Black spot disease (BSD), induced by Alternaria alternata, constitutes a significant menace to chrysanthemum. Identifying resistant germplasm resources underscores its critical importance in chrysanthemum breeding. To elucidate the genetic basis and candidate genes underpinning chrysanthemum BSD resistance, we conducted a multi-locus genome-wide association study (GWAS) using a panel of 152 accessions and 351555 single nucleotide polymorphisms (SNPs) via the 3VmrMLM method. We observed extensive phenotypic variation for the disease severity index (DSI) of BSD, with coefficients of variation ranging from 70.79% to 85.00%, and the broad-sense heritability was calculated at 74.36%. GWAS result detected seventy-one quantitative trait nucleotides (QTNs) and seven QTN-by-environment interactions (QEIs), accounting for 1.53%—7.06% and 0.68%—3.16% of the phenotypic variation, respectively. Eighteen stable QTNs were identified in more than two methods, from which eight highly favorable SNP alleles were extracted for BSD resistance. Furthermore, we observed a significant dosage-pyramiding effect (P < 0.001) among the favorable alleles. Among the genes surrounding the QTNs and QEIs, 12 were homologous to known disease-resistance genes in Arabidopsis, and 14 candidate genes were mined by combining the functional annotation and transcriptomics data, respectively. Our results help better understand the genetic architecture of BSD resistance, and the identified significant SNPs and candidate genes pave the way for future molecular breeding of chrysanthemums with enhanced BSD resistance.

Waterlogging is a major stress that impacts the chrysanthemum industry. Large-scale germplasm screening for identifying waterlogging-tolerant resources in a quick and accurate manner is essential for developing new cultivars with improved waterlogging tolerance. To overcome this phenotyping bottleneck, consumer-grade digital cameras have been used to acquire the red-green-blue (RGB) images of 180 chrysanthemum cultivars and their wild relatives under waterlogging stress and well-watered conditions. A total of 103 image-based digital traits (i-traits), including 10 morphological i-traits and 93 texture i-traits, were extracted and systematically analyzed. Most of these i-traits presented high coefficients of variation (CVs) and broad-sense heritability (H²), with an average CV of 34.04 ​% and an average H² of 0.93. We identified several novel texture i-traits associated with the hue (H) component, which strongly correlated with the traditional waterlogging tolerance index, the membership function value of waterlogging (MFVW) (R ​= ​0.63–0.77). We further employed the random forest (RF) and gradient boosting tree (GBT) machine learning algorithms to predict aboveground biomass and MFVW on the basis of different i-trait datasets. The RF model achieved superior predictive performance, with a coefficient of determination (R²) of up to 0.88 for shoot weight and 0.86 for MFVW. Moreover, a subset of the top 13 most important i-traits could accurately predict MFVW (R² ​> ​0.80) via the cross-validation method. A total of 10 highly tolerant resources were selected by traditional and RGB-based evaluation, and 50 ​% belonged to Artemisia. Our findings confirmed that RGB-based technology provides a promising novel approach for quantifying waterlogging response that contributes to future breeding programs and genetic dissection for waterlogging tolerance.

Chrysanthemum is rich in active compounds such as flavonoids and phenolic acids, and its dried head flowers are commonly used for tea and medicinal purposes. However, the genetic determinism underlying chrysanthemum active compounds remains elusive. In this study, we evaluated a panel of 137 chrysanthemum accessions for total flavonoids, chlorogenic acid, luteolin, and isochlorogenic acid A across two consecutive years. The four active compounds exhibited considerable variation, with a coefficient of variation ranging from 44.96 % to 76.30 %. Significant differences were observed in genotype and environments, and the broad-sense heritability was estimated at 0.5–0.63 for all examined traits. Significant pair-wise correlation was found between the four active compounds. Several accessions showing the highest active compounds were figured out for breeding use by integrating the membership function and hierarchical cluster analysis methods. Based on the 327042 high-quality SNPs, a genome-wide association study (GWAS) captured 59 significant SNPs for the four active compounds, of which 24 elite alleles exhibited pyramiding effects. A total of 18 potential candidate genes were mined, among which evm.model.scaffold_1149.273 (QUA1) has one linkage disequilibrium (LD) block corresponding to Hap4 with the highest luteolin content. The findings are beneficial to understanding the genetic basis of the active compounds and provide parental materials and valuable markers for the genetic improvement of active compounds in chrysanthemums.