Integrative linkage mapping, GWAS, and RNA-Seq analysis unravel the genetic architecture and candidate genes for drought tolerance in Chrysanthemum interspecific F₁ progeny

Abstract

Drought stress is a major environmental constraint that severely impacts plant production. However, the genetic basis is primarily misunderstood in chrysanthemum species. The objectives of this study are to examine the genetic variation of drought tolerance in reciprocal F₁ progenies of Chrysanthemum dichrum (drought-tolerant) and Chrysanthemum nankingense (drought-sensitive) and identify candidate genes by integrating linkage mapping, genome-wide association study (GWAS), and RNA-seq analysis. The results revealed extensive variation for the investigated traits in response to drought stress and notable genetic divergence in drought tolerance between the reciprocal crosses. This confirms that the hybridization direction influenced drought tolerance phenotypes. A high-resolution genetic map containing 6677 nonredundant bin markers spanning 1859.31 cM across nine linkage groups (LGs), achieving an average marker density of 0.28 cM, was developed with a genotyping-by-sequencing (GBS) approach. The inclusive composite interval mapping (ICIM) detected 89 significant quantitative trait loci (QTLs), and GWAS identified 1360 significant quantitative trait nucleotides (QTNs) in Single_Env, 394 QTNs, and 114 quantitative epistatic interactions (QEIs) in the Multi_Env algorithm, as well as six pairs of epistatic loci (QEs) related to drought tolerance. Besides the additive effects, we observed considerable adverse dominant and epistatic effects for the significant loci, explaining why drought tolerance exhibits negative heterosis in reciprocal crosses. The integration of QTL mapping and GWAS revealed 38 colocalized loci harboring 10 known and 15 novel candidate genes, eight validated through RNA-seq and qRT-PCR analyses. Moreover, we identified elite haplotypes yielding higher drought tolerance within the candidate gene Cn1062070. The findings help elucidate the genetic architecture of drought tolerance in chrysanthemum species and provide valuable genetic resources for the development of drought-tolerant cultivars.

Acknowledgements

We gratefully acknowledge Professor Yuan-Ming Zhang (Huazhong Agricultural University) for his expert guidance on 3VmrMLM software implementation and interpretation of data outputs, and thank the Bioinformatics Center of Nanjing Agricultural University for providing data analysis platform services. This work was financially supported by the National Natural Science Foundation of China (31870306, 32171857), the China Agriculture Research System (CARS-23-A18), the Fundamental Research Funds for the Central Universities (QTPY2025005), and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

Author contributions

F.Z. conceived the project and planned the trials. L.Z., J.S., Y.X., X.Z., S.W., and Z.G. conducted the experiment. L.Z., J.S., and X.Z. analyzed the data and prepared the statistical analyses. J.J., Z.G., W.F., and F.C. provided the materials and were major contributors in supervising the project. L.Z. and F.Z. wrote, revised, and finalized the manuscript. All authors have read and approved the final manuscript.

Data availability

All data generated or analyzed in this study are included in the main text and its supplementary information files.

Conflict of interest statement

All the authors have no conflicts of interest, and they approved the publication.

Supplementary Data

Supplementary data is available at Horticulture Research online.

References

[1] Vives-Peris V, Pérez-Clemente RM, Gómez-Cadenas A. et al.. Involvement of citrus shoots in response and tolerance to abiotic stress. Hortic Adv. 2024; 2:393

[2] Chen JC, Chen GX, Guo JG. et al.The CsTIE1-CsAGL16 module regulates lateral branch outgrowth and drought tolerance in cucumber. Hortic Res. 2025;12:uhae279

[3] An HL, Luan YT, Zhao DQ. et al. The VQ motif-containing PoVQ31 protein positively modulates drought stress tolerance in Paeonia ostii ’FengDan’. Ornam Plant Res. 2024; 4:e017

[4] Dietz KJ, Zörb C, Geilfus CM. Drought and crop yield. Plant Biol. 2021; 23:881-93

[5] Mukherjee S, Mishra A, Trenberth KE. Climate change and drought: a perspective on drought indices. Curr Clim Chang Rep. 2018; 4:145-63

[6] Mir RR, Zaman-Allah M, Sreenivasulu N. et al. Integrated genomics, physiology and breeding approaches for improving drought tolerance in crops. Theor Appl Genet. 2012; 125:625-45

[7] Liu KY, Xu H, Liu G. et al. QTL mapping of flag leaf-related traits in wheat (Triticum aestivum L.). Theor Appl Genet. 2018; 131:839-49

[8] Birari BM, Deshmukh RB, Lad SL. et al. Screening for drought tolerance in gram. J Maharashtra Agric Univ. 1995; 20:37-40

[9] Ferede B, Mekbib F, Assefa K. et al. Evaluation of drought toler-ance in tef [Eragrostis tef (Zucc.) trotter] genotypes using drought tolerance indices. J Crop Sci Biotechnol. 2020; 23:107-15

[10] Bac-Molenaar JA, Granier C, Keurentjes JJ. et al. Genome-wide association mapping of time-dependent growth responses to moderate drought stress in Arabidopsis. Plant Cell Environ. 2016; 39:88-102

[11] Qin P, Lin Y, Hu YD. et al. Genome-wide association study of drought-related resistance traits in Aegilops tauschii. Genet Mol Biol. 2016; 39:398-407

[12] Yang X, Wang B, Chen L. et al. The different influences of drought stress at the flowering stage on rice physiological traits, grain yield, and quality. Sci Rep. 2019; 9:3742

[13] Guo J, Guo JH, Li L. et al. Combined linkage analysis and asso-ciation mapping identifies genomic regions associated with yield-related and drought-tolerance traits in wheat (Triticum aestivum L.). Theor Appl Genet. 2023; 136:250

[14] Li PT, Chi TH, Liu YX. et al. Genetic polymorphism analysis, evaluation of drought tolerance and association analysis using SSR markers in the interspecific Chrysanthemum dichrum × C. nankingense F1 hybrids. 3Biotech. 2020; 8:226

[15] Luo Y, Hu TT, Huo YY. et al. Transcriptomic and physiologi-cal analyses reveal the molecular mechanism through which exogenous melatonin increases drought stress tolerance in chrysanthemum. Plants. 2023; 12:1489

[16] Sun J, Ren LP, Cheng Y. et al. Identification of differentially expressed genes in Chrysanthemum nankingense (Asteraceae) under heat stress by RNA Seq. Gene. 2014; 552:59-66

[17] Zhao XT, Song LY, Jiang LW. et al. The integration of transcrip-tomic and transgenic analyses reveals the involvement of the SA response pathway in the defense of chrysanthemum against the necrotrophic fungus Alternaria sp. Hortic Res. 2020; 7:80

[18] Su JS, Li CW, Ou XL. et al. CmWAT6.1, mined by high-density genetic map-based QTL mapping, enhances waterlogging toler-ance in chrysanthemum. Environ Exp Bot. 2024; 219:105612

[19] Liu QL, Xu KD, Zhong M. et al. Overexpression of a novel chrysan-themum Cys2/His2-type zinc finger protein gene DgZFP3 con-fers drought tolerance in tobacco. Biotechnol Lett. 2013; 35: 1953-9

[20] Xu Y, Zhao X, Aiwaili P. et al. A zinc finger protein BBX19 interacts with ABF3 to affect drought tolerance negatively in chrysanthemum. Plant J. 2020; 103:1783-95

[21] Wang T, Wei Q, Wang Z. et al. CmNF-YB8 affects drought resis-tance in chrysanthemum by altering stomatal status and leaf cuticle thickness. J Integr Plant Biol. 2022; 64:741-55

[22] Zhang LL, Xu YJ, Liu XN. et al. The chrysanthemum DEAD-box RNA helicase CmRH56 regulates rhizome outgrowth in response to drought stress. JExp Bot. 2022; 73:5671-81

[23] Liu Y, Cheng H, Cheng P. et al. The BBX gene CmBBX22 negatively regulates drought stress tolerance in chrysanthemum. Hortic Res. 2022;9:uhac181

[24] Zhang T, Qu YX, Wang HB. et al. CmSCL4 and CmR1MYB1 syn-ergistically enhance the drought tolerance by regulation of ABA signaling in chrysanthemum. Environ Exp Bot. 2022; 199: 104886

[25] Cheng X, Chen S, Chen F. et al. Creating novel chrysanthemum germplasm via interspecific hybridization and backcrossing. Euphytica. 2011; 177:45-53

[26] Song C, Liu YF, Song AP. et al. The Chrysanthemum nankingense genome provides insights into the evolution and diversifica-tion of chrysanthemum flowers and medicinal traits. Mol Plant. 2018; 11:1482-91

[27] Su JS, Jiang JF, Zhang F. et al. Current achievements and future prospects in the genetic breeding of chrysanthemum: a review. Hortic Res. 2019; 6:10

[28] Xu S, Wu Z, Hou H. et al. The transcription factor CmLEC1 pos-itively regulates the seed-setting rate in hybridization breeding of chrysanthemum. Hortic Res. 2021; 8:1

[29] Xu C, Mao J, Li X. et al. Evaluation of drought tolerance in sugarcane genotypes using the membership function value of drought tolerance (MFVD). Euphytica. 2023; 219:37

[30] Murray M, Thompson W. Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res. 1980; 8:4321-6

[31] Elshire RJ, Glaubitz JC, Sun Q. et al.A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. 2011; 6:e19379

[32] Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010; 26:589-95

[33] McKenna A, Hanna M, Banks E. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010; 20:1297-303

[34] Zhang R, Yang H, Zhou Z. et al. A high-density genetic map of Schima superba based on its chromosomal characteristics. BMC Plant Biol. 2019; 19:41

[35] Hu FC, Chen Z, Wang XH. et al. Construction of high-density SNP genetic maps and QTL mapping for dwarf-related traits in Litchi chinensis Sonn. J Integr Agric. 2021; 20:2900-13

[36] Huang X, Feng Q, Qian Q. et al. High-throughput genotyping by whole-genome resequencing. Genome Res. 2009; 19:1068-76

[37] Zhang LY, Meng L, Wu WC. et al. GACD: integrated software for genetic analysis in clonal F1 and double cross populations. J Hered. 2015; 106:741-4

[38] Lawson DJ, van Dorp L, Falush D. et al. A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots. Nat Commun. 2018; 9:3258

[39] Li M, Zhang YW, Xiang Y. et al. IIIVmrMLM: the R and C++ tools associated with 3VmrMLM, a comprehensive GWAS method for dissecting quantitative traits. Mol Plant. 2022; 15:1251-3

[40] Li M, Zhang YW, Zhang ZC. et al. A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies. Mol Plant. 2022; 15:630-50

[41] Yin L, Zhang H, Tang Z. et al. rMVP: a memory-efficient, visualization-enhanced, and parallel-accelerated tool for genome-wide association study. Genomics Proteomics Bioinformatics. 2021; 19:619-28

[42] Chen SF, Zhou YQ, Chen YR. et al. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884-90

[43] Guo JN, Gao J, Liu ZY. et al. HISAT2 parallelization method based on spark cluster. JPhysConfSer. 2022; 2197:012038

[44] Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15:550

[45] Chen CJ, Chen H, Zhang Y. et al. TBtools: an integrative toolkit developed for interactive analyses of big biological data. Mol Plant. 2020; 13:1194-202

[46] Adhikari M, Kantar MB, Longman RJ. et al. Genome-wide associ-ation study for carcass weight in pasture-finished beef cattle in Hawai’i. Front Genet. 2023; 14:1168150

[47] Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010; 26:841-2

[48] Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods. 2001; 25:402-8

[49] Purcell S, Neale B, Todd-Brown K. et al. PLINK: a tool set for whole-genome association and population-based linkage anal-yses. Am J Hum Genet. 2007; 81:559-75

[50] Barrett JC, Fry B, Maller J. et al. Haploview: analysis and visu-alization of LD and haplotype maps. Bioinformatics. 2005; 21: 263-5

[51] Zhao Q, Zhong M, He L. et al. Overexpression of a chrysanthe-mum transcription factor gene DgNAC1 improves drought toler-ance in chrysanthemum. Plant Cell Tissue Organ Cult. 2018; 135: 119-32

[52] LiuN, ChenJ, WangT. et al. Overexpression of WAX INDUCER1/SHINE1 gene enhances wax accumulation under osmotic stress and oil synthesis in Brassica napus. Int J Mol Sci. 2019; 20:4435

[53] Du MS, Lu DP, Liu XT. The Arabidopsis ubiquitin ligases ATL31 and ATL6 regulate plant response to salt stress in an ABA-independent manner. Biochem Biophys Res Commun. 2023; 685:149156

[54] Chandran D, Rickert J, Huang Y. et al. Atypical E2F transcrip-tional repressor DEL1 acts at the intersection of plant growth and immunity by controlling the hormone salicylic acid. Cell Host Microbe. 2014; 15:506-13

[55] Yamaguchi N, Matsubara S, Yoshimizu K. et al. H3K27me3 demethylases alter HSP22 and HSP17.6C expression in response to recurring heat in Arabidopsis. Nat Commun. 2021; 12:3480

[56] Hong GJ, Xue XY, Mao YB. et al. Arabidopsis MYC2 interacts with DELLA proteins in regulating sesquiterpene synthase gene expression. Plant Cell. 2012; 24:2635-48

[57] Marchi-Werle L, Fischer H, Graef G. et al. Characterization and identification of methods for phenotyping soybean populations with tolerance to the soybean aphid (Hemiptera: Aphididae). J Econ Entomol. 2018; 111:2416-25

[58] Xi Y, Cai JJ, Li GT. et al. High CO2 facilitates fatty acid biosyn-thesis and mitigates cellular oxidative stress caused by CAC2 dysfunction in Arabidopsis. Plant J. 2023; 115:1316-30

[59] Zhao HY, Li D, Liu YQ. et al. Flavin-containing monooxygenases FMOGS-OXs integrate flowering transition and salt tolerance in Arabidopsis thaliana. Physiol Plant. 2024; 176:e14287

[60] Lu ZW, Su JS, Fan HH. et al. Insights into the genetic architecture of the reciprocal interspecifc hybrids derived from Chrysanthe-mum dichrum and C. nankingense. Mol Breeding. 2024; 44:75

[61] VanGeestG, PostA, Arens P. et al. Breeding for postharvest performance in chrysanthemum by selection against storage-induced degreening of disk florets. Postharvest Biol Technol. 2017; 124:45-53

[62] Song XB, Xu YH, Gao K. et al. High-density genetic map construc-tion and identification of loci controlling flower-type traits in chrysanthemum (Chrysanthemum morifolium Ramat.). Hortic Res. 2020; 7:1

[63] Zhao KK, Jia DW, Zhang X. et al. FUL homologous gene CmFL1 is involved in regulating flowering time and floret numbers in Chrysanthemum morifolium. Plant Sci. 2023; 336:111863

[64] Lou L, Su JS, Ou XL. et al. Multi-locus genome-wide association studies reveal genetic loci and candidate genes for leaf angle traits in cut chrysanthemum. Sci Hortic. 2023; 322:112406

[65] Tang R, Zhuang ZL, Bian JW. et al. GWAS and meta-QTL anal-ysis of kernel quality-related traits in maize. Plants-Basel. 2024; 13:19

[66] Su JS, Zhang HM, Yang YY. et al. BSA-seq identified candidate genes and diagnostic KASP markers for anemone type flower in chrysanthemum. Sci Hortic. 2024; 327:112790

[67] Li PJ, Tao Z, Dean C. Phenotypic evolution through variation in splicing of the noncoding RNA COOLAIR. Genes Dev. 2015; 29: 696-701

[68] Lutz U, Nussbaumer T, Spannagl M. et al. Natural haplotypes of FLM non-coding sequences fine-tune flowering time in ambient spring temperatures in Arabidopsis. eLife. 2017; 6:e22114

[69] Janssen EM, Ashrafuzzaman M. et al. Shoot sodium exclusion in salt stressed barley (Hordeum vulgare L.) is determined by allele specific increased expression of HKT1;5. J Plant Physiol. 2019; 241:153029

[70] Al-Mohanna T, Ahsan N, Bokros NT. et al. Proteomics and pro-teogenomics analysis of sweetpotato (Ipomoea batatas)leafand root. JProteomeRes. 2019; 18:2719-34