000			03095nab|a22003497a|4500
001			63678
003			MX-TxCIM
005			20240919020952.0
008			202104s2021||||xxu|||p|op||||00||0|eng|d
022			_a2160-1836
024	8		_ahttps://doi.org/10.1093/g3journal/jkab040
040			_aMX-TxCIM
041			_aeng
100	1		_915939 _aCosta-Neto, G. _8001712813 _gGenetic Resources Program
245	1	0	_aEnvRtype : _ba software to interplay enviromics and quantitative genomics in agriculture
260			_aBethesda, MD (USA) : _bGenetics Society of America, _c2021.
500			_aPeer review
500			_aOpen Access
520			_aEnvirotyping 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.
546			_aText in English
650		7	_aGenotype environment interaction _2AGROVOC _91133
650		7	_aAgriculture _2AGROVOC _91007
650		7	_aGenomics _2AGROVOC _91132
650		7	_aComputer software _2AGROVOC _94868
700	1		_aGalli, G. _98650
700	1		_aFanelli Carvalho, H. _919807
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aFritsche-Neto, R. _96507
773	0		_tG3: Genes, Genomes, Genetics _gv. 11, no. 4, art. jkab040 _dBethesda, MD (USA) : Genetics Society of America, 2021. _w56922 _x2160-1836
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/21501
942			_cJA _n0 _2ddc
999			_c63678 _d63670