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Article

Construction of a High-Density Genetic Linkage Map Based on Bin Markers and Mapping of QTLs Associated with Fruit Size in Jujube (Ziziphus jujuba Mill.)

by
Tianfa Guo
1,2,†,
Qianqian Qiu
1,2,†,
Fenfen Yan
1,2,
Zhongtang Wang
3,
Jingkai Bao
1,2,
Zhi Yang
1,2,
Yilei Xia
1,2,
Jiurui Wang
4,
Cuiyun Wu
1,2,* and
Mengjun Liu
1,2,5,*
1
The National and Local Joint Engineering Laboratory of High Efficiency and Superior-Quality Cultivation and Fruit Deep Processing Technology of Characteristic Fruit Trees in Southern Xinjiang, Aral 843300, China
2
College of Horticulture and Forestry, Tarim University, Aral 843300, China
3
Shandong Institute of Pomology, Tai’an 271000, China
4
College of Forestry, Hebei Agricultural University, Baoding 071000, China
5
College of Horticulture, Hebei Agricultural University, Baoding 071000, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Horticulturae 2023, 9(7), 836; https://doi.org/10.3390/horticulturae9070836
Submission received: 5 May 2023 / Revised: 9 June 2023 / Accepted: 12 June 2023 / Published: 22 July 2023
(This article belongs to the Special Issue Genetics and Molecular Breeding of Fruit Tree Species)
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Abstract

:
Jujube (Ziziphus jujuba Mill.) is a fruit tree that is gaining increasing importance in drought-affected regions worldwide. The fruit size is an important quantitative agronomic trait that affects not only the fruit yield and attractiveness but also consumer preference. Genetic enhancement of fruit appearance is a fundamental goal of jujube breeding programs. The genetic control of jujube fruit size traits is highly quantitative, and development of high-density genetic maps can facilitate fine mapping of quantitative trait loci (QTLs) and gene identification. However, studies regarding the construction of high-density molecular linkage maps and identification of quantitative trait loci (QTLs) targeting fruit size in jujube are limited. In this study, we performed whole-genome resequencing of the jujube cultivars “JMS2” and “Xing16” and their 165 F1 progenies to identify genome-wide single-nucleotide polymorphism (SNP) markers and constructed a high-density bin map of jujube that can be used to assist in the selection of multiple traits in jujube breeding. This analysis yielded a total of 116,312 SNPs and a genetic bin map of 2398 bin markers spanning 1074.33 cM with an average adjacent interval of 0.45 cM. A quantitative genetic analysis identified 15 QTLs related to fruit size and the observed phenotypic variation associated with a single QTL ranged from 9.5 to 13.3%. Through the screening of overlapping and stable QTL regions, we identified 113 candidate genes related to fruit size. These genes were ascertained to be involved in cell division, cell wall metabolism, synthesis of phytohormones (ABA, IAA, and auxin), and encoding of enzymes and transcription factors. These candidate genomic regions will facilitate marker-assisted breeding of fruits with different sizes and shapes and lay a foundation for future breeding and manipulation of fruit size and shape in jujube.

1. Introduction

Chinese jujube (Ziziphus jujuba Mill., 2n = 2x = 24) belongs to the genus Ziziphus and is one of the most economically important members of the Rhamnaceae family [1,2,3,4]. Native to China, it is one of the oldest cultivated fruit trees in the world with over 7000 years of cultivation history and is now a major dry fruit crop [5]. At present, more than 90% of jujube production is concentrated in six provinces: Xinjiang, Hebei, Shandong, Shanxi, Shaanxi, and Henan. It is the foremost dry fruit in terms of production and the main income source of ~20 million farmers in China [2,3]. It is well adapted to various biotic and abiotic stresses, especially drought and salinity, and is considered an ideal cash crop for arid regions, such as Xinjiang. Although jujube cultivation is several thousand years old [5,6], jujube fruit quality traits, such as the size and flesh flavor, still require significant improvement to satisfy consumer preference. Understanding the molecular mechanism of genes controlling fruit quality is the key to developing jujube cultivars with improved fruit quality.
Highly saturated genetic linkage maps are essential for the fine localization of genes, marker-assisted selection (MAS), and structural and functional genomics. Marker-assisted quantitative trait locus (QTL) studies have used genetic linkage maps to dissect the genetic basis of complex traits in several horticulture plants [7,8,9]. The first genetic map of jujube, developed from 72 progeny of the cultivars “Dongzao” and “linyilizao”, was constructed using random amplified polymorphic DNA (RAPD) makers [10]. In this case, the use of RAPD markers led to a separate map for each of the parents, which had unequal number of linkage groups. Due to the lack of genomic information, the number of available markers in the map was small and the repeatability was poor. The inability to distinguish between homo- and heterozygosity meant this method was unable to capture complete genetic information in jujube. Later studies used codominant AFLP and SSR markers for genetic map construction [11,12,13], but the resulting map density was too limited for fine mapping of traits of interest in jujube.
With the availability of draft jujube genome assemblies and genome resequencing data based on next-generation sequencing platforms [14], a genetic linkage map of jujube with the most comprehensive genome coverage, the largest number of markers, and the largest marker density has been constructed [15,16]. The markers can improve genetic resolution to fine map qualitative traits and facilitate map-based gene cloning in jujube [17]. However, the tools and approaches required to develop genetic maps from millions of markers from genome resequencing projects are not available, and some data reduction approaches are useful to obtain the most informative markers for further analysis [18].
Bin markers are continuous SNPs occurring at nonrecombining intervals in the genome. They have the advantages of being computationally less intensive and highly accurate, having better density, and being more precisely mapped and cost-effective [19]. In combination with whole-genome sequencing approaches, the bin mapping strategy helps to construct highly dense genome-wide linkage maps by capturing rare recombination events in segregating populations. For example, Peng et al. used a multiomics approach that integrated whole-genome resequencing-based quantitative trait locus (QTL) mapping with an F1 population, population genomics analysis using germplasm accessions, and transcriptome analysis to identify genomic regions that are potentially associated with fruit weight in loquat [20]. Therefore, the bin mapping approach is highly suited to reducing the sequencing datasets by keeping the most informative markers for high-density genetic map construction and QTL identification [18].
Fruit size is an integral part of fruit quality and directly influences the commodity value and economic return of fruit crops. Linkage-map-based identification of molecular markers or genes of interest and molecular-marker-assisted breeding hold enormous promise. Thus, their application is urgently needed to improve the existing cultivars of jujube. Despite the importance of fruit size, its underlying molecular mechanisms remain understudied in jujube. Studies have shown that fruit size traits are controlled by polygenes with weak inheritability [21] and are directly or indirectly regulated by one or more hormones [22,23].
In this study, we report a high-density bin-marker-based genetic map on fruit size in Chinese jujube. The stably inherited major QTLs were screened, and the candidate genes determining fruit size were mined. The results obtained lay a foundation for employing MAS in breeding programs, fine mapping, and cloning of crucial genes in jujube.

2. Materials and Methods

2.1. Test Materials and Phenotypic Determination

Because it is difficult to construct F2 populations in fruit trees, F1 generation is generally used as the material for constructing genetic maps. For example, in apples [24], pears [18,25], grapes [26], and apricots [27], F1 hybrids have been used as materials for mapping. In this study, an F1 segregating population consisting of 165 progenies obtained from a “JMS2” × “Xing16” cross conducted by Prof. Liu Mengjun and team at the Hebei Agricultural University, Hebei, China, was used. They were planted with a spacing of 1 × 3 m in a jujube orchard (80°28′ E, 40°59′ N) located in Aral City, China, in 2018. In 2019, 2020, and 2021, 30 representative fruits with uniform size, no pests and diseases, and normal development at the fully red mature stage were picked from each progeny and each of their two parents as representative samples. SFW, FLD, and FTD were measured according to the method described in the Germplasm Resources of Chinese Jujube [6].

2.2. Extraction of Genomic DNA, Library Construction, and Sequencing

In mid-May 2020, the healthy young leaves of each progeny and the two parents were harvested, cleaned, placed into numbered cryovials, flash-frozen in liquid N, and stored in a –80 °C ultralow temperature laboratory freezer. Total genomic DNA was extracted by the CTAB method and randomly digested into 150 bp long fragments [28]. The library construction involved end repair, the addition of polyA to the 3′ end, ligation of sequencing adapters, purification, and PCR-based amplification. After stringent quality checks, the libraries were paired end to end and sequenced using an HiSeqTM2500 platform (Illumina, San Diego, CA, USA).

2.3. SNP Marker Screening and Genotyping

The raw reads were filtered to obtain clean reads by eliminating the adapters, reads with >10% of bases, and reads of low quality [29]. The cleaned reads were aligned to the jujube reference genome of “Dongzao” [14] using the Burrows–Wheeler Aligner (BWA) software [30]. The read pairing information and flags on the BWA (sequence alignment/map format) records were first cleaned up using the Duplicates tool (Picard: http://sourceforgr.net/projects/picard/ (accessed on 18 September 2020)) to shield the effect of PCR duplication. InDel realignment was performed using GATK [31], that is, the sites near the insertion–deletion alignment results were partially realigned to correct the alignment error caused by the insertion–deletion. Base recalibration was performed using GATK to correct the base mass value. GATK was used for variant calling, including SNP and InDel [32]. Based on the minimum recombination fragment identified in each progeny, the SNP segments that did not undergo recombination in each progeny were combined, thus forming a bin marker. All of the sequences of the bin markers that were used to construct the linkage map were aligned to the physical sequences of the reference genome. In order to ensure the quality of the bin mapping, the genotype homozygosity; parental marker depths of 1×, 2×, and 3×; and markers located on nonchromosomal markers were not considered. High-depth sequencing parents were used to fill in relatively correct genotypes and correct the genotypes of low-depth offspring to ensure the correctness of offspring typing.

2.4. Construction of Genetic Linkage Map

In order to ensure the quality of the map, the markers were filtered and screened using the following methods: (1) remove markers with homozygous parents; (2) ensure parental marker depth is not less than 4×; (3) remove nonchromosomal markers. According to the parental genotyping of the offspring, high-depth parental sequencing ensures the correctness of offspring typing. The linkage group was divided by the chromosome of the marker, and the genetic linkage test was performed on the two markers. The linkage phase was determined according to the recombination rate of markers. Wrong genotypes were corrected using genotypes with relatively definite linkage. After marker filling and correction, the bin was divided according to the recombination of offspring. The samples were arranged neatly according to the physical position of the chromosome. When there was a typing change in any sample, it was considered that there was a recombination breakpoint. The SNP between the recombination breakpoints was classified as bin, and there was no recombination time in bin. Finally, the genetic map was constructed using the bin as a mapping marker. The bin was divided into 15 linkage groups based on known information. The linear arrangement of markers in the linkage group was analyzed by HighMap (http://highmap.biomarker.com.cn (accessed on 24 February 2021)) [33] software, and the genetic distance between adjacent markers was estimated.

2.5. Gene Mapping and Nomenclature of QTLs

The CIM method in R/QTL [34] was used to locate the QTLs related to SFW, FLD, and FTD in the F1 population, and those with an LOD ≥3 were considered to be effective in determining fruit size [35,36]. The QTL determining a specific phenotypic trait was named using the following pattern: abbreviation of the English name of the trait + year + the number of the LG it is mapped to + the code number of QTL, e.g., FW19.1.1 indicates that the trait of SFW identified in 2019 was located in LG1 and belonged to QTL1. If the same trait overlapped in the same location repeatedly over a period of two or more years and demonstrated an LOD ≥ 3.0 and a PVE ≥ 10%, the QTL identified at that location was considered to exist stably. Thus, the genes in these stable QTLs had a higher probability of regulating the fruit size and could be screened to serve as candidate genes for this trait.

2.6. Screening and Annotation of Candidate Genes

The markers within the acceptable confidence interval associated with the stable QTLs described in the previous section were compared with the genome of “Dongzao” as a reference [GCF_000826755.1_ZizJuj_1.1] (https://www.ncbi.nlm.nih.gov/genome/15586?genome_assembly_id=219393 (accessed on 1 February 2021)). Functional annotation and alignment were performed using the relevant sequences obtained from the COG, GO, KEGG, Swissprot, and Nr5 databases. Based on the screening results of the functional annotation studies, the candidate genes not related to the trait of fruit size were excluded but those related to fruit size were identified as putative genes.

2.7. Data Processing and Analysis

The software OriginPro 8.5 (OriginLab, Northampton, MA, USA) was used to draw the frequency distribution histogram of the SFW, FLD, and FTD parameters. SPSS 17.0 (IBM, San Jose, CA, USA) was used to determine if the parameters of fruit size conformed to a pattern of normal distribution. The genetic transmission ability (Ta) of the parents was calculated using the following formula: Ta = the average value of the trait in the hybrid offspring/the average value of the trait in both the parents × 100%.

3. Results

3.1. Identification of SNP Markers Based on Whole-Genome Resequencing Data

A total of 386.5 GB of filtered data were obtained through the whole-genome resequencing (WGRS) of the female parent “JMS2,” the male parent “Xing16”, and their 165 progenies, with 13.93, 9.92, and 362.65 GB of individual data, respectively, with an average Q30 and GC content of 93.21 and 33.80% (Table 1). The sequencing reads of all three groups demonstrated an alignment rate of >90% and average coverage depths of 28×, 19×, and 4.14×, respectively, compared to the genome of the jujube cultivar “Dongzao” used as a reference [14]. The genome coverage was >80% (covering at least 1×) for the two parents and 76.33% (covering at least 1×) for the progeny. The results obtained indicated that the sequenced DNA samples had a low error rate.
A total of 1,569,033 SNPs were detected through a comparative study between the genomes of the parents with a transition/transversion ratio (Ti/Tv) of 1.75 (Table 2). The number of heterozygous and homozygous SNPs in “JMS2” were 615,822 and 953,211, respectively, while those in “Xing16” were 713,815 and 855,218, respectively. A total of 148,738 SNPs with a depth not less than 4× were found suitable for coupling (CP). The parents and their progeny were rigorously screened and filtered for the four genotype-specific markers “nn × np” (n = 41,094), “Im × II” (n = 51,657), “hk × hk” (n = 23,405), and “ef × eg” (n = 156), which accounted for 78.2% of the total number of markers identified. Finally, 116,312 SNPs were retained for constructing bin markers using filtered data by referring to a previously described method [37].

3.2. Construction of a High-Density Genetic Map Using the F1 Segregating Population Obtained from “JMS2” × “Xing16”

Based on the genomic sequence of the jujube cultivar “Dongzao” as a reference (https://www.ncbi.nlm.nih.gov/genome/15586?genome_assembly_id=219393 (accessed on 24 February 2021)), the 116,312 SNPs were combined to form 2398 bin markers (each containing an average of 49 SNP markers) using consecutive SNPs derived from all offspring of the same parent. These bin markers were then used for the molecular mapping of selected genes. A genetic map was constructed with a total map distance of 1074.33 cM containing 12 LGs and an average distance of 0.45 cM between adjacent markers. The 12 LGs ranged in length from 67.43 (LG9) to 124.86 (LG6) cM, the number of bin markers ranged from 135 (LG9) to 263 (LG1) cM, and the average intertag distance ranged from 0.38 (LG1) to 0.52 (LG3) cM. The largest intertag distance was 7.76 cM (LG1), followed by LG2, 3, and 9 (7.06 cM in all). The probability of gaps < 5 cM in length in the 12 LGs ranged from 99.25 to 100%, and the average length of 99.61% of the gaps between consecutive markers was <5 cM. LG6 and 9 were the longest and shortest LGs, respectively (Figure 1), and the largest gap of 82–90 cM was in LG1 (Table 3).

3.3. Phenotypic Analysis of Traits Relating to Fruit Size in the F1 Segregating Population

The values of the three indicators of fruit size in the two parents “JMS2” and “Xing16” and the F1 segregating population derived from them were determined over a period of three years: 2019, 2020, and 2021 (Table 4). The average values of fruit size in “JMS2” were significantly higher than those in “Xing16” in all three years. The transmission of parental genetic information showed the following pattern with regard to importance: FTD > FLD > SFW. The absolute values of skewness and kurtosis in the F1 population were <2, which conformed to a pattern of continuous normal distribution. The results obtained were consistent with the number distribution plot, showing that the three typical quantitative trait characteristics selected were suitable for use in QTL mapping (Figure 2). In addition, three years of phenotypic data showed a significant positive correlation between SFW, FLD, and FTD, indicating that the single fruit weight and the vertical and horizontal diameter can influence each other (Table 5).

3.4. QTL Analysis of Traits Relating to Fruit Size in the F1 Segregating Population Derived from “JMS2” × “Xing16”

Based on the high-density bin marker map described in the previous section and the phenotypic data for SFW, FLD, and FTD obtained over three years, a total of 19 QTLs associated with these three fruit size traits were mapped to seven chromosomes/LGs with a phenotypic interpretation range of 9.5–13.3% (Table 6). The number of QTLs detected on LG2, 4, 6,7, 8, 11, and 12 was 7, 1, 3, 1, 2, 3, and 2, respectively. In 2019 and 2020, QTL FTD2.1 was repeatedly detected on LG2. Its yearly phenotypic interpretation rates were 13.3 and 10.5% and its annual LOD thresholds were 3.08 and 3.35, respectively, due to which it was recognized as the most effective QTL in determining fruit size. All the other QTLs could only be detected in 2019 or 2020. However, the LOD thresholds of FW21.2.2, FLD21.2.1, and FTD21.8.1 were all >3.5 and their contribution rates were >10%, due to which they too were regarded to be equally effective QTLs.
The QTLs were, however, not evenly distributed among all the LGs/chromosomes. Certain LGs contained QTLs influencing multiple indicators of fruit size, with some even clustered in the same regions (Figure 3). There was an overlap between FTD21.1, FW21.1, and FLD21.1 on LG2 with a length of 91.738–92.944 cM, which was linked to all three traits SFW, FLD, and FTD simultaneously. Another overlap zone between FW21.2, FTD21.2, and FLD21.1 was found on LG2 with a length of 94.45–96.259 cM. This region is associated with fruit weight and size. One overlap zone between FW21.1 and FTD21.1 with a length of 28.383 cM on LG12 and another one between FW20.1 and FTD20.1 with a length of 21.382 cM on LG11 were identified with SFW and FTD, respectively. These genetic regions identified with QTLs demonstrating stable effects are worthy of attention in follow-up studies (Table 7).

3.5. Prediction of the Putative Functions of Candidate Genes

The candidate genes influencing fruit size were identified based on the markers mapped within the 15 QTLs and their physical location on the “Dongzao” genome. The genes were mined from five databases, namely, Clusters of Orthologous Groups (COG, [38]), Gene Ontology (GO, http://geneontology.org/ (accessed on 22 July 2021 and 7 July 2022)), Kyoto Encyclopedia of Genes and Genomes (KEGG, Kanehisa Laboratories, Kyoto University), Swissprot (http://www.uniprot.org/ (accessed on 22 July 2021 and 7 July 2022)), and Nr (NCBI), to determine the tag information, markers, and genes in the associated regions. The candidate QTLs that were identified in any two years of the study period concerning at least two of the three indicators concerning fruit size or with LOD threshold ≥3.5 and PVE ≥10% in any one year were selected for gene mining. A total of 727 genes were mined (Table 8).
According to the map positions of the candidate QTLs, 15 candidate genomic regions affecting traits relating to fruit size were identified, the relevant candidate genes were identified, and their functions were predicted in GO and KEGG (Table 9). Consequently, a total of 113 candidate genes possibly associated with the regulation of fruit size were identified. These genes were found to be involved in the processes of cell division, cell cycle, cell wall metabolism, and synthesis of phytohormones (ABA, IAA, and auxin) as well as encoding of enzymes, transcription factors (TFs), and zinc finger proteins (ZFPs). The genes LOC107410242 and LOC107409642 were related to the morphogenesis of anatomical structure and tissue development as well as regulation of gene expression and cellular and developmental processes. The genes LOC107409953, LOC112490770, LOC107409998, LOC107409919, LOC107423923, and LOC107423861 were related to encoding LRR receptor-like serine/threonine protein kinases. LOC107410143 and LOC107423897 were associated with LRR receptor-like proteins and kinases, respectively. LOC107410070 and LOC107423821 were related to the cell cycle. LOC107423912, LOC107423913, LOC107431775, LOC107423905, and LOC107423928 were related to the synthesis of phytohormones such as IAA, auxins, and cytokinins. LOC107409704, LOC107423900, LOC107431772, LOC107423814, LOC107423857, LOC107423922, LOC107423848, LOC107423846, LOC107423858, LOC107423925, LOC107423856, LOC107423903, and LOC107423881 were related to the cell wall formation or metabolic enzyme activity. LOC107423820 and LOC107423815 were associated with pectin esterase. LOC107423811 was related to the TF BHLH. LOC107409987 was associated with a NAC domain-containing protein. LOC107409426 was related to ZFPs. LOC107423816, LOC107423801, LOC107423813, and LOC107431773 were related to E3 ubiquitin protein ligase. These genes may be involved in the regulation of fruit size in jujube.

4. Discussion

4.1. Advantages of Constructing a High-Density Genetic Linkage Map Based on Bin Markers

In this study, the genomes of “JMS2” and “Xing16” and their F1 progeny (165 in number) were resequenced. The average coverage depth of the parental genomes was >20× with an average genome coverage > 90%, while the average coverage depth of the offspring was 4.14× with an average genome coverage > 76.33%. A total of 2398 recombination bin markers comprising 116,312 SNP markers were mapped onto 12 LGs. The total length of the linkage map was 1074.33 cM with an average bin intermarker distance of 0.45 cM. The map presented in this study identified manifold SNP markers (116,312) in comparison to those in the six genetic linkage maps already available (2540–8158) with the highest sequencing depth. In addition, the genetic map presented used a bin-marker-based mapping to classify consecutive SNP markers that did not undergo recombination as bins, thus avoiding the probability of errors occurring in the SNP calculation due to the detection of numerous loci and collection of a large amount of data. Bin markers have been used to develop high-density genetic maps in many crops, such as radish [39], brassica napus [40], and melon [41], but not so much on fruit trees, such as hawthorn [42], pear [18], and grapes [26,43]. Our study found the highest number of SNP markers that had shorter intermarker gaps, were of high quality, and demonstrated enhanced precision in jujube. This will be more conducive to determining the location of trait candidate gene segments.

4.2. Mapping of QTLs Associated with Traits Relating to Fruit Size in Jujube

The identification of QTLs affecting fruit size traits in jujube has been less studied when compared to other fruit tree species. In this study, 15 QTLs associated with fruit size traits were mapped, and candidate QTLs that were identified through molecular analyses in at least two years of a three-year study, related to at least two of the three indicators of fruit size (SFW, FLD, and FTD), or had an LOD threshold ≥ 3.5 and a PVE ≥10% in any one year were selected for gene mining. A total of 113 genes were identified to be located on LG2, 8, and 12. Previous studies have also mapped QTLs related to jujube fruit size, but the results were inconsistent [44,45]. This may be due to differences in groups, environment, and other aspects. Although the location of the QTL varies greatly, we can analyze the function of these 113 genes in other species and may find some candidate genes related to fruit development, laying the foundation for jujube breeding (Table 9).

4.3. In Silico Prediction of the Putative Functions of the Candidate Genes Determining

Fruit Size

Rapid cell division, cell elongation, and duration of the cell cycle determine the final size, shape, and weight of the fruit. The family of serine/threonine protein kinases and cell cycle proteins together with phosphorylated compounds act at two checkpoints to initiate DNA replication and mitosis to regulate the cell cycle [46]. During the growth and development of pear fruits, pectin esterase is associated with the relaxation and extension of the cell wall pulp, which may lead to an increase in the number of cells. Similarly, cellulase facilitates cell division when required while regulating their growth and development [47]. Cell wall biogenesis also plays an important role in cell expansion and unidirectional elongation [48]. A homolog of an Arabidopsis ubiquitin-specific protease, ZjDA3, was found to be a negative regulator of fruit size in jujube [49]. In addition, the MYB TFs are also known to demonstrate regulatory effects on fruit development. The R2R3-MYB TF was found to alter the size and shape of cells in the fruits and leaves of tomatoes [50].
Plant hormones directly regulate the fruit size and shape growth and development by altering the expression of early response genes in horticultural plants. The high auxin content depressed the expression of MdAux/IAA2, and the downregulated expression of MdAux/IAA2 led to the formation of a large fruit size apple [22]. An important role for auxin in the regulation of fruit development, especially at the fruit enlargement stage, and three single nucleotide polymorphism (SNP) markers were also closely associated with fruit weight in loquat [20]. Studies on the direct regulation of fruit size and shape by hormones are more common in tomato and cucumber. ABA participates in the CsTRM5-mediated cell expansion during fruit elongation [51], and the auxin-responsive protein CsARP1 promotes cell expansion and fruit elongation in cucumber [23]. In a study of strawberry fruit shape, different expression genes were mainly enriched in DNA replication, cell cycle, plant hormone synthesis, and signal transduction, including auxin-related genes in elongated strawberry fruit [52]. Six candidate genes for fruit size were found to be involved in the regulation of the cell cycle and hormone biosynthesis pathways, including LOC107404981 and LOC107406728, which may be involved in the molecular regulation of fruit size in jujube [53]. These studies have shown that fruit size and shape are inseparable from plant hormones and can provide certain reference value for further research. In this study, we identified 113 candidate genes regulating fruit size in jujube. These genes were determined to be involved in the regulation of cell division, cell cycle, and cell wall metabolism; biosynthesis of phytohormones (ABA, IAA, and auxin); and encoding of enzymes, transcription factors, and ZFPs.
In conclusion, we carried out high-density genetic bin mapping for the identification of reliable QTLs and candidate genes in a single F1 hybrid population in jujube. These results provide a foundation for further research on fruit size in jujube and also provide a theoretical basis for molecular breeding of new jujube varieties.

Author Contributions

C.W. and M.L. conceived and designed the experiments. F.Y. provided support with experimental materials. Q.Q., T.G., Z.W. and J.W. analyzed the data. T.G., J.B., Z.Y. and Y.X. were involved in the experiment and provided technical and theoretical support for this work. Q.Q. and T.G. wrote the paper. C.W. and M.L. revised the intellectual content of this paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Major Scientific and Technological Projects of Xinjiang Production and Construction Corps (2017DB006), Innovation and Entrepreneurship Platform and Base Construction Project of Xinjiang Production and Construction Corps (2019CB001), and earmarked fund of Xinjiang Jujube Industrial Technology System (XJCYTX-01). We wish to express our thanks for their financial support.

Data Availability Statement

The data presented in the study are deposited in the NCBI repository, accession number is PRJNA996341 (https://www.ncbi.nlm.nih.gov/sra/PRJNA996341).

Acknowledgments

The fruit of jujube cultivars “JMS2” × “Xing16” and their F1 segregating population consisting of 165 progenies were obtained from Hebei Agricultural University, Hebei, China.

Conflicts of Interest

The authors declare no competing interest.

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Horticulturae 09 00836 g001 550
Figure 1. The high-density genetic linkage map. Distribution of bin markers on 12 LGs. A black bar indicates a bin marker. The LG number is shown on the x-axis, and the genetic distance in cM is shown on the y-axis.
Figure 1. The high-density genetic linkage map. Distribution of bin markers on 12 LGs. A black bar indicates a bin marker. The LG number is shown on the x-axis, and the genetic distance in cM is shown on the y-axis.
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Figure 2. Frequency distribution of fruit size traits in the parents and the F1 population over a three-year duration. Frequency distributions of SFW, FLD, and FTD in progenies of the “JMS2” and “Xing16” hybrid cross. The phenotypic data were collected in (A) 2019, (B) 2020, and (C) 2021. The vertical coordinates correspond to the columns in which “JMS2” and “Xing16” are located in the figure and indicate the range of the phenotypic values of SFW, FLD, and FTD in the parents. The horizontal coordinates indicate the number of progenies occurring within the range of phenotypic values.
Figure 2. Frequency distribution of fruit size traits in the parents and the F1 population over a three-year duration. Frequency distributions of SFW, FLD, and FTD in progenies of the “JMS2” and “Xing16” hybrid cross. The phenotypic data were collected in (A) 2019, (B) 2020, and (C) 2021. The vertical coordinates correspond to the columns in which “JMS2” and “Xing16” are located in the figure and indicate the range of the phenotypic values of SFW, FLD, and FTD in the parents. The horizontal coordinates indicate the number of progenies occurring within the range of phenotypic values.
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Figure 3. The QTLs distribution map of fruit size-related traits. The CIM method in R/qtl was used to locate the QTLs regarding SFW, FLD, and FTD in the F1 population, and loci with LOD ≥3 were considered to be effective. The QTL determining a specific phenotypic trait was named using the following pattern: abbreviation of the English name of the trait + year + the number of the LG it is mapped to + the code number of the QTL. The letters LG on the top of the linkage maps represent “linkage group”, and the number following LG indicates the number of the linkage group. The QTLs for SFW, FLD, and FTD are marked with red, pink, and blue colored bars, respectively.
Figure 3. The QTLs distribution map of fruit size-related traits. The CIM method in R/qtl was used to locate the QTLs regarding SFW, FLD, and FTD in the F1 population, and loci with LOD ≥3 were considered to be effective. The QTL determining a specific phenotypic trait was named using the following pattern: abbreviation of the English name of the trait + year + the number of the LG it is mapped to + the code number of the QTL. The letters LG on the top of the linkage maps represent “linkage group”, and the number following LG indicates the number of the linkage group. The QTLs for SFW, FLD, and FTD are marked with red, pink, and blue colored bars, respectively.
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Table
Table 1. Resequencing data statistics of “JMS2” × “Xing16” F1 segregating population.
Table 1. Resequencing data statistics of “JMS2” × “Xing16” F1 segregating population.
SampleTotal Clean Bases (Gb)Q30 Proportion (%)GC Proportion (%)Average Sequencing Depth (×)Mapped (%)Coverage Ratio (%)
Female parent13.9393.1933.7128×97.9187.31
Male parent9.9293.6133.7419×97.8486.24
Offspring362.6592.8233.964.14×97.0476.33
Table
Table 2. Statistics of SNPs obtained from “JMS2” and “Xing16” detection.
Table 2. Statistics of SNPs obtained from “JMS2” and “Xing16” detection.
ParentsSNP NumberTransition NumberTransverison NumberTi/Tv RatioHeterozygous SNP NumberHomozygous SNP Number
Female parent1,569,033998,634570,3991.75615,822953,211
Male parent1,569,033998,634570,3991.75713,815855,218
Table
Table 3. Distribution of bin markers on the high-density genetic map constructed.
Table 3. Distribution of bin markers on the high-density genetic map constructed.
Linkage GroupNumber of
Bin Markers		Number of
SNP Markers		Genetic Length (cM)Average Distance (cM)Max Gap (cM)Gap < 5 cM (%)
LG126316,20399.450.387.7699.62
LG221110,63896.260.467.0699.52
LG3188980298.360.527.0699.47
LG422810,71692.540.414.05100.00
LG518211,39077.140.422.46100.00
LG62499032124.860.505.3799.60
LG7165987867.460.411.83100.00
LG8221820596.890.445.7099.55
LG9135936667.430.507.0699.25
LG10206722899.120.486.3899.51
LG11151694871.710.475.0499.33
LG12199690683.110.425.0499.49
Totals2398116,3121074.33---
Overall average---0.45-99.61
Max----7.76-
Table
Table 4. Statistics of fruit size traits in two parents and their F1 population over three years.
Table 4. Statistics of fruit size traits in two parents and their F1 population over three years.
TraitsYearParentsF1 population
JMS2Xing16RangeMean ± SDTa %SkewnessKurtosis
Single fruit weight (g)201911.002.761.02–8.454.30 ± 1.3862.520.2000.287
202010.502.811.46–13.415.47 ± 1.9982.220.9891.817
20217.822.521.67–9.014.45 ± 1.3886.090.5740.557
Fruit longitudinal diameter (mm)201934.4717.7615.04–27.1920.87 ± 2.6479.920.020−0.509
202035.0418.4914.32–32.4922.33 ± 3.1883.440.2000.108
202132.5817.4514.88–31.3421.40 ± 2.7185.540.5661.205
Fruit transverse diameter (mm)201925.5816.1612.27–23.5719.22 ± 2.4492.10−0.5120.111
202024.8816.5613.60–28.2020.83 ± 2.74100.530.1460.067
202121.7016.4413.71–24.4919.27 ± 2.24101.040.5661.205
Table
Table 5. Correlation analysis of traits relating to fruit size in the F1 segregating population.
Table 5. Correlation analysis of traits relating to fruit size in the F1 segregating population.
YearFW vs. FLDFW vs. FTDFLD vs. FTD
20190.8360 **0.9280 **0.7512 **
20200.8771 **0.9424 **0.7972 **
20210.8291 **0.9370 **0.6832 **
Double asterisks (**) indicated extremely significant correlation at p < 0.01 level; SFW stands for Single fruit weight; FLD stands for Fruit longitudina diameter; FTD stands for Fruit transverse diameter.
Table
Table 6. QTL mapping of traits relating to fruit size in the F1 segregation population.
Table 6. QTL mapping of traits relating to fruit size in the F1 segregation population.
TraitQTLsIntervals on Maps (cM)LODPeak Marker Position (cM)PVE (%)Containing Markers
Single fruit weightFW20.11.120.475–21.3823.1520.47510.72
FW21.2.191.738–92.9443.4492.64410.83
FW21.2.294.45–96.2593.6394.4511.34
FW21.12.128.3833.0228.3839.51
Fruit transverse diameterFTD19.2.191.437–91.7383.0891.43713.32
FTD20.4.118.968–23.9213.2723.0211.15
FTD20.6.1111.2263.02111.22610.31
FTD20.6.2113.029–113.933.15113.3310.74
FTD20.6.3122.753.2122.7510.91
FTD20.11.121.3823.0421.38210.31
FTD20.11.247.29–47.8913.0447.89110.43
FTD21.2.191.738–92.9443.3592.64410.53
FTD21.2.294.453.0794.459.71
FTD21.2.395.352–96.2593.1595.3529.92
FTD21.8.164.38–86.6634.5171.89113.918
FTD21.8.292.0853.0092.0859.51
FTD21.12.127.783–28.6833.4728.38310.94
Fruit longitudinal diameter FLD21.2.191.137–96.2593.9194.4512.111
FLD21.7.138.567–39.4683.1038.8679.74
Table
Table 7. QTL cluster information of traits relating to fruit size.
Table 7. QTL cluster information of traits relating to fruit size.
TraitsCorresponding QTLCoincidence
Interval (cM)		Marker NumberLG
Fruit transverse diameter, Single fruit weight, Fruit longitudinal diameterFTD21.1, FW21.1, FLD21.191.738–92.94432
Single fruit weight, Fruit transverse diameter, Fruit longitudinal diameterFW21.2, FTD21.2, FLD21.194.450–96.25942
Fruit transverse diameter, Single fruit weightFTD21.1, FW21.128.383112
Fruit transverse diameter, Single fruit weightFTD20.1, FW20.121.382111
Table
Table 8. Genes in the associated regions by comparing the databases.
Table 8. Genes in the associated regions by comparing the databases.
LGCoincidence Interval (cM)QTL LlociGene NumberCOG
Anno		GO
Anno		KEGG
Anno		Swissprot
Anno		Nr Anno
291.738FTD19.1934599
FTD21.119010101419
291.738–92.944FTD21.119010101419
FW21.119010101419
FLD21.1993360407199
294.450–96.259FW21.2612136214761
FTD21.2411224163241
FLD21.1993360407199
1228.383FTD21.1301225142830
FW21.1323223
1121.382FTD20.1000000
FW20.1101011
294.45–96.259FW21.2612136214761
291.137–96.259FLD21.1993360407199
864.38–86.663FTD21.11675511168125167
Total--727225450297546727
Table
Table 9. The genes putatively associated with jujube fruit development.
Table 9. The genes putatively associated with jujube fruit development.
Range (cM)QTL NameCandidate GenesCandidate Gene IDGene Annotation
91.738FTD19.2.1---
FTD21.2.1LOC107410242rna-XM_025070976.1anatomical structure morphogenesis;tissue development; regulation of gene expression; cellular process; developmental process; regulation of cellular process
LOC107409642rna-XM_016017070.2regulation of cellular process
91.738–92.944FW21.2.1LOC107410242rna-XM_025070976.1anatomical structure morphogenesis; tissue development; regulation of gene expression; cellular process; developmental process; regulation of cellular process
LOC107409642rna-XM_016017070.2regulation of cellular process
FLD21.2.1LOC107409704rna-XM_016017148.2Cell wall
LOC107409953rna-XM_016017375.2LRR receptor-like serine/threonine-protein At3g47570
LOC112490770rna-XM_025071274.1LRR receptor-like serine/threonine-protein EFR
LOC107410143rna-XM_016017551.2Leucine-rich repeat-containing protein
LOC107410070rna-XM_016017481.1regulation of mitotic cell cycle
LOC107409704rna-XM_016017134.2Cell wall
LOC107409998rna-XM_016017417.2LRR receptor-like serine/threonine-protein EFR
LOC107410070rna-XM_025070726.1regulation of mitotic cell cycle
LOC107410070rna-XM_016017502.2regulation of mitotic cell cycle
LOC107410242rna-XM_025070976.1anatomical structure morphogenesis; tissue development; regulation of gene expression; cellular process; developmental process; regulation of cellular process
LOC107409704rna-XM_016017140.2Cell wall
LOC107409426rna-XM_016016863.2Zinc finger protein
LOC107410070rna-XM_016017488.1regulation of mitotic cell cycle
LOC107409919rna-XM_016017343.2LRR receptor-like serine/threonine-protein At3g47570
LOC107409987rna-XM_016017405.2NAC domain-containing protein
LOC107410070rna-XM_025070727.1regulation of mitotic cell cycle
LOC107410143rna-XM_016017558.2Leucine-rich repeat-containing protein
LOC107409642rna-XM_016017070.2regulation of cellular process
94.450–96.259FW21.2.2LOC107409953rna-XM_016017375.2LRR receptor-like serine/threonine-protein At3g47570
LOC107409987rna-XM_016017405.2NAC domain-containing protein
LOC112490770rna-XM_025071274.1LRR receptor-like serine/threonine-protein EFR
LOC107409919rna-XM_016017343.2LRR receptor-like serine/threonine-protein At3g47570
LOC107409426rna-XM_016016863.2Zinc finger protein
LOC107410143rna-XM_016017551.2Leucine-rich repeat-containing protein
LOC107409998rna-XM_016017417.2LRR receptor-like serine/threonine-protein EFR
LOC107410143rna-XM_016017558.2Leucine-rich repeat-containing protein
FTD21.2.2LOC107409426rna-XM_016016863.2Zinc finger protein
LOC107410143rna-XM_016017551.2Leucine-rich repeat-containing protein
LOC107409953rna-XM_016017375.2LRR receptor-like serine/threonine-protein At3g47570
LOC107410143rna-XM_016017558.2Leucine-rich repeat-containing protein
FLD21.2.1LOC107409704rna-XM_016017148.2Cell wall
LOC107409953rna-XM_016017375.2LRR receptor-like serine/threonine-protein At3g47570
LOC112490770rna-XM_025071274.1LRR receptor-like serine/threonine-protein EFR
LOC107410143rna-XM_016017551.2Leucine-rich repeat-containing protein
LOC107410070rna-XM_016017481.1regulation of mitotic cell cycle
LOC107409704rna-XM_016017134.2Cell wall
LOC107409998rna-XM_016017417.2LRR receptor-like serine/threonine-protein EFR
LOC107410070rna-XM_025070726.1regulation of mitotic cell cycle
LOC107410070rna-XM_016017502.2regulation of mitotic cell cycle
LOC107410242rna-XM_025070976.1anatomical structure morphogenesis; tissue development; regulation of gene expression; cellular process; developmental process; regulation of cellular process
LOC107409704rna-XM_016017140.2Cell wall
LOC107409426rna-XM_016016863.2Zinc finger protein
LOC107410070rna-XM_016017488.1regulation of mitotic cell cycle
LOC107409919rna-XM_016017343.2LRR receptor-like serine/threonine-protein At3g47570
LOC107409987rna-XM_016017405.2NAC domain-containing protein
LOC107410070rna-XM_025070727.1regulation of mitotic cell cycle
LOC107410143rna-XM_016017558.2Leucine-rich repeat-containing protein
LOC107409642rna-XM_016017070.2regulation of cellular process
28.383FTD21.12.1LOC107431773rna-XM_016042763.2E3 ubiquitin-protein ligase
LOC107431775rna-XM_025079541.1Cindole-3-acetic acid amido synthetase activity
LOC107431773rna-XM_016042765.2E3 ubiquitin-protein ligase
LOC107431773rna-XM_025066560.1E3 ubiquitin-protein ligase
LOC107431775rna-XM_016042766.2indole-3-acetic acid amido synthetase activity
LOC107431773rna-XM_025066561.1E3 ubiquitin-protein ligase
LOC107431772rna-XM_016042762.2plant-type secondary cell wall biogenesis
LOC107431773rna-XM_016042764.2E3 ubiquitin-protein ligase
FW21.12.1---
94.450–96.259FW21.2.2LOC107409953rna-XM_016017375.2LRR receptor-like serine/threonine-protein At3g47570
LOC112490770rna-XM_025071274.1LRR receptor-like serine/threonine-protein EFR
LOC107409919rna-XM_016017343.2LRR receptor-like serine/threonine-protein At3g47570
LOC107410143rna-XM_016017551.2Leucine-rich repeat-containing protein
LOC107409998rna-XM_016017417.2LRR receptor-like serine/threonine-protein EFR
LOC107410143rna-XM_016017558.2Leucine-rich repeat-containing protein
91.137–96.259FLD21.2.1LOC107409704rna-XM_016017148.2Cell wall
LOC107409953rna-XM_016017375.2LRR receptor-like serine/threonine-protein At3g47570
LOC112490770rna-XM_025071274.1LRR receptor-like serine/threonine-protein EFR
LOC107410143rna-XM_016017551.2Leucine-rich repeat-containing protein
LOC107410070rna-XM_016017481.1regulation of mitotic cell cycle
LOC107409704rna-XM_016017134.2Cell wall
LOC107409998rna-XM_016017417.2LRR receptor-like serine/threonine-protein EFR
LOC107410070rna-XM_025070726.1regulation of mitotic cell cycle
LOC107410070rna-XM_016017502.2regulation of mitotic cell cycle
LOC107410242rna-XM_025070976.1anatomical structure morphogenesis; tissue development; regulation of gene expression; cellular process; developmental process; regulation of cellular process
LOC107409704rna-XM_016017140.2Cell wall
LOC107409426rna-XM_016016863.2Zinc finger protein
LOC107410070rna-XM_016017488.1regulation of mitotic cell cycle
LOC107409919rna-XM_016017343.2LRR receptor-like serine/threonine-protein At3g47570
LOC107410070rna-XM_025070727.1regulation of mitotic cell cycle
LOC107410143rna-XM_016017558.2Leucine-rich repeat-containing protein
64.380–86.663FTD21.8.1LOC107423900rna-XM_016033552.2cell wall; auxin-activated signaling pathway
LOC107423928rna-XM_025076890.1cytokinin metabolic process
LOC107423913rna-XM_016033565.2indoleacetic acid biosynthetic process; response to auxin
LOC107423813rna-XM_016033450.2E3 ubiquitin-protein ligase
LOC107423814rna-XM_016033451.1cell wall; cell wall modification
LOC107423928rna-XM_025076888.1cytokinin metabolic process
LOC107423861rna-XM_016033508.2protein serine/threonine kinase activity
LOC107423912rna-XM_016033564.2indoleacetic acid biosynthetic process; response to auxin
LOC107423811rna-XM_016033446.2Transcription factor bHLH
LOC107423821rna-XM_025076730.1mitotic cell cycle; regulation of cell cycle
LOC107423857rna-XM_016033503.2cell wall
LOC107423801rna-XM_016033440.1E3 ubiquitin-protein ligase
LOC107423922rna-XM_016033576.2plant-type cell wall biogenesis; regulation of meristem growth
LOC107423928rna-XM_016033582.2cytokinin metabolic process
LOC107423848rna-XM_016033491.2cell wall; Pectin methylesterase
LOC107423928rna-XM_025076887.1cytokinin metabolic process
LOC107423928rna-XM_025076889.1cytokinin metabolic process
LOC107423846rna-XM_016033490.2cell wall; pectinesterase activity; cell wall modification; pectin catabolic process
LOC107423858rna-XM_025076143.1cell wall
LOC107423925rna-XM_016033579.2cell wall
LOC107423923rna-XM_016033577.2protein serine/threonine kinase activity
LOC107423815rna-XM_025076224.1putative pectinesterase/pectinesterase inhibitor 45-like
LOC107423897rna-XM_016033547.2Leucine-rich repeat receptor-like protein kinase
LOC107423905rna-XM_016033556.2response to auxin
LOC107423856rna-XM_025076290.1cell wall
LOC107423846rna-XM_016033489.2cell wall; Pectinesterase PPE8B
LOC107423903rna-XM_016033555.1cell wall
LOC107423821rna-XM_016033457.2mitotic cell cycle; meiotic cell cycle; regulation of cell cycle
LOC107423816rna-XM_016033453.2E3 ubiquitin-protein ligase
LOC107423881rna-XM_016033528.2Cell wall
LOC107423820rna-XM_016033456.2Pectinesterase 54
21.382FTD20.11.1---
FW20.11.1---
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MDPI and ACS Style

Guo, T.; Qiu, Q.; Yan, F.; Wang, Z.; Bao, J.; Yang, Z.; Xia, Y.; Wang, J.; Wu, C.; Liu, M. Construction of a High-Density Genetic Linkage Map Based on Bin Markers and Mapping of QTLs Associated with Fruit Size in Jujube (Ziziphus jujuba Mill.). Horticulturae 2023, 9, 836. https://doi.org/10.3390/horticulturae9070836

AMA Style

Guo T, Qiu Q, Yan F, Wang Z, Bao J, Yang Z, Xia Y, Wang J, Wu C, Liu M. Construction of a High-Density Genetic Linkage Map Based on Bin Markers and Mapping of QTLs Associated with Fruit Size in Jujube (Ziziphus jujuba Mill.). Horticulturae. 2023; 9(7):836. https://doi.org/10.3390/horticulturae9070836

Chicago/Turabian Style

Guo, Tianfa, Qianqian Qiu, Fenfen Yan, Zhongtang Wang, Jingkai Bao, Zhi Yang, Yilei Xia, Jiurui Wang, Cuiyun Wu, and Mengjun Liu. 2023. "Construction of a High-Density Genetic Linkage Map Based on Bin Markers and Mapping of QTLs Associated with Fruit Size in Jujube (Ziziphus jujuba Mill.)" Horticulturae 9, no. 7: 836. https://doi.org/10.3390/horticulturae9070836

APA Style

Guo, T., Qiu, Q., Yan, F., Wang, Z., Bao, J., Yang, Z., Xia, Y., Wang, J., Wu, C., & Liu, M. (2023). Construction of a High-Density Genetic Linkage Map Based on Bin Markers and Mapping of QTLs Associated with Fruit Size in Jujube (Ziziphus jujuba Mill.). Horticulturae, 9(7), 836. https://doi.org/10.3390/horticulturae9070836

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