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Article

An SSR-Base Linkage Map Reveals QTLs for Floral-Related Traits in Nightlily (Hemerocallis citrina)

by
Yuting Su
1,
Zhonghao Liang
1,
Xinyu Zhao
1,
Lijing Shi
1,
Yang Liu
1,
Yang Gao
1,
Xiaojing Cheng
1,
Guoming Xing
1,2,* and
Sen Li
1,2,*
1
College of Horticulture, Shanxi Agricultural University, Taigu, Jinzhong 030801, China
2
Datong Daylily Industrial Development Research Institute, Datong 037008, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(7), 1599; https://doi.org/10.3390/agronomy15071599
Submission received: 2 June 2025 / Revised: 22 June 2025 / Accepted: 28 June 2025 / Published: 30 June 2025
(This article belongs to the Section Horticultural and Floricultural Crops)
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Versions Notes

Abstract

Nightlily (Hemerocallis citrina Baroni) is mainly cultivated for bud consumption with medicinal, nutritional, and economic value. Enhancing nightlily yield is one of the most significant breeding goals of modern agriculture of H. citrina breeding objective, but it also faces great challenges. In this study, an intraspecific hybridization population crossed between two varieties, ‘Liuyuehua’ and ‘Datong Huanghua’ of Hemerocallis, was used to establish 715 F1 individuals. Phenotypic data for eight floral traits, including scape number, bud number, scape length, scape diameter, bud length, bud diameter, fresh flower bud weight, and dry flower bud weight, were collected from 715 F1 individuals over a three-year period (2022, 2023, and 2024). The Simple Repeat Sequence (SSR) markers were validated to genotype the 116 random F1 individuals and to construct a linkage map. The intraspecific hybridization map contained 11 linkage groups. A total of 169 SSR markers were used to construct a linkage map, spanning a total map length of 1605.3 cM, with an average marker interval of 9.50 cM. The linkage map revealed 11 floral QTLs from 7.21% to 24.29% of phenotypic variation. Through collinearity analysis, it was found that 122 markers could be aligned to the published genome sequence of H. citrina. A total of five candidate genes for floral traits were predicted. The linkage map is essential for mapping and marker-assisted progeny selection that will accelerate the pace of nightlily breeding.

1. Introduction

Hemerocallis citrina Baroni (2n = 2x = 22), commonly known as nightlily, is native to southern China, Japan, and the Korean Peninsula and has become a globally cultivated vegetable due to the high nutritional, economic, and medicinal value of its buds [1,2,3,4,5,6,7,8,9]. Nightlily buds are recognized to contain bioactive compounds, including flavonoids, polyphenols, carotenoids, and Vitamin E [10,11], which are potential effective in anti-depression, sleep-promoting, antioxidant, and lactation-promoting, etc. [10,12,13,14,15,16].
Scape number, bud number, scape length, scape diameter, bud length, bud diameter, fresh flower bud weight, and dry flower bud weight are important traits that affect yield. So far, QTLs linked to floral traits have been mapped to different linkage groups, but the investigation into traits related to the floral of nightlily remains incomplete [17,18].
The genetic linkage map serves as an invaluable tool in molecular breeding [19], significantly enhancing the efficiency of the breeding process and reducing costs. Genetic maps serve as a framework of chromosomal organization and can be utilized to identify genetic linkage between markers and desirable traits [20,21]. Such marker–trait information can serve as the basis for map-based QTL analysis [22]. Molecular markers have been utilized to elucidate the genetic diversity and origin of various cultivars [23,24]. Population structure analysis has demonstrated that Simple Repeat Sequence (SSR) microsatellites, as co-dominant markers, hold particular promise for pedigree studies, enhancing breeding programs via marker-assisted selection, analyzing genetic diversity, and uncovering genomic selection signatures [25,26,27,28,29,30]. SSR markers are widely distributed across plant genomes, and they have proven to be effective tools for marker-assisted breeding in numerous crop species [31]. Many genetic linkage maps for perennial plants exhibiting heterozygous genes have been successfully constructed utilizing SSR (Simple Repeat Sequence) markers.
Nightlily is a self-incompatible horticultural plant, and it exhibits complex genetic heterozygosity like woody plants and perennial fruit trees. Consequently, the conventional genetic mapping methods that rely on inbred lines or advanced-generation populations are not suitable for this species. Fortunately, the F1 population exhibits trait segregation like the F2 population; therefore, a method known as the ‘pseudo-testcross’ approach was conceived [32,33,34,35]. In the wolfberry study, researchers employed the pseudo-testcross theory to construct a genetic map for Lycium species using SSR and AFLP markers [36]. The first genetic linkage map for mapping drought-tolerant traits of India tea cultivars was constructed using the two-way pseudo-testcross approach [37].
Floral traits are complex quantitative traits governed by multiple major or minor QTLs. Quantitative trait locus (QTL) mapping is widely used to identify the genomic regions linked to economically important traits. Molecular markers tightly linked to these QTLs can be developed for deployment in marker-assisted breeding [38]. Quantitative trait locus (QTL) mapping represents a highly effective methodology for the genetic dissection of quantitative traits [39]. It has been proved to be useful for identifying genes or genomic regions that control polygenic traits. Currently, extensive research over the years has laid a strong base for gene mapping and functional verification of yield traits in major crops, like rice, wheat, cotton, flax, rapeseed, and peanuts; the majority of yield QTL are capable of influencing one or multiple yield components [40,41,42,43,44,45].
The complex and highly heterozygous nature of the nightlily genome hinders the comprehensive understanding and breeding speed of the floral traits. Marker-assisted breeding for nightlily improvement is currently limited by the lack of high-density SSR linkage maps. Therefore, it is necessary to construct a linkage map to accelerate the genetic improvement in nightlily breeding. The linkage map constructed and the preliminary mapping provided in this study could serve as tools for locating genes associated with other important traits of nightlily. This study will provide a foundation for the fine mapping of floral genes in nightlily, for identifying candidate genes, and for implementing molecular design breeding.

2. Materials and Methods

2.1. Plant Material

In 2019, artificial intraspecific hybridizations were performed, and seeds were harvested. In 2020, seedlings of the F1 population were reared. All 715 individuals and their parents were planted at Shanxi Agricultural University (37°25′ N, 112°59′ E) in 2021, with fixed spacing (Figure 1). The segregating population consisted of 715 F1 individuals produced by crossing H. citrina ‘Liuyuehua (female)’ and H. citrina ‘Datong Huanghua (male)’. Field fertilization and water management were conducted in accordance with standard agricultural practices, with timely pest and disease control measures implemented (Figure S1).

2.2. Collection of Phenotypic Data and Statistical Analysis

Continuous measurements and calculations of key floral traits for the hybrid population and parents over three years (2022–2024). A total of 715 F1 individuals and parents, eight key floral traits were measured, including scape number (SN); bud number (BN); scape length (SL, cm); scape diameter (SD, mm); bud length (BL, mm); bud diameter (BD, mm); fresh flower bud weight (FWOB, g); and dry flower bud weight (DWOB, g). However, due to the gradual development of F1 population of nightlily, most flowers only bloomed in the second year of planting, and in the third year, almost all individual plants bloomed; a total of 408 F1 individuals were surveyed in 2022, 705 F1 individuals in 2023, and 707 F1 individuals in 2024. The investigation methods of floral traits were described in Table S1. Fresh buds were picked at 6 a.m. to measure data.
The three-year survey phenotype data was performed using Excel 2019. R language package lme4 was used to calculate the Best Linear Unbiased Prediction (BLUP) for each material across multiple environments [46]. Calculations of the mean and standard deviation, and coefficient of variation of nightlily, and statistical significance, were made in IBM SPSS Statistics 27. The broad-sense heritability was calculated through the package lem4 (Linear Mixed Effects Model) in R-studio [47]. Correlation and normality analyses were performed using Origin 2024 software. Frequency distribution histograms for each trait were plotted and tested for normal distribution to compare the trends in various traits over a three-year period. PCA analysis was performed using GENE DENOVO (https://www.omicshare.com/ (accessed on 22 April 2025).

2.3. DNA Extraction

To ensure high-quality DNA, fresh, actively growing leaves were collected from both the parental nightlily plants and 715 F1 individuals. Genomic DNA was extracted for each sample following the Magnetic Bead Isopropanol Method [48]. The integrity and purity of each DNA sample were measured by a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). We prepared the DNA stock solution and diluted it to reach a final concentration of 50 ng/μL, and then it was stored for subsequent analysis via polyacrylamide gel electrophoresis [17].

2.4. Development of SSR Markers

Genomic microsatellite search and primer design were derived from the Krait V1.3.3 software [49]. Use electronic PCR to sequentially amplify primers in the genomes of female ‘Liu Yuehua’ and male ‘Datong Huanghua’. The genome sequences of H. citrina (https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_017893485.1/ (accessed on 22 April 2025)) were scanned for all SSR (Simple Repeat Sequence) markers utilizing the Perl script in_silico_PCR (https://github.com/egonozer/in_silico_pcr (accessed on 22 April 2025)). Select primers that amplify individual sequences in the parent genome as candidate primers. Furthermore, primers that showed size differences in the amplified products between the two parental genomes were screened as target synthetic primers. Primers were selected and synthesized by Zhengzhou Qingke Biotechnology Co., Ltd. (Zhengzhou, China).
To facilitate subsequent experimental operations, we employ two gels to run samples from 116 individuals, and random selection to represent the variability of the larger population. This experiment utilized 1561 SSR primers, consisting of 1317 primers from the previous design of the research group and 244 were designed using Krait software based on resequencing results. The primer name started with “Sau” and the sequence number starts with 1.

2.5. Collinearity Analysis

The Perl script in_silico_PCR was used to search all SSR markers against the genome sequences of H. citrina. The linkage map, as well as the H. citrina reference genome, was compared, which revealed a collinearity pattern. The graphical comparative maps were visualized graphical comparisons using TBtools V.2.225 [50].

2.6. Genetic Map Construction and QTL Mapping

The polymorphic EST-SSR primers were screened from 1561 primers, using two parents and six random F1 individuals. PCR amplifications were conducted in a 10 μL reaction volume, which consisted of 2 μL DNA (50 ng/μL), 1 μL each of forward and reverse primers (5 μM), and 3 μL 2× Taq PCR MasterMix (5 U/μL, Real-Times Biotechnology Co., Ltd., Beijing, China). A touch-down PCR procedure was employed. PCR amplification products were detected by 30% polyacrylamide gel electrophoresis, followed by silver staining [17]. Using the polymorphism primers obtained from the screening, polymorphisms were detected by PCR amplification of 116 F1 individuals.
A genetic linkage map of the population was generated via the cross-pollinated (CP) model in software JoinMap4.1. SSR markers with 2–4 alleles were polymorphic and could be divided into five segregation patterns (ab × cd, ef × eg, hk × hk, lm × ll, and nn × np) for further linkage analyses [36]. Used markers with a LOD value of 4.0 as the linkage threshold for grouping and selected markers with a recombination frequency of 0.4 for linkage group partitioning. Maximum Likelihood Estimation (MLE) could obtain the standard error of the estimated value and conduct a significance test on the estimated value [51]. For segregation distortion markers, we attempted to retain all segregation distortion markers in the linkage analysis while closely checking their impact on marker order and map length. However, markers affecting the physical location of other markers were removed. The GACD v.1.2.13 software was used to perform QTL mapping on the phenotypic nightlily data via the Composite Interval Mapping (CIM) model. The LOD thresholds at the genome-wide level were determined by running 1000 permutations [52,53]. QTL with the LOD exceeding the threshold value was considered significant. The naming convention for QTLs was as follows: ‘q’+ trait abbreviation + linkage group + number [12,54]. For instance, the designation ‘qBN6.1’ refers to the first QTL associated with the number of buds located on linkage group 6.

2.7. Candidate Gene Prediction

H. citrina genetic reference data were downloaded from the website, according to the physical position of the major QTLs, and candidate genes were extracted from the predicted gene annotation file [2]; its structure gene annotation file was used to predict candidate genes. KEGG annotations of genes located within QTL intervals were used to screen for candidate genes potentially involved in production. To test the statistical enrichment of differentially expressed genes in KEGG pathways, we utilized GENE DENOVO (https://www.omicshare.com/ (accessed on 22 April 2025)), where genes within the targeted interval serve as the target genes, while the whole-genome annotation file was used as the background file.

3. Results

3.1. Phenotypic Variation of Floral Traits in ‘Liuyuehua’ × ‘Datong Huanghua’ F1 Population

Building on a previous study of nightlily, this study investigated nightlily floral traits in 2022, 2023, and 2024. The phenotypes of eight floral traits of the parental lines are shown in Table S2. The results showed that the F1 floral traits of Hemerocallis ‘Liuyuehua’ and ‘Datong Huanghua’ were different, and the frequency distribution showed a continuous normal distribution pattern, indicating that these were typical quantitative traits. Traits characterized by normal distribution were employed for QTL mapping (Figure 2). The coefficients of variation for the eight floral traits ranged from 8.9% to 63.99%, which suggests that genetic variation accounted for a part of the phenotypic variance in the population. Among all the measured traits, broad-sense heritability varied from 0.1912 to 0.9856. SD reached the top (0.9856), followed by BN (0.9588), SL (0.9299), BL (0.8554), BD (0.8066), SN (0.6294), DWOB (0.2167), and FWOB (0.1912) (Table 1).
The results of the correlation analysis among floral traits are presented in Figure 3. In BLUP phenotypic data, there were significant correlations among the eight floral traits, indicating complex synergistic relationships among various traits. The parameter peaked at 0.62 between BL and SL (p ≤ 0.01, p ≤ 0.05), which could be considered as closely related traits (Figure 3).
A principal component analysis (PCA) was conducted on floral traits of the F1 population. PCA adopted a dimensionality reduction approach to identify the most significant and contributing factors for the samples. The results indicated that the principal components and their respective contribution rates were labeled on the axes PC1 and PC2 (Figure 4). The first principal component (SL) had a contribution rate of 98.08%. Based on the different principal components, eight traits were distributed across four quadrants, indicating significant differences in floral traits.

3.2. Genetic Linkage Map Construction

A total of 1561 EST-SSR primers were developed from nightlily. Selected SSR markers could produce polymorphic between two parents as well as among six F1 individuals, and 244 EST-SSR primers were selected for genetic diversity, with a polymorphic rate of 15.3%. Subsequently, the 244 clearly visible co-dominant polymorphic primer markers were further employed to segregate 116 F1 individuals.
The segregation patterns of 244 polymorphic markers in the 116 F1 individuals are shown. Chi-square (χ2) tests indicated that the segregation of 192 primers conformed to Mendelian ratios (1:1, 1:2:1, 1:1:1:1), while 52 primers exhibited varying degrees of segregation distortion (Table S3). The representative gel pictures for segregation types are shown in Figure S2. The proportion of segregation distortion markers among polymorphic markers was 21.3%. In 244 SSR markers, the <ef × eg> genotype accounts for 62.7% of the total, the <lm × ll> genotype constitutes 14.34%, the <nn × np> genotype accounts for 12.29%, and the <hk × hk> genotype comprises 10.66% (Table 2). Markers with <ab × cd> genotypes were excluded from map construction. A total of 169 SSR markers were used for map construction, following the statistical rules for the CP type as specified in JoinMap4.1 software. The total intraspecific linkage length of the genetic map was 1605.3 cM, with an average distance of 9.49 cM, covered by 169 SSR markers, and a total of 11 linkage groups corresponding to the 11 chromosomes were developed. The linkage group size varied from 87.10 cM to 238.45 cM. The number of markers per chromosome varied from 11 to 22, with an average number of 15.36 markers per linkage group. The largest marker interval was 58.3 cM (Figure 4). The correlation coefficient between genetic distance and physical distance ranged from 36.6% to 99.5%, indicating the existence of a correlation (Table 3).

3.3. Collinearity Analysis of the Intraspecific Hybridization Genetic Linkage Maps and H. citrina Genome

Counting the common SSR markers was used to analyze the collinearity between intraspecific spectra. Collinearity analysis was performed for the linkage groups of the linkage map and the H. citrina reference genome, where identical markers were located, and the linkage map showed good collinearity. The primer sequences of 122 markers from one linkage map matched the sequence of the physical genome map, representing 72.19% of all 169 markers. The alignment of most hit markers matched the order of their corresponding gene sequences on the physical map, indicating strong collinearity between the genetic and physical maps (Figure 5). Certain markers lack specific amplification sites within the H. citrina genome, precluding their utility in assessing collinearity.

3.4. QTL Analysis

Used a linkage map that included 169 markers to analyze the floral traits in BLUP data. A total of 11 QTLs for floral traits have been detected in nightlily, distributed among LG3, LG4, LG5, LG6, LG8, and LG10. The peak QTLs for six floral traits were delineated as follows: a total of 11 QTLs related to floral traits, specifically, two QTLs for BD; four QTLs for the DWOB; one QTL for BN; three QTLs for SL; and one QTL for BL. Maximum LOD scores by trait varied from 4.74 for SL to 7.41 for BL. The phenotypic variation explained by the identified QTLs varied from 7.21% for BN to 24.29% for BL. Of these QTLs, qBN6.1, qSL5.1, qBL8.1, qBD8.1, and qDWOB3.1 showed a relatively higher LOD score, suggesting that these QTLs may be major QTLs for floral traits. QTL analysis detected one QTL on LG8 (qBL8.1). The QTL accounted for 24.29% of phenotypic variation, with the highest LOD score of 7.41 and a confidence interval of 2.38 cM (Table 4). QTL mapping intervals for different traits observed with an overlap. For example, SL and DWOB have the same QTL mapping interval in linkage maps, which are located on LG8 from 73.81 to 87.23 cM. Four floral traits, SL, BD, DWOB, and SD, have all been mapped to linkage group 8 (Figure S3).
Three QTL clusters, LG3, LG5, and LG8, each comprised two or four QTLs. The LG3 contained two QTLs, each controlling qBD3.1 and qDWOB3.1. LG5 contained two loci (qSL5.1 and qDWOB5.1). The LG8 contained four QTLs with qBL8.1, qDWOB8.1, qBD8.1, and qSL8.1 (Figure 6). Both QTL clustering and pleiotropy could explain the observed linkage among different traits. Fine mapping with increased marker density within the interval could be used to distinguish between these phenomena.

3.5. Candidate Gene

Due to the availability of the whole genome sequence of H. citrina, it became possible to identify candidate QTL genes underlying QTLs mapped in this study. The linkage analysis showed that qBL8.1 was located between 2.38 cM and 2.64 cM on LG8, and the physical distance was 19.15 Mb. The qSL5.1 was located on LG5, the physical location was from 45.44 to 63.51 Mb, the LOD value was 6.56, and the PVE value was 19.39%. According to the position of the QTL interval, which had been identified for the nightlily per floral traits on the whole genome, candidate genes were screened from the whole nightlily genome (Tables S4 and S5). In order to identify candidate genes, we conducted gene enrichment. In total, 153 candidate genes of the bud length locus were identified in gene databases, and 80 genes had KEGG annotations. In these KEGG annotations, the plant hormone signal transduction pathway was the most significant pathway. Based on gene annotation information, we selected five candidate genes predicted as being related to the process of biomolecules, and the bud length and scape length may be regulated by plant hormones (Table 5). The genes of the bud length locus were significantly enriched in the Citrate cycle (TCA cycle), and the Pentose glucuronate interconversions pathways were significantly enriched (Figure 7A). We speculated that these genes may have negatively affected nightlily bud growth and size. Based on gene annotation information analysis and their known roles in hormone signaling and cell elongation, we selected two candidate genes predicted as bud length, HHC034511 (ethylene-responsive transcription factor ERF071) and HHC034516 (protein PIN-LIKES 3-like isoform X1). The 349 candidate genes of scale length were identified in gene databases, and 60 genes had KEGG annotations. The Ethylene-responsive transcription factor family was involved in growth and developmental programs; ERFs suppress cell expansion and the cell cycle. The plant hormone signal transduction pathway was the most significant of pathways (Figure 7B). Through gene functional analysis, HHC022036 (auxin-responsive protein IAA30), HHC022110 (auxin-responsive protein SAUR71), and HHC022210 (AP2-like ethylene-responsive transcription factor ANT) were identified as candidate genes for SL. It has been proven that these genes play crucial roles in plant growth and development, potentially promoting SL elongation by regulating plant hormones.

4. Discussion

Plant hybrid breeding can combine desirable traits and is an important breeding method for creating new germplasm. In this study, we find that high heritability is obtained in BN, SL, SD, BL, and BD, with minimal influence from environmental factors; floral traits are significantly influenced by genotype. There are significant correlations among the eight floral traits, indicating complex synergistic relationships among various traits. There is a significant correlation between the scape length and bud number. The first principal component (SL) had a contribution rate of 98.08%, indicating that it is the most important factor affecting yield. In summary, the SL is the most important trait affecting the yield of nightlily. Nightlily is a perennial plant, and its F1 population exhibits stable traits in the 2–3 years of development, allowing for phenotypic trait measures.
The pseudo-testcross strategy, characterized by its use of F1 individuals as the mapping population, effectively tackles the self-incompatibility and long juvenile stage issues in Castanea and has seen widespread application in recent years [55]. The whole-genome sequence has been employed to discover and exploit genomic SSR loci, and has been successfully applied in crops such as cotton [56], millet [57], and Lilium L [58]. Nightlily (Hemerocallis), like blueberry, is a perennial plant. Due to their long generation time, high heterozygosity, and self-incompatibility, they are suitable for improvement through marker-assisted breeding [59]. Selecting a mapping population of 60–250 individuals is generally considered appropriate and important for constructing a high-quality genetic linkage map [60,61,62]. Due to the difficulty of hybridization in perennial plants, the linkage mapping population should be around 100 [63]. We use 116 F1 mapping individuals, which can meet the requirements for constructing genetic maps. Segregation distortion for a particular marker in a mapping population is a result of deviation of its segregation ratios from the fixed Mendelian pattern (1:1, 1:1:1:1, 1:2:1 for dominant markers). In the present study, all SSR markers were analyzed for their goodness of fit using the χ2 test. A total of 52 out of 244 SSR markers—21.31% of the total markers—exhibited deviation from the expected Mendelian segregation ratio. It is believed that markers exhibiting segregation distortion, a common phenomenon in mapping studies of these species, are associated with alleles influencing the viability of these genotypes [64]. A previous study examined marker segregation distortion and found that these markers may exert only a small effect on linkage analysis, map order, or map length [65]. Segregation distortion may occur due to chromosome deletion, human error, genetic incompatibility, small group size, etc. [66]. A genetic linkage map of maize was constructed using 150 SSR and 24 RFLP markers; of the 174 markers covering the whole maize 10 chromosomes, 49 markers (28.1%) showed the genetic distortion [67]. In nightlily, significant marker distortion was observed for 40 markers out of the total 148 on the intraspecific hybridization map, but it did not affect the quality of the map [17]. Small group size was considered to be the primary cause of segregation distortion in nightlily.
Linkage group 8 exhibited a lower R2, potentially attributable to the reduced polymorphism of SSR primers in the pericentromeric region, where the centromere and its surrounding area are characterized by low recombination rates. There was still a gap of more than 50 cm between adjacent markers on LG2 and LG6. The presence of a large gap may be a result of genome regions corresponding to the gap regions of the genetic map, which leads to no recombination events. The QTL mapping intervals for different traits exhibited overlapping regions. The occurrence of such phenomena can generally be attributed to two main factors: One reason may be that there are indeed multiple QTLs forming a QTL cluster in the segment, with each QTL controlling a different trait and being linked to one another. The other reason may be that there is a single QTL within the interval, and one gene within this QTL may regulate the expression of multiple traits, which is known as the pleiotropic effect of one QTL. Such phenomena have been observed in the QTL mapping processes of various crops, including soybean [68], rice [69], maize [70], and wheat [71]. Both QTL clustering and pleiotropy can explain the linkage between different traits. The multiple co-localization phenomena observed in this study may be related to the framework genetic map used for mapping, which could be due to the large size of the intervals and lower marker density. This distinction can be achieved by augmenting the marker density within the interval and conducting fine mapping. The map constructed in this study contains 11 linkage groups. A published article of H. citrina established intraspecific and interspecific hybridization populations of H. citrina, comprising 120 and 55 hybrid progenies, respectively. SSR markers were used to construct the genetic linkage maps, which both comprised 11 linkage groups [17]. Another study detected QTLs of two SLs, four BLs, and one DBW, while they were distributed on LG6, LG11, LG1, LG6, LG7, and LG10, and distributed across 11 linkage groups [18]. In this study, we identified eleven QTLs associated with floral traits, ten of which were newly discovered and did not overlap with previously reported regions. The QTLs of BL were co-located on LG8, and the QTLs of DWOB were co-located on LG5 and LG8; the interval for BL was smaller than that for previous studies, being 2.38 cM. The DWOB trait is consistent with previous reports and has a QTL on LG5 [17]. This indicated that the locus controlling the BL trait of nightlily may be located at the front end of linkage group 8, providing a basis for subsequent fine mapping and gene discovery. Due to the different hybrid populations used for QTL mapping, there are also different QTL loci.
Among the predicted candidate genes of BL, several related studies have elucidated the functions of these genes; the ethylene-responsive transcription factor family is involved in the control of primary and secondary metabolism, growth, and developmental programs, as well as responses to environmental stimuli [72]. Ethylene-responsive transcription factor ERF was predicted to be annotated as the candidate gene for melon petal size [73]. ERFs suppress cell expansion and the cell cycle, negatively affecting Arabidopsis petal growth and size [74]. PIN-LIKES 3-like isoform X1, the PILS is located in the endoplasmic reticulum and can transport auxin, which regulates the homeostasis of auxin and affects the signal transduction of auxin in the nucleus [75]. PILS3/5 results in deformation of flowers, inducing transitions of flower organs to flower buds, formation of extra gynoecia, and unfused carpels [76]. These genes play crucial roles in plant growth and development, potentially promoting bud elongation in nightlily by regulating plant hormones to suppress cell expansion and the cell cycle. The three candidate genes have been identified for the floral trait of SL. Auxin-responsive protein SAUR71 was found to be a gene associated with plant height through GWAS and shown to negatively regulate plant height [77]. Auxin-responsive protein IAA30 is involved in plant hormone synthesis and signal transduction, processes that also play a role in the determination of plant height. A dwarf and highly branched soybean mutant has been identified that exhibits a significantly reduced plant height and increased branching. Through a combination of sequencing, mapping, and linkage analysis, they have found that the GmIAA27 gene is responsible for controlling these traits [78]. AP2/ERF transcription factor is a superfamily in the plant kingdom, which has been reported to be involved in the regulation of plant growth and development, fruit ripening, defense, and metabolism. AP2/ERF TF could feedback modulate phytohormone biosynthesis, including ethylene, cytokinin, gibberellin, and abscisic cytokinin, abscisic acid, and abscisic acid [79]. These genes play crucial roles in plant growth and development, potentially promoting SL elongation by regulating plant hormones.
In future studies, it is necessary to construct a high-density genetic map, perform fine mapping of QTL, and fully detect polymorphic markers between parents and individuals in the target region, which is of great significance for QTL mapping. A total of five candidate genes related to yield traits were predicted. In further studies, functional analysis of candidate genes will be used to validate the candidate genes by fine mapping, sequence analysis, and genetic transformation. However, few studies have been published concerning QTL mapping for floral traits in nightlily. In the future, we will continue studying floral traits through surveys and research to confirm the stability of these QTLs.

5. Conclusions

Here, we successfully constructed the intraspecific hybridization population of H. citrina, containing 715 hybrid individuals. An intraspecific genetic linkage map was constructed using 169 SSR markers and contained 11 linkage groups. This map was successfully used to identify QTLs associated with nightlily floral traits. There was a good match between the 11 linkage groups and the super scaffolds in the genome physical maps. One QTL locus overlapped with previously reported regions. Here, we identified ten novel QTLs and five candidate genes for several nightlily traits, which could be used as candidates for marker-assisted selection to enhance gains in breeding in nightlily. The linkage map possesses fine accuracy, and preliminary mapping provided in this study could serve as a tool for locating genes associated with other important traits of nightlily. Thus, this study will provide a useful reference for gene mapping and genetic studies in nightlily. To achieve better accuracy in gene mapping, it is necessary to further increase the density of the map.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15071599/s1, Figure S1: The cultivation in the field; Figure S2: The representative gel pictures for segregation types; Figure S3: The peak maps of all night floral trait; Table S1: Investigation Methods of floral Traits; Table S2: Parental lines used in intraspecific crosses in this study; Table S3: Segregation of 244 EST-SSR markers in F1 population; Table S4: Candidate gene of BL; Table S5: Candidate gene of SL.

Author Contributions

Conceptualization, G.X. and S.L.; data curation, Y.S.; formal analysis, Y.S., Z.L. and X.Z.; investigation, Y.S., L.S., Y.L., Y.G. and X.C.; methodology, Y.G., X.C. and S.L.; resources, Y.G., X.C.; software, Y.S., Z.L. and X.Z.; validation, Y.S.; writing—original draft, Y.S.; writing—review and editing, Y.G., G.X. and S.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program Project, grant number 2021YFD1600301-2, the Shanxi Agricultural Key Core Technology Research Project, grant number NYGG18, and the Shanxi Modern Agro-industry Technology Research System Project, grant number 2025CYJSTX08.

Data Availability Statement

The data are contained within this article.

Acknowledgments

Here, I wish to offer my heartfelt thanks to all those who have provided me with help. I am particularly grateful to my supervisors for their patient guidance and support, which offered me invaluable academic advice. During moments of discouragement, Xing and Li consistently provided me with encouragement, boosting my confidence and enabling me to complete this thesis more effectively.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
SSRSimple Repeat Sequence
SNScape number
BNThree-letter acronym
SLLinear dichroism
SDScape diameter
BLBud length
BDBud diameter
FWOBFresh flower bud weight
DWOBDry flower bud weight
BLUPBest Liner Unbiased Prediction
PCRPolymerase Chain Reaction
QTLQuantitative trait locus
MLEMaximum Likelihood Estimation
ICIMComposite Interval Mapping
LGLinkage group
ChrChromosomes

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Agronomy 15 01599 g001
Figure 1. Buds of ‘Liuyuehua’ × ‘Datong Huanghua’ and partial F1 individuals. Significant differences in the size of buds were observed among individuals of the F1 population.
Figure 1. Buds of ‘Liuyuehua’ × ‘Datong Huanghua’ and partial F1 individuals. Significant differences in the size of buds were observed among individuals of the F1 population.
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Figure 2. Frequency distribution of eight floral traits. (A): Number of scapes. (B): The total number of buds was divided by the number of scapes. (C): The length of all scapes from the ground to the tip of the inflorescence. (D): Average of the diameter at 1 cm below the inflorescence of the whole scape. (E): Length of bud tip to the bottom of the flower tube. (F): Diameter of the thickest part of flower bud. (G): The weight of the fresh flower buds. (H): Weight of the buds.
Figure 2. Frequency distribution of eight floral traits. (A): Number of scapes. (B): The total number of buds was divided by the number of scapes. (C): The length of all scapes from the ground to the tip of the inflorescence. (D): Average of the diameter at 1 cm below the inflorescence of the whole scape. (E): Length of bud tip to the bottom of the flower tube. (F): Diameter of the thickest part of flower bud. (G): The weight of the fresh flower buds. (H): Weight of the buds.
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Figure 3. Correlation analysis of phenotypic traits in BLUP data (p ≤ 0.01 and p ≤ 0.05). The flatter the circle, the redder the color, indicating greater correlation.
Figure 3. Correlation analysis of phenotypic traits in BLUP data (p ≤ 0.01 and p ≤ 0.05). The flatter the circle, the redder the color, indicating greater correlation.
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Figure 4. PCA scores plots of eight floral traits.
Figure 4. PCA scores plots of eight floral traits.
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Figure 5. (A) Linkage map of nightlily. Including 11 linkage groups, genetic distance (cM) is shown on the left linkage group. Different colors represent varying densities of chromosome markers. Red signifies the lowest density, yellow to green represents intermediate densities, and blue indicates the highest density. (B) This shows the collinearity analysis of H. citrina genetic and physical maps, where same-colored squares represent syntenic block regions between them. The squares connected by lines represent SSR markers on a genetic map that correspond to a physical genome map. ‘Chr’ denotes the chromosomes of the H. citrina reference genome, whereas ‘LG’ signifies the linkage genetic groups.
Figure 5. (A) Linkage map of nightlily. Including 11 linkage groups, genetic distance (cM) is shown on the left linkage group. Different colors represent varying densities of chromosome markers. Red signifies the lowest density, yellow to green represents intermediate densities, and blue indicates the highest density. (B) This shows the collinearity analysis of H. citrina genetic and physical maps, where same-colored squares represent syntenic block regions between them. The squares connected by lines represent SSR markers on a genetic map that correspond to a physical genome map. ‘Chr’ denotes the chromosomes of the H. citrina reference genome, whereas ‘LG’ signifies the linkage genetic groups.
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Figure 6. Identical markers and floral trait QTLs were shown on the nightlily intraspecific maps. The same color indicated the same floral traits.
Figure 6. Identical markers and floral trait QTLs were shown on the nightlily intraspecific maps. The same color indicated the same floral traits.
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Figure 7. KEGG pathway enrichment analysis. The top 10 significant pathways from the KEGG pathway enrichment analysis are displayed. The horizontal axis represents the enrichment factor (Rich Factor), while the vertical axis represents the pathway names. The color of each point corresponds to −log10 (p-value), with darker colors indicating smaller p-values, signifying higher significance of the pathways within the dataset. (A) The trait of BL. (B) The trait of SL.
Figure 7. KEGG pathway enrichment analysis. The top 10 significant pathways from the KEGG pathway enrichment analysis are displayed. The horizontal axis represents the enrichment factor (Rich Factor), while the vertical axis represents the pathway names. The color of each point corresponds to −log10 (p-value), with darker colors indicating smaller p-values, signifying higher significance of the pathways within the dataset. (A) The trait of BL. (B) The trait of SL.
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Table 1. Statistics analysis of phenotypic traits for the 715 F1 population.
Table 1. Statistics analysis of phenotypic traits for the 715 F1 population.
YEAR202220232024BLUPBroad Sense Heritability
TraitMEAN ± SDCV 1 (%)MEAN ± SDCV 1 (%)MEAN ± SDCV 1 (%)MEAN ± SDCV 1 (%)
SN15.43 ± 6.9344.931.44 ± 0.9263.996.20 ± 3.556.4715.41 ± 6.9645.130.6294
BN20.28 ± 7.3036.0115.27 ± 8.9558.6236.87 ± 12.5033.9124.16 ± 7.6531.670.9588
SL (cm)71.45 ± 13.6519.1058.95 ± 13.4222.7699.53 ± 16.116.1776.02 ± 12.2216.070.9299
SD (mm)4.39 ± 0.6314.363.73 ± 0.6918.465.03 ± 0.7815.564.45 ± 0.9922.210.9856
BL (mm)115.01 ± 12.2110.62120.55 ± 11.519.55119.65 ± 11.839.88118.24 ± 10.588.90.8554
BD (mm)9.05 ± 0.9710.699.88 ± 1.2913.039.59 ± 1.1411.929.43 ± 0.869.20.8066
FWOB (g)2.63 ± 0.4818.383.22 ± 0.5817.983.32 ± 0.6519.683.04 ± 0.5016.530.1912
DWOB (g)0.33 ± 0.0516.180.39 ± 0.0615.240.42 ± 0.07160.38 ± 0.04913.030.2167
1 CV (%): coefficients of variation.
Table 2. The pattern of segregation for SSR markers within the F1 population.
Table 2. The pattern of segregation for SSR markers within the F1 population.
Segregation Type 1Progeny
Segregation Ratio		Number and Proportion
nn × np 2nn:np = 1:130 (12.29%)
lm × ll 3lm:ll = 1:135 (14.34%)
hk × hk 4hh;hk;kk = 1:2:126 (10.66%)
ef × eg 5ee:ef:eg:fg = 1:1:1:1153 (62.7%)
ab × cd 6ac:ad:bc:bd = 1:1:1:10
Total 244
1 The letters preceding the “×” symbol denote the female, while those following it denote the male. 2,3 nn × np and lm × ll indicate a locus with two alleles, one paternal type is homozygous and other is heterozygous; 4 hk × hk signifies one genetic locus with two alleles, with two paternal type are heterozygous; 5 ef × eg represents one genetic locus with three alleles; and 6 ab × cd denotes one locus with four alleles.
Table 3. Characteristics of linkage groups and genetic linkage maps of nightlily.
Table 3. Characteristics of linkage groups and genetic linkage maps of nightlily.
LGLG1LG2LG3LG4LG5LG6LG7LG8LG9LG10LG11
R20.923 **0.865 *0.994 **0.749 *0.925 **0.906 **0.793 **0.3660.995 **0.903 **0.876 **
Number of markers1311151322141713161916
Total map distance204.19148.56137.97100.18142.98238.45159.6787.10125.68152.29108.34
Average map distance15.7113.519.207.716.5017.039.396.707.868.026.77
R2: correlation coefficient between genetic distance and physical distance, LG represents different linkage groups. * The correlation coefficient is significant at the 0.05 level, and ** indicates that the correlation coefficient is significant at the 0.01 level.
Table 4. The peak maps of 11 QTLs.
Table 4. The peak maps of 11 QTLs.
TraitQTL LociQTL ModelLGLOD 1LOD 2PVE (%) 3Peak Range (cM)Left Marker and PositionRight Marker
and Position
BNqBN6.1ICIM63.775.657.2130.16146.544176.70
SLqSL5.1ICIM54.446.5619.3921.3855.9877.36
SLqSL8.1ICIM84.444.7413.3913.4273.8187.23
SLqSL10.1ICIM104.444.7613.6610.9181.7592.66
BLqBL8.1ICIM84.377.4124.292.382.645.02
BDqBD3.1ICIM34.654.6610.096.9666.8573.82
BDqBD8.1ICIM84.656.8714.931.465.026.84
DWOBqDWOB3.1ICIM34.716.828.6813.9417.6531.59
DWOBqDWOB4.1ICIM44.715.1513.594.5595.60100.15
DWOBqDWOB5.1ICIM54.716.538.9610.5613.1423.7
LOD 1 means the genome-wide QTL thresholds, which were calculated using the 1000 times permutation test. LOD 2 indicates QTL thresholds on the corresponding linkage group. 3 PVE (%): phenotypic variation explained.
Table 5. Five candidate genes predicted to be related to the process of biomolecules.
Table 5. Five candidate genes predicted to be related to the process of biomolecules.
GeneIDNR_Annotation
HHC034511ethylene-responsive transcription factor ERF071 (Dendrobium catenatum)
HHC034516protein PIN-LIKES 3-like isoform X1 (Asparagus officinalis)
HHC022110auxin-responsive protein SAUR71 (Elaeis guineensis)
HHC022036auxin-responsive protein IAA30 (Elaeis guineensis)
HHC022210AP2-like ethylene-responsive transcription factor ANT (Asparagus officinalis)
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Su, Y.; Liang, Z.; Zhao, X.; Shi, L.; Liu, Y.; Gao, Y.; Cheng, X.; Xing, G.; Li, S. An SSR-Base Linkage Map Reveals QTLs for Floral-Related Traits in Nightlily (Hemerocallis citrina). Agronomy 2025, 15, 1599. https://doi.org/10.3390/agronomy15071599

AMA Style

Su Y, Liang Z, Zhao X, Shi L, Liu Y, Gao Y, Cheng X, Xing G, Li S. An SSR-Base Linkage Map Reveals QTLs for Floral-Related Traits in Nightlily (Hemerocallis citrina). Agronomy. 2025; 15(7):1599. https://doi.org/10.3390/agronomy15071599

Chicago/Turabian Style

Su, Yuting, Zhonghao Liang, Xinyu Zhao, Lijing Shi, Yang Liu, Yang Gao, Xiaojing Cheng, Guoming Xing, and Sen Li. 2025. "An SSR-Base Linkage Map Reveals QTLs for Floral-Related Traits in Nightlily (Hemerocallis citrina)" Agronomy 15, no. 7: 1599. https://doi.org/10.3390/agronomy15071599

APA Style

Su, Y., Liang, Z., Zhao, X., Shi, L., Liu, Y., Gao, Y., Cheng, X., Xing, G., & Li, S. (2025). An SSR-Base Linkage Map Reveals QTLs for Floral-Related Traits in Nightlily (Hemerocallis citrina). Agronomy, 15(7), 1599. https://doi.org/10.3390/agronomy15071599

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