Genomic and metabolic prediction of complex heterotic traits in hybrid maize

Genomic prediction of yield-related traits and genome-based establishment of heterotic pattern in maize hybrid breeding of Southwest China

When genomic prediction is implemented in breeding maize (Zea mays L.), it can accelerate the breeding process and reduce cost to a large extent. In this study, 11 yield-related traits of maize were used to evaluate four genomic prediction methods including rrBLUP, HEBLP|A, RF, and LightGBM. In all the 11 traits, rrBLUP had similar predictive accuracy to HEBLP|A, and so did RF to LightGBM, but rrBLUP and HEBLP|A outperformed RF and LightGBM in 8 traits. Furthermore, genomic prediction-based heterotic pattern of yield was established based on 64620 crosses of maize in Southwest China, and the result showed that one of the parent lines of the top 5% crosses came from temp-tropic or tropic germplasm, which is highly consistent with the actual situation in breeding, and that heterotic pattern (Reid+ × Suwan+) will be a major heterotic pattern of Southwest China in the future.

Prediction of plant complex traits via integration of multi-omics data

The formation of complex traits is the consequence of genotype and activities at multiple molecular levels. However, connecting genotypes and these activities to complex traits remains challenging. Here, we investigate whether integrating genomic, transcriptomic, and methylomic data can improve prediction for six Arabidopsis traits. We find that transcriptome- and methylome-based models have performances comparable to those of genome-based models. However, models built for flowering time using different omics data identify different benchmark genes. Nine additional genes identified as important for flowering time from our models are experimentally validated as regulating flowering. Gene contributions to flowering time prediction are accession-dependent and distinct genes contribute to trait prediction in different genotypes. Models integrating multi-omics data perform best and reveal known and additional gene interactions, extending knowledge about existing regulatory networks underlying flowering time determination. These results demonstrate the feasibility of revealing molecular mechanisms underlying complex traits through multi-omics data integration.

Multi-trait multi-environment genomic prediction of preliminary yield trial in pulse crop

Phenotypic selection of complex traits such as seed yield and protein in the preliminary yield trial (PYT) is often constrained by limited seed availability, resulting in trials with few environments and minimal to no replications. Multi-trait multi-environment enabled genomic prediction (MTME-GP) offers a valuable alternative to predict missing phenotypes of selection candidates for multiple traits and diverse environments. In this study, we assessed the efficiency of MTME-GP for improving seed protein and seed yield in field pea, the top two breeding targets but highly antagonistic traits in pulse crop. We utilized a set of 300 selection candidates in the PYT that virtually represented all possible families of the North Dakota State University field pea breeding program. Selection candidates were evaluated in three diverse, contrasting environments, as indicated by a range of heritability. Using whole- and split-environment cross validation schemes, MTME-GP had higher predictive ability than a standard additive G-BLUP model. Integrating a range of overlapping genotypes in between environments showed improvement on the predictive ability of the MTME-GP model but tends to plateau at 50%-80% training set size. Regardless of the cross-validation scheme, accuracy was among the lowest in stressed environments, presumably due to low heritability for seed protein and yield. This study provided insights into the potential of MTME-GP in a public pulse crop breeding program. The MTME-GP framework can be further improved with more testing environments and integration of additional orthogonal information in the early stages of the breeding pipeline.

Engineering the future cereal crops with big biological data: toward intelligence-driven breeding by design

How to feed 10 billion human populations is one of the challenges that need to be addressed in the following decades, especially under an unpredicted climate change. Crop breeding, initiating from the phenotype-based selection by local farmers and developing into current biotechnology-based breeding, has played a critical role in securing the global food supply. However, regarding the changing environment and ever-increasing human population, can we breed outstanding crop varieties fast enough to achieve high productivity, good quality, and widespread adaptability? This review outlines the recent achievements in understanding cereal crop breeding, including the current knowledge about crop agronomic traits, newly developed techniques, crop big biological data research, and the possibility of integrating them for intelligence-driven breeding by design, which ushers in a new era of crop breeding practice and shapes the novel architecture of future crops. This review focuses on the major cereal crops, including rice, maize, and wheat, to explain how intelligence-driven breeding by design is becoming a reality.

High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions

KEY MESSAGE

Transcriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits. The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plant's response to such climatic factors can be capitalized in prediction models to maximize crop improvement. Implementing multi-omics characterization in field evaluations is challenging due to high costs. It is, however, possible to do it on reference genotypes in controlled conditions. Using omics measured on a platform, we tested different multi-omics-based prediction approaches, using a high dimensional linear mixed model (MegaLMM) to predict genotypes for platform traits and agronomic field traits in a panel of 244 maize hybrids. We considered two prediction scenarios: in the first one, new hybrids are predicted (CV-NH), and in the second one, partially observed hybrids are predicted (CV-POH). For both scenarios, all hybrids were characterized for omics on the platform. We observed that omics can predict both additive and non-additive genetic effects for the platform traits, resulting in much higher predictive abilities than GBLUP. It highlights their efficiency in capturing regulatory processes in relation to growth conditions. For the field traits, we observed that the additive components of omics only slightly improved predictive abilities for predicting new hybrids (CV-NH, model MegaGAO) and for predicting partially observed hybrids (CV-POH, model GAOxW-BLUP) in comparison to GBLUP. We conclude that measuring the omics in the fields would be of considerable interest in predicting productivity if the costs of omics drop significantly.

Growth and antioxidant responses to water stress in eggplant MAGIC population parents, F1 hybrids and a subset of recombinant inbred lines

BACKGROUND

The generation of new eggplant (Solanum melongena L.) cultivars with drought tolerance is a main challenge in the current context of climate change. In this study, the eight parents (seven of S. melongena and one of the wild relative S. incanum L.) of the first eggplant MAGIC (Multiparent Advanced Generation Intercrossing) population, together with four F1 hybrids amongst them, five S5 MAGIC recombinant inbred lines selected for their genetic diversity, and one commercial hybrid were evaluated in young plant stage under water stress conditions (30% field capacity; FC) and control conditions (100% FC). After a 21-day treatment period, growth and biomass traits, photosynthetic pigments, oxidative stress markers, antioxidant compounds, and proline content were evaluated.

RESULTS

Significant effects (p < 0.05) were observed for genotype, water treatments and their interaction in most of the traits analyzed. The eight MAGIC population parental genotypes displayed a wide variation in their responses to water stress, with some of them exhibiting enhanced root development and reduced foliar biomass. The commercial hybrid had greater aerial growth compared to root growth. The four F1 hybrids among MAGIC parents differed in their performance, with some having significant positive or negative heterosis in several traits. The subset of five MAGIC lines displayed a wide diversity in their response to water stress.

CONCLUSION

The results show that a large diversity for tolerance to drought is available among the eggplant MAGIC materials, which can contribute to developing drought-tolerant eggplant cultivars.

Near-infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments

For nearly two decades, genomic prediction and selection have supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies for predicting complex traits in maize have recently proven beneficial when integrated into across-environment sparse genomic prediction models. One phenomic data modality is whole grain near-infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of hybrid maize grain yield (GY) and 500-kernel weight (KW) across 2 years (2011-2012) and two management conditions (water-stressed and well-watered) were conducted using combinations of reflectance data obtained from high-throughput, F2 whole-kernel scans and genomic data obtained from genotyping-by-sequencing within four different cross-validation (CV) schemes (CV2, CV1, CV0, and CV00). When predicting the performance of untested genotypes in characterized (CV1) environments, genomic data were better than phenomic data for GY (0.689 ± 0.024-genomic vs. 0.612 ± 0.045-phenomic), but phenomic data were better than genomic data for KW (0.535 ± 0.034-genomic vs. 0.617 ± 0.145-phenomic). Multi-kernel models (combinations of phenomic and genomic relationship matrices) did not surpass single-kernel models for GY prediction in CV1 or CV00 (prediction of untested genotypes in uncharacterized environments); however, these models did outperform the single-kernel models for prediction of KW in these same CVs. Lasso regression applied to the NIRS data set selected a subset of 216 NIRS bands that achieved comparable prediction abilities to the full phenomic data set of 3112 bands predicting GY and KW under CV1 and CV00.

OsLSC6 Regulates Leaf Sheath Color and Cold Tolerance in Rice Revealed by Metabolite Genome Wide Association Study

Plant metabolites including anthocyanins play an important role in the growth of plants, as well as in regulating biotic and abiotic stress responses to the environment. Here we report comprehensive profiling of 3315 metabolites and a further metabolic-based genome-wide association study (mGWAS) based on 292,485 SNPs obtained from 311 rice accessions, including 160 wild and 151 cultivars. We identified hundreds of common variants affecting a large number of secondary metabolites with large effects at high throughput. Finally, we identified a novel gene namely OsLSC6 (Oryza sativa leaf sheath color 6), which encoded a UDP 3-O-glucosyltransferase and involved in the anthocyanin biosynthesis of Cyanidin-3-Galc (sd1825) responsible for leaf sheath color, and resulted in significant different accumulation of sd1825 between wild (purple) and cultivars (green). The results of knockout transgenic experiments showed that OsLSC6 regulated the biosynthesis and accumulation of sd1825, controlled the purple leaf sheath. Our further research revealed that OsLSC6 also confers resistance to cold stress during the seedling stage in rice. And we identified that a SNP in OsLSC6 was responsible for the leaf sheath color and chilling tolerance, supporting the importance of OsLSC6 in plant adaption. Our study could not only demonstrate that OsLSC6 is a vital regulator during anthocyanin biosynthesis and abiotic stress responses, but also provide a powerful complementary tool based on metabolites-to-genes analysis by mGWAS for functional gene identification andpromising candidate in future rice breeding and improvement.

Mechanisms underlying key agronomic traits and implications for molecular breeding in soybean

Soybean (Glycine max [L.] Merr.) is an important crop that provides protein and vegetable oil for human consumption. As soybean is a photoperiod-sensitive crop, its cultivation and yield are limited by the photoperiodic conditions in the field. In contrast to other major crops, soybean has a special plant architecture and a special symbiotic nitrogen fixation system, representing two unique breeding directions. Thus, flowering time, plant architecture, and symbiotic nitrogen fixation are three critical or unique yield-determining factors. This review summarizes the progress made in our understanding of these three critical yield-determining factors in soybean. Meanwhile, we propose potential research directions to increase soybean production, discuss the application of genomics and genomic-assisted breeding, and explore research directions to address future challenges, particularly those posed by global climate changes.

Genomic selection in plant breeding: Key factors shaping two decades of progress

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.

Functional insight into multi-omics-based interventions for climatic resilience in sorghum (Sorghum bicolor): a nutritionally rich cereal crop

MAIN CONCLUSION

The article highlights omics-based interventions in sorghum to combat food and nutritional scarcity in the future. Sorghum with its unique ability to thrive in adverse conditions, has become a tremendous highly nutritive, and multipurpose cereal crop. It is resistant to various types of climatic stressors which will pave its way to a future food crop. Multi-omics refers to the comprehensive study of an organism at multiple molecular levels, including genomics, transcriptomics, proteomics, and metabolomics. Genomic studies have provided insights into the genetic diversity of sorghum and led to the development of genetically improved sorghum. Transcriptomics involves analysing the gene expression patterns in sorghum under various conditions. This knowledge is vital for developing crop varieties with enhanced stress tolerance. Proteomics enables the identification and quantification of the proteins present in sorghum. This approach helps in understanding the functional roles of specific proteins in response to stress and provides insights into metabolic pathways that contribute to resilience and grain production. Metabolomics studies the small molecules, or metabolites, produced by sorghum, provides information about the metabolic pathways that are activated or modified in response to environmental stress. This knowledge can be used to engineer sorghum varieties with improved metabolic efficiency, ultimately leading to better crop yields. In this review, we have focused on various multi-omics approaches, gene expression analysis, and different pathways for the improvement of Sorghum. Applying omics approaches to sorghum research allows for a holistic understanding of its genome function. This knowledge is invaluable for addressing challenges such as climate change, resource limitations, and the need for sustainable agriculture.

Toward understanding and utilizing crop heterosis in the age of biotechnology

Heterosis, a universal phenomenon in nature, mainly reflected in the superior productivity, quality, and fitness of F1 hybrids compared with their inbred parents, has been exploited in agriculture and greatly benefited human society in terms of food security. However, the flexible and efficient utilization of heterosis has remained a challenge in hybrid breeding systems because of the limitations of "three-line" and "two-line" methods. In the past two decades, rapidly developed biotechnologies have provided unprecedented conveniences for both understanding and utilizing heterosis. Notably, "third-generation" (3G) hybrid breeding technology together with high-throughput sequencing and gene editing greatly promoted the efficiency of hybrid breeding. Here, we review emerging ideas about the genetic or molecular mechanisms of heterosis and the development of 3G hybrid breeding system in the age of biotechnology. In addition, we summarized opportunities and challenges for optimal heterosis utilization in the future.

Multi-omics assists genomic prediction of maize yield with machine learning approaches

With the improvement of high-throughput technologies in recent years, large multi-dimensional plant omics data have been produced, and big-data-driven yield prediction research has received increasing attention. Machine learning offers promising computational and analytical solutions to interpret the biological meaning of large amounts of data in crops. In this study, we utilized multi-omics datasets from 156 maize recombinant inbred lines, containing 2496 single nucleotide polymorphisms (SNPs), 46 image traits (i-traits) from 16 developmental stages obtained through an automatic phenotyping platform, and 133 primary metabolites. Based on benchmark tests with different types of prediction models, some machine learning methods, such as Partial Least Squares (PLS), Random Forest (RF), and Gaussian process with Radial basis function kernel (GaussprRadial), achieved better prediction for maize yield, albeit slight difference for method preferences among i-traits, genomic, and metabolic data. We found that better yield prediction may be caused by various capabilities in ranking and filtering data features, which is found to be linked with biological meaning such as photosynthesis-related or kernel development-related regulations. Finally, by integrating multiple omics data with the RF machine learning approach, we can further improve the prediction accuracy of grain yield from 0.32 to 0.43. Our research provides new ideas for the application of plant omics data and artificial intelligence approaches to facilitate crop genetic improvements.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01454-z.

Genome-Wide Association Study and Prediction of Tassel Weight of Tropical Maize Germplasm in Multi-Parent Population

Tassel weight (TW) is a crucial agronomic trait that significantly affects pollen supply and grain yield development in maize breeding. To improve maize yield and develop new varieties, a comprehensive understanding of the genetic mechanisms underlying tassel weight is essential. In this study, tropical maize inbred lines, namely CML312, CML373, CML444, and YML46, were selected as female parents and crossed with the elite maize inbred line Ye107, which served as the common male parent, to develop a multi-parent population comprising four F8 recombinant inbred line (RIL) subpopulations. Using 6616 high-quality single nucleotide polymorphism (SNP) markers, we conducted genome-wide association analysis (GWAS) and genomic selection (GS) on 642 F8 RILs in four subpopulations across three different environments. Through GWAS, we identified 16 SNPs that were significantly associated with TW, encompassing two stable loci expressed across multiple environments. Furthermore, within the candidate regions of these SNPs, we discovered four novel candidate genes related to TW, namely Zm00001d044362, Zm00001d011048, Zm00001d011049, and Zm00001d031173 distributed on chromosomes 1, 3, and 8, which have not been previously reported. These genes are involved in processes such as signal transduction, growth and development, protein splicing, and pollen development, all of which play crucial roles in inflorescence meristem development, directly affecting TW. The co-localized SNP, S8_137379725, on chromosome 8 was situated within a 16.569 kb long terminal repeat retrotransposon (LTR-RT), located 22.819 kb upstream and 26.428 kb downstream of the candidate genes (Zm00001d011048 and Zm00001d011049). When comparing three distinct GS models, the BayesB model demonstrated the highest accuracy in predicting TW. This study establishes the theoretical foundation for future research into the genetic mechanisms underlying maize TW and the efficient breeding of high-yielding varieties with desired tassel weight through GS.

Germplasm Resources and Metabolite Marker Screening of High-Flavonoid Tartary Buckwheat (Fagopyrum tataricum)

Tartary buckwheat is an annual minor cereal crop with a variety of secondary metabolites, endowing it with a high nutritional and medicinal value. Flavonoids constitute the primary compounds of Tartary buckwheat. Recently, metabolomics, as an adjunct breeding method, has been increasingly employed in crop research. This study explores the correlation between the total flavonoid content (TFC) and antioxidant capacity in 167 Tartary buckwheat varieties. Ten Tartary buckwheat varieties with significant differences in flavonoid content and antioxidant capacity were selected by cluster analysis. With the use of liquid chromatography-mass spectrometry, 58 flavonoid compounds were identified, namely, 42 flavonols, 10 flavanols, 3 flavanones, 1 isoflavone, 1 anthocyanidin, and 1 proanthocyanidin. Different samples were clearly separated by employing principal component analysis and partial least-squares discriminant analysis. Eight differential flavonoid compounds were further selected through volcano plots and variable importance in projection. Differential metabolites were highly correlated with TFC and antioxidant capacity. Finally, metabolic markers of kaempferol-3-O-hexoside, kaempferol-7-O-glucoside, and naringenin-O-hexoside were determined by the random forest model. The findings provide a basis for the selection and identification of Tartary buckwheat varieties with high flavonoid content and strong antioxidant activity.

Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids

Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost-efficient genotyping using next-generation sequencing. However, accurate genotype calling from next-generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome-wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype-calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum, and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype-calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost-efficiently implement GWAS and genomic prediction in polyploid crops.

Evaluation of eight Bayesian genomic prediction models for three micronutrient traits in bread wheat (Triticum aestivum L.)

In wheat, genomic prediction accuracy (GPA) was assessed for three micronutrient traits (grain iron, grain zinc, and β-carotenoid concentrations) using eight Bayesian regression models. For this purpose, data on 246 accessions, each genotyped with 17,937 DArT markers, were utilized. The phenotypic data on traits were available for 2013-2014 from Powerkheda (Madhya Pradesh) and for 2014-2015 from Meerut (Uttar Pradesh), India. The accuracy of the models was measured in terms of reliability, which was computed following a repeated cross-validation approach. The predictions were obtained independently for each of the two environments after adjusting for the local effects and across environments after adjusting for the environmental effects. The Bayes ridge regression (BayesRR) model outperformed the other seven models, whereas BayesLASSO (BayesL) was the least efficient. The GPA increased with an increase in the size of the training set as well as with an increase in marker density. The GPA values differed for the three traits and were higher for the best linear unbiased estimate (BLUE) (obtained after adjusting for the environmental effects) relative to those for the two environments. The GPA also remained unaffected after accounting for the population structure. The results of the present study suggest that only the best model should be used for the estimations of genomic estimated breeding values (GEBVs) before their use for genomic selection to improve the grain micronutrient contents.

Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain

With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize (Zea mays L.) grain is low. Several large-effect genes with cis-acting variants affecting messenger RNA (mRNA) expression are mostly responsible for tocochromanol variation in maize grain, with other relevant associated quantitative trait loci (QTL) yet to be fully resolved. Leveraging existing genomic and transcriptomic information for maize inbreds could improve prediction when selecting for higher vitamin E content. Here, we first evaluated a multikernel genomic best linear unbiased prediction (MK-GBLUP) approach for modeling known QTL in the prediction of nine tocochromanol grain phenotypes (12-21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines. On average, MK-GBLUP models improved predictive abilities by 7.0-13.6% when compared with GBLUP. In a second approach with a subset of 545 lines from the larger panel, the highest average improvement in predictive ability relative to GBLUP was achieved with a multi-trait GBLUP model (15.4%) that had a tocochromanol phenotype and transcript abundances in developing grain for a few large-effect candidate causal genes (1-3 genes per trait) as multiple response variables. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes.

Diversity of the volatilome and the fruit size and shape in European woodland strawberry (Fragaria vesca)

Woodland strawberry (Fragaria vesca subsp. vesca) is a wild relative of cultivated strawberry (F. × ananassa) producing small and typically conical fruits with an intense flavor and aroma. The wild strawberry species, F. vesca, is a rich resource of genetic and metabolic variability, but its diversity remains largely unexplored and unexploited. In this study, we aim for an in-depth characterization of the fruit complex volatilome by GC-MS as well as the fruit size and shape using a European germplasm collection that represents the continental diversity of the species. We report characteristic volatilome footprints and fruit phenotypes of specific geographical areas. Thus, this study uncovers phenotypic variation linked to geographical distribution that will be valuable for further genetic studies to identify candidate genes or develop markers linked to volatile compounds or fruit shape and size traits.

Rethinking underutilized cereal crops: pan-omics integration and green system biology

MAIN CONCLUSION

Due to harsh lifestyle changes, in the present era, nutritional security is needed along with food security so it is necessary to include underutilized cereal crops (UCCs) in our daily diet to counteract the rising danger of human metabolic illness. We can attain both the goal of zero hunger and nutritional security by developing improved UCCs using advanced pan-omics (genomics, transcriptomics, proteomics, metabolomics, nutrigenomics, phenomics and ionomics) practices. Plant sciences research progressed profoundly since the last few decades with the introduction of advanced technologies and approaches, addressing issues of food demand of the growing population, nutritional security challenges and climate change. However, throughout the expansion and popularization of commonly consumed major cereal crops such as wheat and rice, other cereal crops such as millet, rye, sorghum, and others were impeded, despite their potential medicinal and nutraceutical qualities. Undoubtedly neglected underutilized cereal crops (UCCs) also have the capability to withstand diverse climate change. To relieve the burden of major crops, it is necessary to introduce the new crops in our diet in the way of UCCs. Introgression of agronomically and nutritionally important traits by pan-omics approaches in UCCs could be a defining moment for the population's well-being on the globe. This review discusses the importance of underutilized cereal crops, as well as the application of contemporary omics techniques and advanced bioinformatics tools that could open up new avenues for future study and be valuable assets in the development and usage of UCCs in the perspective of green system biology. The increased and improved use of UCCs is dependent on number of factors that necessitate a concerted research effort in agricultural sciences. The emergence of functional genomics with molecular genetics might gear toward the reawakening of interest in underutilized cereals crops. The need of this era is to focus on potential UCCs in advanced agriculture and breeding programmes. Hence, targeting the UCCs, might provide a bright future for better health and scientific rationale for its use.

Multivariate Genomic Hybrid Prediction with Kernels and Parental Information

Genomic selection (GS) plays a pivotal role in hybrid prediction. It can enhance the selection of parental lines, accurately predict hybrid performance, and harness hybrid vigor. Likewise, it can optimize breeding strategies by reducing field trial requirements, expediting hybrid development, facilitating targeted trait improvement, and enhancing adaptability to diverse environments. Leveraging genomic information empowers breeders to make informed decisions and significantly improve the efficiency and success rate of hybrid breeding programs. In order to improve the genomic ability performance, we explored the incorporation of parental phenotypic information as covariates under a multi-trait framework. Approach 1, referred to as Pmean, directly utilized parental phenotypic information without any preprocessing. While approach 2, denoted as BV, replaced the direct use of phenotypic values of both parents with their respective breeding values. While an improvement in prediction performance was observed in both approaches, with a minimum 4.24% reduction in the normalized root mean square error (NRMSE), the direct incorporation of parental phenotypic information in the Pmean approach slightly outperformed the BV approach. We also compared these two approaches using linear and nonlinear kernels, but no relevant gain was observed. Finally, our results increase empirical evidence confirming that the integration of parental phenotypic information helps increase the prediction performance of hybrids.

Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley

BACKGROUND

Metabolomics measures an intermediate stage between genotype and phenotype, and may therefore be useful for breeding. Our objectives were to investigate genetic parameters and accuracies of predicted breeding values for malting quality (MQ) traits when integrating both genomic and metabolomic information. In total, 2430 plots of 562 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each plot. Metabolomic features used were 24,018 nuclear magnetic resonance intensities measured on each wort sample. Methods for statistical analyses were genomic best linear unbiased prediction (GBLUP) and metabolomic-genomic best linear unbiased prediction (MGBLUP). Accuracies of predicted breeding values were compared using two cross-validation strategies: leave-one-year-out (LOYO) and leave-one-line-out (LOLO), and the increase in accuracy from the successive inclusion of first, metabolomic data on the lines in the validation population (VP), and second, both metabolomic data and phenotypes on the lines in the VP, was investigated using the linear regression (LR) method.

RESULTS

For all traits, we saw that the metabolome-mediated heritability was substantial. Cross-validation results showed that, in general, prediction accuracies from MGBLUP and GBLUP were similar when phenotypes and metabolomic data were recorded on the same plots. Results from the LR method showed that for all traits, except one, accuracy of MGBLUP increased when including metabolomic data on the lines of the VP, and further increased when including also phenotypes. However, in general the increase in accuracy of MGBLUP when including both metabolomic data and phenotypes on lines of the VP was similar to the increase in accuracy of GBLUP when including phenotypes on the lines of the VP. Therefore, we found that, when metabolomic data were included on the lines of the VP, accuracies substantially increased for lines without phenotypic records, but they did not increase much when phenotypes were already known.

CONCLUSIONS

MGBLUP is a useful approach to combine phenotypic, genomic and metabolomic data for predicting breeding values for MQ traits. We believe that our results have significant implications for practical breeding of barley and potentially many other species.

A Bayesian model for genomic prediction using metabolic networks

Motivation

Genomic prediction is now an essential technique in breeding and medicine, and it is interesting to see how omics data can be used to improve prediction accuracy. Precedent work proposed a metabolic network-based method in biomass prediction of Arabidopsis; however, the method consists of multiple steps that possibly degrade prediction accuracy.

Results

We proposed a Bayesian model that integrates all steps and jointly infers all fluxes of reactions related to biomass production. The proposed model showed higher accuracies than methods compared both in simulated and real data. The findings support the previous excellent idea that metabolic network information can be used for prediction.

Availability and implementation

All R and stan scripts to reproduce the results of this study are available at https://github.com/Onogi/MetabolicModeling.

Reciprocal testcross design for genome-wide prediction of maize single-cross performance

KEY MESSAGE

A reciprocal testcross design increases the relatedness among single crosses and testcrosses, thereby increasing the effectiveness of genome-wide prediction in maize. A reciprocal testcross design uses parental inbreds in an opposite heterotic group as testers in maize (Zea mays L.) inbred development. In particular, doubled haploids from the A × B cross are testcrossed with inbreds Y and Z, and doubled haploids from Y × Z are testcrossed with inbreds A and B. Our objective was to determine if a reciprocal testcross design is superior to a traditional, non-reciprocal testcross design. A total of 700 Iowa Stiff Stalk Synthetic (BSSS) doubled haploids and 231 non-BSSS doubled haploids were developed from 10 breeding populations and had data on 11,032 single nucleotide polymorphism markers. Each doubled haploid was testcrossed to one to five testers from the opposite heterotic group, and the resulting 1642 testcrosses were evaluated in multilocation yield trials in 2019. Divergent selection for yield/moisture, on the basis of genome-wide predictions according to a reciprocal testcross design, led to significant responses (in 2020) in all 10 populations for yield/moisture and moisture and in three populations for yield. Predictive ability for yield/moisture and moisture was 0.11 to 0.26 higher with a reciprocal testcross design than with a testcross design. This higher predictive ability was attributed to a stronger relatedness between the training and test populations. No significant difference in predictive ability was found for yield, for which predictive ability was lower. Differences among genetic models that included and excluded specific combining ability were small. Overall, the empirical results supported the usefulness of a reciprocal testcross design in maize breeding.

Genomic prediction in hybrid breeding: I. Optimizing the training set design

KEY MESSAGE

Training sets produced by maximizing the number of parent lines, each involved in one cross, had the highest prediction accuracy for H0 hybrids, but lowest for H1 and H2 hybrids. Genomic prediction holds great promise for hybrid breeding but optimum composition of the training set (TS) as determined by the number of parents (nTS) and crosses per parent (c) has received little attention. Our objective was to examine prediction accuracy ([Formula: see text]) of GCA for lines used as parents of the TS (I1 lines) or not (I0 lines), and H0, H1 and H2 hybrids, comprising crosses of type I0 × I0, I1 × I0 and I1 × I1, respectively, as function of nTS and c. In the theory, we developed estimates for [Formula: see text] of GBLUPs for hybrids: (i)[Formula: see text] based on the expected prediction accuracy, and (ii) [Formula: see text] based on [Formula: see text] of GBLUPs of GCA and SCA effects. In the simulation part, hybrid populations were generated using molecular data from two experimental maize data sets. Additive and dominance effects of QTL borrowed from literature were used to simulate six scenarios of traits differing in the proportion (τSCA = 1%, 6%, 22%) of SCA variance in σG2 and heritability (h2 = 0.4, 0.8). Values of [Formula: see text] and [Formula: see text] closely agreed with [Formula: see text] for hybrids. For given size NTS = nTS × c of TS, [Formula: see text] of H0 hybrids and GCA of I0 lines was highest for c = 1. Conversely, for GCA of I1 lines and H1 and H2 hybrids, c = 1 yielded lowest [Formula: see text] with concordant results across all scenarios for both data sets. In view of these opposite trends, the optimum choice of c for maximizing selection response across all types of hybrids depends on the size and resources of the breeding program.

The Metabolic Profile of Young, Watered Chickpea Plants Can Be Used as a Biomarker to Predict Seed Number under Terminal Drought

Chickpea is the second-most-cultivated legume globally, with India and Australia being the two largest producers. In both of these locations, the crop is sown on residual summer soil moisture and left to grow on progressively depleting water content, finally maturing under terminal drought conditions. The metabolic profile of plants is commonly, correlatively associated with performance or stress responses, e.g., the accumulation of osmoprotective metabolites during cold stress. In animals and humans, metabolites are also prognostically used to predict the likelihood of an event (usually a disease) before it occurs, e.g., blood cholesterol and heart disease. We sought to discover metabolic biomarkers in chickpea that could be used to predict grain yield traits under terminal drought, from the leaf tissue of young, watered, healthy plants. The metabolic profile (GC-MS and enzyme assays) of field-grown chickpea leaves was analysed over two growing seasons, and then predictive modelling was applied to associate the most strongly correlated metabolites with the final seed number plant^-1. Pinitol (negatively), sucrose (negatively) and GABA (positively) were significantly correlated with seed number in both years of study. The feature selection algorithm of the model selected a larger range of metabolites including carbohydrates, sugar alcohols and GABA. The correlation between the predicted seed number and actual seed number was R² adj = 0.62, demonstrating that the metabolic profile could be used to predict a complex trait with a high degree of accuracy. A previously unknown association between D-pinitol and hundred-kernel weight was also discovered and may provide a single metabolic marker with which to predict large seeded chickpea varieties from new crosses. The use of metabolic biomarkers could be used by breeders to identify superior-performing genotypes before maturity is reached.

MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits

Incorporating the genotypic and phenotypic of the correlated traits into the multi-trait model can significantly improve the prediction accuracy of the target trait in animal and plant breeding, as well as human genetics. However, in most cases, the phenotypic information of the correlated and target trait of the individual to be evaluated was null simultaneously, particularly for the newborn. Therefore, we propose a machine learning framework, MAK, to improve the prediction accuracy of the target trait by constructing the multi-target ensemble regression chains and selecting the assistant trait automatically, which predicted the genomic estimated breeding values of the target trait using genotypic information only. The prediction ability of MAK was significantly more robust than the genomic best linear unbiased prediction, BayesB, BayesRR and the multi trait Bayesian method in the four real animal and plant datasets, and the computational efficiency of MAK was roughly 100 times faster than BayesB and BayesRR.

Understandings and future challenges in soybean functional genomics and molecular breeding

Soybean (Glycine max) is a major source of plant protein and oil. Soybean breeding has benefited from advances in functional genomics. In particular, the release of soybean reference genomes has advanced our understanding of soybean adaptation to soil nutrient deficiencies, the molecular mechanism of symbiotic nitrogen (N) fixation, biotic and abiotic stress tolerance, and the roles of flowering time in regional adaptation, plant architecture, and seed yield and quality. Nevertheless, many challenges remain for soybean functional genomics and molecular breeding, mainly related to improving grain yield through high-density planting, maize-soybean intercropping, taking advantage of wild resources, utilization of heterosis, genomic prediction and selection breeding, and precise breeding through genome editing. This review summarizes the current progress in soybean functional genomics and directs future challenges for molecular breeding of soybean.

QTL analysis of important agronomic traits and metabolites in foxtail millet (Setaria italica) by RIL population and widely targeted metabolome

As a bridge between genome and phenotype, metabolome is closely related to plant growth and development. However, the research on the combination of genome, metabolome and multiple agronomic traits in foxtail millet (Setaria italica) is insufficient. Here, based on the linkage analysis of 3,452 metabolites via with high-quality genetic linkage maps, we detected a total of 1,049 metabolic quantitative trait loci (mQTLs) distributed in 11 hotspots, and 28 metabolite-related candidate genes were mined from 14 mQTLs. In addition, 136 single-environment phenotypic QTL (pQTLs) related to 63 phenotypes were identified by linkage analysis, and there were 12 hotspots on these pQTLs. We futher dissected 39 candidate genes related to agronomic traits through metabolite-phenotype correlation and gene function analysis, including Sd1 semidwarf gene, which can affect plant height by regulating GA synthesis. Combined correlation network and QTL analysis, we found that flavonoid-lignin pathway maybe closely related to plant architecture and yield in foxtail millet. For example, the correlation coefficient between apigenin 7-rutinoside and stem diameter reached 0.98, and they were co-located at 41.33-44.15 Mb of chromosome 5, further gene function analysis revealed that 5 flavonoid pathway genes, as well as Sd1, were located in this interval . Therefore, the correlation and co-localization between flavonoid-lignins and plant architecture may be due to the close linkage of their regulatory genes in millet. Besides, we also found that a combination of genomic and metabolomic for BLUP analysis can better predict plant agronomic traits than genomic or metabolomic data, independently. In conclusion, the combined analysis of mQTL and pQTL in millet have linked genetic, metabolic and agronomic traits, and is of great significance for metabolite-related molecular assisted breeding.

Metabolite Profiling of Wheat Response to Cultivar Improvement and Nitrogen Fertilizer

Both genetic improvement and the application of N fertilizer increase the quality and yields of wheat. However, the molecular kinetics that underlies the differences between them are not well understood. In this study, we performed a non-targeted metabolomic analysis on wheat cultivars from different release years to comprehensively investigate the metabolic differences between cultivar and N treatments. The results revealed that the plant height and tiller number steadily decreased with increased ears numbers, whereas the grain number and weight increased with genetic improvement. Following the addition of N fertilizer, the panicle numbers and grain weights increased in an old cultivar, whereas the panicle number and grain number per panicle increased in a modern cultivar. For the 1950s to 2010s cultivar, the yield increases due to genetic improvements ranged from -1.9% to 96.7%, whereas that of N application ranged from 19.1% to 81.6%. Based on the untargeted metabolomics approach, the findings demonstrated that genetic improvements induced 1.4 to 7.4 times more metabolic alterations than N fertilizer supply. After the addition of N, 69.6%, 29.4%, and 33.3% of the differential metabolites were upregulated in the 1950s, 1980s, and 2010s cultivars, respectively. The results of metabolic pathway analysis of the identified differential metabolites via genetic improvement indicated enrichment in 1-2 KEGG pathways, whereas the application of N fertilizer enriched 2-4 pathways. Our results provide new insights into the molecular mechanisms of wheat quality and grain yield developments.

Genetic mapping of maize metabolites using high-throughput mass profiling

Plant metabolites are the basis of human nutrition and have biological relevance in ecology. Farmers selected plants with favorable characteristics since prehistoric times and improved the cultivars, but without knowledge of underlying mechanisms. Understanding the genetic basis of metabolite production can facilitate the successful breeding of plants with augmented nutritional value. To identify genetic factors related to the metabolic composition in maize, we generated mass profiles of 198 recombinant inbred lines (RILs) and their parents (B73 and Mo17) using direct-injection electrospray ionization mass spectrometry (DLI-ESI MS). Mass profiling allowed the correct clustering of samples according to genotype. We quantified 71 mass features from grains and 236 mass features from leaf extracts. For the corresponding ions, we identified tissue-specific metabolic 'Quantitative Trait Loci' (mQTLs) distributed across the maize genome. These genetic regions could regulate multiple metabolite biosynthesis pathways. Our findings demonstrate that DLI-ESI MS has sufficient analytical resolution to map mQTLs. These identified genetic loci will be helpful in metabolite-focused maize breeding. Mass profiling is a powerful tool for detecting mQTLs in maize and enables the high-throughput screening of loci responsible for metabolite biosynthesis.

Genome resequencing reveals independent domestication and breeding improvement of naked oat

As an important cereal crop, common oat, has attracted more and more attention due to its healthy nutritional components and bioactive compounds. Here, high-depth resequencing of 115 oat accessions and closely related hexaploid species worldwide was performed. Based on genetic diversity and linkage disequilibrium analysis, it was found that hulled oat (Avena sativa) experienced a more severe bottleneck than naked oat (Avena sativa var. nuda). Combined with the divergence time of ∼51,200 years ago, the previous speculation that naked oat was a variant of hulled oat was rejected. It was found that the common segments that hulled oat introgressed to naked oat cultivars contained 444 genes, mainly enriched in photosynthetic efficiency-related pathways. Selective sweeps during environmental adaptation and breeding improvement were identified in the naked oat genome. Candidate genes associated with smut resistance and the days to maturity phenotype were also identified. Our study provides genomic resources and new insights into naked oat domestication and breeding.

Large-scale genomic and transcriptomic profiles of rice hybrids reveal a core mechanism underlying heterosis

BACKGROUND

Heterosis is widely used in agriculture. However, its molecular mechanisms are still unclear in plants. Here, we develop, sequence, and record the phenotypes of 418 hybrids from crosses between two testers and 265 rice varieties from a mini-core collection.

RESULTS

Phenotypic analysis shows that heterosis is dependent on genetic backgrounds and environments. By genome-wide association study of 418 hybrids and their parents, we find that nonadditive QTLs are the main genetic contributors to heterosis. We show that nonadditive QTLs are more sensitive to the genetic background and environment than additive ones. Further simulations and experimental analysis support a novel mechanism, homo-insufficiency under insufficient background (HoIIB), underlying heterosis. We propose heterosis in most cases is not due to heterozygote advantage but homozygote disadvantage under the insufficient genetic background.

CONCLUSION

The HoIIB model elucidates that genetic background insufficiency is the intrinsic mechanism of background dependence, and also the core mechanism of nonadditive effects and heterosis. This model can explain most known hypotheses and phenomena about heterosis, and thus provides a novel theory for hybrid rice breeding in future.

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.

Genome-wide identification of genes enabling accurate prediction of hybrid performance from parents across environments and populations for gene-based breeding in maize

Accurate prediction of hybrid offspring complex trait phenotype from parents is paramount to enhanced plant breeding, animal breeding, and human medicine. Here we report genome-wide identification of genes enabling accurate prediction of hybrid offspring complex traits from parents using maize grain yield as the target trait. We identified 181 ZmF1GY genes enabling prediction of maize (Zea mays L.) F₁ hybrid grain yield from parents and tested their utility and efficiency for predicting F₁ hybrid grain yields from parents using their expressions, genic SNPs, and number of favorable alleles (NFAs), respectively. The ZmF1GY genes predicted hybrid grain yields from parents at an accuracy of 0.86, presented by correlation coefficient between predicted and observed phenotypes, within an environment, 0.74 across environments, and 0.64 across populations, outperforming genomic prediction by 27-406%, 23%, and 40%, respectively. Furthermore, we identified nine of the ZmF1GY genes containing SNPs or InDels in parents that increased or decreased hybrid grain yields by 14-46%. When the NFAs of these nine ZmF1GY genes were used for hybrid grain yield prediction from parents, they predicted hybrid grain yields at an accuracy of 0.79, outperforming genomic prediction by 21% that was based on up to tens of thousands of genome-wide SNPs. These results demonstrate the feasibility of developing a gene toolkit for a species enabling gene-based breeding across environments and populations that is much more powerful and efficient than current breeding, thereby helping secure the world's food production. The methodology is applicable to all crops, livestock, and humans.

Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials

Phenomic prediction of wheat grain yield and heading date in different multi-environmental trial scenarios is accurate. Modelling the genotype-by-environment interaction effect using phenomic data is a potentially low-cost complement to genomic prediction. The performance of wheat cultivars in multi-environmental trials (MET) is difficult to predict because of the genotype-by-environment interactions (G × E). Phenomic selection is supposed to be efficient for modelling the G × E effect because it accounts for non-additive effects. Here, phenomic data are near-infrared (NIR) spectra obtained from plant material. While phenomic selection has recently been shown to accurately predict wheat grain yield in single environments, its accuracy needs to be investigated for MET. We used four datasets from two winter wheat breeding programs to test and compare the predictive abilities of phenomic and genomic models for grain yield and heading date in different MET scenarios. We also compared different methods to model the G × E using different covariance matrices based on spectra. On average, phenomic and genomic prediction abilities are similar in all different MET scenarios. Better predictive abilities were obtained when G × E effects were modelled with NIR spectra than without them, and it was better to use all the spectra of all genotypes in all environments for modelling the G × E. To facilitate the implementation of phenomic prediction, we tested MET designs where the NIR spectra were measured only on the genotype-environment combinations phenotyped for the target trait. Missing spectra were predicted with a weighted multivariate ridge regression. Intermediate predictive abilities for grain yield were obtained in a sparse testing scenario and for new genotypes, which shows that phenomic selection is an efficient and practicable prediction method for dealing with G × E.

The field phenotyping platform's next darling: Dicotyledons

The genetic information and functional properties of plants have been further identified with the completion of the whole-genome sequencing of numerous crop species and the rapid development of high-throughput phenotyping technologies, laying a suitable foundation for advanced precision agriculture and enhanced genetic gains. Collecting phenotypic data from dicotyledonous crops in the field has been identified as a key factor in the collection of large-scale phenotypic data of crops. On the one hand, dicotyledonous plants account for 4/5 of all angiosperm species and play a critical role in agriculture. However, their morphology is complex, and an abundance of dicot phenotypic information is available, which is critical for the analysis of high-throughput phenotypic data in the field. As a result, the focus of this paper is on the major advancements in ground-based, air-based, and space-based field phenotyping platforms over the last few decades and the research progress in the high-throughput phenotyping of dicotyledonous field crop plants in terms of morphological indicators, physiological and biochemical indicators, biotic/abiotic stress indicators, and yield indicators. Finally, the future development of dicots in the field is explored from the perspectives of identifying new unified phenotypic criteria, developing a high-performance infrastructure platform, creating a phenotypic big data knowledge map, and merging the data with those of multiomic techniques.

Can biochemical traits bridge the gap between genomics and plant performance? A study in rice under drought

The possibility of introducing metabolic/biochemical phenotyping to complement genomics-based predictions in breeding pipelines has been considered for years. Here we examine to what extent and under what environmental conditions metabolic/biochemical traits can effectively contribute to understanding and predicting plant performance. In this study, multivariable statistical models based on flag leaf central metabolism and oxidative stress status were used to predict grain yield (GY) performance for 271 indica rice (Oryza sativa) accessions grown in the field under well-watered and reproductive stage drought conditions. The resulting models displayed significantly higher predictability than multivariable models based on genomic data for the prediction of GY under drought (Q2 = 0.54-0.56 versus 0.35) and for stress-induced GY loss (Q2 = 0.59-0.64 versus 0.03-0.06). Models based on the combined datasets showed predictabilities similar to metabolic/biochemical-based models alone. In contrast to genetic markers, models with enzyme activities and metabolite values also quantitatively integrated the effect of physiological differences such as plant height on GY. The models highlighted antioxidant enzymes of the ascorbate-glutathione cycle and a lipid oxidation stress marker as important predictors of rice GY stability under drought at the reproductive stage, and these stress-related variables were more predictive than leaf central metabolites. These findings provide evidence that metabolic/biochemical traits can integrate dynamic cellular and physiological responses to the environment and can help bridge the gap between the genome and the phenome of crops as predictors of GY performance under drought.

High-throughput plant phenotyping: a role for metabolomics?

High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.

Genome-wide association study of eigenvectors provides genetic insights into selective breeding for tomato metabolites

Background

Long-term domestication and intensive breeding of crop plants aim to establish traits desirable for human needs, and characteristics related to yield, disease resistance, and postharvest storage have traditionally received considerable attention. These processes have led also to negative consequences, as is the case of loss of variants controlling fruit quality, for instance in tomato. Tomato fruit quality is directly associated to metabolite content profiles; however, a full understanding of the genetics affecting metabolite content during tomato domestication and improvement has not been reached due to limitations of the single detection methods previously employed. Here, we aim to reach a broad understanding of changes in metabolite content using a genome-wide association study (GWAS) with eigenvector decomposition (EigenGWAS) on tomato accessions.

Results

An EigenGWAS was performed on 331 tomato accessions using the first eigenvector generated from the genomic data as a “phenotype” to understand the changes in fruit metabolite content during breeding. Two independent gene sets were identified that affected fruit metabolites during domestication and improvement in consumer-preferred tomatoes. Furthermore, 57 candidate genes related to polyphenol and polyamine biosynthesis were discovered, and a major candidate gene chlorogenate: glucarate caffeoyltransferase (SlCGT) was identified, which affected the quality and diseases resistance of tomato fruit, revealing the domestication mechanism of polyphenols.

Conclusions

We identified gene sets that contributed to consumer liking during domestication and improvement of tomato. Our study reports novel evidence of selective sweeps and key metabolites controlled by multiple genes, increasing our understanding of the mechanisms of metabolites variation during those processes. It also supports a polygenic selection model for the application of tomato breeding.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01327-x.

An R package VIGoR for joint estimation of multiple linear learners with variational Bayesian inference

SUMMARY

An R package that can implement multiple linear learners, including penalized regression and regression with spike and slab priors, in a single model has been developed. Solutions are obtained with fast minorize-maximization algorithms in the framework of variational Bayesian inference. This package helps to incorporate multimodal and high-dimensional explanatory variables in a single regression model.

AVAILABILITY AND IMPLEMENTATION

The R package VIGoR (Variational Bayesian Inference for Genome-wide Regression) is available at the Comprehensive R Archive Network (CRAN) (https://cran.r-project.org/) and at github (https://github.com/Onogi/VIGoR).

SUPPLEMENTARY INFORMATION

Supplementary Materials are provided at the journal homepage. R scripts to reproduce the experiment results and pdf manual of the package are provided at https://github.com/Onogi/VIGoR.

Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure

Due to insufficient identification and in-depth investigation of existing common bean germplasm resources, it is difficult for breeders to utilize these valuable genetic resources. This situation limits the breeding and industrial development of the common bean (Phaseolus vulgaris L.) in China. Genomic prediction (GP) is a breeding method that uses whole-genome molecular markers to calculate the genomic estimated breeding value (GEBV) of candidate materials and select breeding materials. This study aimed to use genomic prediction to evaluate 15 traits in a collection of 628 common bean lines (including 484 landraces and 144 breeding lines) to determine a common bean GP model. The GP model constructed by landraces showed a moderate to high predictive ability (ranging from 0.59-0.88). Using all landraces as a training set, the predictive ability of the GP model for most traits was higher than that using the landraces from each of two subgene pools, respectively. Randomly selecting breeding lines as additional training sets together with landrace training sets to predict the remaining breeding lines resulted in a higher predictive ability based on principal components analysis. This study constructed a widely applicable GP model of the common bean based on the population structure, and encouraged the development of GP models to quickly aggregate excellent traits and accelerate utilization of germplasm resources.

Metabolomic spectra for phenotypic prediction of malting quality in spring barley

We investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes. Data of five MQ traits was measured on 2667 individual plots of 564 malting spring barley lines from three years and two locations. A total of 24,018 metabolomic features (MFs) were measured on each wort sample. Two statistical models were used, a metabolomic best linear unbiased prediction (MBLUP) and a partial least squares regression (PLSR). Predictive ability within location and across locations were compared using cross-validation methods. For all traits, more than 90% of the total variance in MQ traits could be explained by MFs. The prediction accuracy increased with increasing TP size and stabilized when the TP size reached 1000. The optimal number of components considered in the PLSR models was 20. The accuracy using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation from 0.517 to 0.817. In conclusion, the prediction accuracy of metabolomic prediction of MQ traits using MFs was high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.

Phenomic Selection: A New and Efficient Alternative to Genomic Selection

Recently, it has been proposed to switch molecular markers to near-infrared (NIR) spectra for inferring relationships between individuals and further performing phenomic selection (PS), analogous to genomic selection (GS). The PS concept is similar to genomic-like omics-based (GLOB) selection, in which molecular markers are replaced by endophenotypes, such as metabolites or transcript levels, except that the phenomic information obtained for instance by near-infrared spectroscopy (NIRS ) has usually a much lower cost than other omics. Though NIRS has been routinely used in breeding for several decades, especially to deal with end-product quality traits, its use to predict other traits of interest and further make selections is new. Since the seminal paper on PS , several publications have advocated the use of spectral acquisition (including NIRS and hyperspectral imaging) in plant breeding towards PS , potentially providing a scope of what is possible. In the present chapter, we first come back to the concept of PS as originally proposed and provide a classification of selected papers related to the use of phenomics in breeding. We further provide a review of the selected literature concerning the type of technology used, the preprocessing of the spectra, and the statistical modeling to make predictions. We discuss the factors that likely affect the efficiency of PS and compare it to GS in terms of predictive ability. Finally, we propose several prospects for future work and application of PS in the context of plant breeding.

Integrated Metabolomics and Transcriptomics Analyses Reveal the Metabolic Differences and Molecular Basis of Nutritional Quality in Landraces and Cultivated Rice

Rice (Oryza sativa L.) is one of the most globally important crops, nutritionally and economically. Therefore, analyzing the genetic basis of its nutritional quality is a paramount prerequisite for cultivating new varieties with increased nutritional health. To systematically compare the nutritional quality differences between landraces and cultivated rice, and to mine key genes that determine the specific nutritional traits of landraces, a seed metabolome database of 985 nutritional metabolites covering amino acids, flavonoids, anthocyanins, and vitamins by a widely targeted metabolomic approach with 114 rice varieties (35 landraces and 79 cultivars) was established. To further reveal the molecular mechanism of the metabolic differences in landrace and cultivated rice seeds, four cultivars and six landrace seeds were selected for transcriptome and metabolome analysis during germination, respectively. The integrated analysis compared the metabolic profiles and transcriptomes of different types of rice, identifying 358 differentially accumulated metabolites (DAMs) and 1982 differentially expressed genes (DEGs), establishing a metabolite–gene correlation network. A PCA revealed anthocyanins, flavonoids, and lipids as the central differential nutritional metabolites between landraces and cultivated rice. The metabolite–gene correlation network was used to screen out 20 candidate genes postulated to be involved in the structural modification of anthocyanins. Five glycosyltransferases were verified to catalyze the glycosylation of anthocyanins by in vitro enzyme activity experiments. At the same time, the different mechanisms of the anthocyanin synthesis pathway and structural diversity in landrace and cultivated rice were systematically analyzed, providing new insights for the improvement and utilization of the nutritional quality of rice landrace varieties.

Target-oriented prioritization: targeted selection strategy by integrating organismal and molecular traits through predictive analytics in breeding

Genomic prediction in crop breeding is hindered by modeling on limited phenotypic traits. We propose an integrative multi-trait breeding strategy via machine learning algorithm, target-oriented prioritization (TOP). Using a large hybrid maize population, we demonstrate that the accuracy for identifying a candidate that is phenotypically closest to an ideotype, or target variety, achieves up to 91%. The strength of TOP is enhanced when omics level traits are included. We show that TOP enables selection of inbreds or hybrids that outperform existing commercial varieties. It improves multiple traits and accurately identifies improved candidates for new varieties, which will greatly influence breeding.

Leveraging a graft collection to develop metabolome-based trait prediction for the selection of tomato rootstocks with enhanced salt tolerance

Grafting has been demonstrated to significantly enhance the salt tolerance of crops. However, breeding efforts to develop enhanced graft combinations are hindered by knowledge-gaps as to how rootstocks mediate scion-response to salt stress. We grafted the scion of cultivated M82 onto rootstocks of 254 tomato accessions and explored the morphological and metabolic responses of grafts under saline conditions (EC = 20 dS m^-1) as compared to self-grafted M82 (SG-M82). Correlation analysis and Least Absolute Shrinkage and Selection Operator were performed to address the association between morphological diversification and metabolic perturbation. We demonstrate that grafting the same variety onto different rootstocks resulted in scion phenotypic heterogeneity and emphasized the productivity efficiency of M82 irrespective of the rootstock. Spectrophotometric analysis to test lipid oxidation showed largest variability of malondialdehyde (MDA) equivalents across the population, while the least responsive trait was the ratio of fruit fresh weight to total fresh weight (FFW/TFW). Generally, grafts showed greater values for the traits measured than SG-M82, except for branch number and wild race-originated rootstocks; the latter were associated with smaller scion growth parameters. Highly responsive and correlated metabolites were identified across the graft collection including malate, citrate, and aspartate, and their variance was partly related to rootstock origin. A group of six metabolites that consistently characterized exceptional graft response was observed, consisting of sorbose, galactose, sucrose, fructose, myo-inositol, and proline. The correlation analysis and predictive modelling, integrating phenotype- and leaf metabolite data, suggest a potential predictive relation between a set of leaf metabolites and yield-related traits.

Improvement of prediction ability by integrating multi-omic datasets in barley

Background

Genomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three yield-related phenotypic traits using different omic datasets as single predictors compared to a SNP array, where these omic datasets included different types of sequence variants (full-SV, deleterious-dSV, and tolerant-tSV), different types of transcriptome (expression presence/absence variation-ePAV, gene expression-GE, and transcript expression-TE) sampled from two tissues, leaf and seedling, and metabolites (M); (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the predictive performance when using SV, GE, and ePAV from simulated 3’end mRNA sequencing of different lengths as predictors.

Results

The prediction ability from genomic best linear unbiased prediction (GBLUP) for the three traits using dSV information was higher than when using tSV, all SV information, or the SNP array. Any predictors from the transcriptome (GE, TE, as well as ePAV) and metabolome provided higher prediction abilities compared to the SNP array and SV on average across the three traits. In addition, some (di)-similarity existed between different omic datasets, and therefore provided complementary biological perspectives to phenotypic variation. Optimal combining the information of dSV, TE, ePAV, as well as metabolites into GP models could improve the prediction ability over that of the single predictors alone.

Conclusions

The use of integrated omic datasets in GP model is highly recommended. Furthermore, we evaluated a cost-effective approach generating 3’end mRNA sequencing with transcriptome data extracted from seedling without losing prediction ability in comparison to the full-length mRNA sequencing, paving the path for the use of such prediction methods in commercial breeding programs.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08337-7).