Skip to main content

Genome-wide association study for seedling biomass-related traits in Gossypium arboreum L.



Seedling stage plant biomass is usually used as an auxiliary trait to study plant growth and development or stress adversities. However, few molecular markers and candidate genes of seedling biomass-related traits were found in cotton.


Here, we collected 215 Gossypium arboreum accessions, and investigated 11 seedling biomass-related traits including the fresh weight, dry weight, water content, and root shoot ratio. A genome-wide association study (GWAS) utilizing 142,5003 high-quality SNPs identified 83 significant associations and 69 putative candidate genes. Furthermore, the transcriptome profile of the candidate genes emphasized higher expression of Ga03G1298, Ga09G2054, Ga10G1342, Ga11G0096, and Ga11G2490 in four representative cotton accessions. The relative expression levels of those five genes were further verified by qRT-PCR.


The significant SNPs, candidate genes identified in this study are expected to lay a foundation for studying the molecular mechanism for early biomass development and related traits in Asian cotton.

Peer Review reports


Vegetative growth in crop plants is generally associated with the above and below-ground biomass. The vigorous vegetative growth has been used as an indicator for stress resistance and high yield due to the potential source to sink mobilization of reserves stored in vegetative plant parts, viz., stem, leaves, and roots [1,2,3,4]. Therefore, many studies have used derived traits and/or indices of vegetative growth, including fresh weight [5, 6], dry weight [7], index of cell water content [8], Delf’s index [9], stem reserve mobilization [2], surface expansion [9], and leaf succulence [9]. Plant biomass is a complex trait and usually refers to the quality of plants in their natural state. For example, the estimation of water contents by comparing fresh and dry weight is a commonly practiced technique in many crops [10,11,12]. Owing to its complex nature and underlying genetics, it is pertinent to understand the genetic regulators of biomass development in crop plants.

Cotton contributes hefty shares as raw material for the textile industry. Significant variation of biomass-related traits like fresh weight, dry weight, water content, and root shoot ratio, generally influenced by environmental factors, is present among modern cultivars and wild relatives [3, 13]. There was a hypothesis that greater seedling biomass-related traits like water content and root shoot ratio could enhance the chances that cotton seedlings resist pests and diseases, and may improve the ability to tolerate abiotic stress [14,15,16]. Yet, there is a clear lack of studies concerning the genetic regulation of seedling biomass in crop plants, especially in cotton. To date, a still fewer study has been reported for genetic control of seedling biomass-related traits in cotton. Multigenic control and elusive environmental influences make it difficult to understand the genetic control and development of molecular markers for seedling biomass-related traits [17]. Owing to advances in omics, recent reports suggested the identification of molecular markers concerning seedling biomass-related traits in different species, viz., rice [18], Brassica [19], and wheat [20,21,22].

Recent advances in sequencing technology have led to extensive Genome-wide association studies (GWAS) in crop plants [17, 18, 23,24,25,26]. Based on population genetics and genome analysis of natural genetic populations, GWAS has proven to be an efficient tool for understanding the relationship between phenotype and associated genetic variation [27,28,29]. At present, the previously published molecular markers related to seedling biomass-related traits were mainly determined by traditional QTL methods utilizing F2 populations and RIL populations [18,19,20, 22]. Furthermore, diploid Gossypium arboreum, which is the ancestors of the modern cultivated allotetraploid cotton should be an ideal model for basic research in cotton [30].

The present study aimed to systematically investigate the genetic regulation of early plant biomass in the germplasm of G. arboreum accessions by utilizing high-quality resequencing and GWAS. Moreover, we further screened the candidate genes of GWAS by transcriptome data. The yielded information regarding SNPs and putative candidate genes identified could effectively enrich the excellent genetic resources of complex traits like biomass in cotton.


Phenotypic characterization of seedling biomass-related traits in G. arboreum

In the current study, the seedling biomass was evaluated by the fresh weight, dry weight, root shoot ratio, and water content traits. Totally, 11 seedling biomass-related traits were recorded in the 215 G. arboreum accessions (Table S2). Three replicates (R1, R2, R3) were recorded for each trait, as shown in Fig. 1, the range of replicates was similar to their average values (AV). The broad sense heritability for each trait was also calculated, and the result showed the RFW, RSR_FW, SFW, and TFW had higher heritability values (above 96.00%), whereas the RSR_DW had the lowest broad sense heritability (80.40%). Further descriptive analysis showed that the coefficient of variation (CV%) of all the traits ranged from 1.96 to 28.41% (Table 1; Table S3). The water content traits showed the lowest CV% values than other biomass-related traits. As shown in Table 1, the CV of average shoot water content (SWC_AV), average root water content (RWC_AV), and average total water content (TWC_AV) were 1.98, 2.78, and 1.96% respectively. The phenotypes associated with all the biomass-related traits showed a normal or toward a normal distribution, as shown in the frequency distribution plots (Fig. S2) and the Skewness / Kurtosis values (Tables S1; S3), hence, all the traits could meet the follow-up genome-wide association analysis.

Fig. 1
figure 1

Phenotypes and broad sense heritability of seedling biomass-related traits in 215 G. arboreum accessions. a SFW. b RFW. c WFW. d TFW. e RDW. f TDW. g SWC. d TFW. h RWC. i TWC. j RSR_FW. k RSR_DW. l Broad sense heritability of 11 seedling biomass-related traits

Table 1 Descriptive statistics of phenotypes in 215 G. arboreum accessions

Correlations of 11 seedling biomass-related traits

We performed a Pearson correlation analysis among the fresh weight, dry weight, root shoot ratio, and water content traits in 215 G. arboreum accessions. As shown in Fig. 2, the RWC (root water content) has a highly positive correlation with TWC (total water content), SWC (shoot water content), RFW (root fresh weight), and RSR_FW(root shoot ratio of fresh weight). Both the SDW (shoot dry weight) and TDW (total dry weight) have a negative correlation with SWC, TWC, and RWC. The RFW showed a stronger positive correlation with TFW (total fresh weight), RSR_FW, RDW (root dry weight), SDW, SFW (shoot fresh weight), and RSR_DW (root shoot ratio_dry weight).

Fig. 2
figure 2

Correlation analysis among the 11 seedling biomass-related traits


A total of 1,425,003 high-quality SNPs with MAF > 0.05 and a missing rate < 20% were identified in the published studies [30] for the same 215 G. arboreum accessions. These high-quality filtered SNPs were used to perform GWAS for 11 seedling biomass-related traits in our study. We summarized and analyzed the detected SNPs (Figs. S3-S10; Table S4). As shown in Table S4, most SNPs were detected from the SWC and RWC traits, especially for RWC_R2 (455 SNPs). It is worth noting that the RWC_R2 was peaked at Chr11_19663606 on the Chr11 chromosome, and its -log P was 6.26 (Fig. 3; Table S4). We have also detected plenty of SNPs for the TDW traits, but unfortunately, their -log P values were relatively lower (Table S4; Fig. S8). Only two SNPs, namely SNP Chr02_7231541 (−log P = 6.39) and SNP Chr02_7231541 (−log P = 6.33), were reached the significant level. All the fresh weight traits including SFW, RFW, and TFW have detected fewer numbers of SNPs, and their -lop P values were also relatively low with no SNP reaching the threshold (−log P > 6.15) (Table S4; Figs. S3-S5). Interestingly, we also observed that the SFW_R1 and TFW_R1 both peaked at SNP Chr04_96569153 (−log P = 5.65 and 5.61 respectively) and all the fresh traits have detected more SNP numbers on Chr04. Based on the threshold (−log P > 6.15), a total of 83 significant SNPs were detected (Table S5). Most (60) SNPs correspond to the intergenic regions, and only five significant SNPs were present in coding sequences with four SNPs in intronic and one SNP (SNP Chr11_74811634) in the exonic (non-synonymous) region.

Fig. 3
figure 3

Genome-wide association study for RWC. a Manhattan and quantile-quantile (Q-Q) plots of RWC. b The confidence intervals and LD block analysis. c Genotype analysis of SNP Chr07_93706195

Putative candidate genes were detected by LD blocks with significant SNPs. The water content traits had more significant SNPs detected than other traits. For the root water content trait, as shown in Fig. 3, one peak signal (SNP Chr07_93706195) was identified on RWC_AV, RWC_R1, and RWC_R3. This peak was located upstream of Ga07G2433 and its allele was “T/C”. We further performed a genotype analysis for this site, the result found accessions that carried the “T” genotype had significantly higher water content than the “C” genotype. The shoot water content trait has detected a continuous stronger signal in the interval of 73.00-76.00 Mb on chr11 (Fig. 4). However, the LD-block analysis found that SNPs in this interval had a weaker linkage relationship (Fig. 5a). The SNP Chr11:74923286 was the strongest signal on chromosome 11, and it could be detected on SWC_AV, SWC_R2, and SWC_R3 (Figs. 4, 5). The allele of this site was “C/T”, and the further genotype analysis showed the “C” genotype accessions had significantly higher water content than “T” genotype accession (Fig. 5b).

Fig. 4
figure 4

Manhattan plot of SWC

Fig. 5
figure 5

Genotypes analysis of SWC. a The confidence intervals and LD block analysis for SWC. b Genotype analysis of SNP Chr11_74,923,286

As shown in Table S6, a total of 69 candidate genes were identified. Most of them are related to water content traits. Among them, 18 candidate genes associated with the shoot and root water content traits were detected on the Chr11 chromosome. Nevertheless, there are also a few candidate genes of root shoot ratio and dry weight that have been detected. For example, Ga03G1298 and Ga03G1299 were related to RSR_FW_R2. Ga02G0456 and Ga10G1342 were detected from RSR_FW_R2 and RDW_R1 respectively.

Comparison of expression patterns for GWAS candidate genes

In order to further analyze the GWAS candidate genes, we selected four representative materials for candidate gene expression analysis and qRT-PCR verification. Those four Asian cotton accessions were “AHQJZM”, “DPL971”, “WMSLDMH” and “DGXH”. The “WMSLDMH” and “DGXH” accessions have higher fresh weight, and water content than “AHQJZM” and “DPL971”, especially for the root samples (Table S2). And the transcriptome data ( of the four G. arboreum accessions, previously have been sequenced by our group (unpublished study).

We utilized the important transcriptomic profiles and compared the different expression levels of GWAS candidate genes in the four cotton accessions (Table S7). Three periods were analyzed, as shown in Fig. 6, the Ga10G1342 gene was highly expressed in all the three periods, and interestingly, it was higher expressed in “AHQJZM” and “DPL971” than “WMSLDMH” and “DGXH” on the 12d after sowing. Another gene, Ga11G2490 also showed the same rule, with higher expression levels in the root tissues of “AHQJZM” and “DPL971” both on the 8d and 12d after sowing. However, the Ga11G0096 gene showed the opposite expression pattern, with higher expression levels in “WMSLDMH” and “DGXH” than “AHQJZM” and “DPL971”, especially for the periods of 2d and 8d after sowing. Ga03G1298 has a lower expression on the 2d, and it was increased on the 8d and showed different expression levels among the four cotton accessions on the 12d after sowing. Ga09G2054 showed higher expression on the 2d, and the FPKM value of “AHQJZM” and “DPL971” was higher than the other two accessions. While on the 8d after sowing, little difference can be detected among the four accessions, and it showed an obvious expression change on the 12d after sowing.

Fig. 6
figure 6

FPKM expression analysis for GWAS candidate genes

The TPM expression data of the GWAS candidate genes for the Asian cotton standard line cv Shixiya1 have also been downloaded from [31]. Forty genes with TPM expression data of different organizations included root, stem, leaf, and flower, were obtained (Table S8). As shown in Fig. S11, the Ga03G1298 have higher TPM expression levels in the leaf samples, whereas Ga09G2054 and Ga10G1342 have higher TPM expression levels in the root samples.

qRT-PCR verification

As shown in Fig. 7, five genes (Ga03G1298, Ga09G2054, Ga10G1342, Ga11G0096, and Ga11G2490) were selected to test and verify their relative expression level in the four representative Asian cotton accessions. Root and leaf samples of 12 das after sowing were collected and used in this study. The qRT-PCR result showed that the Ga03G1298 gene showed higher expression in the leaf samples than in the root samples. Yet, the other four genes especially for Ga11G0096 had a higher relative expression on the root samples, which were consistent with their FPKM/TPM expression result. In addition, we also found the Ga10G1342 gene has detected higher relative expression levels in root samples of “AHQJZM” and “DPL971” than “DGXH” and “WMSLDMH”.

Fig. 7
figure 7

qRT-PCR verification for 5 selected genes


Early development of plants is critical for providing a strong base for further development. Seedling stage biomass has been extensively studied in plants with its potential relation to the stress environments [4, 5, 19, 20, 23]. Investigating biomass and its related traits is also of significance to major characteristics, viz., yield, plant structure, fertilizer use efficiency. For instance, a study concerning upland cotton suggested that bud fresh weight along with other morphological parameters are important indicators of cold tolerance [32]. Similar reports have been published emphasizing the importance of early plant biomass and its role as an indirect morphological marker for yield and disease resistance in plants [33, 34]. However, due to the complex inheritance of plant biomass, there is an apparent lack of studies identifying biological markers associated with plant biomass.

The progress of molecular markers identification of biomass and its related traits in cotton is relatively slow. At present, only a few studies have reported molecular markers or QTLs associated with seedling biomass-related traits in cotton. Tang et al. (2017) [35] used cross combination xinluzaomian 35 × Yinong 2 to construct a genetic map using F2 population and subsequently identified 3 QTLs of leaf fresh weight and 4 QTLs of leaf dry weight. An association analysis study of salt stress in upland cotton identified SNP D10_61258588 was associated with leaf fresh-weight, and SNP A10_95330133 was associated with the leaf relative water content [36]. Shang et al. (2016) identified 2 QTLs (qRW-chr11-1, qRW-CHR11-2) for root fresh weight and 2 QTLs (qRSR-chr21-1, qRSR-chr25-1) for root shoot ratio trait at 9 days after nitrogen deficiency [16]. Therefore, it is pertinent to perform more systematic molecular identification and provide mechanistic insights for complex traits. In this study, we systematically investigated the early plant biomass and performed a GWAS study for fresh weight, dry weight, water content, and root shoot ratio traits in Asian cotton.

Based on 1,425,003 high-quality SNPs, we identified 83 significant SNPs, and successfully detected 69 putative candidate genes related to seedling biomass-related traits. After integrating with the transcriptome data in four representative accessions, we finally screened out 5 genes (Ga03G1298, Ga09G2054, Ga10G1342, Ga11G0096, and Ga11G2490) that may be most closely related to the seedling biomass related traits. Ga03G1298 was identified for the RSR_FW trait. It could encode the light-harvesting complex-like protein OHP1 and may play a photoprotective role in the thylakoid membrane in response to light stress. In Arabidopsis, a recent study [37] found the OHP1 protein was localized at the thylakoid membranes, and it could form a trimeric complex with OHP2 and High Chlorophyll Fluorescence 244 (HCF244). In this study, we found “AHQJZM” and “DPL971” had lower RSR_FW values than “WMSLDMH” and “DGXH” (Table S2). Interestingly, the TPM and qRT-PCR results both showed the Ga03G1298 expressed higher in leaves than roots. And we also found the Ga03G1298 was higher expressed in the lower RSR_FW cotton accessions (“AHQJZM” and “DPL971”) on leaf samples, and this may further prove the Ga03G1298 gene is closely related to the RSR_FW trait. The gene name of Ga09G2054 is UBC10. It is a Ubiquitin-conjugating enzyme E2 10. This gene was identified by the RWC trait, however, in our qRT-PCR result, it didn’t show a significant expression difference in root samples among the four representative cotton accessions. Ga10G1342 (RPS15) could encode 40S ribosomal protein S15. A recent study revealed that the ribosomal protein RPS15 exhibited bactericidal activity against Gram-positive Staphylococcus aureus in amphioxus Branchiostoma japonicum [38]. In Arabidopsis thaliana, Kathleen et al. (2010) found the ribosomal protein S15a could be classified into two categories (Type I and Type II), and further knock down experiment suggested the RPS15aE isoform (Type II S15a) may act as regulators of translational activity [39]. Whereas in our study, the Ga10G1342 gene was identified in the RDW trait, which means that this gene may be closely associated with the root dry weight in cotton. The FPKM of this gene showed a higher expression level in “AHQJZM” and “DPL971” than “WMSLDMH” and “DGXH” on the period of 12das after sowing. Our qRT-PCR result also showed the same expression pattern in the root, which may further validate the importance of this gene. Ga11G0096 (CYP76A2) was associated with the SWC and TWC trait, which could encode the cytochrome P450 76A2. As early as 1993, Toguri et al. cloned this gene in eggplant seedlings and attributed it to the CYC76 gene family [40]. Unfortunately, still, no study have revealed the detailed function of this gene in plants. Our study found this gene was expressed in the root samples but not expressed in the leaf samples, and this may indicate that the CYC76 gene has tissue expression specificity. Ga11G2490 is a new gene found in cotton. This gene was identified in the shoot water content trait, which means this gene may also be associated with the water content in cotton seedlings.

Conclusion: This study first investigated early plant biomass and utilized previously published 1,425,003 high-quality SNPs to perform a GWAS study for fresh weight, dry weight, water content, and root shoot ratio in 215 G. arboreum accessions. Based on the threshold -log P > 6.15, a total of 83 SNPs were identified, and consequently, 69 candidate genes were identified as putative candidate genes governing early plant biomass in Asian cotton. The GWAS candidate genes were further combined with the transcriptome data of two pairs of root genotype accessions and finally screened out 5 five genes to verify by qRT-PCR. The findings in this study may provide useful molecular markers and targets for breeders to better understand the complex mechanism of fresh weight in cotton.


Plant material and data collection

A panel of 215 G. arboreum accessions obtained from Midterm Gene Bank of Cotton Research Institute, Chinese Academy of Agricultural Sciences, were used for this study. Given the limitations of field evaluation, this study used the seed bag nursery method to study the seedling stage biomass of Asian cotton in 2017 in a greenhouse to avoid environmental influence. Seeds of each accession were first sterilized with 15% H202 for half an hour and rinsed with sterile water at least five times. Next, we carefully planted the seeds in the medium-sized seed germination bag with tweezers, each bag with 12 seeds, and three replicated for each cotton accession. Twenty milliliters sterile water for each bag was irrigation water and cultured in a greenhouse (light/dark photoperiod = 16 h / 8 h, relative humidity 60 - 65%, and day/night temperature = 26 °C / 28 °C). And 2 weeks later, similar plants for each accession were sampled and were directly cut into two parts of the root and the shoot by scissors (Fig. S1). The phenotyping process mainly included three processes. First, fresh weight (FW) recording, samples were absorbed the surface water by filter papers and weighed by an electronic balance. Second, dry weight (DW) recording, samples were put in an oven (80 °C, 30 min firstly, then 105 °C, 12 h) to dry and weighed. Third, the root shoot ratio (RSR) and water content (WC) were calculated by the FW and DW values. Root shoot ratio of fresh weight (RSR_FW) = root fresh weight (RFW) / shoot fresh weight (SFW) × 100%; Root shoot ratio of dry weight (RSR_DW) = root dry weight (RDW) / shoot dry weight (SDW) × 100%; water content (WC) = (FW - DW) / FW × 100%.


The genotype data of the 215 G. arboreum accessions used in this study is the same as the genotype data previously published by our team in Nature Genetics [30]. Software BWA (Burrows–Wheeler Aligner program, ver. 0.7.10) and GATK (Genome Analysis ToolKit, ver.3.2–2) were utilized to perform reads mapping, and SNP calling followed their Standardized process specification, respectively [41, 42]. After filtering, a total of 1,425,003 high-quality SNP markers (MAF > 0.05, missing rate per site< 10%) were screened out and were further utilized to perform GWAS for 11 seedling biomass-related traits (including the average values and replicates for each trait). An EMMAX model (Efficient Mixed-model Association Expedited) [43] was chosen to perform the GWAS in this study, and the -log10 (P) value was calculated for each SNP. The significant threshold was evaluated with the formula P = 0.5/n (where n is the total number of high-quality SNPs) [44], and -log P > 6.15 was set as the significant threshold. The newly updated G. arboreum genome (download from [31] was set as the reference genome. Genes were identified in the regions of significant SNPs and stronger LD-blocks around the significant SNPs.

LD block analysis

The confidence intervals were identified by IGV software [45], and LD blocks around the significant SNPs were estimated by TASSEL 5.2.51 software [46].

Candidate gene expression analysis

The transcriptome data comes from two studies. One study (unpublished) was completed by our team, which mainly completed the mRNA sequencing of the roots of “AHQJZM”, “DPL971”, “WMSLDMH” and “DGXH” in three different periods (samples were collected after 2d, 8d and 12d after sowing, “d” represent “das”). Each root sample had two replicates, and the plant culture method was similar to our methods of planting 215 G. arboreum accessions. We extracted the FPKM (Fragments per kilobase of exon model per million mapped fragments) data of GWAS candidate genes in this study for further comparative analysis. Another study is for G. arboreum cultivar Shixiya1 RNA-seq analysis. We downloaded the TPM (Trans per kilobase of exon model per million mapped reads) expression data from the website (data accession: PRJNA594268). And we further analyzed the TPM data of different tissues (root, stem, and flowers) for our GWAS candidate genes.

For further gene screening analysis, the candidate genes of GWAS were first excluded by the transcriptome data when the gene was not expressed in any tissues. Then, the expression data (FPKM or TPM) of candidate genes were plotted by TBtools v0.67 [47] based on their average FPKM or TPM data. And genes with a larger difference in expression levels among the four materials (“AHQJZM”, “DPL971”, “WMSLDMH”, and “DGXH”) were selected for further qRT-PCR verification. Genes with the expression level fold change > 1.5 or < 0.67 were considered significant.


The root and leaf samples of four cotton accessions (“AHQJZM”, “DPL971”, “WMSLDMH”, and “DGXH”) on the 12 das after sowing were collected and stored at − 80 °C until further RNA extraction. Total RNA was extracted for each root sample by using a Plant RNA Purification Kit (Tiangen, Beijing, China). The qRT-PCR was performed by 7500 Fast ABI (Applied Biosystems, Foster City, CA, USA) with TransStart Top Green qPCR SuperMix kit (TransGen Biotech) according to the manufacturer’s instructions. The qRT-PCR reaction system had a final volume of 20 μL, which consisted of 2 μl of cDNA sample, 10 μl of 2 × TransStart Green qPCR SuperMix, 0.4 μl of Passive Reference Dye, 6.8 μl of ddH2O, and 0.8 μl of primers. The reactions were amplified at 95 °C for 30 s, followed by 40 cycles of 95 °C for 5 s, 60 °C for 15 s, and 72 °C for 10 s, and then 95 °C for 30 s, 60 °C for 1 min, and 95 °C for 15 s. All reactions were set with three independent biological replications. Primers were designed using Primer5.0 software. The gene β-actin was used as a reference. Primers of selected genes are listed in Table S1. The relative expression levels of selected candidate genes were calculated by the 2-ΔΔCt method [48].

Statistical analyses

The basic descriptive analysis and frequency distribution of the 11 seedling biomass-related traits were performed by GraphPad Prism 8 [49]. The broad sense heritability was calculated by lme4 package in R software [50]. A correlation analysis between different seedling biomass-related traits was performed by R software using the “corrplot” package. The grouping significance test of genotype was used the two-tailed t-test in GraphPad Prism 8, and P-value < 0.05 is regarded as significant.

Availability of data and materials

All data in this article are available. The RNA sequences raw data of root samples were deposited in the Biological Research Project Data (BioProject), National Center for Biotechnology Information (NCBI), accession: PRJNA751791. And other phenotypic and gene expression data are included in this article and its supplementary information files.



Genome wide association study


Shoot fresh weight


Root fresh weight


Total fresh weight


Shoot dry weight


Root dry weight


Total dry weight


Shoot water content


Root water content


Total water content


Root shoot ratio


Linkage disequilibrium


Trans per kilobase of exon model per million mapped reads


Fragments per kilobase of exon per million


Quantitative real time polymerase chain reaction


Single nucleotide polymorphism

G. arboreum :

Gossypium arboreum L.


  1. Ohlson EW, Seido SL, Mohammed S, Santos C, Timko MP. QTL mapping of ineffective nodulation and nitrogen utilization-related traits in the IC-1 mutant of cowpea. Crop Sci. 2017;58(1):264–72.

    Google Scholar 

  2. Piepho HP, Nazir MF, Shah MKN. Design and analysis of a trial to select for stress tolerance. Commun Biometry Crop Sci. 2015;11(1):1–9.

    Google Scholar 

  3. Shah AN, Yang G, Tanveer M, Iqbal J. Leaf gas exchange, source–sink relationship, and growth response of cotton to the interactive effects of nitrogen rate and planting density. Acta Physiol Plant. 2017;39(5):119.

    Google Scholar 

  4. Yang M, Wang C, Hassan M, Wu Y, et al. QTL mapping of seedling biomass and root traits under different nitrogen conditions in bread wheat (Triticum aestivum L.). J Integr Agric. 2021;20(5):1180–92.

    CAS  Google Scholar 

  5. Singh K, Gupta N, Dhingra M. Effect of temperature regimes, seed priming and priming duration on germination and seedling growth on American cotton. J Environ Biol. 2018;39(1):83–91.

    CAS  Google Scholar 

  6. Xiao S, Liu L, Wang H, Li D, Li C. Exogenous melatonin accelerates seed germination in cotton(Gossypium hirsutum L.). PLoS One. 2019;14(6):e0216575.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Kabambe VH. Screening cotton (Gossypium hirsutum L.) genotypes for drought tolerance under screen house conditions in Malawi. Acad J. 2018;10:48–57.

    Google Scholar 

  8. Park IS, Kim DI. Significance of fresh weight to dry cell weight ratio in plant cell suspension cultures. Biotechnol Tech. 1993;7(9):627–30.

    Google Scholar 

  9. Mantovani A. A method to improve leaf succulence quantification. Braz Arch Biol Technol. 1999;41(1):9–14.

    Google Scholar 

  10. Huang P, De-Bashan L, Crocker T, Kloepper JW, Bashan Y. Evidence that fresh weight measurement is imprecise for reporting the effect of plant growth-promoting (rhizo)bacteria on growth promotion of crop plants. Biol Fertil Soils. 2016;53:199–208.

    Google Scholar 

  11. Farag A. Effectiveness of exopolysaccharides and biofilm forming plant growth promoting rhizobacteria on salinity tolerance of faba bean (Vicia faba L.). Afr J Microbiol Res. 2018;12(17):399–404.

    Google Scholar 

  12. Huang W, Ratkowsky DA, Hui C, Wang P, Shi P. Leaf fresh weight versus dry weight: which is better for describing the scaling relationship between leaf biomass and leaf area for broad-leaved plants? Forests. 2019;10(3):256.

    Google Scholar 

  13. Fan X, Zhao L, Zhai H, Wang Y, Sun G. Functional characterization of AtNEK6 overexpression in cotton under drought and salt stress. Sci Agric Sin. 2018;51:4230–40.

    Google Scholar 

  14. Ananthi K, Vijayaraghavan H. Development of drought tolerant index in cotton genotypes based on relative water content and yield. Asian J Bio Sci. 2012;7(2):138–44.

    Google Scholar 

  15. Cook CG, El-Zik KM. Cotton seedling and first-bloom plant characteristics: relationships with drought-influenced boll abscission and lint yield. Crop Sci. 1992;32(6):1464–7.

    Google Scholar 

  16. Shang L, Cai S, Ma L, et al. Seedling root QTLs analysis on dynamic development and upon nitrogen deficiency stress in upland cotton. Euphytica. 2016;207:645–63.

    CAS  Google Scholar 

  17. He S, Wang P, Zhang YM, Dai P, Du X. Introgression leads to genomic divergence and responsible for important traits in upland cotton. Front Plant Sci. 2020;11:929.

    PubMed  PubMed Central  Google Scholar 

  18. Nayyeripasand L, Garoosi GA, Ahmadikhah A. Genome-wide association study (GWAS) to identify salt-tolerance QTLs carrying novel candidate genes in Rice during early vegetative stage. Rice. 2021;14(1):1–21.

    Google Scholar 

  19. Hou LT, Wang TY, Jian HJ, Wang J, Liu LZ. QTL mapping for seedling dry weight and fresh weight under salt stress and candidate genes analysis in Brassica napus L. Acta Agron Sin. 2017;43(2):179–89.

    Google Scholar 

  20. Anna I, Daniela M, Anna RM, Pasquale DV, Vito M, Pina F, et al. Mapping QTL for root and shoot morphological traits in a durum wheat × T. dicoccum segregating population at seedling stage. Int J Genomics. 2017;2017:6876393.

  21. Yang Y, Wan H, Yang F, Xiao C, Zhou Y. Mapping QTLs for enhancing early biomass derived from Aegilops tauschii in synthetic hexaploid wheat. PLoS One. 2020;15(6):e0234882.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Zhou XG, Jing RL, Hao ZF, Chang XP, Zhang ZB. Mapping QTL for seedling root traits in common wheat. Sci Agric Sin. 2005;38:1951–7.

    CAS  Google Scholar 

  23. Li D, Komivi D, Zhang Y, Wei X, Wang L, Zhang Y, et al. GWAS uncovers differential genetic bases for drought and salt tolerances in sesame at the germination stage. Genes-Basel. 2018;9(2):87.

    CAS  PubMed Central  Google Scholar 

  24. Liu R, Gong J, Xiao X, Zhang Z, Li J, Liu A, 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.

    PubMed  PubMed Central  Google Scholar 

  25. Su J, Pang C, Wei H, Li L, Liang B, Wang C, et al. Identification of favorable SNP alleles and candidate genes for traits related to early maturity via GWAS in upland cotton. BMC Genomics. 2016;17(1):687.

    PubMed  PubMed Central  Google Scholar 

  26. Wang H, Xu X, Vieira FG, et al. The power of inbreeding: NGS-based GWAS of Rice reveals convergent evolution during Rice domestication. Mol Plant. 2016;9:975–85.

    CAS  PubMed  Google Scholar 

  27. Ahmed H, Nazir MF, Pan Z, Gong W, Du X. Genotyping by sequencing revealed QTL hotspots for trichome-based plant defense in Gossypium hirsutum. Genes-Basel. 2020;11(368).

  28. Fang L, Gong H, Hu Y, Liu C, Zhou B, Huang T, et al. Genomic insights into divergence and dual domestication of cultivated allotetraploid cottons. Genome Biol. 2017;18(1):33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Sariful IM, Thyssen GN, Jenkins JN, et al. A MAGIC population-based genome-wide association study reveals functional association of GhRBB1_A07 gene with superior fiber quality in cotton. BMC Genomics. 2016;17(1):903.

    Article  CAS  Google Scholar 

  30. Du X, Huang G, He S, et al. Resequencing of 243 diploid cotton accessions based on an updated a genome identifies the genetic basis of key agronomic traits. Nat Genet. 2018;50:796–802.

    CAS  PubMed  Google Scholar 

  31. Zhu T, Liang CZ, Meng ZG, Sun GQ, Meng ZH, Guo SD, et al. CottonFGD: an integrated functional genomics database for cotton. BMC Plant Biol. 2017;17:101.

    PubMed  PubMed Central  Google Scholar 

  32. Zhang L, Chen G, Wei H, Wang H, Lu J, Ma Z, et al. Chilling tolerance identification and response to cold stress of Gossypium hirsutum varieties (lines) during germination stage. Sci Agric Sin. 2021;54:19–33.

    Google Scholar 

  33. Liu DL, Helyar KR. Simulation of seasonal stalk water content and fresh weight yield of sugarcane - ScienceDirect. Field Crop Res. 2003;82(1):59–73.

    Google Scholar 

  34. Okechukwu RU, Dixon A. Performance of improved cassava genotypes for early bulking, disease resistance, and culinary qualities in an inland valley ecosystem. Agron J. 2009;101(5):1258–65.

    Google Scholar 

  35. Tang L, Tang Y, Xing S, Li Z, Wei Y. Analysis of QTL mapping for agronomic traits in boll-setting period. Mol Plant Breed. 2017;15:2687–94.

    Google Scholar 

  36. Yasir M, He S, Sun G, Geng X, Pan Z, Gong W, et al. A genome-wide association study revealed key SNPs/genes associated with salinity stress tolerance in upland cotton. Genes-Basel. 2019;10(10):829.

    PubMed Central  Google Scholar 

  37. Myouga F, Takahashi K, Tanaka R, et al. Stable accumulation of photosystem II requires ONE-HELIX PROTEIN1 (OHP1) of the light harvesting-like family. Plant Physiol. 2018;176(3):2277–91.

  38. Chen C, Yuan J, Ji G, et al. Amphioxus ribosomal proteins RPS15, RPS18, RPS19 and RPS30-precursor act as immune effectors via killing or agglutinating bacteria. Fish Shellfish Immunol. 2021;118:11–2.

    Google Scholar 

  39. Kathleen SM, Ammar SZ, Ali SZ, et al. Analysis of RPS15aE, an isoform of a plant-specific evolutionarily distinct ribosomal protein in Arabidopsis thaliana, reveals its potential role as a growth regulator. Plant Mol Biol Report. 2010;28(2):239–52.

  40. Toguri T, Kobayashi O, Umemoto N. The cloning of eggplant seedling cDNAs encoding proteins from a novel cytochrome P-450 family (CYP76). Biochim Biophys Acta. 1993;1216:165–9.

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Mckenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Kang HM, Sul JH, Service SK, Zaitlen NA, Kong SY, Freimer NB, et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 2010;42(4):348–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Li MX, Yeung J, Cherny SS, Sham P. Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum Genet. 2011;131(5):747–56.

    PubMed  PubMed Central  Google Scholar 

  45. Helga T, Robinson JT, Mesirov JP. Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform. 2013;14(2):178–92.

  46. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23(19):2633–5.

    CAS  PubMed  Google Scholar 

  47. Chen C, Chen H, Zhang Y, et al. TBtools: an integrative toolkit developed for interactive analyses of big biological data. Mol Plant. 2020;13(8):1194–202.

  48. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR. Methods. 2002;25(4):402–8.

    Google Scholar 

  49. Lazareno S. GraphPad prism (version 1.02). Trends Pharmacol Sci. 1994;15(9):353–4.

    Google Scholar 

  50. Boeck PD, Bakker M, Zwitser R, et al. The estimation of item response models with the lmer function from the lme4 package in R. J Stat Softw. 2011;39(12).

Download references


We are thankful to the national mid-term genebank for cotton in Institute of Cotton Research of Chinese Academy of Agricultural Sciences (ICR, CAAS) for kindly providing us with Asian cotton seeds.


This work was supported by Independent project of State Key Laboratory of cotton biology (Grant No. CB2019C02). The sponsors had no role in the design or conception of the study; the collection, management and analysis of the data; the preparation, writing and review of the manuscript; or the decision to submit the manuscript for publication.

Author information

Authors and Affiliations



X.D. and D.H. conceived and designed the experiment. D.H. conducted experiments, analyzed the data, and wrote the manuscript. M.F.N revised the grammar, S.H. and G.S. also performed the GWAS analysis, Y.G. finished the qRT-PCR experiment, and all the other authors revised the manuscript. All authors reviewed and approved the final manuscript.

Corresponding author

Correspondence to Xiongming Du.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

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

Supplementary Information

Additional file 1: Table S1.

Primer sequences of selected genes used for qRT-PCR assay in this study. Table S2. Phenotypes of 11 seedling biomass-related traits in 215 G. arboreum accessions. Table S3. Basic descriptive analysis for seedling biomass-related traits. Table S4. Summary of SNPs that detected in 11 seedling biomass-related traits. Table S5. Significant SNPs identified for the seedling biomass-related traits. Table S6. 69 candidate genes associated with seedling biomass-related traits identified by GWAS. Table S7. FPKM of the GWAS candidate genes in 4 representative cotton accessions. Table S8. TPM of the GWAS candidate genes in Shixiya-1.

Additional file 2: Figure S1.

The separating process of root and shoot in cotton seedlings. Figure S2. Frequency distribution of 11 seedling biomass-related traits in 215 G. arboreum accessions. Figure S3. Manhattan plot of SFW. Figure S4. Manhattan plot of RFW. Figure S5. Manhattan plot of TFW. Figure S6. Manhattan plot of SDW. Figure S7 Manhattan plot of RDW. Figure S8. Manhattan plot of TDW. Figure S9. Manhattan plot of RSR. Figure S10. Manhattan plot of TWC. Figure S11. Heat map of TPM expression of candidate genes in different tissues of Shixiya-1.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, D., He, S., Jia, Y. et al. Genome-wide association study for seedling biomass-related traits in Gossypium arboreum L.. BMC Plant Biol 22, 54 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: