Quantitative Trait Loci Mapping and Candidate Gene Analysis for Fiber Quality Traits in Upland Cotton

Abstract

Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4202.12 cM in length. Transgressive segregation patterns and complex correlations in the five tested traits were observed. A total of 108 QTLs were found, including 13 major effect QTLs that contributed more than 10% toward phenotypic variation (PV) and 9 stable QTLs that could be repeatedly mapped in different generations. Chromosome A7 contained 12 QTL, ranking the first. No QTL was found on chromosomes D1 and D11. Two QTLs could be repeatedly detected in three populations, including qFL-D3-2 in F2, F2:4, and F2:5 with 9.18–21.45% of PV and qFS-A11-1 in F2:3, F2:4, and F2:5 with 6.05–10.41% of PV. Another seven stable QTLs could be detected in two populations, including four major effect QTLs: qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1. Fourteen QTL-overlapping regions were found, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The genomic regions of the two QTLs detected in three populations and the four major effect QTLs contain 810 genes. Gene functional analysis revealed that the annotated genes are mainly involved in protein binding and metabolic pathways. Fifteen candidate genes in the qFL-D3-2 region are highly expressed in fiber or ovules during fiber initiation, elongation, secondary cell wall thickening, or maturation stages. qRT-PCR revealed that Ghir_D03G005440.1 and Ghir_D03G011310.1 may play a role in promoting fiber initiation, while Ghir_D03G006470.1 may be beneficial for promoting fiber elongation. This study provides more information for revealing the molecular genetic basis underlying cotton fiber quality.

1. Introduction

There are four cultivated cotton species, two diploids, Gossypium arboretum L. and G. herbaceum L., and two tetraploids G. hirsutum L. and G. barbadense L. Gossypium hirsutum L., also known as upland cotton, is the most widely planted species in the world and contributes to more than 95% of the world’s cotton production [1,2]. Lint fiber is the most important economic cotton product, and superior quality fiber is an important cotton breeding target. In recent years, textile industries have put forward more stringent requirements on fiber quality, and higher-quality fiber can attain higher prices. It is a great challenge to improve fiber quality further under the premise of ensuring cotton yield, and a negative correlation between fiber quality and yield has dragged down the breeding efficiency.

Molecular markers are powerful tools to dissect complex traits such as yield and fiber quality using quantitative trait loci (QTL) mapping as well as genome-wide association analysis (GWAS). Marker-assisted selection is a new approach to increasing breeding efficiency and improving cotton yield and fiber quality simultaneously. Many QTL mapping studies about cotton yield and fiber quality have been reported both in intra- and inter-specific populations, and numerous QTLs have been mapped on the 26 chromosomes [3,4,5,6].

The development of high-throughput sequencing technology has made it convenient to detect the genetic basis underlying a phenotype. The reference genome sequence quality of the standard upland cotton line TM-1 has been improved since its first release [7,8,9,10,11]. The release of the NDM8 high-quality genome sequence will be a great source for modern cultivar genetics research [12]. QTL mapping studies for various traits based on reference genome sequences have been carried out, such as cotton earliness [13,14,15], plant architecture [16,17,18,19], okra leaf [20], and yield and fiber quality [6,21,22,23,24]. Wang et al. adopted restriction site-associated DNA sequencing (RAD-seq) for high-density genetic map construction and detected 33 yield- and fiber quality-related QTLs [21]. Islam et al. used genotyping by sequencing (GBS) for single nucleotide polymorphism (SNP) detection in upland cotton and validated its utility [25]. Qi et al. constructed a high-density genetic map using GBS and mapped 17 QTLs for plant architecture traits [16]. Su et al. used specific-locus amplified fragment sequencing (SLAF-seq) for GWAS analysis and found two genomic regions associated with fiber quality [26]. Sun et al. conducted GWAS analysis using 719 upland cotton accessions and detected 46 significant SNPs associated with five fiber quality traits [27]. Ma et al. resequenced 419 upland cotton accessions and found 7383 unique SNPs associated with 13 fiber-related traits [6]. Wang et al. constructed 239 RILs with chromosome segments introgressed from Gossypium barbadense into G. hirsutum and used them to detect 67 QTL for fiber quality and 37 QTL for yield, while three putative candidate genes were identified for fiber length QTL [1]. Due to the high yield and wide adaptability of G. hirsutum and the super fiber quality of G. barbadense, this research had important reference value for synchronously improving cotton yield and fiber quality. Although more than 1000 QTLs for fiber quality have been published, few QTLs have been mapped repeatedly in different populations, mainly due to the differences in materials or environments [1,3,4,28,29]. The genetic mechanism of fiber quality in high-yield cotton cultivars is still unclear, and effective molecular markers are rare.

Jifeng 914 is a nationally certified variety with a high and stable yield as well as relatively good fiber length (FL) and fiber strength (FS). Jifeng 173 is a super-quality fiber cultivar, especially regarding its FL, FS, and micronaire (MC). To detect the genetic basis underlying fiber quality, an F2 population was constructed using Jifeng 914 as the maternal material and Jifeng 173 as the paternal material. The GBS technique was applied to detect SNP markers and construct a high-density genetic map. Five fiber quality traits were observed in the F2, F2:3, F2:4, and F2:5 populations and used for QTL mapping. Genes in the stable and major QTL regions were annotated, and gene functions were first analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Online RNA-seq data and qRT-PCR were used for gene expression patterns analyses and candidate genes selection.

2. Materials and Methods

2.1. Mapping Population and Trait Evaluation

Two upland cotton varieties, Jifeng 914 and Jifeng 173, bred by the Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, were selected as parents. Jifeng 914 is a nationally certified variety with outstanding high and stable yield and super fiber quality. The most outstanding character of Jifeng 173 is the super fiber quality with longer FL, stronger FS, and better MC. Before hybridization, both Jifeng 914 and Jifeng 173 underwent multiple generations of self-pollination to obtain homozygous lines. In the summer of 2018, Jifeng 914 was used as a maternal cultivar and crossed with Jifeng 173 at Shijiazhuang. All of the F1 seeds were planted and 40 F1 plants were self-pollinated in the following winter at Sanya, Hainan province. A total of 600 g F2 seeds were harvested from the bulked self-pollinated seeds of all F1 plants and planted in 15 rows (7.0 m long and 0.76 cm apart) at Shijiazhuang on 30 April 2019, with 413 plants reserved after final singling. Two hundred normal plants from the F2 population were randomly selected and continuously self-pollinated. The derived F2:3, F2:4, and F2:5 populations were planted in a completely randomized block design with two replicates (5.0 m long and 0.76 cm apart) at Shijiazhuang on 27 April 2020, 2021, and 2022. Field management was performed under local practices.

Thirty naturally opened bolls from each row were hand-harvested. After ginning, five fiber quality traits, including FL (the average distance between the two ends of the upper half fiber in a fully stretched and straightened state), FS (the strength exerted on the fiber when it is stretched until it breaks), fiber uniformity (FU, the ratio of the average length of all fiber to the average length of the upper half fiber), MC (the measurement value of breathability of fixed weight fibers under specified conditions; it is a comprehensive indicator of fineness and maturity), and fiber elongation rate (FE, the ratio of the length of fiber at the time of tensile break to the FL), were evaluated by a high-volume instrument (HFT9000) at the Cotton Quality Testing Center in Institute of Cotton Research, Chinese Academy of Agricultural Science (CRI CAAS). Fiber quality data were analyzed using Excel 2010 and SPSS (17.0) software.

2.2. QTL Mapping

Previously, a high-density genetic map was constructed [30]. A total of 416 G sequence data were obtained by genotyping by GBS, with an average of 25.91 G and 9× depth in the parents and 1.82 G and 0.7× depth in the F2 plants. The Q30 score reached 95.68%. The genetic map contained 11,488 SNPs and spanned 4202.12 cM in length, ranging from 150.74 cM on A3 to 178.90 cM on A9. The SNPs were unevenly distributed on the 26 linkage groups, with only 30 SNPs on A2 and 1318 SNPs on D5. Based on this genetic map, additive effect QTLs were analyzed using inclusive composite interval mapping (ICIM) with QTL IciMapping 4.0 software. The parameters were set as Step = 1cM and PIN = 0.001. LOD scores were determined using a 1000 permutation test. QTLs contributing more than 10% of the phenotypic variation (PV) are denoted as major effect QTLs. QTLs mapped in at least two populations are denoted as stable QTLs.

2.3. Gene Annotation and Candidate Gene Selection

Annotated genes in the major or stable QTLs were obtained using the reference genome sequence information [9] using the CottonFGD database (https://cottonfgd.net/, accessed on 28 November 2023) [31]. Gene expression levels were analyzed using the online database CottonMD (http://yanglab.hzau.edu.cn/CottonMD, accessed on 28 November 2023) [32]. Candidate genes were first screened based on the highly mapped GO and KEGG terms. qRT-PCR was conducted to analyze gene expression patterns, using ovule samples (−1DPA, 0DPA, 1DPA) from Jifeng 914 and Jifeng 173 and fiber samples (5DPA, 10DPA, 15DPA) from Jifeng 4 (JF4) and Jifeng 4xuan (JF4x) as materials. The difference in lint percentage between Jifeng 914 and Jifeng 173 is greater, and JF4 and JF4x are sister lines. JF4 has better fiber quality than JF4x, especially fiber length and fiber strength. Total RNA was extracted using a Plant RNA Purification Kit (Tiangen, Beijing, China). First-strand cDNA was reverse transcribed from 1 μg total RNA using a FastKing gDNA Dispelling RT SuperMix Kit (Tiangen, Beijing, China). qRT-PCR was carried out with the SYBR Premix Ex Taq (TAKARA, Shiga, Japan) on a LightCycler480 instrument (Rotkreuz, Switzerland).

3. Results

3.1. Phenotypic Variation and Trait Correlation Analysis

Five fiber quality traits, including FL, FS, FU, MC, and FE, were investigated. Significant differences were found between the parents, especially in FL, FS, and MC (

Table 1. Basic statistics of the five tested traits in the parent and offspring populations.

Trait	Year	Parents		Offspring Populations
		Jifeng 914	Jifeng 173	Max	Min	Mean	Skew	Kurt	CV(%)
FL (mm)	2019	29.00	32.10 **	32.90	25.90	28.96	−0.06	−0.13	4.62
	2020	29.25	31.65 **	35.10	26.70	30.97	0.00	0.47	4.37
	2021	30.15	32.65 **	35.50	27.60	31.68	0.18	−0.26	4.61
	2022	29.65	32.40 **	33.10	26.80	30.38	−0.16	−0.39	4.14
FS (cn/tex)	2019	31.10	34.70 **	37.50	24.90	30.82	−0.09	−0.56	6.94
	2020	30.50	31.35 **	34.20	26.00	30.43	0.12	0.04	4.96
	2021	30.20	33.85 **	37.00	28.20	32.72	−0.13	−0.02	5.42
	2022	28.50	33.30 **	35.90	26.30	31.25	−0.19	0.22	5.78
FU (%)	2019	84.80	84.90	87.40	81.20	84.45	−0.25	−0.09	1.33
	2020	84.60	84.30	87.30	80.70	84.48	−0.34	0.34	1.27
	2021	82.65	83.70 *	86.30	81.20	83.79	−0.30	−0.03	1.25
	2022	87.80	85.10 **	88.00	82.20	85.53	0.15	−0.28	1.18
MC	2019	5.60	4.50 **	5.80	4.00	5.06	−0.49	−0.09	6.69
	2020	5.30	4.80 **	5.80	4.00	4.92	−0.14	−0.72	8.25
	2021	5.25	4.45 **	5.70	3.70	4.67	−0.42	0.05	7.96
	2022	5.20	4.50 **	5.40	3.50	4.49	−0.24	−0.04	8.35
FE (%)	2019	6.80	6.80	6.90	6.60	6.77	−0.07	−0.22	0.88
	2020	6.80	6.80	7.00	6.50	6.78	−0.24	0.35	1.25
	2021	6.80	6.70	6.90	6.70	6.78	0.81	−0.61	0.67
	2022	6.00	6.00	6.10	6.00	6.00	17.74	4.42	0.35

Note: FL, fiber length; FS, fiber strength; FU, fiber uniformity; MC, micronaire; FE, fiber elongation rate; CV, coefficient variation; *, p = 0.05; **, p = 0.01.

). Jifeng 173 showed longer FL, stronger FS, and smaller MC in all four years. Both the maximum and minimum values in the offspring populations exceeded the parents, indicating the transgressive segregation of the five observed traits, and minor effect alleles controlling these traits existed in both parents (Figure S1). The absolute values of skew and kurt in the tested populations were smaller than one except for FE in 2022, which indicates that most of the observed traits presented nearly normal distributions. MC has the largest coefficient variations (CVs) over four years, followed by FS, FL, FU, and FE. Thus, this population is appropriate for fiber quality-related QTLs mapping.

Complex correlations were observed among FL, FS, FU, MC, and FE over four years (Table S1). Significant positive correlations were observed within the trait pairs, such as FL-FS, FL-FE, FS-FE, MC-FU, and FE-FU. Significant negative correlations were observed between FL and MC, between FS and MC, and between FE and MC. No consistent correlation was observed between FL and FU or between FS and FU. The complex phenotypic correlations reflected the complex genetic interactions underlying these fiber quality traits.

3.2. QTL Mapping for Fiber Quality Traits

In this study, a total of 108 QTLs were found, including 27 for FL, 16 for FS, 24 for MC, 17 for FU, and 24 for FE (Table S2). Chromosome A7 contained the highest number of QTLs with 12. No QTL was found on chromosomes D1 and D11. Each QTL contributed 1.98–21.45% to the PV, 13 QTLs contributed more than 10% to the PV and nine QTLs could be mapped repeatedly (Figure 1). Jifeng 914 conferred favorable alleles for 49 QTLs (positive values of the additive effect), and Jifeng 173 conferred another 59 favorable alleles (negative values of the additive effect).

Fiber length. Twenty-seven QTLs were mapped on 18 chromosomes, including three major effect QTLs and four stable QTLs. The favorable alleles for 13 and 14 QTLs were conferred by Jifeng 914 and Jifeng 173, respectively. The most outstanding QTL was qFL-D3-1 conferred by Jifeng 914, which was repeatedly mapped in the F₂, F_2:4, and F_2:5 generations and contributed 9.18–21.45% to the PV. qFL-A12-3 was mapped in the F_2:3 and F_2:4 generations and contributed 10.18–13.80% to the PV, and was conferred by Jifeng 173. In addition, qFL-A6-1, qFL-A12-1, and qFL-D9-1 were mapped in two generations with favorable alleles conferred by Jifeng 914, qFL-A11-1 contributed 10.00% to the PV with a favorable allele conferred by Jifeng 173.

Fiber strength. Sixteen QTLs were mapped on ten chromosomes, including only two stable and major effect QTLs. Jifeng 914 and Jifeng 173 conferred favorable alleles for nine and seven QTLs, respectively. qFS-A11-1 was mapped in the F_2:3, F_2:4, and F_2:5 generations, contributed 6.05–10.41% to the PV, and was conferred by Jifeng 173. qFS-D10-2 was mapped in the F₂ and F_2:4 generations and contributed 10.54–17.51% to the PV, and was conferred by Jifeng 914.

Micronaire. Twenty-four QTLs were mapped on seventeen chromosomes, and five major effect and two stable QTLs were found. Jifeng 914 and Jifeng 173 conferred favorable alleles for nine and fifteen QTLs, respectively. qMC-D6-2 was mapped in the F₂ and F_2:3 generations and contributed 10.15–10.91% to the PV, and was conferred by Jifeng 173. qMC-D8-1 was mapped in the F_2:3 and F_2:4 generations and contributed 6.39–10.97% to the PV, and was conferred by Jifeng 914. In addition, qMC-D6-1, qMC-D7-1, and qMC-D10-1 contributed 10.81%, 13.16%, and 12.90% to the PV, respectively.

Fiber uniformity. Seventeen QTLs were mapped on 13 chromosomes. Jifeng 914 and Jifeng 173 conferred favorable alleles for eight and nine QTLs, respectively. Only two major QTLs were found, including qFU-D7-1 and qFU-D10-1, which contributed 14.77% and 10.19% to the PV, respectively. No stable QTL was found over four years, indicating a significant environmental effect on FU.

Fiber elongation. Twenty-four QTLs were mapped on 13 chromosomes. Jifeng 914 and Jifeng 173 conferred favorable alleles for 10 and 14 QTL, respectively. In addition, six QTLs were mapped on chromosome D5 and Jifeng 173 conferred favorable alleles, which might reflect the importance of D5 from Jifeng 173 in regulating FE. Only one major effect QTL was found. qFE-D6-1 contributed 10.72% to the PV and was conferred by Jifeng 914. No stable QTL was found over the four years, which indicated a significant environmental effect on FE.

QTL-overlapping regions. A total of 14 QTL-overlapping regions were found on 12 chromosomes (Figure 1). The additive effect directions of the QTL in 10 out of these 14 regions are different, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The 0–4.5 cM region on A11 was covered by three QTLs (qMC-A11-1, qFL-A11-1 and qFS-A11-1). qFL-A11-1 is a major effect QTL, and qFS-A11-1 is a stable QTL with 10.41% contribution ratio to the PV. The 49.5–57.5 cM region on D3 was covered by two QTLs (qFL-D3-2 and qFU-D3-1). qFL-D3-2 is a stable QTL with 9.18–21.45% of contribution ratios to the PV. The 165.5–170 cM region on D6 was covered by two major QTLs (qFE-D6-1 and qMC-D6-2), and qMC-D6-2 could be repeatedly mapped. The 99.5–100.5 cM on D10 was covered by four QTLs (qFL-D10-2, qFS-D10-2, qFU-D10-1, and qMC-D10-1), of which three are major effect QTLs and one is a stable QTL.

3.3. Candidate Gene Analysis

There are six major effect QTLs that could be repeatedly mapped in at least two generations, including qFL-D3-2 and qFS-A11-1, mapped in three generations, and qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1, mapped in two generations. There are no genes in qFS-D10-2 (16596338 bp-16630630 bp), qMC-D6-2 (1054134 bp-1066524 bp), or qMC-D8-1 (9129475 bp-9137306 bp), while 810 genes were annotated in qFS-A11-1, qFL-A12-3, and qFL-D3-2 including 6 genes in qFS-A11-1 (119649722 bp-119686364 bp), 6 genes in qFL-A12-3 (10414004 bp-10555066 bp), and 799 genes in qFL-D3-2 (6090201 bp-41836768 bp) (Table S3). Upon using GO and KEGG analysis, 392 GO terms and 62 KEGG pathways were mapped under the corrected p value < 0.05 (Table S4). The most significant GO term is protein binding, and the most significant KEGG pathway is metabolic pathways. Among the 265 genes in the mapped protein binding term and metabolic pathways, 15 genes located in the qFL-D3-2 region are highly expressed at different fiber development stages (Table S5). Thirteen genes are highly expressed during the fiber elongation stage (5-25DPA fiber), which determines FL. Five genes, Ghir_D03G010890, Ghir_D03G010910, Ghir_D03G006470, Ghir_D03G010430, and Ghir_D03G010470, were highly expressed during the lint fiber initiation stage (−3-3DPA ovule). As FU-related QTL qFU-D3-1 overlapped with qFL-D3-2, these five genes may affect FL and FU by regulating fiber initiation. Based on the present sequencing depth, a total of 116 SNPs and 40 InDels markers were found in the qFL-D3-2 region, and none of these markers were located in protein coding regions of the above mentioned genes (Table S6). Based on the eight mapped SNP markers in the qFL-D3-2 region, it is difficult to narrow down the interval length to fine map qFL-D3-2 (Table S7).

Ovules from the fiber initiation stage and fibers from the fiber elongation stage were used for qRT-PCR. The expression levels of the above mentioned 15 genes in the ovules were higher in Jifeng 914 than in Jifeng 173 (Figure 2). Nine genes showed continuously increased expression trend from -1DPA to 1DPA ovules, and two genes (Ghir_D03G005440.1 and Ghir_D03G011310.1) showed significantly high expression levels in 1DPA ovules. Two genes (Ghir_D03G005100.1 and Ghir_D03G010910.1) showed continuously decreased expression trend from −1DPA to 1DPA ovules. Gene expression patterns in fibers were more complex, and most of the gene expression patterns between JF4 and JF4x were different (Figure 3). Two genes (Ghir_D03G006470.1 and Ghir_D03G007410.1) showed continuously increased expression trend from 5DPA to 15DPA fiber. The expression level of Ghir_D03G006470.1 in 15DPA fiber is fifty times higher than that in 5DPA fiber, and this gene had higher expression level in 5DPA and 10DPA fiber in JF4 and higher expression level in 15DPA fiber in JF4x. It is predicted that Ghir_D03G006470.1 has a more prominent role in promoting fiber elongation.

4. Discussion

Cotton is the leading natural fiber crop and cash crop in the world [5,33]. Yield and fiber quality traits are the most important economic traits in cotton production. Boll weight (BW), lint percentage (LP), and seed index (SI) are three vital yield components. FL, FS, FU, MC, and FE are the first five fiber quality traits. All of these traits are quantitatively genetically controlled, and significant positive or negative correlations exist among them, such as significant positive correlations between FL and FS, FL and SI, and FS and SI, and significant negative correlations between FL and MC, FS and MC, FL and LP, FS and LP, and SI and LP [28]. Similar results were found in our study, and complex correlations were observed among the five fiber quality traits. For example, significant positive correlation between FL and FS, as well as significant negative correlations between FL and MC and between FS and MC, were the same as in previous studies [1,6,28,29], while the correlations of other trait pairs, such as FL-FE, FS-FE, MC-FU, FE-MC, FL-FU, and FS-FU, were not entirely consistent across different studies [1,6,28,29]. The main reason may be the difference in experimental materials and environmental conditions. Improvements in cotton yield and fiber quality through traditional breeding are more difficult to counter these unfavorable correlations.

High-density genetic maps are a prerequisite for molecular genetic detection of vital traits, and reduced-representation sequencing techniques supply more convenience, which makes the molecular marker detection procedure more time-efficient. Wang et al. applied RAD-seq in cotton mapping parents sequencing and found 1323 SSR, 3838 InDel, and 9366 SNP markers [21]. Jia et al. constructed a genetic map containing 6295 SNPs with only 0.63 cM between adjacent markers using RAD-seq [15]. Li et al. adopted GBS in marker detection and constructed a genetic map containing 3978 SNPs [13]. Zhang et al. published a genetic map with 5521 SNPs and 3259.37 cM using SLAF-seq [34]. Ma et al. constructed a genetic map that contained 7709 SNPs and spanned 3433.24 cM using GBS [18]. In this study, we applied GBS in molecular marker detection, and a total of 1,305,642 polymorphic SNPs were found. A high-density genetic map containing 11,488 SNPs was constructed. The genetic map spanned 4202.12 cM, with 0.37 cM between adjacent markers. The quality of the constructed genetic map was evaluated by marker collinearity analysis and heat map analysis. The marker density and map quality were comparable with these previously published high-density genetic maps.

Up to now, more than 1000 fiber quality and yield-related QTLs have been published and are distributed on almost all 26 chromosomes [1,3,4,28]. By comparing with published results [1,27,28,35,36], 32 QTLs in this study have been reported, and 76 new QTLs might have been identified (Table S2). Only three of the sixteen major or stable QTLs overlapped with previously reported QTL regions, which reflected the parental genetic diversity used in this study. Six outstanding QTL regions were hopeful prospects for candidate gene fine mapping, including qFL-D3-2 and qFS-A11-1 mapped in three generations and qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1 mapped in two generations.

Among the 15 genes in the qFL-D3-2 region with high expression levels at different fiber development stages, Ghir_D03G006470 is a SWEET1 gene and Ghir_D03G005100 and Ghir_D03G005720 are CYP89A9 genes. As reported, GhSWEET12 RNAi plants produced shorter fibers [37]. PAG1, one homolog gene of Arabidopsis CYP734A1, plays a crucial role in regulating FL [38]. Thus, Ghir_D03G006470, Ghir_D03G005100, and Ghir_D03G005720 may also have important functions during cotton fiber development. In addition, Ghir_D03G010890 and Ghir_D03G010910 are UDP-glycosyltransferase genes, Ghir_D03G010430 is a eukaryotic translation initiation factor 5A gene, Ghir_D03G010470 is ribosomal protein S5 family protein gene, Ghir_D03G009660 is a luminal-binding protein 5 gene, and Ghir_D03G005440 is a cinnamoyl-CoA reductase-like SNL6 gene. Based on the published literature, UDP-glycosyltransferase, eukaryotic translation initiation factor 5A, ribosomal protein, luminal-binding protein 5, and cinnamoyl-CoA reductase are key factors involved in cotton fiber development [12,39,40,41,42,43]. qRT-PCR results revealed that most of the above mentioned 15 genes showed continuously increased expression from -1DPA to 1DPA ovules, and Ghir_D03G005440.1 and Ghir_D03G011310.1 had significantly high expression levels in 1DPA ovules. These genes may play a role in promoting fiber initiation, which has a significant impact on fiber length and fiber uniformity. Ghir_D03G006470.1 in 15DPA fiber is fifty times higher than that in 5DPA fiber, and this gene had higher expression levels in 5DPA and 10DPA fiber in JF4 and higher expression levels in 15DPA fiber in JF4x, indicating that this gene may be beneficial for promoting fiber elongation. Therefore, more work needs to be performed to fine map these outstanding QTLs and to screen out and validate the key genes regulating fiber development.

In summary, 6 outstanding QTLs out of 108 QTLs were screened in four generations. Fifteen genes in the qFL-D3-2 region were predicted as candidates for regulating FL as they showed high expression levels during the fiber initiation and elongation stages.