Linking genetic markers with an eco-physiological model to pyramid favourable alleles and design wheat ideotypes

Genetic markers can be linked with eco-physiological crop models to accurately predict genotype performance and individual markers' contributions in target environments, exploring interactions between genotype and environment. Here, wheat (Triticum aestivum L.) yield was dissected into seven traits corresponding to cultivar genetic coefficients in an eco-physiological model. Loci for these traits were discovered through the genome-wide association studies (GWAS). The cultivar genetic coefficients were derived from the loci using multiple linear regression or random forest, building a marker-based eco-physiological model. It is then applied to simulate wheat yields and design virtual ideotypes. The results indicated that the loci identified through GWAS explained 46%-75% variations in cultivar genetic coefficients. Using the marker-based model, the normalized root mean square error (nRMSE) between the simulated yield and observed yield was 13.95% by multiple linear regression and 13.62% by random forest. The nRMSE between the simulated and observed maturity dates was 1.24% by multiple linear regression and 1.11% by random forest, respectively. Structural equation modelling indicated that variations in grain yield could be well explained by cultivar genetic coefficients and phenological data. In addition, 24 pleiotropic loci in this study were detected on 15 chromosomes. More significant loci were detected by the model-based dissection method than considering yield per se. Ideotypes were identified by higher yield and more favourable alleles of cultivar genetic traits. This study proposes a genotype-to-phenotype approach and demonstrates novel ideas and tools to support the effective breeding of new cultivars with high yield through pyramiding favourable alleles and designing crop ideotypes.

Modelling tiller growth and mortality as a sink-driven process using Ecomeristem: implications for biomass sorghum ideotyping

BACKGROUND AND AIMS

Plant modelling can efficiently support ideotype conception, particularly in multi-criteria selection contexts. This is the case for biomass sorghum, implying the need to consider traits related to biomass production and quality. This study evaluated three modelling approaches for their ability to predict tiller growth, mortality and their impact, together with other morphological and physiological traits, on biomass sorghum ideotype prediction.

METHODS

Three Ecomeristem model versions were compared to evaluate whether tillering cessation and mortality were source (access to light) or sink (age-based hierarchical access to C supply) driven. They were tested using a field data set considering two biomass sorghum genotypes at two planting densities. An additional data set comparing eight genotypes was used to validate the best approach for its ability to predict the genotypic and environmental control of biomass production. A sensitivity analysis was performed to explore the impact of key genotypic parameters and define optimal parameter combinations depending on planting density and targeted production (sugar and fibre).

KEY RESULTS

The sink-driven control of tillering cessation and mortality was the most accurate, and represented the phenotypic variability of studied sorghum genotypes in terms of biomass production and partitioning between structural and non-structural carbohydrates. Model sensitivity analysis revealed that light conversion efficiency and stem diameter are key traits to target for improving sorghum biomass within existing genetic diversity. Tillering contribution to biomass production appeared highly genotype and environment dependent, making it a challenging trait for designing ideotypes.

CONCLUSIONS

By modelling tiller growth and mortality as sink-driven processes, Ecomeristem could predict and explore the genotypic and environmental variability of biomass sorghum production. Its application to larger sorghum genetic diversity considering water deficit regulations and its coupling to a genetic model will make it a powerful tool to assist ideotyping for current and future climatic scenario.