Genetic Diversity of the Camellia oleifera Abel. Germplasm and Development of a Core Collection Using Simple Sequence Repeat Markers

doi:10.21203/rs.3.rs-2008463/v1

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Genetic Diversity of the Camellia oleifera Abel. Germplasm and Development of a Core Collection Using Simple Sequence Repeat Markers

https://doi.org/10.21203/rs.3.rs-2008463/v1

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published 09 Jan, 2023

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Camellia oleifera is an important oilseed crop. With the increasing attention given to C. oleifera, germplasms have been collected, and banks have recently been established or renewed. The lack of knowledge of genomic SSRs, limited investigation into the genetic diversity of the germplasm bank, and lack of a core collection have hindered the breeding of oil tea. Therefore, genomic libraries were constructed and sequenced, and all clean reads were assembled into 673,863 unigenes. In total, 27,953 SSRs were detected from all the unigenes, and 17,517 SSR primer pairs were successfully designed. Furthermore, we re-evaluated the genetic diversity of the germplasm bank of C. oleifera using 22 SSR markers. The number of alleles (Na) ranged from 4 to 25, with an average of 11.41; the effective number of alleles (Ne) ranged from 2.014 to 9.284, with an average of 4.800; the Shannon index (I) ranged from 0.716 to 2.471, with an average of 1.682; and the polymorphic information index (PIC) ranged from 0.380 to 0.883, with an average of 0.717. Finally, a core collection consisting of 45 samples (15% of the 300 trees in the entire germplasm bank) was developed, the genetic diversity parameters of which showed nonsignificant differences from the entire germplasm bank. Genomic SSR development, the re-evaluation of genetic diversity and the development of a core collection from the germplasm bank in this study provided useful tools, abundant genetic resources and an effective collection for the utilization and deep exploration of C. oleifera.

Camellia oleifera

Germplasm Bank

Genetic Diversity

Core Collection

Camellia oleifera Abel. (2n = 6x = 90), which is commonly known as oil tea, is a member of the Camellia genus and an important oilseed crop (Zhuang 2008). The oil from C. oleifera, which is called tea oil, has serval favorable characteristics such as high unsaturated fatty acid content (Ma et al. 2011), abundant vitamins (Zhuang 2008) and positive health benefits. Because of high nutritional value, tea oil is regarded as a high-quality, healthy oil (Yang et al. 2016). In addition, tea oil is traditionally used as a medicine for new-born baby lotion and burning injury (Yang et al. 2016). In addition, C. oleifera can provide raw materials for other applications, including essential oils, biological pesticides, biofertilizers and wood products (Zhuang 2008). To increase the total output of lipids and meet demand of our country, the extensive planting of oil tea has been vigorously promoted in the last decade in China.

Currently, there are 11.2 million acres of C. oleifera in China, with a total value of output of approximately 20 billion dollars (National Foresty and Glassland Adminstration 2020). The output of tea oil per acre is approximately 240 kilograms (National Foresty and Glassland Adminstration 2020). However, the yield per acre is low. This results from a deficiency in scientific management (Luo et al. 2012), as well as the late development of improved cultivars (Wang and Chen 2015). The development of new cultivars is strongly dependent on the genetic diversity of the germplasm resources (Hallauer et al. 2010). Hence, the evaluation of genetic diversity is vitally important. Many studies of the genetic diversity of C. oleifera have been reported in the last decade. Some studies have addressed only a few good cultivars or elite trees selected previously (Chen et al. 2005, Lin et al. 2010, Yu et al. 2013, Chen et al. 2016) and have identified all cultivars in their research using various molecular markers. Some studies have focused on a small number of wild genetic resources in certain regions or provinces (Zhang et al. 2011, Kao et al. 2014, Zhou et al. 2017, Huang et al. 2018).

Originating in China, oil tea is widely distributed in South China, and its genetic resources are abundant (Zhuang 2008), especially in the Guangxi region (Zhang 2003). To collect, study and utilize oil tea germplasms, oil tea germplasm banks have been established in the Guangxi region for four decades. Some reports on the genetic diversity of oil tea germplasm banks have been documented (Wang et al. 2014, Zhang et al. 2016). In the last decade, there has been increasing interest in the crop, and C. oleifera germplasm banks have been renewed recently. However, to our knowledge, information on the genetic diversity of renewed germplasm banks is limited to date. Understanding and tracking the latest genetic diversity of the germplasm bank in a timely manner is essential for the genetic improvement of oil tea.

However, the management and utilization of large amounts of germplasm resources are the greatest challenges for plant breeders. A core collection is defined as a representative sample of the whole collection with minimal repetitiveness and maximal genetic diversity of a plant species and its relatives (Frankel and Brown 1983). The establishment of a core collection offers a convenient way to study and utilize germplasm resources. The construction of core collections is an efficient use of germplasm resources to identify useful germplasms for crop improvement using molecular marker approaches (Upadhyaya et al. 2006). Among woody plants or fruit trees, core collections have been developed in only a few species, including Eucalyptus cloeziana F. Muell. (Myrtaceae) (Lv et al. 2020), Theobroma cacao (Martínez et al. 2017), Coffea canephora (Leroy et al. 2014), Malus × domestica Borkh. (Lassois et al. 2016), Pyrus pyrifolia (Burm.) (Bu et al. 2012) and Olea europaea L. (Bakkali et al. 2013).

Many kinds of molecular markers have been developed and used in C. oleifera, including rapid amplification polymorphism DNAs (RAPDs) (Chen et al. 2005), inter-simple sequence repeats (ISSRs) (Yu et al. 2013), amplified fragment length polymorphisms (AFLPs) (Jin et al. 2012), and expressed sequence tag–simple sequence repeats (EST-SSRs) (Jia et al. 2015, Li et al. 2017, Wu et al. 2019). Since molecular markers such as RAPDs, ISSRs and AFLPs are dominantly inherited, simple sequence repeat (SSR) markers are more informative genetic markers due to their codominance, multiple alleles, reproducibility and relative abundance (Powell et al. 1996, Varshney et al. 2005). According to sequence sources, SSR markers can be categorized into two classes: EST-SSRs (eSSRs) are derived from transcribed regions, and genomic SSRs (gSSRs) are derived from sequences throughout the genome. Many EST-SSRs have been discovered from the transcriptomes of various tissues from different genotypes of C. oleifera (Jia et al. 2015, Li et al. 2017, Wu et al. 2019). However, most of the developed SSRs have been derived from conserved coding regions or transcriptomes, which has resulted in low polymorphism (Varshney et al. 2005). Compared with these developed SSRs, the polymorphism of gSSRs is relatively higher. This has been confirmed in two previously published works (Meyer et al. 2017, Parthiban et al. 2018). The development of gSSRs has prevailed and has recently been reported in many species (An et al. 2017, Curto et al. 2019, Xiong et al. 2021, Zhang et al. 2021). Although they are very useful, currently, genomic SSRs have not been developed on a massive scale in C. oleifera.

In this study, we aimed to (1) develop a massive, novel set of genomic SSRs from C. oleifera, (2) explore the genetic diversity of the germplasm bank of the Guangxi region using SSR markers, and (3) develop a core collection of oil tea germplasm resources.

Plant materials

The oil tea germplasm bank was established at the National Sanmenjiang Forestry Farm in Liuzhou, Guangxi, China in the 1970s. The bank consisted of 300 individuals. These individuals were from 138 families, with 1-3 individuals in every family. These families were derived from 27 provenances. The detailed information is as follows: seven provenances, TM (TieMao), QT (QiTuan), MZ (MengZai), SBL (SanBoLing), SD (SanDao), SMJ (SanMenJiang) and BY+ (BinYang), in the Liuzhou region, six provenances, LN (LiNong), JT (JiangTang), FL (FuLin), HB (HuangBao), GP (GuPao) and FM (FengMu), in the Hezhou region, three provenances, LK (LiuKui), RY (RenYong), and BY (BanYue), in the Yulin region, three provenances, BM (BaMa), DY (DouYang) and FS (FengShan), in the Hechi region, two provenances, PL (PingLe) and GL (GuiLin), in the Guilin region, two provenances, BS (BaiSe) and TY (TianYang), in the Baise region, LZ (LongZhou) in the Chongzuo region, WM (WuMing) in the Nanning region, and CX (CengXi) in the Wuzhou region. All the samples were selected across the region by the Guangxi Forestry Research Institute and were placed into the QS (pinyin initials of Chinese abbreviation of guangxi forestry research institute) population, and the samples without recorded provenances were put into the UN population. The LN provenance had the most families (31 families), while seven provenances, FM, FS, JT, LK, LZ, MZ, and RY, had only one family each. These individual trees were planted based on a randomized complete block design, with three replicates of single-tree plots at a spacing of 2.0◊3.0 m.

DNA extraction, construction of genomic libraries and high-throughput sequencing

Fresh leaves of 300 individual plants were collected, and DNA was extracted using the modified cetyltrimethyl ammonium bromide (CTAB) method. The concentration and quality of each DNA sample were assayed using 1.0 % agarose gel electrophoresis and a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). All DNAs were diluted with TE (10 mmol L^-1, EDTA 1 mmol L^-1, pH 8) buffer to a working concentration of 25 ng/µl.

For construction of the genomic libraries, fresh leaves of C. oleifera var Cenruan No. 3 were harvested for genomic DNA isolation. Two libraries of 350 nt and 500 nt in length were generated according to the instructions and sequenced with the Illumina HiSeq^TM 4000 platform.

Assembly of genomic sequences and identification of genomic SSRs

After adaptors and low-quality reads including more than 5 % uncertain nucleotides or 20 % low-quality bases (Q < 10) were removed from the raw reads, all clean reads were assembled into unigenes using SOAPdenovo with a kmer of 81 and extended further with GapCloser software. The Perl script MISA was employed to search for SSR loci with a minimum of 10, 6, 5, 4, and 4 nucleotides for 2-, 3-, 4-, 5-, and 6-nucleotide repeat units, respectively. Corresponding primers were designed by Primer3.

Polymerase chain reaction (PCR) amplification and SSR genotyping

Six DNAs were randomly selected as templates, and then polymorphic SSRs were screened. Twenty-two polymorphic SSRs were employed to genotype the 300 trees, these SSRs consisted of sixteen genomic SSRs, prefixed with cog to represent the first three letters of the phrase camellia oleifera genome, and six EST SSRs, prefixed with the three letters coe. The cogSSR markers were developed in this study, while the coeSSR markers were previously reported (Wu et al. 2019).

PCR was carried out in a 10 µl volume including 5 µl 2◊SanTaq PCR Mix (Sangon Biotech Co., Ltd., Shanghai, China), 1 µl DNA at 25 ng µl^-1, 2 µl sterilized ddH₂O, and 1 µl of each forward and reverse primer (10 µmol L^-1). The PCR program was as follows: 5 min at 95°C, 33 cycles of 45 sec at 95°C, 45 sec at 60°C, and 60 sec at 72°C, and a final extension of 5 min at 72°C. The PCR products were separated with 8 % denaturing polyacrylamide gels and visualized by silver staining. The clear bands of PCR amplification were recorded for future analysis.

Genetic diversity and structure

PowerMarker V 3.25 (Liu and Muse 2005) and GenAlEx version 6.5 (Peakall and Smouse 2012) were used to calculate the parameters of genetic diversity, including the number of observed alleles (Na), effective number of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), Shannon’s index (I) and polymorphic information index (PIC), for every polymorphic SSR marker in the oil tea germplasm in this study. Shared allele genetic distances between samples were calculated, and then samples were clustered using the neighbor-joining method. The dendrogram was drawn with MEGA X (Molecular Evolutionary Genetics Analysis) (Kumar et al. 2018).

STRUCTURE 2.3.4 software was employed to analyze the genetic structure of the tested germplasm using an admixture model with burn-in periods of 10,000 and 100,000 MCMC replications. The K values, which represented the subpopulation number, were between 2 and 10. Each K value was run 10 times. The most likely number of K values was determined by the highest value of ΔK (Evanno et al. 2005) obtained with STRUCTURE HARVESTER v0.6.94 (Earl and vonHoldt 2012).

Development of a core collection

The R package Core Hunter version 3 was used to generate the core collection with maximized genetic diversity and allelic richness (Beukelaer et al. 2018). The sampling fractions were designated as 5 %, 10 %, 15 %, 20 %, 25 %, 30 %, 35 %, 40 %, 45 % and 50 % of the entire collection. The genetic diversity parameters, including Na, Ne, Ho, He, I and PIC, were estimated for the designated core collections mentioned above and then compared with those of the entire collection using t-tests. The smallest core collection showing nonsignificant differences from the entire collection (P ≤ 0.05) was regarded as the core collection.

Sequencing and assembly

Two genomic libraries were sequenced using an Illumina sequencing platform. The 350 nt library contained 82,819,715 raw reads and 81,531,148 clean reads after the removal of adaptors and low-quality reads, while the 500 nt library contained 81,860,129 raw reads and 80,798,610 clean reads. All clean reads were assembled into contigs by SOAPdenovo, and then gaps were closed by GapCloser software. Finally, 673,863 unigenes were obtained with a total of 578,264,895 nt and an N50 of 672 nt.

Characterization and development of simple sequence repeats

In total, 27,953 SSRs were detected from the unigenes. The density was one SSR locus per 20.687 kb. Overall, most of the SSRs were dinucleotide repeats (12,772, 45.69 %), followed by trinucleotide repeats (5557, 19.88 %), pentanucleotide repeats (3994, 14.29 %), and hexanucleotide repeats (2963, 10.60 %). Tetranucleotide repeats had the lowest number of SSRs (2667, 9.54 %) (Fig. 1).

Fig. 1 Frequencies of SSRs with different unit sizes

Repeat motifs with frequencies of more than 1 % included AC/GT, AG/CT, AT/AT, AAC/GTT, AAG/CTT, AAT/ATT, ACC/GGT, AGG/CCT, ATC/ATG, AAAC/GTTT, AAAT/ATTT, AAAAC/GTTTT, AAAAG/CTTTT, AAAAT/ATTTT, AAACC/GGTTT, AAAAAG/CTTTTT and AAAAAT/ATTTTT (Fig. 2). These repeat motifs accounted for 82.55 % of the SSRs. AG/CT-containing SSRs exhibited the highest frequency (38.87 %), followed by AAG/CTT (6.89 %) and AAAT/ATTT (5.58 %). The remaining repeat motifs had a frequency of < 5 %. In total, 17,517 SSR primer pairs were successfully designed using Primer3 software.

Fig. 2 The 17 most frequent SSR repeat motifs.

Screening of polymorphic SSR primers

A set of 100 genomic SSRs was randomly selected and amplified for polymorphism screening. Among the DNAs from six plants, seventy-five SSRs were polymorphic, nine generated uniform amplicons, and sixteen produced no amplicons. Thus, the genomic SSRs had a polymorphism rate of 75 %. Among the thirty EST-SSRs from a previous report (Wu et al. 2019), six were polymorphic, resulting in a 20 % polymorphism rate. Six EST-SSRs and a set of 16 genomic SSRs were utilized in the subsequent genetic diversity studies of C. oleifera.

Genetic diversity, cluster analysis and structure

All 300 individuals in the C. oleifera germplasm bank were investigated for the selected 22 polymorphic SSRs. As shown in Table 1, the Na ranged from 4 to 25, with an average of 11.410, the Ne ranged from 2.014 to 9.284, with an average of 4.800, the I ranged from 0.716 to 2.471, with an average of 1.682, the Ho ranged from 0.546 to 1.000, with an average of 0.957, the He ranged from 0.503 to 0.892, with an average of 0.756, and the PIC ranged from 0.380 to 0.883, with an average of 0.717. Twenty-one SSRs showed high polymorphism (PIC > 0.5), while one was moderately polymorphic (0.25 < PIC < 0.5).

Table 1 Genetic diversity of the germplasm bank at 22 SSR loci

When estimated in the form of shared alleles between two samples, the genetic distances ranged from 0.235 to 0.718, with an average of 0.529. In this study, the samples HZ28-2 and QS10-3 had the minimum genetic distance, however, the samples HZ28-2 and LZ95-2 had the maximum genetic distance. The dendrogram showed that many samples in the same provenance clustered into different groups, and some samples from different provenances clustered into the same group (Fig. 3). The phylogenetic tree was very complicated.

Fig. 3 Neighbor-joining tree based on the genetic distances of 300 individuals of C. oleifera in the germplasm bank in the Guangxi region.

To infer the genetic structure of the germplasm, the molecular data of the population were analyzed using Structure 2.3.4 software. The results showed that there was no obvious maximum LnP(D) corresponding to K values ranging from 2 to 10, and the number of subpopulations (K) could not be predicted accurately. However, the K value was estimated to be 6 according to the maximum ΔK value of 28.024 (Fig. 4). Therefore, the genetic resources of the oil tea germplasm bank were grouped into six classes (Fig. 5). The first subpopulation consisted of 42 individuals mainly from the Hezhou region, the second subpopulation included 73 individuals mainly from the Liuzhou region, the third subpopulation harbored 26 samples from the Guilin and Hezhou regions, the fourth subpopulation was composed of 63 individuals mainly from the Guilin region, the fifth subpopulation had 72 individuals mainly from the Hezhou and Guilin regions, and the sixth subpopulation included 24 individuals from the Guilin, Hezhou and Liuzhou regions.

Fig. 4 Determination of the number of subpopulations (K)

Fig. 5 Population structure of the germplasm samples

Development of core collections

To avoid redundancy of the genetic resources and to save space, ten different fractions were compared with the overall genetic resources in terms of genetic diversity parameters (Na, Ne, Ho, He, I and PIC) (Table 2). Of these fractions, 5 % and 10 % indicated significant differences (P ≤ 0.05) in Na, while other parameters were not significantly different from those of the overall genetic resources. Hence, the optional fraction was set at 15 %. The final core collection developed herein was composed of 45 individuals from 300 samples. Compared with the entire gene bank, the core collection harbored 77.3 %, 105.2 %, 102.4 %, 100.3 %, 101.9 % and 102.9 % of the diversity in Na, Ne, Ho, He, I and PIC, respectively. This demonstrated that the core collection could effectively represent all of the genetic resources.

Table 2 Comparison of genetic diversity for core collection subsets sampled at various fractions

SSR Markers

SSR markers are powerful tools for genetic variation analysis. A few SSR markers have been developed from the transcriptomes of C. oleifera (Jia et al. 2014, Li et al. 2017, Wu et al. 2019). These studies have shown that dinucleotide repeat motifs are the most frequent, excluding mononucleotide repeat motifs, and that AG/CT is the most common dinucleotide motif (Jia et al. 2014, Li et al. 2017, Wu et al. 2019). These results are similar to those in this study, suggesting that C. oleifera gSSRs and EST SSRs share common repeat motifs.

The SSR density (2.5 kb per SSR) is similar between the transcriptomes of different tissues based on similar criteria in C. oleifera (Li et al. 2017, Wu et al. 2019). However, based on the SSR identification criteria herein, the SSR density in the root transcriptome was 10.084 kb, which was only half of that of the gSSRs in this study. This suggests that SSR loci are primarily located in gene-rich regions in the genome of C. oleifera (Rota et al. 2005) and that microsatellites are more frequent in single/low-copy DNA than in repetitive DNA (Morgante et al. 2002, Wang et al. 2015). Therefore, the microsatellite frequency is lower in the genome than in the transcriptome.

Many of the developed EST-SSRs have been successfully amplified in not only C. oleifera but also its relatives (Jia et al. 2015, Wu et al. 2019) because EST-SSRs are conserved. However, EST-SSRs are significantly less polymorphic than genomic SSRs (Meyer et al. 2017, Parthiban et al. 2018). To reveal more genetic diversity, gSSRs have prevailed and been developed in many species, including Erinaceaus europaeus and E. roumanicus (Curto et al. 2019 ), Oreochromis niloticus (Tibihika et al. 2019), Elymus sibiricus L (Xiong et al. 2021) and Akebia trifoliata (Zhang et al. 2021). Hence, in this study, we adapted high-throughput sequencing technology based on small-fragment genomic libraries to develop genomic SSR molecular markers to advance genetic diversity studies.

Genetic diversity and structure

The genetic diversity of germplasms allows plant breeders to improve cultivars (Govindaraj et al. 2015, Ellegren and Galtier 2016). There are extremely abundant genetic resources of C. oleifera in the Guangxi region in China (Zhang 2003). Almost all representative materials in the Guangxi region were deposited into the germplasm bank, which was established approximately 50 years ago. The genetic diversity of the germplasm bank was reported using SRAP (Wang et al. 2014) and SSR (Zhang et al. 2016) markers based on some representative samples.

As more attention has been given to genetic resources, materials have been continuously renewed and supplied in the germplasm bank in recent years. The genetic diversity of the germplasm used in this study was significantly greater than that in a previous report (Zhang et al. 2016). This may be attributed to the dramatic increase in the number of genetic materials in this study and the rapid expansion of genetic resources in the germplasm bank. On the other hand, genetic diversity is also affected by the SSR loci assayed because of the detection and discrimination capability of different SSR markers. Perhaps the genomic SSRs developed in this study could reveal more genetic diversity than EST SSRs. At the same time, the genetic diversity herein was approximately equal to that of the natural population (Huang et al. 2018), but there were twice as many samples as those in the natural population. This means that there existed redundancies in the genetic resources in this gene bank.

In addition, the observed heterozygosity was nearly equal to 1. C. oleifera is a predominantly outcross species (Zhuang 2008). The Ho was significantly higher than the He in this study, which suggests that C. oleifera may be an allopolyploid because of the hybridization of different species (Huang et al. 2018).

Population structure or population subdivision refers to the presence of subpopulations within a population (Pritchard 2001). Due to adapting to different environments, the differences in allele frequency change in gene pools, resulting in genetic differentiation (Hartl and Clark 2007). In this study, all genetic materials in the germplasm collection were divided into six subpopulations. Each subpopulation had samples from two provenances, LN and QS. Because the QS materials were collected across the Guangxi region, there was no clear indication that the materials were derived from various regions, and a complex genetic structure was revealed (Wang et al. 2014, Zhang et al. 2016), which was also confirmed by the clustering analysis results herein. The results suggest that high levels of gene flow exist in Guangxi. In general, the gene flow of plants includes two modes: pollination and migration of germplasm. C. oleifera is often pollinated by insects, and the maximum distance between plants is approximately 70 meters. However, the distances between many samples clustered in one class were as far as hundreds of miles away. We speculate that germplasm migration is the main type of gene flow in this study. This may be associated with the developmental history of C. oleifera in China. In the 1950s, the industry involving C. oleifera was dramatically developing, and germplasms or cultivars were likely to be exchanged from one place to another (Zhuang 2008). In addition, for the samples from LN scattered in every class, it is speculated that most oil tea samples in this germplasm bank were from the northeastern Guangxi region.

Core collection development

To enhance germplasm efficiency, core collections were proposed (Frankel and Brown 1983, Brown 1989). Because C. oleifera is a highly heterozygous species (Zhang et al. 2016) and may have many alleles, we tested only ten fractions, ranging from 5 % to 50 % of the whole germplasm. Because the R package of Core Hunter version 3 was used herein, the core collection has several features, including a small sample size, broad representation, low redundancy and rich diversity (Beukelaer et al. 2018). By definition, the core collection in general contained approximately 10 % of the full collection of samples (Brown 1989, Guo et al. 2014). Similar ratios have been reported in many plants, including Malus × domestica Borkh. at 12.85 % (278/2163) (Lassois et al. 2016)(Lassois et al. 2016) and Coffea canephora at 13.63 % (77/565) (Leroy et al. 2014). The ratio of the core collection to the entire collection in this study is approximately equal to that of Malus sieversii (Ledeb.) Roem at 14 % (42/300) (Liu et al. 2012) and less than those of Pyrus pyrifolia (Burm.) at 17.91 % (36/201) (Bu et al. 2012) and Theobroma cacao at 34.45 % (185/537) (Martínez et al. 2017). An optimal fraction of a core collection is determined by many factors, including the heterozygosity of the species, germplasm population size, diversity level and sampling strategy. This is the first core collection of C. oleifera to be developed. This collection will contribute to breeding novel cultivars effectively in the future.

This was the first report on the large-scale development of genomic SSR markers based on sequences from a small-insert fragment library of C. oleifera. We developed 17,517 genomic SSR primer pairs that were speculated to reveal more genetic diversity than EST-SSRs. We also re-evaluated the genetic diversity of the germplasm bank of C. oleifera over time. Finally, we developed the first core collection of C. oleifera from the germplasm bank. All these findings will provide a solid foundation for the deep exploration and effective utilization of genetic resources of C. oleifera from the germplasm bank.

Acknowledgments:

This work was supported by the Natural Science Foundation of Guangxi (2020GXNSFAA259063), Open Research Fund of Guangxi Key Laboratory of Special Non-wood Forest Cultivation & Utilization (19-B-01-01), National Natural Science Foundation of China (No. 31460208), Key Research and Development Project of Guangxi (GuiKeAB21076012), and Project of Science and Technology of Qinzhou (201616804, 20137003).

Funding

Conflict of interest

The authors declare that they have no conflicts of interest or competing interests.

Author contributions

PW, LY and ZZ conceived and designed the project. PW, JS, HW, YC, YX, HW, GH, NC, CW and LY collected the samples, performed molecular lab work and scored markers. ZZ, PW and LY analyzed the data and wrote the manuscript with assistance from all other authors. All authors read and approved final manuscript.

Data Availability

The Illumina sequencing data used in this study is deposited in the Sequence Read Archive of NCBI, and the identifier is PRJNA557016.

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Table 1 Genetic diversity of the germplasm bank at 22 SSR loci

Locus	Forward primers (5’-3’)	Reverse primers (5’-3’)	Repeat Motifs	Na	Ne	I	Ho	He	PIC
cogSSR73	GGGTTGTCCGGCTAACATAA	CAGAGGGCTCGTTTAGGAAG	(AAAT)5	10	4.178	1.637	0.902	0.761	0.727
cogSSR69	TTTCCCTTTGCTTCCTCTCTC	CCGATTCGTAATGCCTTGTT	(CT)10	14	6.108	1.967	0.985	0.836	0.815
cogSSR68	CTCACTGCTCACATCGAACC	GGTGGTCAGTCAGAGATGGG	(TTC)6	7	3.141	1.323	0.993	0.682	0.629
cogSSR40	CGCTTGAGCACAATTGACAT	TCCCAAACCTAACTGTCTCACA	(AG)14	16	5.82	2.097	0.986	0.828	0.809
cogSSR10	TACGTCAAGTTTGGGGACAA	TCCCTTAGATTGATCCTTATCCT	(ATA)7	6	2.976	1.283	1.000	0.664	0.606
cogSSR9	TTTCGGCTTTGATCTCTCTCA	TGCAATTCGTTTGATCATCATT	(TG)11	7	2.432	1.042	1.000	0.589	0.502
cogSSR6	ATTCCCAGAACAAAGCAACG	AGGAAAGCGAAAGCGATGTA	(TC)11	7	5.163	1.748	0.996	0.806	0.779
cogSSR3	TATTGCTCACCTACGGCACA	TTTGAGACCTAATTGGTTTGCT	(CCAAA)4	10	5.906	1.923	1.000	0.831	0.809
cogSSR2	AAAAATGTCAATGACCATACAATCTC	TCATGGCCTTTATGGGCTTA	(TTTAT)4	15	5.164	1.85	0.996	0.806	0.780
cogSSR11	TTCTCCTTGATGATGAAGATTTG	TGCCCAAATTAAAAGGCTCA	(CTGTG)4	7	3.687	1.425	0.969	0.729	0.683
cogSSR37	TCGTTTGGGACGTAGATTGA	TTCATTTTGGCCAAGGTCTC	(CAACC)5	25	9.284	2.471	0.945	0.892	0.883
cogSSR34	TCATTGGCCCCTCAAAATAG	ATTTCCCTCGATGCAACTGT	(AATA)5	16	6.199	2.092	1.000	0.839	0.821
cogSSR32	TCCCTTTCCCTTGTTTGCTA	GTGGAGTGGGTGGTAGGAGA	(TTA)6	16	7.593	2.228	1.000	0.868	0.855
cogSSR28	ATTTGCTCCTTCAAGTCCCA	GTCGAATCGGAGTTGGTGAT	(CT)16	18	8.246	2.292	0.546	0.879	0.867
cogSSR24	TGAGGAAGCAACTTGGGAAC	GCCACAATCCAACAACCACT	(TTTA)5	4	2.014	0.716	1.000	0.503	0.380
cogSSR14	AAACCCCCGATACCAAGTTT	GATGCTTCGGTAGTGCATATTG	(AAAT)5	9	2.922	1.311	0.996	0.658	0.608
coeSSR50	TCATGGCGATTAAAGCTGTG	ACGGCATCTGCCTGTAGTTT	(ATCAG)4	9	3.653	1.485	0.993	0.726	0.682
coeSSR45	CCCCTTCCTGCCCTAATAAT	CTCCGAGATCCTCCACAGAG	(TCCAA)4	7	3.737	1.493	0.997	0.732	0.690
coeSSR34	ATCAGGGGTTATTTCGAGCC	CACATGGTCTGAACCCACAA	(GTT)7	14	2.721	1.293	0.891	0.632	0.583
coeSSR20	CTGCTGCATGCACACACTTA	ATGGTGCTGACGATGATGAG	(GGT)7	12	6.111	2.049	0.995	0.836	0.819
coeSSR10	TGTGAGATGGGTGGATCTGA	CCTCTCTCTCTCGCTCTCCA	(GTGGTA) 4gtggtggtgttgat(TGG)5	7	4.354	1.59	0.965	0.770	0.736
coeSSR8	ACCCAATCCAATTCAATCCA	TGACTCTTGGGATCCGAGTC	(CTTTT)4	15	4.195	1.684	0.903	0.762	0.723
Mean	-	-	-	11.409	4.800	1.682	0.957	0.756	0.717

Table 2 Comparison of genetic diversity for core collection subsets sampled at various fractions

Fraction (%)	Na	Ne	I	Ho	He	PIC
5	6.818^**	4.840	1.647	0.932	0.763	0.728
10	7.909^**	4.904	1.689	0.955	0.767	0.735
15	8.818	5.051	1.723	0.960	0.770	0.738
20	9.409	5.059	1.731	0.959	0.769	0.737
25	9.864	4.938	1.715	0.959	0.765	0.733
30	10.000	4.916	1.708	0.960	0.762	0.731
35	10.318	4.875	1.703	0.961	0.760	0.727
40	10.409	4.920	1.708	0.961	0.763	0.730
45	10.636	4.918	1.706	0.961	0.762	0.728
50	10.727	4.911	1.703	0.962	0.761	0.728

^**P ≤ 0.01 for the difference between the core subset and the entire collection.

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Genetic Diversity of the Camellia oleifera Abel. Germplasm and Development of a Core Collection Using Simple Sequence Repeat Markers

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