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

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

• Ortiz Rios, Rodomiro Octavio

  • Department of Plant Breeding, Swedish University of Agricultural Sciences
    

• Reslow, Fredrik

  • Department of Plant Breeding, Swedish University of Agricultural Sciences
    

Global goals (SDG)

SDG2 Zero hunger

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