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Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIRpredicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400-1,900) were measured across 8 years (2012-2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. Endproduct and NIR phenotypes were genetically correlated (0.5-0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIRpredicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69-0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle. [ABSTRACT FROM AUTHOR]

Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM–WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM–WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM–WGP model for heading and physiological maturity dates. However, the CGM–WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM–WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM–WGP modelling and suggest future efforts should focus on calibrating CGM–WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect. [ABSTRACT FROM AUTHOR]

Cellulose microfibrils are the major structural component of plant secondary cell walls. Their arrangement in plant primary cell walls, and its consequent influence on cell expansion and cellular morphology, is directed by cortical microtubules; cylindrical protein filaments composed of heterodimers of α- and β-tubulin. In secondary cell walls of woody plant stems the orientation of cellulose microfibrils influences the strength and flexibility of wood, providing the physical support that has been instrumental in vascular plant colonization of the troposphere. Here we show that a Eucalyptus grandisβ-tubulin gene ( EgrTUB1) is involved in determining the orientation of cellulose microfibrils in plant secondary fibre cell walls. This finding is based on RNA expression studies in mature trees, where we identified and isolated EgrTUB1 as a candidate for association with wood-fibre formation, and on the analysis of somatically derived transgenic wood sectors in Eucalyptus. We show that cellulose microfibril angle (MFA) is correlated with EgrTUB1 expression, and that MFA was significantly altered as a consequence of stable transformation with EgrTUB1. Our findings present an important step towards the production of fibres with altered tensile strength, stiffness and elastic properties, and shed light on one of the molecular mechanisms that has enabled trees to dominate terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

Cellulose microfibrils are the major structural component of plant secondary cell walls. Their arrangement in plant primary cell walls, and its consequent influence on cell expansion and cellular morphology, is directed by cortical microtubules; cylindrical protein filaments composed of heterodimers of alpha- and beta-tubulin. In secondary cell walls of woody plant stems the orientation of cellulose microfibrils influences the strength and flexibility of wood, providing the physical support that has been instrumental in vascular plant colonization of the troposphere. Here we show that a Eucalyptus grandisbeta-tubulin gene (EgrTUB1) is involved in determining the orientation of cellulose microfibrils in plant secondary fibre cell walls. This finding is based on RNA expression studies in mature trees, where we identified and isolated EgrTUB1 as a candidate for association with wood-fibre formation, and on the analysis of somatically derived transgenic wood sectors in Eucalyptus. We show that cellulose microfibril angle (MFA) is correlated with EgrTUB1 expression, and that MFA was significantly altered as a consequence of stable transformation with EgrTUB1. Our findings present an important step towards the production of fibres with altered tensile strength, stiffness and elastic properties, and shed light on one of the molecular mechanisms that has enabled trees to dominate terrestrial ecosystems.