Genetic parameters and selection gain in tropical wheat populations via Bayesian inference / Parâmetros genéticos e ganho de seleção em populações tropicais de trigo via inferência bayesiana

ABSTRACT: The development process of a new wheat cultivar requires time between obtaining the base population and selecting the most promising line. Estimating genetic parameters more accurately in early generations with a view to anticipating selection means important advances for wheat breeding programs. Thus, the present study estimated the genetic parameters of F2 populations of tropical wheat and the genetic gain from selection via the Bayesian approach. To this end, the authors assessed the grain yield per plot of 34 F2 populations of tropical wheat. The Bayesian approach provided an adequate fit to the model, estimating genetic parameters within the parametric space. Heritability (h2) was 0.51. Among those selected, 11 F2 populations performed better than the control cultivars, with genetic gain of 7.80%. The following populations were the most promising: TbioSossego/CD 1303, CD 1303/TbioPonteiro, BRS 254/CD 1303, Tbio Duque/Tbio Aton, and Tbio Aton/CD 1303. Bayesian inference can be used to significantly improve tropical wheat breeding programs.

RESUMO: O processo de desenvolvimento de uma nova cultivar de trigo requer tempo entre a obtenção da população base e a seleção da linhagem mais promissora. Estimar parâmetros genéticos com mais precisão nas primeiras gerações com vistas a antecipar a seleção significa avanços importantes para os programas de melhoramento de trigo. Assim, o presente estudo estima os parâmetros genéticos de populações F2 de trigo tropical e o ganho genético da seleção via abordagem Bayesiana. Para tanto, os autores avaliaram a produtividade de grãos por parcela de 34 populações F2 de trigo tropical. A abordagem Bayesiana proporcionou um ajuste adequado ao modelo, estimando parâmetros genéticos dentro do espaço paramétrico. A herdabilidade (h2) foi de 0,51. Dentre as selecionadas, 11 populações F2 obtiveram desempenho superior às cultivares controle, com ganho genético de seleção de 7,80%. As seguintes populações foram as mais promissoras: Tbio Sossego/CD 1303, CD 1303/Tbio Ponteiro, BRS 254/CD 1303, Tbio Duque/Tbio Aton e Tbio Aton/CD 1303. A inferência Bayesiana pode ser usada para melhorar significativamente programas de melhoramento de trigo tropical.

High-resolution spectral information enables phenotyping of leaf epicuticular wax in wheat.

BACKGROUND: Epicuticular wax (EW) is the first line of defense in plants for protection against biotic and abiotic factors in the environment. In wheat, EW is associated with resilience to heat and drought stress, however, the current limitations on phenotyping EW restrict the integration of this secondary trait into wheat breeding pipelines. In this study we evaluated the use of light reflectance as a proxy for EW load and developed an efficient indirect method for the selection of genotypes with high EW density. RESULTS: Cuticular waxes affect the light that is reflected, absorbed and transmitted by plants. The narrow spectral regions statistically associated with EW overlap with bands linked to photosynthetic radiation (500 nm), carotenoid absorbance (400 nm) and water content (~ 900 nm) in plants. The narrow spectral indices developed predicted 65% (EWI-13) and 44% (EWI-1) of the variation in this trait utilizing single-leaf reflectance. However, the normalized difference indices EWI-4 and EWI-9 improved the phenotyping efficiency with canopy reflectance across all field experimental trials. Indirect selection for EW with EWI-4 and EWI-9 led to a selection efficiency of 70% compared to phenotyping with the chemical method. The regression model EWM-7 integrated eight narrow wavelengths and accurately predicted 71% of the variation in the EW load (mg·dm-2) with leaf reflectance, but under field conditions, a single-wavelength model consistently estimated EW with an average RMSE of 1.24 mg·dm-2 utilizing ground and aerial canopy reflectance. CONCLUSIONS: Overall, the indices EWI-1, EWI-13 and the model EWM-7 are reliable tools for indirect selection for EW based on leaf reflectance, and the indices EWI-4, EWI-9 and the model EWM-1 are reliable for selection based on canopy reflectance. However, further research is needed to define how the background effects and geometry of the canopy impact the accuracy of these phenotyping methods.

Genome-Wide Association of Stem Carbohydrate Accumulation and Remobilization during Grain Growth in Bread Wheat (Triticum aestivum L.) in Mediterranean Environments.

Water deficit represents an important challenge for wheat production in many regions of the world. Accumulation and remobilization of water-soluble carbohydrates (WSCs) in stems are part of the physiological responses regulated by plants to cope with water stress and, in turn, determine grain yield (GY). The genetic mechanisms underlying the variation in WSC are only partially understood. In this study, we aimed to identify Single Nucleotide Polymorphism (SNP) markers that account for variation in a suite of WSC and GY, evaluated in 225 cultivars and advanced lines of spring wheat. These genotypes were established in two sites in the Mediterranean region of Central Chile, under water-limited and full irrigation conditions, and assessed in two growing seasons, namely anthesis and maturity growth periods. A genome-wide association study (GWAS) was performed by using 3243 SNP markers. Genetic variance accounted for 5 to 52% of phenotypic variation of the assessed traits. A rapid linkage disequilibrium decay was observed across chromosomes (r2 ≤ 0.2 at 2.52 kbp). Marker-trait association tests identified 96 SNPs related to stem weight (SW), WSCs, and GY, among other traits, at the different sites, growing seasons, and growth periods. The percentage of SNPs that were part of the gene-coding regions was 34%. Most of these genes are involved in the defensive response to drought and biotic stress. A complimentary analysis detected significant effects of different haplotypes on WSC and SW, in anthesis and maturity. Our results evidence both genetic and environmental influence on WSC dynamics in spring wheat. At the same time, they provide a series of markers suitable for supporting assisted selection approaches and functional characterization of genes.

High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation.

Plant height (PH) is an essential trait in the screening of most crops. While in crops such as wheat, medium stature helps reduce lodging, tall plants are preferred to increase total above-ground biomass. PH is an easy trait to measure manually, although it can be labor-intense depending on the number of plots. There is an increasing demand for alternative approaches to estimate PH in a higher throughput mode. Crop surface models (CSMs) derived from dense point clouds generated via aerial imagery could be used to estimate PH. This study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years. Multi-temporal and high spatial resolution images were collected by fixed-wing (P l a t F W ) and multi-rotor (P l a t M R ) unmanned aerial vehicle (UAV) platforms over two wheat populations (50 and 150 lines). The PH was measured and compared at four growth stages (GS) using ground-truth measurements (PHground) and UAV-based estimates (PHaerial). The CSMs generated from the aerial imagery were validated using ground control points (GCPs) as fixed reference targets at different heights. The results show that PH estimations using P l a t F W were consistent with those obtained from P l a t M R , showing some slight differences due to image processing settings. The GCPs heights derived from CSM showed a high correlation and low error compared to their actual heights (R 2 ≥ 0.90, RMSE ≤ 4 cm). The coefficient of determination (R 2) between PHground and PHaerial at different GS ranged from 0.35 to 0.88, and the root mean square error (RMSE) from 0.39 to 4.02 cm for both platforms. In general, similar and higher heritability was obtained using PHaerial across different GS and years and ranged according to the variability, and environmental error of the PHground observed (0.06-0.97). Finally, we also observed high Spearman rank correlations (0.47-0.91) and R 2 (0.63-0.95) of PHaerial adjusted and predicted values against PHground values. This study provides an example of the use of UAV-based high-resolution RGB imagery to obtain time-series estimates of PH, scalable to tens-of-thousands of plots, and thus suitable to be applied in plant wheat breeding trials.

Response to Early Generation Genomic Selection for Yield in Wheat.

We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying markers and three different prediction methods: (1) Genomic Best Linear Unbiased Predictor (GBLUP), (2) GBLUP with the imputation of missing genotypic data by Ridge Regression BLUP (rrGBLUP_imp), and (3) Reproducing Kernel Hilbert Space (RKHS) a.k.a. Gaussian Kernel (GK). F2 GEBVs were generated for 1,924 individuals from 38 biparental cross populations between 21 parents selected from the training set. Results showed that F2 GEBVs from the different methods were not correlated. Experiment 1 consisted of selecting F2s with the highest average GEBVs and advancing them to form genomically selected bulks and make intercross populations aiming to combine favorable alleles for yield. F4:6 lines were derived from genomically selected bulks, intercrosses, and conventional breeding methods with similar numbers from each. Results of field-testing for Experiment 1 did not find any difference in yield with genomic compared to conventional selection. Experiment 2 compared the predictive ability of the different GEBV calculation methods in F2 using a set of single plant-derived F2:4 lines from randomly selected F2 plants. Grain yield results from Experiment 2 showed a significant positive correlation between observed yields of F2:4 lines and predicted yield GEBVs of F2 single plants from GK (the predictive ability of 0.248, P < 0.001) and GBLUP (0.195, P < 0.01) but no correlation with rrGBLUP_imp. Results demonstrate the potential for the application of GS in early generations of wheat breeding and the importance of using the appropriate statistical model for GEBV calculation, which may not be the same as the best model for inbreds.

An Update of Recent Use of Aegilops Species in Wheat Breeding.

Aegilops species have significantly contributed to wheat breeding despite the difficulties involved in the handling of wild species, such as crossability and incompatibility. A number of biotic resistance genes have been identified and incorporated into wheat varieties from Aegilops species, and this genus is also contributing toward improvement of complex traits such as yield and abiotic tolerance for drought and heat. The D genome diploid species of Aegilops tauschii has been utilized most often in wheat breeding programs. Other Aegilops species are more difficult to utilize in the breeding because of lower meiotic recombination frequencies; generally they can be utilized only after extensive and time-consuming procedures in the form of translocation/introgression lines. After the emergence of Ug99 stem rust and wheat blast threats, Aegilops species gathered more attention as a form of new resistance sources. This article aims to update recent progress on Aegilops species, as well as to cover new topics around their use in wheat breeding.

Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat.

Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment (G × E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program of the International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker- and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker- and pedigree-based models were marginal when correcting for days to heading. Our results demonstrate the potential of using hyperspectral imaging to predict grain yield within a multi-environment context and also support further studies on the integration of hyperspectral reflectance phenotyping into breeding programs.

Wheat quality improvement at CIMMYT and the use of genomic selection on it.

The International Center for Maize and Wheat Improvement (CIMMYT) leads the Global Wheat Program, whose main objective is to increase the productivity of wheat cropping systems to reduce poverty in developing countries. The priorities of the program are high grain yield, disease resistance, tolerance to abiotic stresses (drought and heat), and desirable quality. The Wheat Chemistry and Quality Laboratory has been continuously evolving to be able to analyze the largest number of samples possible, in the shortest time, at lowest cost, in order to deliver data on diverse quality traits on time to the breeders for making selections for advancement in the breeding pipeline. The participation of wheat quality analysis/selection is carried out in two stages of the breeding process: evaluation of the parental lines for new crosses and advanced lines in preliminary and elite yield trials. Thousands of lines are analyzed which requires a big investment in resources. Genomic selection has been proposed to assist in selecting for quality and other traits in breeding programs. Genomic selection can predict quantitative traits and is applicable to multiple quantitative traits in a breeding pipeline by attaining historical phenotypes and adding high-density genotypic information. Due to advances in sequencing technology, genome-wide single nucleotide polymorphism markers are available through genotyping-by-sequencing at a cost conducive to application for genomic selection. At CIMMYT, genomic selection has been applied to predict all of the processing and end-use quality traits regularly tested in the spring wheat breeding program. These traits have variable levels of prediction accuracy, however, they demonstrated that most expensive traits, dough rheology and baking final product, can be predicted with a high degree of confidence. Currently it is being explored how to combine both phenotypic and genomic selection to make more efficient the genetic improvement for quality traits at CIMMYT spring wheat breeding program.

Changes in gene expression profiles as they relate to the adult plant leaf rust resistance in the wheat cv. Toropi.

Leaf rust, caused by the foliar pathogen Puccinia triticina is a major disease of wheat in the southern region of Brazil and invariably impacts on production, being responsible for high yield losses. The Brazilian wheat cultivar Toropi has proven, durable adult plant resistance (APR) to leaf rust, which uniquely shows a pre-haustorial resistance phenotype. In this study we aimed to understand the interaction between P. triticina and the pre-haustorial APR in Toropi by quantitatively evaluating the temporal transcription profiles of selected genes known to be related to infection and defense in wheat. The expression profiles of 15 selected genes varied over time, grouping into six expression profile groups. The expression profiles indicated the induction of classical defence pathways in response to pathogen development, but also the potential modification of Toropi's cellular status for the benefit of the pathogen. Classical defence genes, including peroxidases, ß-1,3-glucanases and an endochitinase were expressed both early (pre-haustorial) and late (post-haustorial) over the 72 h infection time course, while induction of transcription of other infection-related genes with a potential role in defence, although variable was maintained through-out. These genes directly or indirectly had a role in plant lignification, oxidative stress, the regulation of energy supply, water and lipid transport, and cell cycle regulation. The early induction of transcription of defence-related genes supports the pre-haustorial resistance phenotype in Toropi, providing a valuable source of genes controlling leaf rust resistance for wheat breeding.