Modeling of Flowering Time in <i>Vigna radiata</i> with Approximate Bayesian Computation

Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. A new approach is proposed that uses Approximate Bayesian Computation with Differential Evolution to construct a pool of models for flowering time. The functions for daily progression of th...

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Autores principales:	Andrey Ageev, Cheng-Ruei Lee, Chau-Ti Ting, Roland Schafleitner, Eric Bishop-von Wettberg, Sergey V. Nuzhdin, Maria Samsonova, Konstantin Kozlov
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	flowering time vigna radiata approximate Bayesian computation climatic factors vGWAS climate warming Agriculture S
Acceso en línea:	https://doaj.org/article/eb4ce8cc07a849018cc2e1827c1bbaae
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spelling	oai:doaj.org-article:eb4ce8cc07a849018cc2e1827c1bbaae2021-11-25T16:11:22ZModeling of Flowering Time in <i>Vigna radiata</i> with Approximate Bayesian Computation10.3390/agronomy111123172073-4395https://doaj.org/article/eb4ce8cc07a849018cc2e1827c1bbaae2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4395/11/11/2317https://doaj.org/toc/2073-4395Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. A new approach is proposed that uses Approximate Bayesian Computation with Differential Evolution to construct a pool of models for flowering time. The functions for daily progression of the plant from planting to flowering are obtained in analytic form and depend on daily values of climatic factors and genetic information. The resulting pool of models demonstrated high accuracy on the dataset. Day length, solar radiation and temperature had a large impact on the model accuracy, while the impact of precipitation was comparatively small and the impact of maximal temperature has the maximal variation. The model pool was used to investigate the behavior of accessions from the dataset in case of temperature increase by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.05</mn></mrow></semantics></math></inline-formula>–<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mn>6.00</mn><mo>°</mo></msup></mrow></semantics></math></inline-formula>. The time to flowering changed differently for different accessions. The Pearson correlation coefficient between the SNP value and the change in time to flowering revealed weak but significant association of SNP7 with behavior of the accessions in warming climate conditions. The same SNP was found to have a considerable influence on model prediction with a permutation test. Our approach can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired flowering time.Andrey AgeevCheng-Ruei LeeChau-Ti TingRoland SchafleitnerEric Bishop-von WettbergSergey V. NuzhdinMaria SamsonovaKonstantin KozlovMDPI AGarticleflowering timevigna radiataapproximate Bayesian computationclimatic factorsvGWASclimate warmingAgricultureSENAgronomy, Vol 11, Iss 2317, p 2317 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	flowering time vigna radiata approximate Bayesian computation climatic factors vGWAS climate warming Agriculture S
spellingShingle	flowering time vigna radiata approximate Bayesian computation climatic factors vGWAS climate warming Agriculture S Andrey Ageev Cheng-Ruei Lee Chau-Ti Ting Roland Schafleitner Eric Bishop-von Wettberg Sergey V. Nuzhdin Maria Samsonova Konstantin Kozlov Modeling of Flowering Time in <i>Vigna radiata</i> with Approximate Bayesian Computation
description	Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. A new approach is proposed that uses Approximate Bayesian Computation with Differential Evolution to construct a pool of models for flowering time. The functions for daily progression of the plant from planting to flowering are obtained in analytic form and depend on daily values of climatic factors and genetic information. The resulting pool of models demonstrated high accuracy on the dataset. Day length, solar radiation and temperature had a large impact on the model accuracy, while the impact of precipitation was comparatively small and the impact of maximal temperature has the maximal variation. The model pool was used to investigate the behavior of accessions from the dataset in case of temperature increase by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.05</mn></mrow></semantics></math></inline-formula>–<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mn>6.00</mn><mo>°</mo></msup></mrow></semantics></math></inline-formula>. The time to flowering changed differently for different accessions. The Pearson correlation coefficient between the SNP value and the change in time to flowering revealed weak but significant association of SNP7 with behavior of the accessions in warming climate conditions. The same SNP was found to have a considerable influence on model prediction with a permutation test. Our approach can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired flowering time.
format	article
author	Andrey Ageev Cheng-Ruei Lee Chau-Ti Ting Roland Schafleitner Eric Bishop-von Wettberg Sergey V. Nuzhdin Maria Samsonova Konstantin Kozlov
author_facet	Andrey Ageev Cheng-Ruei Lee Chau-Ti Ting Roland Schafleitner Eric Bishop-von Wettberg Sergey V. Nuzhdin Maria Samsonova Konstantin Kozlov
author_sort	Andrey Ageev
title	Modeling of Flowering Time in <i>Vigna radiata</i> with Approximate Bayesian Computation
title_short	Modeling of Flowering Time in <i>Vigna radiata</i> with Approximate Bayesian Computation
title_full	Modeling of Flowering Time in <i>Vigna radiata</i> with Approximate Bayesian Computation
title_fullStr	Modeling of Flowering Time in <i>Vigna radiata</i> with Approximate Bayesian Computation
title_full_unstemmed	Modeling of Flowering Time in <i>Vigna radiata</i> with Approximate Bayesian Computation
title_sort	modeling of flowering time in <i>vigna radiata</i> with approximate bayesian computation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/eb4ce8cc07a849018cc2e1827c1bbaae
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Modeling of Flowering Time in <i>Vigna radiata</i> with Approximate Bayesian Computation

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