EnvRtype

EnvRtype integrates large-scale enviromics data with quantitative genomics to characterize environmental drivers of phenotypic adaptation and support genomic prediction in agricultural research.

Key Features:

• Remote sensing and data retrieval: get_weather and extract_GIS collect ecophysiological variables and GIS data at local and global scales, and processWTH processes these variables for analysis.
• Environmental characterization: env_typing types environments and profiles descriptors of environmental quality, while W_matrix compiles environmental covariables as quantitative descriptors for predictive modeling.
• Environmental similarity and kernel modeling: get_kernel and kernel_model derive enviromic-based kernels that emulate reaction-norm effects and can be integrated into whole-genome prediction (GP) and genomic best-unbiased predictions (GBLUP).

Scientific Applications:

• Genome-wide prediction enhancement: Integrating enviromics with genomic data to increase the accuracy of genome-wide predictions across untested growing environments.
• Environmental effect characterization: Characterizing how environmental covariables influence genetic expression and phenotypic traits through envirotyping and kernel-based similarity measures.
• Predictive breeding support: Providing enviromic-derived descriptors and kernels for predictive breeding programs aimed at optimizing crop performance under diverse environmental conditions.

Methodology:

Data retrieval and preprocessing via get_weather, extract_GIS and processWTH; environment typing with env_typing and descriptor assembly with W_matrix; kernel derivation and reaction-norm emulation using get_kernel and kernel_model; and a literature-mining approach to fine-tune envirotyping parameters and apply eco-physiologically-informed processing for target species and environments.