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Genetic basis of flavor complexity in sweet corn

Nature Genetics volume 57, pages 2842–2851 (2025)Cite this article

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Abstract

Sweet corn is an important vegetable crop consumed globally. However, the genetic differentiation between field corn and sweet corn, and the impact of breeding on the metabolite composition and flavor (other than sweetness) of sweet corn, remain poorly understood. Here we assembled a cultivated sweet-corn genome de novo and re-sequenced 295 diverse sweet-corn inbred lines. We examined the genetic architecture of sweet-corn kernel quality by combining genetic, metabolite and expression profiling methodologies. New genes (for example, ZmAPS1, ZmSK1 and ZmCRR5) and metabolites associated with flavor and consumer preference were identified, highlighting important target flavor metabolites, including sugars, acids and volatiles. These findings provide valuable knowledge and targets for future genetic breeding of sweet-corn flavor, and to balance grain yield and quality and contribute to our broader understanding of crop diversification.

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Fig. 1: Genetic diversity between sweet-corn and field-corn populations.
Fig. 2: Whole-genome expression analyses.
Fig. 3: Genetics basis of sweet-corn flavor.
Fig. 4: ZmAPS1 functional validation.
Fig. 5: ZmSK1 functional validation.
Fig. 6: Functional validation of ZmCRR5.

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Data availability

All datasets supporting the findings of this study have been deposited into the CNGB Sequence Archive of the China National GeneBank DataBase (https://db.cngb.org/) under the following accession nos.: de novo assembled genomes and raw data at CNP0004684, CNP0003283 and CNP0003295; raw WGS data for the 295 sweet-corn accessions at CNP0003213; RNA-seq data for the 280 sweet-corn accessions at CNP0003294; and RNA-seq for knockout and wild-type lines at CNP0003291 and CNP0004707. Source data are provided with this paper.

Code availability

No customized code was generated for this study. All analyses were performed using publicly available software with parameters detailed in Methods.

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Acknowledgements

The work was supported by National Natural Science Foundation of China (grant nos. 32321005 to J.Y., U1901201 to J.Y. and 32001563 to K.L.), the National Key Research and Development Program of China (grant no. 2022YFD1201502 to G.L.), the Earmarked Fund for CARS (grant no. CARS-02-85 to G.L.), the Guangdong S&T Program (grant no. 2022B0202060003 to Y.Y.), the Science and Technology Major Program of Hubei Province (grant no. 2021ABA011 to J.Y.), the Agricultural Competitive Industry Discipline Team Building Project of Guangdong Academy of Agricultural Sciences (grant no. 202115TD to G.L.), the Science and Technology Program of Guangzhou (grant no. 202102021015 to K.L.), the Project of Collaborative Innovation Center of Guangdong Academy of Agricultural Sciences (grant no. XTXM202203 to S.Y.), the Guangdong Provincial Science and Technology Plan Project (grant no. 2023B1212060038 to G.L.) and the Special Fund for Scientific Innovation Strategy-construction of High-Level Academy of Agriculture Science (grant nos. R2019YJ-YB1002 to K.L. and R2020PY-JX019 to S.Y.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We are grateful to Maize Research Institute of Shandong Academy of Agricultural Sciences for help in collecting sweet corn resources. Computation resources were provided by the high-throughput computing platform of the National Key Laboratory of Crop Genetic Improvement at Huazhong Agricultural University and supported by Hao Liu.

Author information

Author notes

  1. These authors contributed equally: Kun Li, Yongtao Yu, Shijuan Yan, Wenqiang Li.

Authors and Affiliations

  1. Crop Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of Crop Genetic Improvement, Guangzhou, China

    Kun Li, Yongtao Yu, Gaoke Li, Wu Li, Jianhua Liu, Xitao Qi, Yingni Xiao, Nan Zhang, Wenguang Zhu, Tianxiang Wen, Lihua Xie, Yuliang Li, Wenjia Lu, Chunyan Li & Jianguang Hu

  2. National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China

    Kun Li, Wenqiang Li, Jieting Xu, Jingyun Luo, Lu Chen, Songtao Gui, Yingjie Xiao, Ning Yang, Lin Zhuo & Jianbing Yan

  3. Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China

    Shijuan Yan, Wenjie Huang, Qunjie Zhang & Qian Kong

  4. Hubei Hongshan Laboratory, Wuhan, China

    Wenqiang Li, Jingyun Luo, Songtao Gui, Yingjie Xiao, Ning Yang, Lin Zhuo & Jianbing Yan

  5. WIMI Biotechnology Co. Ltd, Qingdao, China

    Jieting Xu

  6. Yazhouwan National Laboratory, Sanya, China

    Liying Feng & Hai-Jun Liu

  7. Department of Molecular Physiology, Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm, Germany

    Alisdair R. Fernie

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  1. Kun Li
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Contributions

J.Y. and J.H. conceived and designed the research. Y.Y., W.Q.L., G.L., W.L., Y.N.X., N.Z., L.Z. and K.L. managed the project. K.L., Y.Y., Q.Z., J.Y.L., L.C., Y.J.X., N.Y., H.-J.L., L.F. and S.G. performed the genome sequencing and bioinformatics. Y.Y., J.H.L., L.X., X.Q., C.L., W.J.L., Y.L. and Y.X. prepared the samples for resequencing and transcriptome profile of the sweet-corn population and contributed to data analysis. W.Q.L. was responsible for the filed corn planting and data collection. S.Y., W.H. and Q.K. performed metabolome analyses using GC–MS and LC–MS platforms. J.X., K.L., W.Z. and T.W. managed the knockout lines and performed the molecular experiments. K.L., H.-J.L., S.Y., A.R.F., J.H. and J.Y. wrote and revised the manuscript.

Corresponding authors

Correspondence to Hai-Jun Liu, Jianguang Hu or Jianbing Yan.

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Competing interests

J.X. is an employee of WIMI Biotechnology Co., Ltd. The other authors declare no competing interests.

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Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Genetic differences of sweet and field corn.

a, Length of specific sequences in the RC genome compared to B73. b, Distribution of specific sequences in the RC genome compared to B73. c, Length of specific sequences in the RC genome compared to Ia453. d, Distribution of specific sequences in the RC genome compared to Ia453. e, Length of specific sequences in the RC genome compared to IL14H. f, Distribution of specific sequences in the RC genome compared to IL14H. g, Length of specific sequences in the RC genome compared to p39. h, Distribution of specific sequences in the RC genome compared to p39. i, Population structure of sweet and field corn populations, inferred using the maximum-likelihood method with three and four ancestral components (K = 3, 4).

Source data

Extended Data Fig. 2 Genetic variation in sh2 and su1.

a, Synteny map highlighting an inversion in sh2 in RC genome compared to the field corn genome TX303 and parviglumis genome TIL11. b, Haplotype network analysis of 264 SNPs in the sh2-RC region, based on the RC gene model that includes the identified inversion. (c) LD plot (r2 values) for sh2 and flanking regions ( < 500 kb) in sweet corn population, and in kernel corn from (d) TEM population and (e) TST population. Nucleotide diversity (π) around (f) sh2 and (g) su1 in both sweet and field corn populations. h, Haplotype network analysis of SNPs in the su1 region. i, Structural analysis identifying 5 alleles in the Su1 gene within sweet corn population. j, Comparison of relative sugar content (sum of sucrose, maltose, glucose, and fructose) among different su1 alleles. k, Comparison of relative pericarp thickness in immature kernels across different alleles of su1. The values to the left of each bar represent the number of sweet corn lines analyzed in (j) and (k). Data points show individual measurements; bar heights represent mean values and error bars represent the mean values ± s.d. Differences between groups were assessed using a two-tailed unpaired t-test. l, Principal component analysis of 63 SNPs discovered in su1 region.

Source data

Extended Data Fig. 3 Knockout of Sh2 and Su1 by CRISPR-Cas9.

Two sgRNAs designed for gene editing on Sh2 (a) and Su1(b), respectively, shown in red. Mutations and deletions are indicated. c, Phenotypes of maize ears from su1 and sh2 CRISPR-knockout line. Scale bars, 1 cm. d, Metabolites identified with significant changes and differentially expressed genes (DEGs) in Sh2 and Su1 mutants compared to wild types. e, DEGs between sh2 and WT in kernel tissues at 20 DAP. f, Metabolomic comparison of sh2 and WT in kernel tissues at 20 DAP; fold change calculated using mean values (WT, n = 12; Mutant, n = 12). g, DEGs between su1 and WT in kernel tissues at 20 DAP. h, Comparison of metabolome of su1 and WT in kernel tissues at 20 DAP (WT, n = 12; Mutant, n = 12). i, Pericarp thickness comparisons between Su1 and Sh2 CRISPR-knockout lines and wild type kernels. Box plots are defined by the median (centre line), the 5th and 95th percentiles (box limits), and the whiskers extend to the minimum and maximum values. Individual data points are overlaid. P values (i) were calculated by one-way ANOVA followed by Tukey’s multiple comparisons test.

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Extended Data Fig. 4 Distribution of eGWAS signals in sweet and field corn populations.

Scatter plots show the genomic positions of genes and corresponding eQTL signals in sweet (a) and field (b) corn populations (upper panels). Distribution of eGWAS signal counts per 500 kb window across the genome in sweet (a) and field (b) corn populations (lower panels). The horizontal dashed line indicates the threshold for signal hotspots (permutation test P < 0.05).

Extended Data Fig. 5 Mapping results for flavor ratings and metabolites.

a, Distribution of mQTL and flavor rating-related QTL across the maize genome. The arrow shows loci where mQTL and flavor rating-related QTL are colocalized. b, Flowchart illustrating the rationale and statistical framework used in the present study. c, Correlation analysis of sweet corn flavor ratings between two biological replicates in 2019. d, Correlation analysis of sweet corn flavor ratings between experiments conducted in 2017 and 2019.

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Extended Data Fig. 6 Functional analysis of su1 alleles.

Significant differences between Su1-RC and Su1-B73 alleles across the sweet corn population in sucrose (a), maltose (b), glucose (c), and fructose (d), respectively. e, Significant differences in expression levels among four su1 alleles. f, Significant differences in flavor values among four alleles of su1. g, Correlation analysis between pericarp thickness from taste rating experiments and su1 expression levels. h, Sweetness from taste rating experiments, showing no difference between Su1-B73 and Su1-RC types. Differences between groups were assessed using a two-tailed unpaired t-test.

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Extended Data Fig. 7 Functional identification of candidate genes.

a, Gene model of ZmAPS1. CRISPR-Cas9 generated mutants in ZmAPS1 are shown, with sgRNAs in red and deletions shown as dashes. Violin plots showing differences of adenosine (b) and methyl-phosphate (c) content between genotypes at the InDel 3 (-/AAC). Knockout of Zm0001eb405310 (d) and Zm0001eb214960 (e) by CRISPR-Cas9. Violin plots of relative erythrose (f) and DL-2aminooctanoic acid (g) content between wild-type and knockout lines. Group comparisons were performed using two-tailed unpaired t-tests.

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Extended Data Fig. 8 Functional identification of ZmSK1.

a, Correlation analysis between ZmSK1 expression and quinic acid content. b, Correlation analysis between quinic acid content and hundred-kernel weight. c, Correlation analysis between pericarp thickness and hundred-kernel weight. d, Knockout of ZmSK1 by CRISPR-Cas9. Mutants of ZmSK1 are shown. e, Violin plots of hundred-kernel weight between wild type and ZmSK1 knockout lines in 2022. Differences between groups were assessed using a two-tailed unpaired t-test.

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Extended Data Fig. 9 Functional identification of ZmCRR5.

a, Knockout of ZmCRR5 by CRISPR-Cas9. Representative images of maize roots (b) and statistical comparison of root numbers (c) between wild type and KO-ZmCRR5 lines. d, Correlation between root numbers at seedling stage and normalized fructofuranose content. e, Hormones levels in immature kernel of ZmCRR5 knockouts and wild types. P-values were analyzed using two-tailed unpaired t-test (n = 12/12). f, Inferred phylogenetic analysis of six CRR genes across maize genome. g, Expression of six CRR genes in short apical meristem tissues of ZmCRR5 knockout and wild types. h, Expression of six CRR genes in immature kernel (20 DAP) of ZmCRR5 knockouts and wild types. Data points (g and h) show individual measurements; bar heights represent mean values and error bars represent the mean values ± s.d. Differences between groups were assessed using two-tailed unpaired t-test.

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Extended Data Fig. 10 Haplotype analysis of ZmCRR5.

a, Identification of haplotype-specific (minimum allele frequency < 15% in one haplotype and corresponding allele frequency > 85% in other two haplotype groups) SNPs in ZmCRR5, shown as vertical lines in gray (introns), red (coding regions), and black (untranslated regions). were presented with gray, red and black vertical lines located in introns, coding regions and untranslated regions, respectively (upper). Below, details of 16 SNPs in coding regions, with allele frequencies for each haplotype group. Haplotype-specific SNPs are highlighted in bold. Violin plots displaying hundred-kernel weight (b) and pericarp thickness (c) across the three haplotype groups. P-values were determined by one-way ANOVA followed by Tukey’s multiple comparisons test.

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Li, K., Yu, Y., Yan, S. et al. Genetic basis of flavor complexity in sweet corn. Nat Genet 57, 2842–2851 (2025). https://doi.org/10.1038/s41588-025-02401-0

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