EnvRtype: Envirotyping Tools in R

A R Interplay between Quantitative Genetics and Ecophysiology for GxE analysis

Current Version: 1.1.0 (1st June 2022)

Last Version: 1.0.0 (31th August 2021)

Envirotyping has proven useful in identifying the non-genetic drivers of phenotypic adaptation in plants cultivaded in diverse growing conditions. Combined with phenotyping and genotyping data, the use of envirotyping data may leverage the molecular breeding strategies to cope with environmental changing scenarios. Over the last ten years, this data has been incorporated in genomic-enabled prediction models aiming to better model genotype x environment interaction (GE) as a function of reaction-norm. However, there is difficult for most breeders to deal with the interplay between envirotyping, ecophysiology, and genetics.

It also can be useful for several fields of agricultural, livestook and ecology research, by delivering high-quality environmental information and environmental grouping appraoches.

Here we present the EnvRtype R package as a new toolkit developed to facilitate the interplay between envirotyping and fields of plant research such as genomic prediction. This package offers three modules: (1) collection and processing data set, (2) environmental characterization, (3) build of ecophysiological enriched predictive models accounting for three different structures of reaction-norm over different sources of genomic relatedness. Thus, EnvRtype is useful for exploratory purposes and predctive breeding for multiple growing conditions.

## Resources

The envirotyping pipeline provided by EnvRtype consists in three modules (1 - Environmental Sensing, 2- Macro-Environmental Characterization and 3 - Enviromic Similarity and Phenotype Prediction). Collectively, the EnvRtyping functions generate a simple workflow to collect, process and integrates envirotyping data into several fields of agricultural research, specially for predictive breeding that may include the use of genomic x enviromic relatedness information.

## Updates and Maintence

rgdal is retired! We fixed it by Oct 21 2023. More info about rgdal’s retirement here
EnvRtype 1.1.0 is online (1st June 2022)
New version of the get_weather() function – now running in parallel (Jun 2022)
Coming Soon: get_soil() function and EPA() [environmental-phenotype associations]
[SOLVED] Date 2021-10-14: NASA POWER server off “Error: Something went wrong with the query, no data were returned. Please see https://power.larc.nasa.gov for potential server issues.”
Join our DISCUSSION FORUM
[SOLVED] PRECTOT variable (rainfall precipitation) is currently off from NASA POWER
Coming soon: tutorial for using external sources of environmental data (from field micro-stations)
[SOLVED] Coming soon (Dec 2021): tutorial for colecting soil data from SoilGrids data base
The current version of the package is 0.0.2 (May 22th 2021)
[SOLVED] From December 15th 2020 to January 10th 2021 this page will be under maintence. This means that we are now working in several updates and some changes will be made in some functions.
The current version of the package is 0.0.1 (Nov 20th 2020)

## Tutorials

Information

Authorship
Acknowledgments
Publications
Getting help

Install

Using devtools in R

library(devtools)
devtools::install_github('allogamous/EnvRtype',force=TRUE) # current version:  1.1.0 (June 2022)
# Enter one or more numbers, or an empty line to skip updates: 3
require(EnvRtype)

Manually installing

If the method above doesn’t work, use the next lines by downloading the EnvRtype-master.zip file

setwd("~/EnvRtype-master.zip") # ~ is the path from where you saved the file.zip
unzip("EnvRtype-master.zip") 
file.rename("EnvRtype-master", "EnvRtype") 
shell("R CMD build EnvRtype") # or system("R CMD build EnvRtype")
install.packages("EnvRtype_1.1.0.tar.gz", repos = NULL, type="source") # Make sure to use the current verision

### Required packages

For some users, it seems that the packages below must downloaded…(I am not a IT guy, I am really don’t know why, sorry).

EnvRtype
raster
nasapower
BGGE
foreach
doParalell

install.packages("foreach")
install.packages("doParallel")
install.packages("raster")
install.packages("nasapower")
install.packages("rgdal")
install.packages("BGGE")
              
or
              
#source("https://raw.githubusercontent.com/gcostaneto/Funcoes_naive/master/instpackage.R");
#inst.package(c("BGGE",'foreach','doParallel','raster','rgdal','nasapower'));

install.packages(c("BGGE",'foreach','doParallel','raster','rgdal','nasapower'))

library(EnvRtype)

Authorship

This package is a initiative from the Allogamous Plant Breeding Lab (University of São Paulo, ESALQ/USP, Brazil).

Developer

Germano Costa Neto, University of Sao Paulo/ Cornell University

Maintence

Germano Costa Neto

Publications

Last update: 2021-10-10

Costa-Neto, G., Crossa, J., and Fritsche-Neto, R. (2021). Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize. Frontiers in Plant Science 12. doi:10.3389/fpls.2021.717552.

Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., and Fritsche-Neto, R. (2021). EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. **G3 Genes

Genomes

Genetics**. doi:10.1093/g3journal/jkab040.

Galli G, Horne DW, Collins SD, Jung J, Chang A, Fritsche‐Neto R, et al. (2020). Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum. Plant Phenome J 3: 1–14.
Costa-Neto G, Fritsche-Neto R, Crossa J (2020). Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity (Edinb).

Acknowledgments

Giovanni Galli
Humberto Fanelli
Jose Crossa, Biometrics and Statistic Unit at CIMMYT.
Roberto Fritsche-Neto
University of São Paulo (ESALQ/USP)
Conselho Nacional de Desenvolvimento Científico e Tecnológico for the PhD scholarship granted to the authors of the package
Pedro L. Longhin for additional support in Git Hub