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Multi-locus genome-wide association study and genomic prediction for flowering time in chrysanthemum

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Abstract

Main conclusion

Multi-locus GWAS detected several known and candidate genes responsible for flowering time in chrysanthemum. The associations could greatly increase the predictive ability of genome selection that accelerates the possible application of GS in chrysanthemum breeding.

Abstract

Timely flowering is critical for successful reproduction and determines the economic value for ornamental plants. To investigate the genetic architecture of flowering time in chrysanthemum, a multi-locus genome-wide association study (GWAS) was performed using a collection of 200 accessions and 330,710 single-nucleotide polymorphisms (SNPs) via 3VmrMLM method. Five flowering time traits including budding (FBD), visible colouring (VC), early opening (EO), full-bloom (OF) and senescing (SF) stages, plus five derived conditional traits were recorded in two environments. Extensive phenotypic variations were observed for these flowering time traits with coefficients of variation ranging from 6.42 to 38.27%, and their broad-sense heritability ranged from 71.47 to 96.78%. GWAS revealed 88 stable quantitative trait nucleotides (QTNs) and 93 QTN-by-environment interactions (QEIs) associated with flowering time traits, accounting for 0.50–8.01% and 0.30–10.42% of the phenotypic variation, respectively. Amongst the genes around these stable QTNs and QEIs, 21 and 10 were homologous to known flowering genes in Arabidopsis; 20 and 11 candidate genes were mined by combining the functional annotation and transcriptomics data, respectively, such as MYB55, FRIGIDA-like, WRKY75 and ANT. Furthermore, genomic selection (GS) was assessed using three models and seven unique marker datasets. We found the prediction accuracy (PA) using significant SNPs identified by GWAS under SVM model exhibited the best performance with PA ranging from 0.90 to 0.95. Our findings provide new insights into the dynamic genetic architecture of flowering time and the identified significant SNPs and candidate genes will accelerate the future molecular improvement of chrysanthemum.

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Data availability

The GBS data are available in the National Center of Biotechnology Information Sequence Read Archieve (SRA) under BioProject accession number PRJNA1004079. The sequencing and mapping profile of 200 cut chrysanthemum entries used in this study have been uploaded to Figshare (https://figshare.com/ndownloader/files/42493344).

Abbreviations

EO:

Early opening stage

FBD:

Budding stage

GBS:

Genotyping-by-sequencing

GS:

Genomic selection

GWAS:

Genome-wide association study

OF:

Full-bloom stage

QEI:

QTN-by-environment interaction

QTN:

Quantitative trait nucleotide

SF:

Senescing stage

SNP:

Single-nucleotide polymorphism

VC:

Visible colouring stage

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Acknowledgements

We wish to thank the high-performance computing platform of Bioinformatics Center, Nanjing Agricultural University for providing data analysis platform services. This work was financially supported by the China Postdoctoral Science Foundation (2019M661870), Jiangsu Agriculture Science and Technology Innovation Fund (CX(21)2004), Natural Science Foundation of Jiangsu Province (BK20210395), the National Science Foundation of China (32230098, 32102421), China Agriculture Research System (CARS-23-A18), and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institution.

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Authors and Affiliations

  1. State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Weigang No. 1, Nanjing, 210095, Jiangsu, People’s Republic of China

    Jiangshuo Su, Zhaowen Lu, Junwei Zeng, Xuefeng Zhang, Xiuwei Yang, Siyue Wang, Fei Zhang, Jiafu Jiang & Fadi Chen

  2. Zhongshan Biological Breeding Laboratory, No. 50 Zhongling Street, Nanjing, 210014, China

    Fei Zhang, Jiafu Jiang & Fadi Chen

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  1. Jiangshuo Su
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  2. Zhaowen Lu
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  3. Junwei Zeng
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  4. Xuefeng Zhang
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Contributions

FC, FZ, and JS conceived and designed the project. JS, ZL, JZ, XZ, XY, and SW conducted the filed experiments. JS performed GWAS analysis and wrote the manuscript. FZ, JJ and FC guided the research. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Fadi Chen.

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Supplementary Information

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Supplementary file1 (XLSX 191 KB)

425_2023_4297_MOESM2_ESM.pdf

Fig. S1 Boxplots indicating the flowering time variation between the disbud and spray accessions. A pairwise t-test (two-tailed) was used to determine the significance. ***, P < 0.001; **, P < 0.01; *, P < 0.05; ns, not significant

425_2023_4297_MOESM3_ESM.pdf

Fig. S2 Population stratification analyses of the 200 chrysanthemum accessions panel. a Means of cross-validation errors. b Population structure analysis. Each bar represents a single entry, and the coloured portions of the bar indicate the proportional contribution of each of the subpopulations to a given entry. c Distribution of pairwise kinship coefficients in the GWAS panel. d Neighbour-joining phylogenetic tree of the 200 chrysanthemum accessions panel, with each coloured spot representative of one cluster of Q1 to Q9. The spray- and disbud-type chrysanthemum accessions are indicated by green and purple branches, respectively

425_2023_4297_MOESM4_ESM.pdf

Fig. S3 Veen diagram indicating the detected common QTNs across traits and datasets E1, E2, M and B represent four datasets where the phenotypic observations for ten flowering time traits collected in environment 1, environment 2, their mean and BLUP values, respectively, using in single-environment analysis. MQ represents the phenotypic observations for ten flowering time traits collected in environment 1 and environment 2, using in multiple environments analysis

425_2023_4297_MOESM5_ESM.pdf

Fig. S4 Information of the detected QTNs for flowering time traits. a Veen diagram indicating the detected common QTNs across five datasets. b, the distribution of the QTNs for ten flowering traits on 27 chromosomes of chrysanthemum

425_2023_4297_MOESM6_ESM.pdf

Fig. S5 Box plots indicating the variation in five unconditional flowering time traits for ten stable QTNs. NS, not significant. The mean values of each flowering time traits across two environments were used as phenotypic values. *, ** and *** indicate statistical significance at P < 0.05, P < 0.01 and P < 0.001, respectively, as calculated by Wilcox test

425_2023_4297_MOESM7_ESM.tif

Fig. S6 Manhattan plots for the five unconditional flowering time traits. a-e indicate the detected QEIs for FBD, VC, EO, OF and SF, respectively. Candidate genes around QEIs are marked with green colour

425_2023_4297_MOESM8_ESM.tif

Fig. S7 Manhattan plots for the five conditional flowering time traits. a-e indicate the detected QEIs for VC_FBD, EO_VC, OF_EO, SF_OF and SF_EO, respectively. Known genes around QEIs are marked with red colour, and candidate genes around QEIs are marked with green colour

425_2023_4297_MOESM9_ESM.pdf

Fig. S8 Box plots indicating the phenotypic variation in senescing stage (SF) for QEI Chr23__100938560. The P values were calculated by Wilcox test. Mean values were indicated by blue dots. The average SF value for each environment is indicated as blue dots and marked on the box plot

Fig. S9 Scree plot of principal component analysis

425_2023_4297_MOESM11_ESM.pdf

Fig. S10 The correlation between predicted and observed phenotypic values for flowering time using SVM and sig dataset. The dots in purple represent predictions from the training set, and in orange represent predictions from the test set

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Su, J., Lu, Z., Zeng, J. et al. Multi-locus genome-wide association study and genomic prediction for flowering time in chrysanthemum. Planta 259, 13 (2024). https://doi.org/10.1007/s00425-023-04297-8

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