Genetic Diversity, Population Structure, and Genome-Wide Association Study of Seven Agronomic Traits in 273 Diverse Upload Cotton Accessions

1  Introduction

Upland cotton (Gossypium hirsutum), a member of the Gossypium genus [1], is a vital source of natural fibers for the global textile industry [2]. Improving the yield and fiber quality with resistance to numerous adversities remains as the top breeding priorities [3]. However, our understanding of the genetic basis of the important agronomic traits is still limited [2,3].

Genetic diversity is crucial for conservation, breeding and biodiversity. Over the years, the genetic diversity of cotton has been studied using various approaches, including morphological traits [4], pedigree information [5] and different molecular markers [6–10]. Next-generation sequencing technology makes it possible to generate thousands to millions of SNPs for hundreds of cotton germplasm accessions [2,3]. However, this approach can be expensive. Alternatively, sequencing accessions at a lower sequence depth [11] or using SNP arrays [11–17] and SLAF-seq [18] can be used to infer the genetic diversity and structure of large cotton germplasm resource.

Genome-wide association study (GWAS) has proven to be an effective and efficient way to identify genetic loci associated with important agronomic traits in cotton, including yield, fiber quality, growth period, and plant type [1,11,13,14,16–26]. More natural accessions harness more genetic diversity and possible recombination events, breaking down the linkage disequilibrium (LD) and identifying causal variants that cannot be detected in linkage populations [27,28]. However, the associated loci identified in different GWASs can be inconsistent, which can be technically referred and evaluated by heterogeneity (the genetic variations observed across different GWASs) and can be caused by various factors, such as Genotype × Environment interactions, genetic background, genetic structure, and linkage disequilibrium [19,29–31]. In this study, we investigated a core cotton collection and performed GWAS on seven important agronomic traits. Our aims were to identify new significantly associated loci to breed new varieties with higher cotton quality.

2  Materials and Methods

2.1 Materials

In this study, a total of 273 diverse cotton accessions were selected from 18 geographical regions (Supplementary Table 1). The field experiment was performed at No. sixteenth regiment experiment field during 2017 and 2018 at Alaer, Xinjiang Province, China, and each accession has three replicates.

2.2 Phenotypes

A total of seven important morphological traits were measured across two years, including pollen vitality (PV), leaf area (LA), chlorophyll content (CC), the number of dry cotton bolls (DBs), the number of the fall-off cotton bolls from the last three branches (FB3), the number of the cotton bolls from the last three branches (CB3), and drop ratio (DR). Pollen viability was assessed using 2,3,5-triphenyl tetrazolium chloride (TTC) solution. Briefly, flowers were sampled and immersed in TTC and were then stored at room temperature for 1 h. Then 2% sulfuric acid was added to stop the staining process. Pollen was then photographed under microscope and the pollen viability was measured as the percentage of normally stained pollens compared to total number of pollens. The chlorophyll content was measured near the leaf main veins and both sides of the functional leaves using the SPAD-502 chlorophyll meter, with three replicates. Leaf area was measured on the top, middle and bottom leaves using the LA-S leaf area measuring instrument. The number of the fall-off cotton bolls from the last three branches, the number of the cotton bolls from the last three branches were manually cross-checked. The boxplot of the phenotypic variation was generated using ggplot2 package in R. The normality of the phenotypes was tested using the Shapiro-Wilk test in R function.

2.3 Genotyping and SNP Calling

Adaptors and low quality sequences were removed using Trimmomatic [32] (v0.39) with parameters “TruSeq3-PE-2.fa:2:30:10:1:TRUE SLIDINGWINDOW:4:20 LEADING:3 TRAILING:3 MINLEN:40”. The clean sequencing data obtained by specific-locus amplified fragment sequencing (SLAF-seq) was realigned to the reference genome of cotton released by Using Zhejiang University Cotton v2.1 (https://www.cottongen.org/species/Gossypiumhirsutum/ZJU-AD1v2.1) using BWA v0.7.15 software [33] with default parameters. Then haplotypeCaller of GATK v4.0 [34] and SAMtools v1.9 [35] were used to detect variation calling separately, and the overlapped SNP markers were identified to create a final reliable SNP dataset. Raw SNPs from GATK were filtered with default hard filtering with parameters ‘QD < 2.0 || FS > 60.0 || MQ < 40.0 || SOR >3.0 || MQRankSum < −12.5 || ReadPosRankSum < −8.0’. SnpEff v4.0 [36] was used to obtain the locations and the functions of the variable sites (intergenic zones, gene zones, or CDS zones; synonymous mutations, nonsynonymous mutations, etc.).

2.4 SNP Quality Control

In order to obtain a final set of high-quality SNPs, the minimal minor allele frequency (MAF) was set to 0.05, with a maximum percentage of missing at 0.3, generating a total of 81,612 high quality SNPs. All these SNPs were used for further population genetic analyses, including phylogenetic tree, population structure, kinship and genome-wide association study.

2.5 Phylogeny

The optimal ML phylogenetic tree model for the 273 cotton core collection was built using IQ-TREE v1.6.12 [37]. In order to reduce the overall computing time and load, we first thinned the 81,612 SNPs to 11,589 SNPs using vcftools with --thin 100000. The optimal fitted model was PMB+F+R7, which was determined based on the lowest BIC value using ModelFinder [38].

2.6 Population Structure

The population structure was calculated using all the 81,612 high quality SNPs from fastSTRUCTURE v1.0 [39]. The optimal number of sub-populations was determined at 7.

2.7 Genome-Wide Association Study

Genome-wide association study was tested using EMMAX [40]. The PCA and the Balding-Nichols (BN) kinship were added as cofactors. Genome-wide significant threshold (2.81E-6) was determined in GEC1 [41]. The candidate region was determined by calculating the LD for the peak SNPs and its nearby SNPs using PLINK [42] with parameters ‘--ld-window 1000000 --ld-window-r2 0.2’.

3  Results

3.1 Morphological Variations

Geographical diversity can result in diverse phenotypic variations. In this study, we mainly focused on seven important quantitative agronomic traits, namely pollen vitality (PV), leaf area (LA), chlorophyll content (CC), the number of dry cotton bolls (DBs), the number of the fall-off cotton bolls from the last three branches (FB3), the number of the cotton bolls from the last three branches (CB3) and drop ratio (DR). We observed significant phenotypic variations among the defined geographical locations, and the overall variation patterns across different geographical locations in 2017 and 2018 were similar (Fig. 1). These findings suggest that geographical locations play a vital role in shaping the diversification and variation of the analyzed phenotypes.

Figure 1: Boxplot of the measured phenotypes for the 273-core cotton collection in 2017 and 2018. PV17, pollen vitality in 2017; PV18, pollen vitality in 2018; LA17, leaf area in 2017; LA18, leaf area in 2018; CC17, chlorophyll content in 2017; CC18, chlorophyll content in 2018; DBs17, the number of dry cotton bolls in 2017; DBs18, the number of dry cotton bolls in 2018; FB317, the number of the fall-off cotton bolls from the last three branches in 2017; FB318, the number of the fall-off cotton bolls from the last three branches in 2018; CB317, the number of the cotton bolls from the last three branches in 2017; DR17, drop ratio in 2017; DR18, drop ratio in 2018. Only geographical background with no less than five accessions were used in this comparison

All the analyzed traits were considered as quantitative traits and should theoretically follow a normal distribution. However, we found that only the chlorophyll content (CC) showed a normal distribution in both the measurement of years 2017 and 2018. On the other hand, the remaining six traits were either only normally distributed in one year or non-normally distributed in both years. Pollen vitality (PV) and the number of dry cotton balls (DBs) even showed a high significant difference compared to normal distribution (Fig. 2). These findings indicate that the analyzed traits not only exhibit high diversity but also deviate from normal distribution.

Figure 2: Distributions of the measured phenotypes for the 273-core cotton collection in 2017 and 2018. The normality was tested using the Shapiro-Wilk test in R function shapiro.test(). PV, pollen vitality; LA, leaf area; CC, chlorophyll content; DBs, the number of dry cotton bolls; FB3, the number of the fall-off cotton bolls from the last three branches; CB3, the number of the cotton bolls from the last three branches; DR, drop ratio. p value < 0.05 indicated that the distribution was significantly different from normal distribution

We also measured the correlations among the seven traits across both years (Supplementary Fig. 1). In the year 2017, we observed a strong positive correlation (0.82) between DR (cotton bolls drop ratio) and FB3 (the number of fall-off cotton bolls from the last three branches), while DR and CB3 (the number of cotton bolls from the last three branches) had a strong negative correlation. However, we did not find any strong correlations among the traits in year 2018. Moreover, we found that only PV (pollen vitality) exhibited a high positive correlation (0.94) between the year 2017 and 2018.

3.2 Genetic Structure

Although the core cotton collection represents a diverse phenotypic and geographical diversity, a comprehensive genetic structure analysis is largely missed. In this study, we first calculated the maximum-likelihood phylogenetic tree (IQ-Tree) (Fig. 3A). We found that this core collection could be mainly subdivided into six subgroups, and then calculated the population structure and found that the optimal number of subpopulation structure was seven. When further cross-checked the population structure ranged from five to eight and found that, although not very highly consistent, the population structure still provided supplementary information to the phylogenetic structure (Fig. 3B). Interpreting the possible causes of the genetic structure would provide explanations of the genetic differentiation and make the structure much more reasonable. However, though some of the accessions, especially from Xinjiang Province, clustered more closely than others, geographical information could explain some of the phylogenetic tree as well as the population structure (Supplementary Table 1). These findings demonstrated a highly complex genetic structure of this core collection.

Figure 3: Phylogenetic and population structure of 273 cotton accessions. (A) Phylogenetic structure of the 273 cotton accessions. (B) Population structure of the 273 cotton accessions with the number of subpopulations ranged from five to eight

3.3 Genome-Wide Association Study

In order to account for the weaker correlations between the phenotypes across 2017 and 2018, we performed the GWAS both on the year-specific and average values. Additionally, we also normalized the phenotypes using the ln() transformation. Our analyses successfully identified 19 significant associations (Table 1), but no significant associations for chlorophyll content. Notably, we identified up to seven significant associations for the number of fall-off cotton balls from the last three branches (FB3), an important parameter related to total yield. These accessions were discovered either in 2018 or in the average value of 2017 and 2018 with five being identified in 2018 (Table 1, Fig. 4A). However, no significant associations were detected in 2017. The quantile-quantile (Q-Q) plot showed proper correction and positive associations (Fig. 4B). The linkage disequilibrium revealed that SNPs near the peak SNP (D10:25394605) decayed quickly to a short distance (Fig. 4C). The nearest candidate gene to D10:25394605 was annotated as a Pollen Ole e 1 allergen/extension (CotAD_63822), which may regulate the number of cotton balls via the regulation of pollens.

Figure 4: Genome-wide association of FB3 in 2018 identified a candidate gene Pollen Ole e 1 allergen/extension (CotAD_63822). (A) Manhattan plot of SNP-trait associations. (B) Quantile-quantile (Q-Q) plot of the associations. (C) Linkage disequilibrium between D10:25394605 (the peak SNP) and its close SNPs. (D) Candidate genes near the peak association. The candidate genes of interests were highlighted in red rectangular

3.4 Trait Heritability

In addition to the analysis of genetic structure and GWAS, we also examined the heritability of the measured traits. Our results showed that all the measured traits had quite low heritability and a large variation between years (Fig. 5). Among these, the heritability of CB3, CC, DR and FB3 were extremely low, and the heritability of DBs and LA were moderate, with an average value of 0.204 and 0.241, respectively. However, the heritability of DBs and LA in 2017 was much larger than that in 2018.

Figure 5: Heritability estimated from the EMMAX for all the traits in 2017 and 2018. PV, pollen vitality; LA, leaf area; CC, chlorophyll content; DBs, the number of dry cotton bolls; FB3, the number of the fall-off cotton bolls from the last three branches; CB3, the number of the cotton bolls from the last three branches; DR, drop ratio

4  Discussion

Improving yield is one of the primary goals in cotton breeding, and it involves several important components, such as boll number, boll weight, drop ratio, all of which are controlled by various QTLs with major and minor genetic effects [13,19,22,26]. Identifying new QTLs associated with cotton yield is crucial to dissect the genetic control of yield-related components and to develop new high-yield cultivars. While GWASs have been identified many yield-related QTLs, our understandings of the genetic control remain limited [2,11–13,17,19,20,22,24,26,43]. In this study, we presented a new cotton collection from the main cultivation area in Xinjiang, China, which complements the publicly available cotton genetic and genomic resources.

Understanding the causes of genetic structure and subgrouping in the phylogenetic tree is important for deepening our understandings of genetic diversity history. Geographical diversification has been shown to be one important factor causing genetic structure in wheat [44], tomato [45], pepper [46], lettuce [47], soybean [48] and others. We found that geographical diversification was an important factor causing population structure, but it could not explain all the diversification, indicating that the genetic structure of this cotton collection was complex.

GWAS has great potentials for identifying causal variants of complex quantitative traits in plants [30,49–55], and its applications in cotton has identified dozens of associated loci for different yield and fiber-related traits [2,11–13,17,19,20,22,24,26,43]. GWAS has also been applied to identify associations for resistances [56–59]. Among the associations detected in this study, we identified Pollen Ole e 1 allergen/extension as a candidate gene associated with the fall-off cotton balls from the last three branches. While this gene is primary responsible for the pollen allergy [60], it also played an important role in regulating pollen germination and fertilization [60,61]. Pollen allergens are derived from large gene families and have diversified during long-term evolution. For example, up to 145 and 107 pollen allergens were predicted in the genome of Arabidopsis and rice, respectively, which could play diverse roles in metabolic processes and stress responses during pollen development [62]. Our study provides new candidate genes for quality breeding of cotton, and further functional validation of these candidate genes will be necessary and helpful for deepening our understanding of the genetic control of these important agronomic traits.

5  Conclusion

In this study, we assessed the phenotypic variation of seven agronomic traits of 273 cotton accessions from 18 geographical regions across two years (2017–2018). We observed significant variations in different geographical regions and only some traits were normally distributed across the collection. Phylogenetic tree and population structure analysis revealed a diverse genetic structure of the core collection, with geographical location accounting for some of the genetic structure. Our genome-wide association study identified 19 significant associated loci, with the Pollen Ole e 1 allergen/extension gene being a candidate gene associated with the fall-off cotton bolls from the last three branches. This study provides deeper insights into the phenotypic and genetic diversity of a new cotton collection and sheds light on new candidate genes associated with important agronomic traits of cotton, which could facilitate the breeding of new cotton varieties.

Acknowledgement: None.

Funding Statement: This work was supported by the Xinjiang Uygur Autonomous Region Major Science and Technology Project (2021A02001-3).

Author Contributions: YL, JZ and JW conceived the project, prepared the data analysis and draft manuscript. XL and QC supervised the project. ZG, ZL, LM, ZZ, ZS, and YQ revised the manuscript. All authors read and approved the final manuscript.

Availability of Data and Materials: The raw reads of upload cotton genome resequencing have been deposited in the CNGBdb-China National GeneBank DataBase (https://db.cngb.org/) under the accession no. CNP0002250. All data generated or analyzed during this study are included in this published article and its supplementary information files.

Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.

Supplementary Materials: The supplementary material is available online at https://doi.org/10.32604/phyton.2023.028755.

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