Brassica juncea L. Czern and Coss) oil; nonetheless, the lack of information on genetic control is hampering its improvement. In this study, six populations  of RLC3 × NPJ203 were evaluated in a family block design to evaluate the inheritance pattern, gene effects, and various other genetic parameters of tocopherol content , using generation mean analysis. The comparison of direct and reciprocal crosses indicated that the tocopherol content was not influenced by maternal inheritance. Negative directional heterosis showed that ATC, GTC, and TTC are governed by recessive genes. Potence ratio and degree of dominance highlighted an over-dominance type of gene interaction for GTC and TTC, whereas ATC was governed by epistatic interactions. Furthermore, the six-parameter model revealed a duplicate gene action for α-tocopherol content. Broad and narrow sense heritability coupled with genetic advances were high.Tocopherol is vital for the nutritional value and stability of Indian mustard ( Brassica oil are α-tocopherol (ATC) and γ-tocopherol (GTC), as well as a small proportion of δ-tocopherol (DTC) , additive variance [D], dominance variance [H], environmental variance [E], and correlation between additive and dominance components [F], were estimated for tocopherol content traits; The genetic parameters, such as broad-sense heritability  for tocopherol traits were selected as parents and were raised at the experimental farm  of the Division of Genetics, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, India, during 017–2018 .1) and NPJ203 (P2) genotypes at the experimental farm of the Division of Genetics, ICAR-IARI during rabi 2018–2019, and F1s seeds were harvested. The F2, BC1P1 (B1: F1 backcrossed with RLC3) and BC1P2  populations were developed at ICAR-IARI regional station, Wellington, Tamil Nadu  during kharif 2019. The F2:F3, BC1P1:F2, and BC1P2:F2 were developed by selfing of F2, BC1P1, and BC1P2 populations, respectively, at the experimental farm of the Division of Genetics, ICAR-IARI during rabi 2019–2020.The direct and reciprocal crosses were attempted using RLC3  were raised at the experimental farm of ICAR-IARI, New Delhi, during rabi 2020–2021 for the estimation of ATC, GTC, and TTC. The parents, F1 and backcross generations (B1 and B2) were sown in two rows and F2 populations in 9 rows, each 5 m in length. The row-to-row and plant-to-plant spacing were kept at 45 cm and 15 cm, respectively. These six generations were replicated twice, employing the family block design. All the recommended agricultural practices, from sowing to harvesting, were followed to raise a healthy crop. Three random plants were selected from each P1, P2, and F1 population, 15 plants each from B1 and B2, and 120 plants from the F2 population of each replication were harvested individually and were used for the estimation of tocopherol traits.The developed six generations  was used to extract tocopherols employing a mobile phase consisting of an n-heptane/isopropanol-mixture . The fluorescence detection (excitation at λ = 290 nm and emission at λ = 328 nm) method was used for tocopherol quantification. The retention times were compared with Merck’s tocopherol kit  to identify specific tocopherol forms , and the concentration of single forms was correlated with signal output to calibrate each tocopherol form  ,51,52,53rol form . AdditioP1, P2, F1, F2, B1, and B2, respectively. If any of the scales were significant (non-zero), it indicates the presence of non-allelic interaction (epistasis) and vice versa , additive effect [d], and dominance effect [h] could be calculated with the following equations ,54:(5)m=However, interactions were calculated separately in the presence of epistatic effects, and the following equations were used for the analysis of each gene effect employing the mean value of each generation :(8)m=F2¯F2 variances:2b) and narrow-sense (H2n) heritability were calculated by employing the following formulae .Genetic advance P=F1Arabidopsis thaliana and Synechocystis sp. PCC6803, information on tocopherol inheritance in Indian mustard is inadequate. The present investigation highlighted that tocopherol content and composition are controlled by recessive nuclear genes and are not affected by maternal effects in seeds of Indian mustard. High heritability coupled with genetic advances for the studied traits indicated that direct selection could improve these traits. The potence ratio and degree of dominance highlighted that GTC and TTC showed an over-dominance gene interaction, whereas ATC showed an epistatic interaction. This is the first study on Indian mustard that elucidated the nature of inheritance and gene effects of tocopherol traits employing generation mean analysis. This study has potential applications for enhancing tocopherol content in the seed oil of Indian mustard for enhancing oil quality by incorporating genes, especially for ATC, into exiting elite lines.Although the tocopherol biosynthesis pathway has been elucidated in model organisms such as


Wolbachia is a genus of intracellular bacterial endosymbionts found in 20–66% of all insect species and a range of other invertebrates. It is classified as a single species, Wolbachia pipientis, divided into supergroups A to U, with supergroups A and B infecting arthropods exclusively. Wolbachia is transmitted mainly via vertical transmission through female oocytes, but can also be transmitted across different taxa by host shift (HS): the direct transmission of Wolbachia cells between organisms without involving vertically transmitted gametic cells. To assess the HS contribution, we recovered 50 orthologous genes from over 1000 Wolbachia genomes, reconstructed their phylogeny and calculated gene similarity. Of 15 supergroup A Wolbachia lineages, 10 have similarities ranging from 95 to 99.9%, while their hosts’ similarities are around 60 to 80%. For supergroup B, four out of eight lineages, which infect diverse and distantly-related organisms such as Acari, Hemiptera and Diptera, showed similarities from 93 to 97%. These results show that Wolbachia genomes have a much higher similarity when compared to their hosts’ genes, which is a major indicator of HS. Our comparative genomic analysis suggests that, at least for supergroups A and B, HS is more frequent than expected, occurring even between distantly-related species. Wolbachia is a genus of gram-negative intracellular endosymbiotic bacteria. First isolated from Culex pipiens, it is currently estimated to be found in 20–66% of all insect species1. Moreover, it also infects species of filarial nematodes, arachnids, and terrestrial crustaceans2. Wolbachia belongs to the Rickettisiales order, the same order of vertebrate pathogens transmitted by arthropod vectors, although there is no evidence of Wolbachia causing disease in vertebrates4. There are a myriad of Wolbachia lineages that differ substantially at the genomic level, but they are all classified under the umbrella of a single species Wolbachia pipientis. Its strains are divided into supergroups, ranging from A to U, which are defined by phylogenetic analysis using the 16S rDNA, ftsZ and wsp markers5. It is estimated that these supergroups diverged around 100 million years ago, first in filarial nematodes and then infecting arthropods. The supergroups A and B have only been found in arthropods so far; the C and D supergroups are specific to filarial nematodes; and the E and F supergroups are mostly found in nematodes, but are also seen in some terrestrial arthropods. The remaining supergroups are distributed among other arthropod clades6.Wolbachia and their hosts have driven the emergence of diverse ecological relationships from mutualism to parasitism, depending on the lineage/supergroup-host pair. Parasitic Wolbachia lineages modulate different aspects of host physiology, such as the reproductive cycle, host behaviour and pathogen susceptibility7. Nematode-infecting Wolbachia usually have a mutualistic association with their hosts, whereas arthropod-infecting Wolbachia are more associated with commensalism or parasitism, modulating their host reproductive system through male-killing, feminization, parthenogenesis or cytoplasmic incompatibility8. The variety of Wolbachia induced phenotypes on their hosts has attracted the attention of the scientific community due to its potential role in host speciation, exploitation as a biological tool of vector-borne diseases control , and to combat filarial neglected tropical diseases9.Long-term evolution of Wolbachia is transmitted mainly via vertical transmission, i.e., it is passed between host generations in the female oocytes10. Wolbachia is also transmitted to other individuals and species through an alternative mechanism called host shift (HS), also referred as horizontal transfer, which is the direct transmission of Wolbachia cells between organisms where there is no feasible mechanism of vertical transfer.11. Wolbachia strains that can manipulate the host reproductive biology achieve a high rate of infection in the new host, substantially enhancing Wolbachia spreading in the next host generation6.HS can alter host fitness by adding phenotypes to the new host that allow it to interact most successfully with the environmentWolbachia is expected to share a long evolutionary journey with their hosts. Nevertheless, there is strong evidence of Wolbachia ancient and recent horizontal transfer events between phylogenetically closely and distantly related host species16. Transfection experiments of Wolbachia were able to show its great capability to infect cells from distantly-related hosts, reinforcing the HS potential of Wolbachia18. Other characteristics that may influence HS include the ability of Wolbachia to survive for months in an extracellular environment, despite being an intracellular symbiont6, as well as genome recombination, which may influence the ability of the bacterium to adapt to new environments due to genome diversification7.As an obligatory endosymbiont that is mainly vertically transmitted, Wolbachia HS in several arthropod hosts, it is still considered a rare phenomenon20. In this study, we leveraged a large dataset of over 1000 draft and complete Wolbachia genomes reconstructed by Scholz et al. performing the most extensive assessment of Wolbachia HS so far. Our in-depth investigation of Wolbachia-host gene divergence revealed several long-range Wolbachia HS events from supergroups A and B among arthropods, suggesting HS is more frequent than normally reported for these abundant and widespread supergroups.Despite the strong evidence on Wolbachia genomes were downloaded in November 2020, from https://www.ebi.ac.uk/ena/browser/view/PRJEB3516721; only Wolbachia genomes belonging to supergroups A and B were kept for analysis. Scholz retrieved existing Wolbachia reference genomes from refseq22 and genbank23, and public shotgun sequencing samples were retrieved from the NCBI sequence read archive (sra) database from all available projects involving taxa that can host Wolbachia. Host genomes were downloaded from https://www.ncbi.nlm.nih.gov/genome using the host species as a query term. The complete list of hosts and Wolbachia assemblies can be seen in Supplementary Table Assembled Wolbachia and their hosts were obtained using the BUSCO v5.1.2 docker image24 using the ‘augustus’ flag. The databases used were ricketisialles_odb10 and arthropoda_odb10 for Wolbachia and hosts, respectively. Fifty single-copy genes  using the tool catfasta2phyml (available at https://github.com/nylander/catfasta2phyml) generating one fasta file with all sequences for hosts and Wolbachia, respectively.Each one of the recovered orthologous genes were codon aligned separately using MACSE v2.0526, version 1.0.9, was used to calculate the similarity between the concatenated aligned Wolbachia sequences, as well as for the host’s aligned sequences, using the following options: ‘--make_similarity_matrix_input’, ‘--make_simmatrix_keepgaps 2’. All descriptive statistics were calculated using the ‘describe’ method from the Python package Pandas. The ‘described’ method was also used to obtain the overall descriptive statistics for the mean, minimum and maximum values of the first generated statistics. The code used is available at https://github.com/Tiago-Minuzzi/wolbachia-hs.The command-line tool CIAlign27 was used to obtain the Wolbachia phylogeny, with the ultrafast bootstrap parameter set to 1000 and model GTR + F + R3 chosen according to BIC; the ITOL web server28 was used to generate the tree visualisation.The software IQ-Tree stable release 1.6.12Wolbachia assemblies were separated into supergroups based on the phylogeny by Scholz et al. A careful assessment of the alignments revealed many identical sequences between different Wolbachia assemblies, thus, the fasta IDs of the identical sequences were grouped, and only a single sequence was kept as a representative. After selection of representative sequences, a reduction of 1044 to 304 sequences occurred for supergroup A and from 20 to 17 for supergroup B. Most of these highly similar genomes were characterised from different populations of some model organisms, such as species from the Drosophila genus.Wolbachia phylogeny using 50 single-copy orthologues for both supergroups A and B to evaluate if the resulting tree agrees with the original dataset from Scholz et al. and showed that it matched as expected. After reconstructing the Wolbachia phylogeny, we grouped sequences in 23 lineages/clades that showed divergence lower than 0.02%  and dipteran Drosophila simulans Wolbachia (WOLB0926) orthologues was 98.51% (minimum 80.48% and maximum 99.9%); the similarity between host orthologues was 48.36% (minimum similarity 16.37% and maximum similarity 68.49%). For Wolbachia of Hymenopteran Diachasma alloeum and dipteran Drosophila melanogaster, WOLB1002 and WOLB0092, respectively, the mean similarity was 99.87% (minimum 99.33% and maximum similarity 99.9%); host mean similarity values were 47.24% .Pairwise gene sequence similarity analysis Table  of Wolbaces Fig. , corroboWolbachia found infecting the arachnid Tetranychus urticae (WOLB0958) and the strain infecting the insect Aedes albopictus Wolbachia (WOLB1128) showed a mean orthologue similarity of 94.37% (minimum 74.35% and maximum 99.67%), while the similarity between host orthologues showed a 40.80% mean similarity (minimum 14.73% and maximum 60.80%). The mean similarity for Wolbachia orthologues of the hemipteran Homalodisca vitripennis and dipteran Drosophila mauritiana, assemblies WOLB0957 and WOLB0080, respectively, was 94.17% (minimum 73.93% and maximum 99.53%); the mean similarity for host orthologues was 47.81% (minimum 25.91% and maximum 71.33%). To more clearly visualise the differences between the hosts and bacteria orthologous gene divergences, they are presented as strip plots for four pair-wise species comparisons  Wolbachia, the mean similarity, in many cases, is greater than 96%, reaching maximum values greater than 97%, with an overall mean similarity of 89.08%, mode of maximum values of 97.2% in a comparison of 150 D. melanogaster and 22 D. virgifera Wolbachia  Wolbachia . The D. citri and Drosophila mauritiana Wolbachia similarity was 93.04% (minimum 88.19% and maximum 94.7%); D. citri Wolbachia similarity with Wolbachia from Homalodisca vitripennis (Hemiptera) was 93.49% (minimum 88.34% and maximum 95.25%); and D. citri Wolbachia has a mean similarity of 90.92% with Wolbachia from A. albopictus (minimum 86.45% and maximum 93.08%).In Supergroup B, orthologue similarity analysis  D. citri and A. albopictus (Diptera), in which their respective Wolbachia from supergroup B (dating from around 112 mya) also shows high similarity , and the Diptera order around 200 mya18, which may be related to claims of HS not being a common event. In our study, using publicly available data, we calculated the within groups similarity of Wolbachia from supergroups A and B, tracing a parallel with their hosts’ similarity. The data showed that many Wolbachia from distantly related hosts share high similarity, while their hosts’ core gene similarity is significantly lower, alongside a divergence between host and Wolbachia phylogenetic trees. We found that 58.8% of host species analysed share two particular Wolbachia lineages, indicating that these lineages have been acquired by HS recently and suggesting that HS events may be more frequent than previously thought. This is evidence for the HS hypothesis being a common outcome of different ecological interactions, explaining at least partially how Wolbachia became such a ubiquitous organism across multiple clades. In addition, epidemiological modelling of Wolbachia transmission demonstrated that it would not be possible to explain Wolbachia incidence in a broad range of clades only considering it as vertically transmitted41, thus it is necessary to take host shift into account to explain the spread of Wolbachia in phylogenetically distant hosts.In the process of genome assembly of eukaryotic organisms, a common step is the removal of bacterial sequences. This process, although important for these studies, reduces the possibility of a proper assessment of symbionts HSWolbachia HS is a known event described by a wide range of literature33, yet it is still somewhat overlooked and sometimes disbelieved as a more common mechanism30, as it is still not very clear how it is established in some cases13. Nonetheless, Wolbachia has an arsenal of well described methods to thrive when first encountering a new host, which may explain its success jumping across clades by HS6. This arsenal consists of the facts that Wolbachia has no problem adapting to new environments7, can, without much effort, move across cells and tissues, as it is a proficient manipulator of its hosts physiology42. Even though Wolbachia may cause reduced host fitness, the opposite is also true, as Wolbachia may alter pathogen susceptibility conferring viral protection for its hosts43. Also, Wolbachia can survive for a limited time in an extracellular environment, albeit being an obligatory intracellular endosymbiont35.21, alongside a phylogenetic reconstruction, we were able to bring focus to Wolbachia HS, estimate the event and compare it in Wolbachia supergroups A and B of close and distant related hosts and their Wolbachia, shedding more light on the importance of HS as a major player in Wolbachia pervasiveness on very distinctive branches of the Arthropoda tree.By using gene similarity of over 1000 reconstructed genomesSupplementary Legends.Supplementary Figure 1.Supplementary Figure 2a.Supplementary Figure 2b.Supplementary Figure 3a.Supplementary Figure 3b.Supplementary Table 1.Supplementary Table 2.Supplementary Table 3.Supplementary Table 4.Supplementary Table 5.Supplementary Table 6.Supplementary Table 7.Supplementary Table 8.Supplementary Table 9.


Zea mays L.) is at the vanguard facing the upcoming breeding challenges. However, both a super pan-genome for the Zea genus and a comprehensive genetic variation map for maize breeding are still lacking.Maize (Zea genome that contains around 4.57-Gb non-B73 reference sequences from fragmented de novo assemblies of 721 pan-Zea individuals. We annotate a total of 58,944 pan-Zea genes and find around 44.34% of them are dispensable in the pan-Zea population. Moreover, 255,821 common structural variations are identified and genotyped in a maize association mapping panel. Further analyses reveal gene presence/absence variants and their potential roles during domestication of maize. Combining genetic analyses with multi-omics data, we demonstrate how structural variants are associated with complex agronomic traits.Here, we construct an approximately 6.71-Gb pan-Zea genome and structural variations to further understand domestication of maize and explore their potential utilization in crop improvement.Our results highlight the underexplored role of the pan-The online version contains supplementary material available at 10.1186/s13059-022-02742-7. The increasing population and unpredictability evoked by global climate change have brought new demands to increase the productivity and quality of our crops . Food prStreptococcus agalactiae [While they harbor great potential, the development of breeding technologies and the explosive growth of biological information have also highlighted the insufficiencies in conventional genomics-assisted breeding strategies. The first of these insufficiencies is the use of a single reference genome. More and more evidences have shown that mapping reads onto a single reference genome can result in reference bias and missing information in highly polymorphic regions and regions that are not present in the genome –8. Thus,alactiae  to eukaralactiae –12, inclalactiae –16. Secoalactiae , 18. Othalactiae , 20, andalactiae . It is halactiae , 23. Givalactiae , that inZea (pan-Zea genome), including maize and wild taxa, and its graphical representation is still lacking. Here, we (i) constructed a pan-Zea genome from 11 public genome assemblies and de novo draft assemblies of 721 accessions, including 507 modern maize, 31 landraces, and 183 teosintes; (ii) revealed the patterns of genes and presence/absence variations in the genus Zea; and (iii) identified SVs among the maize population and systematically analyzed the potential role of the pan-Zea genome and SVs in maize phenotype variations. These resources and analyses will allow us to more comprehensively understand the genetic bases of complex agronomic traits in maize and provide valuable information for future improvements in maize.Maize is a staple crop and a model organism for genetic research . Since tZea genome was constructed from the alignments against the maize B73 reference genome V4 (AGPv4) of the de novo draft assemblies from 721 individuals, including 507 diverse maize inbred lines [Zea genus from previous studies were also included  and ~4.57 Gb of the non-B73 reference sequences . More than half of the NRSs (58.86%) were anchored to the B73 AGPv4 reference genome  of the NAM-NRSs, as well as many (69.52%) additional NRSs that were not identified in the NAM-NRSs , while 99 individuals covered 99% of the pan-Zea genes  patterns for each maize inbred and teosinte individual  had more subspecies-enriched genes and orthologous groups than close relatives of maize (Zea mays ssp. mexicana and Zea mays ssp. parviglumis).The 691 pan-ons Fig. C. The diEnrichment analysis suggested that the gPAV and oPAV clusters may reflect distinct molecular functions among the sub-populations Fig. : Fig. S9To further address the contents of teosinte-specific genes and maize-specific genes, we investigated the sub-population gPAV distribution differences in teosinte versus maize (TEO-Maize) and tropical maize versus temperate maize (TST-TEM) in more detail. As results, 51.09% 3452/6756) of the maize-enriched genes were absent in all teosinte individuals (hereafter referred to as maize-specific genes), while 17.83%  of the teosinte-enriched genes were lost in maize (teosinte-specific genes)  loss of all members of the other group or (ii) gained from exogenous sources that were not available to the other group. While the gPAVs showed a predominance of teosinte-enriched genes, the oPAV clusters showed the opposite trend in that more orthologous groups were enriched in maize than in teosinte  among the different genetic variation types. To address this, we estimated the phenotypic variance explained by the genetic variations from a collection of complex agronomic phenotypes and multi-omics molecular trait data, including gene expression, metabolites, protein contents, and DNA methylation  explained an average of 14.19% and 1.48% more phenotypic variance than the SNPs  and InDels , respectively  and local association analyses for the molecular traits (gene expression and DNA methylation)  gene (Zm00001d048936) at the maize gametophyte factor1 (Ga1) locus was reported to be causative of unilateral cross-incompatibility [Ga1 locus was also detected to underlay the unilateral-crossing seed set ratio  in the Ga1 locus, we also found four more non-reference PME genes   of the tested gPAVs had associated signals  set using a Bayesian-based statistical fine-mapping algorithm . A totalcis-eQTLs along their distance to the transcription start site (TSS). The results showed that the CCVs were enriched in TSS-nearby regions  are located [To further investigate the effect of different genetic variations on gene expression, we estimated the enrichment of CCVs in the  located .ZEAMAP [MaizeGDB), related to drought and ultraviolet stressors [A detailed example is illustrated in Fig. ZEAMAP ) that hatressors  and was tressors  respondetressors   many of the SVs (~37.36%) were not well represented by nearby SNPs; (ii) SVs explained more heritability than SNPs and InDels in the same volume; (iii) SVs were more likely to be the cause of phenotype variation than SNPs and InDels; and (iv) SVs can represent QTLs that cannot be detected by SNPs only. These findings will enhance maize genome-based breeding in the future.We also analyzed the practical value of the summary association statistics, QTL, associated genes, and causal variants for the multi-omics traits. The summary association statistics have shown their forces by being broadly used for humans in analyses involving gene-based association tests, fine-mapping, polygenic prediction, and cross-trait analyses . While GA substantial proportion of the challenges and rewards in crop genetics are dependent upon understanding the genetic architecture of complex agronomic traits. Considerable progress has been made to untangle the puzzle of maize phenotypic variations , 69–71, Estimates and randomization of narrow-sense heritability have shown high levels of maize omics phenotypic variance explained by genome-wide additive genetic factors. Intergenic variations, which were the majority of GWAS association hits, explained most of the phenotypic variations. However, enrichment of candidate causative variations showed that intergenic variants were less likely to be the cause of the GWAS QTL (which need to have large enough effects to be detected) than genic variants. These findings suggest the ubiquitous polygenic nature of maize agronomic traits, a largely additive genotype-to-phenotype relationship in maize, and that non-coding sequences may more likely contribute by adding multiple variants with small effects.Zea genome, the comprehensive genetic variation map, and population-level multi-omics data to reveal genotype-phenotype relationships. Extra efforts would be needed to determine the underlying cause within the QTL when using a single reference genome, even if the association QTL could be detected through SNP-based analyses, particularly when the causative genes are absent in the reference genome. In our results, at least 32.83% of the pan-Zea genes were absent in the commonly used maize reference genome. We detected the associations between the causative gPAV and the unilateral cross-incompatibility in maize by harnessing the pan-Zea genome. In addition, by leveraging multi-omics data and statistical fine-mapping, we propose the potential mechanism of how a TE-derived SV affected drought resistance in maize by tissue-specific changes in gene expression. These cases have not only shown the practicability of harnessing the pan-Zea genome and SVs to substantially reduce the workload of genome-assistant breeding but have highlighted the potential of the pan-Zea genome and the SV map to better understand the internal mechanism of the associations between genetic and phenotypic variations, thus facilitating maize breeding and improvement.In addition to the general genotype-phenotype association patterns, we have also shown the potential of leveraging the pan--Zea genome, analyzed the gene presence/absence patterns, and investigated the impact of the pan-Zea genome and different genetic variant features on maize biochemical and phenotypic variations. These findings will provide useful information for unraveling the genetic architecture of maize complex agronomic traits, accelerate maize molecular breeding, and improve our understanding of maize domestication and adaptation procedure. Still, some limitations have hampered more informative pan-Zea genomic and genetic results in the current study.In summary, we constructed a pan-Zea genome in the current study is the lack of teosinte reference genomes and population-level deep long-read sequencing data for teosintes, landraces, and elite maize. With the fragmental assemblies generated from deep-WGS reads, we had to perform additional data polishing steps to ensure credibility, which inevitably affected the volume of the results, and led to an underestimate of the teosinte sequences and genes within the pan-Zea genome, the large and rare SVs, and the untangled variations in the highly repetitive regions.Perhaps the most obvious holding-back of the pan-Zea genomic information pool with reference genomes and population-level long-read sequences of teosintes and maize landraces, (ii) enlarge the pan-Zea genetic variation matrices and their associations with more biochemicals, (iii) refine the genetic interactions by investigating causation and pleiotropy, and (iv) decrypt the regulatory network of maize phenotypic variation by combining the genetic interactions with other bio-networks, such as interactomes [Foreseeable actions that could address these limitations and boost our understanding of maize breeding and improvement would be to (i) enrich the panractomes  and cellractomes . The conractomes  and genoractomes , it may ractomes . We haveZea gene and ortholog group analyses, (v) characterizing gene features, (vi) genotyping and characterizing the maize genetic variation map, (vii) variant graph constructing, reads simulation, and mapping, (viii) phenotype data collection and normalization, (ix) estimating of narrow-sense heritability, (x) identifying trait-associated QTLs, genes, and variants, (xi) analyses of gPAV and SV cases, and (xii) miscellaneous statistical analyses and visualizations, are available in Additional file The detailed materials and methods, including (i) collection of genomic and transcriptomic data, (ii) de novo assembly, whole-genome comparison, and pan-genome construction, (iii) pan-genome gene annotation, (iv) pan-Zea NGS de novo assemblies. Figure S2. Schematic diagram illustrated the filtering of assembly-to-assembly alignment. Figure S3. Sketch of the non-reference sequence anchoring and clustering pipeline. Figure S4. Pan-Zea gene annotation and functional annotation pipeline. Figure S5. Statistics of the proportions of functionally annotated genes. Figure S6. Validation of the gene present and absent variations (gPAVs). Figure S7. PCA and SNP LD rank analysis of gPAV. Figure S8. Core and dispensable genes and ortholog groups. Figure S9. KEGG pathways of each dispensable ortholog group cluster. Figure S10. The KEGG pathways enriched in maize concentrated ortholog groups when compared with teosinte concentrated ortholog groups. Figure S11. Distribution of sub-population enriched genes and ortholog groups. Figure S12. The sketch of SV calling and genotyping pipeline. Figure S13. Additional features of the maize genetic variation map. Figure S14. Distribution of the associated QTLs and causal variations along the chromosome. Figure S15. Distribution of the features of associated QTLs, genes and causal variations. Figure S16. Multiple sequence alignment of the PME genes in the Ga1 locus. Figure S17. Multiple sequence alignments of the protein sequences of pan-Zea PME genes. Figure S18. Statistics of candidate causal variants. Figure S19. Enrichment of causal variants features within different omics trait classes. Figure S20. Enrichment of cis-eQTL causal variants along different distance to TSS. Figure S21. Expression patterns of the candidate genes of the example SV-QTLs. Figure S22. Blast graphic summary of PZ00001aSV02097079INS. Figure S23. Distribution of the Zm00001d023299 expression patterns in the tenth leaf of V9 stage in the CUBIC offspring population related to the haplotypes flanking 500Kb of Zm00001d023299. Figure S24. The diagram of vectors used in the luciferase reporter assays of Zm00001d023299 promoter with (A) or without (B) the predicted ABRE.Additional file 1: Figure S1. Statistics of pan-Additional file 2: Table S1. Estimating WGS de novo assemblies by whole genome comparison between WGS de novo assemblies and reference genome sequences of B73, SK, Mo17 and HZS.Zea genome construction.Additional file 3: Table S2. Public genome assemblies used in the pan-Zea gene annotationsAdditional file 4: Table S3. Statistics of pan-Zea gene features.Additional file 5: Tables S4. Pan Additional file 6: Table S5. Enrichment of molecular function GO items for each gene PAV cluster.Additional file 7: Table S6. Enrichment of KEGG ontology for each gene PAV cluster.Additional file 8: Table S7. Statistics of the complex and molecular trait variation data in this study.Additional file 9: Table S8. Information of QTLs that cannot be detected by SNPs.Additional file 10: Table S9. Primers used in the luciferase reporter assay of the activity of the predicted ABRE positioned upstream of Zm00001d023299.Additional file 11: Supplementary Text S1.Additional file 12: Supplementary Materials and Methods –159.Additional file 13. Review history.


Daphnia have been a model system for hundreds of years and is among the best studied ecological model organisms to date. Daphnia are planktonic crustaceans with a cyclic parthenogenetic life-cycle. They have a nearly worldwide distribution, inhabiting standing fresh- and brackish water bodies, from small temporary pools to large lakes. Their predominantly asexual reproduction allows for the study of phenotypes excluding genetic variation, enabling us to separate genetic from non-genetic effects. Daphnia are often used in studies related to ecotoxicology, predator-induced defence, host–parasite interactions, phenotypic plasticity and, increasingly, in evolutionary genomics. The most commonly studied species are Daphnia magna and D. pulex, for which a rapidly increasing number of genetic and genomic tools are available. Here, I review current research topics, where the Daphnia model system plays a critical role.Water fleas of the genus  Daphnia is a genus of small planktonic crustaceans with a very wide geographic distribution. Its English name “water flea,” includes other members of the Cladocera order within the class Branchiopoda, united by the morphology of their trunk limbs  and under favourable conditions they can propagate asexually for many years  and then haploid eggs that need fertilization. Production of sons and haploid eggs is regulated on the population level, with some genotypes never producing males  or haploid eggs . , 6. DaphDaphnia can be collected from their natural habitats as either planktonic animals or in the form of their resting stages, which are often found in the sediments. Planktonic females can be cloned naturally, enabling researchers to produce unlimited numbers of clonal offspring and keep genetic lines for many generations under laboratory conditions. Clonal lines are kept in either natural fresh water, commercial mineral water or artificial medium  certainly play a role [m  and arey a role . The oldy a role . Each hay a role .D. magna currently being the most often-used species for routine crosses. It is possible to cross females with males from the same clone, resulting in genetically selfed offspring [Clonal reproduction allows us to produce genetically identical replicates so as to entirely control for genetic background. Sexual reproduction in the lab is more time consuming, because animals  must first be induced to produce male offspring asexually, after which they produce haploid eggs for the males to fertilize . The resffspring , 13. Thiffspring .Daphnia research goes back several hundred years, with many important findings dating back to this old model system. To name just a few: August Weismann established his germ plasm theory working with water fleas [Daphnia (Nobel prize 1908). The concept of phenotypic plasticity was also developed in relation to predator-induced defence in Daphnia [Daphnia and its symbionts [Daphnia fell somewhat out of favour because of the difficulties in doing genetic crosses—a problem that has only recently been solved. Still, Daphnia has remained the prime model in ecological research and Daphnia is one of the best studied organism with regard to their ecology, with past or current focal areas are diel vertical migration [Daphnia research (primarily D. magna and D. pulex), this model system is rising in prominence in the field of environmental genomics.er fleas , and Élier fleas  studied  Daphnia , and theymbionts . When adigration , host—paigration , communiigration  and climigration , 23. MorDaphnia are part of the Branchiopoda, the same group in which the tadpole shrimp Triops (Notostraca) and the brine shrimp Artemia (Anostraca) are placed  suggest that the higher estimate is closer to the real number.A major surprise from the first genome was the large number of genes, with early counts around 30,000 . HoweverDaphnia populations are strongly subdivided. Gene flow is usually moderate, allowing populations to evolve rapidly in line with local environmental conditions. Animals collected from numerous waterbodies have had their genotypes preserved by clonal culture and then tested in common garden experiments  or reciprocal transplant experiments . When genotypes demonstrate superior performance under the environmental conditions the animals have evolved in, it is considered evidence of local adaptation. Daphnia species have been shown to adapt locally to a long and growing list of environmental factors, including heavy metal pollution, predators, high temperature, habitat stability, photoperiod, water salinity, UV light [With their habitat limited to standing water bodies, UV light , 33–39.Daphnia magna Diversity Panel is a standing collection of hundreds of genotypes, each from a different population used for such studies. Indeed, the strong signal of local adaptation observed across these single genotypes suggests that single genotypes are—at least to some degree—representative of entire populations [Although most local adaptation studies are based on comparing a few populations with multiple genotypes per population, a novel approach has been to collect single clones from as many populations as possible, instead of collecting multiple genotypes from each of a few populations. This method allows us to map phenotypes on geographic maps to investigate the influence of geography and climatic conditions  of a ramosa , 63, theinnensis  and Ordoolligata , 66, theuspidata , the chimesnilli  and the mesnilli . Currentmesnilli , 62, wit cuticle .Daphnia genotypes allows separating the effects of nature (genetic effects) and nurture  to a high degree of sophistication. This has been used to map genes involved in phenotypic traits, including resistance to parasites. Strong variation in host resistance has been reported [D. magna. Studies that combine QTL-F2 panel mapping and GWAS have identified a number of regions and candidate genes in the D. magna genome that contribute to this variation, with strong variation in the underlying genetic architecture ranging, from single gene effects (major QTLs) to complex multigene effects , as they are typical for quantitative traits [Cloning of reported , 50 for e traits –73.Daphnia biology is its phenotypic plasticity, defined as phenotypic variation expressed by the same genotype in response to environmental cues [Daphnia has been instrumental in the development and research of this field. The most readily observable phenotypic plastic trait is the switch between asexual and sexual reproduction  require tests, such as the Acute immobilisation test (a short-term or acute toxicity test) and the Daphnia magna Reproduction Test [Without doubt, the field that spurs the most ion Test , 86. TheDaphnia response to changes in freshwater supplies for human consumption. Swimming speed, turn frequencies, acceleration and other parameters have all been observed to change as water quality changes. Using video tracking systems, these swimming-related parameters can be estimated in real time, and in the case of flow-through water surveillance systems, ensure that appropriate actions are taken. These systems are much faster than even the acute immobilization test and have become increasingly popular for research and applied aspects [Moreover, the swimming behaviour of water fleas is increasingly used to quantify the effect of toxins, as well as to monitor the  aspects .Daphnia in ecotoxicology is the genetic variation among Daphnia genotypes (clones). Different genotypes of the same species may differ strongly in the estimates of toxicity parameters [A rarely considered problem with using rameters . Given tDaphnia was not among the main players in the field of developmental biology. Today, D. pulex and D. magna are frequently used in the evo-devo field, often with a focus on comparative aspects [The discovery of the germ plasm by August Weismann in water fleas  was a ma aspects , for exa aspects –93, neur aspects –96 and s aspects –100. Alo aspects  and diveDaphnia research addresses the genetic mechanism underlying phenotypes, with the long-term goal of uncovering gene function in an environmental context. Manipulation of gene expression, as well as gene knock-in and knock-out technology are now possible for Daphnia, with microinjection-based RNA interference (RNAi) used routinely [D. magna [Daphnia, although other avenues such as bacterial feeding are being developed [Daphnia is still the main bottleneck for these methods, because the initially very fragile egg membrane breaks easily, but then hardens fast, so that injection is not possible anymore. The time window with the right conditions is short, even so a method has been develop to stretch it (collect eggs immediately after ovulation in ice-cold medium enriched with 80 mM sucrose) [D. magna can produce clutches of more than 100 eggs. Thus, often eggs from multiple clutches need to be used. On the other hand, the combination of sexual and asexual reproduction is of great advantage when applying knock-in and knock-out methods. Manipulated genomes can be maintained in stable, clonal culture even in heterozygote state.An expanding area of outinely –104. Cluoutinely , 106, buoutinely . LikewisD. magna –110. Theeveloped . Masterisucrose) . AnotherDaphnia with their environment, for example genes for local adaptation, phenotypic plasticity, predator defence, parasite resistance, perception of environmental factors, such as water quality, photoperiod, and toxins, have hardly been manipulated [Current experimental approaches mainly focus on genes known from other organisms for diverse biological roles, such as development, sex expression  , 112–114ipulated , partly Daphnia was based on an F2 D. magna QTL panel. The position of two deleterious, but naturally segregating mutations were mapped [The first attempts to map phenotypes to genes in e mapped . The same mapped , 116 ande mapped , 117. The mapped , 77, 100Daphnia biology, it is an ideal system to study differential gene expression, as different phenotypes of the same genotype can be contrasted. This has been used intensively for diverse questions related to gene expression differences in different environments or treatments, such as responses to different predators, chemicals, environmental toxins, temperature, salinity, heavy metals, resistance to parasites, sex expression [http://www.daphnia-stressordb.uni-hamburg.de/dsdbstart.php) summaries the result of nearly 100 studies published until 2018 [D. magna. Only seven studies of the entire data set analyzed whole-transcriptome expression profiles (using RNA-seq). Besides RNA-seq, proteomics is also rapidly developing for Daphnia as a powerful research tool [With phenotypic plasticity being a hallmark of pression –121. A dtil 2018 . More thrch tool .Daphnia have rather small and condensed chromosomes, which posed severe difficulties on accurate karyotyping [D. magna is worked out to be 10 (N = 1) and is 12 for D. pulexyotyping , 124. Hoyotyping . The numDaphnia community is rather large with a long tradition. A few hundred papers are published every year that feature Daphnia as the main study organism. The Cladocera community normally holds triannual symposia, but this schedule was interrupted due to the Coronavirus pandemic. A younger meeting series are the “Daphnia Genomics Consortium” meetings, which had convened only three times previously.The D. pulex was already sequenced more than 10 years ago [Daphnia species have since then be sequenced [https://www.ncbi.nlm.nih.gov/genome/?term=Daphnia). Genetic maps exist for D. pulex and D. magna [http://wfleabase.org/) gives an overview, but is not always up-to-date.The first genome of ears ago  and seveequenced , 29 . The same lab also houses more than 300 F2 clones of a D. magna QTL panel [D. magna clones from one single population (the Swisspond panel) [Since clonal lines of d panel) .


Genotype by environment (G × E) interaction is fundamental in the biology of complex traits and diseases. However, most of the existing methods for genomic prediction tend to ignore G × E interaction (GEI). In this study, we proposed the genomic prediction method G × EBLUP by considering GEI. Meanwhile, G × EBLUP can also detect the genome-wide single nucleotide polymorphisms (SNPs) subject to GEI. Using comprehensive simulations and analysis of real data from pigs and maize, we showed that G × EBLUP achieved higher efficiency in mapping GEI SNPs and higher prediction accuracy than the existing methods, and its superiority was more obvious when the GEI variance was large. For pig and maize real data, compared with GBLUP, G × EBLUP showed improvement by 3% in the prediction accuracy for backfat thickness, while our findings indicated that the trait of days to 100 kg of pig was not affected by GEI and G × EBLUP did not improve the accuracy of genomic prediction for the trait. A significant advantage was observed for G × EBLUP in maize; the prediction accuracy was improved by ∼5.0 and 7.7% for grain weight and water content, respectively. Furthermore, G × EBLUP was not influenced by the number of environment levels. It could determine a favourable environment using SNP Bayes factors for each environment, implying that it is a robust and useful method for market-specific animal and plant breeding. We proposed G × EBLUP, a novel method for the estimation of genomic breeding value by considering GEI. This method identified the genome-wide SNPs that were susceptible to GEI and yielded higher genomic prediction accuracies and lower mean squared error compared with the GBLUP method. Genomic selection (GS) , which rThe current state-of-the-art methods for the estimation of genomic breeding value without considering GEI include GBLUP  and BayeTo explore GEI, certain G × E interaction-affected methods have been proposed for detecting SNPs. In this study, we proposed a novel approach for the estimation of genomic breeding value by considering GEI, which can handle environment variables in different dimensions. The basic principle of the new approach was to first detect the genome-wide markers affected by GEI (G × EWAS), in which a score-test statistics was implemented to identify the significant GEI-associated SNPs. Next, all markers were classified into SNPs with/without GEI to construct the genomic relationship matrices separately and predict the genomic breeding value using the mixed model (G × EBLUP). For its general application, the efficiency of the proposed method was evaluated through simulation study and real data from pigs and maize.The animal study was reviewed and approved by Animal handling and sample collection were conducted according to protocols approved by the Institutional Animal Care and Use Committee (IACUC) at China Agricultural University. All authors strictly complied with the Regulations on the Administration of Laboratory Animals . There was no use of human participants, data or tissues.y is the vector of observed phenotypic values; y; IG × EWAS extends the conventional linear mixed model by including an additional per-individual effect term accounting for G × E, which can be represented as N :For the parameter inference, we considered the marginalised form of the model in p-value calculation with a complexity that scaled linearly with the number of individuals, provided that there is low-rank environment covariance Using the marginalised model in +Q follows a mixture of p-values were computed using the Davies method  obtained from such comparisons is directly calibrated as the parameter number fitted using maximum likelihood and is independent of the environment variable numbers.Given a variant and set of L environment L = . The real genotypes of 7,334 individuals determined using the Illumina BovineSNP50 BeadChip from the Chinese Holstein population were referred for phenotype and environment simulation, and 45,323 SNPs remained after imputation of missing genotypes and removal of SNPs with a minor allele frequency (MAF) of <0.01. Additional File 3 For the G × E interaction simulations, the parameter Yorkshire pigs were sampled from a breeding company with five breeding farms distributed across China . Similarly, we excluded the pig individuals or maize samples with a call rate of <0.90. Finally, 56,463 and 59,401 SNPs were present in the pig and maize data, respectively, and all genotyped pigs and maize were retained.For real pig and maize data, Beagle 4.1  was usedp-value gradients of 1-E01–1-E05 were chosen as threshold standards to select the SNPs associated with GEI. Five 10-fold cross-validation (10-CV) repetitions were used to assess the genomic prediction using G × EBLUP. In each cross-validation, the reference and validation populations comprised 6,601 and 733 individuals, respectively. The accuracy of genomic prediction was calculated as the Pearson’s correlation between original phenotypes Phe and the genomic estimated breeding values (GEBVs) of all validation individuals r. Moreover, the mean squared error (MSE) of the prediction ability matrix was used to evaluate the performance of the models; MSE was computed as the average square of the difference between Phe and GEBV centred on zero. In each scenario, we performed the comparisons between G × EBLUP and GBLUP  proposed by nd GBLUP  at diffePig data were used to detect the SNPs affected by GEI. The herd-year-season effects, estimated using the conventional pedigree-based BLUP method, were used as environmental covariates, and the corrected phenotype were used as response variables. The calculations for the corrected phenotype value followed the method described by p-values for all markers were calculated using G × EWAS, and a threshold standard with p-value gradients of 1-E01–1-E05 was selected to screen the GEI-associated SNPs. The GBLUP and G × EBLUP methods were then used to estimate GEBVs. The genomic prediction accuracy on the real data was evaluated differently than that on the simulated data, using the correlation between GEBVs and the phenotypic values y (maize data) or corrected phenotypes yc (pig data) in the validation population. MSE was computed as the average square of the difference between y or yc and GEBVs centred on zero. The BF for each environment variable was calculated to obtain the sensitive environments of markers associated with GEI. For simulated data and real data, the improvement in prediction accuracy for G × EBLUP over GBLUP was calculated by subtracting the prediction accuracy obtained by GBLUP from the prediction accuracy obtained by G × EBLUP and then dividing by the prediction accuracy obtained by GBLUP.For the G × EBLUP method, the p-value gradient of 1-E01–1-E05 to determine SNPs associated with GEI. p-values. G × EBLUP showed the highest genomic prediction accuracy and the lowest MSE with a p-value of 1-E03. Further, the prediction accuracy of G × EBLUP improved by 1.7% compared with that of GBLUP. p-values in each repetition. When the GEI variance was 0.25, the G × EBLUP yielded 1.7% higher prediction accuracies than GBLUP. Moreover, G × EBLUP yielded lower MSE than GBLUP, with average MSE values of 1.816 and 1.942, respectively. G × EBLUP performed significantly better than GBLUP when the GEI variance was increased to 1 and 2, yielding 3.9 and 6.4% higher prediction accuracies, respectively, and a lower MSE than GBLUP.The significance level was set at the p-value of 1-E02, the prediction accuracy was improved by 3% compared with that of GBLUP, and the MSE was reduced by 0.107, decreasing from 2.707 to 2.600.p-value of 1-E02, the genomic prediction accuracy was improved by 6.25% compared with that of GBLUP, and MSE was reduced from 46.323 to 46.174. For the trait WC from a randomly selected replicate, the highest prediction accuracy of G × EBLUP was obtained at the p-value of 1-E03, the prediction accuracy was improved by 7.8% compared with that of GBLUP, and MSE was reduced from 721.588 to 720.854. The average prediction accuracy of 5 repetitions of 10-fold CV for GBLUP and G × EBLUP are shown in Compared with pig data, the genomic prediction of G × EBLUP showed considerable improvement in maize data. As shown in p-values were selected for sensitive environmental detection. As shown in The BF for each environmental factor was obtained, and then the sensitive environments were assessed accordingly. In pig population, for AGE and BFT, 10 SNPs each with the smallest . In all scenarios, G × EBLUP runs longer than GBLUP, mainly because running G × EBLUP requires concurrently running G × EWAS, which is a single marker regression model with a long running time, e.g. G × EWAS took an average of 45.8 min in each fold of CV to complete the analysis, requiring considerably less time than GBLUP (15.05 min). In addition, there is no obvious difference here between different traits in the same population due to the same size population and number of SNPs.The average computation time for G × EBLUP and GBLUP to complete each fold of CV is presented in G × E interactions play an important role in livestock and plants and should be considered in breeding programmes to select elite individuals in specific environments . HoweverIn the simulated data, our results indicated the superiority of the G × EBLUP method for genomic prediction in all scenarios, which was more remarkable when the variance of GEI was large , showingEE’) was primarily used because of two appealing properties. First, as the number of samples typically exceeds the number of environments in larger populations (L << N), a low-rank linear covariance is noted, which enables parameter inference with a computational complexity that scales linearly with the increasing population size. Second, a linear covariance is directly interpretable, as there is one-to-one correspondence between G × EWAS and linear regression using L covariates to account for GEI. Compared with the four methods proposed by In this study, we proposed G × EWAS and a score-test statistic to identify the significance of SNP affected by GEI; the details of G × EWAS and its computational complexity can be found in Additional File 1 ti-trait  and reacti-trait . The linp-value of E02 (BFT in pig and WC in maize) or E03 (simulated data and WC in maize)  in all scenarios .Although G × EWAS could detect more significant SNPs associated with GEI using Bonferroni correction, only a small amount of the whole markers were detected. The best performance of G × EBLUP was obtained at the marker selection criterion of cenarios . In factcenarios . TherefoAlong with the mapping of G × E markers, G × EWAS could determine favourable environment using BF of SNPs on each environment. This is extremely helpful for market-specific breeding in animals and plants as it may provide further explanation to those individuals who have a higher risk of being affected by GEI in a certain environment variable (sensitive environment). In this study, farm was identified as a sensitive environment for BFT in pigs, which allows the selection of elite individuals with good performance in specified farms. Further, our results showed that GEI was different for distinct traits, e.g. similar genomic prediction accuracies were obtained for G × EBLUP and GBLUP for AGE, whereas G × EBLUP showed improvement by approximately 3% in the prediction accuracy for BFT in pigs . This obpig data . FurtherOur results showed G × EBLUP is a powerful alternative to the conventional method for the estimation of genomic breeding value. However, there are several limitations in this approach. First, G × EWAS is not a whole-genome regression model and does not account for the genome-wide contribution of all other variants, thus G × EWAS is still a single marker regression model with a long calculation time . G × EWAMoreover, G × EBLUP can be extended to single-step method (ssG × EBLUP), which could improve the genomic prediction accuracy using pedigree and genomic information .The G × EBLUP method proposed in this study showed the following four features: 1) genomic prediction was performed using the G × EBLUP method by considering GEI and yielded higher accuracies and lower MSE in both simulated and real pig and maize data when the variance of G × E interaction is large; 2) it could powerfully detect the genome-wide SNPs subject to GEI; 3) the number of environment levels did not influence the genomic prediction accuracy of the proposed G × EBLUP, circumventing the limitation of current methods; 4) it could determine favourable environment using SNP BF for each environment, thus being useful for market-specific animal and plant breeding.


The importance of milkability as a trait is growing because of the need to efficiently use labor and machinery; therefore, it is crucial to update the statistical model for the trait to improve the accuracy of the estimated breeding values, and thus provide a more accurate tool for decision-making at the farm level. In the Italian Holstein Friesian cattle population, milkability is recorded twice a year by the milk recording system as a binary trait . Data consisted of 7,862,371 records from 2,945,249 cows collected between 2004 and 2021. A single-trait threshold animal model with repeated measures was used, with parity, days in milk class, calving season, and regression of production (fat + protein grams) within days in milk class as fixed effects and herd-year-season of recording, permanent environment, and animal as random effects. The results for heritability and repeatability were 0.275 and 0.5, estimated with the Gibbs sampler THRGIBBS1F90. Genomic validation, carried out using genotyped proven bulls born before 2009 as the training set, gave a result of 0.386 for reliability. The genetic correlations of this trait confirmed that both extremes of the estimated breeding value must be treated cautiously, because correlations with important traits such as mastitis resistance, body condition score, and teat length are unfavorable. Milkability is the ability to secrete milk in a regular, complete, and fast way: it can be defined as “workability,” as it is regarded as a management trait. The importance of this trait in dairy farms is currently increasing because of the need for cost-efficient use of labor and machinery caused by the lowering of profit margins as a result of fluctuations in milk and feedstuff prices. Milkability is a trait in which extreme high and low values must be handled carefully; in fact, although the time requirements of slower cows are a management issue, high milking speed is genetically correlated with a higher SCS and a higher incidence of udder infections . From a In Italy, milkability is recorded twice a year as a binary trait  by the milk recording system, resulting in nearly 2 observations per cow per parity. The observation refers to the subjective score of the farmer. The aim of this study was to update the statistical model on which our milkability breeding values for Italian Holstein are based and estimate the genetic parameters and correlations with other EBVs. This will lead to more efficient use of the huge amount of data coming from the milk recording system and to an improvement in the accuracy of EBVs, with the aim of providing a more accurate tool for decision-making at the farm level. The analyzed data included records from 2004 onward. This study did not involve animals and prior ethical approval was deemed not necessary.HYS) of recording (January–June or July–December). As the number of observations per animal was 1 but the number of recordings per animal was >1, we must define precisely each of the fixed effects. Parity referred to the parity of an animal at the last recording if the ratio was 0, or its parity on the last occasion when it was recorded as “slow.” The random effects were the animal genetic effect and the residuals.The previous model was a linear model in which the observations were the ratios between the number of controls in which an animal was recorded as slow and the total number of records for that animal. The fixed effects were parity, milk yield in kilograms on the day of recording, and contemporary groups of herd-year-season ; season of calving was a binary variable: cold (November–March) and warm (April–October) months; DIM were divided into 7 classes , and all observations before 5 or after 305 DIM were excluded from the analysis. The pedigree went up to 4 generations. Data consisted of 7,862,371 records from 2,945,249 cows collected between 2004 and 2021. An extensive description of the data set can be found in ijklmnY is the underlying liability of slowness; iP is the fixed effect of parity i; jDIM is the fixed effect of DIM group j; kCS is the fixed effect of season of calving k; b1j is the regression coefficient of the linear effect of ijklmnPROD, in which ijklmnPROD is the fat + protein yield on the day of recording, within DIM group j; lhys is the random effect of HYS l; mpe is the random permanent environmental (PE) effect for cow m based on repeated records both within lactation and across parities; ma is the random additive genetic effect for animal m; and ijklmne is the residual of observation n. The HYS effect was treated as a random effect to avoid extreme-case problems  was given byRepeatability (software  and stanEDP) for 2 data sets: a full data set with all the recorded phenotypes and a reduced data set. Then, genomic evaluation using the SNPblup model was run for the reduced data set to simultaneously estimate the effect of all SNPs, using the EDPs calculated from the reduced data set as response variable. The number of SNPs was 68,263 and imputation was performed with PedImpute software (DGV) of 5,504 genotyped bulls with daughters in the full data set but without daughters in the reduced data set . Finally, linear regression was used to regress current EDPs against DGVs of validation bulls: the r2 value of the linear regression is the reliability of the DGVs of validation bulls. To summarize, the result of the validation process is the reliability of the DGVs of the validation bulls calculated with the reduced data set (when they had no daughters).Genomic validation was done as described in software . These SApproximate genetic correlations were estimated from the correlations between genomic EBVs and their reliabilities as in PSD): 0.002] for HYS variance, 0.66 (PSD: 0.01) for PE variance, and 0.54 (PSD: 0.008) for additive genetic variance. The effective sample size for each component was >60, with 10 as the recommended minimum  was found, different from that (0.03) reported by Approximate genetic correlations confirm the hypothesis that milkability can be an intermediate-optimum trait because high values are associated with less genetic resistance to mastitis, lower BCS, and shorter and narrow teats, whereas low values are associated with slower milking.The new model, with a higher genomic reliability (0.386) than the previous model (0.137), increased our ability to estimate the breeding values of animals for this trait, giving farmers a better decision-support tool for their breeding choices. Its genetic correlations, in particular with mastitis resistance (−0.14), indicate that this trait should be handled carefully at its extreme values. Genetic evaluation for milkability can be a tool to lower the financial impact of expensive investments such as automated milking systems by improving the efficiency of the milking routine. Further improvements can be made when additional consistent information from milk flow sensors becomes available.


Flavour and nutritional quality are important goals for tomato breeders. This study aimed to shed light upon transgressive behaviors for fruit metabolic content. We studied the metabolic contents of 44 volatile organic compounds (VOCs), 18 polyphenolics, together with transcriptome profiles in a factorial design comprising six parental lines and their 14 F1 hybrids (HF1) among which were five pairs of reciprocal HF1. After cluster analyses of the metabolome dataset and co-expression network construction of the transcriptome dataset, we characterized the mode of inheritance of each component. Both overall and per-cross mode of inheritance analyses revealed as many additive and non-additive modes of inheritance with few reciprocal effects. Up to 66% of metabolites displayed transgressions in a HF1 relative to parental values. Analysis of the modes of inheritance of metabolites revealed that: (i) transgressions were mostly of a single type whichever the cross and poorly correlated to the genetic distance between parental lines; (ii) modes of inheritance were scarcely consistent between the 14 crosses but metabolites belonging to the same cluster displayed similar modes of inheritance for a given cross. Integrating metabolome, transcriptome and modes of inheritance analyses suggested a few candidate genes that may drive important changes in fruit VOC contents. Solanum lycopersicum L.) is both a model organism to study the physiological and genetic control of fleshy fruit development and composition [S. lycopersicum var. cerasiforme, ‘SLC’) and big-fruited tomato . The goals of breeding programs have evolved starting from the 1950s [The cultivated tomato  and Recombinant Inbred Lines (‘RILs’) derived from biparental populations, or Genome Wide Association Study (‘GWAS’) panels covering the genetic diversity of a species. Hundreds of QTLs ,18,19,20Capsicum anuum [A. thaliana parental lines [Besides the genetic architecture of such traits, knowing the inheritance of metabolic content is highly informative for breeders who seek to improve flavour and nutritional quality in their hybrids and who may exploit QTLs with heterotic effect for these traits. Likewise, phenotypic differences may arise between reciprocal hybrids given the parental line chosen as the female/male parent. In tomato, heterosis for metabolite content has mostly been investigated for primary metabolites such as amino acids, sugars and organic acids ,27,28,29um anuum , or 34 sal lines . The autal lines . On the al lines . StudiesTo take full advantage of the hybrid state for flavour and nutritional quality, the present study aims at providing knowledge concerning: (i) the mode of inheritance of tomato metabolite content; (ii) the mode of inheritance of genes underlying metabolite content; (iii) the possible impact of the cross direction on metabolite content. To reach this aim, we studied the modes of inheritance of metabolome and transcriptome profiles in fruit produced following a factorial design comprising six parental lines and their 14 HF1 obtained from four intra- and ten interspecific crosses. We produced five pairs of reciprocal HF1 to investigate the impact of the genetic cross direction on VOC and phenolic content. We integrated metabolome and transcriptome datasets through a correlation network analysis to further characterize the mode of inheritance of differentially expressed genes correlated to key VOCs and phenolic compounds. Solanum pimpinellifolium –‘SP’ lines, three SLC and one SL), their four intraspecific HF1  and ten interspecific HF1   . The fulLC × SL) . These eLC × SL) . For theTo investigate the link between secondary metabolites, SSC and fruit weight, we calculated correlation coefficients between each pair of traits as illustrated in As for carotenoid-derived VOCs positively associated with consumer liking, we identified distinct clusters. The cyclic apocarotenoids BCYCL and BIONO clustered together in the cluster analysis  with str5 PENAL and C7 to C10 lipid-derived VOCs, while in the second the four C6 lipid-derived volatiles associated with ‘green’/‘grassy’ aroma in addition with 2EFUR, and further though still in the same cluster two C5 and two C7 fatty acid derived VOCs , no significant differences could be found within crosses. Additivity (‘A’) was detected in 19% of the cases, then recessiveness (‘R’) and over-recessiveness (‘OR’)  and finally over-dominance (‘OD’) and dominance (‘D’) patterns in 5% and 2% cases, respectively.We observed transgressions beyond and below parental values for 47 traits relevant to breeding among which SSC, 23 VOCs impacting tomato overall aroma, and polyphenols with antioxidant properties. Except for the terpene-derived LIN and the polyphenol 35DICAFQ that we found both OD and OR among the 14 crosses, transgressions were always of a single type among the 14 crosses for the 47 traits. Crosses involving SP1 and a parent from a different species displayed the highest number of significant differences across traits a. When tOnly few traits exhibited consistency of their mode of inheritance across the 14 crosses. Fruit weight was additive for 11 out of 14 crosses, while crosses between SP1 and SP2 led to over-dominance. Phenylalanine-derived PHENE was additive for eight out of 11 crosses displaying significant differences, and the polyphenolic RUTIN5O was found additive in the 11 crosses showing a significant genotype effect for the trait. Crosses involving SLC2 and SL (SL being the only big-fruited accession) were those with the highest content in benzenoid and terpenoid-derived volatiles. Looking at the inheritance of benzenoid VOCs, we found mostly OR to R patterns, with SL highest content in these VOCs always overridden by the other parent value in the resulting HF1. The benzenoids SALI, GUAIA, MESA and EUGEN were mainly found OR to R whichever the cross. Terpenoid-derived VOCs were OR in every cross that involved SP1 and additive in the other crosses. In addition, SSC was D to OD when we crossed SL with an SLC.Apart from the benzenoid and terpenoid-derived VOCs, no clear pattern could be identified at the metabolic pathway scale. However, when we analysed groups of volatiles in light of the clustering analysis , specifi6 lipid-derived VOCs showed mainly OR to R patterns for SP crosses and no differences for other crosses. On the other hand, long chain lipid-derived VOCs had OD patterns in crosses that involved one or two SLC. As in cluster analysis, we found three distinct groups of carotenoid derived VOCs with specific inheritance patterns: BDAM mode of inheritance closely matched that of SSC with D to OD patterns in crosses involving parental lines from different species. When we assessed significant differences, BCYCL and BIONO showed consistent additivity patterns for a given cross and the remaining linear apocarotenoids were also mostly additive. We split the large family of fatty acid volatiles into two distinct clusters that displayed distinct modes of inheritance. On the one hand the cluster consisting mostly of CWe compared trait values between each of the five pairs of reciprocal HF1. We found significant differences between reciprocal hybrids for seven metabolites, among which two VOCs correlated to consumer liking (MESA and BDAM) and three polyphenolics . Only MESA mode of inheritance changed from “OR” to “A” between reciprocal crosses with a lower content in MESA when SLC2 was used as a female rather than a male parent in SLC2 × SL crosses. Otherwise, modes of inheritance were consistent despite significant differences between reciprocal HF1 values. Although both crosses gathering SL and SLC2 resulted in OD mode of inheritance for the carotenoid-derived BDAM positively correlated to consumer liking, we found higher content of the VOC when SL was used as the female parent. Both polyphenolics NEOCHL and 34DICAFQ showed higher content when we used SP1 as the female parent in SP1 × SL crosses. The polyphenolic DIFER displayed higher content in the HF1 with SLC2 as the female parent in SLC2 × SL crosses. To identify genes whose expression is impacted by a genotype effect, we first filtered genes with low expression between the parental lines and 14 HF1. The quality control filters kept 13,127 genes. The first two components of the PCA performed on this subset accounted for 37% of the total variation of these genes . The genWe first looked at the number of DEGs found between the parental lines of each cross and compared that number to the genetic similarity calculated between parental lines based on 7442 Illumina SNP markers. While we expected the most genetically distant parental lines to display the highest number of DEGs, we found three pairs of parental lines that did not verify that hypothesis . AlthougWe assessed the mode of inheritance of the 12,896 DEGs. The frequency of each pattern was close to what we found for metabolic traits. First came no significant expression difference within crosses . We then found additivity in 20% of the cases, over-dominance and over-recessiveness both in 6% of the cases and finally recessiveness and dominance in 4% and 3% of the cases, respectively. Comparing We found differences in gene expression between three out of the five pairs of reciprocal HF1, with eight to 15 DEGs found between reciprocal HF1 . Among tTo gain insights into the genes related to the synthesis of the metabolites, we constructed gene co-expression modules using the matrix of 12,896 DEGs with WGCNA. We identified 12 co-expression modules, with 39 (green module) to 686 (turquoise module) genes comprised within each module a. We thep-value = 3.20 × 10−7 for the most significant) which play key roles in the synthesis of benzenoid VOCs. The module showed the highest positive correlations with benzenoid VOCs MESA, GUAIA EUGEN and SALI (from 0.42 to 0.66). The turquoise module (n = 686 genes) was enriched in GO terms related to cellular components, namely ‘photosystem’ and ‘photosystem I’ , ‘photosynthetic membrane’ (4.2 × 10−12), the most significant being ‘thylakoid’ . The turquoise module showed positive correlations with linear apocarotenoids such as 6MHON, GRACE or NERAL . Synthesis of these VOC precursors, among which lycopene, takes place in the chloroplast and further enzymatic activity depends on light and photosystems [To verify whether modules showed significant enrichment in the different pathways we studied, we computed GO term enrichments. We found eight co-expression modules with significant enrichment in GO terms . Among tosystems . This coCCD1A and CCD1B correlated with GRACE and 6MHON  and 6MHON (0.56), respectively. For phenolic compounds, we found the cloned gene 4CL, which is annotated as a ‘4-coumarate:CoA ligase’. This gene catalyses the last step of the overall phenylpropanoid pathway before the pathway is ramified into flavones or non-volatile phenylpropanoids [p-value = 5.1 × 10−23). Within this module, we also identified Solyc01g102950 annotated as a ‘Lycopene beta/epsilon cyclase protein’ and not yet cloned as a QTL, with a correlation of 0.58  with 6MHON. In the blue co-expression module, we identified three glycosyltransferase genes  whose expressions were correlated to the contents of four benzenoid VOCs  for Solyc08g006330 and Solyc10g079950, while Solyc11g013490 expression was correlated to three benzenoid VOCs . We found a negative correlation between Solyc08g006330 expression and the four benzenoid VOCs , while the two other genes displayed positive correlations for the VOCs they were correlated to . When we looked at the mode of inheritance of the four benzenoid VOCs in light of the mode of inheritance of Solyc08g006330, we identified consistency (with opposite effect): MESA exhibited over-recessiveness in three crosses where Solyc08g006330 was over-dominant  for 28 out of 33 VOCs impacting tomato aroma . These correlation patterns are consistent with the two clusters of phenolics identified with on the one hand, non-volatile phenylpropanoids and on the other hand, flavones and flavonols  once we accounted for not significant differences (56%) within crosses. However, the frequency of non-additive modes of inheritance exceeds that of additive patterns. Predominant non-additive modes of inheritance for metabolite content have previously been reported in tomato ,27,29 orthaliana  for instthaliana ,37. Howethaliana ,31, whicWe assessed the mode of inheritance of the 12,896 DEGs identified within the factorial design. The overall frequencies of the modes of inheritance were close between the metabolome and DEG datasets. Gene expression was mostly additive (20%) as reported in maize , while oKnowledge about non-additive modes of inheritance for metabolic content is of the utmost importance to breeders: as the agronomic value of a variety will always rank first among breeding program priorities, getting insight into the possible improvement for flavour and nutritional quality will save time and expenses. Although several crops such as rice exhibit strong correlation between heterosis occurrence and genetic distance between parental lines ,46, we rWith the five pairs of reciprocal hybrids, we investigated the impact of the cross direction on metabolite content and gene expression. We report only seven significant differences between reciprocal HF1 metabolic content, three of which for VOCs correlated to consumer liking. We observed an over-recessive mode of inheritance when the cherry type parent was used as a female in the SLC2 × SL cross and additivity when SL was the female parent. Choosing SL as the female parent in SL × SLC2 crosses resulted in higher content in the carotenoid-derived VOC BDAM positively correlated to consumer liking. Three phenolics also exhibited significant differences and choosing a small-fruited parent as female instead of the big-fruited SL was more beneficial for increasing the corresponding metabolite contents. We looked for DEGs between reciprocal hybrids that may drive the significant differences observed at the metabolic level. First, the lowest number of DEGs between the contrasts we considered was found for reciprocal HF1 with zero to 15 DEGs identified. None of these DEGs were correlated to the metabolites displaying significant differences between reciprocal hybrids. However, three DEGs were consistently found among several pairs of reciprocal HF1. Though we didn’t correlate them to metabolite content variation between reciprocal HF1, they may influence other important traits and thus require further investigation. While 46% of the metabolites  investigMoreover, since we found consistency in the mode of inheritance of metabolites belonging to the same clusters, transgressions may be achieved for several compounds concomitantly. For phenylalanine-, carotenoid- and benzenoid VOCs displaying consistent correlations to consumer liking within their own metabolic pathway , this finding may help breeders reduce the number of VOCs to target while increasing the number of crosses investigated as, for a given cross, consistent modes of inheritance and thus increase/decrease of the corresponding metabolites may be achieved. Finally, as we found positive correlations between phenylalanine-derived VOCs and phenylpropanoids, improving both flavour and nutritional quality may be achieved given the identification of common QTLs between the two classes of metabolites, with careful attention to not increase benzenoid VOCs. However, using small-fruited parental lines may prevent increase of benzenoid compounds, although further investigation is necessary. S. pimpinellifolium –“L. pimpinellifolium atypique, site 10” (‘SP1’) and LA1589 (‘SP2’)-, three were cherry type tomatoes –Cervil (‘SLC1’), Stupicke Polni Rane (‘SLC2’) and LA1420 (‘SLC3’)- and the last one was a big-fruited line S. lycopersicum L. var. lycopercisum –Ferum TMV (‘SL’)- also used as a common tester in GWAS panels in [S. lycopersicum species, we considered their HF1 as interspecific to ease the reading. Ten HF1 resulted from five reciprocal crosses: SP1 × SP2, SL × SP1, SL × SP2, SL × SLC2, SL × SLC3 were studied both ways. We studied a factorial design comprising six parental lines and 14 F1 hybrids (HF1). Among the parental lines, two were tomato wild relatives anels in . We prodPlants from the factorial design were grown from April to July 2019 in a plastic greenhouse under soilless and passive irrigation condition on the experimental site of the seed company Gautier Semences in Eyragues, France. Five plants per genotype were cultivated.Three harvests of red ripe fruits, representing three replicates, were conducted during three consecutive weeks, starting from the 2nd truss for the first harvest and finishing around the 5th truss for the 3rd harvest. At least ten fruits were harvested from each plot and each harvest (up to 30 fruits per harvest for SP accessions). The harvested fruits from each plot were divided in two pools. With the first pool of fruits, we measured an average fruit weight (‘weight’) before crushing the fruit pericarps to measure Soluble Solid Content (‘SSC’ in degree Brix). For the second pool, the pericarp from at least five fruits per plot was flash frozen in liquid nitrogen, ground to powder with a cryogenic mill, and then stored at −80 °C. The original powder was subsampled into three vials destined to (i) VOCs profiling; (ii) quantification of polyphenolic compounds; (iii) RNA-seq analysis. Phenotypic values are available in Profiling of volatile compounds was performed at the Instituto de Biología Molecular y Celular de Plantas, at the Universidad Politécnica de Valencia, Spain, following the protocol described in . A homogPhenolic compounds were quantified on the Avignon metabolomic platform. All solvents were analytical or LC/MS grade. Twenty mg of frozen-dried pericarp were extracted with 1.36 mL of methanol acidified with formic acid . 5 µg of Taxifolin was added as internal standard. The samples were homogenized with a vortex, placed in a water bath at 70 °C for 10 min. During the treatment, the samples were homogenized twice with a vortex. Then, 0.44 mL of ultrapure water were added and the extraction was performed as before. The extract was centrifuged at 5300 rcf for 5 min at 4 °C and the supernatant was filtered through a 0.2 µm membrane and stored at −20 °C until analysis.UPLC was carried out using an Acquity UPLC Class I . Chromatographic separation was achieved on a reversed-phase column, BEH C18 1.7 µm 2.1 × 100 mm, equipped with a guard column . The mobile phase, consisting of water with formic acid  (eluent A) and acetonitrile with formic acid  (eluent B), was pumped at 0.4 mL/min. The following gradient was applied: 0–0.5 min, 2% B, 0.5–6.5 min, 44.5% B, 6.5–7.5 min, 100% B, 7.5–9 min, 100% B. Then the concentration of B decreased to 2% and the column was equilibrated before the next injection . The column temperature was kept at 35°C and the samples at 10 °C. 1 µL was injected. The system was controlled by MassLynx version 1.2 . The UPLC system was coupled with a triple quadrupole mass spectrophotometer TQ-XS XEVO  working with an electrospray source (ESI) at 150 °C in negative ion mode. Mass acquisitions were performed in scan mode and MRM mode. The MassLynx software controlled the MS analyser. We quantified 20 polyphenolic compounds in addition to two alkaloids as part of the untargeted analysis. Major metabolites were identified according to their MS/MS data with pure standard. Other compounds were identified by comparing their MS/MS data with published data. UPLC-DAD-ESI-TQ data were processed with the TargetLynx software  to integrate peaks in MRM mode. Results were expressed in area of peak per gram of fresh matter.We performed RNA extraction on the three biological replicates obtained from pools of ripe tomato pericarp. For each sample, total RNA was extracted using the “Spectrum Plant Total RNA” Kit  following the manufacturer’s protocol and treated for 15 mn at 20 °C with “On Column DNAse Digestion Set” (Sigma-Aldrich) to remove genomic DNA traces. After extractions, RNA purity was assessed on Nanodrop 1000 ; all ratios (A260/280 and A260/230) were comprised between 1.8 and 2.2. RNA integrity was assessed on Bioanalyzer 2100  using RNA Nano 6000 kit; all RNA integrity numbers (RIN) were between 7.7 and 9.2 and samples were not degraded. RNA concentration was assessed on Qubit 3.0 Fluorometer  using Qubit RNA Broad Range Assay Kit.9 bp, 2.72 × 109 bp and 2.54 × 109 bp, respectively. Raw sequencing data quality was assessed using FASTQC v.0.11.8 software [https://solgenomics.net (accessed on 27 April 2022)) and raw read counts per gene were generated for each library using STAR v.2.7.3 with two passes and providing the tomato gene model (annotation v4.0) to support the mapping process. Alignments were filtered to keep only concordantly mapped reads using SAMtools v.1.9 [Library construction and sequencing (100 bp paired-end strand) of the 60 samples were subcontracted to BGI Genomics. The minimal, maximal, and average amounts of raw sequencing data per sample were estimated to be 2.05 × 10software  and aggrsoftware . Sequencsoftware . On averls v.1.9 . DiCoExpress with recommended parameters [DiCoExpress with recommended parameters. After normalization, a principal component analysis (PCA) was performed to assess the diversity of the transcriptome data in the 6 lines and 14 F1 hybrids. Then DEGs were detected for all possible contrasts -190- between the 14 HF1 and six parental lines using the edgeR package: first, we detected genes with significant expression differences between the 20 genotypes; second, we identified genes with significant expression differences between the five pairs of reciprocal F1 hybrids. The p-values were corrected for multiple comparisons using the false discovery rate [DiCoExpress.On average, 95.8% of read pairs were uniquely mapped per library  and 2.1% multi-mapped . We filtered out genes mapping on chromosome 0 and performed quality control, read count normalization and sampling of genes expressed in the experiment using the workspace rameters . A totalery rate  using a car package on the 66 secondary metabolites, SSC and fruit weight to test for genotype effect with the following model:ijy is the trait value of genotype i in harvest j, μ is the intercept, ig and jr represent the fixed effects of the genotype and the harvest, respectively, and ijε the residual effect. Broad-sense heritability (h²) was then computed for every trait with the lme4 package by using the following linear mixed model:ijy is the trait value of genotype i in harvest j, μ is the intercept, ig the random effect of genotype i, jr the fixed effect of harvest j, and ijε the random residual effect. Then heritability was derived from the variance components of the model as:Gσ² and eσ² are the genetic and residual variance, respectively.We used the R software v.3.6.2  to perfop-value threshold with the R packages corrplot and the ‘hclust’ clustering method.We computed pairwise non-parametric Spearman’s rank correlation coefficients between the 68 scaled traits. We represented significant pairwise correlations using a 0.05 ComplexHeatmap and dendextend on scaled datasets. Clusters for metabolites and accession profiles were produced with the R package hclust and the ‘average’ method on scaled dataset. Metabolic clusters were further validated with the pvclust R package based on ‘Euclidean’ distance, ‘average’ method, and n = 1000 bootstrap replications as explained in [Metabolomic profiles from each biological replicate were produced with the R packages ained in .p < 0.05), we estimated additive (A) and dominance (D) components of the genetic variation as described in [We compared the values of the 14 HF1 to their respective parent values for every trait to decipher trait inheritance pattern. Likewise, we compared candidate gene expression levels within each cross to assess gene inheritance. We first computed a one-way ANOVA to assess the genotype effect of gene and trait variation within each cross. For significant tests  transgressions were mostly of a single type for a metabolite whichever the cross; (ii) modes of inheritance were scarcely consistent for a metabolite between the 14 crosses, except for terpenoid-derived VOCs and benzenoid VOCs; (iii) metabolites belonging to the same cluster displayed similar modes of inheritance for a given cross. Knowing these patterns and focusing on key compounds where non-additive patterns may be exploited, breeders may screen their hybrids for the combination of alleles needed (i) in F1 hybrids outperforming their parents for the metabolites they are interested in; (ii) at markers associated to the metabolites in previous GWAS and linkage mapping analyses. The co-expression network we build using the DEGs, followed by correlation analysis with metabolites, provided information on candidate genes that might be targets for improving metabolite contents. Future perspectives lie in confirming these results by studying a larger experimental design where an expression GWAS combined with GWAS for metabolite content may further link the correlations herein highlighted between transcriptome and metabolome, in addition to identifying causal polymorphisms to be used in MAS: as a medium-term objective, the expensive quantification of VOCs that cannot be assessed routinely in a breeding program could be avoided with the development of efficient markers to screen in breeding material.