TY  - JA
AU  - Costa-Neto,G.
AU  - Galli,G.
AU  - Fanelli Carvalho,H.
AU  - Crossa,J.
AU  - Fritsche-Neto,R.
TI  - EnvRtype : : a software to interplay enviromics and quantitative genomics in agriculture
SN  - 2160-1836
PY  - 2021///
CY  - Bethesda, MD (USA)
PB  - Genetics Society of America
KW  - Genotype environment interaction
KW  - AGROVOC
KW  - Agriculture
KW  - Genomics
KW  - Computer software
N1  - Peer review; Open Access
N2  - 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
UR  - https://hdl.handle.net/10883/21501
T2  - G3: Genes, Genomes, Genetics
DO  - https://doi.org/10.1093/g3journal/jkab040
ER  -