Target enrichment sequencing coupled with GWAS identifies MdPRX10 as a candidate gene in the control of budbreak in apple.

The timing of floral budbreak in apple has a significant effect on fruit production and quality. Budbreak occurs as a result of a complex molecular mechanism that relies on accurate integration of external environmental cues, principally temperature. In the pursuit of understanding this mechanism, especially with respect to aiding adaptation to climate change, a QTL at the top of linkage group (LG) 9 has been identified by many studies on budbreak, but the genes underlying it remain elusive. Here, together with a dessert apple core collection of 239 cultivars, we used a targeted capture sequencing approach to increase SNP resolution in apple orthologues of known or suspected A. thaliana flowering time-related genes, as well as approximately 200 genes within the LG9 QTL interval. This increased the 275 223 SNP Axiom® Apple 480 K array dataset by an additional 40 857 markers. Robust GWAS analyses identified MdPRX10, a peroxidase superfamily gene, as a strong candidate that demonstrated a dormancy-related expression pattern and down-regulation in response to chilling. In-silico analyses also predicted the residue change resulting from the SNP allele associated with late budbreak could alter protein conformation and likely function. Late budbreak cultivars homozygous for this SNP allele also showed significantly up-regulated expression of C-REPEAT BINDING FACTOR (CBF) genes, which are involved in cold tolerance and perception, compared to reference cultivars, such as Gala. Taken together, these results indicate a role for MdPRX10 in budbreak, potentially via redox-mediated signaling and CBF gene regulation. Moving forward, this provides a focus for developing our understanding of the effects of temperature on flowering time and how redox processes may influence integration of external cues in dormancy pathways.

Enhancing grapevine breeding efficiency through genomic prediction and selection index.

Grapevine (Vitis vinifera) breeding reaches a critical point. New cultivars are released every year with resistance to powdery and downy mildews. However, the traditional process remains time-consuming, taking 20-25 years, and demands the evaluation of new traits to enhance grapevine adaptation to climate change. Until now, the selection process has relied on phenotypic data and a limited number of molecular markers for simple genetic traits such as resistance to pathogens, without a clearly defined ideotype, and was carried out on a large scale. To accelerate the breeding process and address these challenges, we investigated the use of genomic prediction, a methodology using molecular markers to predict genotypic values. In our study, we focused on 2 existing grapevine breeding programs: Rosé wine and Cognac production. In these programs, several families were created through crosses of emblematic and interspecific resistant varieties to powdery and downy mildews. Thirty traits were evaluated for each program, using 2 genomic prediction methods: Genomic Best Linear Unbiased Predictor and Least Absolute Shrinkage Selection Operator. The results revealed substantial variability in predictive abilities across traits, ranging from 0 to 0.9. These discrepancies could be attributed to factors such as trait heritability and trait characteristics. Moreover, we explored the potential of across-population genomic prediction by leveraging other grapevine populations as training sets. Integrating genomic prediction allowed us to identify superior individuals for each program, using multivariate selection index method. The ideotype for each breeding program was defined collaboratively with representatives from the wine-growing sector.

RNAseq based variant dataset in a black poplar association panel.

OBJECTIVE: Black poplar (Populus nigra L.) is a species native to Eurasia with a wide distribution area. It is an ecologically important species from riparian ecosystems, that is used as a parent of interspecific (P. deltoides x P. nigra) cultivated poplar hybrids. Variant detection from transcriptomics sequences of 241 P. nigra individuals, sampled in natural populations from 11 river catchments (in four European countries) is described here. These data provide new valuable resources for population structure analysis, population genomics and genome-wide association studies. DATA DESCRIPTION: We generated transcriptomics data from a mixture of young differentiating xylem and cambium tissues of 480 Populus nigra trees sampled in a common garden experiment located at Orléans (France), corresponding to 241 genotypes (2 clonal replicates per genotype, at maximum) by using RNAseq technology. We launched on the resulting sequences an in-silico pipeline that allowed us to obtain 878,957 biallelic polymorphisms without missing data. More than 99% of these positions are annotated and 98.8% are located on the 19 chromosomes of the P. trichocarpa reference genome. The raw RNAseq sequences are available at the NCBI Sequence Read Archive SPR188754 and the variant dataset at the Recherche Data Gouv repository under https://doi.org/10.15454/8DQXK5 .

Genetic structure and first genome-wide insights into the adaptation of a wild relative of grapevine, Vitis berlandieri.

In grafted plants, such as grapevine, increasing the diversity of rootstocks available to growers is an ideal strategy for helping plants to adapt to climate change. The rootstocks used for grapevine are hybrids of various American Vitis, including V. berlandieri. The rootstocks currently use in vineyards are derived from breeding programs involving very small numbers of parental individuals. We investigated the structure of a natural population of V. berlandieri and the association of genetic diversity with environmental variables. In this study, we collected seeds from 78 wild V. berlandieri plants in Texas after open fertilization. We genotyped 286 individuals to describe the structure of the population, and environmental information collected at the sampling site made it possible to perform genome-environment association analysis (GEA). De novo long-read whole-genome sequencing was performed on V. berlandieri and a STRUCTURE analysis was performed. We identified and filtered 104,378 SNPs. We found that there were two subpopulations associated with differences in elevation, temperature, and rainfall between sampling sites. GEA identified three QTL for elevation and 15 QTL for PCA coordinates based on environmental parameter variability. This original study is the first GEA study to be performed on a population of grapevines sampled in natural conditions. Our results shed new light on rootstock genetics and could open up possibilities for introducing greater diversity into genetic improvement programs for grapevine rootstocks.

Use of near-infrared spectroscopy to estimate physical, anatomical and hydraulic properties of Eucalyptus wood.

Tree breeding programs and wood industries require simple, time- and cost-effective techniques to process large volumes of samples. In recent decades, near-infrared spectroscopy (NIRS) has been acknowledged as one of the most powerful techniques for wood analysis, making it the most used tool for high-throughput phenotyping. Previous studies have shown that a significant number of anatomical, physical, chemical and mechanical wood properties can be estimated through NIRS, both for angiosperm and gymnosperm species. However, the ability of this technique to predict functional traits related to drought resistance has been poorly explored, especially in angiosperm species. This is particularly relevant since determining xylem hydraulic properties by conventional techniques is complex and time-consuming, clearly limiting its use in studies and applications that demand large amounts of samples. In this study, we measured several wood anatomical and hydraulic traits and collected NIR spectra in branches of two Eucalyptus L'Hér species. We developed NIRS calibration models and discussed their ability to accurately predict the studied traits. The models generated allowed us to adequately calibrate the reference traits, with high R2 (≥0.75) for traits such as P12, P88, the slope of the vulnerability curves to xylem embolism or the fiber wall fraction, and with lower R2 (0.39-0.52) for P50, maximum hydraulic conductivity or frequency of ray parenchyma. We found that certain wavenumbers improve models' calibration, with those in the range of 4000-5500 cm-1 predicting the highest number of both anatomical and functional traits. We concluded that the use of NIRS allows calibrating models with potential predictive value not only for wood structural and chemical variables but also for anatomical and functional traits related to drought resistance in wood types with complex structure as eucalypts. These results are promising in light of the required knowledge about species and genotypes adaptability to global climatic change.

Interest of phenomic prediction as an alternative to genomic prediction in grapevine.

BACKGROUND: Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum provides information on the biochemical composition within a tissue, itself being under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been mainly applied in several annual crop species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour. A major novelty of this study was to collect spectra and phenotypes several years apart from each other. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. RESULTS: For the first time, we showed that the similarity between spectra and genomic relationship matrices was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Applying a mixed model on spectra data increased phenomic predictive ability, while using spectra collected on wood or leaves from one year or another had less impact. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, a significant positive correlation was found across traits between predictive ability of genomic and phenomic predictions. CONCLUSION: NIRS is a new low-cost alternative to genotyping for predicting complex traits in perennial species such as grapevine. Having spectra and phenotypes from different years allowed us to exclude genotype-by-environment interactions and confirms that phenomic prediction can rely only on genetics.

eQTLs are key players in the integration of genomic and transcriptomic data for phenotype prediction.

BACKGROUND: Multi-omics represent a promising link between phenotypes and genome variation. Few studies yet address their integration to understand genetic architecture and improve predictability. RESULTS: Our study used 241 poplar genotypes, phenotyped in two common gardens, with xylem and cambium RNA sequenced at one site, yielding large phenotypic, genomic (SNP), and transcriptomic datasets. Prediction models for each trait were built separately for SNPs and transcripts, and compared to a third model integrated by concatenation of both omics. The advantage of integration varied across traits and, to understand such differences, an eQTL analysis was performed to characterize the interplay between the genome and transcriptome and classify the predicting features into cis or trans relationships. A strong, significant negative correlation was found between the change in predictability and the change in predictor ranking for trans eQTLs for traits evaluated in the site of transcriptomic sampling. CONCLUSIONS: Consequently, beneficial integration happens when the redundancy of predictors is decreased, likely leaving the stage to other less prominent but complementary predictors. An additional gene ontology (GO) enrichment analysis appeared to corroborate such statistical output. To our knowledge, this is a novel finding delineating a promising method to explore data integration.

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.

Across-population genomic prediction in grapevine opens up promising prospects for breeding.

Crop breeding involves two selection steps: choosing progenitors and selecting individuals within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.

Tree architecture, light interception and water-use related traits are controlled by different genomic regions in an apple tree core collection.

Tree architecture shows large genotypic variability, but how this affects water-deficit responses is poorly understood. To assess the possibility of reaching ideotypes with adequate combinations of architectural and functional traits in the face of climate change, we combined high-throughput field phenotyping and genome-wide association studies (GWAS) on an apple tree (Malus domestica) core-collection. We used terrestrial light detection and ranging (T-LiDAR) scanning and airborne multispectral and thermal imagery to monitor tree architecture, canopy shape, light interception, vegetation indices and transpiration on 241 apple cultivars submitted to progressive field soil drying. GWAS was performed with single nucleotide polymorphism (SNP)-by-SNP and multi-SNP methods. Large phenotypic and genetic variability was observed for all traits examined within the collection, especially canopy surface temperature in both well-watered and water deficit conditions, suggesting control of water loss was largely genotype-dependent. Robust genomic associations revealed independent genetic control for the architectural and functional traits. Screening associated genomic regions revealed candidate genes involved in relevant pathways for each trait. We show that multiple allelic combinations exist for all studied traits within this collection. This opens promising avenues to jointly optimize tree architecture, light interception and water use in breeding strategies. Genotypes carrying favourable alleles depending on environmental scenarios and production objectives could thus be targeted.

Genomic Signatures of a Major Adaptive Event in the Pathogenic Fungus Melampsora larici-populina.

The recent availability of genome-wide sequencing techniques has allowed systematic screening for molecular signatures of adaptation, including in nonmodel organisms. Host-pathogen interactions constitute good models due to the strong selective pressures that they entail. We focused on an adaptive event which affected the poplar rust fungus Melampsora larici-populina when it overcame a resistance gene borne by its host, cultivated poplar. Based on 76 virulent and avirulent isolates framing narrowly the estimated date of the adaptive event, we examined the molecular signatures of selection. Using an array of genome scan methods based on different features of nucleotide diversity, we detected a single locus exhibiting a consistent pattern suggestive of a selective sweep in virulent individuals (excess of differentiation between virulent and avirulent samples, linkage disequilibrium, genotype-phenotype statistical association, and long-range haplotypes). Our study pinpoints a single gene and further a single amino acid replacement which may have allowed the adaptive event. Although our samples are nearly contemporary to the selective sweep, it does not seem to have affected genome diversity further than the immediate vicinity of the causal locus, which can be explained by a soft selective sweep (where selection acts on standing variation) and by the impact of recombination in mitigating the impact of selection. Therefore, it seems that properties of the life cycle of M. larici-populina, which entails both high genetic diversity and outbreeding, has facilitated its adaptation.

RNAi suppression of DNA methylation affects the drought stress response and genome integrity in transgenic poplar.

Trees are long-lived organisms that continuously adapt to their environments, a process in which epigenetic mechanisms are likely to play a key role. Via downregulation of the chromatin remodeler DECREASED IN DNA METHYLATION 1 (DDM1) in poplar (Populus tremula × Populus alba) RNAi lines, we examined how DNA methylation coordinates genomic and physiological responses to moderate water deficit. We compared the growth and drought response of two RNAi-ddm1 lines to wild-type (WT) trees under well-watered and water deficit/rewatering conditions, and analyzed their methylomes, transcriptomes, mobilomes and phytohormone contents in the shoot apical meristem. The RNAi-ddm1 lines were more tolerant to drought-induced cavitation but did not differ in height or stem diameter growth. About 5000 differentially methylated regions were consistently detected in both RNAi-ddm1 lines, colocalizing with 910 genes and 89 active transposable elements. Under water deficit conditions, 136 differentially expressed genes were found, including many involved in phytohormone pathways; changes in phytohormone concentrations were also detected. Finally, the combination of hypomethylation and drought led to the mobility of two transposable elements. Our findings suggest major roles for DNA methylation in regulation of genes involved in hormone-related stress responses, and the maintenance of genome integrity through repression of transposable elements.

Favorable Conditions for Genomic Evaluation to Outperform Classical Pedigree Evaluation Highlighted by a Proof-of-Concept Study in Poplar.

Forest trees like poplar are particular in many ways compared to other domesticated species. They have long juvenile phases, ongoing crop-wild gene flow, extensive outcrossing, and slow growth. All these particularities tend to make the conduction of breeding programs and evaluation stages costly both in time and resources. Perennials like trees are therefore good candidates for the implementation of genomic selection (GS) which is a good way to accelerate the breeding process, by unchaining selection from phenotypic evaluation without affecting precision. In this study, we tried to compare GS to pedigree-based traditional evaluation, and evaluated under which conditions genomic evaluation outperforms classical pedigree evaluation. Several conditions were evaluated as the constitution of the training population by cross-validation, the implementation of multi-trait, single trait, additive and non-additive models with different estimation methods (G-BLUP or weighted G-BLUP). Finally, the impact of the marker densification was tested through four marker density sets. The population under study corresponds to a pedigree of 24 parents and 1,011 offspring, structured into 35 full-sib families. Four evaluation batches were planted in the same location and seven traits were evaluated on 1 and 2 years old trees. The quality of prediction was reported by the accuracy, the Spearman rank correlation and prediction bias and tested with a cross-validation and an independent individual test set. Our results show that genomic evaluation performance could be comparable to the already well-optimized pedigree-based evaluation under certain conditions. Genomic evaluation appeared to be advantageous when using an independent test set and a set of less precise phenotypes. Genome-based methods showed advantages over pedigree counterparts when ranking candidates at the within-family levels, for most of the families. Our study also showed that looking at ranking criteria as Spearman rank correlation can reveal benefits to genomic selection hidden by biased predictions.

Gene expression predictions and networks in natural populations supports the omnigenic theory.

BACKGROUND: Recent literature on the differential role of genes within networks distinguishes core from peripheral genes. If previous works have shown contrasting features between them, whether such categorization matters for phenotype prediction remains to be studied. RESULTS: We measured 17 phenotypic traits for 241 cloned genotypes from a Populus nigra collection, covering growth, phenology, chemical and physical properties. We also sequenced RNA for each genotype and built co-expression networks to define core and peripheral genes. We found that cores were more differentiated between populations than peripherals while being less variable, suggesting that they have been constrained through potentially divergent selection. We also showed that while cores were overrepresented in a subset of genes statistically selected for their capacity to predict the phenotypes (by Boruta algorithm), they did not systematically predict better than peripherals or even random genes. CONCLUSION: Our work is the first attempt to assess the importance of co-expression network connectivity in phenotype prediction. While highly connected core genes appear to be important, they do not bear enough information to systematically predict better quantitative traits than other gene sets.

Accuracy of RNAseq based SNP discovery and genotyping in Populusnigra.

BACKGROUD: Populus nigra is a major tree species of ecological and economic importance for which several initiatives have been set up to create genomic resources. In order to access the large number of Single Nucleotide Polymorphisms (SNPs) typically needed to carry out a genome scan, the present study aimed at evaluating RNA sequencing as a tool to discover and type SNPs in genes within natural populations of P. nigra. RESULTS: We have devised a bioinformatics pipeline to call and type SNPs from RNAseq reads and applied it to P. nigra transcriptomic data. The accuracy of the resulting RNAseq-based SNP calling and typing has been evaluated by (i) comparing their position and alleles to those previously reported in candidate genes, (ii) assessing their genotyping accuracy with respect to a previously available SNP chip and (iii) evaluating their inter-annual repeatability. We found that a combination of several callers yields a good compromise between the number of variants type and the accuracy of genotyping. We further used the resulting genotypic data to carry out basic genetic analyses whose results confirm the quality of the RNAseq-based SNP dataset. CONCLUSIONS: We demonstrated the potential and accuracy of RNAseq as an efficient way to genotype SNPs in P. nigra. These results open prospects towards the use of this technology for quantitative and population genomics studies.

Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar.

Genomic selection - the prediction of breeding values using DNA polymorphisms - is a disruptive method that has widely been adopted by animal and plant breeders to increase productivity. It was recently shown that other sources of molecular variations such as those resulting from transcripts or metabolites could be used to accurately predict complex traits. These endophenotypes have the advantage of capturing the expressed genotypes and consequently the complex regulatory networks that occur in the different layers between the genome and the phenotype. However, obtaining such omics data at very large scales, such as those typically experienced in breeding, remains challenging. As an alternative, we proposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits, and coined this new approach "phenomic selection" (PS). We tested PS on two species of economic interest (Triticum aestivum L. and Populus nigra L.) using NIRS on various tissues (grains, leaves, wood). We showed that one could reach predictions as accurate as with molecular markers, for developmental, tolerance and productivity traits, even in environments radically different from the one in which NIRS were collected. Our work constitutes a proof of concept and provides new perspectives for the breeding community, as PS is theoretically applicable to any organism at low cost and does not require any molecular information.

Winter-dormant shoot apical meristem in poplar trees shows environmental epigenetic memory.

Trees have a long lifespan and must continually adapt to environmental pressures, notably in the context of climate change. Epigenetic mechanisms are doubtless involved in phenotypic plasticity and in stress memory; however, little evidence of the role of epigenetic processes is available for trees growing in fields. Here, we analyzed the possible involvement of epigenetic mechanisms in the winter-dormant shoot apical meristem of Populus × euramericana clones in memory of the growing conditions faced during the vegetative period. We aimed to estimate the range of genetic and environmentally induced variations in global DNA methylation and to evaluate their correlation with changes in biomass production, identify differentially methylated regions (DMRs), and characterize common DMRs between experiments. We showed that the variations in global DNA methylation between conditions were genotype dependent and correlated with biomass production capacity. Microarray chip analysis allowed detection of DMRs 6 months after the stressful summer period. The 161 DMRs identified as common to three independent experiments most notably targeted abiotic stress and developmental response genes. Results are consistent with a winter-dormant shoot apical meristem epigenetic memory of stressful environmental conditions that occurred during the preceding summer period. This memory may facilitate tree acclimation.

Changes in the epigenome and transcriptome of the poplar shoot apical meristem in response to water availability affect preferentially hormone pathways.

The adaptive capacity of long-lived organisms such as trees to the predicted climate changes, including severe and successive drought episodes, will depend on the presence of genetic diversity and phenotypic plasticity. Here, the involvement of epigenetic mechanisms in phenotypic plasticity toward soil water availability was examined in Populus×euramericana. This work aimed at characterizing (i) the transcriptome plasticity, (ii) the genome-wide plasticity of DNA methylation, and (iii) the function of genes affected by a drought-rewatering cycle in the shoot apical meristem. Using microarray chips, differentially expressed genes (DEGs) and differentially methylated regions (DMRs) were identified for each water regime. The rewatering condition was associated with the highest variations of both gene expression and DNA methylation. Changes in methylation were observed particularly in the body of expressed genes and to a lesser extent in transposable elements. Together, DEGs and DMRs were significantly enriched in genes related to phytohormone metabolism or signaling pathways. Altogether, shoot apical meristem responses to changes in water availability involved coordinated variations in DNA methylation, as well as in gene expression, with a specific targeting of genes involved in hormone pathways, a factor that may enable phenotypic plasticity.

Identification of major loci and genomic regions controlling acid and volatile content in tomato fruit: implications for flavor improvement.

Plant metabolites are important to world food security due to their roles in crop yield and nutritional quality. Here we report the metabolic profile of 300 tomato accessions (Solanum lycopersicum and related wild species) by quantifying 60 primary and secondary metabolites, including volatile organic compounds, over a period of 2 yr. Metabolite content and genetic inheritance of metabolites varied broadly, both within and between different genetic groups. Using genotype information gained from 10 000 single nucleotide polymorphism markers, we performed a metabolite genome-wide association mapping (GWAS) study. We identified 79 associations influencing 13 primary and 19 secondary metabolites with large effects at high resolution. Four genome regions were detected, highlighting clusters of associations controlling the variation of several metabolites. Local linkage disequilibrium analysis and allele mining identified possible candidate genes which may modulate the content of metabolites that are of significant importance for human diet and fruit consumption. We precisely characterized two associations involved in fruit acidity and phenylpropanoid volatile production. Taken together, this study reveals complex and distinct metabolite regulation in tomato subspecies and demonstrates that GWAS is a powerful tool for gene-metabolite annotation and identification, pathways elucidation, and further crop improvement.