Genomic Prediction of Sunflower Hybrids Oil Content

Maximizing efficiency in sunflower breeding through historical data optimization

Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS years, we further optimized the TRS size and its genetic composition. We developed the Min_GRM size optimization method which consistently found the optimal TRS size, reducing dimensionality by 20% with an approximately 1% loss in predictive ability. Additionally, the Tails_GEGVs algorithm displayed potential, outperforming the use of all data by using just 60% of it for grain yield, a high-complexity, low-heritability trait. Moreover, maximizing the genetic diversity of the TRS resulted in a consistent predictive ability across the entire range of genotypic values in the test set. Interestingly, the Tails_GEGVs algorithm, due to its ability to leverage heterogeneity, enhanced predictive performance for key hybrids with extreme genotypic values. Our study provides new insights into the optimal utilization of historical data in plant breeding programs, resulting in improved GS model predictive ability.

Genetic control of abiotic stress-related specialized metabolites in sunflower

BACKGROUND

Abiotic stresses in plants include all the environmental conditions that significantly reduce yields, like drought and heat. One of the most significant effects they exert at the cellular level is the accumulation of reactive oxygen species, which cause extensive damage. Plants possess two mechanisms to counter these molecules, i.e. detoxifying enzymes and non-enzymatic antioxidants, which include many classes of specialized metabolites. Sunflower, the fourth global oilseed, is considered moderately drought resistant. Abiotic stress tolerance in this crop has been studied using many approaches, but the control of specialized metabolites in this context remains poorly understood. Here, we performed the first genome-wide association study using abiotic stress-related specialized metabolites as molecular phenotypes in sunflower. After analyzing leaf specialized metabolites of 450 hybrids using liquid chromatography-mass spectrometry, we selected a subset of these compounds based on their association with previously known abiotic stress-related quantitative trait loci. Eventually, we characterized these molecules and their associated genes.

RESULTS

We putatively annotated 30 compounds which co-localized with abiotic stress-related quantitative trait loci and which were associated to seven most likely candidate genes. A large proportion of these compounds were potential antioxidants, which was in agreement with the role of specialized metabolites in abiotic stresses. The seven associated most likely candidate genes, instead, mainly belonged to cytochromes P450 and glycosyltransferases, two large superfamilies which catalyze greatly diverse reactions and create a wide variety of chemical modifications. This was consistent with the high plasticity of specialized metabolism in plants.

CONCLUSIONS

This is the first characterization of the genetic control of abiotic stress-related specialized metabolites in sunflower. By providing hints concerning the importance of antioxidant molecules in this biological context, and by highlighting some of the potential molecular mechanisms underlying their biosynthesis, it could pave the way for novel applications in breeding. Although further analyses will be required to better understand this topic, studying how antioxidants contribute to the tolerance to abiotic stresses in sunflower appears as a promising area of research.

Oil yield prediction for sunflower hybrid selection using different machine learning algorithms

Due to the increased demand for sunflower production, its breeding assignment is the intensification of the development of highly productive oil seed hybrids to satisfy the edible oil industry. Sunflower Oil Yield Prediction (SOYP) can help breeders to identify desirable new hybrids with high oil yield and their characteristics using machine learning (ML) algorithms. In this study, we developed ML models to predict oil yield using two sets of features. Moreover, we evaluated the most relevant features for accurate SOYP. ML algorithms that were used and compared were Artificial Neural Network (ANN), Support Vector Regression, K-Nearest Neighbour, and Random Forest Regressor (RFR). The dataset consisted of samples for 1250 hybrids of which 70% were randomly selected and were used to train the model and 30% were used to test the model and assess its performance. Employing MAE, MSE, RMSE and R2 evaluation metrics, RFR consistently outperformed in all datasets, achieving a peak of 0.92 for R2 in 2019. In contrast, ANN recorded the lowest MAE, reaching 65 in 2018 The paper revealed that in addition to seed yield, the following characteristics of hybrids were important for SOYP: resistance to broomrape (Or) and downy mildew (Pl) and maturity. It was also disclosed that the locality feature could be used for the estimation of sunflower oil yield but it is highly dependable on weather conditions that affect the oil content and seed yield. Up to our knowledge, this is the first study in which ML was used for sunflower oil yield prediction. The obtained results indicate that ML has great potential for application in oil yield prediction, but also selection of parental lines for hybrid production, RFR algorithm was found to be the most effective and along with locality feature is going to be further evaluated as an alternative method for genotypic selection.

Plant breeding for harmony between sustainable agriculture, the environment, and global food security: an era of genomics-assisted breeding

MAIN CONCLUSION

Genomics-assisted breeding represents a crucial frontier in enhancing the balance between sustainable agriculture, environmental preservation, and global food security. Its precision and efficiency hold the promise of developing resilient crops, reducing resource utilization, and safeguarding biodiversity, ultimately fostering a more sustainable and secure food production system. Agriculture has been seriously threatened over the last 40 years by climate changes that menace global nutrition and food security. Changes in environmental factors like drought, salt concentration, heavy rainfalls, and extremely low or high temperatures can have a detrimental effects on plant development, growth, and yield. Extreme poverty and increasing food demand necessitate the need to break the existing production barriers in several crops. The first decade of twenty-first century marks the rapid development in the discovery of new plant breeding technologies. In contrast, in the second decade, the focus turned to extracting information from massive genomic frameworks, speculating gene-to-phenotype associations, and producing resilient crops. In this review, we will encompass the causes, effects of abiotic stresses and how they can be addressed using plant breeding technologies. Both conventional and modern breeding technologies will be highlighted. Moreover, the challenges like the commercialization of biotechnological products faced by proponents and developers will also be accentuated. The crux of this review is to mention the available breeding technologies that can deliver crops with high nutrition and climate resilience for sustainable agriculture.

Interaction of iron and zinc fortification and late-season water deficit on yield and fatty acid composition of Dragon's Head (Lallemantia iberica L.)

Dragon's head (Lallemantia iberica) is a rich source of alpha-linolenic acid, linoleic acid, essential oil, protein, and mucilage. Therefore, the aim of this study was to evaluate the effects of foliar application of three different concentrations of Fe and Zn (control, 4, and 8 g lit-1) at two different developmental stages (vegetative stage (VS) and reproductive stage (RS)) on the quantity and quality of dragon's head seed yield and fatty acid composition in two crop seasons (2018 and 2019) under two environments (normal irrigation as control (NI) and post-anthesis water deficit (WD). In NI, average yields of seed, oil, and protein were 1155, 340, and 183 kg ha-1, respectively, and in the WD, they were 879, 283, and 148 kg ha-1, respectively. By applying Zn and Fe, the mean values of seed, oil, and protein yields in the NI were 1425, 478, and 264 kg ha-1, while in the WD, they were 1011, 354, and 200 kg ha-1, respectively. Furthermore, the application of WD resulted in a significant increase in zinc concentration, protein percentage, and saturated fatty acid percentage in seeds. Unlike WD, iron and zinc treatments decreased the percentage of saturated fatty acids and increased the percentage of unsaturated fatty acids. The number of capsules per plant had the most positive indirect effect on grain yield. The results showed that foliar spraying of Fe and Zn could effectively mitigate the adverse effects of WD on the quality and quantity of seed and oil yield dragon's head.

Molecular Advances to Combat Different Biotic and Abiotic Stresses in Linseed (Linum usitatissimum L.): A Comprehensive Review

Flax, or linseed, is considered a "superfood", which means that it is a food with diverse health benefits and potentially useful bioactive ingredients. It is a multi-purpose crop that is prized for its seed oil, fibre, nutraceutical, and probiotic qualities. It is suited to various habitats and agro-ecological conditions. Numerous abiotic and biotic stressors that can either have a direct or indirect impact on plant health are experienced by flax plants as a result of changing environmental circumstances. Research on the impact of various stresses and their possible ameliorators is prompted by such expectations. By inducing the loss of specific alleles and using a limited number of selected varieties, modern breeding techniques have decreased the overall genetic variability required for climate-smart agriculture. However, gene banks have well-managed collectionns of landraces, wild linseed accessions, and auxiliary Linum species that serve as an important source of novel alleles. In the past, flax-breeding techniques were prioritised, preserving high yield with other essential traits. Applications of molecular markers in modern breeding have made it easy to identify quantitative trait loci (QTLs) for various agronomic characteristics. The genetic diversity of linseed species and the evaluation of their tolerance to abiotic stresses, including drought, salinity, heavy metal tolerance, and temperature, as well as resistance to biotic stress factors, viz., rust, wilt, powdery mildew, and alternaria blight, despite addressing various morphotypes and the value of linseed as a supplement, are the primary topics of this review.

Integrated omics approaches for flax improvement under abiotic and biotic stress: Current status and future prospects

Flax (Linum usitatissimum L.) or linseed is one of the important industrial crops grown all over the world for seed oil and fiber. Besides oil and fiber, flax offers a wide range of nutritional and therapeutic applications as a feed and food source owing to high amount of α-linolenic acid (omega-3 fatty acid), lignans, protein, minerals, and vitamins. Periodic losses caused by unpredictable environmental stresses such as drought, heat, salinity-alkalinity, and diseases pose a threat to meet the rising market demand. Furthermore, these abiotic and biotic stressors have a negative impact on biological diversity and quality of oil/fiber. Therefore, understanding the interaction of genetic and environmental factors in stress tolerance mechanism and identification of underlying genes for economically important traits is critical for flax improvement and sustainability. In recent technological era, numerous omics techniques such as genomics, transcriptomics, metabolomics, proteomics, phenomics, and ionomics have evolved. The advancements in sequencing technologies accelerated development of genomic resources which facilitated finer genetic mapping, quantitative trait loci (QTL) mapping, genome-wide association studies (GWAS), and genomic selection in major cereal and oilseed crops including flax. Extensive studies in the area of genomics and transcriptomics have been conducted post flax genome sequencing. Interestingly, research has been focused more for abiotic stresses tolerance compared to disease resistance in flax through transcriptomics, while the other areas of omics such as metabolomics, proteomics, ionomics, and phenomics are in the initial stages in flax and several key questions remain unanswered. Little has been explored in the integration of omic-scale data to explain complex genetic, physiological and biochemical basis of stress tolerance in flax. In this review, the current status of various omics approaches for elucidation of molecular pathways underlying abiotic and biotic stress tolerance in flax have been presented and the importance of integrated omics technologies in future research and breeding have been emphasized to ensure sustainable yield in challenging environments.

Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance

Genomic selection in maize (Zea mays L.) has been one factor that has increased the rate of genetic gain when compared with other cereals. However, the technological foundations in maize also exist in other cereal crops that would allow prediction of hybrid performance based on general (GCA) and specific (SCA) combining abilities applied through genomic-enabled prediction models. Further, the incorporation of genotype × environment (G × E) interaction effects present an opportunity to deploy hybrids to targeted environments. To test these concepts, a factorial mating design of elite yet divergent grain sorghum lines generated hybrids for evaluation. Inbred parents were genotyped, and markers were used to assess population structure and develop the genomic relationship matrix (GRM). Grain yield, height, and days to anthesis were collected for hybrids in replicated trials, and best linear unbiased estimates were used to train classical GCA-SCA-based and genomic (GB) models under a hierarchical Bayesian framework. To incorporate population structure, GB was fitted using the GRM of both parents and hybrids. For GB models, G × E interaction effects were included by the Hadamard product between GRM and environments. A leave-one-out cross-validation scheme was used to study the prediction capacity of models. Classical and genomic models effectively predicted hybrid performance and prediction accuracy increased by including genomic data. Genomic models effectively partitioned the variation due to GCA, SCA, and their interaction with the environment. A strategy to implement genomic selection for hybrid sorghum [Sorghum bicolor (L.) Moench] breeding is presented herein.

Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview

Genomic selection (GS) is becoming an essential tool in breeding programs due to its role in increasing genetic gain per unit time. The design of the training set (TRS) in GS is one of the key steps in the implementation of GS in plant and animal breeding programs mainly because (i) TRS optimization is critical for the efficiency and effectiveness of GS, (ii) breeders test genotypes in multi-year and multi-location trials to select the best-performing ones. In this framework, TRS optimization can help to decrease the number of genotypes to be tested and, therefore, reduce phenotyping cost and time, and (iii) we can obtain better prediction accuracies from optimally selected TRS than an arbitrary TRS. Here, we concentrate the efforts on reviewing the lessons learned from TRS optimization studies and their impact on crop breeding and discuss important features for the success of TRS optimization under different scenarios. In this article, we review the lessons learned from training population optimization in plants and the major challenges associated with the optimization of GS including population size, the relationship between training and test set (TS), update of TRS, and the use of different packages and algorithms for TRS implementation in GS. Finally, we describe general guidelines to improving the rate of genetic improvement by maximizing the use of the TRS optimization in the GS framework.

Genotyping and lipid profiling of 601 cultivated sunflower lines reveals novel genetic determinants of oil fatty acid content

BACKGROUND

Sunflower is an important oilseed crop domesticated in North America approximately 4000 years ago. During the last century, oil content in sunflower was under strong selection. Further improvement of oil properties achieved by modulating its fatty acid composition is one of the main directions in modern oilseed crop breeding.

RESULTS

We searched for the genetic basis of fatty acid content variation by genotyping 601 inbred sunflower lines and assessing their lipid and fatty acid composition. Our genome-wide association analysis based on the genotypes for 15,483 SNPs and the concentrations of 23 fatty acids, including minor fatty acids, revealed significant genetic associations for eleven of them. Identified genomic regions included the loci involved in rare fatty acids variation on chromosomes 3 and 14, explaining up to 34.5% of the total variation of docosanoic acid (22:0) in sunflower oil.

CONCLUSIONS

This is the first large scale implementation of high-throughput lipidomic profiling to sunflower germplasm characterization. This study contributes to the genetic characterization of Russian sunflower collections, which made a substantial contribution to the development of sunflower as the oilseed crop worldwide, and provides new insights into the genetic control of oil composition that can be implemented in future studies.

Multi-omics-based prediction of hybrid performance in canola

Complementing or replacing genetic markers with transcriptomic data and use of reproducing kernel Hilbert space regression based on Gaussian kernels increases hybrid prediction accuracies for complex agronomic traits in canola. In plant breeding, hybrids gained particular importance due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new top performing hybrids requires labour-intensive and costly breeding programmes, including testing of large numbers of experimental hybrids, the prediction of hybrid performance is of utmost interest to plant breeders. In this study, we tested the effectiveness of hybrid prediction models in spring-type oilseed rape (Brassica napus L./canola) employing different omics profiles, individually and in combination. To this end, a population of 950 F₁ hybrids was evaluated for seed yield and six other agronomically relevant traits in commercial field trials at several locations throughout Europe. A subset of these hybrids was also evaluated in a climatized glasshouse regarding early biomass production. For each of the 477 parental rapeseed lines, 13,201 single nucleotide polymorphisms (SNPs), 154 primary metabolites, and 19,479 transcripts were determined and used as predictive variables. Both, SNP markers and transcripts, effectively predict hybrid performance using (genomic) best linear unbiased prediction models (gBLUP). Compared to models using pure genetic markers, models incorporating transcriptome data resulted in significantly higher prediction accuracies for five out of seven agronomic traits, indicating that transcripts carry important information beyond genomic data. Notably, reproducing kernel Hilbert space regression based on Gaussian kernels significantly exceeded the predictive abilities of gBLUP models for six of the seven agronomic traits, demonstrating its potential for implementation in future canola breeding programmes.

Revisiting hybrid breeding designs using genomic predictions: simulations highlight the superiority of incomplete factorials between segregating families over topcross designs

Simulations showed that hybrid performances issued from an incomplete factorial between segregating families of two heterotic groups enable to calibrate genomic predictions of hybrid value more efficiently than tester-based designs. Genomic selection offers new opportunities to revisit hybrid breeding by replacing extensive phenotyping of hybrid combinations by genomic predictions. A key question remains to identify the best design to calibrate genomic prediction models. We proposed to use single-cross hybrids issued from an incomplete factorial design between segregating populations and compared this strategy with a conventional approach based on topcross evaluation. Two multiparental segregating populations of lines, each specific of one heterotic group, were simulated. Hybrids considered as training sets were generated using either (1) a parental line from the opposite group as tester or (2) following an incomplete factorial design. Different specific combining ability (SCA) proportions were simulated by considering different levels of group divergence and dominance effects for the simulated QTL. For the incomplete factorial design, for a same number of hybrids, we considered different numbers of parental lines and different contributions of lines (one to four) to calibration hybrids. We evaluated for different training set sizes prediction accuracies of new hybrids and genetic gains along three generations. At a given training set size, factorial design was as efficient (considering accuracy) as tester design in additive scenarios, but significantly outperformed tester design when SCA was present. The contribution number of each parental line to the incomplete factorial design had a small impact on accuracies. Our simulations confirmed experimental results and showed that calibrating models on hybrids between two multiparental populations is a cost-efficient way to perform genomic predictions in both groups, opening prospects for revisiting reciprocal recurrent selection schemes.

Hybrid Breeding for MLN Resistance: Heterosis, Combining Ability, and Hybrid Prediction

Prior knowledge on heterosis and quantitative genetic parameters on maize lethal necrosis (MLN) can help the breeders to develop numerous resistant or tolerant hybrids with optimum resources. Our objectives were to (1) estimate the quantitative genetic parameters for MLN disease severity, (2) investigate the efficiency of the prediction of hybrid performance based on parental per se and general combining ability (GCA) effects, and (3) examine the potential of hybrid prediction for MLN resistance or tolerance based on markers. Fifty elite maize inbred lines were selected based on their response to MLN under artificial inoculation. Crosses were made in a half diallel mating design to produce 307 F1 hybrids. All hybrids were evaluated in MLN quarantine facility in Naivasha, Kenya for two seasons under artificial inoculation. All 50 inbreds were genotyped with genotyping-by-sequencing (GBS) SNPs. The phenotypic variation was significant for all traits and the heritability was moderate to high. We observed that hybrids were superior to the mean performance of the parents for disease severity (−14.57%) and area under disease progress curve (AUDPC) (14.9%). Correlations were significant and moderate between line per se and GCA; and mean of parental value with hybrid performance for both disease severity and AUDPC value. Very low and negative correlation was observed between parental lines marker based genetic distance and heterosis. Nevertheless, the correlation of GCA effects was very high with hybrid performance which can suggests as a good predictor of MLN resistance. Genomic prediction of hybrid performance for MLN is high for both traits. We therefore conclude that there is potential for prediction of hybrid performance for MLN. Overall, the estimated quantitative genetic parameters suggest that through targeted approach, it is possible to develop outstanding lines and hybrids for MLN resistance.

Training set optimization of genomic prediction by means of EthAcc

Genomic prediction is a useful tool for plant and animal breeding programs and is starting to be used to predict human diseases as well. A shortcoming that slows down the genomic selection deployment is that the accuracy of the prediction is not known a priori. We propose EthAcc (Estimated THeoretical ACCuracy) as a method for estimating the accuracy given a training set that is genotyped and phenotyped. EthAcc is based on a causal quantitative trait loci model estimated by a genome-wide association study. This estimated causal model is crucial; therefore, we compared different methods to find the one yielding the best EthAcc. The multilocus mixed model was found to perform the best. We compared EthAcc to accuracy estimators that can be derived via a mixed marker model. We showed that EthAcc is the only approach to correctly estimate the accuracy. Moreover, in case of a structured population, in accordance with the achieved accuracy, EthAcc showed that the biggest training set is not always better than a smaller and closer training set. We then performed training set optimization with EthAcc and compared it to CDmean. EthAcc outperformed CDmean on real datasets from sugar beet, maize, and wheat. Nonetheless, its performance was mainly due to the use of an optimal but inaccessible set as a start of the optimization algorithm. EthAcc's precision and algorithm issues prevent it from reaching a good training set with a random start. Despite this drawback, we demonstrated that a substantial gain in accuracy can be obtained by performing training set optimization.

Sunflower Hybrid Breeding: From Markers to Genomic Selection

In sunflower, molecular markers for simple traits as, e.g., fertility restoration, high oleic acid content, herbicide tolerance or resistances to Plasmopara halstedii, Puccinia helianthi, or Orobanche cumana have been successfully used in marker-assisted breeding programs for years. However, agronomically important complex quantitative traits like yield, heterosis, drought tolerance, oil content or selection for disease resistance, e.g., against Sclerotinia sclerotiorum have been challenging and will require genome-wide approaches. Plant genetic resources for sunflower are being collected and conserved worldwide that represent valuable resources to study complex traits. Sunflower association panels provide the basis for genome-wide association studies, overcoming disadvantages of biparental populations. Advances in technologies and the availability of the sunflower genome sequence made novel approaches on the whole genome level possible. Genotype-by-sequencing, and whole genome sequencing based on next generation sequencing technologies facilitated the production of large amounts of SNP markers for high density maps as well as SNP arrays and allowed genome-wide association studies and genomic selection in sunflower. Genome wide or candidate gene based association studies have been performed for traits like branching, flowering time, resistance to Sclerotinia head and stalk rot. First steps in genomic selection with regard to hybrid performance and hybrid oil content have shown that genomic selection can successfully address complex quantitative traits in sunflower and will help to speed up sunflower breeding programs in the future. To make sunflower more competitive toward other oil crops higher levels of resistance against pathogens and better yield performance are required. In addition, optimizing plant architecture toward a more complex growth type for higher plant densities has the potential to considerably increase yields per hectare. Integrative approaches combining omic technologies (genomics, transcriptomics, proteomics, metabolomics and phenomics) using bioinformatic tools will facilitate the identification of target genes and markers for complex traits and will give a better insight into the mechanisms behind the traits.