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Research article2023Peer reviewedOpen access

Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments

Ortiz, Rodomiro; Reslow, Fredrik; Montesinos-López, Abelardo; Huicho, José; Pérez-Rodríguez, Paulino; Montesinos‑López, Osval A.; Crossa, Jose

Abstract

It is of paramount importance in plant breeding to have methods dealing with large numbers of predictor variables and few sample observations, as well as efficient methods for dealing with high correlation in predictors and measured traits. This paper explores in terms of prediction performance the partial least squares (PLS) method under single-trait (ST) and multi-trait (MT) prediction of potato traits. The first prediction was for tested lines in tested environments under a five-fold cross-validation (5FCV) strategy and the second prediction was for tested lines in untested environments (herein denoted as leave one environment out cross validation, LOEO). There was a good performance in terms of predictions (with accuracy mostly > 0.5 for Pearson's correlation) the accuracy of 5FCV was better than LOEO. Hence, we have empirical evidence that the ST and MT PLS framework is a very valuable tool for prediction in the context of potato breeding data.

Published in

Scientific Reports
2023, Volume: 13, number: 1, article number: 9947

      SLU Authors

      • Sustainable Development Goals

      SDG2 End hunger, achieve food security and improved nutrition and promote sustainable agriculture

      UKÄ Subject classification

      Genetics and Breeding
      Agricultural Science
      Horticulture

      Publication identifier

      DOI: https://doi.org/10.1038/s41598-023-37169-y

      Permanent link to this page (URI)

      https://res.slu.se/id/publ/122513