Next Article in Journal
Functional Principles of Morphological and Anatomical Structures in Pinecones
Next Article in Special Issue
Search for New Allergens in Lolium perenne Pollen Growing under Different Air Pollution Conditions by Comparative Transcriptome Study
Previous Article in Journal
Drought-Induced Responses of Nitrogen Metabolism in Ipomoea batatas
Previous Article in Special Issue
Chloroplast Genome Analysis of Two Medicinal Coelogyne spp. (Orchidaceae) Shed Light on the Genetic Information, Comparative Genomics, and Species Identification
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Plants
Volume 9
Issue 10
10.3390/plants9101342
plants-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Linkage Map Development by EST-SSR Markers and QTL Analysis for Inflorescence and Leaf Traits in Chrysanthemum (Chrysanthemum morifolium Ramat.)

by
Min Fan
,
Yike Gao
*,
Zhiping Wu
and
Qixiang Zhang
Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Centre for Floriculture, School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Plants 2020, 9(10), 1342; https://doi.org/10.3390/plants9101342
Submission received: 12 September 2020 / Revised: 25 September 2020 / Accepted: 28 September 2020 / Published: 11 October 2020
(This article belongs to the Special Issue Applications of DNA Markers in Plant Science)
Browse Figures
Versions Notes

Abstract

:
Chrysanthemums (Chrysanthemum morifolium Ramat.) are famous ornamental crops with high medicinal and industrial values. The inflorescence and leaf traits are key factors that affect the yield and quality of chrysanthemum. However, the genetic improvement of those traits is slow within chrysanthemum because of its hexaploidy, high heterozygosity and enormous genome. To study the genetic control of the important traits and facilitate marker-assisted selection (MAS) in chrysanthemum, it is desirable to populate the genetic maps with an abundance of transferrable markers such as microsatellites (SSRs). A genetic map was constructed with expressed sequence tag–simple sequence repeat (EST-SSR) markers in an F1 progeny of 192 offspring. A total of 1000 alleles were generated from 223 EST-SSR primer pairs. The preliminary maternal and paternal maps consisted of 265 marker alleles arranged into 49 and 53 linkage groups (LGs), respectively. The recombined parental maps covered 906.3 and 970.1 cM of the genome, respectively. Finally, 264 polymorphic loci were allocated to nine LGs. The integrated map spanned 954.5 cM in length with an average genetic distance of 3.6 cM between two neighbouring loci. Quantitative trait loci (QTLs) analysis was performed using the integrated map for inflorescence diameter (ID), central disc flower diameter (CDFD), number of whorls of ray florets (NWRF), number of ray florets (NRF), number of disc florets (NDF), number of florets (NF), ray floret length (RFL), ray floret width (RFW), ray floret length/width (RFL/W), leaf length (LL), leaf width (LW) and leaf length/width (LL/W). Overall, 36 (21 major) QTLs were identified. The successful mapping of inflorescence and leaf traits QTL demonstrated the utility of the new integrated linkage map. This study is the first report of a genetic map based on EST-SSR markers in chrysanthemum. The EST-SSR markers, genetic map and QTLs reported here could be valuable resources in implementing MAS for chrysanthemums in breeding programs.

1. Introduction

Chrysanthemum (Chrysanthemum morifolium Ramat.) belongs to the family Asteraceae and is a well-known ornamental and medicinal crop throughout the world. They are cultivated as cut flowers and potted or garden plants, which occupy a very important position in the global flower industry. It also possesses diverse medicinal properties, including antibacterial, antioxidant, anti-inflammation, anticancer and cardiovascular protection [1]. The dried capitulum of chrysanthemum is a valuable herbal medicine, which can be used for scattering cold, cleaning heat and toxin as well as brightening eyes [2]. It is also used as tea and seasoning, as it is abundant in bioactive compounds, such as polyphenols and flavonols. Nevertheless, linkage analysis and marker-assisted selection (MAS) breeding in chrysanthemum have been challenging due to the hexaploidy (2n = 6x = 54), high heterozygosity, huge genome as well as self-incompatible and outcrossing nature [3]. Although the genome information of C. nankingense [4] and C. seticuspe [5] has been published, the genomic information of cultivated chrysanthemum has not been reported. Several hypotheses have been proposed in previous reports regarding the ploidy of chrysanthemum, which has been characterized as allopolyploid [6,7], segmental allohexaploid [8,9] and autohexaploid [10]. However, clear conclusions about the ploidy type of chrysanthemum have not yet been reached.
Genetic and quantitative trait loci (QTL) mapping is a powerful approach to identify the genomic regions to control the important traits [11]. That method can greatly enhance the efficiency and precision of conventional breeding. However, to achieve the linkage mapping in polyploids is very difficult. A commonly used approach is to use single-dose (SD) markers combined with pseudotestcross approach, which has been used to construct the genetic maps in several polyploid species, including strawberry [12], sweetpotato [13] and sugarcane [14]. Linkage and quantitative trait loci (QTL) mapping in chrysanthemum is at a preliminary stage. The previous linkage maps in chrysanthemum have been constructed mainly using random amplified polymorphic DNA (RAPD), intersimple sequence repeats (ISSR), amplified fragment length polymorphism (AFLP) markers, sequence-related amplified polymorphism (SRAP) and single nucleotide polymorphism (SNP) markers [15,16,17,18,19,20,21]. In short, most previous genetic maps have been constructed with dominant markers, which are difficult to transfer from one mapping population to another.
Expressed sequence tag–simple sequence repeat (EST-SSR) markers are defined by specific primer pairs, which are transferable from one progeny to another, even among distantly related species. Therefore, EST-SSR markers are handy tools for map comparison and consensus. Moreover, they may be directly associated with the candidate genes and can be exploited as anchor markers for comparative mapping and evolutionary studies [22], which has been a successful approach in a few species, such as carnation [23] and tea plant [24]. The multiallelic nature of EST-SSR markers also make them more likely to tag a haplotype (or trait) than SNPs in polyploid species [25]. However, in recent years, the researchers have increasingly turned to rely on SNP markers for genotyping. Sequencing data requires greater curation and bioinformatics skills and it may contain more erroneous and missing data, and the tools they used may be not suitable for polyploid datasets [26]. Hence, EST-SSR marker technology remains important.
Although EST-SSR markers have been employed for linkage map construction in many ornamental crops [23,27,28], few studies have been conducted in chrysanthemums. The inflorescence is the main ornamental and medicinal part for chrysanthemum. The leaf traits not only affect the quality of the flowers but also contribute to the overall appearance of the plant. In this study, the genetic linkage map of chrysanthemum was developed based on EST-SSR markers. The obtained genetic map was used to perform QTL analyses of the inflorescence and leaf traits.

2. Results

2.1. EST-SSR Marker Segregation

Among the 262 EST-SSR primer pairs, 223 (85%) were informative and amplified alleles around the expected size, which were used for genotyping the mapping population (Table S1). As a result, a total of 1000 alleles were scored in two parents and 192 F1 individuals, among which 285 were paternal-specific, 271 were maternal-specific and 444 were shared. The classification of SSR alleles based on their segregation ratios is listed in Table 1. Of these, 94 alleles (9.4%) were monomorphic in the progeny. The dose of the 906 polymorphic alleles was estimated by χ2 test (α = 0.01) to the appropriate expected segregation ratios of hexasomic (random pairing) and disomic (preferential pairing) inheritance, respectively. Among the 906 alleles, 362 uniparental (183 paternal-specific and 179 maternal-specific) and 94 biparental simplex marker alleles were not affected by the inheritance mode, segregating in a 1:1 and 3:1 ratio, respectively.
Under the hypothesis of hexasomic inheritance, 87 and 38 uniparental SSR alleles gave fits to 4:1 and 19:1 segregation ratios expected for duplex and triplex marker alleles, respectively. In case of biparental SSR marker alleles, while 79 SSR marker alleles gave fits to 9:1 segregation ratio’s expected for duplex-simplex alleles, the dose of 158 alleles could not be identified owing to the multiple fits to different types of segregation ratios. And 88 SSR alleles showed distorted segregation at α < 0.01 under this hexasomic inheritance assumption. On the other hand, under the hypothesis of disomic inheritance, 56 and 49 uniparental SSR alleles gave fits to 3:1 and 7:1 segregation ratios expected for duplex and triplex marker alleles, respectively. While 45 SSR marker alleles gave fits to 7:1 segregation ratio’s expected for duplex-simplex alleles, the dose of 164 alleles could not be identified owing to the multiple fits to different types of segregation ratios. Furthermore, 136 SSR alleles showed distorted segregation at α < 0.01 under this disomic inheritance assumption (Table 1).

2.2. Linkage Map Construction

Firstly, 265 (37.1%) marker alleles were mapped successfully on the preliminary maternal and paternal maps, respectively (Figure S1a,b) (Table S2). Furthermore, three duplex marker alleles were mapped on the preliminary maternal and paternal maps, respectively, which can be used to identify homologous LGs. The preliminary maternal and paternal maps consisted of 49 and 53 LGs, respectively, which putatively corresponded to the number of chromosomes in chrysanthemum (2n = 6x = 54). Those LGs covered a total length of 1800.89 cM in the maternal parent and 2033.52 cM in the paternal parent and had a mean chromosome length of 36.75 and 38.37 cM, respectively. The LGs ranged in size from a low of 2.64 (M49) to a high of 99.44 cM (M1) in the female parent and from 1.06 (F53) to 122.72 (F1) in the male parent (Figure S1a,b) (Table S2).
Using a set of common SSR markers among LGs, the preliminary maps were mapped to generate recombined parental maps. A total of 270 EST-SSR markers were placed successfully when the two parental maps were considered in step 2, among these markers, 141 originated from female parent and 129 originated from male parent. The recombined map consisted of 10 homologous groups for female and male parent, respectively (Figure S2a,b) (Table S3). The map lengths were 906.28 cM and 970.13 cM, with a mean distance between markers of 6.43 and 7.52 cM for female and male parent, respectively (Table S3).
The two recombined maps were combined to form a single integrated map. The final map was composed of 264 loci amplified from 187 polymorphic primer pairs, which formed nine LGs with a total length of 954.46 cM (Figure 1) (Table 2). Nevertheless, the alleles were unevenly distributed, with the number of alleles ranging from 5 (LG 9) to 58 (LG 3) and the mean distance between markers varied from 2.08 (LG3) to 5.32 (LG 9) cM (Table 2).

2.3. Phenotypic Evaluation

The descriptive statistics for inflorescence and leaf traits are listed in Table 3. The frequency distributions of phenotypic values for 12 traits are shown in Figure 2. As some offspring cannot bloom in outdoor conditions, not always all 192 offspring were used. Significant differences between parents were observed for all those traits in at least one year (p < 0.01) except RFL/W, LW and LL/W. The correlation analysis revealed that most of the traits were correlated with each other, and a strong degree of correlation was observed between some traits. Significant positive correlations (correlation coefficient > 0.5) were observed between ID and RFL, ID and RFW, CDFD and NDF, NDF and NF, NRF and NWRF, RFL and RFW as well as LL and LW (Table S4). Significant negative correlations (correlation coefficient < −0.5) were observed between CDFD and NWRF (Table S4). Broad-sense heritability of the 12 traits ranged from 0.76 (LW) to 0.95 (NRF). Except for NRF, transgressive genotypes existed for other traits.

2.4. QTL Identification

After permutation test, regions with a logarithm of odds (LOD) score of 3.6 were considered as candidate QTL intervals. Overall, 36 QTLs (21 major) were identified (Figure 1) (Table 4). In 2015, eight QTLs (all major) were identified; in 2016, seven QTLs (all major) were found; in 2017, 11 (2 major) QTLs were detected; and 10 (four major) were identified for average data. The PVE ranged from 6.8 to 18.9%.

2.4.1. Inflorescence Traits

Seven and two QTLs were identified to influence ID and CDFD, respectively (Table 4). The qID8-2016 and qID8-mean were colocalized at the same position on LG 8, with overlapping LOD confidence intervals. Interestingly, qID3-mean-1 and qRFL3-mean-1 were colocalized at the same position on LG 3. Furthermore, the qID3-mean-2, qRFL3-2016 and qRFL3-mean-2 were colocalized at another same position on LG 3. In addition, the qCDFD4-2015 was mapped on the SSR locus 242-c77190-290 on LG 4 where qNDF4-2015 and qNF4-2015 were also identified.
For NWRF, NRF, NDF and NF, two, one, three and four QTLs were identified, respectively. Interestingly, the qNWRF3-mean, qNRF3-mean and qNDF3-2016 were mapped between the SSR locus 196-c55093-348 and 27-c65910-144 on LG 3, with overlapping LOD confidence intervals.
A total of three, one and three QTLs were detected to influence RFL, RFW and RFL/W. Interestingly, qRFL3-2016 and qRFL3-mean-2 were colocalized at the same position on LG 3, with overlapping LOD confidence intervals. In addition, qRFL/W3-2017-1 and qRFL/W3-mean were mapped on the SSR locus 64-c71464-216 on LG 3, with overlapping LOD confidence intervals.

2.4.2. Leaf Traits

A total of two, one and one QTLs were detected to influence LL, LW, LL/W, respectively (Table 4). The qLL8-2017 and qLW8-2017 were colocalized at the same position on LG 8.

3. Discussion

In summary, we have developed an integrated linkage map for chrysanthemum using EST-SSR markers, which spanned 954.5 cM in length with an average genetic distance of 3.6 cM between two neighbouring loci. Moreover, 36 (21 major) QTLs were identified for 12 inflorescence and leaf traits. Besides that, it is expected to provide valuable anchor markers to integrate information from future genetic maps. The SSR markers, genetic map and QTL reported here could be valuable resources in chrysanthemum breeding.

3.1. EST-SSR Markers

Most SSR primer pairs (190 over 223) provided multilocus amplification in chrysanthemum, which can result from the duplication of zones of genome during the evolution in chrysanthemums, which was also observed in other polyploid species, such as alfalfa [29], Zoysia matrella [30] and rose [28]. Besides, EST-SSR markers were derived from the conserved expressed regions of the genome, and thus had greater potential for finding associations with functional genes. In the future, researchers can transfer the EST-SSR markers mapped in this study to other population in the genus Chrysanthemum.

3.2. Genetic Map Construction

Using 223 EST-SSR primer pairs, the first moderately saturated EST-SSR-based integrated genetic map was constructed in chrysanthemums. A mean interval between the markers on the map reached 3.6 cM, which can meet the requirements of QTL mapping. Due to the homogeneity of chromosome segment between the parents and inadequate markers used, the gaps within the LGs were inevitable. An existence of minor LGs (triplets and doublets) and a number of unlinked markers in the preliminary parental maps indicated that there are several large gaps with few markers [31]. In the integrated map, the gaps larger than 20 cM were observed in LG 1, LG 4 and LG 7. Hence, in order to fill those gaps and increase the density, more markers (SNP markers and SSR markers) are needed to saturate the map.

3.3. Phenotypic Characterization

For the progeny used in this study, there are many traits showing substantial variation. Therefore, such opportunity was used to obtain a comprehensive understanding of the inflorescence and leaf traits. The skewness and kurtosis values of 12 traits indicated that the segregation pattern, in most cases, fitted a normal distribution model approximately which was suitable for QTL identification. One reason for the abnormal distribution of some phenotypic trait in 2015 might be the absence of a few individuals’ phenotypic data. Phenotypic trait distributions of NRF were less uniform than the other traits, especially in 2015 and 2016 (Figure 2).
The phenotypic correlations among 12 traits suggested a stable association between them in this study. A relatively high heritability (>0.50) for 12 traits were observed (Table 3), which is consistent with the results in Song et al. (2018) [32]. For example, the heritability of NWRF was 0.93 in this study and the value was 0.81 in Song et al. (2018). Those results might indicate that genetic composition plays a major role in determining the inflorescence and leaf traits. There are many transgressive genotypes compared to the parental lines observed in this study. The reason for transgressive genotypes may be the combination of alleles from both parents with effects in the same direction [33], which was also confirmed by the study on the trait of days to heading in rice that a few genes and their combinations expanded the variability whose parents exhibit similar phenotypes [34].

3.4. QTL Mapping

Identification of QTLs underlying target trait is prerequisite for MAS. Zhang et al. [15] identified four QTLs for ID, NWRF and RFL, respectively. In contrast, seven, two and three QTLs for ID, NWRF and RFL were identified in the present study, respectively. For ID, Zhang et al. [15] identified two QTLs on LG Y1. Similarly, two QTLs on LG 3 (qID3-mean-1, qID3-mean-2) were identified herein. Van Geest et al. [18] detected two minor QTLs for NRF. In contrast, a major QTL for NRF was identified in this study. The phenomenon demonstrated that QTL mapping had population-specific effects.
QTLs controlling correlated traits are usually located in the same or close LG regions [35]. Previous studies have reported QTL clusters for inflorescence traits in chrysanthemum [15]. Zhang et al. [17] observed two clusters of QTL for LL and LW. Similarly, a cluster of QTLs for LL and LW was found on LG 8. On LG 3, we found two clusters for ID and RFL, and another cluster for CDFD, NDF, NRF and NWRF. In addition, a cluster was found for CDFD, NDF and NF on LG 4. Those results are consistent with the significant correlation between them. The traits controlled by clusters might be explained by QTL with pleiotropy or a set of closely linked loci [36,37].

4. Materials and Method

4.1. Plant Materials and DNA Extraction

In 2014, two phenotypically different cultivars were used to obtain an F1 population by manual cross pollination. The F1 mapping population was formed in 2015 by randomly selecting 192 individuals from a total population of 546 plants, which were then propagated via cuttings. The progeny was maintained in the experimental fields at Xiaotangshan, Beijing Forestry University, Beijing, China (40.0° N 116.3° E).
Genomic DNA was isolated from fresh young leaves with a DNA extraction kit (Demeter Biotech, Beijing, China). DNA quality was checked on 1 % agarose gels. The DNA samples were stored at −80℃.

4.2. Genotyping of Mapping Population

A total of 262 EST-SSR primer pairs were analysed, among which 245 primer pairs were developed from the “Jinbudiao” EST database [38] and 17 primer pairs were reported in [39]. All the SSR primer pairs were labelled with fluorescent dyes, and SSR genotyping was carried out using a three-primer strategy, including a forward/reverse primer labelled with FAM, HEX or TAMRA (Beijing Microread Genetics Co., Ltd, Beijing, China), as detailed in the protocol of Sun et al [40].
All the primer pairs were initially screened for polymorphisms among six randomly chosen segregating individuals and the two parental samples. The EST-SSR markers that generated reproducible polymorphisms were then used to screen all the 194 samples (192 F1 individuals and two parents).
The EST-SSR products (1 μL) were then analysed on an ABI3730 fluorescent analyser with 0.5 μL Rox-500 HD size standard (Microread) and 8.5 μL Hi-Di formamide. The data were analysed using GeneMapper (version 3.2).

4.3. Marker Scoring and Marker Segregation Type

Each allele of specific primer pairs was read, respectively. The alleles were scored by assigning “1” or “0” for the presence or absence of segregating fragments, respectively. The monomorphic bands in the progeny were excluded from segregation analysis. According to the double pseudotestcross mapping strategy [41], the markers were divided by their presence in the maternal parent, the paternal parent and in both.
The dosage of each marker was determined by analysing the segregation ratios of EST-SSR marker alleles (presence vs. absence) in the mapping population, according to the expected segregation ratio of the two inheritance models, hexasomic (random pairing) and disomic (preferential pairing) (Table 5) [9]. The markers were divided into four groups based on their segregation ratios: (a) Simplex markers that are present in a single copy only in one parent and that segregate in a 1:1 (presence:absence) ratio in the progeny; (b) Duplex markers that are present in one parent in two copies and that segregate in a hexasomic (4:1), or disomic (3:1) ratio; (c) Triplex markers that are present in one parent in three copies and that segregate in a hexasomic (19:1), or disomic (7:1) ratio; and (d) Simplex-simplex markers present in both parents in a single copy that segregate in a 3:1 ratio in the progeny (Table 5). Alleles at higher dosages were not analysed as our progeny was too small for segregation analysis in higher dosage situations. The χ2 test (α = 0.01) was performed to analyse the goodness-of-fit to the expected segregation ratios for all markers. If the markers did not fit with the Mendelian segregation ratios, they were defined as segregation distortion.

4.4. Linkage Map Construction

The progeny was analysed with a double pseudotestcross mapping strategy [41]. JoinMap 4.0 software [42] was used to construct the linkage maps using the cross pollinator (CP) population type code. The genetic distances between markers in centimorgan (cM) were calculated by Kosambi’s [43] mapping function. Firstly, simplex and simplex-simplex markers were used to construct the framework map for each parent at logarithm of odds (LOD) score of 7.0–10.0. Then, duplex markers were inserted into the framework maps [44]. Afterwards, the module “combine groups for map integration” in JoinMap was used to construct a recombined map for each parent separately. Finally, the two data sets were merged for linkage groups on the basis of a subset of common markers that were present in both recombined parental maps [44]. Linkage groups with fewer than five markers were omitted. The linkage groups were drawn by using graphical package MapChart 2.2 for Windows [45].

4.5. Phenotyping and Statistical Analysis

Phenotypic data of the parents and the F1 progeny were collected during three consecutive flowering seasons (2015, 2016 and 2017). Nine inflorescence traits were investigated, including inflorescence diameter (ID), central disc flower diameter (CDFD), number of whorls of ray florets (NWRF), number of ray florets (NRF), number of disc florets (NDF), number of florets (NF), ray floret length (RFL), ray floret width (RFW) and ray floret length/width (RFL/W). The leaf traits were characterized by the leaf length (LL), leaf width (LW) and leaf length/width (LL/W). They were measured from three samples per plant. Statistical analysis of phenotypic data was conducted using Microsoft Excel 2016 or IBM SPSS Statistic 20.0 software. The difference in the traits between two parents was compared using a t test (p < 0.05). Pearson’s phenotypic correlation coefficients between different observations of each trait were calculated using the means of three years, respectively. Variance components of the trait scores were estimated by an analysis of variance (ANOVA) using the general linear model procedure of the statistical software IBM SPSS Statistic 20.0 with years as fixed effects. The results were used to calculate broad sense heritability according to the equation: h2 = σ2g / (σ2g+ σ2g∗y/ m + σ2e/ rm), where (σ2g), (σ2g∗y/m) and (σ2e/rm) are the genetic, genotype × year interaction, and residual variance components, m is the number of years and r is the number of replications. In 2015, the data obtained was less than in the other two years due to some individuals’ phenotypic data not being measured. In addition, a dozen chrysanthemums cannot bloom in outdoor conditions every year, which lead to an absence of some individuals’ phenotypic data. The average values of all traits each year were calculated for QTL analysis.

4.6. QTL Analysis

QTL analysis was performed using MapQTL6.0 [46]. A permutation test (1000 times) was performed with a significance level of 5% to calculate the LOD score as the threshold value for QTL detection. First, interval mapping (IM) was used to find QTL regions associated to each of the traits tested. The markers that were closely linked to the positions with the highest LOD score were taken as cofactors, and tested using the automatic cofactor selection (ACS) procedure, with the P-value cutoff for elimination of a cofactor set at p = 0.02. Using the set of cofactors, multiple QTL mapping (MQM) was conducted. According to the percentage of phenotypic variation explained (PVE), QTL with PVE more than 10.0 was classified as major QTL, while that with PVE less than 10.0 was classified as minor QTL. The identified QTL was named with q, followed by a trait abbreviation, a LG number, a hyphen (-) and a number indicating the year of its expression. If two or more QTL were identified for a trait on the same LG in the same year, a hyphen (-) with a serial number was suffixed. For example, qID1-2017-2 indicates the second QTL underlying ID on LG 1 by analysing the data from 2017. The linkage groups representing QTL were drawn using MapChart 2.2 [45].

Supplementary Materials

The following are available online at https://www.mdpi.com/2223-7747/9/10/1342/s1, Figure. S1 (a) Preliminary linkage groups of the maternal parent derived from an F1 population of 192 offspring in chrysanthemum. Map distances (cM) and SSR marker alleles are shown on the left and right side of each linkage group, respectively. Duplex markers are marked in green. Distorted segregating markers are underlined, which are indicated by a significant p-value in the chi-squared test: p < 0.01, (b) Preliminary linkage groups of the paternal parent derived from an F1 population of 192 offspring in chrysanthemum. Map distances (cM) and SSR marker alleles are shown on the left and right side of each linkage group, respectively. Duplex markers are marked in green. Distorted segregating markers are underlined, which are indicated by a significant p-value in the chi-squared test: p < 0.01; Figure S2. (a) Recombined maternal map derived from an F1 population of 192 offspring in chrysanthemum. Map distances (cM) and SSR marker alleles are shown on the left and right side of each linkage group, respectively. Bridge markers are marked in red. Duplex markers are marked in green. Distorted segregating markers are underlined, which are indicated by a significant p-value in the chi-squared test: p < 0.01; (b) Recombined paternal map derived from an F1 population of 192 offspring in chrysanthemum. Map distances (cM) and SSR marker alleles are shown on the left and right side of each linkage group, respectively. Bridge markers are marked in red. Duplex markers are marked in green. Distorted segregating markers are underlined, which are indicated by a significant p-value in the chi-squared test: p < 0.01. Table S1. Characteristics of the 223 EST-SSR primer pairs used on the map (248–262 were reported in Liu et al., 2015); Table S2. Distribution of markers on the preliminary maternal and paternal maps and linkage group statistics; Table S3. Distribution of markers on the recombined maternal and paternal maps and linkage group statistics; Table S4. Pearson correlation analysis among inflorescence and leaf traits.

Author Contributions

Conceptualization, M.F. and Y.G.; Data curation, M.F.; Investigation, M.F.; Supervision, Y.G. and Q.Z.; Writing—original draft, M.F.; Writing—review and editing, Z.W. and S.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

This study was sponsored by the National Natural Science Foundation of China (NSFC, 31971706) and the National High Technology Research and Development Program of China (863 Program, 2013AA102706). We deeply thank Liangdong Xu for helping to analyse the data.

Conflicts of Interest

The authors declare that they have no conflict of interest.

References

  1. Yang, Y.; Jiang, Z.; Guo, J.; Yang, X.; Xu, N.; Chen, Z.; Hao, J.; Li, J.; Pang, J.; Shen, C.; et al. Transcriptomic analyses of Chrysanthemum morifolium Ramat. under UV-B radiation treatment reveal variations in the metabolisms associated with bioactive components. Ind. Crop. Prod. 2018, 124, 475–486. [Google Scholar] [CrossRef]
  2. Pharmacopoeia of People’s Republic of China. The State of Pharmacopoeia Commission of People’s Republic of China; Chemical Industry Press: Beijing, China, 2010; Volume 1. [Google Scholar]
  3. Anderson, N.O.; Ascher, P.D. Fertility changes in inbred families of self-incompatible chrysanthemums (Dendranthema grandiflora). J. Am. Soc. Hortic. Sci. 2000, 125, 619–625. [Google Scholar] [CrossRef]
  4. Song, A.; Song, C.; Liu, Y.; Dong, G.; Dong, B.; Zhao, H.; Sun, W.; Ramakrishnan, S.; Wang, S.; Wang, Y.; et al. The Chrysanthemum nankingense Genome Provides Insights into the Evolution and Diversification of Chrysanthemum Flowers and Medicinal Traits. Mol. Plant. 2018, 11, 1482–1491. [Google Scholar] [CrossRef] [Green Version]
  5. Hirakawa, H.; Sumitomo, K.; Hisamatsu, T.; Nagano, S.; Shirasawa, K.; Higuchi, Y.; Kusaba, M.; Koshioka, M.; Nakano, Y.; Yagi, M.; et al. De novo whole-genome assembly in Chrysanthemum seticuspe, a model species of Chrysanthemums, and its application to genetic and gene discovery analysis. DNA Res. 2019, 26, 195–203. [Google Scholar] [CrossRef] [Green Version]
  6. Zhang, F.; Chen, S.; Chen, F.; Fang, W.; Li, F. A preliminary genetic linkage map of chrysanthemum (Chrysanthemum morifolium) cultivars using RAPD, ISSR and AFLP markers. Sci. Hortic. 2010, 125, 422–428. [Google Scholar] [CrossRef]
  7. Zhang, F.; Chen, S.; Chen, F.; Fang, W.; Deng, Y.; Chang, Q.; Liu, P. Genetic analysis and associated SRAP markers for flowering traits of chrysanthemum (Chrysanthemum morifolium). Euphytica 2011, 177, 15–24. [Google Scholar] [CrossRef]
  8. Klie, M.; Schie, S.; Linde, M.; Debener, T. The type of ploidy of chrysanthemum is not black or white: A comparison of a molecular approach to published cytological methods. Front. Plant. Sci. 2014, 5, 479. [Google Scholar] [CrossRef] [Green Version]
  9. Park, S.K.; Arens, P.; Esselink, D.; Lim, J.H.; Shin, H.K. Analysis of inheritance mode in chrysanthemum using EST-derived SSR markers. Sci. Hortic. 2015, 192, 80–88. [Google Scholar] [CrossRef]
  10. Van Geest, G.; Voorrips, R.E.; Esselink, D.; Post, A.; Visser, R.G.; Arens, P. Conclusive evidence for hexasomic inheritance in chrysanthemum based on analysis of a 183 k SNP array. BMC Genomics 2017, 18, 585. [Google Scholar] [CrossRef] [Green Version]
  11. Tanksley, S.D.; Young, N.D.; Bonierbale, M.W. RFLP Mapping in Plant Breeding: New Tools for an Old Science. Biotechnology 1989, 7, 257–264. [Google Scholar] [CrossRef]
  12. Sargent, D.J.; Passey, T.; Šurbanovski, N.; Lopez Girona, E.; Kuchta, P.; Davik, J.; Harrison, R.; Passey, A.; Whitehouse, A.B.; Simpson, D.W. A microsatellite linkage map for the cultivated strawberry (Fragaria × ananassa) suggests extensive regions of homozygosity in the genome that may have resulted from breeding and selection. Theor. Appl. Genet. 2012, 124, 1229–1240. [Google Scholar] [CrossRef] [PubMed]
  13. Zhao, N.; Yu, X.; Jie, Q.; Li, H.; Li, H.; Hu, J.; Zhai, H.; He, S.; Liu, Q. A genetic linkage map based on AFLP and SSR markers and mapping of QTL for dry-matter content in sweetpotato. Mol. Breed. 2013, 32, 807–820. [Google Scholar] [CrossRef]
  14. Ukoskit, K.; Posudsavang, G.; Pongsiripat, N.; Chatwachirawong, P.; Klomsa-ard, P.; Poomipant, P.; Tragoonrung, S. Detection and validation of EST-SSR markers associated with sugar-related traits in sugarcane using linkage and association mapping. Genomics 2019, 111, 1–9. [Google Scholar] [CrossRef] [PubMed]
  15. Zhang, F.; Chen, S.; Chen, F.; Fang, W.; Chen, Y.; Li, F. SRAP-based mapping and QTL detection for inflorescence-related traits in chrysanthemum (Dendranthema morifolium). Mol. Breed. 2011, 27, 11–23. [Google Scholar] [CrossRef]
  16. Zhang, F.; Jiang, J.; Chen, S.; Chen, F.; Fang, W. Mapping single-locus and epistatic quantitative trait loci for plant architectural traits in chrysanthemum. Mol. Breed. 2012, 30, 1027–1036. [Google Scholar] [CrossRef]
  17. Zhang, F.; Jiang, J.; Chen, S.; Chen, F.; Fang, W. Detection of quantitative trait loci for leaf traits in chrysanthemum. J. Hortic. Sci. Biotechnol. 2012, 87, 613–618. [Google Scholar] [CrossRef]
  18. Van Geest, G.; Bourke, P.M.; Voorrips, R.E.; Marasek-Ciolakowska, A.; Liao, Y.; Post, A.; van Meeteren, U.; Visser, R.G.F.; Maliepaard, C.; Arens, P. An ultra-dense integrated linkage map for hexaploid chrysanthemum enables multi-allelic QTL analysis. Theor. Appl. Genet. 2017, 130, 2527–2541. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Su, J.; Yang, X.; Zhang, F.; Wu, S.; Xiong, S.; Shi, L.; Guan, Z.; Fang, W.; Chen, F. Dynamic and epistatic QTL mapping reveals the complex genetic architecture of waterlogging tolerance in chrysanthemum. Planta 2018, 247, 899–924. [Google Scholar] [CrossRef]
  20. Zhang, F.; Chen, S.; Jiang, J.; Guan, Z.; Fang, W.; Chen, F. Genetic mapping of quantitative trait loci underlying flowering time in chrysanthemum (Chrysanthemum morifolium). PLoS ONE 2013, 8, e83023. [Google Scholar] [CrossRef] [Green Version]
  21. Song, X.; Xu, Y.; Gao, K.; Fan, G.; Zhang, F.; Deng, C.; Dai, S.; Huang, H.; Xin, H.; Li, Y. High-density genetic map construction and identification of loci controlling flower-type traits in Chrysanthemum (Chrysanthemum × morifolium Ramat.). Hortic. Res. 2020, 7, 108. [Google Scholar] [CrossRef]
  22. Truco, M.J.; Antonise, R.; Lavelle, D.; Ochoa, O.; Kozik, A.; Witsenboer, H.; Fort, S.B.; Jeuken, M.J.W.; Kesseli, R.V.; Lindhout, P.; et al. A high-density, integrated genetic linkage map of lettuce (Lactuca spp.). Theor. Appl. Genet. 2007, 115, 735–746. [Google Scholar] [CrossRef] [PubMed]
  23. Yagi, M.; Kimura, T.; Yamamoto, T.; Isobe, S.; Tabata, S.; Onozaki, T. QTL analysis for resistance to bacterial wilt (Burkholderia caryophylli) in carnation (Dianthus caryophyllus) using an SSR-based genetic linkage map. Mol. Breed. 2012, 30, 495–509. [Google Scholar] [CrossRef]
  24. Tan, L.; Wang, L.; Xu, L.; Wu, L.; Peng, M.; Zhang, C.; Wei, K.; Bai, P.; Li, H.; Cheng, H.; et al. SSR-based genetic mapping and QTL analysis for timing of spring bud flush, young shoot color, and mature leaf size in tea plant (Camellia sinensis). Tree Genet. Genomes 2016, 12. [Google Scholar] [CrossRef]
  25. Van Geest, G. Disentangling hexaploid genetics: Towards DNA-informed breeding for postharvest performance in chrysanthemum. Ph.D. Thesis, Wageningen University, Wageningen, The Netherlands, 2017. [Google Scholar]
  26. Bourke, P.M.; Voorrips, R.E.; Visser, R.G.F.; Maliepaard, C. Tools for genetic studies in experimental populations of polyploids. Front. Plant. Sci. 2018, 9, 513. [Google Scholar] [CrossRef] [Green Version]
  27. Zhang, Y.; Sledge, M.K.; Bouton, J.H. Genome mapping of white clover (Trifolium repens L.) and comparative analysis within the Trifolieae using cross-species SSR markers. Theor. Appl. Genet. 2007, 114, 1367–1378. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Yu, C.; Luo, L.; Pan, H.; Guo, X.; Wan, H.; Zhang, Q. Filling gaps with construction of a genetic linkage map in tetraploid roses. Front. Plant. Sci. 2015, 5, 796. [Google Scholar] [CrossRef] [Green Version]
  29. Julier, B.; Flajoulot, S.; Barre, P.; Cardinet, G.; Santoni, S.; Huguet, T.; Huyghe, C. Construction of two genetic linkage maps in cultivated tetraploid alfalfa (Medicago sativa) using microsatellite and AFLP markers. BMC Plant. Biol. 2003, 3, 9. [Google Scholar] [CrossRef] [Green Version]
  30. Li, M.; Yuyama, N.; Hirata, M.; Wang, Y.; Han, J.; Cai, H. An integrated SSR based linkage map for Zoysia matrella L. and Z. japonica Steud. Mol. Breed. 2010, 26, 467–476. [Google Scholar] [CrossRef]
  31. Kesseli, R.V.; Michelmore, I.P.A.R. Analysis of a detailed genetic linkage map of Lactuca sativa (Lettuce) constructed from RFLP and RAPD markers. Genetics 1994, 136, 1435–1446. [Google Scholar]
  32. Song, X.; Zhao, X.; Fan, G.; Gao, K.; Dai, S.; Zhang, M.; Ma, C.; Wu, X. Genetic analysis of the corolla tube merged degree and the relative number of ray florets in chrysanthemum (Chrysanthemum × morifolium Ramat.). Sci. Hortic. 2018, 242, 214–224. [Google Scholar] [CrossRef]
  33. DeVicente, M.C.; Tanksley, S.D. QTL analysis of transgressive segregation in an interspecific tomato cross. Genetics 1993, 134, 585–596. [Google Scholar] [PubMed]
  34. Koide, Y.; Sakaguchi, S.; Uchiyama, T.; Ota, Y.; Tezuka, A.; Nagano, A.J.; Ishiguro, S.; Takamure, I.; Kishima, Y. Genetic properties responsible for the transgressive segregation of days to heading in rice. G3 2019, 9, 1655–1662. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Lan, T.; He, K.; Chang, L.; Cui, T.; Zhao, Z.; Xue, J.; Liu, J. QTL mapping and genetic analysis for maize kernel size and weight in multi-environments. Euphytica 2018, 214, 119. [Google Scholar] [CrossRef]
  36. Xin, F.; Zhu, T.; Wei, S.; Han, Y.; Zhao, Y.; Zhang, D.; Ma, L.; Ding, Q. QTL mapping of kernel traits and validation of a major QTL for kernel length-width ratio using SNP and bulked segregant analysis in wheat. Sci. Rep. 2020, 10, 25. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Lemmon, Z.H.; Doebley, J.F. Genetic dissection of a genomic region with pleiotropic effects on domestication traits in maize reveals multiple linked QTL. Genetics 2014, 198, 345–353. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Fan, M.; Gao, Y.; Gao, Y.; Wu, Z.; Liu, H.; Zhang, Q. Characterization and development of EST-SSR markers from transcriptome sequences of chrysanthemum (Chrysanthemum × morifolium Ramat.). Hortscience 2019, 54, 772–778. [Google Scholar] [CrossRef] [Green Version]
  39. Liu, H.; Zhang, Q.X.; Sun, M.; Pan, H.T.; Kong, Z.X. Development of expressed sequence tag-simple sequence repeat markers for Chrysanthemum morifolium and closely related species. Genet. Mol. Res. 2015, 14, 7578–7586. [Google Scholar] [CrossRef]
  40. Sun, L.; Yang, W.; Zhang, Q.; Cheng, T.; Pan, H.; Xu, Z.; Zhang, J.; Chen, C. Genome-wide characterization and linkage mapping of simple sequence repeats in mei (Prunus mume Sieb. et Zucc.). PLoS ONE 2013, 8, e59562. [Google Scholar] [CrossRef] [Green Version]
  41. Grattapaglia, D.; Sederoff, R. Genetic linkage maps of Eucalyptus grandis and Eucalyptus urophylla using a pseudo-testcross: Mapping strategy and RAPD markers. Genetics 1994, 137, 1121–1137. [Google Scholar]
  42. Van Ooijen, J.W. JoinMap ® 4, Software for the Calculation of Genetic Linkage Maps in Experimental Populations; Kyazma B.V.: Wageningen, The Netherlands, 2006. [Google Scholar]
  43. Kosambi, D.D. The estimation of map distances from recombination values. Ann. Eugen 1943, 12, 172–175. [Google Scholar] [CrossRef]
  44. Cervantes-Flores, J.C.; Yencho, G.C.; Kriegner, A.; Pecota, K.V.; Faulk, M.A.; Mwanga, R.O.M.; Sosinski, B.R. Development of a genetic linkage map and identification of homologous linkage groups in sweetpotato using multiple-dose AFLP markers. Mol. Breed. 2008, 21, 511–532. [Google Scholar] [CrossRef]
  45. Voorrips, R.E. MapChart: Software for the graphical presentation of linkage maps and QTLs. J. Hered. 2002, 93, 77–78. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Van Ooijen, J.W. MapQTL ® 6, Software for the Mapping of Quantitative Trait Loci in Experimental Populations of Diploid Species; Kyazma B.V.: Wageningen, The Netherlands, 2009. [Google Scholar]
Plants 09 01342 g001 550
Figure 1. Final integrated map derived from an F1 population of 192 offspring in chrysanthemum. Map distances (cM) and SSR marker alleles are shown on the left and right side of each linkage group, respectively. Duplex markers are marked in green. Bridge markers are marked in red. Distorted segregating markers are underlined, which are indicated by a significant p value in the chi-squared test: p < 0.01; Quantitative trait loci are located on the far right of each LG.
Figure 1. Final integrated map derived from an F1 population of 192 offspring in chrysanthemum. Map distances (cM) and SSR marker alleles are shown on the left and right side of each linkage group, respectively. Duplex markers are marked in green. Bridge markers are marked in red. Distorted segregating markers are underlined, which are indicated by a significant p value in the chi-squared test: p < 0.01; Quantitative trait loci are located on the far right of each LG.
Plants 09 01342 g001
Plants 09 01342 g002 550
Figure 2. Frequency distributions of 12 inflorescence and leaf traits for the F1 progeny in 2015, 2016 and 2017.
Figure 2. Frequency distributions of 12 inflorescence and leaf traits for the F1 progeny in 2015, 2016 and 2017.
Plants 09 01342 g002
Table
Table 1. Classification of 1000 alleles detected in 223 expressed sequence tag–simple sequence repeat (EST-SSR) loci using the χ2 goodness of fit test (α = 0.01, df = 1) depending on the segregation ratios of hexasomic and disomic inheritance.
Table 1. Classification of 1000 alleles detected in 223 expressed sequence tag–simple sequence repeat (EST-SSR) loci using the χ2 goodness of fit test (α = 0.01, df = 1) depending on the segregation ratios of hexasomic and disomic inheritance.
Marker TypesAutohexaploid (Hexasomic) Allohexaploid (Disomic)
No. of Alleles Present in the Paternal ParentNo. of Alleles Present in the Maternal ParentNo. of Alleles Present in Both ParentsTotalNo. of Alleles Present in the Paternal ParentNo. of Alleles Present in the Maternal ParentNo. of Alleles Present in Both ParentsTotal
Monomorphic328994328994
Polymorphic
Simplex × nulliplex183179-362183179-362
Duplex × nulliplex5037-872828-56
Triplex × nulliplex2315-382821-49
Simplex × simplex--9494--9494
Duplex × simplex 7979--4545
Unidentified dose0015815886150164
Distorted at α < 0.0126382488353566136
Total28527144410002852714441000
Table
Table 2. Distribution of markers on the final integrated map and linkage group statistics.
Table 2. Distribution of markers on the final integrated map and linkage group statistics.
Linkage GroupNumber of SSR Marker AllelesDuplex MarkersNumber of Markers Exhibiting Segregation Distortion (p < 0.01)Map Length (cM)Map Density (loci/cM)Average Distance Between Markers (cM)Largest Gap Between Markers (cM)
LG14532187.80 0.244.1723.49
LG22910131.22 0.224.5219.44
LG35842120.79 0.482.0810.92
LG42310120.39 0.195.2321.64
LG52303108.81 0.214.7319.15
LG62410102.32 0.234.2613.43
LG7331283.48 0.402.5324.92
LG8240173.04 0.333.0411.16
LG950026.61 0.195.3218.96
Total2641110954.46 0.28 3.6224.92
Table
Table 3. Parent mean (± SD) and population mean (± SD) and range for 12 inflorescence and leaf traits. Skewness and kurtosis (± SE) and values of broad-sense heritability for 12 traits are also listed.
Table 3. Parent mean (± SD) and population mean (± SD) and range for 12 inflorescence and leaf traits. Skewness and kurtosis (± SE) and values of broad-sense heritability for 12 traits are also listed.
TraitTrait CodeYearParent Mean ± SDSignificant Mean Difference Among Parental Values (t-Test)F1 Population Mean ± SDRangeSkewnessKurtosisHeritability
FM
Inflorescence diameterID201544.52 ± 2.245.79 ± 2.84n.s40.57 ± 7.9516.68–60.7−0.32 0.23 0.81
201663.26 ± 1.4247.62 ± 5.16Yes, p < 0.00145.86 ± 7.426.20–63.27−1.04 4.59
201759.98 ± 2.1141.09 ± 2.94Yes, p < 0.0145.01 ± 8.5820.06–65.86−0.01 −0.45
Central disc flower diameterCDFD201510 ± 0.75 3.03 ± 0.80Yes, p < 0.018.80 ± 1.693.95–12.05−0.39 −0.18 0.90
201610.16 ± 0.84 3.32 ± 0.46Yes, p < 0.0018.58 ± 1.992.92–14.33−0.46 0.61
201712.79 ± 0.493.5 ± 0.28 Yes, p < 0.0018.72 ± 1.714.11–12.95−0.15 −0.07
Number of whorls of ray floretsNWRF20151 ± 09.67 ± 1.53Yes, p < 0.052.83 ± 1.440–8.51.20 2.43 0.93
20161 ± 08.5 ± 0.50Yes, p < 0.053.28 ± 1.521–12.671.95 7.98
20171 ± 08.67 ± 0.58Yes, p < 0.053.32 ± 1.441–8.330.95 0.63
Number of ray floretsNRF201514.33 ± 0.58209.67 ± 43.11Yes, p < 0.0160.01 ± 31.6013–1731.31 1.45 0.95
201613.33 ± 1.15239 ± 14.11Yes, p < 0.00170.71 ± 38.517.67–245.671.52 3.34
201716 ± 2.00297.33 ± 25.17Yes, p < 0.00176.51 ± 40.3421–213.331.02 0.72
Number of disc floretsNDF2015118 ± 14.7316.67 ± 3.06 Yes, p < 0.01131.28 ± 54.4012–274.330.41 −0.08 0.88
201692.33 ± 10.5017.67 ± 6.03Yes, p < 0.001121.56 ± 49.9616–2920.43 0.32
2017226 ± 6.0029 ± 6.56Yes, p < 0.001144.53 ± 46.0341.33–265.330.27 −0.24
Number of floretsNF2015132.33 ± 15.28226.33 ± 40.80n.s191.67 ± 55.7453.00–3550.460.160.85
2016105.67 ± 11.50256.67 ± 20.13Yes, p < 0.001192.41 ± 57.3595.67–396.330.660.39
2017242 ± 8.00326.33 ± 30.11Yes, p < 0.01221.57 ± 42.22122.67–421.50.822.23
Ray floret lengthRFL201526.47 ± 2.0221.18 ± 1.54Yes, p < 0.0521.86 ± 4.2313.17–33.9−0.05 0.02 0.83
201634.77 ± 0.6221.63 ± 3.55Yes, p < 0.0523.56 ± 3.5410.51–32.54−0.22 1.00
201732.7 ± 0.2521.52 ± 0.83Yes, p < 0.0523.15 ± 4.4811.12–35.790.16 0.06
Ray floret widthRFW20157 ± 0.58 5.40 ± 0.43Yes, p < 0.055.39 ± 0.992.9–8.61−0.01 0.76 0.86
20168.14 ± 1.124.75 ± 0.30Yes, p < 0.055.97 ± 1.323.2–15.422.81 16.57
20177.97 ± 0.30 4.91 ± 0.05Yes, p < 0.055.90 ± 1.172.36–9.06−0.24 0.25
Ray floret length/widthRFL/W20153.78 ± 0.083.93 ± 0.37n.s4.12 ± 0.672.98–7.521.30 4.90 0.90
20164.32 ± 0.554.54 ± 0.50n.s4.06 ± 0.622.71–15.420.46 −0.04
20174.11 ± 0.134.39 ± 0.20n.s4.04 ± 0.871.88–9.501.69 7.92
Leaf lengthLL201544.05 ± 0.5860.07 ± 7.49 Yes, p < 0.0571.94 ± 15.9539.95–111.210.45 −0.15 0.78
201646.15 ± 4.8358.04 ± 3.59Yes, p < 0.0558.93 ± 14.1322.47–110.240.75 1.13
201745.91 ± 3.13 51.43 ± 4.74n.s54.25 ± 15.3123.82–110.591.04 1.80
Leaf widthLW201526.11 ± 0.4536.69 ± 8.43n.s40.48 ± 9.2624.1–63.80.51 −0.25 0.76
201630.28 ± 3.3433.52 ± 3.30n.s35.18 ± 8.356.22–61.470.33 0.63
201728.48 ± 3.3430.43 ± 2.64n.s31.82 ± 9.7315.77–771.54 4.24
Leaf length/widthLL/W20151.69 ± 0.051.66 ± 0.17n.s1.80 ± 0.201.32–2.230.00 −0.31 0.82
20161.53 ± 0.04 1.74 ± 0.15n.s1.70 ± 8.351.24–3.844.09 32.89
20171.62 ± 0.141.69 ± 0.07n.s1.73 ± 0.201.31–2.370.33 −0.01
n.s. nonsignificant.
Table
Table 4. Characterization of QTLs for inflorescence and leaf traits in chrysanthemum.
Table 4. Characterization of QTLs for inflorescence and leaf traits in chrysanthemum.
TraitsQTLYearLinkage GroupMarker IntervalQTL Position (cM)Max LOD ValueContributions (%)
Inflorescence diameterqID5-201520155127-c75747-22113.9683.6914.7
qID6-20152015696-c73599-18437.8843.8715.3
qID8-201620168260-CEM15-42935.6024.4112.4
qID3-201720173226-c73810-207; 43-c67517-24948.0313.819.3
qID3-mean-1Mean398-c73873-17437.5794.5110.5

qID3-mean-2
Mean
3		226-c73810-234; 226-c73810-207; 43-c67517-249; 52-c70441-250; 205-c64830-25949.4035.4512.6
qID8-meanMean8260-CEM15-429; 36-c66938-19536.6024.489.1
Central disc flower diameterqCDFD4-201520154242-c77190-29075.1583.614.2
qCDFD3-201620163196-c55093-34895.1744.2811.9
Number of whorls of ray floretsqNWRF6-201720176203-c64705-23270.4533.719.1
qNWRF3-meanMean3196-c55093-348; 27-c65910-14499.4684.049.5
Number of ray floretsqNRF3-meanMean3196-c55093-348; 27-c65910-14467.7343.196.8
Number of disc floretsqNDF4-201520154242-c77190-29076.1584.9218.9
qNDF6-201520156238-c76774-22930.2093.6914.5
qNDF3-201620163196-c55093-348; 27-c65910-144102.4784.4512.4
Number of floretsqNF4-201520154242-c77190-29076.1583.7914.9
qNF7-20162016794-c73532-33436.3574.0311.4
qNF8-2017-120178257-CEM12-28503.999.8
qNF8-2017-22017834-c66815-14950.8543.638.9
Ray floret lengthqRFL3-201620163115-c75008-25941.7483.7410.7
qRFL8-meanMean8260-CEM15-42936.6023.979.3
qRFL3-mean-1Mean398-c73873-174; 64-c71464-20837.5794.2910
qRFL3-mean-2Mean3226-c73810-234; 226-c73810-207; 43-c67517-249; 52-c70441-250; 205-c64830-25947.975
48.031		5.14
5.14		11.9
11.9
Ray floret widthqRFW5-201520155127-c75747-22116.2613.6914.9
qRFW3-201720173226-c73810-203106.0193.989.7
qRFW8-20172017824-c65571-167; 85-c7300529.1284.079.9
qRFW7-meanMean728-c66048-25350.4773.678.6
Ray floret length/widthqRFL/W2-201620162124-c75428-144117.1584.412.4
qRFL/W3-2017-12017364-c71464-21683.3123.779.2
qRFL/W3-2017-220173196-c55093-338102.4783.919.6
qRFL/W7-201720177254-CEM9-20847.7083.829.4
qRFL/W3-meanMean364-c71464-21683.3123.658.6
Leaf lengthqLL5-20152015563-c71333-217; 121-c75378-26420.3833.6814.8
qLL8-201720178165-c53556-249; 141-c76325-24573.0384.6511.3
Leaf widthqLW8-201720178165-c53556-249; 141-c76325-24573.038512.1
Leaf length/widthqLL/W4-201620164209-c66049-24397.2415.6715.7
Table
Table 5. Expected segregation ratios for the inheritance of a dominant marker in hexaploid chrysanthemum according to two cytological hypotheses (Park et al. 2015).
Table 5. Expected segregation ratios for the inheritance of a dominant marker in hexaploid chrysanthemum according to two cytological hypotheses (Park et al. 2015).
Marker DosageHypothesis IHypothesis II
Autohexaploid (Hexasomic)Allohexaploid (Disomic)
Simplex × nulliplex1:11:1
Duplex × nulliplex4:13:1
1:0
Triplex × nulliplex19:17:1
1:0
Simplex × simplex3:13:1
Duplex × simplex9:17:1
Triplex × simplex39:115:1
Duplex × duplex24:115:1

Share and Cite

MDPI and ACS Style

Fan, M.; Gao, Y.; Wu, Z.; Zhang, Q. Linkage Map Development by EST-SSR Markers and QTL Analysis for Inflorescence and Leaf Traits in Chrysanthemum (Chrysanthemum morifolium Ramat.). Plants 2020, 9, 1342. https://doi.org/10.3390/plants9101342

AMA Style

Fan M, Gao Y, Wu Z, Zhang Q. Linkage Map Development by EST-SSR Markers and QTL Analysis for Inflorescence and Leaf Traits in Chrysanthemum (Chrysanthemum morifolium Ramat.). Plants. 2020; 9(10):1342. https://doi.org/10.3390/plants9101342

Chicago/Turabian Style

Fan, Min, Yike Gao, Zhiping Wu, and Qixiang Zhang. 2020. "Linkage Map Development by EST-SSR Markers and QTL Analysis for Inflorescence and Leaf Traits in Chrysanthemum (Chrysanthemum morifolium Ramat.)" Plants 9, no. 10: 1342. https://doi.org/10.3390/plants9101342

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop