ArticleG3 (Bethesda, Md.)2021
EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture.
Article in G3 (Bethesda, Md.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review.Frontiers in plant science · 2021Pooled it
- Genetic dissection of southern corn leaf blight resistance in sweet corn through genome-wide association studies and genomic selection.The plant genome · 2026Article
- Integrating genomic selection into potato breeding: A comparison of genotyping platforms and cross-environmental predictions.The plant genome · 2026Article
- Phenomics-assisted sparse testing for potato breeding.Scientific reports · 2026Article
- Genetic architecture and candidate loci associated with growth and yield-related traits in sweet fig banana (Musa acuminata cv Sotoumon, AA).BMC genomics · 2026Article
- Leveraging Genetic Diversity for Abiotic Stress Tolerance.International journal of molecular sciences · 2026Article
- Omics Technologies in Molecular Biology.International journal of molecular sciences · 2026Article
- Multimodal deep learning improves cross-environment prediction of durum wheat yield components.BMC plant biology · 2026Article
- Predicting enviromically adapted varieties with big data.Genome biology · 2026Article
- Genomewide association and prediction of phenotypic stability in barley.The plant genome · 2025Article
- Genomic prediction of agronomic traits in perennial ryegrass (Lolium perenne L.) and genotype x environment interactions at the limit of the species distribution.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Prediction of Australian wheat genotype by environment interactions and mega-environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- The genetic basis of chloride exclusion in grapevines.G3 (Bethesda, Md.) · 2025Article
- Phenotypic plasticity in maize grain yield: Genetic and environmental insights of response to environmental gradients.The plant genome · 2025Article
- Improving genomic prediction for plant disease using environmental covariates.Plant methods · 2025Article
- Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Evaluating the effectiveness of selection indices and their genomic prediction using environmental and historical rice data.G3 (Bethesda, Md.) · 2025Article
- Leveraging Automated Machine Learning for Environmental Data-Driven Genetic Analysis and Genomic Prediction in Maize Hybrids.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Tester selection for combining ability estimation of storage root yield and sweetpotato virus disease in sweetpotato breeding.Scientific reports · 2025Article
- Integrative multi-environmental genomic prediction in apple.Horticulture research · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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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