- Yingjie Xue1 na1,
- Shipeng Ma1 na1,
- Zhihao Liu1 na1,
- Liwen Xu1 na1,
- Shaoxi Zhu1,
- Jianrong Ge1,
- Fei Xie1,
- Weiwei Wang1,
- Xuelei Shen1,
- Wei Zhao1,
- Yikun Zhao1,
- Jiuran Zhao1 &
- …
- Fengge Wang1
We are providing an unedited version of this manuscript to give early access to its findings. Before final publ…
- Yingjie Xue1 na1,
- Shipeng Ma1 na1,
- Zhihao Liu1 na1,
- Liwen Xu1 na1,
- Shaoxi Zhu1,
- Jianrong Ge1,
- Fei Xie1,
- Weiwei Wang1,
- Xuelei Shen1,
- Wei Zhao1,
- Yikun Zhao1,
- Jiuran Zhao1 &
- …
- Fengge Wang1
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
Abstract
Background
Maize, as an important dual-purpose grain and forage crop all over the world, exhibits extensive heritable and phenotypic diversity. Taking the breeding patterns as the core and developing advanced genetic breeding tools with the characteristics of Chinese maize germplasms will significantly advance the genetic dissection of complex agronomic traits and facilitate targeted genetic improvement in maize.
Results
Here, based on the predominant heterotic pattern “X group × SPT group,” we developed the first whole chromosome substitution line (WCSL) population in maize, designated the MOSAIC population. We ensured the near-complete substitution of single chromosome and consistent genetic backgrounds as much as possible in each WCSL from the MOSAIC population. The de novo genome assembly and characteristic analysis of the parental lines revealed abundant genetic variants between WCSLs and their parents. Three key major QTL loci associated with tassel main axis length, anthocyanin accumulation at the base of anther glumes, and tassel branch number were rapidly identified and mapped using the MOSAIC population. Meanwhile, we established the MOSAIC molecular breeding and data sharing platform (MOSAIC-DB), which integrates diverse data resources including pedigrees, phenotypes, genotypes, assembled genomes, and structural variants along with integrated analysis modules.
Conclusions
This study provides a powerful new genetic resource for uncovering the genetic basis of complex traits and for genetic improvement, which facilitates the exploration of the molecular mechanisms underlying key agronomic traits and enables more directed breeding strategies by integrating genetics and genomics in maize.
Data availability
Assembled genome data and annotation data of Jing92 and Jing724 and variant data of the MOSAIC population have been deposited into the European Nucleotide Archive (ENA, https://www.ebi.ac.uk/ena/browser/home) with accession number PRJEB101938. Other relevant data are presented within the article and supporting information.
References
Brunner S, Fengler K, Morgante M, Tingey S, Rafalski A. Evolution of DNA sequence nonhomologies among maize inbreds. Plant Cell. 2005;17(2):343–60. https://doi.org/10.1105/tpc.104.025627.
Yang N, Xu XW, Wang RR, Peng WL, Cai L, Song JM, et al. Contributions of Zea mays subspecies mexicana haplotypes to modern maize. Nat Commun. 2017;8(1):1874. https://doi.org/10.1038/s41467-017-02063-5.
Liu W, Zhang Y, He H, He G, Deng XW. From hybrid genomes to heterotic trait output: challenges and opportunities. Curr Opin Plant Biol. 2022;66:102193. https://doi.org/10.1016/j.pbi.2022.102193.
Singh B, Kukreja S, Goutam U. Milestones achieved in response to drought stress through reverse genetic approaches. F1000Res. 2018;7:1311. https://doi.org/10.12688/f1000research.15606.1.
Schneeberger K, Weigel D. Fast-forward genetics enabled by new sequencing technologies. Trends Plant Sci. 2011;16(5):282–8. https://doi.org/10.1016/j.tplants.2011.02.006.
Zou C, Wang P, Xu Y. Bulked sample analysis in genetics, genomics and crop improvement. Plant Biotechnol J. 2016;14(10):1941–55. https://doi.org/10.1111/pbi.12559.
Liu HJ, Wang X, Xiao Y, Luo J, Qiao F, Yang W, et al. CUBIC: an atlas of genetic architecture promises directed maize improvement. Genome Biol. 2020;21(1):20. https://doi.org/10.1186/s13059-020-1930-x.
Dell’Acqua M, Gatti DM, Pea G, Cattonaro F, Coppens F, Magris G , et al. Genetic properties of the MAGIC maize population: a new platform for high definition QTL mapping in Zea mays. Genome Biol. 2015; 16(1):167.https://doi.org/10.1186/s13059-015-0716-z 1.
Yu J, Holland JB, McMullen MD, Buckler ES. Genetic design and statistical power of nested association mapping in maize. Genetics. 2008;178(1):539–51. https://doi.org/10.1534/genetics.107.074245.
Chen Z, Hu K, Yin Y, Tang D, Ni J, Li P, et al. Identification of a major QTL and genome-wide epistatic interactions for single vs. paired spikelets in a maize-teosinte F(2) population. Mol Breed. 2022;42(2):9. https://doi.org/10.1007/s11032-022-01276-x.
Xue Y, Dong H, Huang H, Li S, Shan X, Li H, et al. Mutation in Mg-protoporphyrin IX monomethyl ester (oxidative) cyclase gene ZmCRD1 causes chlorophyll-deficiency in maize. Front Plant Sci. 2022;13:912215. https://doi.org/10.3389/fpls.2022.912215.
Yang C, Liu J, Rong TZ. Detection of quantitative trait loci for ear row number in F2 populations of maize. Genet Mol Res. 2015;14(4):14229–38. https://doi.org/10.4238/2015.November.13.6.
Li B, Wang Z, Jiang H, Luo JH, Guo T, Tian F, et al. ZmCCT10-relayed photoperiod sensitivity regulates natural variation in the arithmetical formation of male germinal cells in maize. New Phytol. 2023;237(2):585–600. https://doi.org/10.1111/nph.18559.
Kohls S, Stamp P, Knaak C, Messmer R. QTL involved in the partial restoration of male fertility of C-type cytoplasmic male sterility in maize. Theor Appl Genet. 2011;123(2):327–38. https://doi.org/10.1007/s00122-011-1586-8.
Wang G, Zhao Y, Mao W, Ma X, Su C. Qtl analysis and fine mapping of a major qtl conferring kernel size in maize (Zea mays). Front Genet. 2020;11:603920. https://doi.org/10.3389/fgene.2020.603920.
Paul C, Naidoo G, Forbes A, Mikkilineni V, White D, Rocheford T. Quantitative trait loci for low aflatoxin production in two related maize populations. Theor Appl Genet. 2003;107(2):263–70. https://doi.org/10.1007/s00122-003-1241-0.
Yang DE, Jin DM, Wang B, Zhang DS, Nguyen HT, Zhang CL, et al. Characterization and mapping of Rpi1, a gene that confers dominant resistance to stalk rot in maize. Mol Genet Genomics. 2005;274(3):229–34. https://doi.org/10.1007/s00438-005-0016-5.
Cao Y, Liang X, Yin P, Zhang M, Jiang C. A domestication-associated reduction in K(+) -preferring HKT transporter activity underlies maize shoot K(+) accumulation and salt tolerance. New Phytol. 2019;222(1):301–17. https://doi.org/10.1111/nph.15605.
Han Q, Zhu Q, Shen Y, Lee M, Lübberstedt T, Zhao G. Qtl mapping low-temperature germination ability in the maize IBM syn10 DH population. Plants. 2022;11(2):214. https://doi.org/10.3390/plants11020214.
Hou W, Lu Q, Ma L, Sun X, Wang L, Nie J, et al. Mapping of quantitative trait loci for leaf rust resistance in the wheat population “Xinmai 26/Zhoumai 22.” J Exp Bot. 2023;74(10):3019–32. https://doi.org/10.1093/jxb/erad085.
Yuan G, Li Y, He D, Shi J, Yang Y, Du J, et al. A combination of QTL mapping and GradedPool-seq to dissect genetic complexity for gibberella ear rot resistance in maize using an IBM Syn10 DH population. Plant Dis. 2023;107(4):1115–21. https://doi.org/10.1094/pdis-05-22-1183-re.
Xiao Y, Tong H, Yang X, Xu S, Pan Q, Qiao F, et al. Genome-wide dissection of the maize ear genetic architecture using multiple populations. New Phytol. 2016;210(3):1095–106. https://doi.org/10.1111/nph.13814.
Yang M, Chen L, Wu X, Gao X, Li C, Song Y, et al. Characterization and fine mapping of qkc7.03: a major locus for kernel cracking in maize. Theor Appl Genet. 2018;131(2):437–48. https://doi.org/10.1007/s00122-017-3012-3.
McMullen MD, Kresovich S, Villeda HS, Bradbury P, Li H, Sun Q, et al. Genetic properties of the maize nested association mapping population. Science. 2009;325(5941):737–40. https://doi.org/10.1126/science.1174320.
Jiménez-Galindo JC, Malvar RA, Butrón A, Santiago R, Samayoa LF, Caicedo M, et al. Mapping of resistance to corn borers in a MAGIC population of maize. BMC Plant Biol. 2019;19(1):431. https://doi.org/10.1186/s12870-019-2052-z.
Giraud H, Lehermeier C, Bauer E, Falque M, Segura V, Bauland C, et al. Linkage disequilibrium with linkage analysis of multiline crosses reveals different multiallelic QTL for hybrid performance in the flint and dent heterotic groups of maize. Genetics. 2014;198(4):1717–34. https://doi.org/10.1534/genetics.114.169367.
Kump KL, Bradbury PJ, Wisser RJ, Buckler ES, Belcher AR, Oropeza-Rosas MA, et al. Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet. 2011;43(2):163–8. https://doi.org/10.1038/ng.747.
Wang B, Hou M, Shi J, Ku L, Song W, Li C, et al. De novo genome assembly and analyses of 12 founder inbred lines provide insights into maize heterosis. Nat Genet. 2023;55(2):312–23. https://doi.org/10.1038/s41588-022-01283-w.
Hufford MB, Seetharam AS, Woodhouse MR, Chougule KM, Ou S, Liu J, et al. De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes. Science. 2021;373(6555):655–62. https://doi.org/10.1126/science.abg5289.
C MM, Shi Y, Viswanathan V, Sawers RJH, Kemanian AR, Lasky JR. Maladaptation in cereal crop landraces following a soot-producing climate catastrophe. Nat Commun. 2025; 16(1):4289.https://doi.org/10.1038/s41467-025-59488-6 1.
Wu S, Zhang H, Fang Z, Li Z, Yang N, Yang F. Genetic dissection of ear-related trait divergence between maize and teosinte. Plant J. 2025;121(2):e17202. https://doi.org/10.1111/tpj.17202.
Zhang F, Ding Y, Zhang J, Tang M, Cao Y, Zhang L, et al. Comparative transcriptomic reveals the molecular mechanism of maize hybrid Zhengdan538 in response to water deficit. Physiol Plant. 2022;174(6):e13818. https://doi.org/10.1111/ppl.13818.
Xie Y, Zhao Y, Chen L, Wang Y, Xue W, Kong D, et al. ZmELF3.1 integrates the RA2-TSH4 module to repress maize tassel branching. New Phytol. 2024;241(1):490–503. https://doi.org/10.1111/nph.19329.
Chen Y, Chen J, Wu J. Fine mapping of gene Rab1 for red glume collar in maize. Acta Agriculturae Boreali-Sinica. 2014; 29(2):7–12.https://doi.org/10.7668/hbnxb.2014.02.002 1.
Xue Y, Zhao Y, Zhang Y, Wang R, Li X, Liu Z, et al. Insights into the genomic divergence of maize heterotic groups in China. J Integr Plant Biol. 2025. https://doi.org/10.1111/jipb.13884.
Balakrishnan D, Surapaneni M, Mesapogu S, Neelamraju S. Development and use of chromosome segment substitution lines as a genetic resource for crop improvement. Theor Appl Genet. 2019;132(1):1–25. https://doi.org/10.1007/s00122-018-3219-y.
Boldizsár Á, Carrera D, Gulyás Z, Vashegyi I, Novák A, Kalapos B, et al. Comparison of redox and gene expression changes during vegetative/generative transition in the crowns and leaves of chromosome 5A substitution lines of wheat under low-temperature condition. J Appl Genet. 2016;57(1):1–13. https://doi.org/10.1007/s13353-015-0297-2.
Calvo-Baltanás V, Wijnen CL, Yang C, Lukhovitskaya N, de Snoo CB, Hohenwarter L, et al. Meiotic crossover reduction by virus-induced gene silencing enables the efficient generation of chromosome substitution lines and reverse breeding in Arabidopsis thaliana. Plant J. 2020;104(5):1437–52. https://doi.org/10.1111/tpj.14990.
Qi G, Si Z, Xuan L, Han Z, Hu Y, Fang L, et al. Unravelling the genetic basis and regulation networks related to fibre quality improvement using chromosome segment substitution lines in cotton. Plant Biotechnol J. 2024;22(11):3135–50. https://doi.org/10.1111/pbi.14436.
Saha S, Wu J, Jenkins JN, McCarty JC, Stelly DM. Interspecific chromosomal effects on agronomic traits in Gossypium hirsutum by AD analysis using intermated G. barbadense chromosome substitution lines. Theor Appl Genet. 2013; 126(1):109–17.https://doi.org/10.1007/s00122-012-1965-9 1.
Schilmiller A, Shi F, Kim J, Charbonneau AL, Holmes D, Daniel Jones A, et al. Mass spectrometry screening reveals widespread diversity in trichome specialized metabolites of tomato chromosomal substitution lines. Plant J. 2010;62(3):391–403. https://doi.org/10.1111/j.1365-313X.2010.04154.x.
Sun Q, Hu A, Mu L, Zhao H, Qin Y, Gong D, et al. Identification of a candidate gene underlying qHKW3, a QTL for hundred-kernel weight in maize. Theor Appl Genet. 2022;135(5):1579–89. https://doi.org/10.1007/s00122-022-04055-6.
Teng F, Zhai L, Liu R, Bai W, Wang L, Huo D, et al. ZmGA3ox2, a candidate gene for a major QTL, qPH3.1, for plant height in maize. Plant J. 2013;73(3):405–16. https://doi.org/10.1111/tpj.12038.
Horsnell R, Leigh FJ, Wright TIC, Burridge AJ, Ligeza A, Przewieslik-Allen AM, et al. A wheat chromosome segment substitution line series supports characterization and use of progenitor genetic variation. Plant Genome. 2024;17(1):e20288. https://doi.org/10.1002/tpg2.20288.
Huang J, Zhang Y, Li Y, Xing M, Lei C, Wang S, et al. Haplotype-resolved gapless genome and chromosome segment substitution lines facilitate gene identification in wild rice. Nat Commun. 2024;15(1):4573. https://doi.org/10.1038/s41467-024-48845-6.
Jafari F, Wang B, Wang H, Zou J. Breeding maize of ideal plant architecture for high-density planting tolerance through modulating shade avoidance response and beyond. J Integr Plant Biol. 2024;66(5):849–64. https://doi.org/10.1111/jipb.13603.
Chuck G, Whipple C, Jackson D, Hake S. The maize SBP-box transcription factor encoded by tasselsheath4 regulates bract development and the establishment of meristem boundaries. Development. 2010;137(8):1243–50. https://doi.org/10.1242/dev.048348.
Liu Y, Wu G, Zhao Y, Wang HH, Dai Z, Xue W, et al. DWARF53 interacts with transcription factors UB2/UB3/TSH4 to regulate maize tillering and tassel branching. Plant Physiol. 2021;187(2):947–62. https://doi.org/10.1093/plphys/kiab259.
Guo Y, Lu X, Li D, Kuang X, Song W, Ye X , et al. A novel genetic framework reveals transcriptional "butterfly effect" underlying heterosis in maize. J Adv Res. 2025; online.https://doi.org/10.1016/j.jare.2025.08.021 1.
Chen J, Wang Z, Tan K, Huang W, Shi J, Li T, et al. A complete telomere-to-telomere assembly of the maize genome. Nat Genet. 2023;55(7):1221–31. https://doi.org/10.1038/s41588-023-01419-6.
Nie S, Wang B, Ding H, Lin H, Zhang L, Li Q, et al. Genome assembly of the Chinese maize elite inbred line RP125 and its EMS mutant collection provide new resources for maize genetics research and crop improvement. Plant J. 2021;108(1):40–54. https://doi.org/10.1111/tpj.15421.
Zhao Y, Wang Y, Ma D, Feng G, Huo Y, Liu Z, et al. A chromosome-level genome assembly and annotation of the maize elite breeding line Dan340. GigaByte. 2022;2022:gigabyte63. https://doi.org/10.46471/gigabyte.63.
Yang N, Liu J, Gao Q, Gui S, Chen L, Yang L, et al. Genome assembly of a tropical maize inbred line provides insights into structural variation and crop improvement. Nat Genet. 2019;51(6):1052–9. https://doi.org/10.1038/s41588-019-0427-6.
Ou S, Chen J, Jiang N. Assessing genome assembly quality using the LTR Assembly Index (LAI). Nucleic Acids Res. 2018; 46(21):e126-e.https://doi.org/10.1093/nar/gky730 1.
Liu J, Seetharam AS, Chougule K, Ou S, Swentowsky KW, Gent JI, et al. Gapless assembly of maize chromosomes using long-read technologies. Genome Biol. 2020;21(1):121. https://doi.org/10.1186/s13059-020-02029-9.
Tian H, Yang Y, Yi H, Xu L, He H, Fan Y, et al. New resources for genetic studies in maize (Zea mays L.): a genome-wide Maize6H-60K single nucleotide polymorphism array and its application. Plant J. 2021;105(4):1113–22. https://doi.org/10.1111/tpj.15089.
Zhao Y, Tian H, Li C, Yi H, Zhang Y, Li X, et al. HTPdb and HTPtools: exploiting maize haplotype-tag polymorphisms for germplasm resource analyses and genomics-informed breeding. Plant Commun. 2022;3(4):100331. https://doi.org/10.1016/j.xplc.2022.100331.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20. https://doi.org/10.1093/bioinformatics/btu170.
Tarasov A, Vilella AJ, Cuppen E, Nijman IJ, Prins P. Sambamba: fast processing of NGS alignment formats. Bioinformatics. 2015;31(12):2032–4. https://doi.org/10.1093/bioinformatics/btv098.
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303. https://doi.org/10.1101/gr.107524.110.
Wingett S, Ewels P, Furlan-Magaril M, Nagano T, Schoenfelder S, Fraser P, et al. HiCUP: pipeline for mapping and processing Hi-C data. F1000Res. 2015;4:1310. https://doi.org/10.12688/f1000research.7334.1.
Zhang X, Zhang S, Zhao Q, Ming R, Tang H. Assembly of allele-aware, chromosomal-scale autopolyploid genomes based on Hi-C data. Nat Plants. 2019; 5(8):833–45.https://doi.org/10.1038/s41477-019-0487-8 1.
Zhang J, Zhang X, Tang H, Zhang Q, Hua X, Ma X, et al. Allele-defined genome of the autopolyploid sugarcane Saccharum spontaneum L. Nat Genet. 2018;50(11):1565–73. https://doi.org/10.1038/s41588-018-0237-2.
Seppey M, Manni M, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness. Methods Mol Biol. 2019; 1962:227–45.https://doi.org/10.1007/978-1-4939-9173-0_14 1.
Parra G, Bradnam K, Korf I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics. 2007;23(9):1061–7. https://doi.org/10.1093/bioinformatics/btm071.
Su W, Ou S, Hufford MB, Peterson T. A Tutorial of EDTA: Extensive De Novo TE Annotator. Methods Mol Biol. 2021; 2250:55–67.https://doi.org/10.1007/978-1-0716-1134-0_4 1.
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60. https://doi.org/10.1093/bioinformatics/btp324.
Rhie A, Walenz BP, Koren S, Phillippy AM. Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies. Genome Biol. 2020;21(1):245. https://doi.org/10.1186/s13059-020-02134-9.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9. https://doi.org/10.1093/bioinformatics/btp352.
Xu Z, Wang H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 2007; 35(Web Server issue):W265–8.https://doi.org/10.1093/nar/gkm286 1.
Edgar RC, Myers EW. PILER: identification and classification of genomic repeats. Bioinformatics. 2005; 21 Suppl 1:i152–8.https://doi.org/10.1093/bioinformatics/bti1003 1.
Benson G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999;27(2):573–80. https://doi.org/10.1093/nar/27.2.573.
Bedell JA, Korf I, Gish W. Maskeraid: a performance enhancement to RepeatMasker. Bioinformatics. 2000;16(11):1040–1. https://doi.org/10.1093/bioinformatics/16.11.1040.
Jiao Y, Peluso P, Shi J, Liang T, Stitzer MC, Wang B, et al. Improved maize reference genome with single-molecule technologies. Nature. 2017;546(7659):524–7. https://doi.org/10.1038/nature22971.
Hamilton John P, Li C, Buell CR. The rice genome annotation project: an updated database for mining the rice genome. Nucleic Acids Res. 2024; 53(D1):D1614-D22.https://doi.org/10.1093/nar/gkae1061 1.
Sloan DB, Wu Z, Sharbrough J. Correction of persistent errors in Arabidopsis reference mitochondrial genomes. Plant Cell. 2018;30(3):525–7. https://doi.org/10.1105/tpc.18.00024.
He Q, Tang S, Zhi H, Chen J, Zhang J, Liang H, et al. A graph-based genome and pan-genome variation of the model plant Setaria. Nat Genet. 2023;55(7):1232–42. https://doi.org/10.1038/s41588-023-01423-w.
Paterson AH, Bowers JE, Bruggmann R, Dubchak I, Grimwood J, Gundlach H , et al. The Sorghum bicolor genome and the diversification of grasses. Nature. 2009; 457(7229):551–6.https://doi.org/10.1038/nature07723 1.
She R, Chu JS, Wang K, Pei J, Chen N. GenBlastA: enabling BLAST to identify homologous gene sequences. Genome Res. 2009;19(1):143–9. https://doi.org/10.1101/gr.082081.108.
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