Genome-wide association study of fiber yield-related traits uncovers the novel genomic regions and candidate genes in Indian upland cotton (Gossypium hirsutum L.)

Upland cotton (Gossypium hirsutum L.) is a major fiber crop that is cultivated worldwide and has significant economic importance. India harbors the largest area for cotton cultivation, but its fiber yield is still compromised and ranks 22nd in terms of productivity. Genetic improvement of cotton fiber yield traits is one of the major goals of cotton breeding, but the understanding of the genetic architecture underlying cotton fiber yield traits remains limited and unclear. To better decipher the genetic variation associated with fiber yield traits, we conducted a comprehensive genome-wide association mapping study using 117 Indian cotton germplasm for six yield-related traits. To accomplish this, we generated 2,41,086 high-quality single nucleotide polymorphism (SNP) markers using genotyping-by-sequencing (GBS) methods. Population structure, PCA, kinship, and phylogenetic analyses divided the germplasm into two sub-populations, showing weak relatedness among the germplasms. Through association analysis, 205 SNPs and 134 QTLs were identified to be significantly associated with the six fiber yield traits. In total, 39 novel QTLs were identified in the current study, whereas 95 QTLs overlapped with existing public domain data in a comparative analysis. Eight QTLs, qGhBN_SCY_D6-1, qGhBN_SCY_D6-2, qGhBN_SCY_D6-3, qGhSI_LI_A5, qGhLI_SI_A13, qGhLI_SI_D9, qGhBW_SCY_A10, and qGhLP_BN_A8 were identified. Gene annotation of these fiber yield QTLs revealed 2,509 unique genes. These genes were predominantly enriched for different biological processes, such as plant cell wall synthesis, nutrient metabolism, and vegetative growth development in the gene ontology (GO) enrichment study. Furthermore, gene expression analysis using RNAseq data from 12 diverse cotton tissues identified 40 candidate genes (23 stable and 17 novel genes) to be transcriptionally active in different stages of fiber, ovule, and seed development. These findings have revealed a rich tapestry of genetic elements, including SNPs, QTLs, and candidate genes, and may have a high potential for improving fiber yield in future breeding programs for Indian cotton.

1 Introduction

Cotton is one of the most important cash crops used for natural textile fibers and oilseeds. It is cultivated worldwide as the primary raw material for the textile industry (Chen et al., 2007). It is derived from the Arabic word “quotn,” “gutum,” or “kutum” meaning those crops which possess spinnable fibers on their seed coat (Lee, 1984). Among the plant species commonly used by people, fiber-yielding crops hold the second position after food crops. Cotton serves as an excellent model for studying various aspects, such as plant cell elongation, evolution, cellulose biosynthesis, and plant polyploidization (Qin and Zhu, 2011; Huang et al., 2021). It belongs to the genus Gossypium in the family Malvaceae and is one of the largest genera in the Gossypieae tribe owing to its high species diversity. It encompasses approximately 44 diploid (2n = 2x = 26) and seven tetraploid (2n = 4x = 52) species (Grover et al., 2015; Ditta et al., 2018).

Cotton is cultivated in more than 123 countries, encompassing regions ranging from arid to semi-arid areas in the tropics and subtropics. Among the four cultivated species, upland cotton (Gossypium hirsutum) constitutes ~95% of the global cotton production because of its high adaptability to various environments and significant yield (Chen et al., 2007). Thus, compared with other cultivated species, most breeding and improvement programs related to fiber yield, fiber quality, insect resistance, and drought tolerance, are mainly focused on upland cotton. Genetic improvement for high cotton fiber yield has always remained the primary focus of cotton breeders to increase their productivity. With progress and advancement in the textile industry, the demand for high fiber yield and quality is growing exponentially. Although fiber quality is an essential trait in cotton breeding programs (Rong et al., 2007; Said et al., 2013), enhancing cotton fiber yield using Indian cotton varieties is still a primary goal. In comparison to the rest of the world, India has the largest land area under cotton cultivation (12,150 thousand hectares), yet its productivity is greatly compromised (only 457 kg/ha) by 2022 (US Department of Agriculture, https://usda.library.cornell.edu/). In India, most cotton cultivars are released using conventional breeding techniques based on morphological traits that are affected by agronomic practices and environmental changes. Consequently, the diversity and quality of elite cotton are reduced daily because of the narrow genetic background, domestication, and selection of elite cotton cultivars (Iqbal et al., 2001; Rungis et al., 2005; Abdurakhmonov et al., 2012); thus, genetic improvement in cotton fiber yield and other quality traits is still challenging.

Cotton fiber yield is a multifaceted quantitative trait influenced by various traits, including boll weight (BW), boll number (BN), seed cotton weight (SCW), lint percentage (LP), lint index (LI), seed index (SI), first fruit branch position (FFBP), plant height (PH), flowering period (FP), fruit spur branch number (FSBN), and many others (Li F. et al., 2018; Sun et al., 2018). Such quantitative traits are governed by both quantitative trait loci (QTLs) and environmental factors, which are difficult to concurrently improve using traditional breeding techniques. However, the advancement of applied genomics research has introduced the use of QTL-linked or QTL-associated molecular markers in marker-assisted selection (MAS) and/or genomic selection programs. These emerging techniques offer promising avenues for enhancing the efficiency of cotton breeding and simultaneously targeting specific traits. QTL mapping has been extensively employed to analyze the genetic variations underlying complex traits in cotton, including fiber quality and yield component traits (Rong et al., 2007; Said et al., 2013; Said et al., 2015). Over the last 20 years, biparental linkage mapping in upland cotton has led to the identification of numerous QTLs associated with cotton yield (Zhang et al., 2005; Abdurakhmonov et al., 2007; Shen et al., 2007; Wan et al., 2007; Liu et al., 2012; Yu et al., 2013a; Yu et al., 2013b; Wang et al., 2014; Liu D. et al., 2015). Nevertheless, biparental QTL mapping encounters two main challenges: limited allelic diversity and restricted genomic resolution due to the relatively low number of recombination events that occur throughout the development of the mapping population (Jannink et al., 2001; Flint-Garcia et al., 2005; Hall et al., 2010).

To overcome these limitations of bi-parental QTL mapping, association mapping offers an alternative approach for mapping QTL, which relies on linkage disequilibrium (LD). It determines whether particular alleles in a population are more frequently associated with specific phenotypes than expected, thereby providing insights into the genetic basis of complex traits (Flint-Garcia et al., 2003). In addition, this analysis can be applied to a large natural population, allowing the identification of traces of genetic crossovers and loci responsible for traits at a much higher resolution than previously reported techniques. In recent years, genome-wide association studies (GWASs) have emerged as a more precise and cost-effective method for identifying significant QTLs or genes linked to complex traits than linkage mapping. The first association mapping study in cotton focusing on fiber quality was reported in G. arboretum (Kantartzi and Stewart, 2008). Subsequently, association mapping has been extensively applied in upland cotton to investigate crucial economic traits, including fiber quality (Abdurakhmonov et al., 2008; Nie et al., 2016; Liu et al., 2020; Song et al., 2021), seed oil content (Liu G. et al., 2015; Yuan et al., 2018; Zhao et al., 2019), fiber yield (Ademe et al., 2017; Sun et al., 2018; GUO et al., 2021; Niu et al., 2023), and biotic and abiotic stresses (Fang et al., 2010; Bardak et al., 2021; Xu et al., 2021; Zhang et al., 2021; Zhao et al., 2021), as well as those associated with epistasis and environmental interactions (Jia et al., 2014).

To date, studies on association mapping using Indian germplasm are scarce and were first reported by Handi et al. (2017), who used a cotton 63 K SNP chip to associate fiber yield and fiber quality traits in 201 upland cotton germplasm lines. Owing to their limitations, SNP chip-based studies only fetch SNPs at a specific location in the genome (where relevant information is gathered), and genotyping-by-sequencing (GBS) methods, on the other hand, proved to be an attractive approach for discovering and genotyping high-density SNPs. Although other marker classes can also be used for this study (for example, Kumar et al. (2021) used SSRs), SNPs demonstrated higher resolution in revealing genetic relatedness as well as delineating population structure in crops. Additionally, since SNP markers are abundant and primarily derived from genes, genetic diversity studies using these markers can reveal the functional variation that can be used in association mapping studies for specific traits (Singh et al., 2013).

Therefore, the specific goals of the present study were to (a) examine the phenotypic variability within the upland cotton germplasm released from different cotton growing belts in India; (b) explore the QTNs (qualitative trait nucleotides) or SNP markers underlying fiber yield traits; (c) compare identified QTLs with previously reported QTLs for yield traits, if available; and (d) identify and validate through expressional profiling of putative candidate genes found within the genomic regions controlling yield traits. Our results using 117 diverse Indian germplasms and 2,41,086 SNPs identified many stable and novel QTL/genes that may offer crucial information on the genetic control of fiber yield traits in cotton. This information will aid in improving cotton yield and the development of elite Indian cotton varieties through marker-assisted breeding programs.

2 Materials and methods

2.1 Plant material

An association panel consisting of 117 Indian upland cotton (G. hirsutum L.) germplasm was procured from All India Coordinated Research Project (AICRP) on Cotton, ICAR-Central Institute for Cotton Research, Regional Station, Coimbatore, India. These materials were selected based on their phenotypic expression with reference to fiber yield. The germplasms used in this study constitute the varieties released from diverse cotton-growing belts (Northern, Southern, and Central) of India. Out these, 31, 43, and 32 germplasms were from the northern, southern, and central zones, respectively, and information was unavailable for 11 germplasms zones (Supplementary Table S1).

2.2 Field experiment locations

Field experiments for precise phenotyping were conducted in two different natural environments in India’s cotton-growing regions: the northern and southern zones. In the northern zone, the investigated plant materials were evaluated at the Punjab Agriculture University, Regional Research Station, Faridkot (30°40’32.4”N 74°44’57.3” E; hereafter referred as E1) and in the southern zone, it was grown at Tamil Nadu Agriculture University, Coimbatore (11°07’3.36”N 76°59’39.91” E; hereafter referred as E2). There was a significant disparity in the agro-climatic conditions between the two cotton-growing regions, including variations in the soil type, rainfall, temperature, and growing season. Cotton cultivation in E1 was done in alluvial soil during May–November 2021 (under high temperatures), while in E2, plant materials were grown under red soil at relatively lower temperatures.

2.3 Experimental design and phenotypic trait measurements

The plants comprising the association panels were sown following a randomized block design (RBD) with ten biological replicates of each germplasm accession. The plant-to-plant spacing was 45 cm, whereas the row-to-row spacing was 90 cm, and there were 10 plants in each row.

The cotton crop in E1 was established following regular practices such as field preparation with fine tilth, sowing with a single seed/hill, fertilizer applications (basal: 150 kg of urea and 50 kg muriate of potash, and top dressing: 50 kg urea and 25 kg diammonium phosphate). Imidacloprid was applied to control mites and sucking pest infestation that was noticed during the early period of the cropping program (25th DAS). In E2, cotton cultivation was supplemented with 12 kg/acre of phosphorus as a pre-planting application, followed by separate applications of 15 kg/acre of nitrogen during both the thinning and flowering stages. Osheen 20 SG (dinotefuran) was applied to safeguard the cotton crop against sucking pests such as whiteflies and jassids to maintain healthy crops.

At maturity, all opened bolls were harvested from the surviving healthy individual plants to estimate the fiber yield traits, such as BN, BW(g), LI (g), LP (%), SCY(g/plant), and SI (g). For BN, the total number of bolls was counted from each plant, BW was measured as the average weight of 10 mature healthy bolls from each plant, and SCY per plant was the total weight of the seed along with the lint. Other traits were subsequently measured after ginning the cotton bolls; LP (Ginning Out Turn) is defined as the percentage of lint weight obtained from a given weight of seed cotton and was calculated using the formula given below; LI is the weight of the lint produced by the 100 seeds and it was calculated using the formula given below; SI was measured as the weight of 100 healthy seeds.

$$\text{Lint}\%(\text{GOT})=\frac{\text{Lint\hspace{0.33em}weight\hspace{0.33em}in\hspace{0.33em}a\hspace{0.33em}sample}}{\text{Seed\hspace{0.33em}cotton\hspace{0.33em}weight}}\times 100$$

$$\text{Lint\hspace{0.33em}Index}(\text{LI})=\frac{\text{SI}\times \text{Lint}\%}{100-\text{Lint}\%}$$

2.4 Phenotypic data analysis

The mean, coefficient of variation (CV), standard deviation (SD), Pearson’s linear correlation coefficients, variance components, Broad sense heritability (h²), and BLUP-based prediction of the mean for the multi-environment trait (two locations) were calculated using the “METAN,” “Phenotype,” and “corrplot” packages in R environment (Team, 2016). GWAS analysis was performed using the individual mean of the two locations and the BLUPed mean of two location trait data of 117 germplasms.

2.5 Genomic DNA extraction, GBS library preparation, genotyping, and SNP call

Genomic DNA was extracted from fresh young leaves using a modified cetyltrimethylammonium bromide (CTAB) method (Shukla et al., 2021). To ensure the accuracy and reliability of the DNA samples, stringent quality control (QC) procedures were performed using agarose gel electrophoresis, NanoDrop® 2000 spectrophotometer, and Qubit® 2.0 fluorometer. For library preparation, 0.3–0.6 0μg of high-quality genomic DNA was digested completely with the in silico optimized restriction enzyme set MseI (frequent cutter) and HaeIII_MspI (rare cutter) followed by efficient adapter ligation. After library preparation, high-throughput paired-end DNA sequencing was performed using the Illumina® HiSeq 2500 platform. Variant calling was performed using the Genome Analysis Toolkit (GATK package, version 4.2.6.1) (Van der Auwera et al., 2013). In brief, the fastq files were converted into uBAM (unmapped BAM) format, followed by Marking of Illumina adapter sequence with the MarkIlluminaAdapters function. The marked uBAM files were converted back to fastq format and aligned to the G. hirsutum TM-1 reference genome (https://www.ncbi.nlm.nih.gov/assembly/GCF_007990345.1/) using BWA-mem, and then a clean BAM file was created using MergeBamAlignment (Li, 2013). Clean BAM files were sorted using Picard Sort Sam and marked for duplicate reads using a mark-duplicate function. Single genotype variant identification was performed with Haplotype Caller, and 117 VCF generated was used to create a variant database using GenomicsDBImport, followed by joint genotyping of 117 genotypes. The variants were filtered for QD< 2.0, FS > 60.0, MQ< 40.0, MQRankSum< −12.5, ReadPosRankSum< −8.0, SOR > 3.0, indel, minor allele frequency >0.05 and max-missing 0.1 for SNP trait association analysis (Van der Auwera and O’Connor, 2020).

2.6 Population structure, PCA, and genetic diversity analysis

To estimate the genetic differences between these 117 Indian cotton germplasms, population structure, molecular phylogenetic (neighbor-joining), principal component analysis (PCA), and kinship (k) analysis were performed. The filtered variants were pruned based on linkage disequilibrium (indep 50 5 0.5) using Plink (version 1.9) for PCA and population structure analysis (Purcell et al., 2007). The pruned variants were converted to a structure format using PGDSpider_2.1.1.5 (Lischer and Excoffier, 2012). STRUCTURE software (version 2.3.1) (Falush et al., 2007) was used to investigate the presence of subgroups in our association panel using Bayesian clustering. The Structure parameters used were 1–10 k, 10 replicates at each k, 100,000 burn-in, and 100,000 MCMC reps after burn-in, after which, the structure harvester was used to calculate the delta k value and prepare the Clumpp individual file. Clumpp was used to calculate the consensus membership coefficient value from the 10 replicates of the k run, and the inferred membership from Clumpp was used for further analysis and cluster visualization (Earl and VonHoldt, 2012). These links were used for principal component analysis (PCA). TASSEL5 was used for tree construction using the neighbor-joining method based on a modified Euclidean distance matrix (Bradbury et al., 2007).

2.7 SNP trait association analysis

The Genome Association and Prediction Integrated Tool (GAPIT), an R package was used for SNP (marker) trait association analysis. Three single-locus models (GLM, MLM, and CMLM), and three multi-locus models (MLMM, FarmCPU, and BLINK) were used (Wang and Zhang, 2021). The kinship coefficient matrix calculated in TASSEL5 was used as co-variables in the GWAS model and the PCA based on the Bayesian Information Content parameter implemented in GAPIT was used to account for population stratification for each trait with setting “model.selection=TRUE” to reduce the false discovery. The Manhattan and QQ plots were drawn using a significance threshold of p<0.000031 (−logP >4.5) using the ‘CMplot’ R package.

2.8 Identification of trait-associated QTLs and annotation of candidate genes

Following previously reported methods (Song et al., 2019; Sun Z. et al., 2017), LD sizes of ±200 kb upstream and downstream regions of significant SNPs were defined as QTLs, and SNPs within these regions were of the same locus. The co-location study of our GWAS-identified loci and previously reported results was implemented using the following steps: (1) all the previously reported QTLs and GWAS signals for yield-related traits were obtained from the http://cotton.zju.edu.cn/Qtl_phe.html database and association mapping reports; (2) the physical location/genomic coordinates of the SNPs in the QTL loci were retrieved through BLASTn (250 Bp flanks) against the G. hirsutum genome (Zhang et al., 2015; Hu et al., 2019); and (3) the coordinates of previous QTLs were compared with the QTLs identified in this study. Haplotype analysis was performed using the geneHapR package (Zhang et al., 2023), where haplogroups detected in five or more germplasms were considered for association analysis. The phenotypic value of each haplotype was assessed by calculating the average phenotypic value across the germplasm with each type of SNP locus linked to a specific target trait. In this study, favorable haplogroups were defined as the haplotype (combination of SNPs) that showed the highest average values over the other haplotypes. The gene located within the identified QTL region was mined and functional annotation was performed using BLAST2GO. Gene ontology enrichment analysis of the identified genes for individual traits was performed using the clusterProfiler package in R (Wu et al., 2021).

2.9 Expression analysis of genes in the associated region

RNA-Seq datasets obtained from various cotton tissues, including root, stem, leaf, torus, seed, cotyledon, ovule, fiber at 5 days post-anthesis (DPA), fiber at 10 DPA, fiber at 20 DPA, and fiber at 25 DPA, were downloaded from NCBI BioProject with the accession number PRJNA248163.The data were preprocessed for quality and adapter trimming using the Trimmomatic tool in the PE mode (Bolger et al., 2014). Alignment with the cotton genome was performed using the splice-aware aligner Hisat2. StringTie was used for count gene-level fragments per kilobase per million mapped read (FPKM) (Pertea et al., 2016). The FPKM count was normalized to zFPKM transformation with the zFPKM package of R, and genes with zFPKM values of ±3 in at least one sample were considered as expressed genes (Hart et al., 2013). The R package “pheatmap” (Kolde, 2019) was used to generate heat maps depicting the expression patterns of potential candidate genes.

3 Results

3.1 Phenotypic variability

The present study evaluated the phenotypic variability of six yield-related traits for an association panel of 117 Indian upland cotton germplasms in two environments, E1 and E2. A significant and extensive range of phenotypic variation was observed for all investigated traits (Table 1). BN, BW, LI, LP, SCY, and SI, exhibit values ranging from 17–39.2 (per plant), 2.24–5 (g), 3.44–5.89 (g), 31.2–46.2 (%), 45.8–177 (g/plant), and 5.55–10.3 (g), with an average of 24.6 g, 3.56 g, 4.54 g, 36.2% 88.1 (g/plant), and 7.87 g in E1, while for E2, all the six traits ranged from 15.3–30 (per plant), 2.12–4.8 (g), 3.4–6.53 (g), 32–44.3 (%), 42–120 (g/plant), and 5.65–10.8 (g), with an average of 22.9 g, 3.03 g, 4.52 g, 37.6%, 69.6 (g/plant), and 7.35 g, respectively.

TABLE 1

Table 1 Description of phenotypic traits in two environments (E1 and E2).

The coefficient of variance (CV) and heritability (h²) ranged from 6.32% to 24.7% and 63.3% to 90.6%, respectively for E1, whereas in E2 the same was ranging from 6.17% to 20.1% and 46.6% to 78.3%, respectively for all six yield-related traits (Table 1). When compared with E1, E2 had lower heritability for all traits, indicating that the environmental conditions of E2 critically influence all six traits. The highest heritability was exhibited for SCY (90.6%) in E1, whereas the lowest was exhibited for LP (46.6%) in E2. The distribution pattern, box plot and phenotypic correlation analysis for yield-contributing traits were also calculated using the mean data of both environments as well as the data obtained from the BLUP analysis. Box plots and frequency distribution plots were drawn using BLUPed data (Figures 1A, B) and the trait mean data from two environments, E1 and E2 (Supplementary Figures S1, S2) to better understand the distribution pattern of the phenotypic data. All traits exhibited an approximately normal distribution pattern indicating that they were quantitative traits influenced by multiple genes. The correlation analysis among traits of the BLUP data (Figure 2) showed that SCY had a strong positive correlation with BN (0.80) and BW (0.74), a moderate positive correlation with SI (0.26), a weak positive correlation with LI (0.16), and a weak negative correlation with LP (−0.13). In addition, LP exhibited the highest negative correlation with SI (−0.56) and BW (−0.29). Moreover, a similar trend of correlation pattern was observed with the trait data obtained from E1, E2, and BLUPed-treated data (Supplementary Table S2).

FIGURE 1

Figure 1 Phenotypic value distribution of 117 individual cotton genotypes using BLUP breeding values. (A) Box plot and (B) frequency distribution of six yield-related traits; the x-axis has trait labels and the y-axis contains the phenotypic value of each trait. BN, Boll Number; BW, Boll Weight; LI, Lint Index; LP, Lint Percentage; SCY, Seed Cotton Yield; SI, Seed Index.

FIGURE 2

Figure 2 Correlation analysis of six yield-related traits. The color legend at the top shows the Pearson’s correlation coefficient value, and at the bottom, the significance level based on the p-value is given in which ns represents non-significant. BN, Boll Number; BW, Boll Weight; LI, Lint Index; LP, Lint Percentage; SCY, Seed Cotton Yield; SI, Seed Index.

The effects of genotype (G), environment (E), and genotype-environment interaction (G × E) for all six traits were assessed using analysis of variance (ANOVA). Significant variation (P<0.001) was observed, indicating that adequate variability was present for all six quantitative traits and was controlled by G, E, and G × E effects (Supplementary Table S3). Thus, the phenotypic data showed significant variation among the Indian cotton germplasms for yield-related traits, some of which were strongly correlated with others and were suitable for association mapping studies.

3.2 SNP genotyping and its data analysis

The GBS library constructed from 117 upland cotton germplasm, generated a vast amount of data of ~1,003.22 million pair-end reads (approximately 144.46 GB data, equates to an average of 8.574 million reads per sample) using Illumina® HiSeq 2500 platform. Details of the sequence read statistics are provided in Supplementary Table S4.

The SNP call performed with the GATK pipeline resulted in a total of 14,46,969 SNPs, of which 2,41,086 high-quality SNPs were retained after filtering (heterogeneity<0.3, missing<0.1, and MAF >0.05), which was used further for genetic variation and GWAS analysis. SNPs had transitions (A/G or C/T) of 5,549,405 bp and transversions (A/C, A/T, C/G, or G/T) of 2,339,098 bp, with a Ts/Tv ratio of 2.37%. The SNP distribution across the genome was not evenly distributed, depending on the genome content and gene density, with 1,47,347 and 93,739 SNPs in the At and Dt sub-genomes, respectively. The number of markers varied among the chromosomes, with a maximum number of SNPs (31,550) in Chr A08 and a minimum number of SNPs (2,860) found in Chr D03. The average SNP density throughout the genome was approximately one SNP per 11.36 kb (Table 2, Figure 3A). The density plot of SNPs for the 5 Mb region is represented as a heat map in Figure 3B.

TABLE 2

Table 2 Chromosome-wise distribution of SNPs.

FIGURE 3

Figure 3 Genomic distribution of SNPs (A) Bar plot showing the total number of SNPs in 26 chromosomes of cotton; (B) Density plot of SNPs in the cotton genome, the horizontal bar indicates chromosomal length, and the color depicts the SNP density in the 5-Mb window. The color legend represents SNP density.

3.3 Population structure and kinship

This study employed multiple approaches to analyze the population structure, including Bayesian clustering using STRUCTURE software, principal component analysis (PCA), neighbor-joining (NJ) phylogenetic analysis, and kinship coefficient analysis. Understanding population structure is crucial in genome-wide association studies (GWAS) because the presence of a structure can influence the reliability of the association found. The LnP(K) value continuously increased from K = 1 to 10 (Figure 4A) with no inflection point in this panel. Moreover, Evanno’s ΔK showed a sharp peak at K = 2 (Figure 4B), suggesting that our population was divided into two subgroups (Figure 4C) designated as CPG1 and CPG2. CPG1 contained 65 and CPG2 contained 13 genotypes of cotton while 39 genotypes could not match the membership probability cut-off (0.8) of any cluster and were considered an admixture. CPG-1 consists of 12 (central), 20 (northern), 28 (southern), and five (unknown) genotypes, whereas CPG-2 consists of seven (central), one (northern), four (southern), and one (unknown), with an admixture containing 13 (central), 10 (northern), 11 (southern), and five (unknown) genotypes (Supplementary Table S5).

FIGURE 4

Figure 4 Population structure of the 117 cotton germplasms. (A) Mean LnP(K) values plotted from 1 to 10; (B) Ln(ΔK) values plotted from 1 to 10, for each value of K ten independent run was considered; (C) Population structure based on STRUCTURE where K = 2, the colored subsection within vertical bar represents membership coefficient value; (D) Plot of Principal component analysis showing genetic variation in cotton genotypes using first three principal component, the color label is based on STRUCTURE derived membership coefficient. Genotypes with ≥80% membership coefficient value were labeled as CG1 and CG2, the other were labeled as admixture; (E) NJ tree based on Nei’s genetic distances, the whole population was divided into two clusters; (F) Heatmap of Kinship coefficient matrix revealing the relationship between 117 individual Indian cotton genotypes.

The STRUCTURE results were substantiated by principal component analysis (Figure 4D), a neighbor-joining (NJ) tree (Figure 4E), and a kinship matrix (Figure 4F). The first two principal components accounted for 14.34% of the total genetic variation, with PC1 and PC2 explaining 7.75% and 6.59%, respectively. The neighbor-joining (NJ) tree based on Nei’s genetic distances also demonstrated a division of the population into two main clusters, consistent with the STRUCTURE analysis. In addition, the kinship relatedness matrix showed two distinct subpopulations among 117 germplasms in this population. The kinship matrix revealed a relatively low degree of genetic relatedness, as indicated by the average pairwise relative kinship coefficient of 0.049. Among the cotton genotypes, most pairs (63.56%) exhibited estimated kinship values of zero. Approximately 31.05% of the kinship values ranged from 0 to 0.1, 3.66% ranged from 0.1 to 0.3, and the remaining pairs of genotypes (1.72%) displayed kinship values exceeding 0.3 (Supplementary Figure S3). This result indicates that the 117 Indian cotton germplasms are distantly and weakly related.

3.4 Genome-wide association mapping of fiber yield traits

To identify the most suitable model for conducting GWAS analysis of our datasets (BLUPed traits and 2,41,086 SNPs), six statistical models (GLM, MLM, CLMM, MLMM, FarmCPU, and Blink) were compared using a quantile–quantile (Q–Q) plot (Supplementary Figure S4). From the Q–Q Plot, MLMM has the best fit followed by FarmCPU and Blink, whereas the GLM, CLMM, and MLM models deviated early from the expectation line. Based on the Q–Q plots, the MLMM model was selected as the best model for identifying significantly associated SNPs for the six studied yield-related traits. This underscores the importance of choosing an appropriate model for GWAS to avoid false positives and to increase the accuracy of the results. In total, 205 SNPs or quantitative trait nucleotides (QTNs) with 90, 67, and 48 in E1, E2, and BLUP, respectively were identified to be significantly associated with six traits above the significance threshold of −logP >4.5 (Figure 5, Supplementary Figures S5, S6; Supplementary Table S6). All SNPs were scattered unevenly, with 98 and 107 significant SNPs in the A and D sub-genomes, respectively. The maximum number of markers associated with the traits was found in Chr D06 (23), followed by Chr A08 (21), while Chr D13 did not contain any SNPs, and D03 had only one significant SNP. For BN, 28 significant SNPs were distributed on chromosomes A06, A08, A12, D01, D05, D06, D08, and D11, with the highest number in D11 (nine SNPs). Among these SNPs, NC_053442.1_45198190 had the highest positive phenotypic effect (3.107), with a −log₁₀(P) value of 4.99, and NC_053441.1_63281905 had a negative effect (−3.606) with a −log₁₀(P) value of 5.08. For BW, 27 significant SNPs were located on chromosomes A06, A08, A09, A10, A11 A12, A13, D04, D06, D07, D09, D10, D11, and D12, with the maximum number in A11 (five SNPs). Of these SNPs, NC_053432.1_54551322 showed a positive phenotypic effect (0.430) with a −log₁₀(P) value of 5.49, and NC_053433.1_116352175 showed a negative effect (−0.465) with a −log₁₀(P) value of 7.36. For LI, 50 significant SNPs were detected on chromosomes A01, A03, A04, A05, A06, A07, A11, A13, D01, D04, D06, D08, D09, D10, and D12 with six SNPs in A06. The phenotypic effect size of these SNPs ranged from 0.480 (NC_053429.1_20687114) to −0.47 (NC_053442.1_33992120) with −log₁₀(P) values of 5.75 to 5.47, respectively. For LP, 40 significant markers were observed in 14 chromosomes of the cotton genome, with the maximum number in Chr D10 (nine SNPs). They explained the −2.072 (NC_053444.1_64810245) to 2.285 (NC_053441.1_58365634) range of phenotypic effect size having a P-value of 5.1 to 5.89. A total of 28 significant markers were observed for SCY located on 12 chromosomes, with the highest occurrence at D06 (nine SNPs), contributing 16.696 to −15.449 phenotypic effect size. Loci NC_053427.1_2009750 and NC_053431.1_72867934 had positive and negative effects on SCY, respectively, with −log₁₀(P) values of 6.66 to 4.53, respectively. Similarly, SI had a total of 32 significant markers distributed on chromosomes A02, A05 (six SNPs), A07, A08, A10, A13, D01, D02, D07, D08, D09, and D12, explaining −0.876 to 0.959. The −log₁₀(P) values of the loci contributing to positive effects (NC_053428.1_79218940) and adverse effects (NC_053431.1_70272704) were 5.45 to 4.99, respectively.

FIGURE 5

Figure 5 Summary of GWAS results for six yield-related traits (BN, BW, LI, LP, SCY, and SI) using BLUP data. (A) Manhattan plot for all six traits. The X-axis represents the chromosomal position of the SNPs in the cotton genome, and the Y-axis represents the −log10 of the P-values. The red dashed line indicates the significance threshold (−logP>4.5); (B) Q–Q plots for all six traits. The Y-axis represents the observed −log10 P-values, and the X-axis represents the expected -log10 P-values. The red points indicate significant SNP.

3.5 Identification and comparison of QTLs

According to the definition of QTLs from the previous methods (Sun Z. et al., 2017; Song et al., 2019), a total of 134 QTLs were obtained from 205 significant markers in this study, out of which maximum QTLs were identified for LI (30) followed by LP (25), SCY (21), SI (20), BW (18), and BN (12) traits (Supplementary Table S7). Similar to the significant markers, these candidate loci were also scattered among different chromosomes of the cotton genomes harboring 69 QTLs in the A subgenome and 65 in the D subgenome. Interestingly, most of the QTLs contained only one significant SNP, except for 37 QTL loci with more than one associated SNP. For instance, QTL (qGhBN_D11-1) had seven significant markers. All QTLs and GWAS signals for yield-related traits were retrieved from the database to compare our QTLs with those in previous reports. A total of 535 QTLs were reported for yield-related traits in 33 r QTL mapping studies. Similarly, details of the significant markers and their genomic positions were extracted from six reports of association mapping studies. Among the 134 QTLs identified in these studies, 39 were novel and newly identified in the Indian germplasm, and the remaining 95 QTLs overlapped with previously reported QTLs (Supplementary Table S7). In addition, we identified eight QTL that exhibited pleiotropic associations with more than one trait. Of these, three QTLs (qGhBN_SCY_D6-1, qGhBN_SCY_D6-2, and qGhBN_SCY_D6-3) on chromosome D06, were found to be associated with BN and SCY having common significant markers (NC_053442.1_36092628, NC_053442.1_37082185, NC_053442.1_37984052) within the same genomic intervals (35.8–38.1 Mb). Also, three QTLs (qGhSI_LI_A5, qGhLI_SI_A13, qGhLI_SI_D9) exhibited pleiotropic association for SI and LI, of these qGhLI_SI_A13 had a common marker (NC_053436.1_78553469) for LI and SI while the other two had overlapping QTL intervals in terms of physical position (25.54–25.94 Mb in A05 and 5.67–6.07 Mb in D09). Moreover, BW and SCY had one pleiotropic QTL (qGhBW_SCY_A10) because of the presence of a common SNP marker (NC_053433.1_116352175) within genomic intervals of 116.15–116.55 Mb. In addition, one QTL (qGhLP_BN_A8) showed an association between LP and BN owing to the overlapping of QTL intervals in Chr A08 (59.74–60.14 Mb). As these traits showed a significant correlation at the phenotypic level (BN and SCY = 0.80, BW and SCY = 0.74, and SI and LI = 0.65), they were also found to have pleiotropic-associated markers at the genomic level. This result implies that a network of QTLs with multiple phenotypic effects may control fiber yield traits.

3.6 Identification of favorable haplotypes of pleiotropic QTLs

To identify the cumulative effects of favorable SNPs, haplotype analysis using eight QTL loci exhibiting pleiotropic associations was conducted. Haplotype analysis of QTL qGhBN_SCY_D6-2, associated with BN and SCY traits on Chromosome D06, formed a haplotype block with five SNP markers, which consisted of six haplogroups in our association panel (Figure 6A). All five markers showed substantial LD (Figure 6B), and variations in these haplotype alleles led to significant differences in the phenotypes of BN and SCY. The average BN values of the haplogroups were 22.72, 23.22, 27.05, 23.14, 24.92, and 25.63 per plant, respectively, and for SCY, the average values among the six haplogroups were 74.71, 76.25, 95.99, 76.34, 79.51, and 88.54 g/plant, respectively, for which H003 (haplotype 3) showed significantly higher BN and SCY values than those of the other haplogroups (Figures 6C, D). Haplotype analysis of qGhSI_LI_A5 QTL resulted in the formation of four haplogroups with seven SNPs having substantial LD on Chromosome A05 (Supplementary Figures S7A, B) among the 117 cotton germplasms. The average LI and SI of H003 (haplotype 3) were 4.85 g and 8.56 g, respectively, higher than those of the other three haplogroups (Supplementary Figures S7C, D) showing significant phenotypic variation.

FIGURE 6

Figure 6 Haplotype analysis of pleiotropic QTL qGhBN_SCY_D6-2, (A) haplogroups observed in our 117-association panel using five SNP markers, (B) genomic location of five SNP loci and LD based on the pairwise R² values between the SNPs estimated in Chromosome D06. The R² values are indicated using the color bar. (C, D) Phenotypic differences of boll number (BN) and seed cotton yield (SCY) g/plant among the six haplogroups.

Eleven SNP markers were associated with pleiotropic QTL qGhLI_SI_D9, which represents four haplogroups in our cotton germplasm (Figure 7A). Substantial phenotypic variation for LI and SI and strong LD was observed among the 11 haplotype alleles present within these haplotype groups on Chromosome D09 (Figures 7B–D). The average LI values of the four haplotype groups were 4.59, 4.33, 4.48, and 4.36 (g), respectively, similarly, the average value of SI among the haplogroups was 8.00, 7.47, 7.57, and 7.76 (g), respectively. The average value of H001 (haplotype 1) was greater than that of the other haplotypes for both LI and SI. In addition, qGhBW_SCY_A10 represented six haplogroups consisting of 22 SNP markers with strong LD (Supplementary Figures S8A, B). Among all the six haplogroups, haplotype 3 (H003) exhibits the highest average BW and SCY value of 3.56 g and 93.91 g/plant, respectively (Supplementary Figures S8C, D). Furthermore, the other four QTLs, qGhBN_SCY_D6-1, qGhBN_SCY_D6-3, qGhLI_SI_A13, and qGhLP_BN_A8, showed phenotypic differences; however, the haplotype alleles of these QTLs did not have any gene features within the 10–20 kb window.

FIGURE 7

Figure 7 Haplotype analysis of pleiotropic QTL qGhLI_SI_D9. (A) Haplogroups observed in our 117-association panel using 11 SNP markers, (B) Genomic location of eleven SNP loci and LD based on the pairwise R² values between the SNPs estimated in Chromosome D09. The R² values are indicated using the color bar. (C, D) Phenotypic differences of lint index (LI) g and seed index (SI) g among the four haplogroups.

3.7 Identification of candidate genes

Genomic intervals of 134 reported QTLs were extracted and annotated to identify the putative candidate genes associated with each trait. Functional annotation of these regions retrieved 2,509 unique genes comprising 1,966 protein-coding, 277 lncRNAs, 204 snoRNAs, 30 tRNAs, 29 snRNAs, and three miscRNAs. The distribution of these genes in the QTL regions ranged from 1 to 65, except for qGhLI_D4-1 and qGhBN_SCY_D6-1, which did not harbor any putative genes. Only 14 (~10%) QTLs had<5 putative genes, 33 QTLs (~24%) had >5–<10 putative genes, while all the remaining 85 QTLs (~63%) covered >10 genes (Supplementary Table S7). A total of 192, 432, 557, 591, 398, and 441 genes were associated with BN, BW, LI, LP, SCY, and SI, respectively. Moreover, the distribution of these genes across the cotton genome was uneven, with 1211 genes located in the A sub-genome and 1,298 genes in the D sub-genome, respectively. Chromosome D08 had the maximum number of genes (205 genes), whereas the minimum number of genes (13 genes) were in chromosome A13. Further gene ontology (GO) enrichment analysis for each trait was conducted to understand the function of each candidate gene, and it was found that these genes within the QTL regions were predominantly enriched for different biological processes. The top ten biological processes enriched for each yield-related trait are shown in Figure 8. For BN, the enrichment analysis results showed four terms that belong to phosphate metabolism: regulation, transport, homeostasis, and cellular response to phosphate starvation. BW includes terms related to RNA processing, DNA replication, maintenance, and mitosis, reflecting cell growth-associated processes enriched in this region. The LI region has genes associated with cellular fate determination, polarity specification, toxin catabolism, and cell wall organization. The LP region regulates gene expression at the epigenetic (DNA demethylation and histone deacetylation), transcriptional, and translation levels, and genes responsive to salicylic acid and gibberellin. SCY has stress-responsive, purine metabolism, and cell fate-related gene ontology. SI contains sugar signaling, trehalose synthesis and metabolism, plant cell wall synthesis, photoperiodism, and vegetative growth development-related genes enriched within its region.

FIGURE 8

Figure 8 Biological process gene ontology term enrichment for genes located in the QTL region of yield-related traits. The top 10 BP ontology terms are shown for each trait. The y-axis represents the BP and the x-axis enrichment scores for each trait. The color scale shows the p-value of enrichment and bubble size enrichment score.

In addition, to identify the most active genes related to six yield traits, gene expression analysis was performed using RNA-seq data from diverse cotton tissues (seed, cotyledon, root, stem, leaf, torus, calycle, petal, stamen, pistil, ovule, and fiber). Of the 2,509 candidate genes, 870 were found to be transcriptionally active, showing a zFPKM value of ±3 in at least one tissue (Supplementary Figure S9). The numbers of active genes in different QTLs for BN, BW, LI, LP, SCY, and SI were 73, 169, 185, 208, 137, and 143, respectively. Furthermore, to identify the highly expressed active genes for yield-related traits, the expression profiles were mainly focused on the ovule, fiber, and seed tissues. For BN, qGhBN_A6-1, qGhBN_D5-1, and qGhBN_D6-4 contained highly expressed active genes such as GhCYP (high expression at 5DPA of fiber development stage), GhGELP (highest expression at 20 DPA fiber development stage), and GhCBSX5 (highly active in stamen tissue and at the 10 DPA of fiber development stage). For the BW trait, qGhBW_D9-3 had two highly expressed genes (GhAGX2 and GhEP1-3) showing higher expression at 10 and 20 DPA at the fiber development stage, respectively. In addition, the BW QTL qGhBW_A11-1, qGhBW_D9-1, and qGhBW_D9-2 contained the highly expressed gene GhSMP1 (higher activity specifically at 25 and 35 DPA of ovule development), GhGRP4 (expression in all stages of ovule development), and GhCSGL3 (positive activity in seeds at 5 and 10 h), respectively. The qGhLI_A4-2 QTL for LI has two genes, GhbZIP11 (showing a higher expression profile in ovule and fiber development stages) and GhLEA (showing higher expression in ovule development). Other LI QTLS, qGhLI_A6-1, qGhLI_D6-2, qGhLI_D9-1, and qGhLI_D9-2 show expression profiles for GhKNAP2 (expressed in root tissue), GhCYP (showing higher activity in all tissues except seed), GhMIF2 (higher activity in ovules and fibers at mid developmental stages), and GhGA20OX1 (highest expression 20 DPA of fiber development) genes. Seven LP QTLs, qGhLP_A5-1, qGhLP_A6-1, qGhLP_A10-1, qGhLP_A10-3, qGhLP_A10-6, qGhLP_D8-2, and qGhLP_D10-3 contained GhPDF1 (highest activity in ovule and fiber developmental stages), GhMADS23 (higher activity in ovule developmental stages), GhGUX1 (expression at 20 DPA of fiber stage), GhBBE18 (expression at 25 DPA of fiber stage), GhLEA-D-19 (high expression at 0 and 5 h of seed development and 35 DPA of ovule development), GhchlADH1(showing higher expression in 20 and 25 DPA fibers, ovule 20 DPA, root, and leaf tissues), and GhABCC (higher activity in 20 and 25 DPA fiber, 20 DPA of ovule development along with root and leaf). The qGhSCY_A4-1 QTL of SCY contains seven genes (five GhPG, one GhPL, and one GhlncRNA) and showed interesting expression profiles that were highly expressed in stamen tissues in contrast to other tissues, especially the pistil. In addition, qGhSCY_D2-1 for the SCY trait had two expressed genes (GhPUP4 and GhDCTPP1) that were highly expressed in ovules at all stages (except at 35 DPA). The other two SCY QTLs (qGhSCY_A8-2 and qGhSCY_D2-2) showed a high expression profile for GhAP2/ERF_AIL5 (highly expressed in the later stage of ovule and fiber development along with all stages of seed development) and GhGGAT2 (expressed 25 and 35 DPA of fiber development), respectively. For SI, qGhSI_D2-3 contains two genes, GhAGP9 (highest activity at all stages of fiber development) and GhZAT10 (activity at 25 DPA of both ovule and fiber development). Similarly, the qGhSI_D8-2 QTL for SI had two active genes, GhACO3 and GhFLA7 (which showed the highest activity during the ovule and fiber development stages). In addition, qGhSI_D12-1 contained GhMYB22, which showed higher expression at an early stage of ovule development (−1 to 1 DPA). In addition, of the eight pleiotropic QTLs, two QTL qGhBN_SCY_D6-3 and qGhSI_LI_A5, governed three active genes. qGhBN_SCY_D6-3 had two active genes, GhPPR (higher expression in ovules at −3, 0, 3, and 5 DPI), and GhCHUP1 (highest activity at 5 and 10 DPA of fiber development and 20 and 25 DPA of ovule development) while qGhSI_LI_A5 had GhSCPL42 (higher expression at 5, 10, and 20 DPA of ovule development). Of the 870 active genes, 40 candidate genes were selected for six yield-related traits that showed more contrasting expression profiles in the ovule, fiber, and seed developmental stages in comparison to other cotton tissues (Figure 9). Additionally, among the 40 genes significantly expressed in cotton yield traits, 15 genes were reported to be associated with different fiber development stages, eight genes were reported for different biotic and abiotic stresses, and 17 genes were novel genes associated with fiber development (Supplementary Table S8).

FIGURE 9

Figure 9 A heatmap of putative candidate genes for six yield traits preferentially active in the ovule, fiber, and seed tissues of cotton. The color scale in the heatmap represents the zFPKM transformed value. Column label is provided for type of tissue and row label is provided for gene biotype, chromosomal location of gene and candidate gene traits.

4 Discussion

Cotton is one of the most important natural fibers and is a raw material for many textile industries. Its yield can significantly influence the textile industry because it is directly related to overall supply and demand dynamics, production capacity, pricing, innovation, global trade, and sustainability efforts within the industry. Cotton yield is a complex quantitative trait governed by multiple yield-related descriptors that are difficult to improve simultaneously using traditional breeding methods (Wang et al., 2019). Thus, the identification and characterization of genetic factors for targeted traits and their manipulation through conventional breeding coupled with genomic tools have consistently been the primary goals for cotton breeders, enabling a more precise selection of genotypes in the pursuit of developing varieties with higher yield potential. In the present study, GWAS was performed based on the natural population of 117 Indian upland cotton germplasms and genotyping-by-sequencing methods to deploy markers for six yield-related traits. Association mapping study is an effective tool for mapping complex quantitative traits to identify of key genes associated with such traits in many plants (Huang et al., 2012; Horton et al., 2014; Crowell et al., 2016; Varshney et al., 2019b; Wang et al., 2020). It is an analytical method and its ability to resolve associated loci relies on factors such as the size of the experimental population, marker density, and the selection of appropriate statistical tools or GWAS models for marker-trait identification (Liu et al., 2016). Population size has a great impact on association mapping studies, and it has been reported that a relatively large population size ensures sufficient genetic variation, thereby influencing the detection of significant QTLs (Su et al., 2016; Huang et al., 2017). Population sizes ranging from 95 to 800 have been previously studied in upland cotton in association studies (Gapare et al., 2017; Sun Z. et al., 2017; Dong et al., 2019; Kumar et al., 2021). Although our population size of 117 released varieties from different agro-climatic zones was not sufficiently large, they are suitable for mapping analysis because they stabilize after many years of genetic recombination (Nordborg et al., 2002). Along with population size, phenotypic variances in a single environment affect the accuracy of phenotypic data, thereby affecting the reliability of the mapping study. To rectify this error, the use of multi-environment and unbiased prediction data in association analysis has been proven effective in previous studies (Roorkiwal et al., 2018; Tomar et al., 2021). Phenotyping from multiple field locations effectively eliminates the influence of the environment and aids in the interpretation of environment-specific as well as general QTL (Gutiérrez et al., 2015). In the present study, we used two sites for the evaluation of six yield-related phenotypic traits: Punjab Agricultural University, Punjab (E1), and Tamil Nadu Agricultural University (E2), which are the two main cotton-growing belts in India. In addition, there is a large difference in the geographical position as well as the climatic conditions between these two zones (https://www.cicr.org.in/pdf/long_staple), and all six traits exhibited abundant phenotypic variation ranging from 6.32% to 24.7% and 6.17% to 20.1% in E1 and E2, respectively. Our findings also showed the presence of medium to high heritability (>50%, except for LP in E2), illustrating the importance of these germplasm lines for genetic improvement and showing the same phenotypic expression if sown in the same environment (Bhat et al., 2022). High and stable heritability has always been proven to be useful in determining the strong association between markers and traits of interest (Courtois et al., 2013). In addition, the correlation study among the traits showed a significant positive correlation between SCY and BN, and BW, and a maximum negative correlation between LP and SI (Figure 2) which corroborates the findings of Dong et al. (2018) and Sun et al. (2018). Thus, the presence of significant variation among the genotypes in different environments along with the strong correlation led us to explore markers associated with six yield-related traits through GWAS analysis.

Association mapping using various SSR or SNP chips has been previously reported in upland cotton (Mei et al., 2013; Gapare et al., 2017; Handi et al., 2017; Huang et al., 2017; Kumar et al., 2021); however, the development of high-resolution sequencing technologies such as GBS, SLAF-seq, and RAD-seq offers significant advantages and has led to tremendous progress in the development of numerous SNP markers for genetic mapping in cotton (Islam et al., 2016; Su et al., 2016; Geng et al., 2020; Wang et al., 2021) and other crops (Arruda et al., 2016; Maldonado et al., 2019; Ravelombola et al., 2021). Genotyping by sequencing (GBS) provides a comprehensive view of the genome by generating genetic markers that spread across the entire genome. In contrast, SSR markers and SNP chips have limited coverage, and may not capture the full genetic variation present in a population. This increased marker density improves the resolution and efficiency of GWAS, enabling the detection of more genetic associations and fine mapping of genomic regions. In addition, SNP chips often rely on preselected SNPs that may not fully represent the genetic diversity of a particular population or species, conversely, GBS allows for unbiased genotyping, reduces ascertainment bias, and enables a more accurate assessment of genetic variation. GBS technologies have enabled the rapid sequencing and genotyping of breeding populations, allowing plant breeders to accomplish genomic diversity, GWAS, genomic selection (GS), and marker development in many species without prior knowledge of the species genomes (Poland and Rife, 2012). In our study, a substantial number of high-quality SNP markers (2,41,086) were identified using the GBS method with an average density of 1SNPs/11.36 kb. The number of markers reported in our study was relatively higher than that reported by Wang et al. (2021); however, it was almost consistent with the findings of Geng et al. (2020). The higher number of markers in the current study was sufficient to conduct GWAS analysis, offering potential advantages in uncovering additional prominent loci and candidate genes (Wang et al., 2018).

Upland cotton has an extensive and intricate history of domestication and breeding, with a narrow genetic background, and is mostly influenced by geographical isolation and gene flow (Huang et al., 2017). Furthermore, understanding the population structure and relatedness in the association mapping panel is crucial for elucidating the heterogeneity of the genetic architecture and controlling false associations (Lu et al., 2015). Therefore, it is crucial to consider the population structure and degree of relatedness among individuals in association-mapping studies. In our study, the association panel was categorized into two clades (CGP1 and CGP2) based on molecular analyses using STRUCTURE software but were not completely consistent according to the geographical origin, as reported in previous studies (Gapare et al., 2017; Sun Z. et al., 2017; Song et al., 2019). In addition, clustering analysis using Nei’s genetic distance, kinship matrix, and PCA calculation methods showed good consistency with population structure analysis. The absence of geographical correlation in our germplasm might be attributed to the relatively high level of gene exchange and interspersed introduction or crossbreeding among the germplasms across different geographical regions of India. Overall, these results highlighted that the germplasms were not highly structured and exhibited weak relatedness, but considering the greater continuous phenotypic variations among the six yield-related traits (Table 1), the association population was further used in the GWAS analysis (Yano et al., 2016; Sun Z. et al., 2017; Song et al., 2019).

Several studies have indicated that effective control of the discovery of false positives resulting from population structure in crops may not be entirely achieved (Hamblin et al., 2011; Lipka et al., 2015). Therefore, to address these errors, we investigated various statistically robust models for the genome-wide association study using the GAPIT package in the ‘R’ programming. The strategy of utilizing two or more different models to identify significant markers in cotton has been reported in several studies (Badigannavar and Myers, 2015; Abdullaev et al., 2017; Sethi et al., 2017; Li C. et al., 2018; Su et al., 2018). In the present study, we implemented six different models; three univariate models (GLM, MLM, and CLMM) and three multivariate mixed models (MLMM, FarmCPU, and Blink). According to Segura et al. (2012), the use of a multivariate GWAS method addresses the issue of confounding between covariates and the test marker, and lowers the false discovery rate (FDR) compared to univariate GWAS when employing the same threshold, enabling the detection of a greater number of QTLs. By examining the Q–Q plots, we determined the most suitable models, and our findings demonstrated that the MLMM exhibited the best-fit model for our trait data, unlike the other models that exhibited early deviations from the expected line. The MLMM model has also shown successful results in other plants, such as Cannabis (Watts et al., 2021), tomato (Zhao et al., 2022), apricot (Omrani et al., 2019), and wheat (Mihalyov et al., 2017). Detected by the MLMM, 205 significant SNP from two environments and BLUP were found to be associated with six yield-related traits (Supplementary Table S6). These markers were unevenly distributed on all chromosomes of cotton (except for Chr D13), suggesting that the regulation of these traits involves a complex gene network. This finding aligns with those of previous reports, highlighting the intricate genetic control of cotton yields (Rong et al., 2007; Sun et al., 2018). Using these significantly associated markers, QTL were determined within the ±200 kb upstream and downstream regions, as reported previously (Sun Z. et al., 2017; Song et al., 2019). In this study, 134 QTLs were derived from 205 significant markers (Supplementary Table S7). Interestingly, some of these significant SNPs or QTLs were associated with multiple yield-related traits at pleiotropic loci. It has been reported that complexity and linkage between complex traits are common in many crops (Van Tienderen et al., 1996; Yan et al., 2011; Raman et al., 2019; Li A. et al., 2022). In upland cotton, four pleiotropic loci related to fiber yield, fiber quality, and flowering date have been reported by Wang et al. (2021). Similarly, four and nine pleiotropic loci have been reported for concurrent alterations in both lint yield and fiber quality traits in cotton (Geng et al., 2020; Li Y. et al., 2023). In our study, eight QTLs (six within the same genomic interval and two overlapping with adjacent QTLs) were found to be associated with six yield-related traits. QTL qGhBN_SCY_D6-1, qGhBN_SCY_D6-2, qGhBN_SCY_D6-3 was simultaneously associated with BN and SCY, having common significant markers NC_053442.1_36092628, NC_053442.1_37082185, NC_053442.1_37984052. Three other QTLs, qGhSI_LI_A5, qGhLI_SI_A13, and qGhLI_SI_D9, were concurrently associated with SI and LI, of which qGhLI_SI_A13 had a common marker (NC_053436.1_78553469), while the other two were overlapping QTLs. Similarly, qGhBW_SCY_A10 and qGhLP_BN_A8 were pleiotropic QTLs for BW/SCY and LP/BN, respectively. Additionally, correlation analysis of these yield-related traits showed a significant positive correlation (between SCY and BW, BN), and the maximum negative correlation between LP and SI indicated that these are the favorable loci that could be the genetic foundation for the correlation between these traits. Thus, the pleiotropic loci identified in this study provide information to explore the molecular mechanism that explains the simultaneous enhancement of six intricate yield-related traits in cotton, which will be helpful in the selection of germplasms in crop improvement programs for yield traits in Indian upland cotton.

In the past few decades, hundreds of QTLs or GWAS signals associated with cotton fiber yield have been identified in different intraspecific and interspecific populations through various linkage and association mapping methods (Said et al., 2015; Huang et al., 2017; Ma Z. et al., 2018). Of the 134 QTLs detected in the present study, 95 QTLs overlapped within the genomic interval or were adjacent to the genomic position of the QTLs and GWAS signals identified in a previous study (Supplementary Table S7). Interestingly, some of the QTLs were trait-specific, as reported earlier within the same genomic interval. For instance, QTLs (qGhBN_D5-1, qGhBN_D6-1, qGhBN_D6-2, qGhBN_D6-3, and qGhBN_D6-4) for BN overlap within TM58714_TM58742_TM58749 (Zhu et al., 2021), and qBPP-1-43.5 (Zhang et al., 2011). Similarly, for BW, QTLs (qGhBW_A13-2, qGhBW_D4-2, qGhBW_D7-1, qGhBW_D9-3, qGhBW_D12-1) showed consistent results with TM47610_TM47614, TM56685_TM56686, TM63749 (Zhu et al., 2021), qBW-C16-1 (Wu et al., 2009) i15830Gh (Sun et al., 2018), and qBW-06A-c26-1 (Yu et al., 2013b) BW QTLs reported previously. LI (qGhLI_D6-2, qGhLI_D9-2) corresponds to QTLs qLI-D6-1 (Liu et al., 2012) and Br7_Lt%_23 (3,47+) (Lacape et al., 2013) LI QTLs reported in previous studies. For LP, three QTL (qGhLP_A6-2, qGhLP_D6-1, and qGhLP_D8-1) matched with the LP GWAS signals reported by Ma Z. et al. (2018) (A06_102555770), and QTL reported by Yu et al. (2013a) (F2:3-qLP-c25-1), and Chen et al. (2010) (qLP-D8-1/qLP-F2:3-JES-1a), respectively. Four QTLs (qGhSCY_A13-1, qGhSCY_D6-3, qGhSCY_D2-1, and qGhSCY_D2-2) were associated with SCY in the present and in the previously reported SCY QTLs: qSCY-07A-c13-1, qSCY-06A-c25-1 (Yu et al., 2013b), qSY-D2-1 (Wang et al., 2007), and TC-qSCY-c14-1 (Yu et al., 2013a), respectively. Interestingly, pleiotropic QTLs for BN and SCY (qGhBN_SCY_D6-1, qGhBN_SCY_D6-2, and qGhBN_SCY_D6-3) showed congruency for both BN (qBPP-1-43.5) and SCY (qSCY-06A-c25-1) traits, as reported previously (Zhang et al., 2011; Yu et al., 2013b). Pleiotropic QTL (qGhBW_SCY_A10 and qGhLI_SI_D9) were consistent with the BW association signal A10_99131954 (Ma Z. et al., 2018) and qSI-D9-1 (Shen et al., 2007) located on chromosomes A10 and D09, respectively. Therefore, our findings corroborate those of previous studies, validate the authenticity of current GWAS results, and increase confidence in the reliability of some QTLs/SNPs. These QTL/SNPs, exhibit stable inheritance and were consistently detected across diverse segregating populations with varying genetic backgrounds and through different mapping methods, and have a significant potential for future breeding programs aimed at improving cotton yield in India.

High-quality allelic loci are precious assets for agricultural breeding initiatives, and the identification of favorable alleles is an efficient approach to enhance the traits within crop plants (Su et al., 2016). Many recent studies have identified haplotype alleles for important traits, such as haplotype analysis of yield-related traits in soybean (Bhat et al., 2022), agronomically important traits in Arabidopsis (Lu et al., 2019), grain quality traits in rice (Wang et al., 2017), high thousand-kernel weight in wheat (Sun C. et al., 2017), grain yield, and flowering time under drought and heat stress conditions in maize (Yuan et al., 2019), which have shown considerable potential for the identification of traits and crop improvement. In cotton, Song et al. (2019) reported the haplotype analysis of two significantly associated SNPs with the lint percentage (LP) trait had a positive effect on LP, and these favorable alleles can be pyramided in a target line by marker-assisted selection. Similarly, two major haplotypes for fiber length and strength in cotton have been identified on chromosomes Dt11 and At07 (Sun Z. et al., 2017). In the present study, haplotype analysis of pleiotropic QTLs identified for different traits was conducted using a 10–20 kb window. The combination of favorable alleles identified within H003 (haplotype 3) was significantly higher for BN, BW, SCY, LI, and SI in the pleiotropic QTL qGhBN_SCY_D6-2, qGhSI_LI_A5, and qGhBW_SCY_A10, respectively. Eleven SNP combinations within haplogroup H001 were greater than other haplotype groups in the pleiotropic QTL qGhLI_SI_D9. Our results revealed that the combination of favorable alleles led to the identification of haplogroups that regulate a diverse range of phenotypes and significant phenotypic variation in yield-related traits in cotton. All identified diverse haplotypes can be further pyramided as a targeted line by marker-assisted breeding in cotton breeding programs. Thus, haplotype-based breeding strategies will aid in choosing favorable plant genotypes that carry advantageous haplotype alleles that have great potential for crop improvement (Varshney et al., 2019a).

Several genes associated with yield traits, such as Gh_D08G2376 (Huang et al., 2017), Gh_D12G2344 (Sun et al., 2018), Gh_D05G1124 (Song et al., 2019), Gh_A02G1268 (Su et al., 2016), Gh_D02G0025 (Ma Z. et al., 2018), AHP5 (Fang et al., 2017), Gh_A02G0111 (Niu et al., 2023), and Ghir_A08G009110 (Feng et al., 2022) have been previously reported in GWAS using different association mapping studies. In the present study, 2,509 candidate genes were identified within the confidence interval of the identified QTLs. Previous studies have suggested that genes preferentially expressed at different stages of fiber, ovule, and seed development may be involved in fiber yield and quality (Huang et al., 2017); therefore, we selected 40 highly active genes showing significant expression profiles (Supplementary Table S8). For BN, three candidate genes were identified; the GhGELP isoform has been reported to have a direct function in ovule, fiber, and seed development in cotton (Ma R. et al., 2018). GhCBSX5 has reported to be involved in other biological processes (Ali et al., 2021); however, its role in fiber development in cotton has not yet been reported. GhCYP has no functional information in cotton; its isoform has been reported to play a significant role in drought tolerance in wheat (Zang et al., 2010). Gene annotation of the BW reported QTL intervals identified five active genes. GhCSGL3 plays a direct role in secondary cell wall biosynthesis leading to enhanced lint yield and quality in cotton (Li et al., 2015; Zhang et al., 2015), whereas GhAGXT2 and GhEP1-3 play a positive role in abiotic stress in cotton (Li L. et al., 2022). Two other genes, GhSMP1 and GhGRP4, were reported to be novel genes governing boll weight traits in the present study. Six candidate genes have been reported for LI traits of which two genes (GhKNAP2 and GhGA20OX1) were functionally validated to play a major role in the cotton fiber development process (Xiao et al., 2010; Gong et al., 2014). The other two genes (GhLEA and GhbZIP11) have been reported to be stress-responsive in cotton (Liang et al., 2016; Magwanga et al., 2018). In addition, the functional roles of GhCYP and GhMIF2 have not been reported in cotton, but its isoform has been reported to play a vital role in petal elongation in Gerbera hybrida (Han et al., 2017). Similarly, expressional analysis of candidate genes for LP traits resulted in the identification of seven active genes. Three genes (GhLEA-D-19, GhPDF1, and GhABCC15) have direct roles in cotton seed germination, fiber cell initiation, and elongation (Dure III and Galau, 1981; Zhu et al., 2003; Deng et al., 2012). The other four genes (GhGUX1, GhBBE18, GhMADS23, and GhChlADR1) were newly identified genes and might play a vital role during fiber development based on their expression profile; however, the functions of these in cotton remain to be further explored. Interestingly, for SCY highest number of genes was found (eleven) out of five GhPG, one GhPL, and one lncRNA reported encodes the highly significant marker (NC_053427.1_2009750) showing the highest phenotypic effects (~16.69%) could be the most promising genes found in the present study that could play a major role in fiber yield trait, which also corroborates with previously reported studies (Li Z. et al., 2023; Sun et al., 2020). Another gene, GhAP2/ERF/AIL5, has also been reported to play a key role in the growth and development of cotton plants (Zafar et al., 2022). GhPUP4, GhDCTPP1, and GhGGAT2 have no direct role in cotton; however, GhPUP4 has been reported to play a major role in the enhancement of grain size increase in rice (Xiao et al., 2019). SI has five active genes, (GhMYB22, GhAGP9, GhZAT10, GhACO3, and GhFLA7), which play an active role in fiber initiation elongation and freezing tolerance, as reported previously (Shi et al., 2006; Huang et al., 2013; Li P. et al., 2023). Additionally, pleiotropic QTLs (qGhBN_SCY_D6-3 and qGhSI_LI_A5) have three active genes (GhPPR, GhCHUP1, and GhSCPL42), whose functions in cotton fiber development have not been deciphered; however, their isoforms have been reported to play a functional role in defense mechanisms against abiotic and biotic stress (Wang et al., 2022). All 23 stable and 17 novel genes identified in the current study exhibited a high expression profile, which renders them promising candidate genes for future investigations and their functional validation would reveal their role in cotton yield improvement through functional genomics approaches. In conclusion, the present study unveiled a rich source of genetic elements, including SNPs, QTLs, and putative candidate genes associated with fiber yield traits in Indian upland cotton.

5 Conclusion

The present study has made significant strides in understanding the genetic structure and diversity of the Indian cotton germplasm, the identification of SNPs associated with fiber yield traits, and the subsequent identification of potential candidate genes. A weak population structure in the Indian cotton germplasm revealed two subgroups when the population structure was analyzed using a variety of approaches, including PCA, NJ tree, and kinship analysis. A low level of genetic relatedness among the genotypes was observed in the kinship matrix, which is required for breeding programs because it preserves the genetic diversity with wide adaptability. A total of 205 significant SNPs associated with six yield-related traits were identified through the GWAS and further delineated into 134 QTLs. Interestingly, several of these QTLs showed pleiotropic effects, indicating that these loci govern several traits and are thus advantageous candidate loci for introduction into breeding programs aimed at enhancing fiber yield. Additionally, 2,509 unique candidate genes were identified within the vicinity of these QTLs. The biological processes of the trait-associated genes were revealed using gene ontology enrichment analysis. While analyzing the public domain RNA-seq data, we identified 40 potential candidate genes across various cotton fiber developmental stages, several of which are known to be associated with fiber yield, while others need further functional validation to decipher their role in cotton yield. In conclusion, the present study unveiled a rich source of genetic elements, including SNPs, QTLs, and putative candidate genes associated with fiber yield traits in Indian upland cotton. These findings provide a solid foundation for further research on the functional roles of these genetic elements and their potential utilization in breeding programs to improve cotton fiber yield in India. To clearly determine the involvement of candidate genes governing cotton yield traits, further functional validation is necessary. It is anticipated that this effort will make a substantial contribution to the MAS breeding of high-fiber-yielding cotton varieties, thereby enhancing cotton farming productivity and the sustainability of the Indian cotton industry.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NCBI SRA accession number: PRJNA982386.

Ethics statement

The manuscript number allotted by the ethical committee is CSIR-NBRI_MS/2023/06/10.

Author contributions

SNJ and SVS conceptualized the idea of the project. NMB and HK performed field cultivation of cotton germplasms at TNAU, Coimbatore, Tamil Nadu, and PAU Faridkot, Punjab, respectively. BJ conducted the phenotyping and performed all the experiments, whereas GJT participated in phenotyping only. SS, BJ, SJ, and MI conducted all the computational and statistical data analyses. BJ wrote the first draft of the manuscript and SS helped into it. GJT, NMB, DA, SJ, DK, SVS, and SNJ revised the manuscript and provided various suggestions. BJ includes all the corrections and suggestions in the final manuscript. All authors contributed to the article and approved the submitted version.

Funding

This research was supported and funded by the CSIR FBR Project MLP-0047.

Acknowledgments

The authors are thankful to the former as well as the present Director of CSIR-NBRI for providing lab facilities for conducting all experiments. We acknowledge Dr. R. K. Singh (ADG, ICAR-Commercial Crops), and Dr. A. H. Prakash, AICRP on Cotton, ICAR-CICR, Regional Station, Coimbatore for providing the seed materials of cotton germplasm used in this study. We would like to thank Dr. Pankaj Rathore, Director, Punjab Agricultural University, Regional Station, Faridkot and Director of Research, Tamil Nadu Agricultural University, Coimbatore for allowing us to conduct field trials at both locations. In addition, BJ acknowledges the Department of Science and Technology (DST), New Delhi, for granting a research fellowship (DST/INSPIRE Fellowship/IF160405). We are grateful to Eurofin Pvt. Limited, India, for executing RAD sequencing. Eventually, we are thankful to the Director, ICAR-IASRI, New Delhi for providing the high-performance computational facility, ASHOKA.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1252746/full#supplementary-material

References

Abdullaev, A. A., Salakhutdinov, I. B., Egamberdiev, S. S., Khurshut, E. E., Rizaeva, S. M., Ulloa, M., et al. (2017). Genetic diversity, linkage disequilibrium, and association mapping analyses of Gossypium barbadense L. germplasm. PloS One 12 (11), e0188125. doi: 10.1371/journal.pone.0188125

PubMed Abstract | CrossRef Full Text | Google Scholar

Abdurakhmonov, I. Y., Abdukarimov, A., Pepper, A. E., Abdullaev, A. A., Kushanov, F., John, Z. Y., et al. (2012). Genetic diversity in Gossypium genus. Genetic Diversity in Plants. InTech. doi: 10.5772/35384

CrossRef Full Text | Google Scholar

Abdurakhmonov, I., Buriev, Z., Saha, S., Pepper, A., Musaev, J., Almatov, A., et al. (2007). Microsatellite markers associated with lint percentage trait in cotton, Gossypium hirsutum. Euphytica 156, 141–156. doi: 10.1007/s10681-007-9361-2

CrossRef Full Text | Google Scholar

Abdurakhmonov, I. Y., Kohel, R. J., Yu, J., Pepper, A., Abdullaev, A., Kushanov, F., et al. (2008). Molecular diversity and association mapping of fiber quality traits in exotic G. hirsutum L. germplasm. Genomics 92 (6), 478–487. doi: 10.1016/j.ygeno.2008.07.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Ademe, M. S., He, S., Pan, Z., Sun, J., Wang, Q., Qin, H., et al. (2017). Association mapping analysis of fiber yield and quality traits in Upland cotton (Gossypium hirsutum L.). Mol. Genet. Genomics 292, 1267–1280. doi: 10.1007/s00438-017-1346-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Ali, F., Li, Y., Li, F., Wang, Z. (2021). Genome-wide characterization and expression analysis of cystathionine β-synthase genes in plant development and abiotic stresses of cotton (Gossypium spp.). Int. J. Biol. Macromol. 193, 823–837. doi: 10.1016/j.ijbiomac.2021.10.079

PubMed Abstract | CrossRef Full Text | Google Scholar

Arruda, M. P., Brown, P., Brown-Guedira, G., Krill, A. M., Thurber, C., Merrill, K. R., et al. (2016). Genome-wide association mapping of Fusarium head blight resistance in wheat using genotyping-by-sequencing. Plant Genome 9 (1), 1–14. plantgenome2015.2004.0028. doi: 10.3835/plantgenome2015.04.0028

CrossRef Full Text | Google Scholar

Badigannavar, A., Myers, G. O. (2015). Genetic diversity, population structure and marker trait associations for seed quality traits in cotton (Gossypium hirsutum). J. Genet. 94, 87–94. doi: 10.1007/s12041-015-0489-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Bardak, A., Çelik, S., Erdoğan, O., Ekinci, R., Dumlupinar, Z. (2021). Association mapping of Verticillium wilt disease in a worldwide collection of cotton (Gossypium hirsutum L.). Plants 10 (2), 306. doi: 10.3390/plants10020306

PubMed Abstract | CrossRef Full Text | Google Scholar

Bhat, J. A., Adeboye, K. A., Ganie, S. A., Barmukh, R., Hu, D., Varshney, R. K., et al. (2022). Genome-wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield-related traits in soybean (Glycine max L.). Front. Genet. 13. doi: 10.3389/fgene.2022.953833

CrossRef Full Text | Google Scholar

Bolger, A. M., Lohse, M., Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30 (15), 2114–2120. doi: 10.1093/bioinformatics/btu170

PubMed Abstract | CrossRef Full Text | Google Scholar

Bradbury, P. J., Zhang, Z., Kroon, D. E., Casstevens, T. M., Ramdoss, Y., Buckler, E. S. (2007). TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23 (19), 2633–2635. doi: 10.1093/bioinformatics/btm308

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, H., Qian, N., Guo, W., Song, Q., Li, B., Deng, F., et al. (2010). Using three selected overlapping RILs to fine-map the yield component QTL on Chro. D8 in Upland cotton. Euphytica 176, 321–329. doi: 10.1007/s10681-010-0204-1

CrossRef Full Text | Google Scholar

Chen, Z. J., Scheffler, B. E., Dennis, E., Triplett, B. A., Zhang, T., Guo, W., et al. (2007). Toward sequencing cotton (Gossypium) genomes. Plant Physiol. 145 (4), 1303–1310. doi: 10.1104/pp.107.107672

PubMed Abstract | CrossRef Full Text | Google Scholar

Courtois, B., Audebert, A., Dardou, A., Roques, S., Ghneim-Herrera, T., Droc, G., et al. (2013). Genome-wide association mapping of root traits in a japonica rice panel. PloS One 8 (11), e78037. doi: 10.1371/journal.pone.0078037

PubMed Abstract | CrossRef Full Text | Google Scholar

Crowell, S., Korniliev, P., Falcao, A., Ismail, A., Gregorio, G., Mezey, J., et al. (2016). Genome-wide association and high-resolution phenotyping link Oryza sativa panicle traits to numerous trait-specific QTL clusters. Nat. Commun. 7 (1), 10527. doi: 10.1038/ncomms10527

PubMed Abstract | CrossRef Full Text | Google Scholar

Deng, F., Tu, L., Tan, J., Li, Y., Nie, Y., Zhang, X. (2012). GbPDF1 is involved in cotton fiber initiation via the core cis-element HDZIP2ATATHB2. Plant Physiol. 158 (2), 890–904. doi: 10.1104/pp.111.186742

PubMed Abstract | CrossRef Full Text | Google Scholar

Ditta, A., Zhou, Z., Cai, X., Wang, X., Okubazghi, K. W., Shehzad, M., et al. (2018). Assessment of genetic diversity, population structure, and evolutionary relationship of uncharacterized genes in a novel germplasm collection of diploid and allotetraploid Gossypium accessions using EST and genomic SSR markers. Int. J. Mol. Sci. 19 (8), 2401. doi: 10.3390/ijms19082401

PubMed Abstract | CrossRef Full Text | Google Scholar

Dong, C., Wang, J., Chen, Q., Yu, Y., Li, B. (2018). Detection of favorable alleles for yield and yield components by association mapping in upland cotton. Genes Genomics 40, 725–734. doi: 10.1007/s13258-018-0678-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Dong, C., Wang, J., Yu, Y., Ju, L., Zhou, X., Ma, X., et al. (2019). Identifying functional genes influencing Gossypium hirsutum fiber quality. Front. Plant Sci. 9, 1968. doi: 10.3389/fpls.2018.01968

PubMed Abstract | CrossRef Full Text | Google Scholar

Dure, L., III, Galau, G. A. (1981). Developmental Biochemistry of Cottonseed Embryogenesis and Germination: XIII. Regulation of biosynthesis of principal storage proteins. Plant Physiol. 68 (1), 187–194. doi: 10.1104/pp.68.1.187

PubMed Abstract | CrossRef Full Text | Google Scholar

Earl, D. A., VonHoldt, B. M. (2012). STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361. doi: 10.1007/s12686-011-9548-7

CrossRef Full Text | Google Scholar

Falush, D., Stephens, M., Pritchard, J. K. (2007). Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol. Ecol. Notes 7 (4), 574–578. doi: 10.1111/j.1471-8286.2007.01758.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Fang, L., Wang, Q., Hu, Y., Jia, Y., Chen, J., Liu, B., et al. (2017). Genomic analyses in cotton identify signatures of selection and loci associated with fiber quality and yield traits. Nat. Genet. 49 (7), 1089–1098. doi: 10.1038/ng.3887

PubMed Abstract | CrossRef Full Text | Google Scholar

Fang, D. D., Xiao, J., Canci, P. C., Cantrell, R. G. (2010). A new SNP haplotype associated with blue disease resistance gene in cotton (Gossypium hirsutum L.). Theor. Appl. Genet. 120, 943–953. doi: 10.1007/s00122-009-1223-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Feng, Z., Li, L., Tang, M., Liu, Q., Ji, Z., Sun, D., et al. (2022). Detection of stable elite haplotypes and potential candidate genes of boll weight across multiple environments via GWAS in upland cotton. Front. Plant Sci. 13. doi: 10.3389/fpls.2022.929168

CrossRef Full Text | Google Scholar

Flint-Garcia, S. A., Thornsberry, J. M., Buckler, E. S., IV (2003). Structure of linkage disequilibrium in plants. Annu. Rev. Plant Biol. 54 (1), 357–374. doi: 10.1146/annurev.arplant.54.031902.134907

PubMed Abstract | CrossRef Full Text | Google Scholar

Flint-Garcia, S., Thuillet, A., Yu, J., Pressoir, G., Romero, S. (2005). Maize genes for lignin abundance and saccharification yield association population: a high-resolution platform for quantitative trait locus dissection. Plant J. 44, 1054–1064. doi: 10.1111/j.1365-313X.2005.02591.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Gapare, W., Conaty, W., Zhu, Q.-H., Liu, S., Stiller, W., Llewellyn, D., et al. (2017). Genome-wide association study of yield components and fibre quality traits in a cotton germplasm diversity panel. Euphytica 213, 1–22. doi: 10.1007/s10681-017-1855-y

CrossRef Full Text | Google Scholar

Geng, X., Sun, G., Qu, Y., Sarfraz, Z., Jia, Y., He, S., et al. (2020). Genome-wide dissection of hybridization for fiber quality-and yield-related traits in upland cotton. Plant J. 104 (5), 1285–1300. doi: 10.1111/tpj.14999

PubMed Abstract | CrossRef Full Text | Google Scholar

Gong, S.-Y., Huang, G.-Q., Sun, X., Qin, L.-X., Li, Y., Zhou, L., et al. (2014). Cotton KNL1, encoding a class II KNOX transcription factor, is involved in regulation of fibre development. J. Exp. Bot. 65 (15), 4133–4147. doi: 10.1093/jxb/eru182

PubMed Abstract | CrossRef Full Text | Google Scholar

Grover, C. E., Gallagher, J. P., Jareczek, J. J., Page, J. T., Udall, J. A., Gore, M. A., et al. (2015). Re-evaluating the phylogeny of allopolyploid Gossypium L. Mol. Phylogenet. Evol. 92, 45–52. doi: 10.1016/j.ympev.2015.05.023

PubMed Abstract | CrossRef Full Text | Google Scholar

Guo, C., Pan, Z., You, C., Zhou, X., Huang, C., Shen, C., et al. (2021). Association mapping and domestication analysis to dissect genetic improvement process of upland cotton yield-related traits in China. J. Cotton Res. 4 (1), 1–12. doi: 10.1186/s42397-021-00087-3

CrossRef Full Text | Google Scholar

Gutiérrez, L., Germán, S., Pereyra, S., Hayes, P. M., Pérez, C. A., Capettini, F., et al. (2015). Multi-environment multi-QTL association mapping identifies disease resistance QTL in barley germplasm from Latin America. Theor. Appl. Genet. 128, 501–516. doi: 10.1007/s00122-014-2448-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Hall, D., Tegström, C., Ingvarsson, P. K. (2010). Using association mapping to dissect the genetic basis of complex traits in plants. Briefings Funct. Genomics 9 (2), 157–165. doi: 10.1093/bfgp/elp048

CrossRef Full Text | Google Scholar

Hamblin, M. T., Buckler, E. S., Jannink, J.-L. (2011). Population genetics of genomics-based crop improvement methods. Trends Genet. 27 (3), 98–106. doi: 10.1016/j.tig.2010.12.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Han, M., Jin, X., Yao, W., Kong, L., Huang, G., Tao, Y., et al. (2017). A mini zinc-finger protein (MIF) from Gerbera hybrida activates the GASA protein family gene, GEG, to inhibit ray petal elongation. Front. Plant Sci. 8, 1649. doi: 10.3389/fpls.2017.01649

PubMed Abstract | CrossRef Full Text | Google Scholar

Handi, S. S., Katageri, I. S., Adiger, S., Jadhav, M. P., Lekkala, S. P., Reddy Lachagari, V. B. (2017). Association mapping for seed cotton yield, yield components and fibre quality traits in upland cotton (Gossypium hirsutum L.) genotypes. Plant Breed. 136 (6), 958–968. doi: 10.1111/pbr.12536

CrossRef Full Text | Google Scholar

Hart, T., Komori, H. K., LaMere, S., Podshivalova, K., Salomon, D. R. (2013). Finding the active genes in deep RNA-seq gene expression studies. BMC Genomics 14, 1–7. doi: 10.1186/1471-2164-14-778

PubMed Abstract | CrossRef Full Text | Google Scholar

Horton, M. W., Bodenhausen, N., Beilsmith, K., Meng, D., Muegge, B. D., Subramanian, S., et al. (2014). Genome-wide association study of Arabidopsis thaliana leaf microbial community. Nat. Commun. 5 (1), 5320. doi: 10.1038/ncomms6320

PubMed Abstract | CrossRef Full Text | Google Scholar

Hu, Y., Chen, J., Fang, L., Zhang, Z., Ma, W., Niu, Y., et al. (2019). Gossypium barbadense and Gossypium hirsutum genomes provide insights into the origin and evolution of allotetraploid cotton. Nat. Genet. 51 (4), 739–748. doi: 10.1038/s41588-019-0371-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Huang, G.-Q., Gong, S.-Y., Xu, W.-L., Li, W., Li, P., Zhang, C.-J., et al. (2013). A fasciclin-like arabinogalactan protein, GhFLA1, is involved in fiber initiation and elongation of cotton. Plant Physiol. 161 (3), 1278–1290. doi: 10.1104/pp.112.203760

PubMed Abstract | CrossRef Full Text | Google Scholar

Huang, G., Huang, J.-Q., Chen, X.-Y., Zhu, Y.-X. (2021). Recent advances and future perspectives in cotton research. Annu. Rev. Plant Biol. 72, 437–462. doi: 10.1146/annurev-arplant-080720-113241

PubMed Abstract | CrossRef Full Text | Google Scholar

Huang, C., Nie, X., Shen, C., You, C., Li, W., Zhao, W., et al. (2017). Population structure and genetic basis of the agronomic traits of upland cotton in China revealed by a genome-wide association study using high-density SNP s. Plant Biotechnol. J. 15 (11), 1374–1386. doi: 10.1111/pbi.12722

PubMed Abstract | CrossRef Full Text | Google Scholar

Huang, X., Zhao, Y., Wei, X., Li, C., Wang, A., Zhao, Q., et al. (2012). Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat. Genet. 44 (1), 32–39. doi: 10.1038/ng.1018

CrossRef Full Text | Google Scholar

Iqbal, M., Reddy, O., El-Zik, K., Pepper, A. (2001). A genetic bottleneck in the’evolution under domestication’of upland cotton Gossypium hirsutum L. examined using DNA fingerprinting. Theor. Appl. Genet. 103, 547–554. doi: 10.1007/PL00002908

CrossRef Full Text | Google Scholar

Islam, M. S., Thyssen, G. N., Jenkins, J. N., Zeng, L., Delhom, C. D., McCarty, J. C., et al. (2016). A MAGIC population-based genome-wide association study reveals functional association of GhRBB1_A07 gene with superior fiber quality in cotton. BMC Genomics 17 (1), 1–17. doi: 10.1186/s12864-016-3249-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Jannink, J.-L., Bink, M. C., Jansen, R. C. (2001). Using complex plant pedigrees to map valuable genes. Trends Plant Sci. 6 (8), 337–342. doi: 10.1016/S1360-1385(01)02017-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Jia, Y., Sun, X., Sun, J., Pan, Z., Wang, X., He, S., et al. (2014). Association mapping for epistasis and environmental interaction of yield traits in 323 cotton cultivars under 9 different environments. PloS One 9 (5), e95882. doi: 10.1371/journal.pone.0095882

PubMed Abstract | CrossRef Full Text | Google Scholar

Kantartzi, S., Stewart, J. M. (2008). Association analysis of fibre traits in Gossypium arboreum accessions. Plant Breed. 127 (2), 173–179. doi: 10.1111/j.1439-0523.2008.01490.x

CrossRef Full Text | Google Scholar

Kolde, R. (2019). pheatmap: pretty heatmaps. R package version 1.0. 12.

Google Scholar

Kumar, P., Nimbal, S., Sangwan, R. S., Budhlakoti, N., Singh, V., Mishra, D. C., et al. (2021). Identification of novel marker–trait associations for lint yield contributing traits in upland cotton (Gossypium hirsutum L.) using SSRs. Front. Plant Sci. 12, 653270. doi: 10.3389/fpls.2021.653270

PubMed Abstract | CrossRef Full Text | Google Scholar

Lacape, J.-M., Gawrysiak, G., Cao, T.-V., Viot, C., Llewellyn, D., Liu, S., et al. (2013). Mapping QTLs for traits related to phenology, morphology and yield components in an inter-specific Gossypium hirsutum× G. barbadense cotton RIL population. Field Crops Res. 144, 256–267. doi: 10.1016/j.fcr.2013.01.001

CrossRef Full Text | Google Scholar

Lee, J. A. (1984). Cotton as a world crop. Cotton 24, 1–25. doi: 10.2134/agronmonogr24.c1

CrossRef Full Text | Google Scholar

Li, H. (2013). Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv:1303.3997. doi: 10.48550/arXiv.1303.3997

CrossRef Full Text | Google Scholar

Li, Z., Cao, X., Wang, H., Liu, Y., Liu, W., Zhang, H., et al. (2023). Comprehensive identification of polygalacturonases in cotton: Genomic analysis, potential regulatory mechanisms and expression patterns in anthers. Ind. Crops Prod. 200, 116874. doi: 10.1016/j.indcrop.2023.116874

CrossRef Full Text | Google Scholar

Li, F., Fan, G., Lu, C., Xiao, G., Zou, C., Kohel, R. J., et al. (2015). Genome sequence of cultivated Upland cotton (Gossypium hirsutum TM-1) provides insights into genome evolution. Nat. Biotechnol. 33 (5), 524–530. doi: 10.1038/nbt.3208

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, C., Fu, Y., Sun, R., Wang, Y., Wang, Q. (2018). Single-locus and multi-locus genome-wide association studies in the genetic dissection of fiber quality traits in upland cotton (Gossypium hirsutum L.). Front. Plant Sci. 9, 1083. doi: 10.3389/fpls.2018.01083

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, A., Hao, C., Wang, Z., Geng, S., Jia, M., Wang, F., et al. (2022). Wheat breeding history reveals synergistic selection of pleiotropic genomic sites for plant architecture and grain yield. Mol. Plant 15 (3), 504–519. doi: 10.1016/j.molp.2022.01.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, Y., Si, Z., Wang, G., Shi, Z., Chen, J., Qi, G., et al. (2023). Genomic insights into the genetic basis of cotton breeding in China. Mol. Plant 16 (4), 662–677. doi: 10.1016/j.molp.2023.01.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, P., Wang, M., Zhou, Y., Wu, Q., Shen, Y., Cui, Z., et al. (2023). Freezing transcriptome analysis showed that GhZAT10 regulates freezing tolerance through a partially CBF-dependent pathway in upland cotton (Gossypium hirsutum L.). Environ. Exp. Bot. 208, 105263. doi: 10.1016/j.envexpbot.2023.105263

CrossRef Full Text | Google Scholar

Li, F., Wen, W., He, Z., Liu, J., Jin, H., Cao, S., et al. (2018). Genome-wide linkage mapping of yield-related traits in three Chinese bread wheat populations using high-density SNP markers. Theor. Appl. Genet. 131, 1903–1924. doi: 10.1007/s00122-018-3122-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, L., Yan, X., Li, J., Wu, X., Wang, X. (2022). Metabolome and transcriptome association analysis revealed key factors involved in melatonin mediated cadmium-stress tolerance in cotton. Front. Plant Sci. 13, 995205. doi: 10.3389/fpls.2022.995205

PubMed Abstract | CrossRef Full Text | Google Scholar

Liang, C., Meng, Z., Meng, Z., Malik, W., Yan, R., Lwin, K. M., et al. (2016). GhABF2, a bZIP transcription factor, confers drought and salinity tolerance in cotton (Gossypium hirsutum L.). Sci. Rep. 6 (1), 1–14. doi: 10.1038/srep35040

PubMed Abstract | CrossRef Full Text | Google Scholar

Lipka, A. E., Kandianis, C. B., Hudson, M. E., Yu, J., Drnevich, J., Bradbury, P. J., et al. (2015). From association to prediction: statistical methods for the dissection and selection of complex traits in plants. Curr. Opin. Plant Biol. 24, 110–118. doi: 10.1016/j.pbi.2015.02.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Lischer, H. E., Excoffier, L. (2012). PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics 28 (2), 298–299. doi: 10.1093/bioinformatics/btr642

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, S., Fan, C., Li, J., Cai, G., Yang, Q., Wu, J., et al. (2016). A genome-wide association study reveals novel elite allelic variations in seed oil content of Brassica napus. Theor. Appl. Genet. 129, 1203–1215. doi: 10.1007/s00122-016-2697-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, D., Liu, F., Shan, X., Zhang, J., Tang, S., Fang, X., et al. (2015). Construction of a high-density genetic map and lint percentage and cottonseed nutrient trait QTL identification in upland cotton (Gossypium hirsutum L.). Mol. Genet. Genomics 290, 1683–1700. doi: 10.1007/s00438-015-1027-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, G., Mei, H., Wang, S., Li, X., Zhu, X., Zhang, T. (2015). Association mapping of seed oil and protein contents in upland cotton. Euphytica 205, 637–645. doi: 10.1007/s10681-015-1450-z

CrossRef Full Text | Google Scholar

Liu, W., Song, C., Ren, Z., Zhang, Z., Pei, X., Liu, Y., et al. (2020). Genome-wide association study reveals the genetic basis of fiber quality traits in upland cotton (Gossypium hirsutum L.). BMC Plant Biol. 20, 1–13. doi: 10.1186/s12870-020-02611-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, R., Wang, B., Guo, W., Qin, Y., Wang, L., Zhang, Y., et al. (2012). Quantitative trait loci mapping for yield and its components by using two immortalized populations of a heterotic hybrid in Gossypium hirsutum L. Mol. Breed. 29, 297–311. doi: 10.1007/s11032-011-9547-0

CrossRef Full Text | Google Scholar

Lu, X., Fu, X., Wang, D., Wang, J., Chen, X., Hao, M., et al. (2019). Resequencing of cv CRI-12 family reveals haplotype block inheritance and recombination of agronomically important genes in artificial selection. Plant Biotechnol. J. 17 (5), 945–955. doi: 10.1111/pbi.13030

PubMed Abstract | CrossRef Full Text | Google Scholar

Lu, Q., Zhang, M., Niu, X., Wang, S., Xu, Q., Feng, Y., et al. (2015). Genetic variation and association mapping for 12 agronomic traits in indica rice. BMC Genomics 16 (1), 1–17. doi: 10.1186/s12864-015-2245-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Ma, Z., He, S., Wang, X., Sun, J., Zhang, Y., Zhang, G., et al. (2018). Resequencing a core collection of upland cotton identifies genomic variation and loci influencing fiber quality and yield. Nat. Genet. 50 (6), 803–813. doi: 10.1038/s41588-018-0119-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Ma, R., Yuan, H., An, J., Hao, X., Li, H. (2018). A Gossypium hirsutum GDSL lipase/hydrolase gene (GhGLIP) appears to be involved in promoting seed growth in Arabidopsis. PloS One 13 (4), e0195556. doi: 10.1371/journal.pone.0195556

PubMed Abstract | CrossRef Full Text | Google Scholar

Magwanga, R. O., Lu, P., Kirungu, J. N., Dong, Q., Hu, Y., Zhou, Z., et al. (2018). Cotton late embryogenesis abundant (LEA2) genes promote root growth and confer drought stress tolerance in transgenic Arabidopsis thaliana. G3: Genes, Genomes. Genetics 8 (8), 2781–2803. doi: 10.1534/g3.118.200423

CrossRef Full Text | Google Scholar

Maldonado, C., Mora, F., Scapim, C. A., Coan, M. (2019). Genome-wide haplotype-based association analysis of key traits of plant lodging and architecture of maize identifies major determinants for leaf angle: Hap LA4. PloS One 14 (3), e0212925. doi: 10.1371/journal.pone.0212925

PubMed Abstract | CrossRef Full Text | Google Scholar

Mei, H., Zhu, X., Zhang, T. (2013). Favorable QTL alleles for yield and its components identified by association mapping in Chinese Upland cotton cultivars. PloS One 8 (12), e82193. doi: 10.1371/journal.pone.0082193

PubMed Abstract | CrossRef Full Text | Google Scholar

Mihalyov, P. D., Nichols, V. A., Bulli, P., Rouse, M. N., Pumphrey, M. O. (2017). Multi-locus mixed model analysis of stem rust resistance in winter wheat. Plant Genome 10 (2), plantgenome2017.2001.0001. doi: 10.3835/plantgenome2017.01.0001

CrossRef Full Text | Google Scholar

Nie, X., Huang, C., You, C., Li, W., Zhao, W., Shen, C., et al. (2016). Genome-wide SSR-based association mapping for fiber quality in nation-wide upland cotton inbreed cultivars in China. BMC Genomics 17 (1), 1–16. doi: 10.1186/s12864-016-2662-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Niu, H., Kuang, M., Huang, L., Shang, H., Yuan, Y., Ge, Q. (2023). Lint percentage and boll weight QTLs in three excellent upland cotton (Gossypium hirsutum): ZR014121, CCRI60, and EZ60. BMC Plant Biol. 23 (1), 179. doi: 10.1186/s12870-023-04147-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Nordborg, M., Borevitz, J. O., Bergelson, J., Berry, C. C., Chory, J., Hagenblad, J., et al. (2002). The extent of linkage disequilibrium in Arabidopsis thaliana. Nat. Genet. 30 (2), 190–193. doi: 10.1038/ng813

PubMed Abstract | CrossRef Full Text | Google Scholar

Omrani, M., Roth, M., Roch, G., Blanc, A., Morris, C. E., Audergon, J.-M. (2019). Genome-wide association multi-locus and multi-variate linear mixed models reveal two linked loci with major effects on partial resistance of apricot to bacterial canker. BMC Plant Biol. 19 (1), 1–18. doi: 10.1186/s12870-019-1631-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Pertea, M., Kim, D., Pertea, G. M., Leek, J. T., Salzberg, S. L. (2016). Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 11 (9), 1650–1667. doi: 10.1038/nprot.2016.095

PubMed Abstract | CrossRef Full Text | Google Scholar

Poland, J., Rife, T. (2012). Genotyping-by-sequencing for plant breeding and genetics. Plant Genome 5, 92–102. Go to original source. doi: 10.3835/plantgenome2012.05.0005

CrossRef Full Text | Google Scholar

Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., et al. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81 (3), 559–575. doi: 10.1086/519795

PubMed Abstract | CrossRef Full Text | Google Scholar

Qin, Y.-M., Zhu, Y.-X. (2011). How cotton fibers elongate: a tale of linear cell-growth mode. Curr. Opin. Plant Biol. 14 (1), 106–111. doi: 10.1016/j.pbi.2010.09.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Raman, H., Raman, R., Qiu, Y., Yadav, A. S., Sureshkumar, S., Borg, L., et al. (2019). GWAS hints at pleiotropic roles for FLOWERING LOCUS T in flowering time and yield-related traits in canola. BMC Genomics 20 (1), 1–18. doi: 10.1186/s12864-019-5964-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Ravelombola, W., Qin, J., Shi, A., Song, Q., Yuan, J., Wang, F., et al. (2021). Genome-wide association study and genomic selection for yield and related traits in soybean. PloS One 16 (8), e0255761. doi: 10.1371/journal.pone.0255761

PubMed Abstract | CrossRef Full Text | Google Scholar

Rong, J., Feltus, F. A., Waghmare, V. N., Pierce, G. J., Chee, P. W., Draye, X., et al. (2007). Meta-analysis of polyploid cotton QTL shows unequal contributions of subgenomes to a complex network of genes and gene clusters implicated in lint fiber development. Genetics 176 (4), 2577–2588. doi: 10.1534/genetics.107.074518

PubMed Abstract | CrossRef Full Text | Google Scholar

Roorkiwal, M., Jarquin, D., Singh, M. K., Gaur, P. M., Bharadwaj, C., Rathore, A., et al. (2018). Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype× environment interaction on prediction accuracy in chickpea. Sci. Rep. 8 (1), 11701. doi: 10.1038/s41598-018-30027-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Rungis, D., Llewellyn, D., Dennis, E., Lyon, B. (2005). Simple sequence repeat (SSR) markers reveal low levels of polymorphism between cotton (Gossypium hirsutum L.) cultivars. Aust. J. Agric. Res. 56 (3), 301–307. doi: 10.1071/AR04190

CrossRef Full Text | Google Scholar

Said, J. I., Lin, Z., Zhang, X., Song, M., Zhang, J. (2013). A comprehensive meta QTL analysis for fiber quality, yield, yield related and morphological traits, drought tolerance, and disease resistance in tetraploid cotton. BMC Genomics 14, 1–22. doi: 10.1186/1471-2164-14-776

PubMed Abstract | CrossRef Full Text | Google Scholar

Said, J. I., Song, M., Wang, H., Lin, Z., Zhang, X., Fang, D. D., et al. (2015). A comparative meta-analysis of QTL between intraspecific Gossypium hirsutum and interspecific G. hirsutum× G. barbadense populations. Mol. Genet. Genomics 290, 1003–1025. doi: 10.1007/s00438-014-0963-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Segura, V., Vilhjálmsson, B. J., Platt, A., Korte, A., Seren, Ü., Long, Q., et al. (2012). An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat. Genet. 44 (7), 825–830. doi: 10.1038/ng.2314

PubMed Abstract | CrossRef Full Text | Google Scholar

Sethi, K., Siwach, P., Verma, S. K. (2017). Linkage disequilibrium and association mapping of fibre quality traits in elite Asiatic cotton (Gossypium arboreum) germplasm populations. Czech J. Genet. Plant Breed. 53 (4), 159–167. doi: 10.17221/142/2016-CJGPB

CrossRef Full Text | Google Scholar

Shen, X., Guo, W., Lu, Q., Zhu, X., Yuan, Y., Zhang, T. (2007). Genetic mapping of quantitative trait loci for fiber quality and yield trait by RIL approach in Upland cotton. Euphytica 155, 371–380. doi: 10.1007/s10681-006-9338-6

CrossRef Full Text | Google Scholar

Shi, Y.-H., Zhu, S.-W., Mao, X.-Z., Feng, J.-X., Qin, Y.-M., Zhang, L., et al. (2006). Transcriptome profiling, molecular biological, and physiological studies reveal a major role for ethylene in cotton fiber cell elongation. Plant Cell 18 (3), 651–664. doi: 10.1105/tpc.105.040303

PubMed Abstract | CrossRef Full Text | Google Scholar

Shukla, R. P., Tiwari, G. J., Joshi, B., Song-Beng, K., Tamta, S., Boopathi, N. M., et al. (2021). GBS-SNP and SSR based genetic mapping and QTL analysis for drought tolerance in upland cotton. Physiol. Mol. Biol. Plants 27 (8), 1731–1745. doi: 10.1007/s12298-021-01041-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Singh, N., Choudhury, D. R., Singh, A. K., Kumar, S., Srinivasan, K., Tyagi, R., et al. (2013). Comparison of SSR and SNP markers in estimation of genetic diversity and population structure of Indian rice varieties. PloS One 8 (12), e84136. doi: 10.1371/journal.pone.0084136

PubMed Abstract | CrossRef Full Text | Google Scholar

Song, C., Li, W., Pei, X., Liu, Y., Ren, Z., He, K., et al. (2019). Dissection of the genetic variation and candidate genes of lint percentage by a genome-wide association study in upland cotton. Theor. Appl. Genet. 132, 1991–2002. doi: 10.1007/s00122-019-03333-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Song, J., PEI, W., Ma, J., Yang, S., Jia, B., Bian, Y., et al. (2021). Genome-wide association study of micronaire using a natural population of representative upland cotton (Gossypium hirsutum L.). J. Cotton Res. 4 (1), 1–12. doi: 10.1186/s42397-021-00089-1

CrossRef Full Text | Google Scholar

Su, J., Fan, S., Li, L., Wei, H., Wang, C., Wang, H., et al. (2016). Detection of favorable QTL alleles and candidate genes for lint percentage by GWAS in Chinese upland cotton. Front. Plant Sci. 7, 1576. doi: 10.3389/fpls.2016.01576

PubMed Abstract | CrossRef Full Text | Google Scholar

Su, J., Ma, Q., Li, M., Hao, F., Wang, C. (2018). Multi-locus genome-wide association studies of fiber-quality related traits in Chinese early-maturity upland cotton. Front. Plant Sci. 9, 1169. doi: 10.3389/fpls.2018.01169

PubMed Abstract | CrossRef Full Text | Google Scholar

Sun, H., Hao, P., Gu, L., Cheng, S., Wang, H., Wu, A., et al. (2020). Pectate lyase-like gene GhPEL76 regulates organ elongation in Arabidopsis and fiber elongation in cotton. Plant Sci. 293, 110395. doi: 10.1016/j.plantsci.2019.110395

PubMed Abstract | CrossRef Full Text | Google Scholar

Sun, Z., Wang, X., Liu, Z., Gu, Q., Zhang, Y., Li, Z., et al. (2017). Genome-wide association study discovered genetic variation and candidate genes of fibre quality traits in Gossypium hirsutum L. Plant Biotechnol. J. 15 (8), 982–996. doi: 10.1111/pbi.12693

PubMed Abstract | CrossRef Full Text | Google Scholar

Sun, Z., Wang, X., Liu, Z., Gu, Q., Zhang, Y., Li, Z., et al. (2018). A genome-wide association study uncovers novel genomic regions and candidate genes of yield-related traits in upland cotton. Theor. Appl. Genet. 131, 2413–2425. doi: 10.1007/s00122-018-3162-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Sun, C., Zhang, F., Yan, X., Zhang, X., Dong, Z., Cui, D., et al. (2017). Genome-wide association study for 13 agronomic traits reveals distribution of superior alleles in bread wheat from the Yellow and Huai Valley of China. Plant Biotechnol. J. 15 (8), 953–969. doi: 10.1111/pbi.12690

PubMed Abstract | CrossRef Full Text | Google Scholar

Team, R. C. (2016). R: A language and environment for statistical computing (Vienna, Austria: R Foundation for Statistical Computing). Available at: http://www.R-project.org/.

Google Scholar

Tomar, V., Singh, D., Dhillon, G. S., Chung, Y. S., Poland, J., Singh, R. P., et al. (2021). Increased predictive accuracy of multi-environment genomic prediction model for yield and related traits in spring wheat (Triticum aestivum L.). Front. Plant Sci. 12, 720123. doi: 10.3389/fpls.2021.720123

PubMed Abstract | CrossRef Full Text | Google Scholar

Van der Auwera, G. A., Carneiro, M. O., Hartl, C., Poplin, R., Del Angel, G., Levy-Moonshine, A., et al. (2013). From FastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr. Protoc. Bioinf. 43 (1), 11.10. 11–11.10.33. doi: 10.1002/0471250953.bi1110s43

CrossRef Full Text | Google Scholar

Van der Auwera, G. A., O’Connor, B. D. (2020). Genomics in the cloud: using Docker, GATK, and WDL in Terra (O’Reilly Media).

Google Scholar

Van Tienderen, P. H., Hammad, I., Zwaal, F. C. (1996). Pleiotropic effects of flowering time genes in the annual crucifer Arabidopsis thaliana (Brassicaceae). Am. J. Bot. 83 (2), 169–174. doi: 10.1002/j.1537-2197.1996.tb12693.x

CrossRef Full Text | Google Scholar

Varshney, R. K., Pandey, M. K., Bohra, A., Singh, V. K., Thudi, M., Saxena, R. K. (2019a). Toward the sequence-based breeding in legumes in the post-genome sequencing era. Theor. Appl. Genet. 132 (3), 797–816. doi: 10.1007/s00122-018-3252-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Varshney, R. K., Thudi, M., Roorkiwal, M., He, W., Upadhyaya, H. D., Yang, W., et al. (2019b). Resequencing of 429 chickpea accessions from 45 countries provides insights into genome diversity, domestication and agronomic traits. Nat. Genet. 51 (5), 857–864. doi: 10.1038/s41588-019-0401-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Wan, Q., Zhang, Z., Hu, M., Chen, L., Liu, D., Chen, X., et al. (2007). T 1 locus in cotton is the candidate gene affecting lint percentage, fiber quality and spiny bollworm (Earias spp.) resistance. Euphytica 158, 241–247. doi: 10.1007/s10681-007-9446-y

CrossRef Full Text | Google Scholar

Wang, B., Guo, W., Zhu, X., Wu, Y., Huang, N., Zhang, T. (2007). QTL mapping of yield and yield components for elite hybrid derived-RILs in upland cotton. J. Genet. Genomics 34 (1), 35–45. doi: 10.1016/S1673-8527(07)60005-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, P., He, S., Sun, G., Pan, Z., Sun, J., Geng, X., et al. (2021). Favorable pleiotropic loci for fiber yield and quality in upland cotton (Gossypium hirsutum). Sci. Rep. 11 (1), 15935.

PubMed Abstract | Google Scholar

Wang, Y., Li, G., Guo, X., Sun, R., Dong, T., Yang, Q., et al. (2019). Dissecting the genetic architecture of seed-cotton and lint yields in Upland cotton using genome-wide association mapping. Breed. Sci. 69 (4), 611–620. doi: 10.1270/jsbbs.19057

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, M., Li, C., Wang, Q. (2014). Quantitative trait loci mapping and genetic dissection for lint percentage in upland cotton (Gossypium hirsutum). J. Genet. 93, 371–378. doi: 10.1007/s12041-014-0385-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, B., Lin, Z., Li, X., Zhao, Y., Zhao, B., Wu, G., et al. (2020). Genome-wide selection and genetic improvement during modern maize breeding. Nat. Genet. 52 (6), 565–571. doi: 10.1038/s41588-020-0616-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, X., Pang, Y., Wang, C., Chen, K., Zhu, Y., Shen, C., et al. (2017). New candidate genes affecting rice grain appearance and milling quality detected by genome-wide and gene-based association analyses. Front. Plant Sci. 7, 1998. doi: 10.3389/fpls.2016.01998

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, B., Wu, Z., Li, Z., Zhang, Q., Hu, J., Xiao, Y., et al. (2018). Dissection of the genetic architecture of three seed-quality traits and consequences for breeding in Brassica napus. Plant Biotechnol. J. 16 (7), 1336–1348. doi: 10.1111/pbi.12873

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, J., Zhang, Z. (2021). GAPIT version 3: boosting power and accuracy for genomic association and prediction. Genomics Proteomics Bioinf. 19 (4), 629–640. doi: 10.1016/j.gpb.2021.08.005

CrossRef Full Text | Google Scholar

Wang, Y., Zhao, J., Deng, X., Wang, P., Geng, S., Gao, W., et al. (2022). Genome-wide analysis of serine carboxypeptidase-like protein (SCPL) family and functional validation of Gh_SCPL42 unchromosome conferring cotton Verticillium der Verticillium wilt stress in Gossypium hirsutum. BMC Plant Biol. 22 (1), 1–13. doi: 10.1186/s12870-022-03804-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Watts, S., McElroy, M., Migicovsky, Z., Maassen, H., van Velzen, R., Myles, S. (2021). Cannabis labelling is associated with genetic variation in terpene synthase genes. Nat. Plants 7 (10), 1330–1334. doi: 10.1038/s41477-021-01003-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Wu, J., Gutierrez, O. A., Jenkins, J. N., McCarty, J. C., Zhu, J. (2009). Quantitative analysis and QTL mapping for agronomic and fiber traits in an RI population of upland cotton. Euphytica 165, 231–245. doi: 10.1007/s10681-008-9748-8

CrossRef Full Text | Google Scholar

Wu, T., Hu, E., Xu, S., Chen, M., Guo, P., Dai, Z., et al. (2021). clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation 2 (3), 100141. doi: 10.1016/j.xinn.2021.100141

PubMed Abstract | CrossRef Full Text | Google Scholar

Xiao, Y.-H., Li, D.-M., Yin, M.-H., Li, X.-B., Zhang, M., Wang, Y.-J., et al. (2010). Gibberellin 20-oxidase promotes initiation and elongation of cotton fibers by regulating gibberellin synthesis. J. Plant Physiol. 167 (10), 829–837. doi: 10.1016/j.jplph.2010.01.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Xiao, Y., Liu, D., Zhang, G., Gao, S., Liu, L., Xu, F., et al. (2019). Big Grain3, encoding a purine permease, regulates grain size via modulating cytokinin transport in rice. J. Integr. Plant Biol. 61 (5), 581–597. doi: 10.1111/jipb.12727

PubMed Abstract | CrossRef Full Text | Google Scholar

Xu, P., Guo, Q., Meng, S., Zhang, X., Xu, Z., Guo, W., et al. (2021). Genome-wide association analysis reveals genetic variations and candidate genes associated with salt tolerance related traits in Gossypium hirsutum. BMC Genomics 22 (1), 1–14. doi: 10.1186/s12864-020-07321-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Yan, W.-H., Wang, P., Chen, H.-X., Zhou, H.-J., Li, Q.-P., Wang, C.-R., et al. (2011). A major QTL, Ghd8, plays pleiotropic roles in regulating grain productivity, plant height, and heading date in rice. Mol. Plant 4 (2), 319–330. doi: 10.1093/mp/ssq070

PubMed Abstract | CrossRef Full Text | Google Scholar

Yano, K., Yamamoto, E., Aya, K., Takeuchi, H., Lo, P.-C., Hu, L., et al. (2016). Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat. Genet. 48 (8), 927–934. doi: 10.1038/ng.3596

PubMed Abstract | CrossRef Full Text | Google Scholar

Yu, J., Yu, S., Gore, M., Wu, M., Zhai, H., Li, X., et al. (2013a). Identification of quantitative trait loci across interspecific F 2, F 2: 3 and testcross populations for agronomic and fiber traits in tetraploid cotton. Euphytica 191, 375–389. doi: 10.1007/s10681-013-0875-5

CrossRef Full Text | Google Scholar

Yu, J., Zhang, K., Li, S., Yu, S., Zhai, H., Wu, M., et al. (2013b). Mapping quantitative trait loci for lint yield and fiber quality across environments in a Gossypium hirsutum× Gossypium barbadense backcross inbred line population. Theor. Appl. Genet. 126, 275–287. doi: 10.1007/s00122-012-1980-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Yuan, Y., Cairns, J. E., Babu, R., Gowda, M., Makumbi, D., Magorokosho, C., et al. (2019). Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and flowering time under drought and heat stress conditions in maize. Front. Plant Sci. 9, 1919. doi: 10.3389/fpls.2018.01919

PubMed Abstract | CrossRef Full Text | Google Scholar

Yuan, Y., Wang, X., Wang, L., Xing, H., Wang, Q., Saeed, M., et al. (2018). Genome-wide association study identifies candidate genes related to seed oil composition and protein content in Gossypium hirsutum L. Front. Plant Sci. 9, 1359. doi: 10.3389/fpls.2018.01359

PubMed Abstract | CrossRef Full Text | Google Scholar

Zafar, M. M., Rehman, A., Razzaq, A., Parvaiz, A., Mustafa, G., Sharif, F., et al. (2022). Genome-wide characterization and expression analysis of Erf gene family in cotton. BMC Plant Biol. 22 (1), 134. doi: 10.1186/s12870-022-03521-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Zang, Q.-W., Wang, C.-X., Li, X.-Y., Guo, Z.-A., Jing, R.-L., Zhao, J., et al. (2010). Isolation and characterization of a gene encoding a polyethylene glycol-induced cysteine protease in common wheat. J. Biosci. 35, 379–388. doi: 10.1007/s12038-010-0043-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, Y., Chen, B., Sun, Z., Liu, Z., Cui, Y., Ke, H., et al. (2021). A large-scale genomic association analysis identifies a fragment in Dt11 chromosome conferring cotton Verticillium wilt resistance. Plant Biotechnol. J. 19 (10), 2126–2138. doi: 10.1111/pbi.13650

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, W., Fang, L., Shao-Hui, L., Wei, W., Chun-Ying, W., Zhang, X.-D., et al. (2011). QTL analysis on yield and its components in recombinant inbred lines of upland cotton. Acta Agronomica Sin. 37 (3), 433–442. doi: 10.1016/S1875-2780(11)60013-8

CrossRef Full Text | Google Scholar

Zhang, T., Hu, Y., Jiang, W., Fang, L., Guan, X., Chen, J., et al. (2015). Sequencing of allotetraploid cotton (Gossypium hirsutum L. acc. TM-1) provides a resource for fiber improvement. Nat. Biotechnol. 33 (5), 531–537. doi: 10.1038/nbt.3207

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, R., Jia, G., Diao, X. (2023). geneHapR: an R package for gene haplotypic statistics and visualization. BMC Bioinf. 24 (1), 199. doi: 10.1186/s12859-023-05318-9

CrossRef Full Text | Google Scholar

Zhang, Z.-S., Xiao, Y.-H., Luo, M., Li, X.-B., Luo, X.-Y., Hou, L., et al. (2005). Construction of a genetic linkage map and QTL analysis of fiber-related traits in upland cotton (Gossypium hirsutum L.). Euphytica 144, 91–99. doi: 10.1007/s10681-005-4629-x

CrossRef Full Text | Google Scholar

Zhao, Y., Chen, W., Cui, Y., Sang, X., Lu, J., Jing, H., et al. (2021). Detection of candidate genes and development of KASP markers for Verticillium wilt resistance by combining genome-wide association study, QTL-seq and transcriptome sequencing in cotton. Theor. Appl. Genet. 134, 1063–1081. doi: 10.1007/s00122-020-03752-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhao, W., Kong, X., Yang, Y., Nie, X., Lin, Z. (2019). Association mapping seed kernel oil content in upland cotton using genome-wide SSRs and SNPs. Mol. Breed. 39, 1–11. doi: 10.1007/s11032-019-1007-2

CrossRef Full Text | Google Scholar

Zhao, J., Sauvage, C., Bitton, F., Causse, M. (2022). Multiple haplotype-based analyses provide genetic and evolutionary insights into tomato fruit weight and composition. Horticult. Res. 9, uhab009. doi: 10.1093/hr/uhab009

CrossRef Full Text | Google Scholar

Zhu, G., Hou, S., Song, X., Wang, X., Wang, W., Chen, Q., et al. (2021). Genome-wide association analysis reveals quantitative trait loci and candidate genes involved in yield components under multiple field environments in cotton (Gossypium hirsutum). BMC Plant Biol. 21 (1), 1–11. doi: 10.1186/s12870-021-03009-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhu, Y.-Q., Xu, K.-X., Luo, B., Wang, J.-W., Chen, X.-Y. (2003). An ATP-binding cassette transporter GhWBC1 from elongating cotton fibers. Plant Physiol. 133 (2), 580–588. doi: 10.1104/pp.103.027052

PubMed Abstract | CrossRef Full Text | Google Scholar