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Structural variations and environmental specificities of flowering time-related genes in Brassica napus

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Abstract

Key message

We found that the flowering time order of accessions in a genetic population considerably varied across environments, and homolog copies of essential flowering time genes played different roles in different locations.

Abstract

Flowering time plays a critical role in determining the life cycle length, yield, and quality of a crop. However, the allelic polymorphism of flowering time-related genes (FTRGs) in Brassica napus, an important oil crop, remains unclear. Here, we provide high-resolution graphics of FTRGs in B. napus on a pangenome-wide scale based on single nucleotide polymorphism (SNP) and structural variation (SV) analyses. A total of 1337 FTRGs in B. napus were identified by aligning their coding sequences with Arabidopsis orthologs. Overall, 46.07% of FTRGs were core genes and 53.93% were variable genes. Moreover, 1.94%, 0.74%, and 4.49% FTRGs had significant presence-frequency differences (PFDs) between the spring and semi-winter, spring and winter, and winter and semi-winter ecotypes, respectively. SNPs and SVs across 1626 accessions of 39 FTRGs underlying numerous published qualitative trait loci were analyzed. Additionally, to identify FTRGs specific to an eco-condition, genome-wide association studies (GWASs) based on SNP, presence/absence variation (PAV), and SV were performed after growing and observing the flowering time order (FTO) of plants in a collection of 292 accessions at three locations in two successive years. It was discovered that the FTO of plants in a genetic population changed a lot across various environments, and homolog copies of some key FTRGs played different roles in different locations. This study revealed the molecular basis of the genotype-by-environment (G × E) effect on flowering and recommended a pool of candidate genes specific to locations for breeding selection.

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Data availability

The supporting data of Figures and Tables are available in Supplemental Tables S1–S23. The raw reads of the rapeseed accessions have been deposited in the public database of National Center of Biotechnology Information under SRP155312 (https://www.ncbi.nlm.nih.gov /sra/SRP155312) and China National Center for Bioinformation (NGDC) (https://ngdc.cncb.ac.cn/gsa/browse/CRA001854).

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Acknowledgements

We thank Dr. Ulrike Lohwasser from Leibniz Institute of Plant Genetics and Crop Plant Research, Germany, for providing a part of the rapeseed accessions for this study, Dr. Li Ruimin from Zhejiang Meteorological Bureau, for providing the meteorological data of the experimental sites, and Mr. Rui Sun from Agricultural Experiment Station of Zhejiang University for the management of field experiments.

Funding

The work was financially sponsored by Natural Science Foundation of China (code nos. 32130076 and 31961143008), Key Science and Technology Project of Zhejiang Province (2021C02057), and Jiangsu Collaborative Innovation Centre for Modern Crop Production.

Author information

Authors and Affiliations

  1. Institute of Crop Science, Zhejiang University, Hangzhou, 310058, China

    Ying Xu, Yunhai Lu, Jie Dong, Yang Zhu & Lixi Jiang

  2. Jiguang Gene Biotechnology Co., Ltd., Nanjing, 210000, China

    Xiangdong Kong

  3. College of Agronomy, Northwest A&F University, Yangling, 712100, China

    Yuan Guo & Mingxun Chen

  4. Jiaxing Academy of Agricultural Sciences, Jiaxing, 31400, China

    Ruisen Wang & Xiangtan Yao

  5. Jinhua Academy of Agricultural Sciences, Jinhua, 321017, China

    Xiaoyang Chen

  6. College of Agronomy, Hunan Agricultural University, Changsha, 410128, China

    Tao Yan & Dezhi Wu

  7. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China

    Haiyan Cen

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  1. Ying Xu
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  2. Xiangdong Kong
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  3. Yuan Guo
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  14. Lixi Jiang
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Contributions

LJ conceived the experiments. YX carried out the field experiments and data analyses. XK contributed a lot to programming and data analyses. YG, RW, XY, XC, YL, JD, YZ, MC, and HC participated in field experiments in multiple locations and years. TY and DW were involved in data analyses and many constructive discussions. YX and LJ wrote the manuscript.

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Correspondence to Lixi Jiang.

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Communicated by Isobel AP Parkin.

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Supplementary Information

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122_2023_4326_MOESM1_ESM.pdf

Fig. S1 Venn diagrams showing the presence-frequency difference (PFD) number of flowering time-related genes (FTRGs) in winter and semi-winter (blue color circle), spring and semi-winter (yellow color circle), spring and winter (red color circle) ecotypes, and the overlapped number of FTRGs (PDF 93 KB)

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Fig. S2 The number of structural variations (SVs) in different frequency intervals in the 991 germplasms population (PDF 113 KB)

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Fig. S3 Scatter plot showing structural variation (SV) frequency difference between the spring and semi-winter ecotypes (PDF 2029 KB)

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Fig. S4 Line graph showing the frequencies of flowering time-related structural variations (SVs) that are significantly different between the spring and semi-winter ecotypes. The IDs of the genes are listed in Table S20 (PDF 116 KB)

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Fig. S5 Scatter plot showing structural variation (SV) frequency difference between the spring and winter ecotypes (PDF 3065 KB)

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Fig. S6 Line graph showing the frequencies of flowering time-related structural variations (SVs) that are significantly different between the spring and winter ecotypes. The IDs of the genes are listed in Table S21 (PDF 136 KB)

122_2023_4326_MOESM7_ESM.pdf

Fig. S7 Scatter plot showing structural variation (SV) frequency difference between the winter and semi-winter ecotypes (PDF 3359 KB)

122_2023_4326_MOESM8_ESM.pdf

Fig. S8 Line graph showing the frequencies of flowering time-related structural variations (SVs) that are significantly different between the winter and semi-winter ecotypes. The IDs of the genes are listed in Table S22 (PDF 141 KB)

122_2023_4326_MOESM9_ESM.pdf

Fig. S9 Histograms of the days from sowing to flowering (DSF) in the three locations in two successive years (i.e., six environments) (PDF 508 KB)

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Fig. S10 Manhattan plots of GWAS-SNP on the days from sowing to flowering (DSF) in three locations in two successive years (i.e., six environments) (PDF 4637 KB)

122_2023_4326_MOESM11_ESM.pdf

Fig. S11 Accumulated temperature (a), precipitation (b), and sunshine hours (c) during the vegetative growth stage in the three locations in two successive years (i.e., six environments) (PDF 209 KB)

122_2023_4326_MOESM12_ESM.pdf

Fig. S12 Number of accessions and proportions of each ecotype in three categories differing from each other in the degree of flowering time rank (FTR) change across six environments. (a) Bar chart showing the number of accessions of each ecotype, (b)–(f) pie charts showing the number of accessions in each category (b), proportions of each ecotype in the whole GWAS population (c), in the FTR-consistent category (d), the FTR-moderately fluctuated category (e), and the FTR-drastically fluctuated category (f) (PDF 161 KB)

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Xu, Y., Kong, X., Guo, Y. et al. Structural variations and environmental specificities of flowering time-related genes in Brassica napus. Theor Appl Genet 136, 42 (2023). https://doi.org/10.1007/s00122-023-04326-w

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