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The Enviromic marker
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AI
DOI：10.1590/1984-70332026v26n1n1.png)
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Abstract
En 中文
We formalize enviromic markers as modeling units parallel to DNA markers, but herein for genotype-environment (G & times; E) prediction. Four operational premises (linearity; site potential; heterogeneous favorability; and envirotypic covariates (ECs)-genotype-dependence) are presented to enable their use in linear mixed models and also to motivate four construction strategies: (i) using raw environmental covariates as linear markers; (ii) applying transformations to capture mild nonlinearities; (iii) deriving ecophysiological functions; and (iv) engineering markers with Artificial Intelligence (AI) models which learn nonlinear environment -> phenotype mappings for linear downstream use. Environmental data quality control is detailed, including checks of spatial coverage and resolution, variance within the TPE, collinearity control, and spatial/ temporal validation without leakage. Envirome data are linked with GIS to compute environmental kernels, quantify covariate shifts, and deliver pixel-level predictions with uncertainty diagnostics. The framework clarifies assumptions and standardizes the use of enviromic markers for predictive breeding analyses.
Keywords:
Envirotyping
genotype-by-environment interaction (G & times
E)
environmental or envirotypic covariates (ECs)
target population of environments (TPE)
geographic information systems (GIS)
Journal
C
IF:
1.1
Papers:
14
Citations:
0