Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity

Abstract

Quantitative genetics states that phenotypic variation is a consequence of genetic and environmental factors and their subsequent interaction. Here, we present an enviromic assembly approach, which includes the use of ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as E-GP). We propose that the quality of an environment is defined by the core of environmental typologies (envirotype) and their frequencies, which describe different zones of plant adaptation. From that, we derive markers of environmental similarity cost-effectively. Combined with the traditional genomic sources (e.g., additive and dominance effects), this approach may better represent the putative phenotypic variation across diverse growing conditions (i.e., phenotypic plasticity). Additionally, we couple a genetic algorithm scheme to design optimized multi-environment field trials (MET), combining enviromic assembly and genomic kinships to provide in-silico realizations of the future genotype-environment combinations that must be phenotyped in the field. As a proof-of-concept, we highlight E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity using optimized phenotyping efforts. Our approach was tested using two non-conventional cross-validation schemes to better visualize the benefits of enviromic assembly in sparse experimental networks. Results on tropical maize show that E-GP outperforms benchmark GP in all scenarios and cases tested. We show that for training accurate GP models, the genotype-environment combinations' representativeness is more critical than the MET size. Furthermore, we discuss theoretical backgrounds underlying how the intrinsic envirotype-phenotype covariances within the phenotypic records of (MET) can impact the accuracy of GP and limits the potentialities of predictive breeding approaches. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.

Enviromics: bridging different sources of data, building one framework

“…Enviromics is the large-scale envirotyping, based on the collections of environmental data across time and space to establish a global association between the crops envirome (the core of TPEs) and phenotypic variation of key factors driving G×E. From enviromics, it is possible to derive some useful outputs, such as an environmental relatedness matrix, which supposes that the core of typologies is a marker of "environmental polymorphisms" (Resende et al 2020, Costa-Neto et al 2021a, Costa-Neto et al 2021c. Another important goal of enviromics is its further integration in systems biology approaches, which can support the understanding of genomics × enviromics interactions, but also its relation with different omics sources (e.g., transcriptomic, proteomic) and its impact on improving accuracy and resolution of predictive breeding platforms (Costa-Neto et al 2021a, Costa-Neto et al 2021b, Rogers et al 2021.…”

“…Similar to the classical GBLUP, in which molecular markers shape a genomic relationship matrix (G), the environmental relatedness must also be created using the linear covariance among W covariates (W). In addition, the authors showed a more intuitive approach to model G×E, in which now the Hadamard product (#) between genomics, GW~ZgGZg T # ZeWZe T, and what was called later as 'enviromic-based matrix' (Costa-Neto et al 2021a, Costa-Neto et al 2021c) is the core of putative reaction-norms driving the expected G×E (Jarquin et al 2014). This approach was expanded by Morais-Júnior et al ( 2018), which involved a wide number of W covariates and another genetic relatedness, both from genomics and pedigree-based records.…”

“…But the efforts of GP at MET only started in 2012 with the marker environment models (Burgueño et al 2012, Schulz-Streeck et al 2013. In this scenario, we must consider that all phenotypic records carry the indissoluble effects of macro-environmental fluctuations of certain weather and soil factors during crop growth and development (Costa-Neto et al 2021a, Costa-Neto et al 2021c). Because of that, every phenotypic record carries an inner covariance with its growing environment, in which this phenomenon is visualized when the same model is trained for the same germplasm evaluated under different growing conditions.…”

“…To evolve the current GP platforms for the next level, which includes increasing the accuracy and resolution of predicting particular genotypes for complex future scenarios (e.g., climate change), we envisage that the use of explicit covariates in a wise manner must also evolve. There are three ways to implement it: 1) the classical reaction-norms to consider whole-genome regressions with key-environmental factors, and also hierarchical trait-by-trait interactions , Ly et al 2018, Millet et al 2019, Guo et al 2020, Jarquín et al 2020; 2) get a better understanding of crops envirome to formulate a mathematical description of the environmental relatedness (Jarquín et al 2014, Morais-Júnior et al 2018, Monteverde et al 2019, de los Campos et al 2020, Costa-Neto et al 2021a, Costa-Neto et al 2021b, Costa-Neto et al 2021c, Rogers et al 2021, in which; 3) integrate different prediction approaches, such as crop growth models in the GP platforms (Cooper et al 2016, Messina et al 2018, Toda et al 2020, Robert et al 2020, in which enviromics is an output of the environmental factors and mechanistic process shaped by genotype-specific parameters. To rethinking the idea of MET GP, perhaps a generalized enviromic-genomic prediction model for modeling the phenotypic variation might be written as:…”

“…If the envirotyping analytics pass by to a second step, where the envirotyping data is associated with some trait-specific phenotype, then it's possible to see a breeding programspecific TPE (Crespo-Herrera et al 2021). The use of remote sensing-based envirotyping analytic tools (e.g., EnvRtype) easily implements those approaches as diverse global sites might share similar environmental patterns according to their planting dates (Costa-Neto et al 2021a, Costa-Neto et al 2021b, Costa-Neto et al 2021c. A next step might be implementing well-consolidated enviromes for each crop species, considering the all-possible typologies that some crop species might face.…”