen and Sjefke Allefs (Agrico Research BV), Maurice Schehr (HZPC Holland BV), Guus Heselmans (C. Meijer BV), Peter Oldenkamp (KWS Potato BV), Eric Bonnel and Gisèle Joly (Germicopa SAS), Hervé van den Wyngaert (Binst Breeding Selection), Jens Kr. Ege Olsen (LKF Vandel, Danespo A/S), Sylvie Marhadour (FN3PT), Curzio Caravati (Kenosha Potato Project) and Olivier Mahieu (CARAH asbl) for providing the plant material and associated information used in the present study. The help of Jeromy Hrabovecky and the anonymous reviewer is kindly acknowledged for the revision of the manuscript.
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MS and TM conceived the experiments. MS and JR performed the plant material sampling. MS, MM, JR, AN and CD achieved DNA extractions. MS and MM executed the genotyping analysis. MS, TM, AN and MB analyzed and interpreted the results. DL supervised the experiments and revised the draft manuscript. All authors read and approved the final manuscript.
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Plant material and associate data were collected with the best intentions by donors and the authors, respectively. Although no abnormality was detected, the authors are not responsible for any potential inaccuracy in the results except for those arising from genotyping analyses. Legal issues or disputes raised by owners of germplasm cannot be based on data or conclusions disclosed in this publication. The authors declare that they have no conflicts of interest.
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Spanoghe, M.C., Marique, T., Rivière, J. et al. Genetic patterns recognition in crop species using self-organizing map: the example of the highly heterozygous autotetraploid potato (Solanum tuberosum L.). Genet Resour Crop Evol 67, 947–966 (2020). https://doi.org/10.1007/s10722-020-00894-8
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DOI: https://doi.org/10.1007/s10722-020-00894-8