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Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials
Kernel methods are flexible and easy to interpret and have been successfully used in genomic-enabled prediction of various plant species. Kernel methods used in genomic prediction comprise the linear genomic best linear unbiased predictor (GBLUP or GB) kernel, and the Gaussian kernel (GK). In genera...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Genetics Society of America
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6723142/ https://www.ncbi.nlm.nih.gov/pubmed/31289023 http://dx.doi.org/10.1534/g3.119.400493 |
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author | Cuevas, Jaime Montesinos-López, Osval Juliana, Philomin Guzmán, Carlos Pérez-Rodríguez, Paulino González-Bucio, José Burgueño, Juan Montesinos-López, Abelardo Crossa, José |
author_facet | Cuevas, Jaime Montesinos-López, Osval Juliana, Philomin Guzmán, Carlos Pérez-Rodríguez, Paulino González-Bucio, José Burgueño, Juan Montesinos-López, Abelardo Crossa, José |
author_sort | Cuevas, Jaime |
collection | PubMed |
description | Kernel methods are flexible and easy to interpret and have been successfully used in genomic-enabled prediction of various plant species. Kernel methods used in genomic prediction comprise the linear genomic best linear unbiased predictor (GBLUP or GB) kernel, and the Gaussian kernel (GK). In general, these kernels have been used with two statistical models: single-environment and genomic × environment (GE) models. Recently near infrared spectroscopy (NIR) has been used as an inexpensive and non-destructive high-throughput phenotyping method for predicting unobserved line performance in plant breeding trials. In this study, we used a non-linear arc-cosine kernel (AK) that emulates deep learning artificial neural networks. We compared AK prediction accuracy with the prediction accuracy of GB and GK kernel methods in four genomic data sets, one of which also includes pedigree and NIR information. Results show that for all four data sets, AK and GK kernels achieved higher prediction accuracy than the linear GB kernel for the single-environment and GE multi-environment models. In addition, AK achieved similar or slightly higher prediction accuracy than the GK kernel. For all data sets, the GE model achieved higher prediction accuracy than the single-environment model. For the data set that includes pedigree, markers and NIR, results show that the NIR wavelength alone achieved lower prediction accuracy than the genomic information alone; however, the pedigree plus NIR information achieved only slightly lower prediction accuracy than the marker plus the NIR high-throughput data. |
format | Online Article Text |
id | pubmed-6723142 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Genetics Society of America |
record_format | MEDLINE/PubMed |
spelling | pubmed-67231422019-09-17 Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials Cuevas, Jaime Montesinos-López, Osval Juliana, Philomin Guzmán, Carlos Pérez-Rodríguez, Paulino González-Bucio, José Burgueño, Juan Montesinos-López, Abelardo Crossa, José G3 (Bethesda) Genomic Prediction Kernel methods are flexible and easy to interpret and have been successfully used in genomic-enabled prediction of various plant species. Kernel methods used in genomic prediction comprise the linear genomic best linear unbiased predictor (GBLUP or GB) kernel, and the Gaussian kernel (GK). In general, these kernels have been used with two statistical models: single-environment and genomic × environment (GE) models. Recently near infrared spectroscopy (NIR) has been used as an inexpensive and non-destructive high-throughput phenotyping method for predicting unobserved line performance in plant breeding trials. In this study, we used a non-linear arc-cosine kernel (AK) that emulates deep learning artificial neural networks. We compared AK prediction accuracy with the prediction accuracy of GB and GK kernel methods in four genomic data sets, one of which also includes pedigree and NIR information. Results show that for all four data sets, AK and GK kernels achieved higher prediction accuracy than the linear GB kernel for the single-environment and GE multi-environment models. In addition, AK achieved similar or slightly higher prediction accuracy than the GK kernel. For all data sets, the GE model achieved higher prediction accuracy than the single-environment model. For the data set that includes pedigree, markers and NIR, results show that the NIR wavelength alone achieved lower prediction accuracy than the genomic information alone; however, the pedigree plus NIR information achieved only slightly lower prediction accuracy than the marker plus the NIR high-throughput data. Genetics Society of America 2019-07-09 /pmc/articles/PMC6723142/ /pubmed/31289023 http://dx.doi.org/10.1534/g3.119.400493 Text en Copyright © 2019 Cuevas et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Genomic Prediction Cuevas, Jaime Montesinos-López, Osval Juliana, Philomin Guzmán, Carlos Pérez-Rodríguez, Paulino González-Bucio, José Burgueño, Juan Montesinos-López, Abelardo Crossa, José Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials |
title | Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials |
title_full | Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials |
title_fullStr | Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials |
title_full_unstemmed | Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials |
title_short | Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials |
title_sort | deep kernel for genomic and near infrared predictions in multi-environment breeding trials |
topic | Genomic Prediction |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6723142/ https://www.ncbi.nlm.nih.gov/pubmed/31289023 http://dx.doi.org/10.1534/g3.119.400493 |
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