**4. Discussion**

In the two consecutive years of EN2019 and EN2020, the number of upper primary branches, number of lateral flower buds and primary branch length presented the highest CV values; these traits were also the key traits used to determine the output of spray cut chrysanthemum. These results indicated that the architectural traits of the 195 spray cut chrysanthemum varieties selected by artificial breeding were diverse and controlled by complex gene pathways. As such, when selecting the appropriate spray cut chrysanthemum, breeders should consider these three traits to have high priority.

During their growth, plants have limited resources. They can allocate resources reasonably through different gene pathways to complete their life cycle. In the present study, plant height had significant slightly large positive Pearson coefficients with number of leaf nodes and total number of lateral buds, but had significant large negative Pearson coefficients with number of upper flower branches and number of lateral flower buds. Plant height was also found to positively correlate with tiller number in sorghum [38], and negatively correlated with fruit branch length in Chinese upland cotton [39]. The correlation relationships in chrysanthemums reflect the mutual negative relationship between vegetative growth and reproductive growth. The number of upper primary branches had very significant negative correlations with primary branch diameter, primary branch angle and primary branch length, which are key traits determining the quality of spray cut chrysanthemum. Therefore, balancing the number of upper primary branches and their quality is highly important.

Plant architecture is species specific, and influenced by environmental conditions such as light, temperature, humidity and nutrient status [40]. Low temperature can lead to the dwarfed rosette and leaves with increased thickness in Arabidopsis [41]. Main differences between EN2019 and EN2020 were monthly precipitation and monthly average relative humidity according to Table S2. Monthly precipitation and monthly average relative humidity of EN2020 in nearly each month were larger than that of EN2019. The mean values of plant height, stem diameter, primary branch diameter, primary branch angle and primary branch length decreased in EN2020 compared with EN2019, indicating a weaker growth state in EN2020. These differences in growth might be attributed to three main factors. First, the growth of chrysanthemum is sensitive to continuously cropped soils, which is related to changes in physicochemical properties, soil microorganisms and allelopathy of plants [42]. Second, the plum rain season in Nanjing was longer in 2020 than in 2019, as mentioned above, which increased the air humidity during the rooting period of the seedlings and the initial root growth stages. Changes in vapor pressure deficit (VPD) and relative humidity (RH) affect the height and flowering time of chrysanthemum plants [43,44]. This explained why the growth of seedlings in EN2020 was rather poor to some extent. Lastly, the seedlings of EN2020 were collected from mother plants overwintering in EN2019, whose growth state might be worse than that in EN2019. Number of leaf nodes, number of lateral flower buds and primary branch angle showed no significant difference in two years, which means that they might not be significantly influenced by a changed environment.

In this experiment, due to the differences in each trait in the two years, two cluster methods were used to cluster the data from the two years. After the trials, the best K value was set as 5. According to the clustering results, we divided the spray cut chrysanthemum varieties into five categories; these categories could be used as typical architecture types for summarize the architectural traits of spray cut chrysanthemum.

A low red light:far-red light ratio (R:FR) can lead to shade avoidance syndrome of plants, resulting in enhanced shoot elongation and reduced branching, and phyB is a major sensor of R:FR signal [45]. phyB has been found to control shooting branching together with photosynthetic photon flux density (PPFD) and PIF4/PIF5 in Arabidopsis, regulating related hormone pathway and expression levels of downstream genes such as *BRC1* [46,47]. The *phyB* mutant in sorghum also showed enhanced apical dominance and shortened bud length and the expression levels of *TEOSINTE BRANCHED1* (*TB1*), *Dormancy-associated gene-1* (*DRM1*) and *MORE AXILLARY BRANCHES2* (*MAX2*) were found to increase in the axillary buds [48,49]. The number of upper primary branches in chrysanthemums was found to correlate with *phyB* in our study, indicating the functional role of controlling plant architecture. Three other genes were also found to participate in plant development regulation and hormone signal pathway. *BRH1* is a BR-responsive gene, and overexpression of *BRH1* results in the production of rounded leaves and may result in the growth and development of rosette leaves [50]. By promoting the conversion of nonhair cells to root hair cells, the R3-type MYB transcription factor protein CAPRICE (CPC) was shown to induce root hair formation in the root epidermis [51]. bZIP16 can promote seed germination and hypocotyl elongation in the initial stages of seedling development [52]. These three genes also play a role in plant development in other plants, which might control plant architecture in chrysanthemums.

#### **5. Conclusions**

In this study, we found that the number of leaf nodes, number of lateral flower buds and primary branch angle were less influenced by environmental factors, while plant height, stem diameter, total number of lateral buds, number of upper primary branches, primary branch diameter and primary branch length were significantly influenced by environmental factors. The number of upper primary branches, number of lateral flower buds and primary branch length presented larger variation degree in 195 species. The number of upper primary branches had very significant negative correlations with primary

branch diameter, primary branch angle and primary branch length. We also summarized five clusters with typical architecture and predicted four candidate functional genes (*phyB*, *BRH1*, *CPC* and *bZIP16*) which might control plant architecture in chrysanthemums.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/horticulturae8050458/s1, Table S1: 195 spray cut chrysanthemum varieties tested; Table S2. The monthly average temperature, monthly precipitation and monthly average relative humidity of EN2019 and EN2020 in planting location, Jiangning District, Nanjing, China (E118◦85 , N31◦95 ) Table S3: 44 spray cut chrysanthemum varieties sequencing completed; Table S4: SNPs (GAPIT-cMLM) for the architecture traits of plant height and number of leaf nodes; Table S5: SNPs (GAPIT-cMLM) for the architecture traits of number of upper primary branches, number of lateral flower buds and stem diameter; Table S6: SNPs (GAPIT-cMLM) for the architecture traits of stem diameter, primary branch diameter and primary branch angle; Table S7: SNPs (GAPITcMLM) for the architecture traits of primary branch length; Table S8: SNPs (GAPIT-cMLM) for the architecture traits of primary branch length; Table S9: SNPs (Tassel-cMLM) for the architecture traits of plant height, number of leaf nodes, total number of lateral buds, number of upper primary branches and number of lateral flower buds; Table S10: SNPs (Tassel-cMLM) for the architecture traits of number of lateral flower buds, stem diameter, primary branch diameter and primary branch length; Table S11: SNPs (Tassel-MLM) for the architecture traits of plant height, number of leaf nodes and total number of lateral buds; Table S12: SNPs (Tassel-MLM) for the architecture traits of number of upper primary branches number of lateral flower buds and stem diameter; Table S13: SNPs (Tassel-MLM) for the architecture traits of stem diameter, primary branch diameter, primary branch angle and primary branch length; Table S14: SNPs (Tassel-MLM) for the architecture traits of primary branch length; Table S15: SNPs (Tassel-MLM) for the architecture traits of primary branch length; Table S16: SNPs (Tassel-MLM) for the architecture traits of primary branch length. Supplementary Figures S1–S18: The Manhattan plots of SNPs detected by the cMLM model of TASSEL software.

**Author Contributions:** Conceptualization, A.S.; methodology, A.S. and J.S.; software, A.S., J.S. and D.S.; validation, D.S., L.Z. and Q.Y.; formal analysis, D.S. and L.Z.; writing—original draft preparation, D.S. and L.Z.; writing—review and editing, D.S., L.Z., J.S., Q.Y., J.Z., W.F., H.W., Z.G., F.C. and A.S.; funding acquisition, A.S., F.C., W.F. and H.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by China Agriculture Research System (CARS-23-A18), National Natural Science Foundation of China (32172609, 31870694, 31872149), the earmarked fund for Jiangsu Agricultural Industry Technology System (JATS [2021]454), and a project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institution.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Data analysis was supported by the high-performance computing platform of Bioinformatics Center, Nanjing.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

