Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction

Abstract

Genomic prediction of complex traits across environments, breeding cycles, and populations remains a challenge for plant breeding. A potential explanation for this is that underlying non-additive genetic (GxG) and genotype-by-environment (GxE) interactions generate allele substitution effects that are non-stationary across different contexts. Such non-stationary effects of alleles are either ignored or assumed to be implicitly captured by most gene-to-phenotype (G2P) maps used in genomic prediction. The implicit capture of non-stationary effects of alleles requires the G2P map to be re-estimated across different contexts. We discuss the development and application of hierarchical G2P maps that explicitly capture non-stationary effects of alleles and have successfully increased short-term prediction accuracy in plant breeding. These hierarchical G2P maps achieve increases in prediction accuracy by allowing intermediate processes such as other traits and environmental factors and their interactions to contribute to complex trait variation. However, long-term prediction remains a challenge. The plant breeding community should undertake complementary simulation and empirical experiments to interrogate various hierarchical G2P maps that connect GxG and GxE interactions simultaneously. The existing genetic correlation framework can be used to assess the magnitude of non-stationary effects of alleles and the predictive ability of these hierarchical G2P maps in long-term, multi-context genomic predictions of complex traits in plant breeding.

Breeding crops for drought-affected environments and improved climate resilience

“…This insight emerging from the use of the infinitesimal model in predictive breeding should be of interest to plant and crop physiologists investigating the mechanistic and ecophysiological bases of the G2P relationships for the traits controlling plant growth and development within environments. Results from the use of predictive algorithms based on the infinitesimal model provide a relevant experimental control against which the utilization of any additional prior biological knowledge used to predict G2P relationships can be judged for its merits in improving the opportunities for a plant breeder to predict response to selection for breeding objectives ( Figure 1 ; Jackson et al, 1996 ; Cooper et al, 2005 , 2021b ; Messina et al, 2009 ; Technow et al, 2015 ; Araus et al, 2018 ; Hammer et al, 2019 ; Powell et al, 2021 , 2022 ; Diepenbrock et al, 2022 ). In this review, we apply the breeder’s equation within this framework to discuss the use of molecular and physiological knowledge of traits for any applications to accelerate breeding for drought resistance and improved climate resilience ( Chapman et al, 2012 ; Voss-Fels et al, 2019a ; Langridge et al, 2021 ).…”

“…In their compilation of the gene targets that had been tested for yield efficacy in maize, Simmons et al (2021) highlighted the importance of gene targets in the key hormonal pathways that are involved in regulating plant growth and development. These important hormonal pathways have many direct and indirect influences on plant growth and development, that can operate across scales from cells to whole plants, suggesting there is opportunity to use such gene targets for combined experimental and modeling research efforts to predict from the genome level to multi-trait networks that impact the yield reaction-norm phenotypes at the ecosystem level ( Figure 5 ; Hammer et al, 2006 , 2019 ; Powell et al, 2021 , 2022 ; Diepenbrock et al, 2022 ; Gleason et al, 2022 ). A further promising opportunity that has been investigated is the use of these novel sources of trait genetic diversity originating from the discovery programs as components of integrated breeding strategies, where selection is targeted to co-develop complementary natural genetic diversity to exploit the positive interactions and ameliorate the potential negative consequences of the genes in different genetic backgrounds ( Simmons et al, 2021 ; Linares et al, 2022a , 2022b ).…”

“…This has stimulated optimism that we can further accelerate breeding for complex challenges, such as improved crop drought tolerance, to develop more climate-resilient crops and reduce on-farm yield-gaps ( Ramstein et al, 2019 ; Voss-Fels et al, 2019a ; Peng et al, 2020 ; Cooper et al, 2021a ; Langridge et al, 2021 ; Varshney et al, 2021b ; Messina et al, 2022a , 2022c ). Nevertheless, major gaps in our understanding of the genetic architecture and G2P relationships for traits and trait networks persist ( Hammer et al, 2006 ; Reynolds et al, 2021 ; Roeder et al, 2021 ; Powell et al, 2021 , 2022 ). Despite the advances, much of our prior G2P knowledge of the traits and trait networks contributing to plant growth and development has yet to be fully utilized and applied in most of the breeding programs that target improved drought resistance for agricultural environments.…”

Methods for Evaluating Effects of Transgenes for Quantitative Traits

“…Quantitatively inherited traits are controlled by multiple quantitative trait loci (e.g., Lynch and Walsh, 1998; Falconer and Mackay, 1996). Generally, quantitative trait loci (QTL) for complex traits such as grain yield of crop plants can have drastically different effects depending on the genetic background evaluated (Kramer, 2009; Cheng et al, 2012; Powell et al, 2021). For instance, in maize, the detection and effect size estimates of QTL identified using two different tester inbreds showed considerable differences for grain yield but not for other traits such as plant height and grain moisture (Melchinger et al, 1998).…”

Methods for evaluating effects of transgenes for quantitative traits

“…such as grain yield of crop plants can have drastically different effects depending on the genetic background and/or environments evaluated (Boer et al, 2007;Cheng et al, 2012;Kramer et al, 2009;Powell et al, 2021). For instance, in maize (Zea mays L.), the detection and effect size estimates of QTL identified using two different tester inbreds showed considerable differences for grain yield but not for other traits such as plant Crop Science.…”