EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture

Envirotyping is an essential technique used to unfold the nongenetic drivers associated with the phenotypic adaptation of living organisms. Here, we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To star...

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Main Authors: Costa-Neto, Germano, Galli, Giovanni, Carvalho, Humberto Fanelli, Crossa, José, Fritsche-Neto, Roberto
Format: Journal Article
Language:Inglés
Published: Oxford University Press 2021
Subjects:
Online Access:https://hdl.handle.net/10568/164346
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author Costa-Neto, Germano
Galli, Giovanni
Carvalho, Humberto Fanelli
Crossa, José
Fritsche-Neto, Roberto
author_browse Carvalho, Humberto Fanelli
Costa-Neto, Germano
Crossa, José
Fritsche-Neto, Roberto
Galli, Giovanni
author_facet Costa-Neto, Germano
Galli, Giovanni
Carvalho, Humberto Fanelli
Crossa, José
Fritsche-Neto, Roberto
author_sort Costa-Neto, Germano
collection Repository of Agricultural Research Outputs (CGSpace)
description Envirotyping is an essential technique used to unfold the nongenetic drivers associated with the phenotypic adaptation of living organisms. Here, we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments.
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spelling CGSpace1643462025-01-24T14:11:46Z EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture Costa-Neto, Germano Galli, Giovanni Carvalho, Humberto Fanelli Crossa, José Fritsche-Neto, Roberto genetics(clinical) genetics molecular biology Envirotyping is an essential technique used to unfold the nongenetic drivers associated with the phenotypic adaptation of living organisms. Here, we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments. 2021-04-15 2024-12-19T12:53:45Z 2024-12-19T12:53:45Z Journal Article https://hdl.handle.net/10568/164346 en Open Access Oxford University Press Costa-Neto, Germano; Galli, Giovanni; Carvalho, Humberto Fanelli; Crossa, José and Fritsche-Neto, Roberto. 2021. EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3-Genes Genomes Genetics, Volume 11, no. 4; jkab040 (20 pages)
spellingShingle genetics(clinical)
genetics
molecular biology
Costa-Neto, Germano
Galli, Giovanni
Carvalho, Humberto Fanelli
Crossa, José
Fritsche-Neto, Roberto
EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture
title EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture
title_full EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture
title_fullStr EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture
title_full_unstemmed EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture
title_short EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture
title_sort envrtype a software to interplay enviromics and quantitative genomics in agriculture
topic genetics(clinical)
genetics
molecular biology
url https://hdl.handle.net/10568/164346
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