Identification of SSR markers linked to the abscission of cotton bolls traits and mining germplasm in Cotton

doi:10.21203/rs.3.rs-3627611/v1

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Identification of SSR markers linked to the abscission of cotton bolls traits and mining germplasm in Cotton

https://doi.org/10.21203/rs.3.rs-3627611/v1

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Background: Cotton is one of the most important economic crops. To effectively improve yield of cotton, an urgent way is decreasing the abscission of cotton bolls and buds. However, unclear genetic and molecular mechanism underlying cotton bolls abscission traits hindered the genetic improvement of cotton.

Methods: In this study, the correlation analysis between abscission traits and other agronomically important traits were carried out by Pearson correlation analysis. Then, a genome-wide association study for these traits were performed based on 145 alleles locus with highly polymorphism and uniformly distributed on 26 chromosomes by TASSEL V2.1 software. The loci of elite allelic variation and typical materials carrying elite alleles were determined by association study.

Results: The Pearson correlation analysis showed a significant positive correlation among AR1 (boll abscission rate 1) and AR2 (boll abscission rate 2). There were 18, 46 and 62 markers significantly associated with abscission traits, fiber quality traits and yield traits (P < 0.05), and explaining 1.75% ~ 7.13%, 1.16% ~ 9.58% and 1.40% ~ 5.44% of the phenotypic variation respectively. Among these markers, MON_CGR5732aand MON_CGR5732b markers were associated with cotton abscission traits, fiber quality traits and yield traits loci, and 13 marker loci have been reported by previous studies. In addition, based on phenotypic effects six typical materials with elite allele loci related to fiber quality traits were identified and they will be widely used in practical breeding.

Conclusions: Phenotypic analysis of 238 accessions showed that the traits were relatively stable under four environmental conditions, and AR2 was an important indicator of abscission. Association analysis based on SSR markers provides two excellent allelic variants associated with low abscission rate and the six typical materials with excellent alleles.

SSR

Genome wide association study

Abscission

Favorable alleles

Cotton is one of the most important economic crops, providing the most important natural textile fibers, oil and biofuel in the world (Chen et al. 2020). China is one of the world’s largest producers, consumers and importers of cotton. Data released by the National Bureau of Statistics of China showed that the cotton produced 5.98 million tons of cotton in 2022. Currently, Xinjiang Province is the main cotton producing area in China, produced 5.39 million tons of cotton, representing 90% of total value of cotton crops (http://data.stats.gov.cn). The critical period of cotton yield formation is the flowering and bolling stage, which accounts for 58.7% ~ 60.4% of the total cotton growth period (Wang et al. 2017). Moreover, the flowering and boll stage is the peak risk periods of the abscission of flower buds and cotton boll, respectively (Guinn et al. 1990). The abscission of cotton bolls traits, which is a premature aging symptom during cotton growth, including flower bud abscission before flowering and cotton boll abscission after flowering, is a physiological phenomenon and a complex trait influenced by multiple factors, including genetic and non-genetic factors such as weather events. In production, the abscission rate is generally between 60 and 70%, but when it reaches more than 80% (Yu et al. 1999; Zhang et al. 2021), the cotton yield will decline. Therefore, reducing the abscission rate of flower buds and cotton bolls is one of the main tasks for cotton yield in the flowering and boll period. Many studies showed that the abscission rate varies between different cultivars, the abscission rate of flower buds was significantly higher that of cotton bolls, in upland cotton, and abscission occurring more frequently on the central fruit branch (Wang et al. 2020). The abscission rate of four different sea island cotton were compared, the abscission rate of high-quality varieties was higher than that of high-yield varieties (Hu et al. 2020). The abscission rate of sea island cotton is 50%, the abscission rate of flower bud was higher than that of cotton boll, the varieties with high abscission rate had low-yield (Zhang et al. 2007).

Abscission refers to the process of abscission of leaves, branches, flowers, fruits and other organs from the parent plant, and the separation layer called abscission zone (AZ). During abscission, cell enlargement begins and activating gene expression, increasing the activity of cell-wall-degrading enzymes, including cellulase, polygalacturonases, pectin methyl esterase, etc, finally leads to detachment of the organ (Li et al. 2015). A few studies suggested the role of organs shedding depends on the activation of the abscission zone (Patharkar OR et al. 2018). On the abscission layer, the INFLORESCENCE DEFICIENT IN ABSCISSION (IDA) peptide functions as a ligand of the receptor-like kinases HAESA and HAESA-LIKE2 (HAE/HSL2), presumed to activate a mitogen-activated protein kinase (MAPK) cascade that includes MKK4/5 (MAPKK) and MPK3/6 (MAPK). MADS-domain transcription factor AGAMOUS like-15 (AGL15), KNAT1 and KNAT2/6, participate in Arabidopsis floral shedding. EVERSHED (EVR), CAST AWAY (CST), NEVERSHED (NEV) and somatic embryogenesis receptor kinase1 (SERK1) likely negatively regulates IDA to play a role in floral organ abscission in Arabidopsis (Glazinska et al. 2017).

Moreover, many studies showed that the ethylene and auxin play an important role in the abscission of leaves, flowers, young fruits and mature fruits, and the endogenous ethylene content will increase before organ abscission (Goldental-Cohen et al. 2017). LcEIL2/3 was involved in regulation of ethylene biosynthesis and cell wall remodeling during litchi fruit ripening, via inducing ethylene production, and resulting in the activation of fruit abscission (Ma et al. 2020). The expression of genes related to expansins, pectinases and cellulases were induced by interacting effects of ethylene and auxin, and leaded to separation of an organ from a plant. The abscission of plant organs is associated with changes in the auxin gradient across the AZ, which is affected by ethylene (Glazinska et al. 2017). The abscission process is accompanied by a series of physiological responses, including the activity of cell wall-degrading enzymes (CWDEs) and polygalacturonase (PGs) (Du et al. 2014). In Arabidopsis, flower organ abscission is dependent on IDA precursor protein, the IDA-HAE/HSL2 pathway activated downstream mitogen-activated protein (MAP) kinase cascades (Meng et al. 2016). The transcription factor AGAMOUS-like 15 (AGL15) bind to the promoter of HAE, the reduction of function of HAE results in delayed abscission of floral organs in Arabidopsis (Rahul et al. 2015). Previous studies show that exogenous application of GA3 induces translationally controlled tumor protein (TCTP) widely expressed in the roots and stems of cotton, which lead to the abscission of cotton boll (Zhang 2018). Other studies showed that the BOP1 promotes the abscission layer development to promote leaf shedding (Chang et al. 2015). Transcriptome analysis of separation layer formation between leaf and petiole in cotton, eight miRNAs involved exclusively in flower abscission were identified (Guo et al. 2018). In addition, the cytokinin and ethylene could induce cotton leaves abscission (Xu et al. 2019).

In this study, by selected 145 polymorphic SSR markers, the genetic analysis diversity among the population containing 238 varieties were performed. Moreover, the phenotype of the abscission of flower buds and cotton bolls traits in four different environments were detected. Further, the allelic variants associated with abscission and search for the typical carrier material with elite allele identified by using GWAS. These results provide the basis for understanding the regulation mechanism of cotton bolls abscission and molecular breeding of cotton, and can improve cotton yield through molecular breeding.

Plant Materials and Field Experiments

To evaluate the abscission traits, 178 Gossypium hirsutum cultivars and 60 Gossypium barbadense cultivars were collected from the Institute of Cotton Research Center of Shihezi Academy of Agricultural Sciences (Table S1), and was planted in two locations in Shihezi (SHZ), Xinjiang and Kuerle (KEL), Xinjiang during 2018 and 2019 via a randomized block design with two replicates.

Phenotype analysis

To determine the phenotype of abscission traits, ten consecutive cotton plants was labeled in the middle of each line. Ten days after flowering, the number of boll abscission and unabscission were investigated, and the abscission rate was calculated as rate of boll abscission 1 (AR1). In the full boll stage, the second survey of boll abruption rate was carried out, denoted as rate of boll abscission 2 (AR2). Rate of boll abscission (AR) was calculated according to the formula of AR (%) = the number of boll abscission / the total number of boll × 100%. Moreover, the flowering period (FP) and the whole growth period (WGP), which is the days from seedling to flowering and the days from seedling to boll opening respectively, were detected. During the cotton boll opening period, lint samples derived from 20 normally opened bolls of each variety were collected, and the fiber quality were detected by using the HVI1000 Automatic Fiber Determination System, including the fiber upper half mean length (FUHML), fiber strength (FS), micronaire value (MV). During the cotton boll opening period, 20 spontaneously opened bolls were gathered from middle part of the plants from each accession in each replicate. For each cotton sample’s bolls weight, seed weight (SW) and their lint weight (LW) were measured. Lint percentage (LP) was calculated according to the formula of

LP (%) = LW (g) /BW (g) × 100%.

The variance, normality statistical analyses and basic statistics including extreme values, mean value, standard deviation, variable coefficient, and the statistics for the correlation between traits were performed by using SPSS software 22.0. Correlation analysis and boxplot between environments were drawn using R software (Guo et al. 2021).

Population structure and divergence analysis

Structure analysis was conducted through STRUCTURE version 2.3.4. To obtain the optimal K value, the K value that maximized the statistic ΔK were calculated as described by Pritchard JK (2000). In details, the number of clusters (K) from 1 to 10 were set and conducted 10 independent runs for each value of K with the parameter of the length of burnin period to 10 000, the number of MCMC reps after burnin to 100 000, and the number of populations assumed (K) to 2 to 10.

Genetic diversity and Association analysis

Genetic diversity including number of alleles (Na), major allele frequency, polymorphic information content (PIC), expected heterozygosity or Gene Diversity (He) and observed heterozygosity (Ho) (Meki et al. 2019), was calculated by PowerMarker version 3.25 (http://statgen.ncsu.edu/powermarker/downloads.htm). The association relationship between phenotypic and genotypic were detected by using TASSEL V2.1 software with MLM (G + P + Q + K) models (Bradbury PJ et al. 2007).

Phenotype analysis of the cotton bolls abscission among 238 cotton accessions

To evaluate the abscission of cotton bolls traits in the natural population, the AR1, AR2, FP and WGP were detected in four environments during 2018 and 2019. Then, the phenotype data analysis for four abscission of cotton bolls traits were performed, and showed that the genetic variation of the FP were minimum, with a range of 4.30%-6.13%, and the largest coefficient of variation was found in AR2 ranging from 31.99–81.49% (Table 1), which indicated that the variation of AR2 trait was more influenced by environmental factors. In addition, the extensive phenotype variations were found across 238 accessions in four environments. The AR1, AR2, and WGP varied from 0.43–41.45%, 11.67–74.94%, and 111.50 d to 159.50 d, respectively (Table 1). The absolute values of skewness and kurtosis for most of the abscission of cotton bolls traits were less than one in the environments, except for the 2018KEL-AR1, 2019KEL-WGP. The boxplot analysis showed that the cotton bolls abscission traits were stability under different environments, meanwhile FP and WGP were more stable than AR1 and AR2 under four environments, while large variations in the abscission traits in cotton bolls were found among different growing environments (Fig. 1). Correlation analysis showed the yield-related traits were positively correlated to each other, while no significant relationship with the AR1. A positive relationship was found between seed cotton weight and FP (P < 0.005), and between lint cotton weight (LW) and FT (P < 0.005) (Fig. 2). There was no significant relationship between AR1 and the yield traits. In addition, a positive association amongst quality traits and a negative association between FP and FS, FUHML was found (Table 3). These results provide a solid foundation for genome-wide association analysis.

Table 1

Statistics of cotton the abscission of cotton bolls traits in cotton
Trait	Env	Min	Max	Mean	SD	CV%	Skewness	Kurtosis	H²
AR1	2018KRL	2.11	31.32	10.45	4.58	27.02	0.90	1.73	0.6
	2018SHZ	0.43	41.45	14.72	9.39		0.58	-0.57
	2019KEL	21.22	63.30	41.03	7.91		0.12	-0.43
	2019SHZ	1.65	34.68	13.51	6.73		0.71	0.27
AR2	2018KRL	23.01	73.89	43.31	9.25	56.34	0.50	0.38	0.6
	2018SHZ	11.67	58.65	33.74	10.87		0.53	-0.49
	2019KEL	35.15	74.94	55.54	6.81		-0.12	-0.04
	2019SHZ	15.64	60.17	38.91	7.94		0.01	-0.07
FP	2018KRL	77.85	88.65	83.85	2.15	5.15	-0.27	-0.40	0.75
	2018SHZ	66.25	85.90	77.77	4.72		-0.58	-0.65
	2019KEL	69.00	80.00	73.22	2.24		-0.58	-0.09
	2019SHZ	72.80	91.00	82.17	4.09		-0.53	-0.67
WGP	2018SHZ	132.55	148.00	140.37	3.64	19.85	0.41	-0.39	0.32
	2019KEL	111.50	145.00	132.04	4.65		-0.39	1.28
	2019SHZ	135.55	159.50	147.40	4.54		0.30	-0.05

The diversity analysis of molecular markers

To identify the efficiency and polymorphism of the markers, analysis of efficiency and polymorphism were performed on 75 SSR primer pairs with 145 alleles locus (Li X et al. 2012). The result showed that the 145 alleles locus amplified clear fragments in each of 238 accessions, which suggested that those SSR markers had the high reproducibility, abundant polymorphisms and uniform distribution on the 26 cotton chromosomes with an average of 2.8 SSR per chromosome, ranging from 1 to 5, and the average distance between the markers was 87.43 Mb. The Polymorphism information content (PIC) at each locus ranged from 0.022 to 0.521 with an average of 0.345 loci (Table S2). NAU3774b locatedon chromosome D13, and showed high polymorphism. These results indicate that these SSR have abundant variants in the population.

Population genetic structure analysis

Based on the 145 alleles locus polymorphic markers, the genetic diversity and population structure analysis were performed by using the software STRUCTURE 2.3.4 with the admixture model (Fig. 3). The findings the LnP (K) values gradually increased with K-value from 1 to 10, ΔK based on rate of change of LnP (K) between successive K values, and a sharp peak of ΔK was observed at K = 2 (Fig. 3), which indicated that the 238 cotton accessions were divided into two sub-population. Further, the maximum likelihood method was applied to assign every cotton individual to a cluster. Subcluster-1 contained 174 varieties (including 39.08% of Xinluzao series, 43.10% of Xinluzhong series, 2.30% of Xinhai and 15.52% of Xincai), subcluster-2 contained 62 varieties (including 95.16% of Xinhai series, 4.84% of Xinluzhong series).

A: Ln(P(D)) for K ranging 1 to 10; B: Lines chart showing Evanno’s △K; C: Population genetic structure of cotton cultivars.

Association mapping QTL for the abscission of cotton bolls traits

The association analysis was performed on 238 materials in four environments with 145 SSR loci. 18 loci associated with the abscission of cotton bolls traits, which the range in phenotypic explanation rates was 0.97% ~ 7.13%, the mean (± SD) value was 2.55%, were detected (Table S3). Two SSR loci (MON_SHIN-1584b and NBRI_HQ526730b) associated with AR1 were detected with 23.02% and 3.13% phenotypic variations explained rate in both 2018 and 2019, respectively. Four SSR loci associated with AR2 were detected with 1.51% ~6.24% phenotypic explanation rates and 2.79% mean value. NAU3419c is the loic with the highest phenotypic variations explained rate. Mon_shin-1584b is the locus with the lowest phenotypic variations explained rate. Eight SSR loci associated with FP were detected, the range of phenotypic explanation rates was 0.97%-6.59%, the mean value was 2.14%. MON_DPL0504aa is the loic with the highest phenotypic variations explained rate. HAU1968a is the locus with the lowest phenotypic variations explained rate. Four SSR loci associated with WGP were detected, the range of phenotypic explanation rates was 2.41% ~ 7.13%, the mean value was 3.47%. HAU2588b is the locus with the highest phenotypic variations explained rate. HAU1952bb is the locus with the lowest phenotypic variations explained rate

QTL mapping for fiber quality and yield traits

To investigate the effect of the abscission of cotton bolls traits on cotton quality and yield, association analysis was conducted for fiber and yield traits. In this study, 46 locus (phenotypic explanation rates, 0.44%-9.58%) associated with fiber quality traits were identified, and a mean (± SD) value of 2.77%. Among them, 18 SSR loci (phenotypic explanation rates, 0.91% ~ 4.10%), 15 SSR loci (phenotypic explanation rates, 0.44% ~ 4.10%) and 13 SSR loci (phenotypic explanation rates, 1.57% ~ 9.58%) were associated with FUHML, FS and MV respectively (Table S3). NAU3827b (for FUHML), HAU1952bc (for FS) and HAU2588a (for MV) is the locus with the highest phenotypic variations explained rate. Gh330c (for FUHML), HAU3193 (for FS) and MON_SHIN-1584a (for MV) is the locus with the lowest phenotypic variations explained rate. Moreover, 62 loci (phenotypic explanation rates, 0.44%-9.58%) associated with yield traits were identified, and a mean (± SD) value of 2.77%. Among them, 18 SSR loci (phenotypic explanation rates, 1.00%-4.06%), 30 SSR loci (phenotypic explanation rates, 0.56%-4.25%) and 14 SSR loci (phenotypic explanation rates, 1.23%-5.44%) were associated with SW, LW and LP respectively (Table S3). MON_DPL0504ab (for SW and LW) and HAU2588a (for LP) is the locus with the highest phenotypic variations explained rate. MGHES31a, HAU2846c and MON_SHIN-1584a is the locus with the lowest phenotypic variations explained rate.

Table 2

Markers associated with multiple-effect traits
Locus	Chr	PB	Trait	Other research Trait
MON_CGR5732a	Chr05	155	AR2, WGP, MV	LP, FS (Xia et al. 2014, Shao et al. 2014)
MON_CGR6012b	Chr05	155	AR2, MV	FUHML (Shao et al. 2014)
BNL3043a	Chr19	296	FUHML, LW	MV (Liu et al. 2013)
NAU2126a	Chr19	186	FUHML, FS	LW, FUHML (Shao et al. 2014, Lin et al. 2009)
NAU2126b	Chr19	178	FUHML, FS, LW, SW	LW, FUHML (Shao et al. 2014, Lin et al. 2009)
NAU3346ba	Chr15	305	FUHML, FS	FE (Yu et al. 2013)
NAU3346bb	Chr15	289	FUHML, FS	FE (Yu et al. 2013)
NAU3827a	Chr18	149	FUHML, FS, LW, SW	LW (Yu et al. 2013)
NAU3827b	Chr18	136	FUHML, FS, LW, SW	LW (Yu et al. 2013)
MUSS422aa	Chr01	207	MV, LP, LW	MV (Yu et al. 2013)
MUSS422ab	Chr01	191	MV, LP, LW	MV (Yu et al. 2013)
DPL0062	Chr21	137	LW, SW	MV (Wang et al. 2015)
NAU3298a	Chr25	385	LW, SW	FS, SW (Sun et al. 2012, Zhao 2016)

Identification of favorable alleles and typical materials for cotton boll shedding traits

Based on the effect values of allelic variant loci associated with the abscission of cotton bolls traits, six typical materials (Xinluzhong 36, Xinhai 12, Xinhai 63, Xinhai 63, Xinhai 37, Xinluzao 30 and Xinhai 51) associated with the abscission of cotton bolls traits, were identified (Table 4). According to the effect values of allelic variation loci, different types of allelic variation corresponding to typical materials were detected. MON_SHIN-1584b and NBRI_HQ526730b were associated with AR1. Among them, MON_SHIN-1584b was the allelic variation locus with the negative phenotypic effect (-0.16%) in Xinluzhong 36, NBRI_HQ526730b was the allelic variation locus with the positive phenotypic effect (+ 0.52%) in Xinluzhong 36.

Four allelic loci (MON_SHIN-1584b, MON_CGR5732a, MON_CGR6012b and NAU3419c) were associated with AR2. Among them, MON_SHIN-1584b was the allelic variation locus with the negative phenotypic effect (-0.60%) in Xinhai 63. The typical materials of MON_CGR5732a, MON_CGR6012b and NAU3419c were all Xincai12, but the phenotypic effects of each mutation locus were different, MON_CGR5732a (+ 0.16%), MON_CGR6012b (+ 0.68%) were the positive phenotypic effect allelic variation locus, NAU3419c (-0.50%) was the negative phenotypic effect allelic variation locus.

Eight allelic loci associated with FP, HAU1952bc, HAU1968a, MON_DPL0504aa and MON_DPL0893a were all alleles for the positive phenotypic effect. The typical carrier materials for the polymerization of four alleles were Xinhai 37. HAU1952bc (+ 0.73d) was the allelic variation locus with the maximum positive phenotypic effect. The typical carrier materials of HAU3071aa, NAU5172b and HAU1952bb alleles were Xinluzao 30. HAU3071aa (+ 0.07d) was the allelic variation locus with the positive phenotypic effect. NAU5172b (-0.34d) and HAU1952bb (-0.26d) were the allelic variation locus with the negative phenotypic effect.

Two allelic loci associated with WGR, HAU2588b (-1.17d) was the allelic variation locus with the maximum negative phenotypic effect, MON_CGR5732b (+ 0.66d) was the allelic variation locus with the positive phenotypic effect, the typical carrier material for polymerization of two alleles is Xinluzao 30.

Table 3

Allelic variation of loci significantly associated with the abscission of cotton bolls traits and corresponding phenotypic effects
Traits	Locus-Allele	Phenotypic effect	Typical material
AR1	MON_SHIN-1584b	-0.16	Xinluzhong36
AR1	NBRI_HQ526730b	0.52	Xinluzhong36
AR2	MON_CGR5732a	0.16	Xincai12
	MON_CGR6012b	0.68	Xincai12
	NAU3419c	-0.50	Xincai12
	MON_SHIN-1584b	-0.60	Xinhai63
FP	HAU1952bc	0.73	Xinhai37
	HAU1968a	0.28	Xinhai37
	MON_DPL0504aa	0.60	Xinhai37
	MON_DPL0893a	0.05	Xinhai37
	HAU3071aa	0.07	Xinluzao30
	NAU5172b	-0.34	Xinluzao30
	HAU1952bb	-0.26	Xinluzao30
WGP	HAU2588b	-1.17	Xinluzao30
	MON_CGR5732b	0.66	Xinluzao30
	CCRI303b	-0.92	Xinhai51

In this study, seven excellent allelic locus including MON_CGR5732b, MON_Shin-1584b, MON_CGR5732a, DPL0062, MGHES31a, NAU2126b and HAU2588b (Table S4)were identified. And a total of 42 typical carrier materials (22 materials of positive phenotypic effects and 20 materials loci of negative phenotypic effects) were identified. Two allelic loci (MON_CGR5732b and MON_SHIN-1584b) were associated with AR1 and AR2, a total of five typical carrier materials (Xinluzao 28, Xinluzhong 1, Xinluzhong 28, Xinluzhong 35 and Xinluzhong 86) were identified, three materials with positive effect are Xinluzhong 28, Xinluzhong 35 and Xinluzhong 86. Two allelic loci associated with FP and WGP were MON_CGR5732a and DPL0062. A total of three typical carrier materials (xinluzao 30, Xinhai 37 and Xinluzhong 26) were identified, both of which showed positive effects. The loci associated with yield and quality traits were DPL0062, MGHES31a, HAU2588b, MON_CGR5732a and NAU2126b. A total of four typical materials were identified, and three of them (Xinluzao 25, Xinhai 63 and Xincai 12) had positive effects. Only one material with negative effect is Xinluzao 32. These results indicate that the abscission of cotton bolls traits might be regulated by multiple co-effect loci.

Cotton bud shedding is a quantitative trait controlled by micro-effect polygenes, which is the results of the interaction between genotype and environmental factors. Previous studies indicated that the abscission of flower buds even cotton bolls were caused by disasters, extreme weather events and cotton species and so on, finally leading to serious yield and quality losses (Goldental-Cohen et al. 2017;Cheng G et al., 2020). In this study, the phenotypes of the abscission of flower buds and the abscission of cotton bolls traits of 238 Xinjiang cotton varieties under different environments were compared and analyzed. The variance of the average coefficient of variation of traits is between 5.15% and 56.34%. Which showed that the test materials in this study have abundant genetic variation among the four agronomic traits. Correlation analysis results show that FP is positively correlated with SW and LW, and negatively correlated with AR, which is consistent with previous studies (Li L et al., 2021; Su J et al., 2016; Li C et al., 2018). And suggests that planting early-maturing cotton increases lint yield by increasing the number of bolls per unit area, which is an effective way to increase cotton planting density and meet demand. Therefore, FP, WGP, AR1 and AR2 can be used as important reference indicators to measure cotton yield, and comprehensive evaluation of cotton yield traits can be carried out based on these indicators. While improving cotton flower buds and bolls shedding, ensure cotton yield and quality, and improve cotton economic benefits. Based on the above considerations, this study also used these four bud-boll shedding-related traits, three yield traits, and three quality traits to carry out correlation analysis on these 10 traits, and excavate excellent allelic variation sites, which have great influence on cotton breeding.

In recent years, genome-wide association analysis (GWAS) has been extensively used to dissect the natural allelic variation responsible for complex quantitative traits in crops (Wang C et al., 2019; Liu S et al., 2020; Zhang P et al., 2020). However, a large number of genomic loci and candidate genes were identified for fiber development (Thyssen GN et al., 2019; Zhao H et al., 2022), Yield formation (Shen C et al., 2019), early maturity-related traits (Zhang J et al., 2021) but less commonly in the abscission of flower buds and the abscission of cotton bolls traits. Eight loci significantly associated with multiple traits were detected in this study, which could be divided into three categories: the first category was the loci significantly associated with the rate of boll abscission. Mon_shin-1584b was significantly correlated with AR1 and AR2 with phenotypic variation explained of 3.02 and 3.38. The second category was significantly associated with the rate of boll abscission and micronaire value. MON_CGR5732a and MON_CGR6012b were significantly correlated with AR2 and MV and the explanatory rates were 4.46, 4.3, 2.79 and 2.71, respectively. The third category, HAU1952bc, HAU1968a, MON_DPL0504aa, and NAU5172b, were significantly correlated with flowering time, fiber length, single boll seed cotton weight, and single boll lint weight, with explanatory rates ranging from 1.84 to 6.59. We speculated that these eight traits had pleiotropy and might control several traits with high correlation at the same time. Moreover, six typical carrier materials related to boll abscission were identified according to multi-effect marker sites.

The 145 marker loci (P < 0.05) associated with four abscission of cotton bolls traits in this study were compared to other reported QTL in cotton. Thirteen marker loci identified in our study coincided with previous research, of which seven marker loci were detected with the same traits (Table 5). There are few reports on QTL loci associated with boll abscission traits, and two loci MON_CGR5732a and MON_CGR6012b were detected in this study. In previous studies showed that MON_CGR5732a, located on chromosome-5, is associated with fiber strength with a high interpretation rate of phenotypic variation, while MON_CGR6012b, located on chromosome 26, is associated with fiber length with a high interpretation rate. MON_CGR5732a is located on chromosome 19, which is associated with coat fraction with a high interpretation rate of phenotypic variation, while MON_CGR6012b is located on chromosome 26, which is associated with seed weight and has a high interpretation rate. there results showed that the above markers could be detected in multiple reported populations, although this locus was only associated with yield and quality traits in previous reports.

Traditional crop breeding has the disadvantage of long cycle, low efficiency, meanwhile abscission of cotton bolls traits is controlled by multiple genes. In this study, we have applied the molecular marker techniques to identified markers in the candidate region that are tightly associated with abscission of cotton bolls traits, these markers will be beneficial for molecular marker-assisted selection breeding and accelerate breeding progress in the future. Most of the associated loci in this study have not been reported, which may be due to the low level of marker coincidence and the greater influence of environment and research materials on some QTLs, so different results were detected results under different analysis conditions. In the future, the associated regions can be delineated according to the results, and high-density SNP markers can be developed in associated regions combined with gene functional annotation, so as to obtain marker loci closely linked to target traits, which can be applied to the assisted selection of related traits to accelerate the process of variety breeding. In addition, it will provide reference for fine mapping and candidate gene mining of QTLs for yield and important agronomic traits in the future.

To detect the related markers and allelic variation for the abscission of cotton bolls traits, the association analysis was performed on the abscission of cotton bolls traits derived from 38 cottons in China based on 145 SSR markers by using TASSEL. The results showed that twelve QTLs were detected repeatedly in more than three environments, indicating high-confidence QTLs, which suggested that these QTLs were genetically stable, and play an important role in further study and quantifying their effect on phenotype. Our study would provide valuable information to molecular marker-assisted selection (MAS) in cotton breeding, supplies new resource for the improvement of yield in future cotton breeding efforts, and offer a foundation for fine gene mapping.

Acknowledgements

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

Authors’ contributions

Nie XH, Song GL and You CY designed the experiments. Nurimanguli, Guo CP, Ma XM and Zhu B performed the experiments. Shui GL wrote the main manuscript text and prepared all Figures. Lin HR and Hang P performed data analysis. Wu YL and Pan ZY revised and polished the manuscript. All authors contributed in the interpretation of results, read and approved the final manuscript.

Funding

This work was Sponsored by State Key Laboratory of Cotton Biology Open Fund (CB2022A11), the National Natural Science Foundation of China (32260510), the Innovation talent Program in Sciences and Technologies of Xinjiang production and Construction Corps, China (2021CB028), the Key Programs for Science and Technology Development of Shihezi city, Xinjiang production and Construction Crops, China (2022NY01), the Science and Technology Planning of shuanghe city, Xinjiang production and Construction Crops, China (2021NY02), and the key programs for science and technology development in agricultural field of Xinjiang production and Construction Corps, China.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

Not applicable

Consent for publication

All Authors have provided ethical approval and consent to participate as well as consent for publication.

Competing interests

The authors have declared that no competing interests exist.

Author details

¹Key Laboratory of Oasis Ecology Agricultural of Xinjiang Bingtuan, Agricultural College, Shihezi University, Shihezi 832003, Xinjiang, China. ²Agricultural Science Research Institute of the 5th Division of Xinjiang Production and Construction Corps, Shuanghe 833408, ³Cotton Institute, Xinjiang Academy of Agriculture and Reclamation Science, Shihezi 832000, Xinjiang, China. Xinjiang, China.⁴Modern Agricultural College, Kashi University, Kashi 844000, Xinjiang, China. ³Cotton Research Institute, Shihezi Academy of Agriculture Science, Shihezi 832011, Xinjiang, China. ⁵Cotton Research Institute, Shihezi Academy of Agriculture Science, Shihezi 832011, Xinjiang, China. ⁶State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China

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Supplementary.xlsx
Supplementary Information Supplementary Table S1 Information for the 238 cotton Supplementary Table S2 The Polymorphism information content of SSR Supplementary Table S3 SSR locus associated with the abscission of cotton bolls traits, yield and fiber quality related traits Supplementary Table S4 The Phenotypic effect of Typical material

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Identification of SSR markers linked to the abscission of cotton bolls traits and mining germplasm in Cotton

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Version 1

Abstract

Figures

Background

Materials and methods

Plant Materials and Field Experiments

Phenotype analysis

Population structure and divergence analysis

Genetic diversity and Association analysis

Results

Phenotype analysis of the cotton bolls abscission among 238 cotton accessions

The diversity analysis of molecular markers

Population genetic structure analysis

Association mapping QTL for the abscission of cotton bolls traits

QTL mapping for fiber quality and yield traits

Identification of favorable alleles and typical materials for cotton boll shedding traits

Discussion

Conclusion

Declarations

References

Supplementary Files

Status:

Version 1