Genomic insights of a native bacterial consortium for wheat production sustainability.

The use of plant growth-promoting bacteria as bioinoculants is a powerful tool to increase crop yield and quality and to improve nitrogen use efficiency (NUE) from fertilizers in plants. This study aimed to bioprospecting a native bacterial consortium (Bacillus cabrialesii subsp. cabrialesii TE3T, Priestia megaterium TRQ8, and Bacillus paralicheniformis TRQ65), through bioinformatic analysis, and to quantify the impact of its inoculation on NUE (measured through 15N-isotopic techniques), grain yield, and grain quality of durum wheat variety CIRNO C2008 grown under three doses of urea (0, 120, and 240 kg N ha-1) during two consecutive agricultural cycles in the Yaqui Valley, Mexico. The inoculation of the bacterial consortium (BC) to the wheat crop, at a total N concentration of 123-225 kg N ha-1 increased crop productivity and maintained grain quality, resulting in a yield increase of 1.1 ton ha-1 (6.0 vs. 7.1 ton ha-1, 0 kg N ha-1 added, 123 kg N ha-1 in the soil) and of 2.0 ton ha-1 (5.9 vs. 7.9 ton ha-1, 120 kg N ha-1 added, 104 kg N ha-1 in the soil) compared to the uninoculated controls at the same doses of N. The genomic bioinformatic analysis of the studied strains showed a great number of biofertilization-related genes regarding N and Fe acquisition, P assimilation, CO2 fixation, Fe, P, and K solubilization, with important roles in agroecosystems, as well as genes related to the production of siderophores and stress response. A positive effect of the BC on NUE at the studied initial N content (123 and 104 kg N ha-1) was not observed. Nevertheless, increases of 14 % and 12.5 % on NUE (whole plant) were observed when 120 kg N ha-1 was applied compared to when wheat was fully fertilized (240 kg N ha-1). This work represents a link between bioinformatic approaches of a native bacterial inoculant and the quantification of its impact on durum wheat.

Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials.

Implementing genomic-based prediction models in genomic selection requires an understanding of the measures for evaluating prediction accuracy from different models and methods using multi-trait data. In this study, we compared prediction accuracy using six large multi-trait wheat data sets (quality and grain yield). The data were used to predict 1 year (testing) from the previous year (training) to assess prediction accuracy using four different prediction models. The results indicated that the conventional Pearson's correlation between observed and predicted values underestimated the true correlation value, whereas the corrected Pearson's correlation calculated by fitting a bivariate model was higher than the division of the Pearson's correlation by the squared root of the heritability across traits, by 2.53-11.46%. Across the datasets, the corrected Pearson's correlation was higher than the uncorrected by 5.80-14.01%. Overall, we found that for grain yield the prediction performance was highest using a multi-trait compared to a single-trait model. The higher the absolute genetic correlation between traits the greater the benefits of multi-trait models for increasing the genomic-enabled prediction accuracy of traits.

Genome-based prediction of multiple wheat quality traits in multiple years.

Wheat quality improvement is an important objective in all wheat breeding programs. However, due to the cost, time and quantity of seed required, wheat quality is typically analyzed only in the last stages of the breeding cycle on a limited number of samples. The use of genomic prediction could greatly help to select for wheat quality more efficiently by reducing the cost and time required for this analysis. Here were evaluated the prediction performances of 13 wheat quality traits under two multi-trait models (Bayesian multi-trait multi-environment [BMTME] and multi-trait ridge regression [MTR]) using five data sets of wheat lines evaluated in the field during two consecutive years. Lines in the second year (testing) were predicted using the quality information obtained in the first year (training). For most quality traits were found moderate to high prediction accuracies, suggesting that the use of genomic selection could be feasible. The best predictions were obtained with the BMTME model in all traits and the worst with the MTR model. The best predictions with the BMTME model under the mean arctangent absolute percentage error (MAAPE) were for test weight across the five data sets, whereas the worst predictions were for the alveograph trait ALVPL. In contrast, under Pearson's correlation, the best predictions depended on the data set. The results obtained suggest that the BMTME model should be preferred for multi-trait prediction analyses. This model allows to obtain not only the correlation among traits, but also the correlation among environments, helping to increase the prediction accuracy.