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Genome-wide analysis of deletions in maize population reveals abundant genetic diversity and functional impact

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Abstract

Key message

Two read depth methods were jointly used in next-generation sequencing data to identify deletions in maize population. GWAS by deletions were analyzed for gene expression pattern and classical traits, respectively.

Abstract

Many studies have confirmed that structural variation (SV) is pervasive throughout the maize genome. Deletion is one type of SV that may impact gene expression and cause phenotypic changes in quantitative traits. In this study, two read count approaches were used to analyze the deletions in the whole-genome sequencing data of 270 maize inbred lines. A total of 19,754 deletion windows overlapped 12,751 genes, which were unevenly distributed across the genome. The deletions explained population structure well and correlated with genomic features. The deletion proportion of genes was determined to be negatively correlated with its expression. The detection of gene expression quantitative trait loci (eQTL) indicated that local eQTL were fewer but had larger effects than distant ones. The common associated genes were related to basic metabolic processes, whereas unique associated genes with eQTL played a role in the stress or stimulus responses in multiple tissues. Compared with the eQTL detected by SNPs derived from the same sequencing data, 89.4% of the associated genes could be detected by both markers. The effect of top eQTL detected by SNPs was usually larger than that detected by deletions for the same gene. A genome-wide association study (GWAS) on flowering time and plant height illustrated that only a few loci could be consistently captured by SNPs, suggesting that combining deletion and SNP for GWAS was an excellent strategy to dissect trait architecture. Our findings will provide insights into characteristic and biological function of genome-wide deletions in maize.

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Data availability

The sequencing data of 270 inbred lines are available from NCBI SRA PRJNA389800 and can be also downloaded from CyVerse Data Store: /iplant/home/shared/panzea/raw_seq_282/bam/ (Bukowski et al. 2017). The VCF files of SNP genotyping data with AGPv3 coordinates can be downloaded in the directory: /iplant/home/shared/commons_repo/curated/Qi_Sun_Zea_mays_haplotype_map_2018/282_onHmp321. Sequencing data of RNA-seq have been deposited in the Sequence Read Archive under accession number SRP115041 and in BioProject under accession number PRJNA383416 (Kremling et al. 2018).

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Acknowledgements

XZ thanks Sara Miller, Xiaolei Liu, Dong Zhang, and Tao Zuo for the help of working in the Buckler lab and gratefully acknowledges China Scholarship Council (CSC) for financial support during studying in USA. The authors also thank Dan Liu, Ling Wu, and Bowen Luo for the help on analyzing data and Duojiang Gao and Shiqiang Gao for the help on data collection in Sichuan Agricultural University of China.

Funding

This work was supported by Sichuan Science and Technology Support Project 2021YFYZ0027, 2021YFYZ0020, and 2021YFFZ0017, China Agriculture Research System of MOF and MARA, the National Natural Science Foundation of China (31971955), and the US Department of Agriculture–Agricultural Research Service and the National Science Foundation grant IOS-1238014 to E.S.B.

Author information

Author notes

  1. Xiao Zhang and Yonghui Zhu contributed equally to this work.

Authors and Affiliations

  1. Maize Research Institute, Sichuan Agricultural University, Chengdu, Sichuan, China

    Xiao Zhang & Shibin Gao

  2. Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Ministry of Agriculture, Chengdu, Sichuan, China

    Xiao Zhang & Shibin Gao

  3. Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, NY, USA

    Xiao Zhang, Karl A. G. Kremling, M. Cinta Romay, Edward S. Buckler & Fei Lu

  4. Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu, Sichuan, China

    Yonghui Zhu

  5. Bioinformatics Facility, Institute of Biotechnology, Cornell University, Ithaca, NY, USA

    Robert Bukowski & Qi Sun

  6. USDA-ARS, R. W. Holley Center, Cornell University, Ithaca, NY, USA

    Edward S. Buckler

  7. State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China

    Fei Lu

  8. CAS-JIC Centre of Excellence for Plant and Microbial Science (CEPAMS), Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China

    Fei Lu

  9. University of Chinese Academy of Sciences, Beijing, China

    Fei Lu

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  1. Xiao Zhang
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Contributions

XZ performed bioinformatics analysis and wrote the manuscript. YZ performed bioinformatics analysis and review the manuscript. KK participated in large-scale sample collection, RNA-seq and expression data production, and revised the manuscript. MCR managed the field work, collected tissues for next-generation sequencing of the population, and reviewed the manuscript. RB and QS performed raw sequencing data processing and data management. SG, ESB, and FL helped in manuscript discussion and writing. FL and XZ conceived the project. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xiao Zhang or Fei Lu.

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The authors declare no competing interests.

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Supplementary Information

Below is the link to the electronic supplementary material.

122_2021_3965_MOESM1_ESM.pdf

Fig. S1 Correlation between read depth (q > 30) and average read count, average copy number per window. a. Correlation between read depth (q > 30) and average read count per window in HMMCopy method. b. Correlation between read depth (q > 30) and average read count per window in dynamic window method. c. Correlation between read depth (q > 30) and average copy number per window in HMMCopy method. d. Correlation between read depth (q > 30) and average copy number per window in dynamic window method (PDF 455 kb)

Fig. S2 Frequency of BUSCOs overlapped and non-overlapped deletions (PDF 12 kb)

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Fig. S3 Deletion allele frequency and inbred line frequency for deletion windows in this population. a. Deletion allele frequency distribution of inbred lines. b. Inbred line frequency for deletion windows (PDF 39 kb)

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Fig. S4 Comparison of polymorphism SNP and deletion allele frequency derived from B73. a. Proportion of the polymorphism SNPs and deletions according to different allele frequencies derived from B73. b. Correlation between allele frequency of the polymorphism deletion and mean SNP frequency derived from B73 within the deletion window (PDF 1890 kb)

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Fig. S5 A simulation of PCA analysis by different deletion window numbers. PCA analysis using 5000 (a), 10,000 (b), 20,000 (c), 30,000 (d), 40,000 (e), 100,000 (f) randomly selected windows (PDF 1182 kb)

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Fig. S6 A simulation of PCA analysis by different SNP numbers. PCA analysis using 5000 (a), 10,000 (b), 20,000(c), 30,000 (d), 40,000 (e), 100,000 (f) randomly selected SNPs (PDF 1074 kb)

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Fig. S7 Linkage disequilibrium decay distance of deletions in the population and the different subgroups. a. Linkage disequilibrium decay distance in the population. b. Linkage disequilibrium decay distance in the NSS group. c. Linkage disequilibrium decay distance in the SS group. d. Linkage disequilibrium decay distance in the TS group. The dashed lines on each graph mean the r2 threshold for the LD decay distance (PDF 210 kb)

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Fig. S8 Correlation matrix of deletion frequency and six genomic features (Recombination, Sites of GERP > 2, Sites of GERP > 0, Gene density, Repeat and Centromere distance). Pairwise P value and correlation coefficient are labeled in this graph (PDF 3291 kb)

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Fig. S9 Correlation of expression rank and mean deletion proportion in seven tissues. a. GRoot. b. GShoot. c. Kern. d. L3Base. e. L3Tip. f. LMAD. g. LMAN (PDF 241 kb)

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Fig. S10 The relative distance between eQTL and associated genes analyzed by deletions. a. GRoot. b. GShoot. c. Kern. d. L3Base. e. L3Tip. f. LMAD. g. LMAN (PDF 462 kb)

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Fig. S11 Distribution of eQTL hotspots in total genome. The color gradient means gene number located in each hotspot (PDF 167 kb)

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Fig. S12 Significantly enriched GO of genes with eQTL identified by deletions. a. GRoot. b. GShoot. c. Kern. d. L3Base. e. L3Tip. f. LMAD. g. LMAN (PDF 325 kb)

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Fig. S13 Significantly enriched GO of unique genes with eQTL identified by deletion in each tissue. a. GRoot. b. GShoot. c. Kern. d. L3Base. e. L3Tip. f. LMAD. g. LMAN (PDF 357 kb)

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Fig. S14 The number of significantly associated genes detected in paired relevant tissues. a. GShoot and GRoot. b. LMAN and LMAD. c. L3Base and L3Tip. The pie plots below are the proportion of shared eQTL and unique eQTL detected in paired relevant tissues (PDF 245 kb)

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Fig. S15 The relative distance between eQTL and associated genes analyzed by SNPs. a. GRoot. b. GShoot. c. Kern. d. L3Base. e. L3Tip. f. LMAD. g. LMAN. h. Total (PDF 641 kb)

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Fig. S16 Comparison of effect (R2) between distant eQTL and local eQTL detected by SNPs in different tissues. ** means they show very significant difference at P < 0.01 (PDF 1796 kb)

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Fig. S17 Comparison of effect (R2) of top eQTL between deletion (DEL) and SNP for the same genes. a. Distant eQTL. b. Local eQTL. c. Total eQTL (PDF 524 kb)

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Fig. S18 Manhattan plots of GWAS result using deletion alleles. a. DTA. b. DTS. c. EHdivPH. d. GDD_DTA. e. GDD_DTS (PDF 2452 kb)

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Fig. S19 QQ plots of GWAS for five traits using deletion alleles generated in FarmCPU. a. DTA. b. DTS. c. EHdivPH. d. GDD_DTA. e. GDD_DTS (PDF 1044 kb)

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Fig. S20 Manhattan plots of GWAS result using SNP alleles. a. ASI. b. DTA. c. DTS. d. EH. e. EHdivPH. f. GDD_ASI. g. GDD_DTA. h. GDD_DTS. i. PH (PDF 2811 kb)

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Fig. S21 QQ plots of GWAS for nine traits using SNP alleles generated in FarmCPU. a. ASI. b. DTA. c. DTS. d. EH. e. EHdivPH. f. GDD_ASI. g. GDD_DTA. h. GDD_DTS. i. (PDF 1659 kb)

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Fig. S22 Comparison of GWAS significant sites between deletion and SNP in flowering time and plant height trait (PDF 16 kb)

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Table S1 List of pedigree, sequencing depth, mean read length, and the suitable window size of inbred lines in the population (XLSX 28 kb)

Table S2 PAV length of CML247, Mo17, and W22 relative to B73 (DOCX 16 kb)

Table S3 PAV length of B73 relative to CML247, Mo17, and W22 (DOCX 15 kb)

Table S4 List of deletion windows enriched more than 95% inbred lines in this population (XLSX 10 kb)

Table S5 List of deletion regions that included more than 20 continuous deletion windows (XLSX 11 kb)

Table S6 Multiple comparison of deletion frequency in different groups (DOCX 15 kb)

Table S7 Descriptive statistics of deletion frequency for different group (DOCX 15 kb)

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Table S8 List of genes show significant differences of expression in inbred lines between deletion overlap or not overlap. Data in this table are the P values, only significant genes in seven tissues are included. NS means ‘not significant’ (XLSX 68 kb)

Table S9 Summary of eQTL detected by deletions in seven tissues (DOCX 16 kb)

Table S10 List of deletion windows enriched more than ten eQTL in seven tissues (XLSX 12 kb)

Table S11 KEGG pathway enrichment analysis of genes extracted from distant eQTL hotspots (XLSX 11 kb)

Table S12 List of 301 genes consistently associated with eQTL in all seven tissues (XLSX 20 kb)

Table S13 Summary of eQTL detected by SNPs in seven tissues (DOCX 16 kb)

Table S14 List of unique eQTL consistently detected by deletions and SNPs inside the deletion window (XLSX 14 kb)

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Table S15 Significant deletion alleles and associated gene information. Left and right boundaries are determined by LD decay (XLSX 13 kb)

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Table S16 Significant SNP alleles and associated gene information. Left and right boundaries are determined by LD decay (XLSX 14 kb)

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Zhang, X., Zhu, Y., Kremling, K.A.G. et al. Genome-wide analysis of deletions in maize population reveals abundant genetic diversity and functional impact. Theor Appl Genet 135, 273–290 (2022). https://doi.org/10.1007/s00122-021-03965-1

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