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Modelling [Formula: see text] with historical weather information improves genomic prediction in new environments

MOTIVATION: Interaction between the genotype and the environment ([Formula: see text]) has a strong impact on the yield of major crop plants. Although influential, taking [Formula: see text] explicitly into account in plant breeding has remained difficult. Recently [Formula: see text] has been predi...

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Autores principales: Gillberg, Jussi, Marttinen, Pekka, Mamitsuka, Hiroshi, Kaski, Samuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6792123/
https://www.ncbi.nlm.nih.gov/pubmed/30977782
http://dx.doi.org/10.1093/bioinformatics/btz197
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author Gillberg, Jussi
Marttinen, Pekka
Mamitsuka, Hiroshi
Kaski, Samuel
author_facet Gillberg, Jussi
Marttinen, Pekka
Mamitsuka, Hiroshi
Kaski, Samuel
author_sort Gillberg, Jussi
collection PubMed
description MOTIVATION: Interaction between the genotype and the environment ([Formula: see text]) has a strong impact on the yield of major crop plants. Although influential, taking [Formula: see text] explicitly into account in plant breeding has remained difficult. Recently [Formula: see text] has been predicted from environmental and genomic covariates, but existing works have not shown that generalization to new environments and years without access to in-season data is possible and practical applicability remains unclear. Using data from a Barley breeding programme in Finland, we construct an in silico experiment to study the viability of [Formula: see text] prediction under practical constraints. RESULTS: We show that the response to the environment of a new generation of untested Barley cultivars can be predicted in new locations and years using genomic data, machine learning and historical weather observations for the new locations. Our results highlight the need for models of [Formula: see text]: non-linear effects clearly dominate linear ones, and the interaction between the soil type and daily rain is identified as the main driver for [Formula: see text] for Barley in Finland. Our study implies that genomic selection can be used to capture the yield potential in [Formula: see text] effects for future growth seasons, providing a possible means to achieve yield improvements, needed for feeding the growing population. AVAILABILITY AND IMPLEMENTATION: The data accompanied by the method code (http://research.cs.aalto.fi/pml/software/gxe/bioinformatics_codes.zip) is available in the form of kernels to allow reproducing the results. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-67921232019-10-18 Modelling [Formula: see text] with historical weather information improves genomic prediction in new environments Gillberg, Jussi Marttinen, Pekka Mamitsuka, Hiroshi Kaski, Samuel Bioinformatics Original Papers MOTIVATION: Interaction between the genotype and the environment ([Formula: see text]) has a strong impact on the yield of major crop plants. Although influential, taking [Formula: see text] explicitly into account in plant breeding has remained difficult. Recently [Formula: see text] has been predicted from environmental and genomic covariates, but existing works have not shown that generalization to new environments and years without access to in-season data is possible and practical applicability remains unclear. Using data from a Barley breeding programme in Finland, we construct an in silico experiment to study the viability of [Formula: see text] prediction under practical constraints. RESULTS: We show that the response to the environment of a new generation of untested Barley cultivars can be predicted in new locations and years using genomic data, machine learning and historical weather observations for the new locations. Our results highlight the need for models of [Formula: see text]: non-linear effects clearly dominate linear ones, and the interaction between the soil type and daily rain is identified as the main driver for [Formula: see text] for Barley in Finland. Our study implies that genomic selection can be used to capture the yield potential in [Formula: see text] effects for future growth seasons, providing a possible means to achieve yield improvements, needed for feeding the growing population. AVAILABILITY AND IMPLEMENTATION: The data accompanied by the method code (http://research.cs.aalto.fi/pml/software/gxe/bioinformatics_codes.zip) is available in the form of kernels to allow reproducing the results. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2019-10-15 2019-04-12 /pmc/articles/PMC6792123/ /pubmed/30977782 http://dx.doi.org/10.1093/bioinformatics/btz197 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Gillberg, Jussi
Marttinen, Pekka
Mamitsuka, Hiroshi
Kaski, Samuel
Modelling [Formula: see text] with historical weather information improves genomic prediction in new environments
title Modelling [Formula: see text] with historical weather information improves genomic prediction in new environments
title_full Modelling [Formula: see text] with historical weather information improves genomic prediction in new environments
title_fullStr Modelling [Formula: see text] with historical weather information improves genomic prediction in new environments
title_full_unstemmed Modelling [Formula: see text] with historical weather information improves genomic prediction in new environments
title_short Modelling [Formula: see text] with historical weather information improves genomic prediction in new environments
title_sort modelling [formula: see text] with historical weather information improves genomic prediction in new environments
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6792123/
https://www.ncbi.nlm.nih.gov/pubmed/30977782
http://dx.doi.org/10.1093/bioinformatics/btz197
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AT mamitsukahiroshi modellingformulaseetextwithhistoricalweatherinformationimprovesgenomicpredictioninnewenvironments
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