Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
19 06 2023
19 06 2023
Historique:
received:
11
10
2022
accepted:
17
06
2023
medline:
21
6
2023
pubmed:
20
6
2023
entrez:
19
6
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37336933
doi: 10.1038/s41598-023-37169-y
pii: 10.1038/s41598-023-37169-y
pmc: PMC10279678
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
9947Informations de copyright
© 2023. The Author(s).
Références
Theor Appl Genet. 2014 Mar;127(3):595-607
pubmed: 24337101
Front Genet. 2022 Jul 08;13:920689
pubmed: 36313422
Heredity (Edinb). 2015 Jul;115(1):29-36
pubmed: 25873147
Plant Genome. 2016 Nov;9(3):
pubmed: 27902799
Front Plant Sci. 2022 Feb 07;13:785196
pubmed: 35197995
Front Plant Sci. 2019 Nov 08;10:1311
pubmed: 31787990
Front Plant Sci. 2016 Nov 22;7:1666
pubmed: 27920780
Trends Plant Sci. 2017 Nov;22(11):961-975
pubmed: 28965742
Front Genet. 2022 Sep 05;13:966775
pubmed: 36134027
G3 (Bethesda). 2018 Dec 10;8(12):3829-3840
pubmed: 30291108
G3 (Bethesda). 2019 May 7;9(5):1355-1369
pubmed: 30819822
Plant Genome. 2017 Nov;10(3):
pubmed: 29293806
Brief Bioinform. 2007 Jan;8(1):32-44
pubmed: 16772269
Plants (Basel). 2020 Dec 24;10(1):
pubmed: 33374406
Genes (Basel). 2023 Feb 02;14(2):
pubmed: 36833322
Genetics. 2012 Dec;192(4):1513-22
pubmed: 23086217
G3 (Bethesda). 2017 Jun 7;7(6):1995-2014
pubmed: 28455415
Adv Appl Bioinform Chem. 2009;2:57-70
pubmed: 21918616
Theor Appl Genet. 2018 Mar;131(3):685-701
pubmed: 29198016
Nat Food. 2021 Jul;2(7):453
pubmed: 37117682
Genetics. 2001 Apr;157(4):1819-29
pubmed: 11290733
Genet Sel Evol. 2011 Jul 05;43:26
pubmed: 21729282
Bioinformatics. 2016 Jun 15;32(12):i37-i43
pubmed: 27307640
G3 (Bethesda). 2015 Feb 06;5(4):569-82
pubmed: 25660166
G3 (Bethesda). 2016 Sep 08;6(9):2725-44
pubmed: 27342738
G3 (Bethesda). 2023 Feb 9;13(2):
pubmed: 36477309
G3 (Bethesda). 2019 Oct 7;9(10):3381-3393
pubmed: 31427455
Trends Plant Sci. 2014 Sep;19(9):592-601
pubmed: 24970707