Improving Wheat Yield Prediction Using Secondary Traits and High-Density Phenotyping Under Heat-Stressed Environments

A primary selection target for wheat (Triticum aestivum) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0–0.6), while the correlation between grain yield and secondary traits ranged from −0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58–0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.

Evaluation of advanced back cross lines for drought tolerance in rice (Oryza sativa L.)

Drought is the largest abiotic constraint to rice production which cause significant yield loss depending upon the severity. Development of rice varieties with tolerance to drought and high use water use efficiency is the need of the hour. Despite the importance of drought as major abiotic constraint, the efforts to develop drought tolerant rice varieties are very low. Breeding efforts until recent past were focused on understanding and improvement of secondary traits that are putatively associated with drought tolerance. However, the genetic gain in yield by improvement of secondary traits is very low. Hence, improvement of yield per se under drought conditions will be better solution. Introgression of yield QTLs under drought in the genetic background of high yielding varieties will be helpful to overcome the problem to a certain extent. In the present study, 31 advanced back cross lines (BILs) derived from drought susceptible mega variety Samba Mahsuri (BT 5204) and a drought tolerant tolerant land race Azucena were evaluated under drought. Thirty one advanced back cross inbred lines (BC2F4) lines having yield QTLs viz., qDTY3.1 on chromosome 3 and qDTY2.1 on chromosome 2 were phenotyped under drought conditions. The results suggested that wide range of variation was observed for yield and its component traits in the BILs generated in the background of BPT 5204 and direct selection for yield under water stress coupled with marker assisted screening would help in development of drought tolerant version of mega varieties with improved yield under stress. Thermo tolerance studies indicated that high variability was observed for the BILs in terms of % seedling survival, % reduction in root and shoot growth under stress.

Improving Genomic Prediction Using High-Dimensional Secondary Phenotypes

In the past decades, genomic prediction has had a large impact on plant breeding. Given the current advances of high-throughput phenotyping and sequencing technologies, it is increasingly common to observe a large number of traits, in addition to the target trait of interest. This raises the important question whether these additional or “secondary” traits can be used to improve genomic prediction for the target trait. With only a small number of secondary traits, this is known to be the case, given sufficiently high heritabilities and genetic correlations. Here we focus on the more challenging situation with a large number of secondary traits, which is increasingly common since the arrival of high-throughput phenotyping. In this case, secondary traits are usually incorporated through additional relatedness matrices. This approach is however infeasible when secondary traits are not measured on the test set, and cannot distinguish between genetic and non-genetic correlations. An alternative direction is to extend the classical selection indices using penalized regression. So far, penalized selection indices have not been applied in a genomic prediction setting, and require plot-level data in order to reliably estimate genetic correlations. Here we aim to overcome these limitations, using two novel approaches. Our first approach relies on a dimension reduction of the secondary traits, using either penalized regression or random forests (LS-BLUP/RF-BLUP). We then compute the bivariate GBLUP with the dimension reduction as secondary trait. For simulated data (with available plot-level data), we also use bivariate GBLUP with the penalized selection index as secondary trait (SI-BLUP). In our second approach (GM-BLUP), we follow existing multi-kernel methods but replace secondary traits by their genomic predictions, with the advantage that genomic prediction is also possible when secondary traits are only measured on the training set. For most of our simulated data, SI-BLUP was most accurate, often closely followed by RF-BLUP or LS-BLUP. In real datasets, involving metabolites in Arabidopsis and transcriptomics in maize, no method could substantially improve over univariate prediction when secondary traits were only available on the training set. LS-BLUP and RF-BLUP were most accurate when secondary traits were available also for the test set.

Aspects in breeding maize for drought tolerance: Progress and modern breeding approaches

Breeding for drought tolerance in plants is challenging because it is controlled by multiple genes. Grain yield, the trait of primary interest in maize, is characterized by low heritability, high genotype by environment interaction, and low variation under drought conditions, which further complicates selection. To circumvent these challenges, secondary traits that are highly correlated with grain yield are indirectly selected under drought conditions. Although considerable success in terms of yield gains under drought stress has been reported in both temperate and tropical maize breeding programmes, climate change exacerbated droughts coupled with high population growth necessitates continuous improvement of maize varieties. Understanding the genetics of associated secondary traits is essential in elucidating drought tolerance. The identification of quantitative trait loci (QTL) and candidate genes conferring drought tolerance and the subsequent development of relevant molecular markers ushered in the era of molecular assisted breeding. There is empirical evidence that marker-assisted selection and genomic selection have great potential to increase the yield gains while comprehensive and accurate phenotyping using secondary traits remains the pillar of drought tolerance breeding

Combining Genomic and Phenomic Information for Predicting Grain Protein Content and Grain Yield in Spring Wheat

Genomics and high throughput phenomics have the potential to revolutionize the field of wheat (Triticum aestivum L.) breeding. Genomic selection (GS) has been used for predicting various quantitative traits in wheat, especially grain yield. However, there are few GS studies for grain protein content (GPC), which is a crucial quality determinant. Incorporation of secondary correlated traits in GS models has been demonstrated to improve accuracy. The objectives of this research were to compare performance of single and multi-trait GS models for predicting GPC and grain yield in wheat and to identify optimal growth stages for collecting secondary traits. We used 650 recombinant inbred lines from a spring wheat nested association mapping (NAM) population. The population was phenotyped over 3 years (2014–2016), and spectral information was collected at heading and grain filling stages. The ability to predict GPC and grain yield was assessed using secondary traits, univariate, covariate, and multivariate GS models for within and across cycle predictions. Our results indicate that GS accuracy increased by an average of 12% for GPC and 20% for grain yield by including secondary traits in the models. Spectral information collected at heading was superior for predicting GPC, whereas grain yield was more accurately predicted during the grain filling stage. Green normalized difference vegetation index had the largest effect on the prediction of GPC either used individually or with multiple indices in the GS models. An increased prediction ability for GPC and grain yield with the inclusion of secondary traits demonstrates the potential to improve the genetic gain per unit time and cost in wheat breeding.

Bayesian optimization of multivariate genomic prediction models based on secondary traits for improved accuracy gains and phenotyping costs

Key messageWe propose a novel approach to the Bayesian optimization of multi-variate genomic prediction models based on secondary traits to improve accuracy gains and phenotyping costs via efficient Pareto frontier estimation.Multivariate genomic prediction based on secondary traits, such as data from various omics technologies including high-throughput phenotyping (e.g., unmanned aerial vehicle-based remote sensing), has attracted much attention because it offers improved accuracy gains compared with genomic prediction based only on marker genotypes. Although there is a trade-off between accuracy gains and phenotyping costs of secondary traits, no attempt has been made to optimize these trade-offs. In this study, we propose a novel approach to optimize multivariate genomic prediction models for secondary traits measurable at early growth stages for improved accuracy gains and phenotyping costs. The proposed approach employs Bayesian optimization for efficient Pareto frontier estimation, representing the maximum accuracy at a given cost. The proposed approach successfully estimated the optimal secondary trait combinations across a range of costs while providing genomic predictions for only about 20% of all possible combinations. The simulation results reflecting the characteristics of each scenario of the simulated target traits showed that the obtained optimal combinations were reasonable. Analysis of real-time target trait data showed that the proposed multivariate genomic prediction model had significantly superior accuracy compared to the univariate genomic prediction model.

Secondary Traits for Maize (Zea mays L.) Tolerance to Drought Combined with Reduced N Fertilization Rate

The secondary trait for a given abiotic stress tolerance, should be of strong correlation (r) with grain yield, high heritability (h2b) and high genetic advance (GA) under stressed conditions. The main objective of the present investigation was to identify secondary trait(s) for drought and/or low-N tolerance in maize genotypes. A two-year experiment was conducted, using a split-split-plot design. Main plots were allotted to two irrigation regimes, i.e. well watering (WW) and water stress at flowering (WS), sub-plots to three N fertilizer rates, i.e. low (LN), medium (MN) and high (HN) and sub-sub-plots to nineteen maize genotypes. Analysis of variance of randomized complete blocks design (RCBD) was also performed under each of the six environments (WW-HM, WW-MN, WW-LN, WS-HN, WS-MN and WS-LN). Tolerance to drought and/or low-N was strongly correlated with grain yield/plant (GYPP) under stressed environments. GYPP had high (h2b) and (GA); thus it is considered the best indicator of drought, low N or both stresses tolerance. The best secondary traits are high 100-kernel weight (100-KW), ears/plant (EPP), kernels/row (KPR), and short anthesis-silking interval (ASI) for low-N tolerance, high EPP, 100-KW, plant height (PH) and short ASI for drought tolerance, high 100-KW, EPP, KPR, PH and short ASI, for tolerance to drought combined with low N, and high 100-KW, rows/ear (RPE) and KPR under optimum conditions (WW-HN), since they show high (r), high (h2b) and high (GA) estimates under the respective environments. Under low-N and/or drought, future research should focus on the incorporation of secondary traits such as EPP, KPR, 100-KW, PH, ASI in the selection programs along with the grain yield trait.