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QTL mapping for plant height and fruit branch number based on RIL population of upland cotton

Journal of Cotton Research volume 3, Article number: 5 (2020) Cite this article

Abstract

Background

Plant height (PH) and fruit branch number (FBN) are important traits for improving yield and mechanical harvesting of cotton. In order to identify genes of PH and FBN in cotton germplasms to develop superior cultivars, quantitative trait loci (QTLs) for these traits were detected based on the phenotypic evaluation data in nine environments across four locations and 4 years and a previously reported genetic linkage map of an recombinant inbred line (RIL) population of upland cotton.

Results

In total, 53 QTLs of PH and FBN, were identified on 21 chromosomes of the cotton genome except chromosomes c02, c09-c11, and c22. For PH, 27 QTLs explaining 3.81%–8.54% proportions of phenotypic variance were identified on 18 chromosomes except c02, c08-c12, c15, and c22. For FBN, 26 QTLs explaining 3.23%–11.00% proportions of phenotypic variance were identified on 16 chromosomes except c02-c03, c06, c09-c11, c17, c22-c23, and c25. Eight QTLs were simultaneously identified in at least two environments. Three QTL clusters containing seven QTLs were identified on three chromosomes (c01, c18 and c21). Eleven QTLs were the same as previously reported ones, while the rest were newly identified.

Conclusions

The QTLs and QTL clusters identified in the current study will be helpful to further understand the genetic mechanism of PH and FBN development of cotton and will enhance the development of excellent cultivars for mechanical managements in cotton production.

Introduction

Agronomic traits, especially plant morphological attributes such as PH, FBN, height of the node of first fruiting branch, and angle between stem and fruiting branch, play a decisive role in the architectural construction of crops, which impact agricultural practices, including reasonable increases in planting density and mechanical managements of crops (Mei et al. 2016; Shang et al. 2016). Among them, PH and FBN are important plant morphological attributes, which have a certain impact on the formation of yield (Ge et al. 2012; Hussain et al. 2000; Li et al. 2010; Tang et al. 2009). In rice, a point mutation in OsSPL14 perturbs OsmiR156-directed regulation of OsSPL14, generating an ideal plant with a reduced tiller number, increased lodging resistance and enhanced grain yield (Jiao et al. 2010; Miura et al. 2010). In maize, a valuable PH gene ZmRPH1 was demonstrated to be useful in molecular breeding to improve PH and lodging resistant traits (Li et al. 2019).

Cotton is an important cash crop and a major source of natural fiber for the textile industry (Paterson et al. 2012). Upland cotton (Gossypium hirsutum L.) is planted worldwide because of its high yield and good fiber quality (Chen et al. 2007; Huang et al. 2017). PH is an important component of ideal plant architecture and plays an important role in cotton breeding (Jiao et al. 2010; Ma et al. 2019b; Miura et al. 2010; Wang et al. 2018). Studies demonstrated that PH and FBN had important effects on cotton yield and mechanical harvesting (Su et al. 2018; Ma et al. 2019b), but it is still necessary for researchers to understand the genetic basis of PH and FBN and how they impact plant architecture (Qi et al. 2017; Shang et al. 2016; Song and Zhang 2009; Wang et al. 2006; Zhang et al. 2006). Therefore, further study on these agronomic traits will be of great significance for cotton plant-type breeding and the application and distribution of mechanical harvesting technologies in cotton production.

The genetic linkage maps have been used to detect quantitative trait locus (QTL) for cotton fiber quality, yield and various agronomic traits, which is of great significance for both marker-assisted selection as well as functional studies of candidate genes (Ma et al. 2019a; Zhang et al. 2016). However, the disadvantages of previous genetic maps, such as low marker density, asymmetric distribution of mapped markers, and unavailability of reference genomes for upland cotton, hindered the above-mentioned applications of the QTL detection results (Deschamps et al. 2012; Jamshed et al. 2016; Yang et al. 2015). Due to the rapid development of high-throughput sequencing technologies, the reduction of sequencing cost, and the establishment of the reference genome of upland cotton (TM-1), a number of high-density genetic maps have been constructed by single nucleotide polymorphism (SNP) markers including genotyping by sequencing (GBS) (Diouf et al. 2018; Qi et al. 2017), restriction-site associated DNA sequencing (RAD-Seq) (Hegarty et al. 2013; Kundu et al. 2015; Wang et al. 2017), specific locus-amplified fragment sequencing (SLAF-seq) (Ali et al. 2018; Zhang et al. 2016), CottonSNP63K array (Hulse-Kemp et al. 2015; Li et al. 2016; Li et al. 2018a; Zhang et al. 2016), and CottonSNP80K array (Cai et al. 2017; Tan et al. 2018; Liu et al. 2018; Zou et al. 2018). These high-density genetic maps significantly improved QTL detection accuracy (Ma et al. 2019a; Su et al. 2018; Jia et al. 2016).

This study was based on a previously constructed high-density genetic map through chip-SNP genotyping (cottonSNP80K array) (Cai et al., 2017; Liu et al., 2018). The field phenotypes of PH and FBN were evaluated and analyzed across multiple environments, and their QTLs were detected. Our results will be helpful to further understand the genetic mechanism of these important agronomic traits and lay a promising foundation for developing excellent cultivars to meet the challenges of mechanical harvesting technologies in the future.

Materials and methods

Experimental materials and field management

A segregation population consisting of 231 F6:8 RIL individuals was developed from an intra-specific cross of G. hirsutum between two homozygous cultivars Lumianyan28 (LMY28) and Xinluzao24 (XLZ24). The attributes of the two parental lines and the development procedures of the population were previously described (Liu et al. 2018). Briefly, the cross was made in an experimental farm at the Institute of Cotton Research of Chinese Academy of Agricultural Sciences in Anyang in 2008. Then, the RIL population was developed via multiple cycles of selfing, and a random single plant selection was made the F6 generation to form F6:8 seeds. F6:8 and beyond generations were regarded as RILs. From 2013 to 2016, phenotypes of the target traits of the RILs were evaluated in three different locations throughout China with a randomized complete block design in two biological replications in each environment.

Phenotyping

The phenotypes of PH and FBN were evaluated throughout a four-year-three-location experiment arrangement, composed from a total of six environments (Table 1). PH was usually evaluated from the cotyledonary node to the apex of the stem. In the experiment locations of this study, removing the stem apex was a normal practice in cotton production for plant architectural control. According to local practices, the stem apex was pinched off manually (in Anyang and Quzhou) or with chemicals (in Kuerle) in July, and PH was evaluated in September before harvest. PH was measured immediately from the soil surface to the pinching point of the plant. FBN was the number of effective branches on which mature bolls set. These phenotype data across multiple environments were collected and analyzed with SPSS21.0 software. The heritability of PH and FBN across environments was evaluated by QTLIciMapping software (version 4.1) (Meng et al. 2015; Ma et al. 2019a).

Table 1 Details of seven environments used to evaluate 231 F6:8 RIL individuals and their parents
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QTL mapping

QTLs for the target traits were identified with Windows QTL Cartographer 2.5 software (Wang et al. 2007) with composite interval mapping (CIM) algorithms. The threshold of logarithm of odds (LOD) for a significant QTL declaration was calculated by a 1 000 permutations test and a walking speed of 1.0 cM. QTLs for the same trait identified in different environments were regarded as the same QTL when their confidence intervals were fully or partially overlapped. The QTL identified at least in two environments was declared as a stable one. Nomenclature of QTL was designated following Sun’s description (Sun et al. 2012). MapChart 2.2 (Voorrips 2002) was used to graphically present the QTLs on the genetic map.

The candidate gene annotation

The genes contained in the physical interval of stable QTLs underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses using BMKCloud (www.biocloud.net). The transcriptome sequencing data of root, stem, and leaf of TM-1 (Zhang et al. 2015) were referenced to reveal the expression pattern of candidate genes. The expression heatmap was drawn by TBtools software (Chen et al. 2018).

Result

Evaluation of phenotype performances

We observed that all of the traits showed continuous variations and that a transgressive segregation phenomenon was detected. The values of skewness and kurtosis of all traits in six environments showed that they fit normal distributions (Table 2). The heritabilities of PH and FBN were 0.76 and 0.52, respectively. We also identified significant G × E influences for both PH and FBN (Additional file 1: Table S1).

Table 2 The descriptive statistical analysis of the parents and the recombinant inbred lines (F6:8) population
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QTL mapping the target traits

A total of 53 QTLs for the target traits were identified on 21 chromosomes except c02, c09-c11, and c22, using the composite interval mapping method. These QTLs could explain 3.23%–11.00% of the observed phenotypic variances (PVs) (Additional file 2: Table S2). Among them, eight QTLs were simultaneously identified in at least two environments on c03-c04, c14, c17-c19, and c25, which were regarded as stable ones which could explain 3.29%–8.54% of the total observed PVs (Fig. 1; Table 3).

Fig. 1
figure 1

The stable QTL for PH and FBN were distributed in chromosomes

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Table 3 The stable QTLs for agronomic traits identified by the composite intervalmapping (CIM) in multiple environments
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Plant height

Twenty-seven QTLs for PH were detected, which could explain 3.81%–8.54% of the observed PVs and were distributed on 18 chromosomes except c02, c08-c12, c15, and c22. Six stable QTLs could be simultaneously detected in at least two environments, with an overall explanation of 3.89%–8.54% of the observed PVs, which were identified on c03, c04, c17, c19, and c25. That is, qPH-c03–1, qPH-c04–1, qPH-c04–3, qPH-c17–1, qPH-c19–1, and qPH-c25–1 could explain 4.53%–4.98%, 3.97%–4.11%, 5.43%–6.84%, 3.89%–5.82%, 7.17%–8.54%, and 5.77%–7.11% of the observed PV, respectively.

Fruiting branch number

Twenty-six QTLs for FBN were detected on 16 chromosomes, except c02-c03, c06, c09-c11, c17, c22-c23, and c25, which could explain 3.23%–11.00% of the observed PV. Two stable QTLs on c14 and c18 were simultaneously detected in at least two environments, with an overall explanation of 3.29%–8.49% of the observed PV. That is, qFBN-c14–1 and qFBN-c18–2 could explain 6.23%–8.49% and 3.29%–5.25% of the observed PV, respectively.

QTL clusters

The QTL cluster was defined as a DNA region that harbored at least two QTLs for different traits (Jamshed et al. 2016; Palanga et al. 2017; Said et al. 2013). In the current study, when confidence intervals of QTLs for different traits fully or partially overlapped, we defined these QTLs to form a QTL cluster. Three QTL clusters were formed from 7 out of 53 QTLs for PH and FBN, and the marker intervals of these clusters were less than 20 cM on the genetic map (Said et al. 2013). They were identified on three chromosomes, namely c01, c18, and c21 (Additional file 3: Table S3). The cluster on c21, clu-c21–1, harbored three QTLs, namely, qFBN-c21–3(−), qPH-c21–1(−), and qFBN-c21–4(+), explained 4.64%–7.18% of the observed PV. The cluster on c01, clu-c01–1, harbored two QTLs, namely, qPH-c01–1(+) and qFBN-c01–1(+), explained 5.56%–6.82% of the observed PV. The cluster on c18, clu-c18–1, harbored two QTLs, namely qFBN-c18–2(+) and qPH-c18–1(+), explained 3.29%–6.64% of the observed PV. All of the QTLs in clu-c18–1 showed positive additive effects, in which FBN-c18–2 was a stable QTL identified across three environments.

The gene annotation

In total, 925 and 437 genes in the physical interval of the QTLs for PH and FBN were identified and annotated by Gene Ontology (GO) and Kyoto Encyclopedia and Genomes (KEGG) analysis, respectively. In GO term analysis, the genes of both PH and FBN were mainly assorted into three categories of cellular component, molecular function, and biological process. The genes in the cellular component were further enriched in subcategories of cell part, cell, and organelle. The genes in molecular function were enriched in catalytic activity and binding, and the genes in the biological process were enriched in metabolic process, cellular process, and single-organism process (Fig. 2). When the P-value < 0.05 was used to define the significance of functional enrichment (Additional file 4: Table S4), for PH, a total of 106 genes were enriched in molecular function, in which 22 were found to act with sequence-specific DNA binding transcription factor activities and 11 to have sequence-specific DNA binding functions. Thirteen genes were enriched in cellular components, in which three were found to function in the “proteasome complex” and “proton-transporting ATP synthase complex and catalytic core F(1)”. One hundred forty-five genes were enriched in biological processes, in which 33 genes were found to act in “regulation of transcription, DNA-templated” processes and 10 genes in “lipid metabolic processes”. For FBN, a total of 59 genes were enriched in molecular function, in which 12 and 10 genes were found to act in “nucleic acid binding” and “binding” activities, respectively. Five genes were enriched in cellular components, and 98 genes in biological processes (Additional file 4: Table S4). KEGG pathway analysis revealed that, when a significance level of P-value < 0.05 was used to define the effectiveness of functional enrichment for PH, most possible pathways were “Carbon metabolism” (enriched 16 genes), “Oxidative phosphorylation” (enriched 12 genes), “Glycerolipid metabolism” (enriched 7 genes), and “Glycerophospholipid metabolism” (enriched 7 genes). For FBN, most possible pathways were “Spliceosome” (enriched 6 genes), “Pentose and glucuronate interconversions” (enriched 5 genes), and “Glycerolipid metabolism” (enriched 4 genes) (Additional file 5: Table S5).

Fig. 2
figure 2

The GO classification of the genes for PH (a) and FBN (b) in stable QTL

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Discussion

The significance of QTL mapping for agronomic traits

With the continuous reducing of total cotton planting acreages due to the shortage of labor force and the increase of labor cost in production, the full mechanization of cotton production becomes inevitable in the future development in China (Lu et al. 2018). Mechanical managements in the whole growth procedure of cotton in China have not been fully applied in practical productions, probably due to the following reasons. First, there are relatively few excellent cotton varieties suitable for mechanization because mechanical harvesting has certain strict requirements on plant architecture, such as at least a 20-cm node-height of the first fruiting branch above the ground and a plant height of 100–120 cm (Gao et al. 2016). Second, cotton is planted in small acreage of scales. The lack of large batches of planting scales is mainly due to the planting of various alternative crops, including corn and soybean, which have advantages of high degree of mechanization, short growth period, and easy management (Lei et al. 2014). Third, to some extent, mechanical picking partially reduces fiber qualities. Studies indicated that mechanical harvesting might result in a loss of 1–2 mm fiber length and an increasing of impurity rate (Mao et al. 2016; Shi and Zhou 2014). Therefore, it would be of great importance to breed improved cotton varieties suitable for mechanized operations through molecular marker-assisted selections for these important agronomic traits.

Comparison with previous QTLs

Plenty of genetic maps have been constructed, based on which QTLs of target traits were identified in upland cotton. Compared with QTLs identified for fiber quality and yield traits, QTLs for agronomic traits are comparatively less reported (Li et al. 2014; Song and Zhang 2009; Wang et al. 2006; Zhang et al. 2006). Therefore, it is necessary to map QTLs for agronomic traits using high-density genetic maps. In the current study, QTL mapping for agronomic traits is based on a high-density genetic map that covers a total genetic distance of 2 477.99 cM, composing 4 729 SNP markers and 122 SSR markers. Comparing the results of this study with previous common QTLs summarized with meta-analysis (Said et al. 2013), and QTLs identified in recent years (Jia et al. 2016; Su et al. 2018; Zhang et al. 2019a; Zhang et al. 2019b; Ma et al. 2019a), QTLs on c04 for PH and those on c01, c07, c12, c20-c21, c24, and c26 for FBN were all newly identified ones. As the existence of significant G × E interactions, QTLs identified in every environment moved around. Windows QTL Cartographer 2.5 is unable to evaluate the G x E influences. In order to increase QTL mapping accuracy, phenotypic data across multiple environments were evaluated and used to identify the QTL in our study. The stable QTLs that could be detected across multiple environments were probably more reliable, while the environment-specific QTLs revealed the interaction between the G x E influences.

QTL-wise comparisons were also conducted with the physical position of the markers harbored in the QTL confidence intervals. When a QTL for a correspondent trait shared a fully or partially overlapped physical fragment with a previously identified one, it was regarded as a repetitive identification of a common QTL. We found that 9 of the 27 QTLs for PH might be common ones (Additional file 2: Table S2), of which qPH-c03–1, qPH-c17–1, and qPH-c19–1 were stable in the current study. The rest were probably newly discovered QTLs. Two of the 26 QTLs for FBN may be common ones, while the rest were probably newly discovered QTLs. In previous studies, when SSR markers were applied to construct the linkage maps, the QTLs in different studies were usually compared through common markers in their confidence intervals. When the SSR markers were aligned back to the reference genome, their positions in the physical map were very often not unique, possibly misleading the mapping results. However, in current studies, when SNPs were applied to map the QTL, although it was not easy to compare common markers, it was convenient to identify the physical position of the QTL. In recent studies (Su et al. 2018; Zhang et al. 2019a), the physical positions of stable QTLs for PH and FBN traits were clearly shown. When comparing these studies with our current study, the QTLs of qPH-c17–1 and qPH-c19–1 were probably previously identified by Zhang et al. (2019a) and Su et al. (2018), respectively. This alternative comparison of common QTL might provide a promising choice of comparing the QTLs which were identified in different studies.

Candidate gene functioning analysis

Some genes which may play an important role in the growth and development of PH and FBN were identified by functional annotation of homologous genes in Arabidopsis based on GO and KEGG analysis and Arabidopsis annotation information (Additional file 5: Table S5). In stable QTLs of the current study, 723 of 925 genes for PH and 335 of 437 genes for FBN had annotation information (Additional file 6: Table S6). In previous studies, Gh_D03G0922 (MADS-box family gene; AT5G60910) and Gh_D01G1471 (GhPIN3; AT1G70940) were, respectively, annotated as AGAMOUS-like 8 and Auxin efflux carrier family protein in Arabidopsis and were verified to be responsible for PH in cotton (Su et al. 2018; Ma et al. 2019a). OsPIN2 and ZmPIN1a, which were also the PIN gene family members, were verified to have an effect on PH of rice and maize (Chen et al. 2012; Li et al. 2018b). However, in the current study, the gene in qPH-c03–1, Gh_A03G0634 (AT5G60910), was also annotated as AGAMOUS-like 8 in Arabidopsis, and Gh_A03G1052 (AT1G23080), Gh_A03G1053 (AT1G70940), Gh_A03G1054 (AT5G57090), and Gh_A03G1069 (AT1G71090) were annotated as Auxin efflux carrier family proteins in Arabidopsis (Additional file 5: Table S5). An expression heat-map revealed that Gh_A03G1069 and Gh_A04G1054 had a specific expression in stem in TM-1 (Zhang et al. 2015) (Fig. 3). Therefore, these genes could also have a certain role in plant height determination in cotton. Evidence indicated that gibberellin caused a reduction in plant height (Monna et al. 2002; Sakamoto et al. 2004; Braun et al. 2019; Annunziata. 2018). In this study, Gh_A03G0973 (AT4G21200) in qPH-c03–1 and Gh_D03G0239 (AT2G14900) in qPH-c17–1, were respectively annotated as gibberellin 2-oxidase 8 and Gibberellin-regulated family protein genes, which could be involved in gibberellin biosynthesis. Gh_A04G1054 (AT4G34710) in qPH-c04–1 was annotated as an arginine decarboxylase 2 gene, which could be involved in Polyamines biosynthesis (Watson et al. 1998). Gh_D03G0284 (AT4G37760) in qPH-c17–1 was annotated as a squalene epoxidase 3 (SQE3) gene, which may be involved in sterol biosynthesis (Laranjeira et al. 2015). Gh_D13G0612 (AT5G23190) and Gh_D13G0806 (AT2G23180) in qFBN-c18–2 were annotated as cytochrome P450 genes, which may be involved in brassinosteroid (BR) biosynthesis (Wu et al. 2016). Gh_D13G0732 (AT1G68640) in qFBN-c18–2 was annotated as bZIP transcription factor family protein, which may be involved in multiple biological processes in plants (Hu et al. 2016; Lozano-Sotomayor et al. 2016; Yan et al. 2019). In general, these candidate genes for PH and FBN could play an important role in cell elongation, and tissue and organ differentiation and formation in plant development, but their specific functions need to be further verified. The results of this study will not only contribute to promote an understanding of the genetic mechanism of PH and FBN formation of cotton, but also enhance the practical application for plant-type breeding through MAS.

Fig. 3
figure 3

The expression information of the important candidate genes for target traits in TM-1. Note: The data is the original expression data in expression pattern

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Conclusions

In this study, QTLs for PH and FBN were detected, based on the phenotypic evaluations of an intraspecific RIL population of upland cotton across six environments in three locations from 2013 to 2016 and the previously reported (Liu et al. 2018) genetic linkage map of that population. A total of 27 QTLs for PH and 26 QTLs for FBN were identified, in which six for PH and two for FBN were stable QTLs, and seven QTLs formed three QTL clusters. The possible candidate genes behind the QTLs were also identified and annotated. The results could be of great importance to further understand the genetic mechanism of plant type determination of cotton and for pragmatic applications in future breeding programs for cultivar development to meet the challenges of mechanization in cotton production.

Availability of data and materials

The data and materials for supporting the results of this article are included within the article and its supplementary material files.

References

  1. Ali I, Teng Z, Bai Y, et al. A high density SLAF-SNP genetic map and QTL detection for fibre quality traits in Gossypium hirsutum. BMC Genomics. 2018;19(1):879–96. https://doi.org/10.1186/s12864-018-5294-5.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. Annunziata MG. The long and the short of it: GA 2-oxidaseA9 regulates plant height in wheat. Plant Physiol. 2018;177(1):3–4. https://doi.org/10.1104/pp.18.00235.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. Braun E-M, Tsvetkova N, Rotter B, et al. Gene expression profiling and fine mapping identifies a gibberellin 2-oxidase gene co-segregating with the dominant dwarfing gene ddw1 in rye (Secale cereale L.). Front Plant Sci. 2019;10:857–75. https://doi.org/10.3389/fpls.2019.00857.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Cai C, Zhu G, Zhang T, et al. High-density 80 K SNP array is a powerful tool for genotyping G. hirsutum accessions and genome analysis. BMC Genomics. 2017;18(1):654–67. https://doi.org/10.1186/s12864-017-4062-2.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Chen C, Hao R, He Y. TBtools, a toolkit for biologists integrating various HTS-data handling tools with a user-friendly interface. bioRxiv. 2018. https://doi.org/10.1101/289660.

  6. Chen Y, Fan X, Song W, et al. Over-expression of OsPIN2 leads to increased tiller numbers, angle and shorter plant height through suppression of OsLAZY1. Plant Biotechnol J. 2012;10(2):139–49. https://doi.org/10.1111/j.1467-7652.2011.00637.x.

    Article  Google Scholar 

  7. Chen ZJ, Scheffler BE, Dennis E, et al. Toward sequencing cotton (Gossypium) genomes. Plant Physiol. 2007;145(4):1303–10. https://doi.org/10.1104/pp.107.107672.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Deschamps S, Llaca V, May GD. Genotyping-by-sequencing in plants. Biology (Basel). 2012;1(3):460–83. https://doi.org/10.3390/biology1030460.

    Article  Google Scholar 

  9. Diouf L, Magwanga RO, Gong W, et al. QTL mapping of fiber quality and yield-related traits in an intra-specific upland cotton using genotype by sequencing (GBS). Int J Mol Sci. 2018;19(2):441–64. https://doi.org/10.3390/ijms19020441.

    CAS  Article  PubMed Central  Google Scholar 

  10. Gao P, Xia S, Zhao Z, et al. Exploring the suitable cultivation measures for mechanized harvesting of cotton in North Jiangxi. Cotton Sciences. 2016;38(1):42–4,60. https://doi.org/10.3969/j.issn.2095-3143.2016.01.09.

  11. Ge R, Lan M, Shi Y, et al. Correlation and path coefficient analysis of main agronomic characters in BC4F3 and BC4F4 generations from Gossypium hirsutum L. × Gossypium barbadense L. Chin Agric Sci Bull. 2012;28(3):127–30. https://doi.org/10.11924/j.issn.1000-6850.2011-2089.

  12. Hegarty M, Yadav R, Lee M, et al. Genotyping by RAD sequencing enables mapping of fatty acid composition traits in perennial ryegrass (Lolium perenne (L.)). Plant Biotechnol J. 2013;11:572–81. https://doi.org/10.1111/pbi.12045.

    CAS  Article  PubMed  Google Scholar 

  13. Hu W, Yang H, Yan Y, et al. Genome-wide characterization and analysis of bZIP transcription factor gene family related to abiotic stress in cassava. Sci Rep. 2016;6:22783–94. https://doi.org/10.1038/srep22783.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Huang C, Nie X, Shen C, et al. Population structure and genetic basis of the agronomic traits of upland cotton in China revealed by a genome-wide association study using high-density SNPs. Plant Biotechnol J. 2017;15(11):1374–86. https://doi.org/10.1111/pbi.12722.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. Hulse-Kemp AM, Lemm J, Plieske J, et al. Development of a 63K SNP array for cotton and high-density mapping of intraspecific and interspecific populations of Gossypium spp. G3 (Bethesda). 2015;5(6):1187–209. https://doi.org/10.1534/g3.115.018416.

    Article  Google Scholar 

  16. Hussain SS, Azhar FM, Mahmood I. Path coefficient and correlation analysis of some important plant traits of Gossypium hirsutum L. Pak J Biol Sci. 2000;3(9):1399–400. https://doi.org/10.3923/pjbs.2000.1399.1400.

    Article  Google Scholar 

  17. Jamshed M, Jia F, Gong J, et al. Identification of stable quantitative trait loci (QTLs) for fiber quality traits across multiple environments in Gossypium hirsutum recombinant inbred line population. BMC Genomics. 2016;17(1):197–209. https://doi.org/10.1186/s12864-016-2560-2.

  18. Jia X, Pang C, Wei H, et al. High-density linkage map construction and QTL analysis for earliness-related traits in Gossypium hirsutum L. BMC Genomics. 2016;17:909–22. https://doi.org/10.1186/s12864-016-3269-y.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Jiao Y, Wang Y, Xue D, et al. Regulation of OsSPL14 by OsmiR156 defines ideal plant architecture in rice. Nat Genet. 2010;42(6):541–4. https://doi.org/10.1038/ng.591.

    CAS  Article  PubMed  Google Scholar 

  20. Kundu A, Chakraborty A, Mandal NA, et al. A restriction-site-associated DNA (RAD) linkage map, comparative genomics and identification of QTL for histological fibre content coincident with those for retted bast fibre yield and its major components in jute (Corchorus olitorius L., Malvaceae s. l.). Mol Breed. 2015;35(1):19–35. https://doi.org/10.1007/s11032-015-0249-x.

    CAS  Article  Google Scholar 

  21. Laranjeira S, Amorim-Silva V, Esteban A, et al. Arabidopsis squalene epoxidase 3 (SQE3) complements SQE1 and is important for embryo development and bulk squalene epoxidase activity. Mol Plant. 2015;8(7):1090–102. https://doi.org/10.1016/j.molp.2015.02.007.

    CAS  Article  Google Scholar 

  22. Lei Y, Wei X, Liu Z. Present status and outlook of cotton industry development in China. Agric Outlook. 2014;10(9):43–7. https://doi.org/10.3969/j.issn.1673-3908.2014.09.008.

    Article  Google Scholar 

  23. Li C, Dong Y, Zhao T, et al. Genome-wide SNP linkage mapping and QTL analysis for fiber quality and yield traits in the upland cotton recombinant inbred lines population. Front Plant Sci. 2016;7:1356–71. https://doi.org/10.3389/fpls.2016.01356.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Li C, Song L, Zhao H, et al. Quantitative trait loci mapping for plant architecture traits across two upland cotton populations using SSR markers. J Agric Sci. 2014;152(2):275–87. https://doi.org/10.1017/S0021859613000063.

    CAS  Article  Google Scholar 

  25. Li C, Wang Q, Peng W, et al. Relationship between lint yield and main agronomic characters in F2 generation of upland cotton. Guizhou Agric Sci. 2010;38(9):14–6,21. https://doi.org/10.3969/j.issn.1001-3601.2010.09.005.

    Article  Google Scholar 

  26. Li C, Wang Y, Ai N, et al. A genome-wide association study of early-maturation traits in upland cotton based on the CottonSNP80K array. J Integr Plant Biol. 2018a;60(10):970–85. https://doi.org/10.1111/jipb.12673.

    CAS  Article  PubMed  Google Scholar 

  27. Li W, Ge F, Qiang Z, et al. Maize ZmRPH1 encodes a microtubule-associated protein that controls plant and ear height. Plant Biotechnol J. 2019:13292. https://doi.org/10.1111/pbi.13292.

  28. Li Z, Zhang X, Zhao Y, et al. Enhancing auxin accumulation in maize root tips improves root growth and dwarfs plant height. Plant Biotechnol J. 2018b;16(1):86–99. https://doi.org/10.1111/pbi.12751.

    CAS  Article  PubMed  Google Scholar 

  29. Liu R, Gong J, Xiao X, et al. GWAS analysis and QTL identification of fiber quality traits and yield components in upland cotton using enriched high-density SNP markers. Front Plant Sci. 2018;9:1067–71. https://doi.org/10.3389/fpls.2018.01067.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Lozano-Sotomayor P, Chavez Montes RA, Silvestre-Vano M, et al. Altered expression of the bZIP transcription factor DRINK ME affects growth and reproductive development in Arabidopsis thaliana. Plant J. 2016;88(3):437–51. https://doi.org/10.1111/tpj.13264.

    CAS  Article  PubMed  Google Scholar 

  31. Lu X, Jia X, Niu J. The present situation and prospects of cotton industry development in China. Sci Agric Sin. 2018;51(1):26–36. https://doi.org/10.3864/j.issn.0578-1752.2018.01.003.

    Article  Google Scholar 

  32. Ma J, Pei W, Ma Q, et al. QTL analysis and candidate gene identification for plant height in cotton based on an interspecific backcross inbred line population of Gossypium hirsutum × Gossypium barbadense. Theor Appl Genet. 2019a;132(9):2663–76. https://doi.org/10.1007/s00122-019-03380-7.

    CAS  Article  Google Scholar 

  33. Ma J, Tu Y, Zhu J, et al. Flag leaf size and posture of bread wheat: genetic dissection, QTL validation and their relationships with yield-related traits. Theor Appl Genet. 2019b:03458. https://doi.org/10.1007/s00122-019-03458-2.

    Article  Google Scholar 

  34. Mao S, Li Y, Zhi X, et al. Technology advancement of China's cotton cultivation. Agric Outlook. 2016;12(1):57–64. https://doi.org/10.3969/j.issn.1673-3908.2016.01.013.

    Article  Google Scholar 

  35. Mei Y, Yu J, Xue A, et al. Association mapping of genetic network for plant morphological traits in cotton. J Zhejiang Univ. 2016;42(2):127–36. https://doi.org/10.3785/j.issn.1008-9209.2016.01.191.

    Article  Google Scholar 

  36. Meng L, Li H, Zhang L, et al. QTL IciMapping: integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J. 2015;3:269–83. https://doi.org/10.1016/j.cj.2015.01.001.

    Article  Google Scholar 

  37. Miura K, Ikeda M, Matsubara A, et al. OsSPL14 promotes panicle branching and higher grain productivity in rice. Nat Genet. 2010;42:545–9. https://doi.org/10.1038/ng.592.

    CAS  Article  Google Scholar 

  38. Monna L, Kitazawa N, Yoshino R, et al. Positional cloning of rice semidwarfing gene, sd-1: rice “green revolution gene” encodes a mutant enzyme involved in gibberellin synthesis. DNA Res. 2002;9(1):11–7. https://doi.org/10.1093/dnares/9.1.11.

    CAS  Article  PubMed  Google Scholar 

  39. Palanga KK, Jamshed M, Rashid HO, et al. Quantitative trait locus mapping for Werticillium wilt resistance in an upland cotton recombinant inbred line using SNP-based high density genetic map. Fron Plant Sci. 2017;8:382–94. https://doi.org/10.3389/fpls.2017.00382.

  40. Paterson AH, Wendel JF, Gundlach H, et al. Repeated polyploidization of Gossypium genomes and the evolution of spinnable cotton fibres. Nature. 2012;492(7429):423–7. https://doi.org/10.1038/nature11798.

    CAS  Article  PubMed  Google Scholar 

  41. Qi H, Wang N, Qiao W, et al. Construction of a high-density genetic map using genotyping by sequencing (GBS) for quantitative trait loci (QTL) analysis of three plant morphological traits in upland cotton (Gossypium hirsutum L.). Euphytica. 2017;213(4):83–99. https://doi.org/10.1007/s10681-017-1867-7.

    CAS  Article  Google Scholar 

  42. Said J, Lin Z, Zhang X, et al. A comprehensive meta QTL analysis for fiber quality, yield, yield related and morphological traits, drought tolerance, and disease resistance in tetraploid cotton. BMC genomics. 2013;14(1):776. https://doi.org/10.1186/1471-2164-14-776.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. Sakamoto T, Miura K, Itoh H, et al. An overview of gibberellin metabolism enzyme genes and their related mutants in rice. Plant Physiol. 2004;134(4):1642–53. https://doi.org/10.1104/pp.103.033696.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. Shang L, Liu F, Wang Y, et al. Dynamic QTL mapping for plant height in upland cotton (Gossypium hirsutum). Plant Breed. 2016;134(6):703–12. https://doi.org/10.1111/pbr.12316.

    CAS  Article  Google Scholar 

  45. Shi LY, Zhou J. The development status and prospect of Xinjiang machine picked cotton. Prog Text Sci Technol. 2014;3:1–3. https://doi.org/10.19507/j.cnki.1673-0356.2014.03.001.

    Article  Google Scholar 

  46. Song X, Zhang T. Quantitative trait loci controlling plant architectural traits in cotton. Plant Sci. 2009;177(4):317–23. https://doi.org/10.1016/j.plantsci.2009.05.015.

    CAS  Article  Google Scholar 

  47. Su J, Li L, Zhang C, et al. Genome-wide association study identified genetic variations and candidate genes for plant architecture component traits in Chinese upland cotton. Theor Appl Genet. 2018;131:1299–314. https://doi.org/10.1007/s00122-018-3079-5.

    CAS  Article  PubMed  Google Scholar 

  48. Sun FD, Zhang JH, Wang SF, et al. QTL mapping for fiber quality traits across multiple generations and environments in upland cotton. Mol Breed. 2012;30(1):569–82. https://doi.org/10.1007/s11032-011-9645-z.

    Article  Google Scholar 

  49. Tan Z, Zhang Z, Sun X, et al. Genetic map construction and fiber quality QTL mapping using the cottonSNP80K array in upland cotton. Front Plant Sci. 2018;9:225–35. https://doi.org/10.3389/fpls.2018.00225.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Tang FY, Wang XF, Mo WC, et al. Relation analysis of several agronomic traits and single plant lint yield in upland cotton with high quality. J Anhui Agric Sci. 2009;10(2):90–2. https://doi.org/10.16175/j.cnki.1009-4229.2009.02.021.

    Article  Google Scholar 

  51. Voorrips R. MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered. 2002;93(1):77–8. https://doi.org/10.1093/jhered/93.1.77.

    CAS  Article  Google Scholar 

  52. Wang B, Smith SM, Li J. Genetic regulation of shoot architecture. Annu Rev Plant Biol. 2018;69(1):437–68. https://doi.org/10.1146/annurev-arplant-042817-040422.

    CAS  Article  PubMed  Google Scholar 

  53. Wang BH, Yao-Ting WU, Huang NT, et al. QTL mapping for plant architecture traits in upland cotton using RILs and SSR markers. Acta Genet Sin. 2006;33(2):161–70. https://doi.org/10.1016/S0379-4172(06)60035-8.

    CAS  Article  PubMed  Google Scholar 

  54. Wang J, Wang Z, Du X, et al. A high-density genetic map and QTL analysis of agronomic traits in foxtail millet [Setaria italica (L.) P. Beauv.] using RAD-seq. PLoS One. 2017;12(6):e0179717. https://doi.org/10.1371/journal.pone.0179717.

    Article  Google Scholar 

  55. Wang S, Basten C, Zeng Z. Windows QTL Cartographer 2.5. Raleigh, NC: Department of Statistics, North Carolina State University; 2007. http://statgen.ncsu.edu/qtlcart/WQTLCart.htm.

  56. Watson MB, Emory KK, Piatak RM, et al. Arginine decarboxylase (polyamine synthesis) mutants of Arabidopsis thaliana exhibit altered root growth. Plant J. 1998;13(2):231–9. https://doi.org/10.1046/j.1365-313X.1998.00027.x.

    CAS  Article  Google Scholar 

  57. Wu Y, Fu Y, Zhao S, et al. CLUSTERED PRIMARY BRANCH 1, a new allele of DWARF11, controls panicle architecture and seed size in rice. Plant Biotechnol J. 2016;14(1):377–86. https://doi.org/10.1111/pbi.12391.

    CAS  Article  PubMed  Google Scholar 

  58. Yan Q, Wu F, Ma T, et al. Comprehensive analysis of bZIP transcription factors uncovers their rolesduring dimorphic floret differentiation andstress response in Cleistogenes songorica. BMC Genomics. 2019;20(1):760–76. https://doi.org/10.1186/s12864-019-6092-4.

  59. Yang H, Li C, Lam HM, et al. Sequencing consolidates molecular markers with plant breeding practice. Theor Appl Genet. 2015;128(5):779–95. https://doi.org/10.1007/s00122-015-2499-8.

    CAS  Article  PubMed  Google Scholar 

  60. Zhang K, Kuraparthy V, Fang H, et al. High-density linkage map construction and QTL analyses for fiber quality, yield and morphological traits using CottonSNP63K array in upland cotton (Gossypium hirsutum L.). BMC Genomics. 2019b;20(1):889–914. https://doi.org/10.1186/s12864-019-6214-z.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  61. Zhang PT, Zhu XF, Guo WZ, et al. Inheritance and QTLs tagging for ideal plant architecture of Simian 3 using molecular markers. Cotton Sci. 2006;18(1):13–8. https://doi.org/10.3969/j.issn.1002-7807.2006.01.003.

  62. Zhang T, Hu Y, Jiang W, et al. Sequencing of allotetraploid cotton (Gossypium hirsutum L. acc. TM-1) provides a resource for fiber improvement. Nat Biotechnol. 2015;33(5):531–7. https://doi.org/10.1038/nbt.3207.

    CAS  Article  PubMed  Google Scholar 

  63. Zhang Z, Liu A, Huang Z, et al. Quantitative trait locus mapping for plant height and branch number in an upland cotton recombinant inbred line with an SNP-based high-density genetic map. Euphytica. 2019a;215:110–21. https://doi.org/10.1007/s10681-019-2428-z.

    CAS  Article  Google Scholar 

  64. Zhang Z, Shang H, Shi Y, et al. Construction of a high-density genetic map by specific locus amplified fragment sequencing (SLAF-seq) and its application to quantitative trait loci (QTL) analysis for boll weight in upland cotton (Gossypium hirsutum.). BMC Plant Biol. 2016;16:79–97. https://doi.org/10.1186/s12870-016-0741-4.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  65. Zou X, Gong J, Duan L, et al. High-density genetic map construction and QTL mapping for fiber strength on C24 across multiple environments in a CCRI70 recombinant inbred lines population. Euphytica. 2018;214(6):102–15. https://doi.org/10.1007/s10681-018-2177-4.

    CAS  Article  Google Scholar 

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Acknowledgements

We would like to thank the anonymous reviewers for their valuable comments and helpful suggestions which help to improve the manuscript.

Funding

This work was funded by the National Key R&D Program of China (2017YFD0101600; 2016YFD0100505), the Fundamental Research Funds for Central Research Institutes (Y2017JC48), the Natural Science Foundation of China (31371668, 31471538).

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Affiliations

  1. State Key Laboratory of Cotton Biology / Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, Henan, China

    LIU Ruixian, XIAO Xianghui, GONG Juwu, LI Junwen, ZHANG Zhen, LIU Aiying, LU Quanwei, SHANG Haihong, SHI Yuzhen, GE Qun, IQBAL Muhammad Sajid, YUAN Youlu & GONG Wankui

  2. Department of Agronomy / Key Laboratory of Agriculture Biological Technology, Xinjiang Agriculture University, Urumqi, 830052, China

    LIU Ruixian, CHEN Quanjia & YUAN Youlu

  3. School of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang, 455000, Henan, China

    LU Quanwei

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  1. LIU Ruixian
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  2. XIAO Xianghui
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Contributions

Gong WK, Yuan YL initiated the research; Gong WK and Liu RX designed the experiments; Liu RX, Xiao XH, Zhang Z, Gong JW, Li JW, Liu AY, Shang HH, Shi YZ, Ge Q, Iqbal MS, Lu QW, and Chen QJ conducted the phenotypic evaluations and collected the data from the field; Liu RX, Gong WK and Yuan YL performed the analysis; Liu RX drafted the manuscript; Yuan YL and Gong WK finalized the manuscript. All authors contributed in the interpretation of results and approved the final manuscript.

Corresponding authors

Correspondence to YUAN Youlu or GONG Wankui.

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

Supplementary information

Additional file 1: Table S1.

The ANOVA analysis and heritability analysis of PH and FBN traits.

Additional file 2: Table S2.

The QTLs for agronomic traits identified in multiple environments.

Additional file 3: Table S3.

Distribution of the QTL clusters.

Additional file 4: Table S4.

The genes in stable QTL for PH and FBN used for GO analysis.

Additional file 5: Table S5.

The genes in stable QTLs for PH and FBN used for KEGG analysis.

Additional file 6: Table S6.

The genes annotation information in stable QTLs for PH and FBN.

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LIU, R., XIAO, X., GONG, J. et al. QTL mapping for plant height and fruit branch number based on RIL population of upland cotton. J Cotton Res 3, 5 (2020). https://doi.org/10.1186/s42397-020-0046-x

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Keywords

  • Upland cotton
  • RIL population
  • Agronomic traits
  • QTL
  • Plant height
  • Fruiting branch number

Journal of Cotton Research

ISSN: 2523-3254

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