Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments
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.
Main Authors: | , , , , , , |
---|---|
Format: | Article biblioteca |
Language: | English |
Published: |
Nature Publishing Group
2023
|
Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Potato Traits, Cross-Validation, Breeding Data, LEAST SQUARES METHOD, POTATOES, ENVIRONMENT, PLANT BREEDING, Genetic Resources, |
Online Access: | https://hdl.handle.net/10883/22633 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
dig-cimmyt-10883-22633 |
---|---|
record_format |
koha |
spelling |
dig-cimmyt-10883-226332023-12-08T15:11:05Z Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments Ortiz, R. Reslow, F. Montesinos-Lopez, A. Huicho, J. Perez-Rodriguez, P. Montesinos-Lopez, O.A. Crossa, J. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Potato Traits Cross-Validation Breeding Data LEAST SQUARES METHOD POTATOES ENVIRONMENT PLANT BREEDING Genetic Resources 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. 2023-06-29T20:10:11Z 2023-06-29T20:10:11Z 2023 Article Published Version https://hdl.handle.net/10883/22633 10.1038/s41598-023-37169-y English https://hdl.handle.net/11529/10548617 https://hdl.handle.net/11529/10548784 CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose Open Access London (United Kingdom) Nature Publishing Group 1 13 2045-2322 Scientific Reports 9947 |
institution |
CIMMYT |
collection |
DSpace |
country |
México |
countrycode |
MX |
component |
Bibliográfico |
access |
En linea |
databasecode |
dig-cimmyt |
tag |
biblioteca |
region |
America del Norte |
libraryname |
CIMMYT Library |
language |
English |
topic |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Potato Traits Cross-Validation Breeding Data LEAST SQUARES METHOD POTATOES ENVIRONMENT PLANT BREEDING Genetic Resources AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Potato Traits Cross-Validation Breeding Data LEAST SQUARES METHOD POTATOES ENVIRONMENT PLANT BREEDING Genetic Resources |
spellingShingle |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Potato Traits Cross-Validation Breeding Data LEAST SQUARES METHOD POTATOES ENVIRONMENT PLANT BREEDING Genetic Resources AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Potato Traits Cross-Validation Breeding Data LEAST SQUARES METHOD POTATOES ENVIRONMENT PLANT BREEDING Genetic Resources Ortiz, R. Reslow, F. Montesinos-Lopez, A. Huicho, J. Perez-Rodriguez, P. Montesinos-Lopez, O.A. Crossa, J. Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments |
description |
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. |
format |
Article |
topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Potato Traits Cross-Validation Breeding Data LEAST SQUARES METHOD POTATOES ENVIRONMENT PLANT BREEDING Genetic Resources |
author |
Ortiz, R. Reslow, F. Montesinos-Lopez, A. Huicho, J. Perez-Rodriguez, P. Montesinos-Lopez, O.A. Crossa, J. |
author_facet |
Ortiz, R. Reslow, F. Montesinos-Lopez, A. Huicho, J. Perez-Rodriguez, P. Montesinos-Lopez, O.A. Crossa, J. |
author_sort |
Ortiz, R. |
title |
Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments |
title_short |
Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments |
title_full |
Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments |
title_fullStr |
Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments |
title_full_unstemmed |
Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments |
title_sort |
partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments |
publisher |
Nature Publishing Group |
publishDate |
2023 |
url |
https://hdl.handle.net/10883/22633 |
work_keys_str_mv |
AT ortizr partialleastsquaresenhancemultitraitgenomicpredictionofpotatocultivarsinnewenvironments AT reslowf partialleastsquaresenhancemultitraitgenomicpredictionofpotatocultivarsinnewenvironments AT montesinoslopeza partialleastsquaresenhancemultitraitgenomicpredictionofpotatocultivarsinnewenvironments AT huichoj partialleastsquaresenhancemultitraitgenomicpredictionofpotatocultivarsinnewenvironments AT perezrodriguezp partialleastsquaresenhancemultitraitgenomicpredictionofpotatocultivarsinnewenvironments AT montesinoslopezoa partialleastsquaresenhancemultitraitgenomicpredictionofpotatocultivarsinnewenvironments AT crossaj partialleastsquaresenhancemultitraitgenomicpredictionofpotatocultivarsinnewenvironments |
_version_ |
1787233023000838144 |