Genomic selection for target traits in the Australian lentil breeding program.

Genomic selection (GS) uses associations between markers and phenotypes to predict the breeding values of individuals. It can be applied early in the breeding cycle to reduce the cross-to-cross generation interval and thereby increase genetic gain per unit of time. The development of cost-effective, high-throughput genotyping platforms has revolutionized plant breeding programs by enabling the implementation of GS at the scale required to achieve impact. As a result, GS is becoming routine in plant breeding, even in minor crops such as pulses. Here we examined 2,081 breeding lines from Agriculture Victoria's national lentil breeding program for a range of target traits including grain yield, ascochyta blight resistance, botrytis grey mould resistance, salinity and boron stress tolerance, 100-grain weight, seed size index and protein content. A broad range of narrow-sense heritabilities was observed across these traits (0.24-0.66). Genomic prediction models were developed based on 64,781 genome-wide SNPs using Bayesian methodology and genomic estimated breeding values (GEBVs) were calculated. Forward cross-validation was applied to examine the prediction accuracy of GS for these targeted traits. The accuracy of GEBVs was consistently higher (0.34-0.83) than BLUP estimated breeding values (EBVs) (0.22-0.54), indicating a higher expected rate of genetic gain with GS. GS-led parental selection using early generation breeding materials also resulted in higher genetic gain compared to BLUP-based selection performed using later generation breeding lines. Our results show that implementing GS in lentil breeding will fast track the development of high-yielding cultivars with increased resistance to biotic and abiotic stresses, as well as improved seed quality traits.

Development and Validation of a Phenotyping Computational Workflow to Predict the Biomass Yield of a Large Perennial Ryegrass Breeding Field Trial.

Increasing dry matter yield (DMY) is the most important objective in perennial ryegrass breeding programs. Current yield assessment methods like cutting are time-consuming and destructive, non-destructive measures such as scoring yield on single plants by visual inspection may be subjective. These assessments involve multiple measurements and selection procedures across seasons and years to evaluate biomass yield repeatedly. This contributes to the slow process of new cultivar development and commercialisation. This study developed and validated a computational phenotyping workflow for image acquisition, processing and analysis of spaced planted ryegrass and investigated sensor-based DMY yield estimation of individual plants through normalized difference vegetative index (NDVI) and ultrasonic plant height data extraction. The DMY of 48,000 individual plants representing 50 advanced breeding lines and commercial cultivars was accurately estimated at multiple harvests across the growing season. NDVI, plant height and predicted DMY obtained from aerial and ground-based sensors illustrated the variation within and between cultivars across different seasons. Combining NDVI and plant height of individual plants was a robust method to enable high-throughput phenotyping of biomass yield in ryegrass breeding. Similarly, the plot-level model indicated good to high-coefficients of determination (R 2) between the predicted and measured DMY across three seasons with R 2 between 0.19 and 0.81 and root mean square errors (RMSE) values ranging from 0.09 to 0.21 kg/plot. The model was further validated using a combined regression of the three seasons harvests. This study further sets a foundation for the application of sensor technologies combined with genomic studies that lead to greater rates of genetic gain in perennial ryegrass biomass yield.

High-throughput physiological phenotyping and screening system for the characterization of plant-environment interactions.

We present a simple and effective high-throughput experimental platform for simultaneous and continuous monitoring of water relations in the soil-plant-atmosphere continuum of numerous plants under dynamic environmental conditions. This system provides a simultaneously measured, detailed physiological response profile for each plant in the array, over time periods ranging from a few minutes to the entire growing season, under normal, stress and recovery conditions and at any phenological stage. Three probes for each pot in the array and a specially designed algorithm enable detailed water-relations characterization of whole-plant transpiration, biomass gain, stomatal conductance and root flux. They also enable quantitative calculation of the whole plant water-use efficiency and relative water content at high resolution under dynamic soil and atmospheric conditions. The system has no moving parts and can fit into many growing environments. A screening of 65 introgression lines of a wild tomato species (Solanum pennellii) crossed with cultivated tomato (S. lycopersicum), using our system and conventional gas-exchange tools, confirmed the accuracy of the system as well as its diagnostic capabilities. The use of this high-throughput diagnostic screening method is discussed in light of the gaps in our understanding of the genetic regulation of whole-plant performance, particularly under abiotic stress.

Water Balance, Hormone Homeostasis, and Sugar Signaling Are All Involved in Tomato Resistance to Tomato Yellow Leaf Curl Virus.

Vacuolar water movement is largely controlled by membrane channels called tonoplast-intrinsic aquaporins (TIP-AQPs). Some TIP-AQP genes, such as TIP2;2 and TIP1;1, are up-regulated upon exposure to biotic stress. Moreover, TIP1;1 transcript levels are higher in leaves of a tomato (Solanum lycopersicum) line resistant to Tomato yellow leaf curl virus (TYLCV) than in those of a susceptible line with a similar genetic background. Virus-induced silencing of TIP1;1 in the tomato resistant line and the use of an Arabidopsis (Arabidopsis thaliana) tip1;1 null mutant showed that resistance to TYLCV is severely compromised in the absence of TIP1:1. Constitutive expression of tomato TIP2;2 in transgenic TYLCV-susceptible tomato and Arabidopsis plants was correlated with increased TYLCV resistance, increased transpiration, decreased abscisic acid levels, and increased salicylic acid levels at the early stages of infection. We propose that TIP-AQPs affect the induction of leaf abscisic acid, which leads to increased levels of transpiration and gas exchange, as well as better salicylic acid signaling.

Risk-taking plants: anisohydric behavior as a stress-resistance trait.

Water scarcity is a critical limitation for agricultural systems. Two different water management strategies have evolved in plants: an isohydric strategy and an anisohydric strategy. Isohydric plants maintain a constant midday leaf water potential (Ψleaf) when water is abundant, as well as under drought conditions, by reducing stomatal conductance as necessary to limit transpiration. Anisohydric plants have more variable Ψleaf and keep their stomata open and photosynthetic rates high for longer periods, even in the presence of decreasing leaf water potential. This risk-taking behavior of anisohydric plants might be beneficial when water is abundant, as well as under moderately stressful conditions. However, under conditions of intense drought, this behavior might endanger the plant. We will discuss the advantages and disadvantages of these two water-usage strategies and their effects on the plant's ability to tolerate abiotic and biotic stress. The involvement of plant tonoplast AQPs in this process will also be discussed.