Skip to main content

A high-density genetic map of Schima superba based on its chromosomal characteristics



Schima superba (Theaceae) is a popular woody tree in China. The obscure chromosomal characters of this species are a limitation in the development of high-density genetic linkage maps, which are valuable resources for molecular breeding and functional genomics.


We determined the chromosome number and the karyotype of S. superba as 2n = 36 = 36 m, which is consistent with the tribe Schimeae (n = 18). A high-density genetic map was constructed using genotyping by sequencing (GBS). A F1 full-sib with 116 individuals and their parents (LC31 × JO32) were sequenced on the Illumina HiSeq™ platform. Overall, 343.3 Gb of raw data containing 1,191,933,474 paired-end reads were generated. Based on this, 99,966 polymorphic SNP markers were developed from the parents, and 2209 markers were mapped onto the integrated genetic linkage map after data filtering and SNP genotyping. The map spanned 2076.24 cM and was distributed among 18 linkage groups. The average marker interval was 0.94 cM. A total of 168 quantitative trait loci (QTLs) for 14 growth traits were identified.


The chromosome number and karyotype of S. superba was 2n = 36 = 36 m and a linkage map with 2209 SNP markers was constructed to identify QTLs for growth traits. Our study provides a basis for molecular-assisted breeding and genomic studies, which will contribute towards the future research and genetic improvement of S. superba.


Schima superba, based on its original description, is formally placed in the tribe Schimeae (≡ Gordonieae) in Theaceae [1,2,3]. Theaceae is a family of subtropical and tropical trees in Asia containing approximately 17–20 genera and 500 species [1]. The genus Schima is closely related to the genera Franklinia and Gordonia and has approximately 20 species. Schima is an economically and ecologically important genus and is mainly distributed in southern China and the adjacent parts of East Asia, with 13 species (six endemic) present in China [1]. Schima superba is a typical large tree and dominant element in the subtropical evergreen broad-leaved forests of southern China. This species is valued commercially for its timber, and the wood is used for furniture and in construction. Additionally, these trees are used as fire breaks and thus help protect forests from fires [4,5,6].

Schima is distinct from other genera within Theaceae with regards to its chromosome number. The family Theaceae comprises three major tribes: Theeae, Schimeae, and Stewartieae. The tribe Stewartieae was the earliest to show differentiation at nearly 48 mya and has a chromosome number of n = 17. Theeae (n = 15) and Schimeae have a closer relationship, but their chromosome numbers are different. Previous studies indicate that all members of the tribe Theeae have the dominant basic chromosome number of n = 15 [3, 7, 8]. A chromosome number of 17 (n = 17) is the base number within the entire Stewartiae tribe [9]. In the tribe Schimeae, the basic chromosome numbers are either n = 15 or n = 18, and the count of n = 18 is more widely recognized in Schima than the original count number of n = 15 [2, 10,11,12,13]. The chromosome number of S. superba within Schima is still unclear, which limits studies on this species.

Due to the long (up to 30 years) breeding cycle of woody trees, there is an urgent need to improve upon traditional breeding methods [14]. Marker-assisted selection (MAS) is a useful tool for reducing the breeding cycle and has been used for many decades [15,16,17,18]. The development of a saturated genetic linkage map using molecular markers with high genome coverage is a prerequisite for the application of molecular plant breeding [19]. A molecular genetic linkage map is required as a fundamental resource for MAS; however, no high-density genetic maps have been constructed for S. superba. Therefore, an advanced breeding strategy and genetic maps are necessary for the identification of genes associated with important genotypes in S. superba. Genotyping by sequencing (GBS) has been used for the rapid development of thousands of segregating single nucleotide polymorphism (SNP) markers in large mapping populations at a low cost [20]. GBS has been widely used in high-density genetic linkage map construction [18, 21, 22] and quantitative trait locus (QTL) mapping in several plant species [23,24,25,26].

In the present study, we report the chromosome number of S. superba and describe its karyotype. We further performed hybridization between the individuals “LC31” and “JO32”. Based on the F1 full-sib, a high-density genetic map was constructed using the GBS approach. Phenotypic traits related to height, stem base diameter, growth rate, crown width, branching characters, and other growth-related parameters were mapped on this linkage map, and the associated SNPs were identified.


Chromosome data

The mapping population consisted of 116 full-sibs derived from two selected trees from natural S. superba forests: LC31 (female parent, 50-years-old) and JO32 (male parent, 37-years-old). The parents exhibited differences in growth rate, woody yield, and wood quality. The hybridization was performed in 2013. A total of 116 full-sibs were harvested and planted on the forest farm of Longquan in 2014 and were used for the genetic map construction.

The mitotic metaphase indicated a chromosome number of 2n = 36 for S. superba (Fig. 1a). The absolute chromosome lengths ranged from 0.8 to 2.7 μm and were therefore classified as small chromosome types (Table 1). The chromosomes were determined to be 36 median (Fig. 1b; Table 1), which corresponded to a 3B karyotype symmetry. A secondary constriction was not observed. The arm ratios of most of the chromosomes were lower than 1.5 except one that was between 1.5 and 2.0 (Table 1) indicating low intra-chromosomal asymmetry.

Fig. 1

Cytological observations of Schima superba. a Metaphase, b Karyotype, formulated as 2n = 36 = 36 m

Table 1 Mitotic metaphase chromosomes of Schima superbaa

Sequencing data quality assessment

The number of clean reads obtained from the female parent, male parent, and progeny were 37,926,886, 50,742,572, and 9,981,084–45,923,854 with an average of 20,550,577 clean reads. The 2.96 Gb of individual raw data were generated using a Hi-Seq platform for the parents and 116 progenies, yielding 343.3 Gb of high-quality sequences with an average Q20 ratio of 94.87%, a Q30 ratio of 87.83%, and a GC content of 35.17%. The average MseI enzyme capture rate was 98.63%, validating the quality of the enzyme digest. No abnormal SNP calls were found, validating the genotyping accuracy.

In the absence of a suitable reference genome, a 137 Mb tag clustering by the male parent ‘JO32’ was used as the reference genome. A summary of the parental reads and reference genome alignment is shown in Table 2. For each progeny, the clean reads ranged from 9,981,084 to 45,923,854 bp, with an average of 20,550,577 bp (Additional file 1: Table S1). Moreover, the average mapping rate of the 116 full-sibs was 89.17%.

Table 2 Sequence depth and coverage statistics

GBS-based SNP identification

We used the GATK (type UnifiedGenotyper) software to determine 298,332 and 190,522 SNPs in the female and male parents, respectively. For F1 individuals, an average of 276,582 SNPs was discovered for an individual progeny. The heterozygosis SNP rate of the females and males was 72.04 and 98.86%, respectively. The progeny SNP results are provided in the Additional file 2: Table S2 File. To avoid false positive SNPs, the base number of the parent SNP was set as ≥4 and the base number of the progeny was ≥2.

A Bayesian model was used to detect 99,966 polymorphic loci (Fig. 2), which could be classified into eight segregation types according to the CP model in JoinMap 4.0. Among these, three major patterns including hk × hk, nn × np, and lm × ll accounted for nearly 94.92% of the loci, while the other five patterns, ab× cd, ab × cc, cc × ab, ef × eg, and aa × bb, accounted for only 5.08%. Thus, only segregation types hk × hk, nn × np, and lm × ll were selected for genotyping in F1 individuals, resulting in a total number of 94,883 polymorphic loci.

Fig. 2

Segregation types of polymorphic SNP markers. The x-axis indicates the eight segregation types; the y-axis indicates the corresponding number of markers

Fig. 3

Genotyping-by-sequencing-based high-density genetic map of 116 full-sibs. The 2076.24 cM map included 2209 SNPs

High-density genetic map development

A total of 28,856 markers were ultimately obtained after filtering the markers with complete coverage during genotyping. The available markers were filtered for < 65% integrity using a Chi-squared test with a threshold of P < 0.001. Ultimately, 519 markers with hk × hk, 639 markers with lm × ll, and 1051 markers with nn × np segregation types were used for map construction. Following data preparation, 1569 markers with types nn × np and hk × hk and 1137 markers with types lm × ll and hk × hk were used for the construction of male and female maps, respectively. On the male map, 1569 markers were placed into 18 LGs, and the genetic length was 1583.97 cM with an average marker interval distance of 1.01 cM (Additional file 3: Table S3). On the female map, 1137 markers were placed into 18 linkage groups (LGs), and the genetic length was 1459.19 cM with an average marker interval of 1.28 cM (Additional file 4: Table S4). The heat maps reflect the linkage relationships between the markers in a single linkage group (Additional file 5: Figure S1).

The two parent maps were then merged, and the integrated map spanned 2076.24 cM with 2209 markers placed into 18 LGs (Fig. 3). Among the 18 LGs, LG06 was the largest group with a genetic distance of 173.97 cM and 231 markers. LG18 was the smallest group with 84 markers, spanning 70.52 cM. The average marker interval ranged from 0.57 to 2.66 cM, with an average distance of 0.94 cM (Table 3). Between the markers, 2161 gaps (97.83%) were less than 5 cM, 40 gaps were between 5 and 10 cM, seven gaps were between 10 and 20 cM, and only one gap was larger than 20 cM, which was located on LG13.

Table 3 Genetic linkage group statistics of the integrated map

QTL mapping of growth traits

QTLs were mapped using the phenotypic data (Table 4) of 14 growth traits at an LOD (logarithmic) threshold of 3.0, from which 168 QTLs were identified (Table 5, Additional file 6: Table S5). These 14 traits could be classified into four categories namely I, II, III, and IV. Category I included the height characteristics: height of one-year-old seedlings (H1), height of three-year-old seedlings (H3), and height growth rate per year (HGR). The stem characteristics were designated in category II as stem base diameter of one-year-old-seedlings (SBD1), stem base diameter of three-year-old-seedlings (SBD3), and stem base diameter growth rate (SBDGR). The leaf characteristics leaf length (LL) and leaf width (LW) were classified in the third category (III). Crown width (CW), primary shoot number (PSN), maximum branching angle (MBA), maximum branching diameter (MBD), bifurcate trunk (BT), and height of bifurcate trunk (HBT) were classified in category IV for the branching characters. Thirty-four QTLs were identified for category I on chromosome LGs 1, 2, 3, 5, 8,10, 11, 12, 13, 16, and 17 with the percentage of phenotypic variation explained (PVE) by each QTL varying from 11.2 to 16.1%. The mean PVEs of the same traits were different in different years. Since the QTLs exerted the main- and side-effects, not all phenotypes were revealed at the same time. The mean PVEs of the QTLs were 12.1% for H1 and 14.1% for H3, and higher PVEs of the QTLs were located on LG13. The intervals of the five QTLs located on LG13 for HGR were 0.51, 22, 0.21, and 0.42 cM, respectively. The markers SsSNPLG13lm3242 and SsSNPLG13np4091 flanked the significant QTL qHGR-LG13. The two genotype calls AA and AG of SsSNPLG13lm3242 had an average HGR of 26 and 59%, respectively, and the two genotypes AA and AG of SsSNPLG13np4091 had an average HGR of 65 and 11%, respectively (Table 6).

Table 4 Variation analysis of phenotypic data of 14 growth traitsa
Table 5 QTL identified for different growth traits
Table 6 Summary of QTL for HGR, SBDGR, CW, PSN, MBA, BT and HBT traits identified in the full-sib populations of LC31 × JO32

For (II), 26 QTLs were identified on chromosome LGs 4, 6, 10, 13, 16, and 17 with PVEs varying from 11.2 to 18.2%. The mean PVEs of the QTLs were 11.9% for SBD1 and 14.2% for SBD3, and the QTL on LG4 had the highest PVE (18.2%) (Additional file 6: Table S5). The intervals of the six QTLs located on LG4 for SBD growth rate per year (SBDGR) were 13.21, 25.7, 0.03, 0.9, and 14.53 cM, respectively. The markers SsSNPLG4np2901 revealed the significant QTL qSBDGR-LG4. The genotype calls TG and TT had average SBDGRs of 25 and 49%, respectively (Table 6).

We identified 37 QTLs for category III on LGs 1, 2, 3, 4, 5, 8, 11, 13, and 17 with PVEs varying from 11.2 to 16.0%. Category IV had 71 QTLs that were identified on LGs 1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 13, 15, 17, and 18 with PVEs varying from 12.8 to 22.8%. The QTL on the LG4 marker SsSNPLG4np2206 had the highest PVE (19.4%) for CW, and another marker SsSNPLG4np2901 (identified in SBDGR) had a higher PVE = 18.6%. The SsSNPLG17lm2346 marker had the highest PVE (22.8%) for PSN. The LG10 SsSNPLG10np8947 marker had the highest PVE (17.7%) for MBA; the LG12 SsSNPLG12mp7972 marker had the highest PVE (20.8%) for BT, the LG17 SsSNPLG17np727 marker had the highest PVE (18.5%) for BT, and the LG17 SsSNPLG17hk3434 had the highest PVE (18.9%) for HBT.


Morphological and karyotype analysis of S. superba indicated that it has a diploid chromosome number (n = 18) with three large, five medium-large, seven medium-small, and three small sized chromosomes with a median centromere (Fig. 1, Table 1). Gordonia and Schima are the two biggest genera in the tribe Schimeae (≡Gordonieae). Gordonia has the highest species count with n = 15 [2, 10, 27,28,29], and only one North American species (G. lasianthus) has a different chromosome number of n = 18 [30]. Yang (2004) indicated that Gordonia should be further classified into two genera: one being the Chinese Gordonia species with n = 15, which should be classified into Polyspora in tribe Theeae, whereas the other North American Gordonia (G. lasianthus) with n = 18 should form the monotypic genus Gordonia s.str [2]. Our results confirmed that the species chromosomal number in Schima is n = 18. Previous studies showed that n = 18 for most species in this genus [28, 29, 31, 32], and only Schima wallichii has n = 15 [33] or n = 18 [2, 34]. Bloembergen and Keng regarded the genus as monotypic and subdivided S. wallichii into nine or more geographically separated species [35, 36]. Our results corroborated the study of Wang (2006) where the base chromosome numbers in tribes Theeae, Schimeae (≡Gordonieae), and Stewartieae in Theaceae are n = 15, n = 18, and n = 17, respectively [3].

The construction of genetic linkage maps and the identification of growth trait-related QTLs will facilitate future genetic and breeding studies in S. superba. However, the absence of a genetic map has prevented the use of QTLs from this species in breeding programs. GBS is a rapid, efficient, and cost-effective strategy for SNP development, genetic linkage map construction, marker-based complex trait selection, and draft genome assembly in many species with or without reference genomes [18, 20, 37,38,39,40]. We generated 343.3 Gb of raw sequences from which 99,966 SNPs and 94,883 (94.9%) polymorphic SNPs were detected from the two parents and the 116 full sibs. The integrated genetic linkage map was divided into 18 LGs and comprised 2209 SNP markers, which spanned 2076.24 cM with an average marker interval of 0.94 cM. Accordingly, the high-quality SNP-based map will provide a basis for MAS and genomic studies, which should contribute to the genetic improvement of S. superba.

Plant morphological traits influence cultivation area, cultivation patterns, yields, and planting efficiency in forests. Woody plant traits such as higher individual height, thicker stem base diameter, fewer branches or bifurcate trunks, fast growth rate, higher wood density, wood durability, lower wood shrinkage and fissure, and greater wood strength are favored by forest geneticists and breeders [14, 41]. We have a good understanding of the effect of individual QTLs on phenotypes and their position in the genome [42]. However, QTLs discovered in Pinus radiata experimental populations explain only 0.78–3.8% of the variation in juvenile wood density and diameter [43]. We developed 168 QTLs from 14 growth traits, which varied from 11.2–22.8% (Table 4).

Plant growth and branching pattern traits are complex dynamic traits that are regulated by the interactions of many genes that may behave differently during different growth stages. Some chromosome segments may be associated with different traits at different growth periods, and QTLs can be detected throughout various stages of plant development. However, certain QTLs are conditional and are found only at specific growth stages [44, 45]. For example, the QTL affecting height located in the interval from 45.95–143.94 cM on LG13 and the QTL affecting SBD located in the interval from 21.21–94.78 cM on LG17 appeared to be unconditional and were detected in the measurements from years one and three (Additional file 6: Table S5). However, the minor QTL of height located at 92.17 cM at LG2 was found only at the one-year stage, and the QTL of height located at 69.55 cM at LG10 was found only at the three-year stage (Additional file 5: Table S5). Ten out of the 168 QTLs with LOD scores of 3.78–5.67 affecting seven growth traits were detected, and 15.8–22.8% of the phenotypic variance was explained by the QTLs (Table 5).


We further examined the association between the actual segregation of the SNP markers closest to the QTL peaks and the traits of interest in the mapping population. The relationship between the genotypes of the linked markers and the average phenotypic values are displayed in Table 5. For SsSNPLG13lm3242 and SsSNPLG13np4091, the individuals harboring the homozygous AA genotype exhibited significantly lower HGR. The “G” allele, in this case, was strongly correlated with increased vertical height. However, the presence of “G” in the marker SsSNPLG4np2901 was associated with increased stem base diameter (SBD). Other markers, including SsSNPLG4np2901, SsSNPLG4np2206, and SsSNPLG10np8947, showed a “co-dominant” effect, where the average phenotypes of the heterozygous “TG,” “TA”, and “GA” individuals were higher than those of the homozygous groups. The genotypes of all of these SNPs for these QTLs in the full-sib were much more similar to the father line (JO32) and showed higher phenotype trait values (Table 5).


Morphological and karyotype characters

Seeds and seedlings of S. superba were collected from the Longquan region of Zhejiang province, China. The seeds were germinated in Petri dishes in a growth chamber, and the seedlings were cultivated in the nursery of the Longquan Forestry Academy.

Roots (1 cm) were removed from the seedlings or germinating seeds and pretreated with 8-hydroxy quinoline for 4 h at 4 °C, fixed for 24 h in Carnoy’s fluid (absolute alcohol: glacial acetic acid =3:1) at 4 °C, washed with 70% alcohol (v/v), washed with distilled water, macerated in 1 M hydrochloric acid at room temperature for 10 min and at 60 °C for 20 min, and then macerated in distilled water for 1 h. The root tips were then removed, compressed, and stained with carbol Fuchs. The cytological classification of the resting and mitotic prophase was performed as described by Tanaka [46]; the classification of karyotype symmetry was according to Stebbins [47]; and the use of symbols for the description of the chromosomes was according to Levan [48].

Experimental population and phenotypic measurements

The mapping population consisted of 116 full-sibs derived from two select trees from natural S. superba forests: LC31 (female parent) and JO32 (male parent). The parents exhibited obvious differences in several phenotypes, such as growth rate, woody yield, and quality. They were twig-grafted in 2009 and kept in Longquan, Zhejiang, China (latitude: 28°03’N, longitude: 119°06′E, mean altitude: 200–300 m). The mean annual air temperature was 17.6 °C, and the rainfall was 1664.8–1706.2 mm. The hybridization was performed in 2013. A total of 116 full-sibs were harvested and planted on the forest farm of Longquan in 2014 and were used for the genetic map construction.

In November 2015, H1, SBD1, LL, and LW of one-year-old seedlings were measured, and in November of 2017, H3, SBD3, HGR, SBDGR, CW, PSN, MBA, MBD, BT, and HBT of three-year-old seedlings were measured. All measurements were used for the QTL analysis.

DNA extraction

Young leaf samples were individually collected from the two parents and 116 full-sibs for DNA extraction. All samples were immediately frozen in liquid nitrogen and preserved at − 80 °C until extraction. The genomic DNA was extracted using a Plant Genomic DNA Isolation kit (Dingguo, Beijing, China) following the manufacturer’s instructions. The DNA purity and concentration were determined using a NanoDrop 1000 (Thermo Fisher Scientific, USA) and assessed on 1% agarose gels.

GBS protocol and library construction

A GBS strategy (Novogene, Beijing, China) was used to develop the SNP markers. First, we performed a GBS pre-design for restriction enzyme selection. The GBS library was constructed by digesting the genomic DNA with a Mse I, EcoR I, and Hae III enzyme combination with subsequent ligation to barcodes, after which each sample was amplified in the multiplex PCR. The desired fragments were selected for library construction.

Next, we performed a standard analysis of the raw data. The Illumina HiSeq™ sequencing platform (Illumina, San Diego, CA, USA) was used for paired-end (PE) 150 sequencing. Then, based on the analysis of the original data, we conducted advanced analyses, and DNA library assembly was followed by HiSeq sequencing with the removal of reads with adapters, low-quality base calls, or uncalled bases.

Finally, in the progeny GBS-Seq analysis, we analyzed the number of reads cut by MseI at both ends of each screened read, and the reads that did not contain these restriction sites were discarded. The specific reads and the ratio of the total number of reads to the number of enzyme-captured reads were also recorded. Then, Burrows-Wheeler Aligner (BWA) software [49] was used to align the clean reads against the reference genome (settings: mem -t 4 -k 32 -M -R). The reference genome was selected using a large amount of data from the male parent and was clustered and built to obtain a consistent sequence. The reads of the male parents, allowing up to six base mismatches, were clustered using the Stacks software [50, 51] and used to select the groups that contained read support numbers up to 3. These were clustered to obtain the final reference sequence. The alignment files were converted to bam files using SAMtools software [52]. If multiple read pairs had identical external coordinates, only the pair with the highest mapping quality was retained.

SNP identification and genotyping

SNP calling was performed for parents and progenies using SAMtools software [49]. Then, a Perl script was used to filter the SNPs that had more than two genotypes. Polymorphic markers between the two parents were detected and classified into eight segregation patterns (ab × cd, ab × cc, cc × ab, ef × eg, hk × hk, nn × np, lm × ll, and aa × bb) according to the CP model in JoinMap 4.0 software [53]. After the parental markers were developed, the 116 progeny lines were genotyped for the loci at which the parents differed.

Linkage map construction

Markers indicating significantly distorted segregation (P < 0.001), integrity (> 65%), or containing abnormal bases were filtered by JoinMap 4.0 (JoinMap® 4.0: Software for the calculation of genetic linkage maps in experimental populations). The segregation patterns hk × hk and nn × np were used for the construction of the male parent map, while the patterns lm × ll and hk × hk were used for the female parent map using JoinMap 4.0. The regression algorithm, three times circulation sequence, and Kosambi mapping functions were used in marker distance calculation [54]. The LOD value was set to 2.0–10. The integrated map for both the male and female parents was computed using the combined group for map integration function in MergeMap software [55]. A Perl script SVG was used to visualize the exported maps, and heat maps were constructed to evaluate the maps.

QTL analysis

QTLs were detected using the software MapQTL (6.0) [56]. Multiple QTL mapping (MQM) was applied to map the QTLs and estimate their effects. The LOD score of the significant QTLs was determined by conducting test analyses (a permutation test with 1000 permutations). However, when this was done, very few QTLs were detected. We thus used LOD = 3.0 for the further analyses.



Bifurcate trunk


Crown width


Genotyping by sequencing


Total genetic distance of chromosomes


Seedling height of 1 year


Seedling height of 3 year


The height of bifurcate trunk


Height growth rate per year


Leaf length


Leaf width


Marker Assisted Selection


Maximum branching angle


Maximum branching diameter


Primary shoot numbers


Percentage of phenotypic variation explained by each QTL


Quantitative trait loci


Stem base diameter of 1 year


Stem base diameter of 3 year


SBD growth rate per year


Subtropical evergreen broad-leaved forests


  1. 1.

    Stevens PF, Dressler S, Weitzman AL. Theaceae. In: Flowering plants Dicotyledons. Berlin: Springer; 2004. p. 463–71.

    Google Scholar 

  2. 2.

    Yang SX, Yang JB, Lei LG, Li DZ, Yoshino H, Ikeda T. Reassessing the relationships between Gordonia and Polyspora (Theaceae) based on the combined analyses of molecular data from the nuclear, plastid and mitochonodrial genomes. Plant Syst Evol. 2004;248:45–55.

    CAS  Article  Google Scholar 

  3. 3.

    Wang YH, He H, Min TL, Zhou LH, Fritsch PW. The phylogenetic position of Apterosperma (Theaceae) based on morphological and karyotype characters. Plant Syst Evol. 2006;260:39–52.

    Article  Google Scholar 

  4. 4.

    Zhang R, Zhou ZC, Luo WJ, Wang Y, Feng ZP. Effects of nitrogen deposition on growth and phosphate efficiency of Schima superba of different provenances grown in phosphorus-barren soil. Plant Soil. 2013;370:435–45.

    CAS  Article  Google Scholar 

  5. 5.

    Yang HB, Zhang R, Zhou ZC. Pollen dispersal, mating patterns and pollen contamination in an insect-pollinated seed orchard of Schima superba Gardn. Et champ. New Forest. 2017;48:431–44.

    Article  Google Scholar 

  6. 6.

    Yang HB, Zhang R, Song P, Zhou ZC. The floral biology, breeding system and pollination efficiency of Schima superba Gardn. Et Champ Forests. 2017;8:404.

    Article  Google Scholar 

  7. 7.

    Kondo K. Chromosome numbers in the genus Camellia. Biotropica. 1977;9:86–94.

    Article  Google Scholar 

  8. 8.

    Li GT, Liang T. Chromosome numbers and karyotypes in the genus Camellia. Guihaia. 1990;10:127–38.

    Google Scholar 

  9. 9.

    Horiuchi K, Oginuma K. Karyomorphology of three species of Stewartia (Theaceae) in Japan. Chromosome Sci. 2001;5:79–82.

    Google Scholar 

  10. 10.

    Santamour FA. Cytological studies in the Theaceae. Morris Arbor Bull. 1963;14:51–3.

    Google Scholar 

  11. 11.

    Goldblatt P. Index to plant chromosome numbers 1975–1978. In: Monogr Syst bot Missouri bot Gard, vol. 5. St. Louis: Missouri Bot Gard Press; 1981.

    Google Scholar 

  12. 12.

    Goldblatt P. Index to plant chromosome numbers 1984–1985. In: Monogr Syst bot Missouri bot Gard, vol. 23. St. Louis: Missouri Bot Gard Press; 1988.

    Google Scholar 

  13. 13.

    Goldblatt P, Johnson DE. Index to plant chromosome numbers 1994–1995. In: Monogr Syst bot Missouri bot Gard, vol. 69. St. Louis: Missouri Bot Gard Press; 1998.

    Google Scholar 

  14. 14.

    Isik F. Genomic selection in forest tree breeding: the concept and an outlook to the future. New Forest. 2014;45:379–401.

    Article  Google Scholar 

  15. 15.

    Plomion C, Durel CE, O’Malley DM. Genetic dissection of height in maritime pine seedlings raised under accelerated growth conditions. Theor Appl Genet. 1996;93:849–58.

    CAS  Article  Google Scholar 

  16. 16.

    Kumar S, Garrick DJ. Genetic response to within family selection using molecular markers in some radiata pine breeding schemes. Can J For Res. 2001;31:779–85.

    Article  Google Scholar 

  17. 17.

    Bernardo R. Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci. 2008;48:1469.

    Article  Google Scholar 

  18. 18.

    Zhang Z, Wei T, Zhong Y, Li X, Huang J. Construction of a high-density genetic map of Ziziphus jujube mill. Using genotyping by sequencing technology. Tree Genet and Genomes. 2016;12:76.

    Article  Google Scholar 

  19. 19.

    Sindhu A, Ramsay L, Sanderson LA, Stonehouse R, Li R, Condie J, Shunmugam AS, Liu Y, Jha AB, Diapari M, Burstin J, Aubert G, Taran B, Bett KE, Warkentin TD, Sharpe AG. Gene-based SNP discovery and genetic mapping in pea. Theor Appl Genet. 2014;127:2225–41.

    CAS  Article  Google Scholar 

  20. 20.

    Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. 2011;6:e25737.

    Article  Google Scholar 

  21. 21.

    Bielenberg DG, Rauh B, Fan S, Gasic K, Abbott AG, Reighard GL, Okie WR, Wells CE. Genotyping by sequencing for SNP-based linkage map construction and QTL analysis of chilling requirement and bloom date in peach [Prunus persica (L.) Batsch]. PLoS One. 2015;10:e0139406.

    Article  Google Scholar 

  22. 22.

    Ýpek A, Ýpek M, Erciþli S, Tangu NA. Transcriptome-based SNP discovery by GBS and the construction of a genetic map for olive. Funct Integr Genomics. 2017;17:493–501.

    Article  Google Scholar 

  23. 23.

    Imai A, Yoshioka T, Hayashi T. Quantitative trait locus (QTL) analysis of fruit-quality traits for mandarin breeding in Japan. Tree Genet and Genomes. 2017;13:79.

    Article  Google Scholar 

  24. 24.

    Liu C, Zhu S, Tang S, Wang H, Zheng X, Chen X, Dai Q, Liu T. QTL analysis of four main stem bark traits using a GBS-SNP-based high-density genetic map in ramie. Sci Rep. 2017;7:13458.

    Article  Google Scholar 

  25. 25.

    Han K, Lee HY, Ro NY, Hur OS, Lee JH, Kwon JK, Kang BC. QTL mapping and GWAS reveal candidate genes controlling capsaicinoid content in Capsicum. Plant Biotechnol J. 2018;16:1546–58.

    CAS  Article  Google Scholar 

  26. 26.

    Soto JC, Ortiz JF, Perlaza-Jiménez L, Vásquez AX, Lopez-Lavalle LA, Mathew B, Léon J, Bernal AJ, Ballvora A, López CE. A genetic map of cassava (Manihot esculenta Crantz) with integrated physical mapping of immunity-related genes. BMC Genomics. 2015;16:190.

    Article  Google Scholar 

  27. 27.

    Mehra PN, Sareen TS. Cytology of some Himalayan trees. Silvae Genet. 1973;22:66–70.

    Google Scholar 

  28. 28.

    Mehra PN. Cytology of Himalayan hardwood. Calcutta: Sree Saraswaty Press; 1976.

    Google Scholar 

  29. 29.

    Oginuma K, Tobe H, Ohba H. Chromosomes of some woody plants from Nepal. Acta Phytotaxonomica et Geobotanica. 1994;45:15–22.

    Google Scholar 

  30. 30.

    Bostick PE. Documented plant chromosome numbers 65: 2. Sida. 1965;2:165–7.

    Google Scholar 

  31. 31.

    Mehra PN. Cytogenetical evolution of hardwoods. Nucleus. 1972;15:64–83.

    Google Scholar 

  32. 32.

    Gill BS, Bir SS, Singhal VK. Cytological studies in some western Himalayan wood species II. Polypetalae. In: Paliwal GS, editor. The vegetation wealth of the Himalayas, puja publishers, Delhi; 1984. p. 497–515.

    Google Scholar 

  33. 33.

    Malla SB, Bhattarai S, Gorkhali M, Saiju H. Kayastha M. IOPB chromosome number reports LVII. Taxon. 1977;26:443–52.

    Google Scholar 

  34. 34.

    Bezbaruah HP. Cytological investigations in the family Theaceae-I. Chromosome numbers in some Camellia species and allied genera. Caryologia. 1971:2165–5391.

  35. 35.

    Bloembergen S. A critical study in the complex-polymorphous genus Schima (Theaceae). Reinwardtia. 1952;2:133–83.

    Google Scholar 

  36. 36.

    Keng H. Flora Malesianae precursors LVIII, part 4: the genus Schima (Theaceae) in Malesia. Gardens’ Bulletin (Singapore). 1994;46:77–87.

    Google Scholar 

  37. 37.

    He J, Zhao X, Laroche A, ZX L, Liu H, Li Z. Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci. 2014;5:484.

    Article  Google Scholar 

  38. 38.

    International Barley Genome Sequencing Consortium, Mayer KF, Waugh R, Brown JW, Schulman A, Langridge P, Platzer M, Fincher GB, Muehlbauer GJ, Sato K, Close TJ, Wise RP, Stein N. A physical, genetic and functional sequence assembly of the barley genome. Nature. 2012;491:711–6.

    Article  Google Scholar 

  39. 39.

    Poland J, Endelman J, Dawson J, Rutkoski J, Wu S, Manes Y, Dreisigacker S, Crossa J, Sánchez-Villeda H, Sorrells M, Jannink JL. Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome. 2012;5:103–13.

    CAS  Article  Google Scholar 

  40. 40.

    Ward JA, Bhangoo J, Fernández-Fernández F, Moore P, Swanson JD, Viola R, Velasco R, Bassil N, Weber CA, Sargent DJ. Saturated linkage map construction in Rubus idaeususing genotyping by sequencing and genome-independent imputation. BMC Genomics. 2013;14:2.

    CAS  Article  Google Scholar 

  41. 41.

    Durán R, Isik F, Zapata-Valenzuela Z, Balocchi C, Valenzuela S. Genomic predictions of breeding values in a cloned Eucalyptus globulus population in Chile. Tree Genet and Genomes. 2017;13:74.

    Article  Google Scholar 

  42. 42.

    Neale DB, Savolainen O. Association genetics of complex traits in conifers. Trends Plant Sci. 2004;9:325–30.

    CAS  Article  Google Scholar 

  43. 43.

    Devey ME, Carson SD, Nolan MF, Matheson AC, Te Riini C, Hohepa J. QTL associations for density and diameter in Pinus radiata and the potential for marker-aided selection. Theor Appl Genet. 2004;108:516–24.

    CAS  Article  Google Scholar 

  44. 44.

    Yang G, Xing Y, Li S, Ding J, Yue B, Deng K, Li Y, Zhu Y. Molecular dissection of developmental behavior of tiller number and plant height and their relationship in rice (Oryza sativa L.). Heredtas. 2006;143:236–45.

    Article  Google Scholar 

  45. 45.

    Pootakham W, Jomchai N, Ruang-areerate P, Shearman JR, Sonthirod C, Sangsrakru D, Tragoonrung S, Tangphatsornruang S. Genome-wide SNP discovery and identification of QTL associated with agronomic traits in oil palm using genotyping-by-sequencing (GBS). Genomics. 2015;105:288–95.

    CAS  Article  Google Scholar 

  46. 46.

    Tanak R. Type of resting nuclei in Orchidaceae. Bot Mag Tokyo. 1971;84:118–22.

    Article  Google Scholar 

  47. 47.

    Stebbins GL. Chromosomal evolution in higher plants. London: Edward Arnold (Publishers) Ltd; 1971. p. 87–93.

    Google Scholar 

  48. 48.

    Levan A, Fredga K, Sandlberg AA. Nomenclature for centromeric position on chromosomes. Hereditary. 1964;52:197–200.

    Google Scholar 

  49. 49.

    Li H, Durbin R. Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics. 2009;25:1754–60.

    CAS  Article  Google Scholar 

  50. 50.

    Catchen J, Amores A, Hohenlohe P, Cresko W, Postlethwait J. Stacks: building and genotyping loci de novo from short-read sequences. G3: Genes, Genomes, Genetics. 2011;1:171–82.

    CAS  Article  Google Scholar 

  51. 51.

    Catchen J, Hohenlohe P, Bassham S, Amores A, Cresko W. Stacks: an analysis tool set for population genomics. Mol Ecol. 2013;22(11):3124–40.

    Article  Google Scholar 

  52. 52.

    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Marth G, Abecasis G, Durbin R. 1000 genome project data processing subgroup. The sequence alignment/map format and SAM tools. Bio informatics. 2009;25:2078–9.

    Google Scholar 

  53. 53.

    VanOoijen JW. Multi point maximum likelihood mapping in a full sib-family of an outbreeding species. Genet Res (Camb). 2011;93:343–9.

    CAS  Article  Google Scholar 

  54. 54.

    Kosambi DD. The estimation of map distances from recombination values. Ann Eugenics. 1944;12:172–5.

    Article  Google Scholar 

  55. 55.

    Wu Y, Close TJ, Lonardi S. On the accurate construction of consensus genetic maps. Comput Syst Bioinformatics Conf. 2008;7:285–96.

    Article  Google Scholar 

  56. 56.

    Van Ooijen JW, Map QTL. 6. Software for the mapping of quantitative trait loci in experimental populations of diploid species. Wageningen: Kyazma BV; 2009.

    Google Scholar 

Download references


This research was supported by the Longquan Academy of Forestry. We thank the staff members of the Longquan Academy of Forestry for field and technical support.


The research was financially supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ19C160002, the Tree Breeding Research Project during the 13th Five-year Period of Zhejiang Province (2016C02056–3), the Fifth Stages of Planting and Seedling Science and Technology of Fujian Province (201605), and the Key projects in Jiangxi Province (201503). Authors declare that none of the funding bodies have any role in the design of the study and collection, analysis, and interpretation of data as well as in writing the manuscript.

Availability of data and materials

The datasets generated or analyzed during this study are included in this article (and its Additional file 1: Table S1, Additional file 2: Table S3, Additional file 3: Table S4, Additional file 4: Figure S1, Additional file 5: Table S5, Additional file 6: Table S2) or are available from the corresponding author on reasonable request. All raw data were submitted to the NCBI Sequence Read Archive: BioProject ID PRJNA510005.

Author information




RZ and ZZ conceived and designed the study. RZ, HY, BS, BW, and JX performed the experiments. RZ wrote the paper. RZ, HY, and ZZ reviewed and edited the manuscript. All authors read and approved the manuscript.

Corresponding authors

Correspondence to Rui Zhang or Zhichun Zhou.

Ethics declarations

Ethics approval and consent to participate

All field studies were performed in accordance with the local legislation in China and complied with the convention on the trade in endangered species.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests. The plant specimens used in our study are not an endangered species.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional files

Additional file 1:

Table S1. Number of clean GBS reads per sample (XLSX 12 kb)

Additional file 2:

Table S2. Total number and heterozygosis SNP rate per sample. (XLSX 14 kb)

Additional file 3:

Table S3. Genetic linkage group statistics of the male map (XLSX 11 kb)

Additional file 4:

Table S4. Genetic linkage group statistics of the female map (XLSX 12 kb)

Additional file 5:

Figure S1. Heat map of the group 1 of the male map (A), female map (B) and integrate map (C). The x-axis and y-axis are the names of the markers. The color is correlated with linkage strength. (DOCX 130 kb)

Additional file 6:

Table S5. QTL locations. (XLSX 19 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, R., Yang, H., Zhou, Z. et al. A high-density genetic map of Schima superba based on its chromosomal characteristics. BMC Plant Biol 19, 41 (2019).

Download citation


  • Theaceae
  • Schima superba
  • Chromosome
  • Karyotype
  • Genotyping by sequencing (GBS)
  • SNP
  • Linkage map
  • Growth traits
  • QTL