EnvRtype: a tool for envirotyping analysis and genomic prediction considering reaction norms 
ATTENTION: package maintenance between 15th October to 31th October 2020.
Background
Environmental typing (envirotyping) has proven useful in identifying the non-genetic drivers of phenotypic adaptation in plant breeding. 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. Here we present the EnvRtype R package as a new toolkit developed to facilitate the interplay between envirotyping and genomic prediction. This package offers three modules: (1) collection and processing data set, (2) environmental characterization, (3) build of ecophysiological enriched genomic prediction models accounting for three different structures of reaction norm. Here we focus our efforts to present a practical use of EnvRtype package in supporting the genome-wide prediction of reaction norms. We provide a intuitive framework to integrate different reaction norm models in Bayesian Genomic Genotype x Environment Interaction (BGGE) package.
Resources
EnvRtype consists in three modules (sections 2-4), which collectively generate a simple workflow to collect, process and integrates envirotyping data into genomic prediction over multiple environments.
Main Functions
- 1. Install and Required Packages
- 2. Environmental Sensing Module
- 3. Environmental Characterization Module
- 4. Reaction-Norm Module
Tutorials
Information
Install
Using devtools in R
library(devtools)
install_github('allogamous/EnvRtype') # current version: 0.1.5
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_0.1.9.tar.gz", repos = NULL, type="source") # Make sure to use the current verision
Required packages
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','doParalell','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, PhD Candidate in Genetics and Plant Breeding
Maintence
- Germano Costa Neto, PhD Candidate in Genetics and Plant Breeding
- Giovanni Galli, PhD in Genetics and Plant Breeding
- Humberto Fanelli, PhD in Genetics and Plant Breeding
Acknowledgments
- Giovanni Galli, PhD in Genetics and Plant Breeding
- Humberto Fanelli, PhD in Genetics and Plant Breeding
- Jose Crossa, Biometrics and Statistic Unit at CIMMYT.
- Roberto Fritsche-Neto, Professor in Genetics and Plant Breeding, Head of Allogamous Plant Breedig Lab (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
Publications
Costa-Neto G, Galli G, Fanelli H, Crossa J, Fritsche-Neto R (2020). EnvRtype : a software to interplay enviromics and quantitative genomics in agriculture. bioRxiv in press
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).
