Sub-pangenome analysis reveals structural variants associated with fruit color and bacterial wilt resistance in eggplant

Domain: chemistry
Published: 2026-02-23
Editorial outcome: major_revision

Abstract
Eggplant (Solanum melongena L.) is a globally important Solanaceae crop, yet trait-relevant genomic variants remain poorly characterized. Here, we perform population genomic analyses of 226 eggplant accessions sampled mainly from a major domestication center spanning Southeast Asia and South China, and find that genetic relationships closely track geographic origin. We generate chromosome-scale assemblies for 11 representative accessions using long-read sequencing and integrate six published genomes to build a pangenome resource. Using this resource, association scans identify a 12.4 Mb inversion on chromosome 10 segregating at 50.44% frequency that is strongly associated with fruit color, likely through hitchhiking with SmMYB1. We also detect variants associated with bacterial wilt resistance, including a premature stop codon in SmCYP82D47 and copy number variations in SmEPS1 and SmRoq1 homologs. Together, our results illuminate the evolution and phenotypic impact of large structural variants and provide genomic resources for eggplant genetics and breeding.

Introduction
Eggplant (Solanum melongena L.), also known as brinjal or aubergine, is a globally important vegetable crop in the Solanaceae. With annual production of 57.4–59.3 million tons worldwide, it ranks as the third most produced solanaceous crop after potato (S. tuberosum L.) and tomato (S. lycopersicum L.) (FAO, 2020–2022). Domesticated in the Old World and cultivated for centuries, eggplant is now widely grown and consumed, particularly in Asia, the Mediterranean basin, and Southeast Europe. However, its origin and domestication history remain debated. The “out of Africa” hypothesis proposes that the wild progenitor of cultivated eggplant, S. insanum, originated in Africa and later spread to Asia, where domestication gave rise to modern S. melongena1. An alternative model suggests multiple independent domestication events in distinct regions, including Southeast Asia and the Indian subcontinent2,3. These hypotheses may not be mutually exclusive, but could reflect different stages of eggplant evolution, domestication, and diffusion. Beyond S. melongena, two other cultivated eggplant species, scarlet eggplant (S. aethiopicum L.) and gboma eggplant (S. macrocarpon L.), are indigenous to Africa and have long been cultivated there, but are now grown more broadly worldwide4. S. aethiopicum is also used as an ornamental crop, whereas S. macrocarpon is valued for both leaves and fruits. These species are thought to have been domesticated from distinct wild relatives, S. anguivi and S. dasyphyllum, respectively5. In contrast to several New World solanaceous crops, eggplant did not experience a strong early domestication bottleneck6. Nonetheless, recent evidence indicates substantial reductions in genomic diversity during subsequent spread and improvement, for example during expansion from Southeast Asia into China and Japan7,8. Defining genome-wide diversity in cultivated eggplant will refine our understanding of domestication and facilitate the discovery and deployment of breeding relevant alleles.

The rapid expansion of eggplant genomic resources has deepened our understanding of its origin, domestication, genome organization, and evolutionary history, and has begun to resolve the genetic architecture of several agronomically important traits. To date, genome assemblies for at least eight eggplant genotypes are available. Two were generated using short read sequencing, including ‘Nakate Shinkuro’9 and ‘67/3’10,11, and six were generated using long read sequencing, including ‘HQ1315’12, ‘GUIQIE1’13, ‘NO211’14, and three additional accessions (PI180485, PI196043, and PI200854)15. Although these assemblies have served as valuable reference resources for genetic and genomic studies, they have not been comprehensively integrated into a pangenome framework. Pangenomes have transformed plant genomics by representing within species diversity more effectively and reducing reference bias. An early eggplant pangenome effort used the ‘67/3’ reference together with resequencing data from 25 accessions, identifying 816 genes absent from the reference and selective sweep regions enriched for candidate genes related to fruit color, prickliness, and fruit shape11. Using the same reference, more than 3,400 accessions were genotyped by Single Primer Enrichment Technology (SPET), supporting a model of independent domestication in Southeast Asia and the Indian subcontinent7. However, these short read based strategies are generally underpowered for detecting large structural variants, particularly insertions16. While our study was in progress, two eggplant pangenome studies were reported17,18. One constructed pangenome graphs from 40 chromosome-scale genome assemblies, whereas the other developed an Asia-representative eggplant pangenome using resequencing data from 22 accessions together with four genome assemblies. These independent efforts further underscore the value of pangenome resources for capturing within-species genomic diversity and for identifying trait-associated variants, including structural variants that contribute to key agronomic traits, such as prickliness, and Fusarium oxysporum f. sp. Melongenae resistance etc.

Genomic resources have also accelerated the dissection of agronomically important traits in eggplant, particularly fruit color and bacterial wilt (BW) resistance19–21. However, most mapping studies for these traits have relied on bi-parental populations22–25, which inherently sample only a limited fraction of the available genetic and phenotypic diversity. Genome-wide association studies (GWAS) using diverse panels offer a complementary strategy and have been applied in eggplant, but have largely focused on single-nucleotide polymorphisms (SNPs) and small insertions and deletions (InDels)22,24,26. In contrast, structural variant-informed genetic mapping in eggplant has only begun to emerge, as exemplified by a recent pangenome study published while our manuscript was under review17. This is given extensive evidence that SVs contribute to speciation and domestication and can reveal trait loci missed by SNP based GWAS, including presence absence variants15,27–30.

Here, we present population genomic analyses of a diverse panel of 226 eggplant accessions primarily collected from China and Southeast Asia, a region proposed as a major center of eggplant domestication. Using whole-genome resequencing data, we characterize population structure and genome-wide diversity and generate de novo chromosome-scale assemblies for 11 representative accessions. By integrating these assemblies and six published genomes with the resequencing dataset, we construct eggplant pangenome graphs using both reference-based and reference-free approaches. We show that this subpangenome resource enables the discovery of variants associated with complex traits. Specifically, we identify a large inversion of up to 12.4 Mb that is strongly associated with fruit color, as well as copy-number variation in candidate genes associated with bacterial wilt resistance. This pangenome resource, together with the related genes and molecular markers, accelerates biological studies and supports genomics-assisted breeding in eggplant.

Results
Genomesequence,populationstructure,andgeneticdiversity
Toexplorethegeneticdiversityandpopulationstructureofeggplantgermplasm and to help guide our sampling for de novo genome sequencing and assembly, we collected resequencing data for 226 eggplant accessions,representingphenotypicandgeneticdiversityofthecultivatedeggplant.Thepanelconsisted of219accessions ofS.melongena,twoofitsclosestwildrelatives (S.insanumandS.incanum),fourscarleteggplantaccessions(S.aethiopicum),andonewildeggplantspecies(S.violaceum)(Fig.1a,bandSupplementaryData1).TheseaccessionsweremainlydistributedacrossEastAsiaandSoutheastAsia,whichishypothesizedtobeamaindomesticationcenterandisalsothelocusof>60%ofworldeggplantproduction2,7,8.Amongthe226re-sequencedaccessions,198weregeneratedinthisstudyand28werefrompreviousstudies11–13. This approach generated 5.50 terabytes of sequencing data, with an average coverage of 20.87× per sample(SupplementaryData1).Tounderstandpopulationgeneticsofthis diverse set, we mapped these reads to a chromosome-scale assembly(describedbelow)of‘S076’,arepresentativebreedinglineofSouthChinathathaspurplefruitsandishighlyresistanttoBWdisease.Altogether,weidentified3,698,811SNPsand349,227InDels(1–50bp)ofhighquality.

To infer population structure, we performed principal componentanalysis(PCA)using153,934SNPsspanningall226accessionsandfilteredbasedonallelefrequency,missingrates,andlinkagedisequilibrium(LD).Thetopthreeprincipalcomponentsaccountedfor55.41%ofthetotalvariance(Fig.1c,d).Giventhenoticeableslowingofthecross-validation(CV)errordeclinestartingatK=4intheadmixtureanalysis,the226accessionswereassignedtofourgroups(C1,C2,C3,andC4).TheassignmentoffourgroupswasfurthersupportedbyPCA,phylogeneticanalysis,andthegeographicaloriginsofthesamples(Fig.1c–fandSupplementaryFigs.1and2).Almostallthesamplesfrom SoutheastAsia(31/35) were assigned to the C1 group. The C2 group contained accessions mainly from South America (9/12) and Europe (10/18). The majority (80.00%, 52/65) of the C3 group containedaccessionsfromawiderangeof19ProvincesinChina.TheC4group primarily consisted (80.77%, 63/78) of members from South China (especiallyGuangdongProvince).Theseresultsindicatethatthepopulationstructureofsampledeggplantsislargelycorrelatedwithgeographic distributions. Additionally, the LD among SNPs rapidly decreased at 105Kb (r2=0.5) (Supplementary Fig. 3). Nucleotide diversity(π)variedamongthegroups,withtheC3(π=0.168)andC4(π=0.183)groupssubstantiallylowerthanthatofC1(π=0.309)andC2 (π=0.224). These π values were comparable to that from two previousstudiesutilizingeitherwildAfricaneggplants(0.03~0.10)31 or a worldwide diversity panel (0.20~0.36) in eggplant7. The most geneticallydivergentgroupswereC2andC4withthehighestFstvalueof0.212,whiletheclosestgeneticdistancewasobservedbetweenC3andC4groups(Fst=0.084). Overall, the C1 and C2 groups from Southeast Asia, South America, and Europe displayed much higher geneticdiversityanddifferentiationcomparedtothetwogroupsfromChina(C3andC4).

Asub-pangenomeofgenespaceineggplant
Weused informationfrom the resequencing data(226accessions) (including ‘HQ-1315’ and ‘GUIQIE-1’) together with four published assemblies to identify 11 accessions (plus six published ones) that representallmajorclustersofaneggplantphylogenetictree(Fig.2a).Tofurtherinvestigatetheextenttowhichthese17samplesrepresentthe genetic diversity of eggplant, a phylogenetic tree comprising 3,673accessions(SupplementaryFig.4)wasconstructedbyfurtherincludingaccessionsfromtwopublishedstudies7,11.The17samplesgenerallyrepresentmostmajorclusters,withafocusoncapturingthe genetic diversity ofeggplantfromEastand SoutheastAsia.To constructa sub-pangenomeprimarilytargetingthis regionaldiversity, we de novo sequenced the 11 accessions with an average of 30.18×PacBioHiFireadsand103.79×Hi-Csequencingdataforgenomeassemblyandanchoring(SupplementaryFig.5).Theresulting11genomes had contig N50s ranging between 38.73~59.67Mb and totalassemblysizesbetween1.14~1.20Gb(SupplementaryData2).

Thesechromosome-scalegenomeassembliesallhadaBenchmarking Universal Single-Copy Orthologs (BUSCO) completeness score exceeding 98.70% and relatively more contiguous sequences comparedwiththatofthefivepreviouslypublishedassembliesexceptforaccessionNO211(Fig.2b),whichsupportedthehighqualityoftheseassemblies.Furthergenomeannotationpredictedbetween68.79%~79.85% repetitive sequences and 33,620~36,174 protein-coding genes ineach genome (SupplementaryData2).A gene-based pangenome analysis was performed utilizing these 17 genomes. The number of pan-gene families increased when more genomes were added and nearly reached a plateau after adding 17 genomes (Fig. 2c). A total of 15,406 (41.15%) core, 3,657 (9.77%) softcore, 17,889 (47.79%) dispensable, and 483 (1.29%) private gene families wereobtained(Fig.2d,e).Thecoregenefamiliesexhibitedhighergeneexpression(Fig.2f)butlowernonsynonymoustosynonymoussubstitution (K /K) ratios than the other types of genes (Fig. 2g, Supplementary Fig. 6a). Since these genomes were selected from different genetic backgrounds and represent phenotypic diversity for fruit color, fruit size, fruit shape, and disease resistance/susceptibility,itispossiblethatthedispensablegenefamiliescontributetophenotypicvariability.Amongthesignificantlyoverrepresentedgeneontology(GO)termsfordispensablegenefamilies,wespecifically identified a series of defense-related terms, such as “defense response”, “defense response to virus”, and “defense response to Gram-negativebacterium”,etc.(SupplementaryData3),whichmayprovide a potential repertoire for identifying disease resistance/susceptibility genes in eggplant. In addition, the 11 de novo sequenced genomes were also used for a gene-based pangenome analysis.Althoughthenumbersofdifferentgenefamilyclasseswereslightly different, the main conclusion and trend were highly consistent(SupplementaryFigs.6band7andSupplementaryData3).

Populationproperties,fitness,andfunctionaleffectsofstructuralvariationsineggplant
ToexploretheimpactofSVsongenomediversityandfunctionineggplant,weemployedadenovoassembly-basedapproach32–34toidentify SVs (≥ 50bp) from the genome assemblies of 16 diverse eggplantaccessions—10generatedinthisstudyand6frompreviousstudies12–15—together with genome assemblies from two outgroup species,S.insanumandS.violaceum.Bycomparingtheothereggplantgenomeassembliesagainstthereferencegenome‘S076’,weidentifiedfourtypesofSVs: insertion (INS), deletion (DEL), translocation (TRA), and inversion (INV), from the pairwise whole genome alignments (see Methods). A cumulative total of 156,540 SVs were identified, with counts per assembly ranging from 5,219 to 23,771, which were fairly consistent with their respective levels of divergencefromthereference(Fig.3aandSupplementaryData4).WethenmergedSVsbydifferenttypesacrossgenomes,resultingina total of 76,481 uniquely located calls (Fig. 3a), with nearly 60% beingaccession-specific(seeexamplesofINSandDELinFig.3b).Asanticipated,smallervariantstendedtobemorecommonthanlargerones,asshownbythesizedistributionofdeletionsandinsertions(Supplementary Fig. 8). This distribution exhibited a peak around 12kb, likely reflecting the length of long terminal repeat (LTR) retrotransposons(SupplementaryFig.8).Wealsoobserved15large(near or larger than 5Mb) inversions segregating in the eggplant population,whichwefurtherconfirmedwithsyntenyorHi-Cmaps(SupplementaryData5andSupplementaryFigs.9and10).Similartoother plant genomes35, the most abundant SVs in eggplant were caused by insertions of transposable elements, followed by the contraction or expansion of dinucleotide microsatellites (e.g., ‘ATAT’),tandemduplications,and,finally,gene-relatedandcomplexvariations(Fig.3c).WeexaminedthefrequenciesofSVsacrossourdatasetof16genomes.Near81.5%ofdeletionsandinsertionsweredetectedinonlyoneortwoassemblies,withlargeronesbeinggenerallylessfrequent,suggestingSVssegregateatlowfrequencieswithinpopulation(SupplementaryFig.11a).Tofurtherexplorethesefrequency patterns, we calculated the minor allele frequency for eachSVtype,excludingtranslocationsduetotheirlownumber.WealsocombinedINSsandDELsintoasinglecategorybecauseoftheirunknownancestralstate.Wefurtherclassifiedthemintosubgroupsbasedontheiroverlapwithdifferentgenomicfeatures,suchasLTRretrotransposons (LTR-TE), DNA transposons (DNA-TE), ‘TATA’ motif satellite sequences, or genic regions. The minor allele frequencyspectra(AFS)confirmedthelowfrequencyforSVsaswellasother variants (i.e., single nucleotide variations/SNVs and small InDels)butitrevealednocompellingpatternthatsuggestedSVsaresegregatingat significantly different frequencies than SNVs and InDels (Supplementary Fig. 11b). We therefore employed an outgroup species, S. insanum, to ascertain the ancestral state and deduced the ancestral status for ~74.3% (56,486/76,002) of deletions and insertions (Supplementary Data 6). The derived allele frequency spectrum (dAFS) showed that SVs segregate at significantlylowerfrequenciesthaneithersynonymousornonsynonymousSNPs,suggestingthatSVsare,onaverage,moredeleterious tofitness than SNPs (Fig. 3d). A similar pattern was also observed whenusingS.violaceumastheoutgroup,althoughancestralstatescouldbeinferredforasmallerproportionofsites(28.1%)(SupplementaryFig.11c).AmongtheSVclassesexamined,inversionssegregated at the lowest frequencies, consistent with previous reports36–38.

WealsoinvestigatedtheeffectsofSVsongenefunction.Overall, SVs were significantly depleted in genic regions, and only a small fraction directly overlapped coding sequences (Fig. 3e),wheretheymaydisruptgenestructure.AmongthevarioustypesofSVs,LTR-TE insertions exhibited a farther distance to genes compared to other typesofSVs(Fig.3f),likelyowingtoinsertionalbiasesoralargereffect ongeneexpression.ToinvestigatethebroadeffectsofSVsongeneexpression, we compared the expression profiles across five tissues (i.e.,leaf,root,stem,flower,andfruit)betweentwosetsofgenes:thosewith overlapping SVs and those without. We measured expression variationusingthePearsonCorrelationCoefficientbetweenthesetwogenesetswithRNA-seqdatafromeightaccessions.ThesetofgeneswithoutSVsoverlappinghadsignificantlylowerexpressionvariancecomparedtothesetofgeneswithoverlappingSVs(p<2.2e-16,Wilcoxontest;Fig.3gandSupplementaryFig.12a).ThesetofgeneswithSVsdirectlyoverlappingtheirexonsgenerallyshowedhigherexpressionvariancethangeneswithSVsinothergeniccontexts,withthisdifference being significant in most comparisons (Supplementary Fig.12b).

ThroughanalysisofRNA-seqdatafrom111eggplantaccessions, we identified 93 SV-affected genes whose fruit expression levels were significantly altered (Supplementary Data 7). For example, a 418-bpdeletionupstreamofevm.model.Chr02.3134(Fig.3h),which encodesaWAT1-relatedprotein,anda10,296-bpdeletionupstreamof evm.model.Chr02.4128 (Fig. 3i), which encodes a BTB/POZ- and MATH-domain–containing protein, were each associated with significantly reduced expression in accessions carrying the deletion. Collectively,theseresultsestablishanintegratedSV–transcriptome resource for eggplant, enabling systematic dissection of genomic diversity and the functional and fitness effects of structural variation.

Graph-basedsub-pangenomeofeggplant
TofurthercaptureSVdiversityincultivatedeggplant,wegeneratedan additionalcallsetcomprising128,575deletionsand11,925insertions(≥ 50bp)fromresequencingdataof226eggplantaccessions(198inthisstudyand28frompreviousworks),usingthreecomplementaryshort-read-basedapproaches(seeMethods).Byintegratingthiscallsetwithour assembly-based callset, we obtained a non-redundant set of 187,412SVs.UsingthisunifiedSVset,weappliedthevgtoolkit39to buildapangenomegraphwithS076asthereferencebackbone.We then used the vg pipeline to genotype graph-embedded SVs (i.e., deletionsandinsertions)acrossallaccessionsusingshort-readdata.Theresultinggenotypesyieldedaderivedallelefrequencyspectrum (dAFS) similar to that obtained from the smaller set of assembled genomes,confirmingthatmostSVssegregateatlowfrequencyinthepopulation(SupplementaryFig.11d).Becauseouraccessionsprimarily representmajorregionsofEastandSoutheastAsiaandEurope,with limitedsamplingfromotherregions(forexample,India,Korea,andJapan),werefertothisresourceasasub-pangenome.

Given that the pangenome graph above was built from a reference-basedvariantcallsetandmaythereforebesubjecttoreference bias, we additionally constructed a reference-free pangenome graphusingthePanGenomeGraphBuilder(PGGB)pipeline40tocapturepotentially missing genetic diversity. The PGGB graph, constructed from the genome assemblies of 16 cultivated eggplant accessions,contains31.8millionnodesand43.9millionedges,correspondingtoameannodedegree(i.e.,thenumberofedgesconnectedtoanode)of1.4.Thetotalsequencelengthrepresentedinthegraph (thesumofnodelengths)is1.96Gb.Deconstructingthegraphusing S076 as the reference backbone, yielding 31,793 structural variants (SVs;≥50bp),2,045,873InDels,and6,939,066SNPs.Thisreference-free pangenome graph provides an important complement to the reference-anchoredgraphandestablishesafoundationfordissecting thefunctionaleffectsofSVsonphenotypicvariationineggplant.

Alargeinversion(~12.4Mb)stronglyassociateswithfruitcolorineggplant
Thefruitcolorofeggplantisamajorqualityattributethatsignificantly influencescommercialvalue.WeinitiatedaGWASacrossoursample of201cultivatedeggplantaccessionsthatexhibitabroadspectrumof fruit colors. For simplicity, we classified the color pattern into two groups: purple (n=148) versus non-purple (i.e., green and white, n=53).Variants derivedfrom the PGGB pipeline largely overlapped those identified by reference based methods. For example, nearly 97.61%ofPGGBSNPswerepresentwithinornear2kbofGATKSNPs (Supplementary Data 8). We therefore first performed GWAS using filteredGATK-derivedSNPsandInDels,togetherwithreference-based vgderivedSVs,whichconsistentlyidentifiedamajorassociationsignal spanningalargegenomicsegmentonchromosome10(Fig.4aand SupplementaryFig.13).Inaddition,wedetectedmultiplesignificant lociacrossthegenome,includingregionsoverlappingknownanthocyanin pathway genes such as SmANS41, SmelAAT42, Sm3GT43,44, SmMYB119,45,46, and SmPAL43,47 (Fig. 4a and Supplementary Data 9). SeveraloftheselociwerealsorecoveredwhenGWASwasperformed using the reference-free PGGBderived variants, although fewer significantlociweredetected,likelyduetothesmallervariantset(Supplementary Fig. 14 and Supplementary Data 10). Together, these results indicate that fruit color is influenced by multiple loci in eggplant,withthechromosome10locusshowingaparticularlypronouncedeffectinourpanel.

By focusing on the genomic region that harbors the most significant signal on Chr10, we found significant loci spanning over a genomicsegment>10Mb(Fig.4a).Thissegmentcontainsfourpreviouslyidentifiedgenesreportedtobeassociatedwithanthocyanin synthesisineggplant19,43,48.Interestingly,thisregioncoincideswitha 12.4Mbinversionsegregatingwithinthepopulation,previouslyidentifiedinoursyntenyanalysis(SupplementaryFig.9).Thesignificant loci are enriched at the two breakpoints of this inversion (Fig. 4b). Moreover, this inversion polymorphism shows a strong correlation with purple and non-purple coloring across 14 genome assemblies (Fig.4c).UsingS.violaceum‘S098’astheoutgroup,weinferredthis inversion to be derived in accessions with purple fruit color. By focusingontheinversionbreakpointsandexaminingtheshort-read mappingresultwithS126(greenfruitwithoutinversion)asthereference,wewereabletofurthergenotypethisinversionacrossatotalof 227eggplantaccessions(198inthisstudy,28fromthepreviouswork, andthe‘NO211’)(Fig.4d).Atotalof115accessionsharborthederived inversion;ofthese,111(96.52%)havepurplefruitcolorandfourare green(Fig.4d).Ofthefourexceptionalsamples,wefound3accessions thatpossessaveryclosephylogeneticrelationship,andcontainan‘A/ T’SNPleadingtoaprematurestopcodoninSmANS19thatfunctionsin anthocyanin biosynthesis. Out of the 112 accessions without the inversion,55(49.11%)havepurplecolorand57(50.89%)displaynon-purple color (Fig. 4d). These results suggest that the inversion on chromosome 10 is strongly associated with the purple phenotype; however,ifitiscausal,itis notthesoledeterminantofpurplecolorationineggplant.

We next attempted to examine the functional impact of this inversion oncolorformation byexamining theexpression ofgenes withinandsurroundingtheinvertedregion.Wefocusedonatotalof 1178 genes annotated within a target region, which included the invertedregionaswellas500kbupstreamanddownstreamofboth breakpoints.WecomparedRNA-seqdatafromfruitbetweensamples thatwerepolymorphicfortheinversion,including55sampleswiththe inversionand55sampleswithouttheinversion.Ofthe1178genes,we identifiedonlyonegene,SmMYB1,whichexhibitedasignificantlydifferentexpressionpattern(p≤0.05,foldchange≥1.5,andTPM≥10) and is located atthe inversion breakpoint (Fig. 4e). This significant differencewasfurthervalidatedbyqRT-PCRin62eggplantaccessions (23 with the inversion and 39 without) (Supplementary Fig. 15a). SmMYB1 has previously been shown to play a role in anthocyanin synthesisineggplant19.A6-bpdeletioninitscodingregionwasfound exclusivelyin eggplantaccessions with green andwhitefruitcolors (n=26)acrosstheresequencingsamples(Fig.4f).Incontrast,those without this deletion were predominantly purple (162/193, 83.94%) (Fig. 4g and Supplementary Data 11). Moreover,the qRT-PCR result showedthateggplantaccessionslackingthisdeletionexhibitedsignificantly higher SmMYB1 expression than those with the deletion (Supplementary Fig. 15b). Therefore, SmMYB1 might be a strong candidateforinteractingwiththeinversiontoinfluencetheformation ofthepurplecolor.

TofurtherinvestigatethecausalrelationshipbetweentheinversionandtheexpressionofSmMYB1,aswellasitssubsequenteffectonthepurplecolorpattern,wecategorizedtheeggplantaccessionswith available fruit RNA-seq data into three groups: purple without inversion (n=27), non-purple withoutinversion (n=28), and purple withinversion (n=55).Pairwise comparisons ofSmMYB1 expression revealed that differential expression of SmMYB1 was observed only betweengroupswithdifferentfruitcolors,butnotbetweengroups withorwithouttheinversion(Fig.4h).Thisresultthereforeindicates thattheinversionisnotdirectlyinvolvedintheexpressionchangeof SmMYB1,raisingthequestionofhowthisinversionistightlylinkedto thepurplecolorofthefruitineggplant.

The12.4Mbinversionlinkedwithreducedgeneticdiversityandextendedhaplotypeblockineggplantpopulation
Inversionsplayaroleinawiderangeofbiologicalprocessesandtheir maintenancewithinpopulationscanbeattributedtointricateevolutionaryforcessuchaslocaladaptation,balancingselection,andnaturalselection.Amongthe15largeinversions(>5Mb)(Supplementary Data5)weidentifieda12.4Mbinversiononchromosome10thatstood outduetoitshigh allelefrequency(50.44%)withinthepopulation. This observation presents an excellent opportunity to explore how largechromosomalrearrangementscanbesustainedindomesticated populationsbytracingtheevolutionarytrajectoryoftheinversion. Our study revealed that this inversion occurred predominantly amongeggplantaccessionsfromChinafromgroupsC3andC4,with only a handful of instances (n=5) occurring in samples from C1 representingotherregions(Fig.5a).Thissuggeststhatthisinversion aroseorhasrisentohighfrequencyduringtheselectivebreedingof eggplant in China, although it remains present globally in eggplant germplasm.Toexplorethepopulationpropertiesofthisinversionand itspotentialeffectsongenomicdiversity,weclassifiedthe226eggplantsamplesintotwogroups:onewithsamplescarryingtheinversion (n=114) and the other presenting the ancestralstatus (n=112). Our analysisofnucleotidediversity(π)onchromosome10revealedthat the ancestral group (i.e., without the inversion) exhibited higher geneticdiversityacrosstheentirechromosome—approximatelytwice that of the inversion group. This difference was even more pronouncedwithintheinversionregionanditssurroundingbreakpoints (Fig. 5b). We also observed a significant increase in the Fst value (a measureofgeneticdifferentiation)acrosstheinvertedregionbetweenthetwogroups(Fig.5c).Furthermore,Tajima’sDvalues,whichcanbe anindicatorofselection,showedagreaterdifferenceinthisinverted regionbetweenthetwogroups.Collectively,thesefindingssuggest thatthisinversionisassociatedwithasubstantialreductionofgenetic diversityintheeggplantpopulation.

Weobservedthatthe12.4Mbinversionislinkedtoalargehaplotypeencompassingtheentireinvertedregion(Fig.5e).AphylogenetictreeconstructedusingSNPsfromthisinversionregionreveals thatnearlyalleggplantaccessionscarrytheinversionclustertogether, forming a distinct group atthe bottom of the tree. This suggests a singlehaplotypeamongtheseaccessions,likelyresultingfromarecent and singular origin. Additionally, we identified a large linkage disequilibrium(LD)blockof~6.0Mbwithinthisinversion(Fig.5e),indicating that recombination events in this region are significantly suppressed. The suppression of recombination within inversions is well-documented;inversionscaninhibitrecombinationbetweenheterokaryotypes,leadingtotheaccumulationofgeneticchangesandthe establishmentofdivergenthaplotypeblocks.Tofurtherexaminethe effectofthisinversiononLDpatterns,weplottedLDforeach10Mb window downstream, upstream, and spanning the inversion region. We observed substantialLDdifferencesamongthesewindows,suggesting a significant impact of the inversion on local LD patterns (Fig.5f).Consideringthesefindingsalongsidethestrongassociationof thisinversionwithfruitcolor,weproposethatitshighfrequencyinthe population is likely due to a de novo origin followed by artificial selectionduringthecultivationandbreedingofeggplant.

Genomiclociassociatedwithbacterialwiltresistance
Justasfruitcolorisacrucialagronomictrait,soistheabilitytoresist disease,particularlybacterialwilt(BW).Toidentifygenomicregions associatedwithBWresistance,weplanted197accessionsinfourbatches (three batches in 2022, one batch in 2023), and scored every seedlingoftheseaccessionsasresistantorsusceptible(deadorwilt) consecutivelyfor5weeksafterinfectionwithRalstoniasolanacearum atthe4-5trueleafseedlingstage(SupplementaryData12).Theincidencerateforeachaccessionwascalculatedasthenumberofdeador wiltseedlingsdividedbythetotalnumberofseedlings(atleast20for eachaccession).Theincidenceratesrangedfrom0~100%(Fig.6a, b).Similarly,weperformedSNP-basedGWASandInDel-basedGWAS using the filtered GATK callsets, and SV-based GWAS using the referencebasedvgderivedSVs,foreachofthefoursetsofphenotypic data. Strong association signals were primarily identified on Chr02, Chr03, Chr04, and Chr05 (Fig. 6c; Supplementary Figs.16and17;andSupplementaryData13)forSNP-GWASandInDel-GWAS,andthesewereidentifiedconsistentlyacrossdatasets.Strikingly,thetopassociatedSNPsonChr04werelocatedwithinagene, evm.model.Chr04.2518, homologous to CYP82D47 and CYP82A3, which confer resistance to powdery mildew in cucumber and to biotic/abiotic stresses in soybean49,50. Importantly, we identified a SNPlocatedwithinthefirstexonofthisgene,leadingtoapremature stopcodonthatreducedtheproteinlengthtolessthanahalf.Asa result,thegeneproductispredictedtolackmostoftheconserved domainofcytochromeP450familyprotein(Fig.6d).TheBWinci- denceratesofaccessionswithaTGG genotype were significantly lowerthanthosewithaTGA(stopcodon)genotype(p=4.90×10−11, two-sidedt-test).

By searchingcandidategenesonChr05,weidentifiedthatthetop associatedSNPsandInDelsweredirectlylocatedwithinagene,evm.model.Chr05.2618,whichwastruncatedandnotexpressed.ThistruncatedgenewashomologoustoEPS1,whichcontrolssalicylicacid(SA) biosynthesis51,52.ThisgeneregionturnedouttoalsoharboraRoq1-like homolog (directly adjacent to the truncated EPS1 homolog) after GSAmancorrectionasmentionedbelow.Thismisannotationwaslikely duetotheerroneousfusionoftwoneighboringgenes.Consistently, SV-GWASidentifiedtwoSVassociationsignals(Chr05:84617337and Chr05:84619808)onChr05(SupplementaryFig.18),whichwerealso located within the above evm.model.Chr05.2618 region (SupplementaryData13).Interestingly,analysis ofEPS1 homologsbased onthe genefamily-basedpangenomeresultrevealedsubstantialvariationin its copy numbers across the 11 genomes examined, leading us to speculate thata single referencegenome maynotreveal thewhole picture of this region. Similarly, both SNP- and InDel-based GWAS analysiswithanotheraccession‘S126’asareferencerevealedthatthe majorityoftopassociationsignalswerelocatedwithinthehomologof EPS1,evm.model.Chr05.2786,aswellaswithinaRoq1-likegene,evm.model.Chr05.2787, encoding putative disease resistance proteins (SupplementaryData13).Importantly,Roq1waspreviouslyreportedto conferresistancetothreebacterialpathogens,includingRalstoniain tomato53.

AlthoughSNP-,InDel-,andSV-GWASsignalsallconvergedonthis region,thespecificcausalvariantsorgenesresponsibleforBWresistanceorsusceptibility remained unresolved. In particular, the SNPs and InDels within these genes did not show strong effects. This promptedadetailedcomparativegenomicanalysis.Toobtainaclear landscapeofEPS1andRoq1-likehomologsinthisarea,wemanually curatedandcorrectedgenemodelsintheseregionsofthe11genomes utilizing transcriptomes of five organ types including roots, stems, leaves,flowers,andfruits,withGSAman(Fig.6eandSupplementaryData14).Asexpected,thenumberofexpressedEPS1(0~4)andRoq1-like(5~17)homologsandthelengthsofsyntenicregionswerehighly variableacrossthe11genomes.Toevaluatetheirpotentialcorrelation withBWresistance,weidentifiedandutilizedanothertwoSVmarkers in these regions, with SVe (4,956bp InDel, Chr05:84599196) differentiating accessions with expanded EPS1 homologs (≥ 3, E+) from thosewithonlyoneorzeroEPS1homolog(E-),andwithSVr(167bp InDel, Chr05:84828646) differentiating accessions with relatively moreRoq1-likehomologs(≥13,R+,like‘S076’and‘S027’)fromthose withlessRoq1-likehomologs(5~11,R-,liketheremainingaccessions). When considered together or in combination with the Chr04 locus (SmCYP82D47), the two SV markers were significantly and highly associatedwithBWresistance(Fig.6f).ByfixingtheChr04locusas either‘TGG’or‘TGA’genotype,thecomparisonrevealedthataccessionswitheitherE-_R+(indicatinglessEPS1sandexpandedRoq1-like homologs) or E+_R- (indicating expanded EPS1s and less Roq1-like homologs)genotypeweresignificantlymoreresistanttothediseasethanthosewithE-_R-(indicatinglessEPS1sandlessRoq1-likehomologs) genotype. Accessions with TGA_E-_R- genotype were the most susceptibletothedisease.Othergenotypeswerenotcompareddueto a small number of available accessions (n<10). Another approach calculating the normalized mean coverage of SmEPS1 and SmRoq1 revealed similar results(SupplementaryFig. 19).The comparisonof geneexpressionsofdifferentEPS1homologsrevealedthatthosefor genomes with multiple copies showed much higher expressions (Supplementary Figs. 20 and 21). To evaluate the functions of SmCYP82D47, SmEPS1 and SmRoq1 homologs, we carried out virus-inducedgenesilencing(VIGS)experimentstargetingtheSmCYP82D47 homolog using accession ‘S065’ (TGG_E-_R-), targeting a conserved fragment of EPS1 and Roq1 homologs using accession ‘S092’ (with TGA_E+_R- genotype) and accession ‘S050’ (with TGA_E-_R+ genotype), respectively. Results showed that the SmCYP82D47-silenced, SmEPS1-silenced, and SmRoq1-silenced plants displayed typical wilt symptomsat10daysafterinfectionwithR.solanacearum,whiletheir control plants showed less or no wilt symptoms (Fig. 6g, h). These resultssupportamodelinwhichSmCYP82D47,SmEPS1,andSmRoq1 homologs,perhapstogetherwithadditionalgenes,jointlycontribute to BW resistance. Collectively, we identified three major candidate genesorgenetypesassociatedwithBWresistance,aswellasthree associatedmolecularmarkers,includingoneSNPonchromosome4 andtwoSVsonchromosome5,thatcanbedirectlyappliedineggplant breeding for BW resistance. These three candidate genes, SmCYP82D47,SmEPS1,andSmRoq1,werealsoamongthesignificant association signals in the GWAS using PGGB-derived variants (SupplementaryFig.14andSupplementaryData10and15).

Methods
Plantmaterials,growthconditions,andphenotyping
A total of 198 eggplant germplasm accessions were obtained from SouthChinaBranchofNationalGenebankforVegetableGermplasm Resources at Vegetable Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China. This diverse collection included 193 eggplantaccessions(S.melongena), 4scarleteggplantaccessions(S.aethiopicum),and1wildeggplantspecies(S.violaceum), whichwerecollectedfrom21provincesofChina,SoutheastAsia,and South America.Toevaluate their phenotype, these accessions were planted at Baiyun Experimental Station of Guangdong Academy of Agricultural Sciences, Guangzhou, China (113.40° N, 23.39° E). Fruit colorswereobservedinthefieldforfourgrowingseasonsin2021-2023 andrecordedaspurple,green,andwhite.Toevaluatethedegreeof resistance/susceptibilitytobacterialwilt,theseedlingsat4-5trueleaf stagemaintainedinadiseasenurserywereinoculatedwithR.solanacearum (strain GMI1000, RS742, DG-1-2, 20181102). The roots of seedlingswereimmersedinapreparedR.solanacearumsuspension (10⁸CFUmL⁻¹)for20min.Followinginoculation,theseedlingswere transplanted into the field for subsequent disease development. A totaloffourbatches(threebatcheseachwithtwocompletelyrandomized blocks in 2022, and one batch with three completely randomized blocks in 2023) of plants were conducted for bacterial wilt inoculation.Eachseedlingwasevaluatedasresistant(normal,score1) orsusceptible(deadorwithwiltsymptom,score0)atfiveweekspost inoculationduringthesummersof2022and2023.Theincidencerate of each accession was calculated as the number of dead or wilt seedlingsdividedbythetotalnumberofseedlings(atleast20foreach accessionperbach).

Short-readandlong-readsequencing
GenomicDNAextractionandlibrary(350bp)constructionwereperformed by a commercial service (Biomarker Technologies, Beijing, China), which were sequenced using the Illumina NovaSeq 6000 platform(150bppaired-end)for198accessions.Forconstructionof Hi-Clibraries,youngleaveswerecollectedfrom11selectedaccessions andimmediatelyputintoliquidnitrogen.Thensampleswerecrosslinkedwith2%formaldehydeviavacuuminfiltration;2.5Mglycinewas addedtothemixturetoquenchthecrosslinkingreaction.Nucleiwere purified,digestedwithenzymeDpnⅡ,andend-labeledviabiotinylation withbiotin-14-dCTP.LigatedDNAwasshearedinto200-600bpfragmentsbysonication,whichwereend-repaired,A-tailed,andpurified. Hi-C sequencing libraries were amplified by PCR (12-14 cycles) and sequencedonIlluminaNovaSeq6000platform(150bppaired-end).
For long-read sequencing,genomic DNA was extracted from young leavesofthe11selectedaccessionsandwasusedtoconstructPacBio HiFi SMRTbell libraries. The libraries were sequenced on a PacBio SequelIIplatformtogenerateHiFilongreadswiththecircularconsensussequencing(CCS)modeintheSMRTLinksoftware(v11.0).

Denovoassemblyandchromosomeanchoring
HiFi long reads were used to generate raw contigs using Hifiasm (v0.16.1)85withdefaultparameters.TheresultingcontigsandHi-Cdata wereusedforchromosomeanchoringwithALLHiC(v0.9.8)86(--min-REs 50 --maxlinkdensity 3 --NonInformativeRabio 2) and Juicebox87 with manual adjustment. Then sequence order and direction were adjusted according to the genome collinearity with the published eggplant genome12 using Mummer (v4.0.0rc1)88 (parameter: -i 90 -l 5000).Tofurtherevaluatetheassemblyquality,weassessedthegene completenesswithBUSCO(v.5.2.2)89.Then,weobtainedthemapping ratesofshort-readstotheassembliesusingtheBWA(v2.2.1)90.

Repeatannotation
Astrategycombininghomologyanddenovopredictionwasusedto identifytransposableelements(TEs)intheassembledgenomes.TandemRepeatswereidentifiedusingTRF(v4.09)91byabinitioprediction. RepeatMasker (v4.1.092) was used for the homology-based method,employingtheRepbasedatabase93withdefaultparameters. The ab initio prediction was carried out by LTR_FINDER v1.0694, RepeatScout (v1.0.5)95, and RepeatModeler (v2.0.1)96 with default parameters.Subsequently,allrepeatsequenceswithlengths>100bp and‘Ns’(gap)<5%constitutedtherawTElibrary,whichwascombined withRepbaseforredundancyremovalbyUCLUST97.Theresultingnon-redundantTE library wassupplied to RepeatMasker forfinalrepeat identification.

Geneannotation
Toannotateprotein-codinggenes,weintegratedhomology-based,ab initio,andRNA-Seqassistedapproachestopredictgenemodels.We downloaded the protein sequences of S. lycopersicum (GCF_000188115.4), S. tuberosum (GCA_000226075.1), Capsicum annuum (GCF_000710875.1), S. melongena11, Nicotinana tabacum (GCF_000715135.1),andArabidopsisthaliana(GCA_000001735.1)from Ensembl (https://useast.ensembl.org/) and NCBI. Then protein sequenceswerealignedtothegenomeusingTblastN(E-value≤1e−5), andthematchingproteinswerealignedtothehomologousgenome sequencesforaccuratesplicedalignmentsusingGeneWise(v2.4.1)98. Forabinitioprediction,weusedAugustus(v3.2.3)99,Geneid(v1.4)100, Genescan(v1.0)101,GlimmerHMM(v3.04)102andSNAP(2013-11-29)102,103 for our automated gene prediction pipeline. For RNA-Seq assisted prediction,totalRNAswereextractedusingTRIzol(Invitrogen,USA) fromfivedifferenttissues,includingroots,stems,leaves,flowers,and fruits,whichwereusedfortranscriptomesequencingontheNovaSeq 6000platform.Inaddition,theRNAsofthesetissueswerealsopooled forfull-lengthtranscriptomesequencingonthePacBioSequelIISMRT platform(exceptforS001andS057).RNA-Seqreadswereassembled usingTrinity(v2.1.1)104forthegenomeannotation.TheRNA-Seqreads weremappedtothecorrespondinggenomeusingHISAT(v.2.0.4)105 withdefaultparameters.Thefull-lengthtranscriptsequencesofeach genome were assembled using StringTie2 (v.1.3.3)106 with default parameters.Finally,anon-redundantgenesetwasobtainedbymerginggenemodelsfromtheabovethreemethodsusingEvidenceModeler(EVM,v1.1.1)106,107andProgramtoAssembleSplicedAlignment (PASA)108.ThetRNAswerepredictedusingtheprogramtRNAscan-SE (v1.4)109.ThereRNAswerepredictedbyBlast,whileotherncRNAswere identifiedusingtheRfam(v14.1)110databasewithdefaultparameters with the Infernal software (v1.1.4)111. Gene functions were assigned accordingtothebestmatchbyaligningtheproteinsequencestothe Swiss-Prot database using Blastp (E-value≤1e−5). Genes were also functionallyannotatedusingInterProScan(v.5.31)112.

SNPandindelcalling
Raw Illumina short reads were filtered with Trimmomatic (v0.39)113 (LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:100) to removelow-qualitybasesandadaptersequences.Thecleanreadswere aligned to the ‘S076’ (named ‘R06112’) or ‘S126’ reference genome usingtheBWA(v2.2.1)90withdefaultparameters.SNPsandIndelswere calledusingtheGenomeAnalysisToolkitHaplotypeCaller(v4.2.6.1)114. Therawvariantswerefilteredusingfollowingparameters:QD<2.0, FS>60.0, MQ<40.0, MQRankSum<−12.5, ReadPosRankSum<−8.0, SOR>3.0;minorallelefrequency≥0.05,missingrate≤0.2.
Tocalculateminorallelefrequency(MAF),weusedVCFtoolswith the--freqcommand.Toestimatederivedallelefrequency(DAF),we usedthegenotypeofS.insanumintheVCFfileastheancestralstate. Specifically, when the genotype of S. insanum was “0/0”, the alternativegenotype“1/1”wastreatedasthederivedallele,andviceversa.

Phylogeneticandpopulationgenomicanalyses
Toinvestigatethepopulationstructureandphylogeneticrelationships ofeggplantgermplasmgermplasmaccessions,theSNPscalledbasedon‘S076’as the reference were further processed. The SNP dataset was pruned usingPlink(v0.98.3)115(--indep-pairwise50100.2),resultingin153,934 SNPs. A neighbor-joining (NJ) tree was constructed using theMEGA7 software116 with the wild species S. violaceum (S098) as the outgroup, and the results were visualized using the iTOL (v6) tool (https://itol.embl.de/).Thegeneticstructureandancestryinformation were inferred using ADMIXTURE (v1.30)117 (-C 0.01 -j 24) with the number of K ranging from 2 to 10. The results were subsequently visualizedusingTBtools(v1.123)118.ForPCAanalysis,thetop20principalcomponentsandtheircorrespondingeigenvalueswereanalyzed usingPlink,andtheclusteringpatternswerevisualizedusingR.The optimal grouping of the 226 accessions was inferred based on the comprehensiveconsiderationoftheirdiversephenotypiccharacteristics, phylogenetic relationships, geographical origins, PCA, and population structure.Thenucleotide diversity(π) within subgroups andfixationindex(Fst)representinggeneticdifferentiationbetween each subgroup pair were calculated using VCFtools (v0.1.16)119. The linkage disequilibrium (LD) decay was evaluated using PopLDdecay (v3.41)119,120withamaximummeasurementdistancesetat1000Kb.

Structuralvariantidentification,analysis,andgraph-based pangenomeconstruction
Weusedtwostrategiestoidentifystructuralvariantsforeggplant.A custom pipeline named SVGAP34 (https://github.com/yiliao1022/ SVGAP)wasusedtoidentifySVs(≥50bp)fromgenomeassemblies. Thispipelineconsistsofsixmajorsteps:(1)Pairwisewholegenome alignmentusingMUMmer488withthedefaultsettings.Inthisstep,the genome assembly ‘S076’ was used as the reference, and the other genomeassemblieswereusedasthequerygenomes.(2)Identification ofsyntenicandorthologousalignmentswiththeChain/Netpipeline121. (3)IdentificationofSVsfrompairwisegenomealignments.(4)Generationofanon-redundantcallsetbymergingSVsacrosssamples.(5)GenotypingeachSVagainacrosseachindividualgenome.(6)AnnotationofSVs:SVswereclassifiedintodifferenttypesbasedontheir overlapwithcorrespondinggenomicfeatures,includinginsertionsof transposableelements(LTRretrotransposonsandDNAtransposons), tandemduplications(i.e.,sequencesthatareinsertedordeletedand are homologous to their flanking regions), gene fragments, ‘TATA’ satellites, and complex SVs consisting of more than two genomic features.Usingthispipeline,weidentifiedandgenotypedatotalof 34,938deletionsand41,064insertions,ranginginsizefrom50bpto 100kb,alongwith315inversions(>10kb)and164translocations(>100 Kb).TheseSVdatasetsareavailableatZendo(https://doi.org/10.5281/ zenodo.18425195).Additionally,wealsocalledInDelsandSNPSusing thispipelinetogeneratefullygenotypedVCFfiles.Theaccuracyofthe SVGAPpipelineforSVdetectionverifiedbyPCRexperimentis87.50% (21/24)(SupplementaryFig.26).
Forshort-readresequencingdata,weusedthreeprograms-SvABA (v1.1.0)122,DELLY(v1.1.6)123,andManta(v1.6.0)124-tocallstructuralvariants(SVs)foreachsample.SvABAwasrunwiththefollowingparameters:‘svabarun-t$BAM-p20-L6-I-agermline_run-G$REF’,while MantaandDELLYwereexecutedwiththeirdefaultsettings.SVsidentified by these three programs for each sample were merged using SURVIVOR (v1.0.7)125 with the parameters: ‘SURVIVOR merge $sample.vcf.files1000211150$sample.merged.vcf’,retainingonlythose SVssupportedbyatleasttwoprograms.TofurtherconsolidateSVcalls acrosssamples,weusedtwoprograms:Jasmine(https://github.com/ mkirsche/Jasmine) and Panpop126. After comparing their results, we retainedthePanpopoutputfordownstreamanalyses,asiteffectively merges and optimizes the majority of multiallelic SVs, while nearly encompassingallthecallsmadebyJasmine(SupplementaryFig.27).
For the genome assembly – based SV callset generated using SVGAP, thegenomeofS.insanumwasobtainedfromarecentstudy;~74.32% (56,486 out of 76,002) of presence/absence variants (PAVs) were assignedanancestralstate.WealsotestedSolanumviolaceum(S098) asanalternativeoutgroupreference,butonly28.09%(21,351/76,002) ofsitescouldbeassignedancestralstates.Nevertheless,thederived allele frequencies estimated using both references were highly consistent(Fig.3dandSupplementaryFig.11c).Fortheshort-read–based SVcallset,thegenotypeofaccessionS225(S.insanum)wasusedasthe ancestralreference.
We constructed graph-based pangenomes using deletions and insertionsidentifiedbytheSVGAPpipelineandcomplementaryshort-read variant callers, using S076 as the reference and the vg toolkit (v1.55.0)39. Presence/absence variants (PAVs) were then genotyped across the eggplant population panel using short-read data. Briefly, reads weremapped to the pangenome graph with ‘vggiraffe’, read supportwassummarizedwith‘vgpack‘,andgenotypesweregeneratedwith‘vgcall’.Inaddition,webuiltareference-freepangenome graphfrom16high-qualitygenomeassembliesusingPGGB(v0.7.4)40 withdefaultsettings.Basic statisticsofthe graphweresummarized usingMultiQCv1.19127.WethendeconstructedthePGGBgraphusing S076 as the reference path to extract SNPs, InDels, and SVs. For population-widegenotyping,variantsofeachtypewereanchoredto theS076genomecoordinates,andcorrespondingpangenomegraphs weresubsequentlyconstructedusingthevgtoolkit.Thesegraphswere then used for read mapping and variant genotyping as describedabove.

Gene-basedpangenomeconstruction
To illustrate the phylogenetic relationship of the four published accessions(PI180485,PI196043,PI200854,NO211)15relativetothe226 accessions,wefirstobtainedSNPsbycomparingtheirassemblieswith thatof‘S076’usingtheSVGAPpipeline.Subsequently,thesharedSNP variantsamong the 230accessionswereusedtoconstructaphylo- genetictreeusingtheNeighbor-Joiningapproachand4-folddegen- eratesites.Besides,toinvestigatetowhatextenttheseassembliesand re-sequencing data represent the genetic diversity of eggplant, the sequencing data for over 3400 SPET-genotyped accessions were downloadedandmappedtotheS076referencegenomeforvariant callingfollowingtheGATKpipeline.CommonSNPsforatotalof3,673 accessionswereusedforphylogenetictreeconstructionwithIQ-Tree (v1.6.12)128. The protein sequences of 17 eggplant accessions were assignedtogenefamiliesusingOrthoFinder(v.2.5.4)129.Thecore(inall 17genomes),softcore(in16genomes),dispensable(in2-15genomes), andprivate(onlyinonegenome)geneswerefurtherdefinedbasedon their presence or absence in each of the 17 genomes. For the non-synonymous to synonymous substitution (K/K) calculation, the a s orthologgenepairsforallpossiblepairwisecomparisonsamongthe17 genomeswereobtainedusingJCVI(v1.3.5)128,130.Theparalogpairswere alsoobtainedbycomparingeachgenomewithitself.Onlygenepairs supported by both JCVI and OrthoFinder were retained for K/K a s comparison.K/K wascalculatedintwoways,eitherincludingonlythe a s ortholog pairs or including both the ortholog and paralog pairs. TBtools-II118 was used to calculate K/K ratios as well as for Gene a s ontology (GO) enrichment analysis. The above analysis was also appliedforasubsetof11denovogenomesusingthesameapproach.

Genome-wideassociationstudy
ThefilteredSNPs,Indels,andSVsbasedon‘S076’referencegenome were used for genome-wide association study (GWAS) analyses, respectively, to identify the genetic loci associated with traits including fruit color and bacterial wilt incidence in eggplants. In addition, the SNP- and Indel-based GWAS analyses were also performedbasedonS126asareferencegenome.Thevariantdatasets werefilteredusingPlink(v0.98.3)115withthecriteriaofmissingrate ≤0.2,allelefrequency≥0.05,andnomulti-allelicvariants.Principal componentanalysis(PCA)wasperformedusingPlink(-allow-extra-chr-pca20)toextractthetopthreeprincipalcomponents.Astandardized kinship matrix generated by GEMMA (version 0.98.1)131 (parameter:-gk2)wasusedascovariates.GWASanalysiswascarried out using the mixed linear model (LMM) in GEMMA (-lmm 1). To determine the GWAS significance threshold, the GEC software (v0.2)132 was used to calculate the threshold at the α=0.05 level. However,forquantitativetraitssuchasdiseaseincidence,arelatively relaxedcriterion(P<1×10−5)wasadoptedasasecondarythreshold. Finally, Manhattan plots were generated using the R software (v4.2.0)133 with packages CMplot (v4.4.1) (https://github.com/YinLiLin/CMplot)andqqman(v0.1.9).
Toevaluatethepotentialcorrelationbetweencopynumbersof SmEPS1 and SmRoq1 with bacterial wilt resistance, the normalized meancoveragevaluesofbothgeneswerecalculatedasfollows.The resequencing data were aligned to a custom reference sequence consistingofasingleSmEPS1(S126_evm.model.Chr05.2786),asingle SmRoq1 (S126_evm.model.Chr05.2787), and a single-copy reference gene (S126_evm.model.Chr05.2898) identified by OrthoFinder gene familyassignmentamongthe17genomes.Themeancoveragesoftwo conserved regions within SmEPS1 and SmRoq1 were obtained using Bedtools(v2.31.1;coverage-mean)134,andsubsequentlynormalizedto thatofthesingle-copygene.SampleswereclassifiedasE+orE−ifthe normalized mean coverage of SmEPS1 was greater or less than 2, respectively, and as R+ or R− if the normalized mean coverage of SmRoq1wasgreaterorlessthan15,respectively.

Quantitativereal-TimePCR
To analyze the expressions of candidate genes for fruit color and bacterialwiltresistance,fruitpeelsfromsampleswithdifferentcolors andleavesfromtheseedlingsintheVIGSexperimentwerecollected with three biological replicates. Total RNA was extracted using the MagicPure® Total RNA Kit (TransGen Biotech, Beijing, China). First-strandcDNAwassynthesizedusingtheTransScript®First-StrandcDNA SynthesisSuperMix,andqRT-PCRwasperformedwiththeTransStart® Top Green qPCR SuperMix (TransGen Biotech, Beijing, China), followingthemanufacturer’sinstructions.Three technical replicates wereperformedforeachcDNAsample.Allprimersusedinthisstudy weredesignedwithPrimer3Plus(https://www.primer3plus.com),and theprimersequenceswereprovidedinSupplementaryData18.The qRT-PCR results were analyzed using the 2−ΔΔCt method, and the SmActinwasusedasthereferencegene.

Virus-inducedgenesilencing
To assess the functions of SmCYP82D47, SmEPS1s, and SmRoq1 in eggplantinresponsetoR.solanacearuminoculation,conservedfragments of274bp,228bp,and678bptargetingSmCYP82D47,SmEPS1s, and SmRoq1 were cloned into the pTRV2 vector, respectively (SupplementaryData18).TheVIGSassaywasconductedasfollows.The Agrobacterium tumefaciens strain GV3101 cells transformed with pTRV2:00, pTRV2: CYP82D47, EPS1, and pTRV2:SmRoq1 were mixed withGV3101cellstransformedwithpTRV1ataratioof1:1,respectively. Eachmixedbacterialsolutionwasinjectedintothreetofourleavesof eggplantseedlings,whichwereincubatedat16°Cfor24hunderdark. Then,theywereculturedundernormalconditions(16hlightat28°C; 8h dark at22°C) for oneto twoweeks. Subsequently,the silenced plantswereinfectedwithR.solanacearumstrainRS742usingaroot-dippinginoculationmethod25.ThestrainRS742wasgrowninnutrient brothat28°Covernightandthensuspendedinsteriledistilledwater. Thebacterialsuspensionwasadjustedtoafinalconcentrationof10⁸ CFUmL⁻¹.Rootsoftheseedlingsweredippedinthepreparedsuspensionfor20min.Afterinoculation,plantsweregrownundera16h light/8h dark photoperiod at 30°C/32°C (day/night) for disease development.Therewereatleast10replicatesforeachtreatment.To evaluatepotentialoff-targeteffects,theVIGStargetfragmentswere alignedagainstallannotated codingsequences(CDSs)ofS092(for SmEPS1)andS076(forSmRoq1),usingBLASTnwithanE-valuecutoffof 1e−5.Similarly,thecorrespondingqRT-PCRprimerpairsweremappedusingBowtie135withtheparameters-S-f-v2-I100-X5000-y.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "You and colleagues reported the populational and pan-genome analysis of eggplant, an important horticulture crop with both scientific and economic importance. They newly assembled 9 genomes, constructed a graph-genome, investigated the effect of SVs on gene expression, and discovered candidate loci involved in eggplant fruit color and disease resistance. Overall, this work offers important genetic resources to understand the historical evolution and to improve eggplant breeding. However, I have some major concerns, e.g., the quality of SV datasets and the association results of fruit colors.\nMajor:\n1.The authors used a customized SV calling pipeline to identify SVs between newly assembled genomes and the reference genome. In this pipeline, three commonly used tools including SvABA, Delly and Manta were used, and those SVs supported by two callers were retained. Although this pipeline seemed reasonable, please offer more details regarding the SV statistics among three callers. In fact, I am more concerned about the merging of SVs from different accessions using SURVIVOR. The strategy of this tool was to collapse SVs with mutual overlaps into a non-redundant SV set, which was not sensitive enough when dealing with complex SV breakpoints. Meanwhile, the integrated populational SVs were not genotyped accurately for each accession. The authors can try additional SV mergers and cross-compare with SURVIVOR results to increase accuracy of population SV, for example, PanPop (Nat Commun, 2024), a recently developed population SV merger which had been optimized for plant species with relatively high repeat contents.\n2.The authors applied association analysis to investigate the genetic basis of eggplant fruit color. They concluded that the large inversion, 6-bp deletion in coding regions, as well as the gene expression variation of MYB1 gene were all strongly associated with fruit color. Chromosome inversions may have impact on gene expression, especially for those genes that near breakpoints. However, inversions have less impact on gene coding regions only if the coding regions overlapped with breakpoints, while it seemed that the coding variation had strongest evidence associated with fruit color (Fig. 4g)? I suggest the authors to re-organize these results to make more clarification regarding the inference of potential causal variant (coding or regulatory) of this trait.\n3.The association analysis of SVs and gene expression was performed by RNA-seq data of only 20 accessions (Line 227-247). However, the sample size was a little bit small to have high-confidence conclusions regarding SV effects on gene expression. To meet the standard of a high-quality publication in Nat Commun, I suggest the authors to further incorporate more populational RNA-seq data (>100 accessions) which can better resolve the causality between SV and gene expression, as well as to help the annotation of GWAS results through eQTL mapping.\nMinor:\n1.The pan-genes in this work was constructed by comparing 9 newly assembled and 5 previous published genomes. The authors should be aware of the potential biases of pan-gene construction originated from different gene annotation methods.\n2.Line 123-124, the genetic diversity (pai) was unconventionally high in eggplant (0.181, 0.187). It was more common to see an estimation of genetic diversity between 1.0*10-3 to 6.0*10-3 among major crops including rice, maize and millet (e.g., Nat Genet 54, 1736–1745 (2022)).\n3.Line 187, it should be ‘By comparing the other 13 genome assemblies against the reference genome of S076’.\n4.The impact of large inversion on recombination and linkage disequilibrium should be further discussed.\n5.Line 344, the authors found a 15-fold increase of genetic diversity near breakpoints of inversions. If this inversion encountered consistent artificial selection during breeding, the genetic diversity of this inversion should exhibit significant reduction due to bottleneck effects. I mean, the authors should be careful to these types of observations which might be originated from the mis-mapping of paralogous reads resulted from SVs.", "reviewer_2": "NCOMMS\nI found the manuscript beautifully detailed and compelling that there is tremendous scientific value in pangenome studies. The use of fruit color and disease resistance are excellent examples. My recommendation for major revision is because of the misportrayal of the work as encompassing global or at least dominantly Asian biodiversity of eggplant but really collections are mostly from China (a major place of great interest!) and the ways eggplant accessions are lumped into larger groupings like countries for some and continents for others makes the study feel inherently biased and hard to follow or trust. The authors should consider how they are categorizing the germplasm and arriving at their conclusions. To me, the value is in the pangenome and GWAS work, not in the domestication confirmation part, which is where the bias and lack of clarity on what the results mean really shows through.\nMy specific comments are below. Thank you for the opportunity to review this manuscript.\nline 44: change 'or' after SE Asia to 'and'\nline 47: about S macrocarpon and S aethiopicum, they are now cultivated around the world as well. I've seen plenty of S. macrocarpon growing in China (e.g. Guizhou) and both species are getting more popular in the Americas as they are consumed by people with African heritage such as Brazil's massive African diaspora. Please revise to explain they are not just locally cultivated. You may also wish to mention S. macrocarpon is consumed for its leaves as well as its fruits, and S. aethiopicum is a popular ornamental crop looking like pumpkins in floral bouquets.\nline 63: Break up the paragraph into two starting with 'the concept'\nline 64: 'the full' is impossible. 'much more' should replace 'the full'. Let's not date ourselves....\nline 65: obviating --> reducing\nline 75: I don't think the goal of 'to enhance the power of genetic discovery' is crisp enough. Simply saying 'to enable domestication and artificial selection studies to be enhanced with less bias' or something similar is more clear and direct.\nline 104,105: This sentence is in direct conflict with the sentence on lines 88-89. You mainly have China and SE Asia representation of cultivar diversity, not worldwide phenotypic and genetic diversity.\nline 125: decreased at 69kb in which group?\nline 128: South Asia is included in C1 too. If South America is pointed out, SE Asia should be pointed out. I also don't understand how the C2 and C3 could be parsed out of PCA. There is no explanation of a geographic pattern or region that is consistent with the map. The clusters are also not separated. It is not ideal to group them. Seek a less subjective grouping system.\nFig 1: I don't understand how admixture could split the populations. I would support these three or four populations based on the phylogenetic tree but there are plenty of accessions in the admixture plot that could be rearranged into different groups based on pop assignment color, like some in the purple-blue group that have more than 50% red. Maybe explaining the decision making process would be helpful here. I am also curious if you go with k=4 if the light blue are a clade of their own in the phylogeny.. If so, it would be nice to highlight on f.\nline 159: a matter of style, but it would help readers to say A total of 16,512 (44.81%) core, 3570 (9.69%) softcore, 16,...dispensable, and 391 (1.06%) private gene families were obtained ().\nFig 2a: these colors are similar to the C1-C3 purple and green. I suggest you change the colors so they don't look similar. The colors may also not be good for colorblind people. What about a dark red-orange for new genomes and black for published genomes?\nFig 2: I don't understand the calculations of GO counts and p values with fold change. What is being compared to what for examining representation as fold enrichment? The legend could be clearer if s little longer for this section .\nFig 3: Here too I cannot easily see the differences in shade. Please choose colors and shades that are more distinguishable from each other.\nline 315: 'Known color-related genes are depicted.' These could be more specifically described as the genes in the Hydroxycinnamic acid synthesis pathway that take from anthocyanin biosynthesis precursors. C4H is the known key enzyme shunting molecules to HCAAs or pigments. I would just advise the authors to make sure they highlight the literature on these pathways and not to oversimplify as color-related genes.\nFig 5a: I cannot easily see the differences between uncertain and other countries but I think this section needs some reconsideration. You have purple arising in another country very early in the tree, both with and without the inversion. The authors don't acknowledge where that came from. It was favored in China but also most accessions chosen were purple eggplants in china -- look how few non-purple eggplants there are from China. I am not convinced the inversion arose in China. It looks like it arose in multiple places. It does seem to be favored in China, so I am in agreement with the authors on that. What would make this more interesting is to get more fine-scale, where the purple eggplants without the inversion from China (the bottom cluster) could be associated with a geolocation (province level? Latitude? Ecoregion?) and so could the varieties on the top from China that are green. Then regarding the purple inversion accession from other countries, how thoroughly were those countries sampled here? Without naming the countries we cannot know. I bet one is from India and there is only one representative. This study unfortunately suffers from being biased goegraphically toward China while doing a broader landscape analysis and thus requires more precaution with caveats to not jump to conclusions about what happened in China versus the rest of Asia. Then it suffers from underutilizing the high resolution of Chinese germplasm in the study, not doing a proper landscape-level analysis.\nline 477: This stat of 65% of eggplant being grown by South America is incorrect. It seemed high so I calculated on my own, and total yield for 2021 (the most recent FAO data), defined by yield in 100g/ha, was 2322313. The proportion of that for South America is 11.2%. If you use another metric, like tons, South America is not even a percent of eggplant in tons. Asia is 94% of eggplant tons production.\nline 486: 'confined to one center' ---> make sure it's clear if China is expected to be derived form the SE Asia center, a South Asian center, or a China center. This will be confusing to readers and it sounds like the authors are unsure themselves about how Chinese germplasm relates to the domestication narratives.\nline 519: 'we ensured a comprehensive' --> you cannot ensure a comprehensive catalog of SVs from a species when you are not sampling everything and you are limited by technology. Avoid hyperbole and say 'we attempted to build a comprehensive catalog' or 'we aimed for a comprehensive catalog'.\nline 532: it would be beneficial to relate this to the experiences of breeders. has breeding for purple color ever been a challenge because this inversion was not known? Probably impossible to find such information but I am curious what reports are out there on breeding for various gradations of purple in eggplant.\nline 535: not global, YOUR samples.. Make sure to describe accurately. The big flaw in this paper is the misportrayal of the diversity examined herein. Be careful.\nline 544: studied in detail in China. Not especially in China. You didn't look enough elsewhere to say 'especially'\nline 658: citation marked twice, no closed parenthese.\nData Availability: I see August 2026 as the date the data are made public. Will that change once the manuscript is in press to align with publication?\n-Rachel Meyer", "reviewer_3": "The present study re-sequenced 198 eggplant accessions and de novo assembled nine genomes from them to represent eggplants' genetic diversity. They also identified a large inversion associated with purple fruit color and two genes associated with bacterial wilt resistance. This study provides a valuable resource for future studies, however, I have some concerns.\nMajor ones:\n(1) As claimed in the title, pangenome analysis was applied in this study, but the benefits of constructing a pangenome are unclear.\n(2) To what extent the assembled genomes can represent the genetic diversity of eggplants? Inclusion of the 25 accessions re-sequenced by Barchi et al. (2021) and the over 3400 SPET-genotped accessions sequenced later (Barchi et al., 2023) may help clarify the representativeness of this genome collection. Especially, it can help estimate the power of the pangenome in SV genotyping with short reads.\n(3) The association between the large inversion and the fruit color may not 'underlie' the formation of purple eggplant fruit. I have several questions related to this part of the analysis.\n(3.1) Line 276-311: Is the strongest signal for related to SmPAL and SmMYB1 genes on the inversion? Given that nearly half of accessions without this inversion are purple, the association between this inversion and the purple color is likely hitchhiked by the suppression of recombination, because the 6-bp deletion on SmMYB1 can better explain the expression change. There might be more surrounding SNPs and SVs with higher association. How far are the two genes, SmHIPP and SmMYB1, distant from the inversion? To further dissect the function of the large inversion and 6-bp deletion, the two variations can be combined to contrast the haplotype versus phenotype in figure 4h.\n(3.2) Line 335-337: It is more likely that this inversion was inherited from the accessions at the base of the tree, including accessions from both China and other regions, if that tree is rooted. No group information is available in this tree.\n(3.3) Line 339-352: No evidence was shown that the allele of SmHIPP linked with this inversion is beneficial to the stress response. The nearly fixed genotype within the inversion-harboring monophyletic group could be explained by narrow genetic diversity caused by founder effect rather than selection against an inversion, especially when this inversion may not be causal.\n(3.4) Line 354-362 & figure 3e & 3f: It is surprising that the LD decays much faster in the INV group than in the non-INV group, given that the nucleotide diversity is much higher in the non-INV group. I would suspect it of being an artificial error caused by using the wrong reference for physical distance calculation. The high LD could disappear if a ‘non-INV’ genome was used as the reference genome.\nBesides, since 'S076' was used as reference to talk about variants, the 'INV' group should be those harboring the 12.4-Mb inversion which are different form 'S076'.\nI also have minor concerns related to the illustration and interpretation of results and details of methods:\n(4) Need more information to illustrate the association between geographic origin and C1, C2, and C3 grouping, for example summarizing location versus population in table S1.\n(5) Line 130-131: Need more evidence to support the conclusion. Higher genetic diversity doesn’t necessarily mean that genetic variants are more important.\n(6) Line 160-162 & 729-731 & figure 2f & 2g: How were the expression profiles obtained? Are they representing the 14 genomes used in this study? Need further information on how Ka/Ks values were calculated. What was the purpose of using an outgroup in TBtools? What type of gene pairs was compared? How to pair genes if there are paralogs?\n(7) Figure 3a & 3b & line 252: The figure legend for 3b is unclear. Additionally, if the value 19,824 represents the number of SVs present in PI180, it doesn’t align with the length of the bar in 3a. The bars in 3a don’t match numbers in supplementary table S4 either. The ‘growth rate’ is sensitive to the order to include samples, so the decline is artificial.\n(8) Line 197-199: ‘S076’ is the reference genome use for comparison, then why is the 12.4-Mb inversion described to happen in ‘S076’ instead of other accessions in supplementary figure 7? is there evidence supporting the ancestry state?\n(9) Line 199-201 & figure 3c: More details are required for this analysis. Different types of TEs can be harbored in the same insertion or deletion. ‘Tandem duplication’ is not a type of TE, so it is weird to include it here. Please also define ‘the most common SVs’.\n(10) Line 201-207: More details are required in the SV calling and genotyping methods. No information is available on the pipeline (SVGAP, https://github.com/yiliao1022) used to call SVs based on genome comparison. The parameters used for each software should be provided. Additionally, how many SVs were included in the final graph? Were there any size filtering on these SVs and in the downstream analyses?\n(11) Line 208-226: The number of sampled genomes is too small to represent the real allele distribution in the population. A reasonable way is including the 201 re-sequenced accessions.\n(12) Line 229-230: Like figure 3c, definition of the SV type classification needs to be clarified. And there is no evidence supporting that LTR-TE insertions have higher ‘deleterious impacts’ on genes.\n(13) Line 231-239: Supplementary figure 6 and 7 mentioned are missing. The large sample sizes for comparisons in figure 3g increase the power, which may increase type I error. Please add a bootstrap-based method to provide a confidence interval.\n(14) Line 266-270: Signal for SmTT8 is absent in figure 4a.\n(15) Figure 6c: GWAS for different phenotypic data should be plotted separately to improve clarity. These plots can be aligned to compare the consistency of signals.\n(16) Line 414-434: Are these two selected SVs signaled in SV-based GWAS as described in line 401? What are the relative locations of selected SVs related to the target gene SmEPS1?", "reviewer_4": "In this study, the role of structural variation in skin color and bacterial wilt resistance of eggplant was discussed by pan-genomic analysis. In addition, the genetic information of 201 eggplant germplasm was analyzed to provide guidance for genetic domestication of eggplant. The results of this study are detailed and have certain value for basic research and molecular breeding of eggplant in the future. In response to this study, the reviewers have the following questions:\n1. What was the basis for the selection of S076 as a reference genome in the population structure analysis? We know that the authors assembled nine genomes from scratch, and I think the authors need to explain the basis for selecting reference genomes for population structure analysis.\n2. Please refine the genome assembly strategy and the different technical approaches to obtain a specific description of the amount of data available.\n3. It is suggested that each attached figure in Figure 2 should choose a color match with more vivid color contrast, which will help readers to distinguish better.\n4. For the obtained structural variation, the author needs to consider the molecular verification of some representative important structural variation to prove the reliability of the data.\n5. Based on numerous previous reports, fruit shape of eggplant is also an important selected trait. Has the author tried to analyze structural variation and fruit shape selection of eggplant?\n6. Regarding the classification of fruit color, we believe that the distinction between purple and non-purple is too simplified. Currently, there are abundant reports on the color of eggplant fruits, and the results obtained by the author are not enough in terms of novelty compared with previous reports. This paper systematically expounds the formation and domestication of eggplant peel color.\n7. The description of bacterial wilt inoculation methods and evaluation criteria as well as biological and technical duplication is not clear enough, please add a detailed description.\n8. This study provides a comprehensive analysis of structural variants (SVs) in eggplant; however, the SV data are not publicly available. The authors should make these data accessible to promote transparency and facilitate further research. Additionally, the description of the methods used for SV detection in insufficient. The authors should provide more detailed information about the methodologies employed for identifying the SVs.\n9. One striking finding in this study is the observation that a large inversion is strongly associated with peel color in eggplant, a phenomenon also observed in other organisms. Although the authors present compelling evidence to identify the causal variants and provide some population genetic analyses to demonstrate the potential artificial selection of this inversion, the current explanation could be further strengthened. I encourage the authors to incorporate additional population analyses or discussion to better elucidate the domestication history and evolutionary significance of this inversion.\n10. To clarify how SmEPS1 associated with bacterial wilt resistance, I encourage the authors provide more analyses, such as comparing the expression and the protein sequences among the different copies of SmEPS1."}, "author_response": "Peer Review File\nThe eggplant sub-pangenome reveals hidden structural\nvariants associated with fruit color and bacterial wilt\nresistance\nCorresponding Author: Dr Qian You\nThis file contains all reviewer reports in order by version, followed by all author rebuttals in order by version.\nVersion 0:\nReviewer comments:\nReviewer #1\n(Remarks to the Author)\n[R1C0]：We thank the referee for the careful and insightful review of our manuscript. We respond to\nyour comments point-by-point below.\nMajor:\n[R1C1]：We would like to thank the reviewer for the valuable comments, particularly regarding the\nidentification and quality of our structural variant (SV) dataset. We recognize that the quality of the SV\ndataset is essential for downstream analyses such as population genomic analyses, pangenome\ngraph construction and GWAS. To ensure that we obtained a comprehensive and accurate SV\ndataset, we employed two complementary strategies for SV identification in eggplant.\nFirst, we used a custom assembly-based pipeline called SVGAP (see Hu et al., 2025, GitHub:\nhttps://github.com/yiliao1022/SVGAP) to call SVs from 15 phylogenetically diverse eggplant\naccessions in relation to the reference genome (S076). This pipeline allowed us to identify 34,938\ndeletions and 41,064 insertions (ranging from 50 bp to 100 kb), as well as other complex variants,\nsuch as 315 inversions and 164 translocations (> 10 kb) (see Fig. 3a). The accuracy of this pipeline\nhas been extensively validated, with experimental verification (see Hu et al., 2025 and our PCR\nexperiments Supplementary Fig. 21) confirming up to 87.5% accuracy and a low false positive rate.\nGiven the broad representation of eggplant germplasm in our selected accessions (from a pool of\n3,673 accessions, Supplementary Fig. 5), we believe this sample set captures a substantial\nproportion of the major structural variants in the eggplant population, providing a solid foundation for\nfurther studies.\nSecond, we applied three short-read-based methods—SvABA, Delly, and Manta—to call SVs from\n201 resequencing datasets, which have an average depth of 20x, ensuring high-quality SV calls.\nThese methods were chosen based on their superior performance in previous benchmark studies\n(see Kosugi et al., 2019; Cameron et al., 2019). We apologize for not clearly presenting these\nmethods and data in the original manuscript. In the revised version, we have introduced the details of\nrunning these methods (see Line 772-807). We appreciate the reviewer’s suggestion to compare\ndifferent methods for merging results, which has significantly improved our understanding of the SV\ndataset.\nAs the reviewer correctly pointed out, the SURVIVOR + Jammin pipeline tends to collapse\noverlapping SVs into a non-redundant SV set, which is less sensitive to complex SV breakpoints.\nConsequently, it detected fewer SVs compared to the SURVIVOR + PanPop pipeline (97,485 vs.\n140,500). Importantly, the calls from the former are highly covered by the latter (up to 99%, see\nSupplementary Fig. 22). We have reported both SV datasets in the revised manuscript, with the final\nanalysis based on the results from the SURVIVOR + PanPop pipeline.\nWe once again thank the reviewer for the valuable feedback. We hope our revised analyses and\nexplanations address your concerns and provide a clearer understanding of the methods used to\nconstruct our SV dataset.\nReference\nCameron, D.L. et al. 2019. Comprehensive evaluation and characterisation of short read general-\npurpose structural variant calling software. Nature Communications, 10(1), p.3240.\nKosugi, S. et al. 2019. Comprehensive evaluation of structural variation detection algorithms for\nwhole genome sequencing. Genome Biology, 20, pp.1-18.\nHu, M. et al., 2025. Benchmarking, detection, and genotyping of structural variants in a population of\nwhole-genome assemblies using the SVGAP pipeline. doi:\nhttps://doi.org/10.1101/2025.02.07.637096\nZheng, Z. et al. 2024. A sequence-aware merger of genomic structural variations at population scale.\nNature Communications, 15(1), p.960.\n[R1C2]：We thank the reviewers for raising this important concern, which has encouraged us to\nreconsider the relationship between the inversion and fruit color, as well as other genomic variants\npreviously identified by us and others (You et al., 2023; He at al., 2019; Zhou et al., 2022), such as\nthe 6-bp deletion in the coding region of SmMYB1 and its association with fruit color. Indeed, there is\nno direct evidence to suggest that the inversion is the causal variant underlying the formation of\npurple fruit color in eggplant, as it does not affect any coding regions of known genes related to fruit\ncolor.\nWhile we observed that eggplant accessions carrying this inversion have significantly higher\nexpression levels of SmMYB1 compared to accessions without it based on 111 RNA-seq data,\nsuggesting that the inversion may influence SmMYB1 expression (see Fig. 4e). We further divided\nthe accessions without the inversion into two groups: one group with purple fruit and the other with\nnon-purple fruit (e.g., green or white). We found no significant difference in SmMYB1 gene\nexpression between the accessions with and without the inversion when the fruit color was purple\n(see Fig. 4f). However, a significant difference in SmMYB1 expression was observed between the\ngreen or white fruit group and any of the purple groups, regardless of whether they carried the\ninversion (see Fig. 4f). This pattern was further confirmed by qRT-PCR analysis (see Supplementary\nFigure 15). Therefore, these results suggest that the inversion may have no effect on SmMYB1\nexpression.\nMoreover, our analysis of gene expression across all the inversion regions did not reveal any other\ngenes associated with the inversion (see Fig. 4e), further indicating that this inversion may not affect\ngenes related to purple fruit color. This conclusion was also supported by the eGWAS analysis, but\nwe didn't detect any confident signals. Therefore, the eGWAS result was not added in the manuscript.\nGiven that we were unable to find direct evidence linking the inversion to fruit color, we reconsidered\nthe population history of this inversion. The association between this 12.4 Mb inversion and fruit color\nis likely due to a founder effect (which is also suggested by reviewer 2 and reviewer 3), where the\ninversion may have originated from a few founder lines and subsequently spread to higher\nfrequencies in specific populations or regions, such as South China, where purple fruit is preferred.\nThis hypothesis is supported by the narrow genetic diversity of the eggplant accessions carrying the\ninversion (see Fig. 5b) and the long homologous haplotype blocks across the inversion region (see\nFig. 5e). Based on this hypothesis and the available evidence, we propose an evolutionary model for\nthe inversion and the evolution of purple fruit color (see Supplementary Figure 20). We also\nacknowledge that including more eggplant germplasm from other geographic regions could further\nvalidate this hypothesis and provide a clearer understanding of the evolution of purple fruit color in\neggplant. We also believe this inversion could serve as an example for studying and interpreting the\nrelationship between inversions and phenotypic traits. As suggested by the reviewer, we reorganized\nour statement of the relationship between the inversion and fruit color, see changing in the Result\nsection at (Line 408-416) and Discussion section at (Line 602-607). We thank the reviewer again for\nhighlighting this important issue and for agreeing with our interpretation of the inversion and its\npotential relation to fruit color.\nReference\nYou, Q. et al. 2023. Mapping and validation of the epistatic and genes controlling anthocyanin\nbiosynthesis in the peel of eggplant (Solanum melongena L.) fruit. Hortic Research, 10:uhac268\nZhou, X. et al. 2022. Integrated metabolome and transcriptome analysis reveals a regulatory network\nof fruit peel pigmentation in eggplant (Solanum melongena L.). International Journal of Molecular\nSciences, 23(21), p.13475.\nHe, Y. et al. 2019. Comparative transcription analysis of photosensitive and non-photosensitive\neggplants to identify genes involved in dark regulated anthocyanin synthesis. BMC Genomics, 20,\n678.\n[R1C3]：We would like to thank the reviewer for this valuable suggestion. As recommended, we\nhave collected additional RNA-seq data from the fruit, bringing the total to 111 eggplant accessions\n(Line 272). These new RNA-seq data have been submitted to a publicly available dataset and are\naccessible for community use (see CNGB database under Project accession number CNP0006177).\nWith this expanded dataset, we were able to confirm our previous findings, providing greater\nconfidence in the results that a substantial number of structural variants (SVs) have a significant\neffect on gene expression in eggplant, especially the SVs that are very close to genes (see\nSupplementary Table 7, Fig. 3h and 3i). Additionally, we used the new RNA-seq data (for fruits) to\nconduct eGWAS analysis for fruit color. However, we did not obtain a confident signal. Therefore, the\neGWAS result was not added in the manuscript.\n[R1C4]：Thank you for this insightful point! We have added two more newly assembled and one\nmore recently published genomes, adding up to 11 newly assembled and six published genomes. It is\na good point that different annotation pipelines may lead to different gene models, therefore causing\nbias in the gene-based pangenome construction. To ameliorate this question, we carried out two\nversions of the gene-based pangenome construction. Version 1 used gene annotations from all 17\ngenomes (11 de novo + 6 published) with 6 published annotations (Figure 2c-h), while version 2 only\nused gene annotations from the 11 de novo genomes with the same annotation pipeline\n(Supplementary Fig. 8). We noticed that while the numbers of different gene family classes were\nslightly different between the two versions, the general conclusion and trend were quite consistent.\nFor example, the order of Core, Softcore, Dispensable, and Private gene families when comparing\ngene expressions and Ka/Ks values, and the enriched GO terms associated with “defense” remained\nsimilar between the two versions. However, for the Ka/Ks analysis, version 2 was able to separate\nDispensable and Private gene families well (significantly different), while this comparison was not\nsignificantly different in version 1. This may imply that utilizing gene annotations from the same\npipeline may be more accurate/comparable, alternatively, this could be due to the different number of\ngenomes being used.\nWe have presented both versions in the manuscript, with version 1 presented in Figure 2c-h and\nversion 2 presented in Supplementary Fig. 8. Considering that the gene-based pangenome was not\nutilized in the following gene mining regarding fruit color and bacterial wilt disease, plus when it\ncomes to comparison of specific candidate genomic regions associated with bacterial wilt resistance\namong different genomes (11 de novo), we did manually correct all gene models within these regions\nusing GSAman and transcriptomes from five different organs. We believe this bias should have\nminimal impact on the major conclusion and biology of the current study. We have updated the\nmanuscript (Line 182-185).\n[R1C5]：Thank you for this point. The genetic diversity (π values) reported in the current study\n(0.181-0.323) did look relatively higher than that reported previously in several major crops, including\nrice (0.024-0.030) (Huang et al., 2012), maize (8.47×10-3 to 1.04×10-2) (Chen et al., 2022), soybean\n(0.0012-0.0029) (Zhou et al., 2015), and millet (0.00042-0.00067) (Chen et al., 2023). Due to a limited\nnumber of studies mentioning π in eggplant, we could only found two previous studies that reported π\nvalues ranging between 0.03~0.10 in African wild eggplants (Omondi et al., 2024), and between\n0.20~0.36 for an eggplant worldwide diversity panel from different geographical areas (Barchi et al.,\n2023). Therefore, we believe the relatively higher π values were most likely due to species difference.\nWe have updated this information in the manuscript (Line 131-133).\nReferences\nBarchi, L. et al. 2023. Analysis of > 3400 worldwide eggplant accessions reveals two independent\ndomestication events and multiple migration‐diversification routes. The Plant Journal, 116(6),\npp.1667-1680.\nChen, L. et al. 2022. Genome sequencing reveals evidence of adaptive variation in the genus Zea.\nNature Genetics, 54(11), pp.1736-1745\nChen, J. et al. 2023. Pangenome analysis reveals genomic variations associated with domestication\ntraits in broomcorn millet. Nature Genetics, 55(12), pp.2243-2254\nHuang, X. et al. 2012. A map of rice genome variation reveals the origin of cultivated rice. Nature 490,\n497–501.\nOmondi, E.O. et al. 2024. Landscape genomics reveals genetic signals of environmental adaptation\nof African wild eggplants. Ecology and Evolution, 14(7), p.e11662.\nZhou, Z. et al. 2015. Resequencing 302 wild and cultivated accessions identifies genes related to\ndomestication and improvement in soybean. Nature Biotechnology, 33, 408–414.\n[R1C6]：Thank you for this suggestion. We revised this sentence to “By comparing the other 13 genome assemblies against the reference genome of S076” in the manuscript (Line 215-216).\n[R1C7]：We thank the reviewer for raising this point. Generally, inversions suppress recombination,\nleading to long regions of linkage disequilibrium (LD). In our previous analysis of LD across the\ninversion, we made a mistake in the calculation by separating all accessions into two groups: one\nwith the inversion and the other without. While we found that the inversion group showed a higher\ndecay in LD than the non-inversion group, this was surprising given that the inversion group displays\na narrower genetic diversity than the non-inversion group.\nUpon closer examination, we found that the subpopulation of eggplant accessions carrying the\ninversion exhibited a long, homogeneous haplotype with very few SNPs (around 10% of the original\nSNPs detected across all accessions, 565 vs. 654,432). We mistakenly used these SNPs for the LD\ncalculation, which resulted in an unexpectedly higher LD, and this was incorrect. We have now\ncorrected this by examining the haplotype structure of the region. Our updated analysis revealed that\nthe inversion forms nearly a single haplotype across the inversion region, suggesting that very few\nrecombination events have occurred since the inversion originated. Consequently, the long\nhomologous haplotype leads to a long LD region. We have updated these results in Fig 5e and Fig 5f,\nas well as the Discussion section for more discussion at Line 596-607.\n[R1C8]： We thank the reviewer for raising this concern. Regarding the inversion, we now believe\nthat it likely originated from a small number of purple-fruited eggplant accessions (founder effect), and\nits increasing frequency in the eggplant population can be attributed to consistent artificial selection\nduring breeding, which aligns with the reviewer’s suggestion. We completely agree with the\nreviewer’s point that mis-mapping can lead to overestimating genetic diversity near the breakpoints of\nSVs. In our case, although we cannot entirely exclude this possibility, we believe it does not affect our\nconclusions. First, the increased genetic diversity is not confined to the breakpoint region but extends\nacross the entire chromosome and the whole inversion segment. Second, the heightened diversity\naround the breakpoints of the inversion extends over several megabases. Given that the inversion is\nrelatively recent, the sequences at the breakpoints have not changed significantly. To better convey\nthis, we revised the sentence to: \"Our analysis of nucleotide diversity (π) on chromosome 10 revealed\nthat the ancestral group (i.e., without the inversion) exhibited higher genetic diversity across the entire\nchromosome—approximately twice that of the inversion group. Notably, this difference was even more\npronounced within the inversion region and its surrounding breakpoints (Fig. 5b).\" (See changes in Line\n390-394). Additionally, to strengthen our results, we performed a haplotype analysis and found that\naccessions carrying the inversion almost form a single haplotype with very few variants (Fig. 5e). This\nfinding further explains the significantly higher genetic diversity in the non-inversion group compared\nto the inversion group. We hope the reviewer is satisfied with our changes.\nReviewer #2\n(Remarks to the Author)\n[R2C0]：We are grateful to reviewer #2 for the positive assessment of our work and critical remarks.\nWe carefully consider and respond to the reviewer’s concerns and comments below.\n[R2C1]：Thank you for your comment! We have changed the ‘or’ to ‘and’ after SE Asia (Line 44).\n[R2C2]：Thank you for pointing this out and a great suggestion! We have revised accordingly by\nadding “S. aethiopicum and S. macrocarpon are cultivated around the world as well”. And we also\nadded the information about these two cultivated eggplant species as suggested: “S. aethiopicum is a\npopular ornamental crop looking like pumpkins in floral bouquets, and S. macrocarpon is consumed\nfor its leaves as well as for its fruits.” (Line 47-49)\n[R2C3]: Thank you for this great suggestion! We have splitted the paragraph into two (Line 66-67) to\nmake our manuscript more logical.\n[R2C4]：Thank you for pointing this out! We have replaced ‘the full’ with ‘much more’ (Line 68).\n[R2C5]：Thank you! We have replaced ‘obviating’ with ‘reducing’ (Line 68).\n[R2C6]：Thank you for pointing this out! To make the sentence more clear and direct, we revised the\nsentence as “to enable domestication and artificial selection studies to be enhanced with less bias” (Line 79-80).\n[R2C7]：We would like to thank the reviewer for this important comment! It is true that the\nrepresentativeness of our accessions is limited. To investigate to what extent our assemblies and\nsequencing data could represent the genetic diversity of eggplants, as suggested by the comment\n[R3C2] from Reviewer#3, we have constructed a new phylogenetic tree (Supplementary Fig. 5) by\nincorporating the sequencing data for a worldwide panel with over 3400 SPET-genotyped accessions\n(Barchi et al., 2023) and the re-seq data for 25 accessions (Barchi et al., 2021). The result showed\nthat the accessions from our study basically covered most of the major branches of the tree.\nHowever, the limitation is the few number of accessions for some branches. We have deleted\n“worldwide” in this sentence (Line 108).\nReference:\nBarchi, L. et al. 2023. Analysis of >3400 worldwide eggplant accessions reveals two independent\ndomestication events and multiple migration-diversification routes. The Plant Journal, 116, 1667–\n1680.\nBarchi, L. et al. 2021. Improved genome assembly and pan-genome provide key insights into\neggplant domestication and breeding. The Plant Journal, 107, 579–596.\n[R2C8]：Thank you for this point. The LD among SNPs rapidly decreased at 69 Kb (r2=0.5) in the\npopulation of 201 accessions. We have added this information (Line 134).\n[R2C9]：Thank you for this insightful point! We apologize for this confusion. It was hard to visualize\nthe separation of C1, C2, and C3 in the PCA figure, probably due to the inclusion of outgroup\nsamples, which made the samples stacked together. We have provided another Supplementary\nFigure (Supplementary Fig. 2) without outgroup samples, which should make the three groups more\nseparable. This grouping was actually summarized based on overall consideration of results from the\nphylogenetic tree, population structure, geographical information, as well as the PCA (Figure 1,\nSupplementary Fig. 1-3, Table S1). More details regarding the geographical information of the\nsamples have been updated in the manuscript (Line 123-128) as follows. Almost all the samples from\nSoutheast Asia (32/34) and South America (11/13) were assigned to the C1 group. The C2 group mainly\nconsisted (74.19%, 46/62) of members from South China (especially Guangdong Province). The majority\n(80.82%, 59/73) of the C3 group contained accessions from a wide range of 17 Provinces in China. We\nhope our revisions could address your concerns.\n[R2C10]：Thank you for this great point! It is interesting and true that if we go with higher K values,\nsuch as K=4 or K=5, more sub-groups may show up as their own clusters in the phylogenetic tree.\nPlease kindly refer to our response to [R2C9], this grouping was actually summarized based on\noverall consideration of results from the phylogenetic tree, population structure, geographical\ninformation, as well as the PCA (Figure 1, Supplementary Fig. 1-3, Table S1). It seems that the\ngrouping with three groups could be reasonably explained by the geographic information. (Line 123-\n128)\n[R2C11]：Thank you for this comment! We have revised this sentence accordingly as suggested\n(Line 171-173).\n[R2C12]：We apologize for this unfriendly color pattern. As suggested by the reviewer, we have now\nchanged the color of Figure 2a to dark red-orange and black, and also updated the colors of Figure 2\nin the revised manuscript.\n[R2C13]：Thank you for this point! We have made more clarifications in the figure legend to make it\nclearer. The GO enrichment analysis was carried out by comparing the GO terms associated with\ndispensable genes to the background of all GO terms associated with all genes in either the 17\ngenomes (version 1, Figure 2h) or the 11 genomes (version 2, Supplementary Fig. 8f). GO counts\nindicate the number of genes associated with a certain GO in dispensable genes. Fold enrichment\nscore was calculated as (No. of dispensable genes with a certain GO/No. of dispensable genes)/(No.\nof all genes with a certain GO/No. of all genes). The Figure legend has been updated (Line 205-208).\n[R2C14]：Thank you for pointing this out. We have revised the colors in Figure 3.\n[R2C15]：Thank you for this comment! We apologize for this inappropriate statement. We have\nrephrased this sentence as “Known anthocyanin biosynthesis pathway-related genes are depicted”\n(Line 357). We also provided more details of these genes including their literatures in Table S8.\n[R2C16]：We thank the reviewer for the thoughtful comments and valuable suggestions. We\nacknowledge that our sample is geographically biased towards China. However, we have made an\neffort to demonstrate how our sample represents the diversity of eggplant by including it in a\nphylogenetic analysis with over 3,600 accessions, based on SPET genotyping data. While this\nanalysis confirmed that our sample is indeed biased towards Chinese-origin accessions, it also shows\nthat our samples encompass major branches of the global phylogenetic tree.\nRegarding the representation of our sample, we can confidently state that it primarily reflects the\nSoutheast Asia domestication center (Barchi et al., 2023), with China representing one subpopulation\nof this center. Additionally, we have included resequencing data from 25 more eggplant accessions,\nmainly from European countries such as Italy and Spain, in the revised manuscript. This new data\nreveals that the inversion is present in four Italian accessions, which supports the reviewer's insight\nthat the inversion likely arose in multiple regions, making it difficult to attribute its origin solely to\nChina. The high frequency of the inversion in China may be associated with the selection for purple\ncolor traits in the region. A model illustrating the evolution of the inversion is provided in\nSupplementary Figure 20. However, several questions remain open, particularly regarding the origin\nof the inversion.\nAs suggested by the reviewer, we also explored whether the inversion correlates with provincial level,\nlatitude, or ecoregion. We did not find evidence to suggest a relationship, as the inversion is randomly\ndistributed across provinces and latitudes. Further studies with more geographically diverse samples\nand related wild accessions may help clarify the evolutionary dynamics of the inversion.\n[R2C17]：We apologize for this grammar error leading to incorrect information. We were trying to\nemphasize that our samples are mostly from Asian countries, which contributes a large proportion of\nglobal eggplant production. We have double-checked the FAO website\n(https://www.fao.org/faostat/en/#data/QCL/visualize), and obtained the global production share of\neggplant for Asia (93.5%, 2021-2023, sum). We have revised this sentence accordingly (Line 537-\n538).\n[R2C18]：We apologize for this misportrayal of information. In order to not make confusions, we\nhave updated this sentence as “Considering that our samples are primarily from China and Southeast\nAsia” (Line 547-548).\n[R2C19]：We apologize for this misportrayal of information. Thank you for the suggestion! We totally\nagree that the limitations in sampling and technology will not be able to ensure a comprehensive\ncatalog of SVs. So we revised the description as “we attempted to build a comprehensive SV catalog”\nas suggested (Line 581).\n[R2C20]：We thank the reviewer for bringing up this point. Selecting for purple color in a breeding\nprogram should not pose significant challenges. In our lab, we have developed several genetic\nmarkers associated with important genes, such as SmMYB1, SmANS, SmDFR, and SmAPRR2-like,\nwhich have been linked to fruit colors, including purple, white, and green. We still anticipate that\ndeveloping a marker for this inversion could be beneficial for purple color breeding.\nAs is well-known, the formation of anthocyanins in eggplant peel color is complex, as it is influenced\nby light, temperature, and other environmental factors. To our knowledge, there are a few reports\naddressing the different gradations of purple in eggplant. However, we previously developed two\nmarkers related to dark purple and purplish-red hues over a decade ago (Liao et al., 2009).\nReference\nYi, L., SUN, B. J., SUN, G. W., LIU, H. C., LI, Z. L., LI, Z. X., & CHEN, R. Y. (2009). AFLP and SCAR\nmarkers associated with peel color in eggplant (Solanum melongena). Agricultural Sciences in China,\n8(12), 1466-1474.\n[R2C21]：We apologize for this misportrayal of information. We have deleted the word “global” and\nchecked the manuscript to avoid this kind of description in the whole manuscript (Line 597).\n[R2C22]：Sorry for this misportrayal of information. Since the writing has been re-organized, we have\ndeleted this sentence.\n[R2C23]：We apologize for this mistake. We have removed one duplicate citation and closed the\nparentheses (Line 714).\n[R2C24]：Thank you for this question! Yes! Of course. We will contact the CNGBdb manager to\nrelease our data immediately to make our data publicly available.\nReviewer #3\n(Remarks to the Author)\n[R3C0]：We thank the referee for the careful and insightful review of our manuscript. We respond to\nyour comments point-by-point below.\n[R3C1]: Thank you for this insightful point! Pangenomes have been playing an increasingly important\nrole in plant genetic and genomic research due to their privilege in representing more genetic\ndiversity and minimizing the bias caused by using a single reference genome (He et al., 2024). Apart\nfrom the gene-based pangenome construction, several strategies are available for pangenome\nconstruction at the DNA sequence level, such as map-to-pan, whole genome alignment, and graph-\nbased (reference-based or reference-free) strategies. In our study, we used the VG method for\npangenome graph construction and subsequent population genotyping using next-generation\nsequencing short reads. The full utilization of pangenomes could also be challenged by technical\nlimitations, such as genotyping a population using short reads.\nIt is admittedly that the pangenome analysis in the current study did not play a major or direct role in\nsome major analyses such as SV characterization and association mapping for fruit color and\nbacterial wilt resistance. However, we believe the pangenome was important and did facilitate several\nfindings as below. (1) The original identification of gene number variations of EPS1 benefited from the\ngene number comparisons in the gene-based pangenome analysis. (2) The analysis of population\ncharacteristics of SVs (Figure 3d) was only possible because of the availability of the pangenome\ngraph and subsequent populational genotyping. (3) The SVs genotyped based on the pangenome\ngraph were used for SV-GWAS (Supplementary Fig. 14 d, e and Supplementary Fig. 18), which\nfacilitated gene mapping and candidate gene identification, especially for bacterial wilt resistance\nGWAS. The two SVs (SVe and SVr) differentiating gene copy numbers of EPS1 and Roq1 were also\nidentified based on their populational genotypes. (4) The graph-based pangenome and associated\npopulational variation data are also a great resource for future mapping numerous other important\ntraits of eggplants, which shall benefit the research community of eggplants.\nReference\nHe, W. et al. 2024. The developments and prospects of plant super pangenomes: demands,\napproaches and applications. Plant Communications, 6:101230.\n[R3C2]: Thank you for this great point! We have downloaded the re-seq data for 25 accessions\n(Barchi et al., 2021) and sequencing data for over 3400 SPET-genotyped accessions (Barchi et al.,\n2023), and performed variant calling using S076 as the reference with the GATK pipeline. By using\ncommon SNPs, we finally obtained a vcf file containing genotypes of 3,673 accessions at 710 SNP\nloci, which was further used to construct a phylogenetic tree with IQ-TREE (Supplementary Fig. 5).\nThe tree was rooted using S. incanum as the outgroup. By highlighting the 11 de novo assembled\nand 6 published genomes, we observed that this set of 17 genomes could well represent the genetic\ndiversity of eggplants. Although a few branches were not covered by an assembled genome, the re-\nsequenced accessions within them could also capture a certain proportion of their genetic variations.\nOverall, we believe the 17 genomes together with re-sequencing data can decently represent a large\nproportion of genetic diversity of eggplants. This has been updated in the manuscript (Line 156-160).\nReference:\nBarchi, L. et al. 2023. Analysis of >3400 worldwide eggplant accessions reveals two independent\ndomestication events and multiple migration-diversification routes. The Plant Journal, 116, 1667–\n1680.\nBarchi, L. et al. 2021. Improved genome assembly and pan-genome provide key insights into\neggplant domestication and breeding. The Plant Journal, 107, 579–596.\n[R3C3]: We thank the reviewer for raising this important concern. After further consideration and\nadditional analyses, we agree that the large inversion is more likely not the causal variant underlying\nthe formation of purple eggplant fruit. Instead, our new analyses suggest that the increased frequency\nof the inversion may be a result of genetic hitchhiking associated with the selection for purple color\nduring domestication or breeding processes, potentially driven by a founder effect (as knowledgeably\nsuggested by the reviewer) or artificial selection. We now respond to the other questions one by one\nbelow.\n[R3C3.1]: We thank the reviewer for their helpful explanation of the observation and valuable\nsuggestions! GWAS based on SNPs, InDels, and SVs consistently identified a large number of\nsignificantly associated loci with the purple color across the inversion region (see Fig. 4a,\nSupplementary Fig. S14). At least four genes previously identified as being related to color\nformation in eggplant, including SmCHI (P-value = 7.78e-09), SmPAL (P-value = 6.87e-08), SmMYB1\n(P-value = 5.25e-25), and Sm3GT (P-value = 1.98e-09), were found to be near or overlap with the\nsignificantly associated loci. The strongest GWAS signals we detected were mostly in SmMYB1 (P-\nvalue = 5.25e-25), which is located very close to the breakpoint of the inversion (~5 Kb apart).\nSmHIPP is located near the other breakpoint (for further discussion on this gene, please refer to our\nresponse to [R3C3.3] below). We did not detect any loci with stronger associations than those linked\nto SmMYB1.\nWith additional analyses, we agree with the reviewer that the association between the inversion and\npurple peel color formation can be better explained by genetic hitchhiking (for further explanation, see\nour response to your comment [R3C3.2] below). We hypothesize that genetic hitchhiking involves not\njust a single genetic locus, but rather a combination of multiple genes related to fruit color formation,\npotentially including genes within the inversion, such as SmMYB1, SmCHI, SmPAL, and even genes\nfrom other chromosomal regions.\nAccessions carrying the 6-bp deletion are non-purple and are exclusively excluded from the inversion,\nindicating that the inversion likely arose in accessions without this 6-bp deletion. The combination of\nthese two variants helps to better explain our proposed model of the evolution of the inversion and\nfruit color in eggplant (see Supplementary Fig. S20).\n[R3C3.2]: We appreciate this insightful comment. Tracing the origin of this inversion without complete\ngermplasm resources is indeed a challenging task. Although most of the eggplant accessions\ncarrying this inversion are from China, some accessions from other regions (like Italy, see\nSupplementary Table 9), as well as those at the base of the tree, also carry the inversion (see Fig.\n5a). Therefore, it is reasonable, as the reviewer commented, that this inversion may have originated\nfrom accessions at the base and existing for a long time. However, we propose that this inversion is\njust a new mutation, and originally occur in only a smaller subset of accessions with purple fruit, then\nit likely underwent expansion in China, though it may have also occurred in other regions.\nThis hypothesis is supported by three lines of evidence: First, the breakpoints of this inversion in the\ngenome are very clear (see Fig. 4c), indicating that this inversion is a relatively recent mutation, and\nthe breakpoints remain novel. Second, accessions carrying the inversion exhibit relatively low genetic\ndiversity compared to those without the inversion, likely due to a founder effect or bottleneck.\nThird, and most importantly, when we used only the SNPs from the inversion region to construct the\ntree, we found that no accessions at the base of the tree carried the inversion (see Fig. 5e). All accessions\nharboring the inversion clustered together at the bottom cluster of the tree. This suggests a\nmonophyletic group of inversion-harboring accessions. The accessions at the base of the tree, which\nwere constructed using genome-wide variants, may not accurately represent the evolutionary history\nof local regions. These accessions could be explained by introgression or recombination during\nbreeding and exchange.\nWe apologize for the lack of group information in Fig. 5a. In the revised manuscript, we have added\nthis information to the tree, highlighted with different colors.\n[R3C3.3]: We thank the reviewer for pointing this out and helping to explain the narrow genetic\ndiversity of the inversion group. We apologize for overstating the function of SmHIPP. While we\ninitially proposed that SmHIPP might be associated with stress response based on the functional\nannotation of this gene and the reported functions of its homologs in Arabidopsis, we now believe this\nmay not be the case. We highlighted this gene because we hypothesized that the higher frequency of\nthe inversion gained during domestication could be beneficial due to its tight linkage with the\npreferred purple peel color during eggplant breeding, as well as potentially other traits like stress\nresponse. However, upon further analysis, we have demonstrated that the inversion has no effect on\ngene expression across the inversion region (see Fig. 4h and Supplementary Figure 15). Therefore,\nwe have removed the description of this gene in the revised manuscript.\nMotivated by the reviewer’s comment, we performed additional analysis to trace the origin of the\ninversion and its relationship with fruit color. 1. We compared the expression patterns between\ndifferent groups: purple with the inversion vs. purple without the inversion, purple with the inversion\nvs. non-purple, and purple without the inversion vs. non-purple. These pairwise comparisons\nconfirmed that the inversion has no significant effect on gene expression. 2. We constructed a\nphylogenetic tree using only the SNPs from the inversion region and found that accessions carrying\nthis inversion almost exclusively form a single group (See Fig. 5e), suggesting they are closely\nrelated and likely share a common genetic origin. 3. Accessions carrying the inversion show a long\nhaplotype across the inversion region. Given that these accessions exhibit narrow genetic diversity\ncompared to the non-inversion group, we are now more confident in believing that the inversion is not\nthe causal variant, and we propose that the founder effect better explains the fixed genotype of the\ninversion-harboring monophyletic group. In the revised manuscript, we provide a model to illustrate\nthe evolutionary history of color evolution and the inversion (see Supplementary Fig.20).\n[R3C3.4]: We sincerely thank the reviewer for carefully reading our manuscript and pointing out the\nsurprising result regarding the LD pattern. Motivated by these comments, we reanalyzed the LD\npattern and identified a mistake in our previous calculation. We had used VCFtools to calculate the\nLD, which can only include heterozygous sites in the analysis. In the entire region across the\ninversion, there are 67,822 SNPs, but for the subgroup of accessions carrying the inversion, only 547\nSNPs (less than 1%) were retained, most of which are homozygous sites. This strongly suggests a\nlong single haplotype for the inversion group in this region. Indeed, haplotype analysis across this\nregion confirmed that the inversion forms a long single haplotype, which explains why LD is expected\nto be very low. When we calculated LD across the inversion using all accessions, we found that the\ninverted region had much lower LD than both the chromosome-wide level and other regions.\nIn the revised manuscript, we have removed the LD analysis for the subgroup, as we believe there\nwere not enough accurate SNPs in the inversion group to perform a reliable analysis. Instead, we\nhave included an LD pattern for all accessions (see Fig. 5f) and a haplotype analysis (see Fig. 5e) of\nthe inversion region, which further supports the hypothesis that the inversion is more likely to have\narisen from a founder effect.\nThe reference ‘S076’ we used is the derived state of the inversion. As suggested by the reviewer, in\nthe revised manuscript, we changed the reference to a non-inversion accession ‘S126’ and performed\nthe analysis in Figure 5 (see Figure 5). The results are highly consistent with results when using\n‘S076’ as the reference.\nThank you again for this insightful comment, we hope you are satisfied with our responses.\n[R3C4]: Thank you for this great suggestion! We have further summarized the locations of our\nsamples corresponding to the populations in Table S1. This part has been revised as follows (Line\n125-128). Almost all the samples from Southeast Asia (32/34) and South America (11/13) were\nassigned to the C1 group. The C2 group mainly consisted (74.19%, 46/62) of members from South\nChina (especially Guangdong Province). The majority (80.82%, 59/73) of the C3 group contained\naccessions from a wide range of 17 Provinces in China. We hope our revisions could address your concerns.\n[R3C5]: Thank you for this comment! We agree that higher genetic diversity doesn’t necessarily\nmean genetic variants are more important. Therefore, we have deleted this sentence.\n[R3C6]: Thank you for this comment! We have clarified this information in the manuscript. We used\nthe transcriptomes of roots, stems, leaves, flowers, and fruits from the 11 samples (2 newly added)\nwith de novo genomes for calculation of expression profiles, since these data were available and\nmore comparable (Line 197-198). They should be able to represent the 17 genomes (11 de novo + 6\npublished), since the goal was to compare gene expressions among different gene family classes.\nThe calculation of Ka/Ks was also clarified in the methods part (Line 821-825). We have revised the\nway of Ka/Ks calculation as follows. First, we obtained ortholog gene pairs for all possible pairwise\ncomparisons among the 17 genomes using JCVI. The paralog pairs were also obtained by comparing\neach genome with itself using JCVI. Then, we carried out two versions of subsequent Ka/Ks\ncalculations. Version 1 considered only ortholog pairs (Figure 2g, Supplementary Fig. 8); version 2\nconsidered both ortholog and paralog pairs (Supplementary Fig. 7). In this way, no outgroup was\nused. It seems that the general trend of the results of the two versions were consistent.\n[R3C7]: We apologize for not making the figure legends and the numbers in each figure and table\nclearer. The inconsistent numbering is due to our use of slightly different SV sets (i.e. filtered with size\nor not) for different analyses (e.g., Table and Figure). For example, we used one SV set (with\nsize >50 bp) and another set (with size between 50 bp and 100 kb). To ensure consistency, in the\nrevised manuscript, we have used the size-filtered SV dataset (size between 50 bp and 100 kb PAVs)\nfor all analyses, and make the numbers consistent in the Figures and Tables.\nThe number in Figure 3b is slightly different from Figures 3a and Table S4 because the SVs in\nFigure 3b were obtained using further genotyping methods.To make the presentation of the SV sets\nmore clearer, we now change to report the Shared and Specific Number of SVs in Figure 3b by\nadding samples with the order based on divergence levels. The shared number of SVs refer to SVs\ndetected in at least two samples, while the specific SVs refer to SV only being detected in one\nsample. We agree with the reviewer that the \"growth rate\" is sensitive to the order in which the\naccessions are included. In the revised manuscript, we have adjusted the order to reflect increasing\ndiversity (see our changes in Fig. 3b).\n[R3C8]: We apologize for this confusing presentation. The inversion has been traced to occur in\npurple fruit eggplant accessions, including the reference ‘S076’. The ancestry state can be\ndetermined by the outgroup S. violaceum (S098). In the revised manuscript, we revised the\ndescription in the figure legend of Supplementary Figure 11 (which is the original Supplementary\nFigure 7). We hope this has addressed your concerns.\n[R3C9]: We apologize for not making the results of Figure 3c clearer and more detailed. Our aim\nwas to describe the proportion of each type of SV relative to the total SVs. Based on our\nobservations, we categorized the SVs into five classes: LTR-TE-related SVs, DNA-TE-related SVs,\nTATA satellite expansion and contraction SVs, tandem duplications, and complex SVs that overlap\nwith both LTR- or DNA-based TEs (as recommended by the reviewer), as well as gene-related SVs,\nwith all other SVs categorized as 'others.' To clarify this classification, we have added a paragraph in\nthe Methods section to describe it in detail (Line 781-785). The number of SVs in each class is also\nincluded in the source data.\nThe term 'most common SVs' was intended to refer to the SVs that appear most frequently in our\ndataset. In the revised manuscript, we have changed this description to 'most abundant SVs.' We\nhope that this revision addresses your concerns.\n[R3C10]: We apologize for not clearly explaining the method for SV calling and genotyping. The\nSVGAP pipeline is now available on GitHub at: https://github.com/yiliao1022/SVGAP, and the\nmanuscript reports this pipeline as a preprint at\nhttps://www.biorxiv.org/content/10.1101/2025.02.07.637096v1.article-metrics. We have also\ncomprehensively revised the method section to describe SV calling with the SVGAP pipeline, as well\nas the parameters for each short-read-based method (i.e., Menta, Delly, and Abavs), which can be\nfound in the Methods section at line 792-802.\nFor SVGAP SVs callset, we used a total of 41064 insertions and 34938 deletions used for the\npangenome construction, with size between 50 bp and 100,000 bp. For the short-reads based SV\ncallsets, we used a total of 140, 500 deletions and insertions (with size > 50 bp and < 100,1000bp) for\nthe pangenome construction. These two pangenomes were all used to GWAS analysis, but only the\nSVGAP-based pangenome was used for population genomic analysis as it is more accurate and\ncomprehensive.\nWe thank you again for pointing this out and helping to make our manuscript more detailed. We hope\nour revisions have addressed your concerns.\n[R3C11]: We thank the reviewer for this helpful suggestion. In the revised manuscript, we have\nadded three new genomes to describe the minor allele frequency in the population. These assemblies\ncan represent the major diversity within the eggplant gene pool (see Supplementary Fig. 5).\nAdditionally, we performed allele frequency analysis using the 201 re-sequenced accessions for SVs,\nas suggested by the reviewer. We report the results of the 16 assemblies in Fig. 3d and the analysis\nwith the 201 re-sequenced accessions in Supplementary Fig. 12c.\n[R3C12]: Thank you for raising this concern. To clarify the method for classifying SV types, we have\nincluded the defining criteria in the Methods section (Lines 781-785). We agree with the reviewer that\nwe did not provide direct evidence to show that LTR-TE insertions have higher 'deleterious impacts'\non genes. In the revision, we have modified the description to: 'likely owing to insertional biases or a\nlarger effect on gene expression' (see our changes on Lines 262-263). We hope this revision\naddresses your concern.\n[R3C13]: We apologize for the incomplete and missing information. In the revised manuscript, we\nhave included the complete information in Supplementary Figure 13 a,b (corresponding to\nSupplementary Fig 6 and 7).\nWe agree with the reviewer’s comment that our analysis in Figure 3g may be susceptible to Type I\nerror. To address this, we have added a bootstrap-based method to provide a confidence interval,\nas suggested by the reviewer (see our changes in Fig. 3g and Supplementary Fig. 13a). Thank you\nagain for this helpful suggestion!\n[R3C14]: We apologize for this misportrayal of information in Fig 4a. When we double checked the\nGWAS candidate genes, we found that the SmTT8 was not in the candidate list. We have removed\nSmTT8 in the manuscript. We apologize for this mistake.\n[R3C15]: Thank you for this point! The GWAS Manhattan plots for different phenotypic data were\nalso plotted separately as Supplementary Fig. 16-18. These plots were aligned and the consistency\nof signals could be well observed.\n[R3C16]: A great point! We apologize for this confusion. The two SV markers (SVe and SVr, Figure\n6e) used to differentiate gene numbers for EPS1 and Roq1 homologs were different with the two SVs\n(Chr05:84617337, Chr05:84619808) signaled in SV-GWAS (Supplementary Fig. 18).\nOriginally, the top signals of SNP-GWAS were directly located within a gene model\n(evm.model.Chr05.2618, Chr05: 84613990-84619320) annotated as an EPS1 homolog in the S076\nreference genome (Figure 6c). Consistently, SV-GWAS also revealed two significant SV signals\nwithin this region (SV_Chr05:84617337, p-value 4.56-e6; SV_Chr05:84619808, p-value 2.89-e8)\n(Supplementary Fig. 18). However, after comparing the haplotypes of these variants between\nresistant and susceptible accessions, we found that none of them could explain the phenotype well\nindividually, even by fixing the genotype of SmCYP82D47 on Chr04. Strikingly, when checking the\nresults of gene-based pangenome analysis, we noticed a great variation in the numbers of EPS1\nhomologs (0~4) of different genomes. This led us to speculate that a single bi-allelic (or multiple-\nallelic) variation (SNP, Indel, or SV) may not be able to explain the phenotype very well, and the\nsituation could be more complicated. Therefore, to obtain a clear picture of this region, we manually\ncorrected/annotated all gene models within this region (R1 in Figure 6e) for the 11 de novo\nassembled genomes. Interestingly in S076, instead of harboring a single EPS1 gene, this region\n(Chr05:84613990-84619320) actually contains two genes right adjacent to each other or intertwined\nwith each other, including one truncated/pseudogene EPS1 and a homolog of Roq1 (previously\nreported to convey bacterial wilt resistance in tomato). Considering the complexity of this area, it is\nhighly likely that Roq1 homologs may also differ in gene numbers among different genomes.\nTherefore, we also manually corrected gene annotations for additional four regions (R2-R5 in Figure\n6e) potentially harboring Roq1 homologs. To further investigate the potential correlation of EPS1 and\nRoq1 homologs with this trait, we identified two SV markers (SVe for EPS1, Chr05:84599196; SVr for\nRoq1, Chr05:84828646) to differentiate the scenario of abundant/expanded homologs with relatively\nless homologs for the two genes. These two markers were not among the significant signals in SV-\nGWAS, likely due to the complexity of structural changes in these regions as discussed above.\nHowever, these two SV markers, when considered together, were highly associated with this trait\n(Figure 6f).\nIn terms of their locations, SVe was located at ~14.7 Kb to the left of the original GWAS signaled\nregion (Chr05: 84613990-84619320), while SVr was located in another region harboring Roq1\nhomolog clusters, ~209.3 Kb to the right of the original region. We have also revised Figure 6e so\nthat the locations of SVe and SVr (colored triangles) relative to EPS1/Roq1 genes can be visualized.\nAll locations of SVs and their associated genes were provided in Table S11.\nReviewer #4\n(Remarks to the Author)\n[R4C0]: We thank the referee for the careful and insightful review of our manuscript. We respond to\nyour comments point-by-point below.\n[R4C1]: Thank you for this great point! The reason why we selected ‘S076’ as a reference genome in\nour population structure analysis was because ‘S076’ is a representative breeding line with purple\nfruits typical in South China. Moreover, ‘S076’ showed high resistance to bacterial wilt disease of\neggplant (Gong et al., 2024), which may be beneficial for identifying disease resistance genes by serving\nas a reference genome. We have clarified these in Lines 117-118.\nReference\nGong, C. et al. 2024. A QTL of eggplant shapes the rhizosphere bacterial community, co-responsible\nfor resistance to bacterial wilt. Horticulture Research, 11(2), p.uhad272.\n[R4C2]: Thank you for this important comment! We apologize for the misportrayal of information. We\nhave refined this section accordingly (Line 708-710).\n[R4C3]: We apologize for this inappropriate color pattern. We have revised the colors in Figure 2.\n[R4C4]: We thank you for this valuable suggestion! As recommended by the reviewer, we applied two\nmethods to verify the reliability of our SV dataset. First, the SVGAP pipeline has been extensively\ntested and shown to provide high accuracy and low false positive rates in SV detection (see Hu et al.,\n2025, GitHub: https://github.com/yiliao1022/SVGAP). Second, we randomly selected 24 SVs for PCR\nverification and achieved 87.5% accuracy (Supplementary Fig. 21).\n[R4C5]: Thank you for this suggestion! We agree that fruit shape is also an important trait for\neggplant. However, it was not included in the association mapping part, since we did not phenotype\nthis trait. We will work on it in this coming growing season, and perform more analyses in the future.\n[R4C6]: Thank you for this insightful point! We agree that the distinction between the purple and non-\npurple color of eggplant fruit might be simple, but it is actually a stable and reliable way to score the\ncolor. As we know, anthocyanins formation of the peel color is complex in eggplant, because\nanthocyanins could be affected by light, temperature, and other environmental factors. It would be\nless stable if we phenotype using “degrees” (high to low). To contrast, we measured anthocyanin\npigmentation in a way as a qualitative trait, and scored the anthocyanin pigmentation as “1” and non-\nanthocyanin pigmentation as “0”. In this way, the presence or absence of anthocyanins was actually\nnot affected by environmental factors in different planting seasons.\nIt is admittedly that while we identified several novel loci for bacterial wilt resistance, the major\ncandidate gene (SmMYB1) for fruit color on Chr10 is a previously known gene. Despite this, we were\nable to identify a 12.4 Mb inversion at a high frequency (50.7%) within the population, which is\nassociated with fruit color, likely due to genetic hitchhiking. We believe this is of great biological\nimportance, since it could serve as an example of large SVs associated with biological traits. Moreover,\nthe association signals on other chromosomes also provide candidate genomic regions/variants for\nfuture investigations.\n[R4C7]: Thank you for this great comment! We apologize that some of these descriptions were\nshown in the Results part instead of in the Methods section. To be more specific, a total of four\nbatches (three batches each with two completely randomized blocks in 2022, and one batch with\nthree completely randomized blocks in 2023) of plants were conducted for bacterial wilt inoculation.\nThe bacterial wilt inoculation followed the method reported previously (Gong et al., 2024). For\nbacterial wilt disease phenotyping, each seedling was scored as resistant (score 1) or susceptible\n(either dead or wilt, score 0) consecutively for 5 weeks after infection with Ralstonia solanacearum at\nthe 4-5 true leaf seedling stage. We have updated these details in the Methods section (Line 680-687).\nReference\nGong, C. et al. 2024. A QTL of eggplant shapes the rhizosphere bacterial community, co-responsible\nfor resistance to bacterial wilt. Horticulture Research, 11(2), p.uhad272.\n[R4C8]: We thank the reviewer for raising this point. In the revised manuscript, we have uploaded our\nSV datasets to https://github.com/yiliao1022/eggplantpangenome, which include SVs identified from\n16 full genome assemblies and up to 200 re-sequenced datasets. These datasets are now freely\navailable for download and use to the public. Additionally, we have carefully revised the Methods\nsection to clarify how the SVs were identified (lines 772-802). We hope our revision addresses your\nconcerns.\n[R4C9]: We thank the reviewer and all the other reviewers for raising this insightful point. Motivated\nby the reviewer’s comment, and with additional analyses, we now have a clearer understanding of the\norigin and its relationship with fruit color. Our findings regarding this inversion are as follows: (1) the\ninversion originated from a small number of purple-fruited accessions; (2) these purple-fruited\naccessions (carrying the inversion) were selected as key breeding materials (founder effect); (3) the\ninversion has been strongly selected due to genetic hitchhiking with favorable traits (e.g., purple fruit)\nin certain regions (e.g., China), leading to an increase in its frequency within the population. For\nfurther details, please refer to our responses to [R1C2], [R2C16], and [R3C3]. We hope you find our\nresponse satisfactory.\n[R4C10]: This is a great point! We have performed additional analyses on comparing EPS1\nhomologs among different genomes (Supplementary Fig. 19). By rooting a phylogenetic tree with\nS098 as an outgroup, the 20 EPS1 homologs were classified to four clusters, corresponding to the\nfour copies of EPS1. Since EPS1 homologs are absent in this region for S027, S054, and S076,\nno expressions were detected. The only EPS1 homolog in S001 also showed low expression levels.\nIn contrast, the EPS1 homologs for genomes with multiple copies showed much higher expression\nlevels. It seems that EPS1 homologs in Cluster 2 and 4 may play an essential role since their\nexpressions were quite high. However, it requires future in-depth study to find out which copy or\ncopies play a role in disease resistance, since not only the gene numbers, but also the protein lengths\nvary among different homologs. We have updated the manuscript (Line 658-661).\nVersion 1:\nReviewer comments:\nReviewer #1\n(Remarks to the Author)\nThe authors have done a nice work in addressing my concerns. The manuscript is now much improved. Thank you!\nFor additional suggestion, I will not put the result of two pan-gene versions into the main text, which will reduce the\nreadability of this section. I agreed with the authors to retain the pan-genes of \"11denovo + 6published\" in Results,\nwhile to re-organize the \"version2 pan-genes\", emphasizing the potential biases of gene annotations into Discussion.\nReviewer #3\n(Remarks to the Author)\nThank you to the authors for the revised manuscript. I appreciate your efforts in addressing my previous comments and\nenhancing the overall quality of the paper. The manuscript provides valuable genomic resources and presents interesting\ntrait associations. Most of my concerns have been addressed. However, after reviewing this revised version, I still have a few\nremaining concerns.\nThe authors have made notable improvements in clarifying the representativeness of the assembled genomes, tracing the\norigin of the large inversion, refining the methodology, and removing inconclusive inferences. Despite these improvements,\nseveral concerns remain unaddressed or insufficiently justified.\nI use numbered tags provided by the authors to trace the comments.\n[R3C1] This response does not adequately address my concern.\n(1) I infer that the authors' statement regarding \"benefiting from the gene number comparisons\" refers to the results described\nin lines 447–461. However, this section does not provide substantive evidence from gene-based pangenome gene family\ndata to support the association between gene copy number variation (CNV) and phenotype. Moreover, the results presented\nraise concerns about the quality of gene annotation. Specifically, the structure of the gene evm.model.Chr05.2618 may have\nresulted from an erroneous fusion of two neighboring genes, evm.model.Chr05.2786 and evm.model.Chr05.2787. This\npotential annotation error is partially supported by subsequent manual curation (lines 462–465). Figure 6e illustrates CNV,\nbut the distinction between \"R+/R-\" needs clarification. Additionally, based on the gene distribution patterns, genotypes\ncarrying \"E+\" seem to originate from the same ancestral sequence, and similarly, those with \"R+\" appear to share a common\nancestral sequence. This suggests that resistance in accessions carrying these genotypes may be driven by functional gene\nvariations rather than solely by a potential dosage effect resulting from gene expansion. Furthermore, if the pangenome\nanalysis only contributes to the identification of the EPS1 gene, then the mention of structural variants (SVs) related to fruit\ncolor in the title could be misleading.\n(2) The genetic diversity of SVs within the studied populations is one of the key contributions of this study. However, the\nanalysis of this diversity does not substantiate the claim made in the title that the study \"uncovers large hidden structural\nvariants associated with fruit color and bacterial wilt resistance.\"\n(3) There is a substantial discrepancy between the revised Suppl. Fig. 14d and the original Suppl. Fig. 9B. Notably, the\nauthors used two different SV datasets, \"SV_panpop\" and \"SV_SVGAP,\" in Suppl. Fig. 14d and 14e, respectively. This\ninconsistency is confusing and does not align with the uniform description of SV detection and genotyping in the main text.\nSimilar inconsistencies are also present in Suppl. Fig. 18. These contradictions undermine the reliability of the analysis.\n(4) The graph-based pangenome and SV population diversity are highly valuable resources. However, they do not\nsufficiently address the core concern—how these resources facilitate the identification of phenotype-associated SVs in this\nstudy.\n[R3C3] Fig. 5e provides strong evidence. Please add ‘Origin’ information as in 5a to the figure to support the inferred origin\nof the Chinese-type inversion.\n[R3C7] The authors should first carefully review their analyses, finalize the dataset they intend to use, and ensure\nconsistency before re-computing SV distributions and conducting subsequent analyses, including figure generation. This\nwill help maintain the credibility of their results by ensuring that the data used is consistent throughout.\nThe revised Figure 3b still does not align with the reported results. According to the results section and Table S4, there are a\ntotal of 76K SVs, yet Figure 3b displays fewer than 71K SVs. Additionally, the authors have released two SV datasets\ncontaining 75,683 and 140,500 SVs, neither of which matches the reported results. Similar inconsistencies are present in\nother parts of the study. Unless there is a specific reason, which should be clearly explained, SV-GWAS should be\nconducted using the final SV genotyping dataset rather than one of these intermediate versions.\n[R3C10] One of the key contributions of this study is the construction of a graph and the precise\nSV genotyping using vg toolkit, enabling more accurate and comprehensive graph-based SV analysis. However, according to this response, the\nactual SV-aware population genomic analyses were conducted using SV genotyping data derived from SVGAP, which is\nbased on a single reference genome. This approach does not fully demonstrate the advantages of assembling multiple\ngenomes and constructing a graph, which is a central strength of this study.\n[R3C16] Please incorporate the genotype data (E/R) from Figure 6f into the dataset presented in Figure 6b. Additionally, the\nauthors should release the SV genotyping results that were consistently used throughout the study, ensuring that they\ninclude all relevant accessions.\nOverall, the manuscript has improved, but the emphasis on gene-based and graph-based pangenome analyses has not yet\nbeen sufficiently supported.\nReviewer #4\n(Remarks to the Author)\nThe revised manuscript has been improved a lot and my concerns have been addressed. Only minor suggestions are given\nas below:\n1. Authors used two accession names, ‘GQIE’ and ‘HQIE’, in Figure 2b, e, and in Figure 4c, d, but they were not defined in\nthe text. Although ‘GQIE’ most likely refers to ‘GUIQIE-1’ and ‘HQIE’ should refer to ‘HQ-1315’, consistent names shall be\nused in order to avoid misunderstanding.\n2. I suggest authors immediately release the genomes and sequencing data once the paper is in press.\nReviewer #5\n(Remarks to the Author)\nReview:\nYou et al performed a pangenome analysis of eggplant, based on 6 published and 11 novel chromosome-level genomes,\nand short read resequencing of 201 accessions. They then proceeded to compute PAVs, SNPs and SVs using a reference-\nbased approach, construct a graph pangenome, and finally focus on two phenotypes: anthocyanin biosynthesis, where they\nfind a large inversion associated (most likely through a founder effect), but not causally related to a difference in fruit color\n, and bacterial wilt resistance, where they conduct an elegant and thorough GWAS analysis of the genes involved,\ndemonstrating the power of using multiple chromosome-scale assemblies. While most of the conclusions are expected, the\nbacterial wilt work is highly novel. However, the experimental approach presents some serious limitations:\n1. The main one, as pointed out also by reviewer 2 (R2C0), is the sampling: the accessions used originate almost\nexclusively from one of the two recognized domestication centers (SE Asia) and from China, with just one accession from\nthe second domestication center (Indian subcontinent) 2 from Central/Northern Europe, and 2 from the Americas (Fig 1a).\nSeveral important domestication/diversity centers (India, Middle East, Southern and Eastern Europe, Africa, Japan and\nKorea) are not represented, or represented just by one accession in India. This limitation is further exacerbated by the bias in\nthe resequenced accessions, that cluster in a narrow region of the diversity tree (Supplementary figure 5). Thus, I don’t think\nthis concern by reviewer 2 has been appropriately addressed. A pangenome should give a more or less complete\nrepresentation of genes within a species or clade, so this double geographic and genetic bias in the population is a serious\nlimitation. The definition “regional pangenome” or “sub-pangenome” seems more appropriate for the present dataset. This\nbias in the accessions has a bearing also on other comments by reviewer 2, such as R2C16 (did the inversion arise in\nChina? Its abundance is due to a founder effect?) or R2C19 (the catalog of eggplant SV is not comprehensive). Without a\ngeographically and genetically balanced sample, these comments cannot be addressed.\n2. A second limitation is the outgroup used for the phylogenetic analyses, composed by 4 S. aethiopicum and 1 S.\nviolaceum resequenced accessions, of which the latter was also assembled to chromosome scale. Both species belong to\nthe Anguivi grade, and are thus phylogenetically distant from S. melongena, whose direct progenitor and sister wild species\nare, respectively, S. insanum and S. incanum. This limitation questions the validity of many of the\nphylogenetic analyses made, such as inferring the ancestral state of SV (Fg. 3d) or the ancestral\ncomponent analysis (Fig 1e). The analyses should be repeated using the direct eggplant progenitor S. insanum and/or the\nsister species S. incanum.\n3. A third limitation is the approach used for pangenome graph construction. The main advantage of graph-based\npangenomes is an unbiased all-vs-all comparison, thus avoiding the so-called reference bias (Secomandi, et al (2025).\nNature Genetics, 1-14). Two main pipelines exist for this purpose: Minigraph-Cactus (Hickey, et al. (2024). Nature\nbiotechnology, 42(4), 663-673) and PanGenome Graph Builder (Garrison, et al. (2024). Nature Methods, 1-5.). I therefore\nwonder why authors decided to first detect SVs with a reference-based approach and then build the graph. The resulting\nreference bias, summed to the geographical and genetic bias mentioned above, is likely to limit the value of this\npangenome. Reference bias can be removed using an all-vs-all comparison, eg based on PGGB.\nOther suggestions for improvement:\n4. The fact that gene families in the pangenome “nearly reached a plateau after adding 17 genomes” (line 171) is largely\nexpected. What about using all genes identified in the 17 genomes?\n5. In the admixture analysis, authors tested 2-5 Ks. In the CV plot however, k from 2 to 8 are analyzed. Why did they select 3\nas best K, since the CV plot shows a plateau at 8 (Fig. 1A)? This point was also raised by reviewer 2 on different grounds\n(R2C10) and needs to be better addressed.\n6. Previous work by other groups has identified several candidate genes for anthocyanin biosynthesis in eggplant, as well\nas, in some cases, the causal mutations (see eg Florio, et al (2021). International Journal of Molecular Sciences, 22(17),\n9174; Mangino, et al (2022). Frontiers in Plant Science, 13, 847789; You, et al (2023). Horticulture Research, 10(2),\nuhac268; Xiao, et al (2024). International Journal of Molecular Sciences, 25(10), 5241). Of these, the only paper that is\ncited (lines 305 and 616) is the You et al paper, by some of the authors of the present paper. In particular, the 6-bp deletion in the\nChr10 Myb gene that is causing the non-purple phenotype (Fig 4f-g in the present paper) has been described by Mangino et\nal (2022) (fig 5j in that paper). Appropriate credit should be given to previous work in the field by other groups.\n7. In the text, the authors conclude that the 12.4 Mb inversion on Chr 10 is not associated with the non-purple color, but it\nreduces genetic diversity in the surrounding chromosomal region. This conclusion in correct, but not thoroughly analyzed\nand inappropriately presented in the abstract:\na) both Pi and Tajima’s D are reduced in the whole pericentromeric heterochromatin (Fig. 5b, d; Chr10 should be\nmetacentric, Fang, H., et al (2025). International Journal of Biological Macromolecules, 284, 138094). How does an\ninversion in a chromosome arm affect these two parameters in pericentromeric heterochromatin, even on the other side of\nthe centromere? Is it due to “gamete culling” (i.e. gametes that have a recombination event in the inverted region contain\nunviable chromosomes, thus reducing recombination over the whole chromosome)? If so, what is the predicted fitness on\nthe chromosome carrying the inversion in a free-pollinating population? This point should be discussed.\nb) The present formulation of the abstract “we identified a 12.4 Mb 27 inversion at a high frequency (50.7%) within the\npopulation, which is associated with fruit color, likely due to genetic hitchhiking” is misleading, in that it suggests some sort\nof causal relationship between the inversion and fruit color. I suggest changing into “…associated with a previously\nidentified mutation for fruit color..”\nMinor points:\n8. I don’t understand fig 2g: It supposedly depicts Ka/Ks values for orthologous pairs in the core, softcore, dispensable, and\nprivate genes. But if a gene is private, how can it be part of an orthologous pair? Could the authors explain better how they\ndid the analyses in this figure and suppl figs 7 and 8e?\n9. Ref 14, which should refer to a chromosome level eggplant assembly, instead refers to phylogenomic discovery of\ndeleterious mutations in potato. Ref 69, which should refer to fruit pigmentation, instead is a review on bacterial wilt\nresistance in Solanaceae. Please correct.\nVersion 2:\nReviewer comments:\nReviewer #3\n(Remarks to the Author)\nMy concerns have been largely addressed in this revision. I have only one remaining comment and one minor suggestion.\nThe authors have effectively highlighted the advantage of graph-based pangenomes in accurately identifying structural\nvariants (SVs). However, the proposed association between gene dosage effects and bacterial wilt (BW) resistance remains\ninsufficiently supported. This association is further complicated by the finding that silencing a single gene, SmEPS1, results\nin a substantial reduction in BW resistance (Fig. 6g,h), even though the accession background of the wild-type control is not\nspecified. Moreover, the connection between the representative SVs (‘SVe’ and ‘SVr’) and the observed copy number\nvariation (CNV) is not yet strongly substantiated, particularly given the limited sampling of only 11 assembled genomes. The\ncontribution of CNVs may be better interpreted as providing a genetic basis for potential mutational events rather than\ndirectly contributing to phenotypic resistance.\nMinor suggestion: The criteria used to define and determine derived alleles should be clearly described in the Methods\nsection.\nReviewer #5\n(Remarks to the Author)\nThe authors have only partially addressed my main concern, which was the scarce representation of accessions from the\nIndian domestication center. They added few resequenced accessions from Europe and Asia, but the Indian domestication\ncenter remains crucially underrepresented, together with other important regions, such as Middle East, Africa, Korea and\nJapan (Fig 1a). Therefore, the definition “sub-pangenome” is the most appropriate. Its limitations should be more extensively\ndiscussed, indicating explicitly that it lacks a sufficient number of accessions from the Indian domestication center and\nexpansions from that center, and therefore many genomic events that occurred in these regions are likely missing from this\nresource, limiting its usefulness.\nAs noted previously, this limitation is likely to be exacerbated by the reference bias introduced by graph construction using\nSVGAP. The authors, in their reply, show that the reference-independent PGGB software is performing as well as SVGAP in\ndetecting bias. A logical next step is to perform GWAS on the PGGB reference-independent graph. This analysis is missing\nin the present revision and should be introduced.\nMy third comment, i.e. the lack of a proper outgroup to anchor the phylogenetic data, has been addressed by using a single,\npublicly available S. insanum chromosome level assembly that, as the authors note, “substantially strengthened” the\nresolution and robustness of their evolutionary inferences. It must be noted that S. insanum is heterogeneous, with some\naccessions being feral or admixed forms with S. melongena, and therefore the resolution and robustness of the evolutionary\ninferences would be further strengthened by including multiple assemblies of S. insanum and of the closely related S.\nincanum. Given the fact that generating additional chromosome-level assemblies is time-consuming and expensive, this is\nnot an absolute requirement.\nMy comments 4-6 have been adequately addressed.\nAdditional comments:\nRalstonia resistance:\nThe relative contribution of CYP82, EPS and Roq1 genes in Ralstonia resistance remains elusive and should be better\nstudied. In order to better understand it, a supplementary figure should be provided in which pie charts are shown for two of\nthe gene classes in genotypes separately for the two classes of the third gene, together with the % average susceptibility of\nthe two classes. Eg: pie charts for the four classes (E+R+,E-R+,E+R-, and E-R-) in TGG and TGA genotypes. And so on, for\nthe E+/E- and R+/R- genotypes. % susceptibility and number of accessions should be indicated for the genotypes in each\nclass.\nSupplementary figure 19 is not very informative in clarifying the role of the different genes in ralstonia resistance. First, it is\nunclear how genes in the different clusters correlate with those in fig. 6e. These should be marked with names or, better, with\ncolors corresponding to the different clusters in supplementary fig 19. Importantly, supplementary figures (or if it’s too\ncomplicated, tables) should be provided, giving the expression of individual genes in populations with different classes of\nsusceptibility to ralstonia (eg, low, intermediate and high) and for all the CYP82, EPS and Roq1 genes analyzed.\nAlternatively, the CYP82, EPS and Roq1 gene composition and expression should be added to Table S10.\nVIGS: Although the authors claim that they performed RT-PCR to evaluate the silencing of the different EPS1 and\nCYP82D47 homologs, I wasn’t able to find anywhere the data. These data should be shown, together with a table with the %\nhomology of the different homologs to the silencing fragments used. It would be also appropriate to show VIGS data also for\nthe Roq1 homologs.\nMinor: The pie chart data on CYP, EPS and Roq1-like genotypes in various classes of ralstonia susceptibility are shown\nonly for four susceptibility classes (0 to 40-60). They should be shown also for the remaining two classes (60-80 and 80-\n100).\nOnce these data are provided, a discussion of the synergic-epistatic relations of the three classes of genes in determining\nRalstonia resistance should be provided.\nOther:\nSupplementary Table 2 provides only the statistics for the 11 de novo assembled genomes. Statistics (except HiFi and HiC,\nif not available) should be provided for all 17 chromosome level genomes used in the analyses, including the newly added\nS. insanum.\nData availability: A search of the Chinese National Genebank with the Project or individual genome accession numbers\nfound no results. Raw sequencing data, genome assemblies, annotations, must be released on a public database and the\ngraph on a public pangenome browser such as PpanG (https://doi.org/10.1186/s12864-024-10302-5 ) upon paper\nacceptance.\nVersion 3:\nReviewer comments:\nReviewer #3\n(Remarks to the Author)\nAll my previous concerns have been fully addressed.\nReviewer #5\n(Remarks to the Author)\nOverall Assessment\nThe authors have made a substantial effort to address my comments, and the manuscript is improved as a result. The\nclarification of the 12.4 Mb inversion on chromosome 10 as a case of genetic hitchhiking rather than a causal variant is a\nvaluable scientific conclusion. The new analysis of the synergic-epistatic interactions of the three bacterial wilt (BW)\nresistance loci (SmCYP82D47, SmEPS1, SmRoq1) is a significant addition that clarifies a complex genetic architecture.\nHowever, the revised manuscript is unfortunately still beset by several critical issues:\n• The authors' response to the request for a reference-independent pangenome-GWAS (using PGGB) is contradictory.\n• In the light of the new data added, the VIGS results are difficult to interpret\n• The manuscript's primary narrative for fruit color focuses on the 12.4 Mb inversion on Chr10 while dismissing other loci as\n\"less significant\". This claim is wrong; the authors' own supplementary table shows SVs on Chr05 and Chr06 that are orders\nof magnitude more statistically significant.\n• The methodological novelty of this paper has been superseded by the recent publications of Gaccione et al. (2025) and Yu\net al (2025).\nBelow the point-by-point evaluation of the authors’ responses:\nComment R5C1. The authors adopted the term \"sub-pangenome\" in the title and abstract. They have added a paragraph to\nthe Discussion acknowledging the geographical bias and calling for future studies to include samples from India, the Middle\nEast, Africa, etc. This is a transparent and appropriate response.\nComment R5C2. This is still a significant shortcoming. The authors claim to have performed the requested PGGB-GWAS,\nthen they dismiss the method, stating it \"reported a substantially lower number of SVs\" and that for BW resistance, \"no\nsignals were detected at the two stable peaks on Chr04 and Chr05.\" They use this \"failure\" to justify retaining their original,\nbiased SVGAP results. This justification does not stand upon closer scrutiny: For BWR, Supplementary Table 13 explicitly\nlists 7 \"PGGB derived SV\" , albeit on different chromosomes and with different p-values than with SVGAP (Supplementary\nTable 11). Fig. 6C and Suppl Fig. 22 are not comparable, since one refers to SNP-GWAS and the other to SV-GWAS and,\nfor fruit color, it is not clear if the Manhattan plot in Fig. 4c refers to SNPs, SVs or both, so the reader is left wondering about\nthe comparison between SVGAP-GWAS and PGGB-GWAS. Since PGGB is a robust, reference free method, in order to\nsubstantiate their claims the authors must report the complete sets of associations (SNPs, short InDels and SVs) obtained\nfor the two traits with the two methods. Their current response is evasive.\nComment R5C3. The authors had already addressed it in the previous revision.\nComment R5C4. The authors provided a new Supplementary Figure 24 and a new Discussion paragraph detailing the\n\"synergic-epistatic interactions.\" This is a valuable addition that significantly strengthens the manuscript.\nComment R5C5. The authors have revised Fig. 6e by adding color labels that correspond to the SmEPS1 gene clusters\nshown in Supplementary Fig. 20, thus addressing my comment.\nComment R5C6. The authors have have performed VIGS for SmRoq1 and updated the Results and Methods sections\n(Lines 497–504, 533–540, and 913–916), as well as revised Fig. 6g and 6h accordingly. The figure provided in the rebuttal\nletter but not in the supplementary materials suggests that, for each VIGS fragment, there are multiple transcripts with high\nenough homology to be silenced by the SmEPS1 and SmRoq1 VIGS fragments (see eg Fernandez-Pozo et al Molecular\nplant, 8(3), 486-488. Also, the reader has no clue on which of these transcripts are recognized by the primers used for the\nRT-PCR. Finally, the exact genotypes of the 'S065', 'S092', 'S050' accessions used for VIGS whose genotypes are not\nspecified. All these shortcomings make the results still difficult to interpret. The authors should, for each of the genotypes\nabove: i) in a new table indicate the genes homologous to the VIGS fragment and their levels of homology; ii) in table S14\nindicate which of the genes are recognized by the RT-PCR oligonucleotides; iii) discuss the results of the VIGS esperiment\nin view of the above information.\nComments R5C7-R5C10. The comments were satisfactorily addressed.\nWhile the authors perform these revisions, they should also address the inconsistencies in Loci returned by GWAS. The\nmanuscript's narrative is built around the 12.4 Mb inversion on Chr10 as the primary example of an SV-trait association. The\ntext explicitly states other loci are \"less significant\", and indeed Fig 4a seems to support this claim. However, in Table S8 , at\nleast one SV-GWAS hit (Chr05_81641732) has a comparable, if not higher significance. Why isn’t it shown in Fig 4a? This\nis very confusing. The authors should revise the Manhattan plots in the main figures to match the data given in\nsupplementary tables both for SNPs and SVs, and present in supplementary figures the comparable results for PGGB-\nGWAS (see comment R5C2).\nVersion 4:\nReviewer comments:\nReviewer #5\n(Remarks to the Author)\nThe authors have made substantial efforts to fully address all my comments, except for data availability, which is presently\npartially addressed.\nR5C1 – Scope of the “sub-pangenome” - Addressed. The term “sub-pangenome” is now used consistently, and the\nDiscussion explicitly acknowledges the strong geographical bias of the panel and the need for broader sampling (India,\nMiddle East, Africa). This is an appropriate clarification.\nR5C2 – Reference-free PGGB-GWAS - Addressed. The authors now present a reference-free PGGB-based GWAS\nalongside the reference-based (GATK/SVGAP) analyses. Complete association results for SNPs, InDels and SVs are\nreported for fruit color and BW resistance (Suppl. Tables 8, 10; Suppl. Fig. 14). High concordance between methods is\ndemonstrated, and all key candidate loci (SmMYB1, SmCYP82D47, SmEPS1, SmRoq1) are supported by both approaches.\nThe previous ambiguity regarding the nature and comparability of the Manhattan plots has been resolved.\nR5C3 - Already addressed in the previous revision.\nR5C4 – Epistatic architecture of BW resistance - Addressed. The new analysis of synergic/epistatic interactions among\nSmCYP82D47, SmEPS1 and SmRoq1, supported by a new supplementary figure and expanded Discussion, significantly\nstrengthens the manuscript.\nR5C5 – SmEPS1 gene clusters in Fig. 6e - Addressed. Fig. 6e is now correctly annotated and consistent with\nSupplementary Fig. 20.\nR5C6 – VIGS experiments - Addressed. Additional VIGS experiments for SmRoq1 were performed. The authors now clarify\n(i) homology and potential off-target effects of VIGS fragments, (ii) RT-PCR primer specificity, and (iii) the genotypes and\nrationale for the accessions used (S065, S092, S050). While VIGS targets multiple homologs, this is now explicit and\nproperly discussed, making the results interpretable.\nR5C7–R5C10 - Addressed satisfactorily in the previous version.\nMethodological novelty vs. recent literature - Addressed. The manuscript now properly cites and contextualizes Gaccione et\nal. (2025) and Yu et al (2025), reframing its contribution as complementary and trait-focused rather than methodologically\npioneering.\nGWAS narrative consistency (Chr10 inversion vs other loci) - Addressed. Inconsistencies between Manhattan plots and\nsupplementary tables have been corrected. The misleading claim that other loci are “less significant” has been removed,\nand the Chr10 inversion is now correctly interpreted as a hitchhiking signal linked to SmMYB1, not as the sole major locus.\nData availability. Partially addressed. An accession number for raw sequencing data, assemblies and annotations is\nprovided (CNGB Project CNP0006177). However, the project is meant to go public on 2026-08-27. The data must be made\npublic by the time the manuscript is published. Also, the full variant callsets and pangenome graph files (VG/PGGB) should\nbe made public, preferably on a dedicated website like SGN.\n\n======================================================================\nATTACHED REVIEWER REPORT — IMPORTANT\nThe text below is a complete reviewer report submitted as a PDF\nattachment. It appears after the Creative Commons license block.\nAssign it to whichever reviewer wrote 'see pdf' or had no inline text.\n======================================================================\n\nTo all reviewers:\nWe respond point-by-point to all reviewer comments below. All original comments from the\nreviewers appear in black text and our responses are below, in blue text. For each reviewer, we have\nnumbered comments in their reviews with tags (e.g. reviewer 1’s first comment is tagged [R1C1]).\nThese tags are included in the manuscript and highlighted in green where appropriate to direct the\nreviewer to the location of the response. Some tagged comments aren’t actually tagged in the\nmanuscript, especially when there is no clear textual anchor (like simple deletions of text or some\nedits to figures). Corrections of typographical errors and insertions of references aren’t tagged.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nYou and colleagues reported the populational and pan-genome analysis of eggplant, an important\nhorticulture crop with both scientific and economic importance. They newly assembled 9 genomes,\nconstructed a graph-genome, investigated the effect of SVs on gene expression, and discovered\ncandidate loci involved in eggplant fruit color and disease resistance. Overall, this work offers\nimportant genetic resources to understand the historical evolution and to improve eggplant breeding.\nHowever, I have some major concerns, e.g., the quality of SV datasets and the association results of\nfruit colors.\n[R1C0]：We thank the referee for the careful and insightful review of our manuscript. We respond to\nyour comments point-by-point below. All original comments from the\nreviewers appear in black text and our responses are below, in blue text. For each reviewer, we have\nnumbered comments in their reviews with tags (e.g. reviewer 1’s first comment is tagged [R1C1]).\nThese tags are included in the manuscript and highlighted in green where appropriate to direct the\nreviewer to the location of the response. Some tagged comments aren’t actually tagged in the\nmanuscript, especially when there is no clear textual anchor (like simple deletions of text or some\nedits to figures). Corrections of typographical errors and insertions of references aren’t tagged.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nYou and colleagues reported the populational and pan-genome analysis of eggplant, an important\nhorticulture crop with both scientific and economic importance. They newly assembled 9 genomes,\nconstructed a graph-genome, investigated the effect of SVs on gene expression, and discovered\ncandidate loci involved in eggplant fruit color and disease resistance. Overall, this work offers\nimportant genetic resources to understand the historical evolution and to improve eggplant breeding.\nHowever, I have some major concerns, e.g., the quality of SV datasets and the association results of\nfruit colors.\n[R1C0]：We thank the referee for the careful and insightful review of our manuscript. We respond to\nyour comments point-by-point below. All original comments from the\nreviewers appear in black text and our responses are below, in blue text. For each reviewer, we have\nnumbered comments in their reviews with tags (e.g. reviewer 1’s first comment is tagged [R1C1]).\nThese tags are included in the manuscript and highlighted in green where appropriate to direct the\nreviewer to the location of the response. Some tagged comments aren’t actually tagged in the\nmanuscript, especially when there is no clear textual anchor (like simple deletions of text or some\nedits to figures). Corrections of typographical errors and insertions of references aren’t tagged.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nYou and colleagues reported the populational and pan-genome analysis of eggplant, an important\nhorticulture crop with both scientific and economic importance. They newly assembled 9 genomes,\nconstructed a graph-genome, investigated the effect of SVs on gene expression, and discovered\ncandidate loci involved in eggplant fruit color and disease resistance. Overall, this work offers\nimportant genetic resources to understand the historical evolution and to improve eggplant breeding.\nHowever, I have some major concerns, e.g., the quality of SV datasets and the association results of\nfruit colors.\n[R1C0]：We thank the referee for the careful and insightful review of our manuscript. We respond to\nyour comments point-by-point below."}]

Decision letter