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Genome sequencing reveals evidence of adaptive variation in the genus Zea

Nature Genetics volume 54, pages 1736–1745 (2022)Cite this article

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Abstract

Maize is a globally valuable commodity and one of the most extensively studied genetic model organisms. However, we know surprisingly little about the extent and potential utility of the genetic variation found in wild relatives of maize. Here, we characterize a high-density genomic variation map from 744 genomes encompassing maize and all wild taxa of the genus Zea, identifying over 70 million single-nucleotide polymorphisms. The variation map reveals evidence of selection within taxa displaying novel adaptations. We focus on adaptive alleles in highland teosinte and temperate maize, highlighting the key role of flowering-time-related pathways in their adaptation. To show the utility of variants in these data, we generate mutant alleles for two flowering-time candidate genes. This work provides an extensive sampling of the genetic diversity of Zea, resolving questions on evolution and identifying adaptive variants for direct use in modern breeding.

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Fig. 1: Phylogeny of the Zea genus.
Fig. 2: Variation in the Zea genus.
Fig. 3: Local adaptation in teosintes and maize.

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Data availability

DNA and RNA sequencing reads from this study were deposited in the NCBI Sequence Read Archive with the accession codes PRJNA641489, PRJNA816255, PRJNA816273 and PRJNA645739. The SNP data can be downloaded from https://ftp.cngb.org/pub/CNSA/data3/CNP0001565/zeamap/02_Variants/PAN_Zea_Variants/Zea-vardb/. Source data are provided with this paper.

Code availability

All of the custom scripts used in this study are available at https://doi.org/10.5281/zenodo.6818334 (ref.132).

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Acknowledgements

We thank S. Taba from CIMMYT for providing the teosinte materials, J. Chen from Henan Agricultural University for providing the DNA of T. dactyloides, A. Kur from North Carolina State University for drawing the picture of teosinte morphological characteristics and T. AuBuchon-Elder and S. Fabbri for growing and supplying germplasm. 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 H. Liu. This research was supported by the National Key Research and Development Program of China (2020YFE0202300), National Natural Science Foundation of China (U1901201 and 31771879) and Science and Technology major program of Hubei Province (2021ABA011) (all to J.Y.), the Young Elite Scientist Sponsorship Program of the China Association for Science and Technology (2019QNRC001 to N.Y), and the US National Science Foundation (grants 1546719 and 1822330) and United States Department of Agriculture Hatch project CADPLS2066H (both to J.R.-I.).

Author information

Author notes

  1. Lu Chen

    Present address: State Key Laboratory of Plant Genomics and National Center for Plant Gene Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China

  2. These authors contributed equally: Lu Chen, Jingyun Luo, Minliang Jin, Ning Yang.

Authors and Affiliations

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

    Lu Chen, Jingyun Luo, Minliang Jin, Ning Yang, Yong Peng, Wenqiang Li, Jiali Yan, Qinghua Zhang, Xiaoting Zhang, Shenshen Wu, Songtao Gui, Wenjie Wei, Yuebin Wang, Yun Luo, Chenglin Jiang, Min Deng, Min Jin, Liumei Jian, Yanhui Yu, Maolin Zhang & Jianbing Yan

  2. Hubei Hongshan Laboratory, Wuhan, China

    Ning Yang & Jianbing Yan

  3. Institute of Agricultural Biotechnology, Jilin Academy of Agricultural Sciences, Changchun, China

    Xiangguo Liu, Qing Liu, Yuejia Yin & Xinnan Ye

  4. Center for Population Biology, University of California Davis, Davis, CA, USA

    Alyssa Phillips

  5. Department of Evolution and Ecology, University of California Davis, Davis, CA, USA

    Alyssa Phillips & Brenda Cameron

  6. Department of Entomology, Texas A&M University, College Station, TX, USA

    Julio S. Bernal

  7. Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, USA

    Rubén Rellán-Álvarez

  8. Department of Plant Science, The Pennsylvania State University, State College, PA, USA

    Ruairidh J. H. Sawers

  9. National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, China

    Xiaohong Yang

  10. Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA

    Matthew B. Hufford

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

    Alisdair R. Fernie

  12. United States Department of Agriculture-Agricultural Research Service: Western Regional Plant Introduction Station, Washington State University, Pullman, WA, USA

    Marilyn L. Warburton

  13. Department of Evolution and Ecology, Center for Population Biology, Genome Center, University of California Davis, Davis, CA, USA

    Jeffrey Ross-Ibarra

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  1. Lu Chen
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Contributions

Jianbing Yan., J.R.-I. and N.Y. designed and supervised the study. Y.P., W.L., A.P., B.C., J.S.B., R.R.-A., R.J.H.S., Jiali Yan., Q.Z., S.W., S.G., Y.W., Y.L., C.J., M.D., M.J., J.L., L.J., Y.Yu., M.Z. and X.Yang. prepared the materials. X.Z. provided the variant calling pipeline. J.L. performed the Sanger validation of SNPs. W.W. uploaded the SNPs and indels to the database. L.C. and J.L. analyzed the data. M.J., X.L., Q.L., Y.Yin. and X.Ye. performed the genetic transformation and mutant validation. L.C. led the population genomics analyses. L.C., M.J., N.Y., M.B.H., A.R.F., M.L.W., J.R.-I. and Jianbing Yan. prepared the manuscript.

Corresponding authors

Correspondence to Ning Yang, Jeffrey Ross-Ibarra or Jianbing Yan.

Ethics declarations

Competing interests

The authors M.J., X.L., J.X., H.L., Y.Yin., L.H., J.G., B.H. and Jianbing Yan. (Chinese patent application number 2021 1 0085348.5; “Methods and genes of changing the flowering time of maize”) and X.L., Q.L., M.J., X.L., J.X., H.L., Y.Yin., J.G., Y.L., L.H., B.H., Y.L., Jianbing Yan. and D.H. (Chinese patent application number 2020 1 1480563.7; “Application of a maize plant height and ear height controlling gene ZmCOL14”) have filed patent applications on technology related to the processes described in this article. The other authors declare no competing interests.

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Nature Genetics thanks Klaus Mayer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 STRUCTURE and principle component analysis (PCA) of 507 maize and 237 teosinte.

a, Population structure for K = 2–16. maize (TEM) indicates temperate maize (including 8 components), maize (TST) indicates tropical maize (including 3 components). Samples with assignment to maize (TEM) or maize (TST) lower than 0.6 were classified as maize (mix), and mexicana or parviglumis samples with assignment lower than 0.6 to any teosinte were classified as teosinte (mix). b, Cross-validation error for K = 2–20 showing K = 15 with the lowest cross validation error. c, PCA of maize and teosinte; points are colored according to the admixture result (K = 15).

Source data

Extended Data Fig. 2 Phylogenetic tree of Zea genus.

The maximum likelihood tree was estimated with SNPhylo54. Populations are colored based on the admixture result for K = 15.

Source data

Extended Data Fig. 3 The distribution of nucleotide diversity.

a, Violin plot for nucleotide diversity analysis (calculated with 5 kb non-overlap window; windows less than 500 covered sites were excluded to filter out regions of poor alignment). Windows used in nicaraguensis (n = 60,778), luxurians (n = 51,956), diploperennis (n = 52,656), perennis (n = 51,306), huehuetenangensis (n = 107,481), mexicana (n = 60,247), parviglumis (n = 61,014) and maize (n = 76,375). Cultivated maize was down-sampled to 110 randomly selected individuals. Horizontal lines in each violin plot represent the median value of nucleotide diversity. b, Line plot of nucleotide diversity analysis along the chromosomes.

Source data

Extended Data Fig. 4 Repeat comparison of Zea.

Boxplot of reads mapped to different repeat classes across the samples in different taxa. Center lines in the boxplot indicate the median, edges represent the 25th and 75th percentiles, whiskers further extend by ±1.5 times the interquartile range from the limits of each box. Each point shows the percentage of mapped reads to different transposon classes in each individual. nicaraguensis (n = 14), luxurians (n = 14), diploperennis (n = 20), perennis (n = 19), huehuetenangensis (n = 5), mexicana (n = 81), parviglumis (n = 70), TST (n = 210) and TEM (n = 280).

Source data

Extended Data Fig. 5 Principal component analysis and haplotype of eight large inversions.

Each group of plots contains a dotplot showing the principal component analysis of SNPs in the inversion region and a heatmap showing genotypes at the inversion identified by invClust116. Each point in the dotplot represents a sample and the color represents the inversion haplotypes. Each row in the heatmap represents the genotyped window used in invClust (500 kb) and each column represents a sample. The bar above the heatmap also shows the inversion cluster identified by PCA.

Source data

Extended Data Fig. 6 Enrichment analysis between highland and high latitude adaptation.

1,000 independent permutations were performed to accesses overlap using a, selected regions or b, differently expressed genes. Grey points represent the statistics of each permutation. Bars represent SD. P-values derived from permutation test are indicated.

Source data

Extended Data Fig. 7 Flowering time related pathway in highland and high latitude adaptation.

Pathway were integrated from Arabidopsis and rice. Genes colored with ‘blue’ represent highland adaptation genes, ‘green’: high latitude adaptation genes, ‘purple’: homologous genes pairs with one copy under selection in the highlands and the other in high latitude, ‘red’: convergent selection genes in highland and high latitude adaptation.

Extended Data Fig. 8 Phenotype analysis of CRISPR/Cas9 mutation for ZmPRR7 in different environments.

a, Gene structure and sequences of ZmPRR7 target regions in wild type, ZmPRR7 CRISPR/Cas9 knockout mutants. b and c, Statics of days to tassel, days to anthesis, days to silk in Hainan province (2019 and 2020; China; E109°, N18°; tropical environment). d and e, Statics of days to tassel, days to anthesis, days to silk in Jilin province (2020 and 2021; China; E125°, N44°; temperate environment). ns: no significance, two-sided t-test P-value shown. Numbers in the blank in x-axis represents the number of individuals. Each point shows the statistics of traits for each individual. Center lines in the boxplot indicate the median, edges represent the 25th and 75th percentiles, whiskers further extend by ±1.5 times the interquartile range from the limits of each box.

Source data

Extended Data Fig. 9 Phenotype analysis of different CRISPR/Cas9 mutation ZmCOL9 in different environments.

a, Gene structure and sequences of ZmCOL9 target regions in wild type, ZmCOLl9 CRISPR/Cas9 knockout mutants 1. b, Statics of days to tassel, days to anthesis, days to silk in Hainan province (2019; China; E109°, N18°; CRISPR/Cas9 mutation). c, Statics of days to tassel, days to anthesis, days to silk in Jilin province (2020; China; E125°, N44°; CRISPR/Cas9 mutation). d, Gene structure and sequences of ZmCOL9 target regions in wild type, ZmCOL9 CRISPR/Cas9 knockout mutants 2. e, Statics of days to tassel, days to anthesis, days to silk in Hainan province (2019; China; E109°, N18°; CRISPR/Cas9 mutation). f, Statics of days to tassel, days to anthesis, days to silk in Jilin province (2020; China; E125°, N44°; CRISPR/Cas9 mutation). ns: no significance, two-sided t-test P-value shown. Numbers in the blank in x-axis represents the number of individuals. Each point shows the statistics of traits in each individual. Center lines in the boxplot indicate the median, edges represent the 25th and 75th percentiles, whiskers further extend by ±1.5 times the interquartile range from the limits of each box.

Source data

Extended Data Fig. 10 Phenotype analysis of different overexpression lines for ZmCOL9 in different environments.

a, Statistics of days to tassel, days to anthesis, days to silk in Hainan province (2019; China; E109°, N18°; overexpression line 1). b, Statics of days to tassel, days to anthesis, days to silk in Hainan province (2019; China; E109°, N18°; overexpression line 2). c, Statics of days to tassel, days to anthesis, days to silk in Jilin province (2020; China; E125°, N44°; overexpression line 1). d, Statics of days to tassel, days to anthesis, days to silk in Jilin province (2020; China; E125°, N44°; overexpression line 2). ns: no significance, two-sided t-test P-value shown. Numbers in the blank in x-axis represents the number of individuals. Each point shows the statistics of traits in each individual. Center lines in the boxplot indicate the median, edges represent the 25th and 75th percentiles, whiskers further extend by ±1.5 times the interquartile range from the limits of each box.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–12 and Tables 8, 11 and 24.

Reporting Summary

Supplementary Tables

Supplementary Table 1–7, 9, 10 and 12–23.

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Chen, L., Luo, J., Jin, M. et al. Genome sequencing reveals evidence of adaptive variation in the genus Zea. Nat Genet 54, 1736–1745 (2022). https://doi.org/10.1038/s41588-022-01184-y

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