4.3 Article

EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture

期刊

G3-GENES GENOMES GENETICS
卷 11, 期 4, 页码 -

出版社

OXFORD UNIV PRESS INC
DOI: 10.1093/g3journal/jkab040

关键词

GxE: genotype x environment interaction; envirotyping; environmental characterization

资金

  1. Bill and Melinda Gates Foundation [INV-003439 BMGF/FCDO]
  2. USAID [9 MTO 069033]
  3. Foundations for Research Levy on Agricultural Products (F.F.J.)
  4. Agricultural Agreement Research Fund (J.A.) in Norway through NFR [267806]
  5. CIMMYT CRP (maize and wheat)

向作者/读者索取更多资源

Envirotyping is a crucial technique for understanding the non-genetic factors associated with phenotypic adaptation of living organisms. The EnvRtype R package is a novel tool kit developed to integrate large-scale envirotyping data with quantitative genomics, providing a user-friendly pipeline for increasing ecophysiological knowledge in genomic best-unbiased predictions.
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 tool kit 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_kemel 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.

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