A comprehensive meta QTL analysis for fiber quality, yield, yield related and morphological traits, drought tolerance, and disease resistance in tetraploid cotton

Co-localization and analysis of miR477b with fiber length quantitative trait loci in cotton

Cotton is an important cash crop for the textile industry. However, the understanding of natural genetic variation of fiber elongation in relation to miRNA is lacking. A miRNA gene (miR477b) was found to co-localize with a previously mapped fiber length (FL) quantitative trait locus (QTL). The miR477b was differentially expressed during fiber elongation between two backcross inbred lines (BILs) differing in FL and its precursor sequences. Bioinformatics and qRT-PCR analysis were further used to analyse the miRNA genes, which could produce mature miR477b. Cotton plants with virus-induced gene silencing (VIGS) constructs to over-express the allele of miR477b from the BIL with longer fibers had significantly longer fibers as compared with negative control plants, while the VIGS plants with suppressed miRNA expression had significantly shorter fibers. The expression level of the target gene (DELLA) and related genes (RDL1 and EXPA1 for DELLA through HOX3 protein) in the two BILs and/or the VIGS plants were generally congruent, as expected. This report represents one of the first comprehensive studies to integrate QTL linkage mapping and physical mapping of small RNAs with both small and mRNA transcriptome analysis, followed by VIGS, to identify candidate small RNA genes affecting the natural variation of fiber elongation in cotton.

Genome-wide association study for boll weight in Gossypium hirsutum races

High yield has always been an essential target in almost all of the cotton breeding programs. Boll weight (BW) is a key component of cotton yield. Numerous linkage mapping and genome-wide association studies (GWAS) have been performed to understand the genetic mechanism of BW, but information on the markers/genes controlling BW remains limited. In this study, we conducted a GWAS for BW using 51,268 high-quality single-nucleotide polymorphisms (SNPs) and 189 Gossypium hirsutum accessions across five different environments. A total of 55 SNPs significantly associated with BW were detected, of which 29 and 26 were distributed in the A and D subgenomes, respectively. Five SNPs were simultaneously detected in two environments. For TM5655, TM8662, TM36371, and TM50258, the BW grouped by alleles of each SNP was significantly different. The ± 550 kb regions around these four key SNPs contained 262 genes. Of them, Gh_A02G1473, Gh_A10G1765, and Gh_A02G1442 were expressed highly at 0 to 1 days post-anthesis (dpa), - 3 to 0 dpa, and - 3 to 0 dpa in ovule of TM-1, respectively. They were presumed as the candidate genes for fiber cell differentiation, initiation, or elongation based on gene annotation of their homologs. Overall, these results supplemented valuable information for dissecting the genetic architecture of BW and might help to improve cotton yield through molecular marker-assisted selection breeding and molecular design breeding.

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.

Unraveling genomic regions and candidate genes for multiple disease resistance in upland cotton using meta-QTL analysis

Verticillium wilt (VW), Fusarium wilt (FW) and Root-knot nematode (RKN) are the main diseases affecting cotton production. However, many reported quantitative trait loci (QTLs) for cotton resistance have not been used for agricultural practices because of inconsistencies in the cotton genetic background. The integration of existing cotton genetic resources can facilitate the discovery of important genomic regions and candidate genes involved in disease resistance. Here, an improved and comprehensive meta-QTL analysis was conducted on 487 disease resistant QTLs from 31 studies in the last two decades. A consensus linkage map with genetic overall length of 3006.59 cM containing 8650 markers was constructed. A total of 28 Meta-QTLs (MQTLs) were discovered, among which nine MQTLs were identified as related to resistance to multiple diseases. Candidate genes were predicted based on public transcriptome data and enriched in pathways related to disease resistance. This study used a method based on the integration of Meta-QTL, known genes and transcriptomics to reveal major genomic regions and putative candidate genes for resistance to multiple diseases, providing a new basis for marker-assisted selection of high disease resistance in cotton breeding.

Genome-Wide Association Study of Lint Percentage in Gossypium hirsutum L. Races

Lint percentage is one of the most essential yield components and an important economic index for cotton planting. Improving lint percentage is an effective way to achieve high-yield in cotton breeding worldwide, especially upland cotton (Gossypium hirsutum L.). However, the genetic basis controlling lint percentage has not yet been systematically understood. Here, we performed a genome-wide association mapping for lint percentage using a natural population consisting of 189 G. hirsutum accessions (188 accessions of G. hirsutum races and one cultivar TM-1). The results showed that 274 single-nucleotide polymorphisms (SNPs) significantly associated with lint percentage were detected, and they were distributed on 24 chromosomes. Forty-five SNPs were detected at least by two models or at least in two environments, and their 5 Mb up- and downstream regions included 584 makers related to lint percentage identified in previous studies. In total, 11 out of 45 SNPs were detected at least in two environments, and their 550 Kb up- and downstream region contained 335 genes. Through RNA sequencing, gene annotation, qRT-PCR, protein-protein interaction analysis, the cis-elements of the promotor region, and related miRNA prediction, Gh_D12G0934 and Gh_A08G0526 were selected as key candidate genes for fiber initiation and elongation, respectively. These excavated SNPs and candidate genes could supplement marker and gene information for deciphering the genetic basis of lint percentage and facilitate high-yield breeding programs of G. hirsutum ultimately.

Identification of quantitative trait loci and candidate genes for seed folate content in soybean

KEY MESSAGE

From 61 QTL mapped, a stable QTL cluster of 992 kb was discovered on chromosome 5 for folate content and a putative candidate gene, Glyma.05G237500, was identified. Folate (vitamin B9) is one of the most essential micronutrients whose deficiencies lead to various health defects in humans. Herein, we mapped the quantitative trait loci (QTL) underlying seed folate content in soybean using recombinant inbred lines developed from cultivars, ZH35 and ZH13, across four environments. We identified 61 QTL on 12 chromosomes through composite interval mapping, with phenotypic variance values ranging from 1.68 to 24.68%. A major-effect QTL cluster (qFo-05) was found on chromosome 5, spanning 992 kb and containing 134 genes. Through gene annotation and single-locus haplotyping analysis of qFo-05 in a natural soybean population, we identified seven candidate genes significantly associated with 5MTHF and total folate content in multiple environments. RNA-seq analysis showed a unique expression pattern of a hemerythrin RING zinc finger gene, Glyma.05G237500, between both parental cultivars during seed development, which suggest the gene might regulate folate content in soybean. This is the first study to investigate QTL underlying folate content in soybean and provides new insight for molecular breeding to improve folate content in soybean.

Comparative physiological and biochemical mechanisms in diploid, triploid, and tetraploid watermelon (Citrullus lanatus L.) grafted by branches

Seed production for polyploid watermelons is costly, complex, and labor-intensive. Tetraploid and triploid plants produce fewer seeds/fruit, and triploid embryos have a harder seed coat and are generally weaker than diploid seeds. In this study, we propagated tetraploid and triploid watermelons by grafting cuttings onto gourd rootstock (C. maxima × C. mochata). We used three different scions: the apical meristem (AM), one-node (1N), and two-node (2N) branches of diploid, triploid, and tetraploid watermelon plants. We then evaluated the effects of grafting on plant survival, some biochemical traits, oxidants, antioxidants, and hormone levels at different time points. We found significant differences between the polyploid watermelons when the 1N was used as a scion. Tetraploid watermelons had the highest survival rates and the highest levels of hormones, carbohydrates, and antioxidant activity compared to diploid watermelons, which may explain the high compatibility of tetraploid watermelons and the deterioration of the graft zone in diploid watermelons. Our results show that hormone production and enzyme activity with high carbohydrate content, particularly in the 2-3 days after transplantation, contribute to a high survival rate. Sugar application resulted in increased carbohydrate accumulation in the grafted combination. This study also presents an alternative and cost-effective approach to producing more tetraploid and triploid watermelon plants for breeding and seed production by using branches as sprouts.

Genome-wide association studies of Striga resistance in extra-early maturing quality protein maize inbred lines

Identification of genes associated with Striga resistance is invaluable for accelerating genetic gains in breeding for Striga resistance in maize. We conducted a genome-wide association study to identify genomic regions associated with grain yield and other agronomic traits under artificial Striga field infestation. One hundred and forty-one extra-early quality protein maize inbred lines were phenotyped for key agronomic traits. The inbred lines were also genotyped using 49,185 DArTseq markers from which 8,143 were retained for population structure analysis and genome wide-association study. Cluster analysis and population structure revealed the presence of 3 well-defined genetic groups. Using the mixed linear model, 22 SNP markers were identified to be significantly associated with grain yield, Striga damage at 10 weeks after planting, number of emerged Striga plants at 8 and 10 weeks after planting and ear aspect. The identified SNP markers would be useful for breeders for marker-assisted selection to accelerate the genetic enhancement of maize for Striga resistance in sub-Saharan Africa after validation.

Deciphering key genomic regions controlling flag leaf size in wheat via integration of meta-QTL and in silico transcriptome assessment

BACKGROUND

Grain yield is a complex and polygenic trait influenced by the photosynthetic source-sink relationship in wheat. The top three leaves, especially the flag leaf, are considered the major sources of photo-assimilates accumulated in the grain. Determination of significant genomic regions and candidate genes affecting flag leaf size can be used in breeding for grain yield improvement.

RESULTS

With the final purpose of understanding key genomic regions for flag leaf size, a meta-analysis of 521 initial quantitative trait loci (QTLs) from 31 independent QTL mapping studies over the past decades was performed, where 333 loci eventually were refined into 64 meta-QTLs (MQTLs). The average confidence interval (CI) of these MQTLs was 5.28 times less than that of the initial QTLs. Thirty-three MQTLs overlapped the marker trait associations (MTAs) previously reported in genome-wide association studies (GWAS) for flag leaf traits in wheat. A total of 2262 candidate genes for flag leaf size, which were involved in the peroxisome, basal transcription factor, and tyrosine metabolism pathways were identified in MQTL regions by the in silico transcriptome assessment. Of these, the expression analysis of the available genes revealed that 134 genes with > 2 transcripts per million (TPM) were highly and specifically expressed in the leaf. These candidate genes could be critical to affect flag leaf size in wheat.

CONCLUSIONS

The findings will make further insight into the genetic determinants of flag leaf size and provide some reliable MQTLs and putative candidate genes for the genetic improvement of flag leaf size in wheat.

Genetic structure and molecular mechanism underlying the stalk lodging traits in maize (Zea mays L.)

Stalk lodging seriously affects yield and quality of crops, and it can be caused by several factors, such as environments, developmental stages, and internal chemical components of plant stalks. Breeding of stalk lodging-resistant varieties is thus an important task for maize breeders. To better understand the genetic basis underlying stalk lodging resistance, several methods such as quantitative trait locus (QTL) mapping and genome-wide association study (GWAS) have been used to mine potential gene resources. Based on different types of genetic populations and mapping methods, many significant loci associated with stalk lodging resistance have been identified so far. However, few work has been performed to compare and integrate these reported genetic loci. In this study, we first collected hundreds of QTLs and quantitative trait nucleotides (QTNs) related to stalk lodging traits in maize. Then we mapped and integrated the QTLs and QTNs in maize genome to identify overlapped hotspot regions. Based on the genomic confidence intervals harboring these overlapped hotspot regions, we predicted candidate genes related to stalk lodging traits. Meanwhile, we mapped reported genes to these hotspot regions. Finally, we constructed molecular regulatory networks underlying stalk lodging resistance in maize. Collectively, this study provides not only useful genetic loci for deeply exploring molecular mechanisms of stalk lodging resistance traits, but also potential candidate genes and targeted strategies for improving stalk lodging resistance to increase crop yields in future.

Dual Domestication, Diversity, and Differential Introgression in Old World Cotton Diploids

Domestication in the cotton genus is remarkable in that it has occurred independently four different times at two different ploidy levels. Relatively little is known about genome evolution and domestication in the cultivated diploid species Gossypium herbaceum and Gossypium arboreum, due to the absence of wild representatives for the latter species, their ancient domestication, and their joint history of human-mediated dispersal and interspecific gene flow. Using in-depth resequencing of a broad sampling from both species, we provide support for their independent domestication, as opposed to a progenitor-derivative relationship, showing that diversity (mean π = 6 × 10-3) within species is similar, and that divergence between species is modest (FST = 0.413). Individual accessions were homozygous for ancestral single-nucleotide polymorphisms at over half of variable sites, while fixed, derived sites were at modest frequencies. Notably, two chromosomes with a paucity of fixed, derived sites (i.e., chromosomes 7 and 10) were also strongly implicated as having experienced high levels of introgression. Collectively, these data demonstrate variable permeability to introgression among chromosomes, which we propose is due to divergent selection under domestication and/or the phenomenon of F2 breakdown in interspecific crosses. Our analyses provide insight into the evolutionary forces that shape diversity and divergence in the diploid cultivated species and establish a foundation for understanding the contribution of introgression and/or strong parallel selection to the extensive morphological similarities shared between species.

Systematic trait dissection in oilseed rape provides a comprehensive view, further insight, and exact roadmap for yield determination

Background

Yield is the most important and complex trait that is influenced by numerous relevant traits with very complicated interrelations. While there are a large number of studies on the phenotypic relationship and genetic basis of yield traits, systematic studies with further dissection focusing on yield are limited. Therefore, there is still lack of a comprehensive and in-depth understanding of the determination of yield.

Results

In this study, yield was systematically dissected at the phenotypic, genetic to molecular levels in oilseed rape (Brassica napus L.). The analysis of correlation, network, and principal component for 21 traits in BnaZN-RIL population showed that yield was determined by a complex trait network with key contributors. The analysis of the constructed high-density single nucleotide polymorphism (SNP) linkage map revealed the concentrated distribution of distorted and heterozygous markers, likely due to selection on genes controlling the growth period and yield heterosis. A total of 134 consensus quantitative trait loci (QTL) were identified for 21 traits, of which all were incorporated into an interconnecting QTL network with dozens of hub-QTL. Four representative hub-QTL were further dissected to the target or candidate genes that governed the causal relationships between the relevant traits.

Conclusions

The highly consistent results at the phenotypic, genetic, and molecular dissecting demonstrated that yield was determined by a multilayer composite network that involved numerous traits and genes showing complex up/down-stream and positive/negative regulation. This provides a systematic view, further insight, and exact roadmap for yield determination, which represents a significant advance toward the understanding and dissection of complex traits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13068-022-02134-w.

Genome-wide meta-QTL analyses provide novel insight into disease resistance repertoires in common bean

BACKGROUND

Common bean (Phaseolus vulgaris) is considered a staple food in a number of developing countries. Several diseases attack the crop leading to substantial economic losses around the globe. However, the crop has rarely been investigated for multiple disease resistance traits using Meta-analysis approach.

RESULTS AND CONCLUSIONS

In this study, in order to identify the most reliable and stable quantitative trait loci (QTL) conveying disease resistance in common bean, we carried out a meta-QTL (MQTL) analysis using 152 QTLs belonging to 44 populations reported in 33 publications within the past 20 years. These QTLs were decreased into nine MQTLs and the average of confidence interval (CI) was reduced by 2.64 folds with an average of 5.12 cM in MQTLs. Uneven distribution of MQTLs across common bean genome was noted where sub-telomeric regions carry most of the corresponding genes and MQTLs. One MQTL was identified to be specifically associated with resistance to halo blight disease caused by the bacterial pathogen Pseudomonas savastanoi pv. phaseolicola, while three and one MQTLs were specifically associated with resistance to white mold and anthracnose caused by the fungal pathogens Sclerotinia sclerotiorum and Colletotrichum lindemuthianum, respectively. Furthermore, two MQTLs were detected governing resistance to halo blight and anthracnose, while two MQTLs were detected for resistance against anthracnose and white mold, suggesting putative genes governing resistance against these diseases at a shared locus. Comparative genomics and synteny analyses provide a valuable strategy to identify a number of well‑known functionally described genes as well as numerous putative novels candidate genes in common bean, Arabidopsis and soybean genomes.

CarrotOmics: a genetics and comparative genomics database for carrot (Daucus carota)

CarrotOmics (https://carrotomics.org/) is a comprehensive database for carrot (Daucus carota L.) breeding and research. CarrotOmics was developed using resources available at the MainLab Bioinformatics core (https://www.bioinfo.wsu.edu/) and is implemented using Tripal with Drupal modules. The database delivers access to download or visualize the carrot reference genome with gene predictions, gene annotations and sequence assembly. Other genomic resources include information for 11 224 genetic markers from 73 linkage maps or genotyping-by-sequencing and descriptions of 371 mapped loci. There are records for 1601 Apiales species (or subspecies) and descriptions of 9408 accessions from 11 germplasm collections representing more than 600 of these species. Additionally, 204 Apiales species have phenotypic information, totaling 28 517 observations from 10 041 biological samples. Resources on CarrotOmics are freely available, search functions are provided to find data of interest and video tutorials are available to describe the search functions and genomic tools. CarrotOmics is a timely resource for the Apiaceae research community and for carrot geneticists developing improved cultivars with novel traits addressing challenges including an expanding acreage in tropical climates, an evolving consumer interested in sustainably grown vegetables and a dynamic environment due to climate change. Data from CarrotOmics can be applied in genomic-assisted selection and genetic research to improve basic research and carrot breeding efficiency.

Database URL

https://carrotomics.org/

Genome-wide association study reveals novel quantitative trait loci and candidate genes of lint percentage in upland cotton based on the CottonSNP80K array

Thirty-four SNPs corresponding with 22 QTLs for lint percentage, including 13 novel QTLs, was detected via GWAS. Two candidate genes underlying this trait were also identified. Cotton (Gossypium spp.) is an important natural textile fiber and oilseed crop cultivated worldwide. Lint percentage (LP, %) is one of the important yield components, and increasing LP is a core goal of cotton breeding improvement. However, the genetic and molecular mechanisms underlying LP in upland cotton remain unclear. Here, we performed a genome-wide association study (GWAS) for LP based on 254 upland cotton accessions in four environments as well as the best linear unbiased predictors using the high-density CottonSNP80K array. In total, 41,413 high-quality single-nucleotide polymorphisms (SNPs) were screened, and 34 SNPs within 22 quantitative trait loci (QTLs) were significantly associated with LP. In total, 175 candidate genes were identified from two major genomic loci (GR1 and GR2), and 50 hub genes were identified through GO enrichment and weighted gene co-expression network analysis. Two candidate genes (Gh_D01G0162 and Gh_D07G0463), which may participate in early fiber development to affect the number of fiber protrusions and LP, were also identified. Their genetic variation and expression were verified by linkage disequilibrium blocks, haplotypes, and quantitative real-time polymerase chain reaction, respectively. The weighted gene interaction network analysis showed that the expression of Gh_D07G0463 was significantly correlated with that of Gh_D01G0162. These identified SNPs, QTLs and candidate genes provide important insights into the genetic and molecular mechanisms underlying variations in LP and serve as a foundation for LP improvement via marker-assisted breeding.

Dynamic Quantitative Trait Loci Mapping for Plant Height in Recombinant Inbred Line Population of Upland Cotton

Plant height (PH) is a key plant architecture trait for improving the biological productivity of cotton. Ideal PH of cotton is conducive to lodging resistance and mechanized harvesting. To detect quantitative trait loci (QTL) and candidate genes of PH in cotton, a genetic map was constructed with a recombinant inbred line (RIL) population of upland cotton. PH phenotype data under nine environments and three best linear unbiased predictions (BLUPs) were used for QTL analyses. Based on restriction-site-associated DNA sequence (RAD-seq), the genetic map contained 5,850 single-nucleotide polymorphism (SNP) markers, covering 2,747.12 cM with an average genetic distance of 0.47 cM. Thirty-seven unconditional QTL explaining 1.03-12.50% of phenotypic variance, including four major QTL and seven stable QTL, were identified. Twenty-eight conditional QTL explaining 3.27-28.87% of phenotypic variance, including 1 major QTL, were identified. Importantly, five QTL, including 4 stable QTL, were both unconditional and conditional QTL. Among the 60 PH QTL (including 39 newly identified), none of them were involved in the whole period of PH growth, indicating that QTL related to cotton PH development have dynamic expression characteristics. Based on the functional annotation of Arabidopsis homologous genes and transcriptome data of upland cotton TM-1, 14 candidate genes were predicted within 10 QTL. Our research provides valuable information for understanding the genetic mechanism of PH development, which also increases the economic production of cotton.

Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs

Researchers have used quantitative genetics to map cotton fiber quality and agronomic performance loci, but many alleles may be population or environment-specific, limiting their usefulness in a pedigree selection, inbreeding-based system. Here, we utilized genotypic and phenotypic data on a panel of 80 important historical Upland cotton (Gossypium hirsutum L.) lines to investigate the potential for genomics-based selection within a cotton breeding program’s relatively closed gene pool. We performed a genome-wide association study (GWAS) to identify alleles correlated to 20 fiber quality, seed composition, and yield traits and looked for a consistent detection of GWAS hits across 14 individual field trials. We also explored the potential for genomic prediction to capture genotypic variation for these quantitative traits and tested the incorporation of GWAS hits into the prediction model. Overall, we found that genomic selection programs for fiber quality can begin immediately, and the prediction ability for most other traits is lower but commensurate with heritability. Stably detected GWAS hits can improve prediction accuracy, although a significance threshold must be carefully chosen to include a marker as a fixed effect. We place these results in the context of modern public cotton line-breeding and highlight the need for a community-based approach to amass the data and expertise necessary to launch US public-sector cotton breeders into the genomics-based selection era.

Meta-QTL analysis and candidate genes identification for various abiotic stresses in maize (Zea mays L.) and their implications in breeding programs

Global climate change leads to the concurrence of a number of abiotic stresses including moisture stress (drought, waterlogging), temperature stress (heat, cold), and salinity stress, which are the major factors affecting maize production. To develop abiotic stress tolerance in maize, many quantitative trait loci (QTL) have been identified, but very few of them have been utilized successfully in breeding programs. In this context, the meta-QTL analysis of the reported QTL will enable the identification of stable/real QTL which will pave a reliable way to introgress these QTL into elite cultivars through marker-assisted selection. In this study, a total of 542 QTL were summarized from 33 published studies for tolerance to different abiotic stresses in maize to conduct meta-QTL analysis using BiomercatorV4.2.3. Among those, only 244 major QTL with more than 10% phenotypic variance were preferably utilised to carry out meta-QTL analysis. In total, 32 meta-QTL possessing 1907 candidate genes were detected for different abiotic stresses over diverse genetic and environmental backgrounds. The MQTL2.1, 5.1, 5.2, 5.6, 7.1, 9.1, and 9.2 control different stress-related traits for combined abiotic stress tolerance. The candidate genes for important transcription factor families such as ERF, MYB, bZIP, bHLH, NAC, LRR, ZF, MAPK, HSP, peroxidase, and WRKY have been detected for different stress tolerances. The identified meta-QTL are valuable for future climate-resilient maize breeding programs and functional validation of candidate genes studies, which will help to deepen our understanding of the complexity of these abiotic stresses.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01294-9.

Identification of Candidate Genes for Lint Percentage and Fiber Quality Through QTL Mapping and Transcriptome Analysis in an Allotetraploid Interspecific Cotton CSSLs Population

Upland cotton (Gossypium hirsutum) has long been an important fiber crop, but the narrow genetic diversity of modern G. hirsutum limits the potential for simultaneous improvement of yield and fiber quality. It is an effective approach to broaden the genetic base of G. hirsutum through introgression of novel alleles from G. barbadense with excellent fiber quality. In the present study, an interspecific chromosome segment substitution lines (CSSLs) population was established using G. barbadense cultivar Pima S-7 as the donor parent and G. hirsutum cultivar CCRI35 as the recipient parent. A total of 105 quantitative trait loci (QTL), including 85 QTL for fiber quality and 20 QTL for lint percentage (LP), were identified based on phenotypic data collected from four environments. Among these QTL, 25 stable QTL were detected in two or more environments, including four for LP, eleven for fiber length (FL), three for fiber strength (FS), six for fiber micronaire (FM), and one for fiber elongation (FE). Eleven QTL clusters were observed on nine chromosomes, of which seven QTL clusters harbored stable QTL. Moreover, eleven major QTL for fiber quality were verified through analysis of introgressed segments of the eight superior lines with the best comprehensive phenotypes. A total of 586 putative candidate genes were identified for 25 stable QTL associated with lint percentage and fiber quality through transcriptome analysis. Furthermore, three candidate genes for FL, GH_A08G1681 (GhSCPL40), GH_A12G2328 (GhPBL19), and GH_D02G0370 (GhHSP22.7), and one candidate gene for FM, GH_D05G1346 (GhAPG), were identified through RNA-Seq and qRT-PCR analysis. These results lay the foundation for understanding the molecular regulatory mechanism of fiber development and provide valuable information for marker-assisted selection (MAS) in cotton breeding.

Construction of a high-density genetic map and identification of QTLs related to agronomic and physiological traits in an interspecific (Gossypium hirsutum × Gossypium barbadense) F2 population

Background

Advances in genome sequencing technology, particularly restriction-site associated DNA sequence (RAD-seq) and whole-genome resequencing, have greatly aided the construction of cotton interspecific genetic maps based on single nucleotide polymorphism (SNPs), Indels, and other types of markers. High-density genetic maps can improve accuracy of quantitative trait locus (QTL) mapping, narrow down location intervals, and facilitate identification of the candidate genes.

Result

In this study, 249 individuals from an interspecific F2 population (TM-1 and Hai7124) were re-sequenced, yielding 6303 high-confidence bin markers spanning 5057.13 cM across 26 cotton chromosomes. A total of 3380 recombination hot regions RHRs were identified which unevenly distributed on the 26 chromosomes. Based on this map, 112 QTLs relating to agronomic and physiological traits from seedling to boll opening stage were identified, including 15 loci associated with 14 traits that contained genes harboring nonsynonymous SNPs. We analyzed the sequence and expression of these ten candidate genes and discovered that GhRHD3 (GH_D10G0500) may affect fiber yield while GhGPAT6 (GH_D04G1426) may affect photosynthesis efficiency.

Conclusion

Our research illustrates the efficiency of constructing a genetic map using binmap and QTL mapping on the basis of a certain size of the early-generation population. High-density genetic map features high recombination exchanges in number and distribution. The QTLs and the candidate genes identified based on this high-density genetic map may provide important gene resources for the genetic improvement of cotton.

Transcriptome Profiling to Dissect the Role of Genome Duplication on Graft Compatibility Mechanisms in Watermelon

Watermelon (Citrullus lanatus) is a popular crop worldwide. Compared to diploid seeded watermelon, triploid seedless watermelon cultivars are in great demand. Grafting in triploid and tetraploid watermelon produces few seedlings. To learn more about how genome duplication affects graft compatibility, we compared the transcriptomes of tetraploid and diploid watermelons grafted on squash rootstock using a splicing technique. WGCNA was used to compare the expression of differentially expressed genes (DEGs) between diploid and tetraploid watermelon grafted seedlings at 0, 3, and 15 days after grafting (DAG). Only four gene networks/modules correlated significantly with phenotypic characteristics. We found 11 genes implicated in hormone, AOX, and starch metabolism in these modules based on intramodular significance and RT-qPCR. Among these genes, two were linked with IAA (r² = 0.81), one with ZR (r² = 0.85) and one with POD (r² = 0.74). In the MElightsteelblue1 module, Cla97C11G224830 gene was linked with CAT (r² = 0.81). Two genes from the MEivory module, Cla97C07G139710 and Cla97C04G077300, were highly linked with SOD (r² = 0.72). Cla97C01G023850 and Cla97C01G006680 from the MEdarkolivegreen module were associated with sugars and starch (r² = 0.87). Tetraploid grafted seedlings had higher survival rates and hormone, AOX, sugar, and starch levels than diploids. We believe that compatibility is a complicated issue that requires further molecular research. We found that genome duplication dramatically altered gene expression in the grafted plants' IAA and ZR signal transduction pathways and AOX biosynthesis pathways, regulating hormone levels and improving plant survival.

Linkage and association analyses reveal that hub genes in energy-flow and lipid biosynthesis pathways form a cluster in upland cotton

Upland cotton is an important allotetraploid crop that provides both natural fiber for the textile industry and edible vegetable oil for the food or feed industry. To better understand the genetic mechanism that regulates the biosynthesis of storage oil in cottonseed, we identified the genes harbored in the major quantitative trait loci/nucleotides (QTLs/QTNs) of kernel oil content (KOC) in cottonseed via both multiple linkage analyses and genome-wide association studies (GWAS). In ‘CCRI70′ RILs, six stable QTLs were simultaneously identified by linkage analysis of CHIP and SLAF-seq strategies. In ‘0-153′ RILs, eight stable QTLs were detected by consensus linkage analysis integrating multiple strategies. In the natural panel, thirteen and eight loci were associated across multiple environments with two algorithms of GWAS. Within the confidence interval of a major common QTL on chromosome 3, six genes were identified as participating in the interaction network highly correlated with cottonseed KOC. Further observations of gene differential expression showed that four of the genes, LtnD, PGK, LPLAT1, and PAH2, formed hub genes and two of them, FER and RAV1, formed the key genes in the interaction network. Sequence variations in the coding regions of LtnD, FER, PGK, LPLAT1, and PAH2 genes may support their regulatory effects on oil accumulation in mature cottonseed. Taken together, clustering of the hub genes in the lipid biosynthesis interaction network provides new insights to understanding the mechanism of fatty acid biosynthesis and TAG assembly and to further genetic improvement projects for the KOC in cottonseeds.

Quantitative Trait Locus Analysis and Identification of Candidate Genes Affecting Seed Size and Shape in an Interspecific Backcross Inbred Line Population of Gossypium hirsutum × Gossypium barbadense

Seed size and shape are key agronomic traits affecting seedcotton yield and seed quality in cotton (Gossypium spp.). However, the genetic mechanisms that regulate the seed physical traits in cotton are largely unknown. In this study, an interspecific backcross inbred line (BIL) population of 250 BC1F7 lines, derived from the recurrent parent Upland CRI36 (Gossypium hirsutum) and Hai7124 (Gossypium barbadense), was used to investigate the genetic basis of cotton seed physical traits via quantitative trait locus (QTL) mapping and candidate gene identification. The BILs were tested in five environments, measuring eight seed size and shape-related traits, including 100-kernel weight, kernel length width and their ratio, kernel area, kernel girth, kernel diameter, and kernel roundness. Based on 7,709 single nucleotide polymorphic (SNP) markers, a total of 49 QTLs were detected and each explained 2.91-35.01% of the phenotypic variation, including nine stable QTLs mapped in at least three environments. Based on pathway enrichment, gene annotation, genome sequence, and expression analysis, five genes encoding starch synthase 4, transcription factor PIF7 and MYC4, ubiquitin-conjugating enzyme E27, and THO complex subunit 4A were identified as candidate genes that might be associated with seed size and shape. Our research provides valuable information to improve seed physical traits in cotton breeding.

Development and Utilization of Functional Kompetitive Allele-Specific PCR Markers for Key Genes Underpinning Fiber Length and Strength in Gossypium hirsutum L

Fiber length (FL) and fiber strength (FS) are the important indicators of fiber quality in cotton. Longer and stronger fibers are preferred for manufacturing finer yarns in the textile industry. Functional markers (FMs) designed from polymorphic sites within gene sequences attributing to phenotypic variation are highly efficient when used for marker-assisted selection (MAS) in breeding superior varieties with longer FL and higher FS. The aims of this study were to develop FMs via kompetitive allele-specific PCR (KASP) assays and to validate the efficacy of the FMs for allele discrimination and the potential value in practice application. We used four single-nucleotide polymorphism markers and 360 cotton accessions and found that two FMs, namely, D11_24030087 and A07_72204443, could effectively differentiate accessions of different genotypes with higher consistency to phenotype. The appeared frequencies of varieties harbored Hap2 (elite alleles G and T) with longer FL (> the mean of accessions with non-elite allele, 28.50 mm) and higher FS (> the mean of accessions with non-elite allele, 29.06 cN•tex-1) were 100 and 72.7%, respectively, which was higher than that of varieties harbored only on a single elite allele (G or T, 77.9 or 61.9%), suggesting a favorable haplotype for selecting varieties with superior FL and FS. These FMs could be valuable for the high-throughput selection of superior materials by providing genotypic information in cotton breeding programs.

Genetics, Breeding and Genetic Engineering to Improve Cottonseed Oil and Protein: A Review

Upland cotton (Gossypium hirsutum) is the world's leading fiber crop and one of the most important oilseed crops. Genetic improvement of cotton has primarily focused on fiber yield and quality. However, there is an increased interest and demand for enhanced cottonseed traits, including protein, oil, fatty acids, and amino acids for broad food, feed and biofuel applications. As a byproduct of cotton production, cottonseed is an important source of edible oil in many countries and could also be a vital source of protein for human consumption. The focus of cotton breeding on high yield and better fiber quality has substantially reduced the natural genetic variation available for effective cottonseed quality improvement within Upland cotton. However, genetic variation in cottonseed oil and protein content exists within the genus of Gossypium and cultivated cotton. A plethora of genes and quantitative trait loci (QTLs) (associated with cottonseed oil, fatty acids, protein and amino acids) have been identified, providing important information for genetic improvement of cottonseed quality. Genetic engineering in cotton through RNA interference and insertions of additional genes of other genetic sources, in addition to the more recent development of genome editing technology has achieved considerable progress in altering the relative levels of protein, oil, fatty acid profile, and amino acids composition in cottonseed for enhanced nutritional value and expanded industrial applications. The objective of this review is to summarize and discuss the cottonseed oil biosynthetic pathway and major genes involved, genetic basis of cottonseed oil and protein content, genetic engineering, genome editing through CRISPR/Cas9, and QTLs associated with quantity and quality enhancement of cottonseed oil and protein.

Quantitative Trait Locus Mapping and Identification of Candidate Genes for Resistance to Fusarium Wilt Race 7 Using a Resequencing-Based High Density Genetic Bin Map in a Recombinant Inbred Line Population of Gossypium barbadense

Fusarium wilt caused by Fusarium oxysporum f. sp. vasinfectum (FOV) is one of the most destructive diseases in cotton (Gossypium spp.) production, and use of resistant cultivars is the most cost-effective method managing the disease. To understand the genetic basis of cotton resistance to FOV race 7 (FOV7), this study evaluated a recombinant inbred line (RIL) population of 110 lines of G. barbadense from a cross between susceptible Xinhai 14 and resistant 06-146 in eight tests and constructed a high-density genetic linkage map with resequencing-based 933,845 single-nucleotide polymorphism (SNP) markers covering a total genetic distance of 2483.17 cM. Nine quantitative trait loci (QTLs) for FOV7 resistance were identified, including qFOV7-D03-1 on chromosome D03 in two tests. Through a comparative analysis of gene expression and DNA sequence for predicted genes within the QTL region between the two parents and selected lines inoculated with FOV7, GB_D03G0217 encoding for a calmodulin (CaM)-like (CML) protein was identified as a candidate gene. A further analysis confirmed that the expression of GB_D03G0217 was suppressed, leading to increased disease severity in plants of the resistant parent with virus induced gene silencing (VIGS).

Genome-Wide Analysis of the GW2-Like Genes in Gossypium and Functional Characterization of the Seed Size Effect of GhGW2-2D

Cotton is one of the most economically important crops worldwide. Seed size is a vital trait for plants connected with yield and germination. GW2 encodes a RING_Ubox E3 ubiquitin ligase that controls seed development by affecting cell growth. Here, are few reports on GW2-like genes in cotton, and the function of GW2 in cotton is poorly understood. In the present study, a genome-wide analysis identified 6 and 3 GW2-like genes in each of the two cultivated tetraploids (Gossypium hirsutum and G. barbadense) and each of their diploid ancestral species (G. arboreum, G. raimondii), respectively. GhGW2-2D has the same functional domain and high sequence similarity with AtDA2 in Arabidopsis. Overexpression of GhGW2-2D in Arabidopsis significantly reduced seed and seedling size, suggesting GhGW2-2D is a potential target for regulating cotton seed size. These results provided information on the genetic and molecular basis of GW2-like genes in cotton, thus establishing a foundation for functional studies of cotton seeds.

Analyzing Quantitative Trait Loci for Fiber Quality and Yield-Related Traits From a Recombinant Inbred Line Population With Gossypium hirsutum Race palmeri as One Parent

Fiber quality and yield-related traits are important agronomic traits in cotton breeding. To detect the genetic basis of fiber quality and yield related traits, a recombinant inbred line (RIL) population consisting of 182 lines was established from a cross between Gossypium hirsutum cultivar CCRI35 and G. hirsutum race palmeri accession TX-832. The RIL population was deeply genotyped using SLAF-seq and was phenotyped in six environments. A high-density genetic linkage map with 15,765 SNP markers and 153 SSR markers was constructed, with an average distance of 0.30 cM between adjacent markers. A total of 210 fiber quality quantitative trait loci (QTLs) and 73 yield-related QTLs were identified. Of the detected QTLs, 62 fiber quality QTLs and 10 yield-related QTLs were stable across multiple environments. Twelve and twenty QTL clusters were detected on the At and Dt subgenome, respectively. Twenty-three major QTL clusters were further validated through associated analysis and five candidate genes of four stable fiber quality QTLs were identified. This study revealed elite loci influencing fiber quality and yield and significant phenotypic selection regions during G. hirsutum domestication, and set a stage for future utilization of molecular marker assisted breeding in cotton breeding programs.

A comprehensive meta-QTL analysis for yield-related traits of durum wheat (Triticum turgidum L. var. durum) grown under different water regimes

Abiotic stress strongly affects yield-related traits in durum wheat, in particular drought is one of the main environmental factors that have effect on grain yield and plant architecture. In order to obtain new genotypes well adapted to stress conditions, the highest number of desirable traits needs to be combined in the same genotype. In this context, hundreds of quantitative trait loci (QTL) have been identified for yield-related traits in different genetic backgrounds and environments. Meta-QTL (MQTL) analysis is a useful approach to combine data sets and for creating consensus positions for the QTL detected in independent studies for the reliability of their location and effects. MQTL analysis is a useful method to dissect the genetic architecture of complex traits, which provide an extensive allelic coverage, a higher mapping resolution and allow the identification of putative molecular markers useful for marker-assisted selection (MAS). In the present study, a complete and comprehensive MQTL analysis was carried out to identify genomic regions associated with grain-yield related traits in durum wheat under different water regimes. A total of 724 QTL on all 14 chromosomes (genomes A and B) were collected for the 19 yield-related traits selected, of which 468 were reported under rainfed conditions, and 256 under irrigated conditions. Out of the 590 QTL projected on the consensus map, 421 were grouped into 76 MQTL associated with yield components under both irrigated and rainfed conditions, 12 genomic regions containing stable MQTL on all chromosomes except 1A, 4A, 5A, and 6B. Candidate genes associated to MQTL were identified and an in-silico expression analysis was carried out for 15 genes selected among those that were differentially expressed under drought. These results can be used to increase durum wheat grain yields under different water regimes and to obtain new genotypes adapted to climate change.

Genome-Wide Association Analysis of Salt-Tolerant Traits in Terrestrial Cotton at Seedling Stage

Soil salinization is the main abiotic stress factor affecting agricultural production worldwide, and salt stress has a significant impact on plant growth and development. Cotton is one of the most salt-tolerant crops. Therefore, the selection and utilization of salt-tolerant germplasm resources and the excavation of salt resistance genes play important roles in improving cotton production in saline-alkali soils. In this study, we analysed the population structure and genetic diversity of a total 149 cotton plant materials including 137 elite Gossypium hirsutum cultivar accessions collected from China and 12 elite Gossypium hirsutum cultivar accessions collected from around the world. Illumina Cotton SNP 70 K was used to obtain genome-wide single-nucleotide polymorphism (SNP) data for 149 elite Gossypium hirsutum cultivar accessions, and 18,430 highly consistent SNP loci were obtained by filtering. It was assessed by using PCA principal component analysis so that the 149 elite Gossypium hirsutum cultivar accessions could be divided into two subgroups, including subgroup 1 with 78 materials and subgroup 2 with 71 materials. Using the obtained SNP and other marker genotype test results, under salt stress, the salt tolerance traits 3d Germination potential, 3d Radicle length drop rate, 7d Germination rate, 7d Radicle length drop rate, 7d Germination weight, 3d Radicle length, 7d Radicle length, Relative Germination potential, Relative Germination rate, 7d Radicle weight drop rate, Salt tolerance index 3d Germination potential index, 3d Radicle length index, 7d Radicle length index, 7d Radicle weight index and 7d Germination rate index were evaluated by GWAS (genome-wide association analysis). A total of 27 SNP markers closely related to the salt tolerance traits and 15 SNP markers closely related to the salt tolerance index were detected. At the SNP locus associated with phenotyping, Gh_D01G0943, Gh_D01G0945, Gh_A01G0906, Gh_A01G0908, Gh_D08G1308 and Gh_D08G1309 related to plant salt tolerance were detected, and they were found to be involved in intracellular transport, sucrose synthesis, osmotic pressure balance, transmembrane transport, N-glycosylation, auxin response and cell amplification. This study provides a theoretical basis for the selection and breeding of salt-tolerant upland cotton varieties.

Quantitative Trait Loci and Transcriptome Analysis Reveal Genetic Basis of Fiber Quality Traits in CCRI70 RIL Population of Gossypium hirsutum

Upland cotton (Gossypium hirsutum) is widely planted around the world for its natural fiber, and producing high-quality fiber is essential for the textile industry. CCRI70 is a hybrid cotton plant harboring superior yield and fiber quality, whose recombinant inbred line (RIL) population was developed from two upland cotton varieties (sGK156 and 901-001) and were used here to investigate the source of high-quality related alleles. Based on the material of the whole population, a high-density genetic map was constructed using specific locus-amplified fragment sequencing (SLAF-seq). It contained 24,425 single nucleotide polymorphism (SNP) markers, spanning a distance of 4,850.47 centimorgans (cM) over 26 chromosomes with an average marker interval of 0.20 cM. In evaluating three fiber quality traits in nine environments to detect multiple environments stable quantitative trait loci (QTLs), we found 289 QTLs, of which 36 of them were stable QTLs and 18 were novel. Based on the transcriptome analysis for two parents and two RILs, 24,941 unique differentially expressed genes (DEGs) were identified, 473 of which were promising genes. For the fiber strength (FS) QTLs, 320 DEGs were identified, suggesting that pectin synthesis, phenylpropanoid biosynthesis, and plant hormone signaling pathways could influence FS, and several transcription factors may regulate fiber development, such as GAE6, C4H, OMT1, AFR18, EIN3, bZIP44, and GAI. Notably, the marker D13_56413025 in qFS-chr18-4 provides a potential basis for enhancing fiber quality of upland cotton via marker-assisted breeding and gene cloning of important fiber quality traits.

Omics-Facilitated Crop Improvement for Climate Resilience and Superior Nutritive Value

Novel crop improvement approaches, including those that facilitate for the exploitation of crop wild relatives and underutilized species harboring the much-needed natural allelic variation are indispensable if we are to develop climate-smart crops with enhanced abiotic and biotic stress tolerance, higher nutritive value, and superior traits of agronomic importance. Top among these approaches are the "omics" technologies, including genomics, transcriptomics, proteomics, metabolomics, phenomics, and their integration, whose deployment has been vital in revealing several key genes, proteins and metabolic pathways underlying numerous traits of agronomic importance, and aiding marker-assisted breeding in major crop species. Here, citing several relevant examples, we appraise our understanding on the recent developments in omics technologies and how they are driving our quest to breed climate resilient crops. Large-scale genome resequencing, pan-genomes and genome-wide association studies are aiding the identification and analysis of species-level genome variations, whilst RNA-sequencing driven transcriptomics has provided unprecedented opportunities for conducting crop abiotic and biotic stress response studies. Meanwhile, single cell transcriptomics is slowly becoming an indispensable tool for decoding cell-specific stress responses, although several technical and experimental design challenges still need to be resolved. Additionally, the refinement of the conventional techniques and advent of modern, high-resolution proteomics technologies necessitated a gradual shift from the general descriptive studies of plant protein abundances to large scale analysis of protein-metabolite interactions. Especially, metabolomics is currently receiving special attention, owing to the role metabolites play as metabolic intermediates and close links to the phenotypic expression. Further, high throughput phenomics applications are driving the targeting of new research domains such as root system architecture analysis, and exploration of plant root-associated microbes for improved crop health and climate resilience. Overall, coupling these multi-omics technologies to modern plant breeding and genetic engineering methods ensures an all-encompassing approach to developing nutritionally-rich and climate-smart crops whose productivity can sustainably and sufficiently meet the current and future food, nutrition and energy demands.

Favorable pleiotropic loci for fiber yield and quality in upland cotton (Gossypium hirsutum)

Upland cotton (Gossypium hirsutum L.) is an important economic crop for renewable textile fibers. However, the simultaneous improvement of yield and fiber quality in cotton is difficult as the linkage drag. Compared with breaking the linkage drag, identification of the favorable pleiotropic loci on the genome level by genome-wide association study (GWAS) provides a new way to improve the yield and fiber quality simultaneously. In our study restriction-site-associated DNA sequencing (RAD-seq) was used to genotype 316 cotton accessions. Eight major traits in three categories including yield, fiber quality and maturation were investigated in nine environments (3 sites × 3 years). 231 SNPs associated with these eight traits (− log₁₀(P) > 5.27) were identified, located in 27 genomic regions respectively by linkage disequilibrium analysis. Further analysis showed that four genomic regions (the region 1, 6, 8 and 23) held favorable pleiotropic loci and 6 candidate genes were identified. Through genotyping, 14 elite accessions carrying the favorable loci on four pleiotropic regions were identified. These favorable pleiotropic loci and elite genotypes identified in this study will be utilized to improve the yield and fiber quality simultaneously in future cotton breeding.

Genome-wide quantitative trait loci mapping on Verticillium wilt resistance in 300 chromosome segment substitution lines from Gossypium hirsutum × Gossypium barbadense

Cotton Verticillium wilt (VW) is a devastating disease seriously affecting fiber yield and quality, and the most effective and economical prevention measure at present is selection and extension of Gossypium varieties harboring high resistance to VW. However, multiple attempts to improve the VW resistance of the most widely cultivated upland cottons have made little significant progress. The introduction of chromosome segment substitution lines (CSSLs) provide the practical solutions for merging the superior genes related with high yield and wide adaptation from Gossypium hirsutum and VW resistance and the excellent fiber quality from Gossypium barbadense. In this study, 300 CSSLs were chosen from the developed BC5F3:5 CSSLs constructed from CCRI36 (G. hirsutum) and Hai1 (G. barbadense) to conduct quantitative trait locus (QTL) mapping of VW resistance, and a total of 40 QTL relevant to VW disease index (DI) were identified. Phenotypic data were obtained from a 2-year investigation in two fields with two replications per year. All the QTL were distributed on 21 chromosomes, with phenotypic variation of 1.05%-10.52%, and 21 stable QTL were consistent in at least two environments. Based on a meta-analysis, 34 novel QTL were identified, while 6 loci were consistent with previously identified QTL. Meanwhile, 70 QTL hotspot regions were detected, including 44 novel regions. This study concentrates on QTL identification and screening for hotspot regions related with VW in the 300 CSSLs, and the results lay a solid foundation not only for revealing the genetic and molecular mechanisms of VW resistance but also for further fine mapping, gene cloning and molecular designing in breeding programs for resistant cotton varieties.

Genomic Approaches to Identify Molecular Bases of Crop Resistance to Diseases and to Develop Future Breeding Strategies

Plant diseases are responsible for substantial crop losses each year and affect food security and agricultural sustainability. The improvement of crop resistance to pathogens through breeding represents an environmentally sound method for managing disease and minimizing these losses. The challenge is to breed varieties with a stable and broad-spectrum resistance. Different approaches, from markers to recent genomic and 'post-genomic era' technologies, will be reviewed in order to contribute to a better understanding of the complexity of host-pathogen interactions and genes, including those with small phenotypic effects and mechanisms that underlie resistance. An efficient combination of these approaches is herein proposed as the basis to develop a successful breeding strategy to obtain resistant crop varieties that yield higher in increasing disease scenarios.

Parallel and Intertwining Threads of Domestication in Allopolyploid Cotton

The two cultivated allopolyploid cottons, Gossypium hirsutum and Gossypium barbadense, represent a remarkable example of parallel independent domestication, both involving dramatic morphological transformations under selection from wild perennial plants to annualized row crops. Deep resequencing of 643 newly sampled accessions spanning the wild-to-domesticated continuum of both species, and their allopolyploid relatives, are combined with existing data to resolve species relationships and elucidate multiple aspects of their parallel domestication. It is confirmed that wild G. hirsutum and G. barbadense were initially domesticated in the Yucatan Peninsula and NW South America, respectively, and subsequently spread under domestication over 4000-8000 years to encompass most of the American tropics. A robust phylogenomic analysis of infraspecific relationships in each species is presented, quantify genetic diversity in both, and describe genetic bottlenecks associated with domestication and subsequent diffusion. As these species became sympatric over the last several millennia, pervasive genome-wide bidirectional introgression occurred, often with striking asymmetries involving the two co-resident genomes of these allopolyploids. Diversity scans revealed genomic regions and genes unknowingly targeted during domestication and additional subgenomic asymmetries. These analyses provide a comprehensive depiction of the origin, divergence, and adaptation of cotton, and serve as a rich resource for cotton improvement.

Evaluation and genome-wide association study of resistance to bacterial blight race 18 in U.S. Upland cotton germplasm

Bacterial blight (BB), caused by Xanthomonas citri pv. malvacearum (Xcm), is a destructive disease to cotton production in many countries. In the U.S., Xcm race 18 is the most virulent and widespread race and can cause serious yield losses. Planting BB-resistant cotton cultivars is the most effective method of controlling this disease. In this study, 335 U.S. Upland cotton accessions were evaluated for resistance to race 18 using artificial inoculations by scratching cotyledons on an individual plant basis in a greenhouse. The analysis of variance detected significant genotypic variation in disease incidence, and 50 accessions were resistant including 38 lines with no symptoms on either cotyledons or true leaves. Many of the resistant lines were developed in the MAR (multi-adversity resistance) breeding program at Texas A&M University, whereas others were developed before race 18 was first reported in the U.S. in 1973, suggesting a broad base of resistance to race 18. A genome-wide association study (GWAS) based on 26,301 single nucleotide polymorphic (SNP) markers detected 11 quantitative trait loci (QTL) anchored by 79 SNPs, including three QTL on each of the three chromosomes A01, A05 and D02, and one QTL on each of D08 and D10. This study has identified a set of obsolete Upland germplasm with resistance to race 18 and specific chromosomal regions delineated by SNPs for resistance. The results will assist in breeding cotton for BB resistance and facilitate further genomic studies in fine mapping resistance genes to enhance the understanding of the genetic basis of BB resistance in cotton.

QTL controlling fiber quality traits under salt stress in upland cotton (Gossypium hirsutum L.)

QTL for fiber quality traits under salt stress discerned candidate genes controlling fatty acid metabolism. Salinity stress seriously affects plant growth and limits agricultural productivity of crop plants. To dissect the genetic basis of response to salinity stress, a recombinant inbred line population was developed to compare fiber quality in upland cotton (Gossypium hirsutum L.) under salt stress and normal conditions. Based on three datasets of (1) salt stress, (2) normal growth, and (3) the difference value between salt stress and normal conditions, 51, 70, and 53 QTL were mapped, respectively. Three QTL for fiber length (FL) (qFL-Chr1-1, qFL-Chr5-5, and qFL-Chr24-4) were detected under both salt and normal conditions and explained 4.26%, 9.38%, and 3.87% of average phenotypic variation, respectively. Seven genes within intervals of two stable QTL (qFL-Chr1-1 and qFL-Chr5-5) were highly expressed in lines with extreme long fiber. A total of 35 QTL clusters comprised of 107 QTL were located on 18 chromosomes and exhibited pleiotropic effects. Thereinto, two clusters were responsible for improving five fiber quality traits, and 6 influenced FL and fiber strength (FS). The QTL with positive effect for fiber length exhibited active effects on fatty acid synthesis and elongation, but the ones with negative effect played passive roles on fatty acid degradation under salt stress.

The cellulose synthase (CesA) gene family in four Gossypium species: phylogenetics, sequence variation and gene expression in relation to fiber quality in Upland cotton

Cellulose synthases (CesAs) are multi-subunit enzymes found on the plasma membrane of plant cells and play a pivotal role in cellulose production. The cotton fiber is mainly composed of cellulose, and the genetic relationships between CesA genes and cotton fiber yield and quality are not fully understood. Through a phylogenetic analysis, the CesA gene family in diploid Gossypium arboreum and Gossypium raimondii, as well as tetraploid Gossypium hirsutum ('TM-1') and Gossypium barbadense ('Hai-7124' and '3-79'), was divided into 6 groups and 15 sub-groups, with each group containing two to five homologous genes. Most CesA genes in the four species are highly collinear. Among the five cotton genomes, 440 and 1929 single nucleotide polymorphisms (SNPs) in the CesA gene family were identified in exons and introns, respectively, including 174 SNPs resulting in amino acid changes. In total, 484 homeologous SNPs between the A and D genomes were identified in diploids, while 142 SNPs were detected between the two tetraploids, with 32 and 82 SNPs existing within G. hirsutum and G. barbadense, respectively. Additionally, 74 quantitative trait loci near 18 GhCesA genes were associated with fiber quality. One to four GhCesA genes were differentially expressed (DE) in ovules at 0 and 3 days post anthesis (DPA) between two backcross inbred lines having different fiber lengths, but no DE genes were identified between these lines in developing fibers at 10 DPA. Twenty-seven SNPs in above DE CesA genes were detected among seven cotton lines, including one SNP in Ghi_A08G03061 that was detected in four G. hirsutum genotypes. This study provides the first comprehensive characterization of the cotton CesA gene family, which may play important roles in determining cotton fiber quality.

Genome-Wide Association Analysis Reveals Loci and Candidate Genes Involved in Fiber Quality Traits Under Multiple Field Environments in Cotton (Gossypium hirsutum)

Fiber length, fiber strength, and fiber micronaire are the main fiber quality parameters in cotton. Thus, mining the elite and stable loci/alleles related to fiber quality traits and elucidating the relationship between the two may accelerate genetic improvement of fiber quality in cotton. Here, genome-wide association analysis (GWAS) was performed for fiber quality parameters based on phenotypic data, and 56,010 high-quality single nucleotide polymorphisms (SNPs) using 242 upland cotton accessions under 12 field environments were obtained. Phenotypic analysis exhibited that fiber length (FL) had a positive correlation with fiber strength (FS) and had a negative correlation with fiber micronaire (Mic). Genetic analysis also indicated that FL, FS, and Mic had high heritability of more than 80%. A total of 67 stable quantitative trait loci (QTLs) were identified through GWAS analysis, including 31 for FL, 21 for FS, and 22 for Mic. Of them, three pairs homologous QTLs were detected between A and D subgenomes, and seven co-located QTLs with two fiber quality parameters were found. Compared with the reported QTLs, 34 co-located with previous studies, and 33 were newly revealed. Integrated with transcriptome analysis, we selected 256, 244, and 149 candidate genes for FL, FS, and Mic, respectively. Gene Ontology (GO) analysis showed that most of the genes located in QTLs interval of the three fiber quality traits were involved in sugar biosynthesis, sugar metabolism, microtubule, and cytoskeleton organization, which played crucial roles in fiber development. Through correlation analysis between haplotypes and phenotypes, three genes (GH_A05G1494, GH_D11G3097, and GH_A05G1082) predominately expressed in fiber development stages were indicated to be potentially responsible for FL, FS, and Mic, respectively. The GH_A05G1494 encoded a protein containing SGS-domain, which is related to tubulin-binding and ubiquitin-protein ligase binding. The GH_D11G3097 encoded 20S proteasome beta subunit G1, and was involved in the ubiquitin-dependent protein catabolic process. The GH_A05G1082 encoded RAN binding protein 1 with a molecular function of GTPase activator activity. These results provide new insights and candidate loci/genes for the improvement of fiber quality in cotton.

Genetic Diversity, QTL Mapping, and Marker-Assisted Selection Technology in Cotton (Gossypium spp.)

Cotton genetic resources contain diverse economically important traits that can be used widely in breeding approaches to create of high-yielding elite cultivars with superior fiber quality and adapted to biotic and abiotic stresses. Nevertheless, the creation of new cultivars using conventional breeding methods is limited by the cost and proved to be time consuming process, also requires a space to make field observations and measurements. Decoding genomes of cotton species greatly facilitated generating large-scale high-throughput DNA markers and identification of QTLs that allows confirmation of candidate genes, and use them in marker-assisted selection (MAS)-based breeding programs. With the advances of quantitative trait loci (QTL) mapping and genome-wide-association study approaches, DNA markers associated with valuable traits significantly accelerate breeding processes by replacing the selection with a phenotype to the selection at the DNA or gene level. In this review, we discuss the evolution and genetic diversity of cotton Gossypium genus, molecular markers and their types, genetic mapping and QTL analysis, application, and perspectives of MAS-based approaches in cotton breeding.

A high-density genetic map and multiple environmental tests reveal novel quantitative trait loci and candidate genes for fibre quality and yield in cotton

A high-density linkage map of an intraspecific RIL population was constructed using 6187 bins to identify QTLs for fibre quality- and yield-related traits in upland cotton by whole-genome resequencing. Good fibre quality and high yield are important production goals in cotton (Gossypium hirsutum L.), which is a leading natural fibre crop worldwide. However, a greater understanding of the genetic variants underlying fibre quality- and yield-related traits is still required. In this study, a large-scale population including 588 F₇ recombinant inbred lines, derived from an intraspecific cross between the upland cotton cv. Nongdamian13, which exhibits high quality, and Nongda601, which exhibits a high yield, was genotyped by using 232,946 polymorphic single-nucleotide polymorphisms obtained via a whole-genome resequencing strategy with 4.3-fold genome coverage. We constructed a high-density bin linkage map containing 6187 bin markers spanning 4478.98 cM with an average distance of 0.72 cM. We identified 58 individual quantitative trait loci (QTLs) and 25 QTL clusters harbouring 94 QTLs, and 119 previously undescribed QTLs controlling 13 fibre quality and yield traits across eight environments. Importantly, the QTL counts for fibre quality in the Dt subgenome were more than two times that in the At subgenome, and chromosome D02 harboured the greatest number of QTLs and clusters. Furthermore, we discovered 24 stable QTLs for fibre quality and 12 stable QTLs for yield traits. Four novel major stable QTLs related to fibre length, fibre strength and lint percentage, and seven previously unreported candidate genes with significantly differential expression between the two parents were identified and validated by RNA-seq. Our research provides valuable information for improving the fibre quality and yield in cotton breeding.

Characterization of black spot resistance in diploid roses with QTL detection, meta-analysis and candidate-gene identification

Two environmentally stable QTLs linked to black spot disease resistance in the Rosa wichurana genetic background were detected, in different connected populations, on linkage groups 3 and 5. Co-localization between R-genes and defense response genes was revealed via meta-analysis. The widespread rose black spot disease (BSD) caused by the hemibiotrophic fungus Diplocarpon rosae Wolf. is efficiently controlled with fungicides. However, in the actual context of reducing agrochemical use, the demand for rose bushes with higher levels of resistance has increased. Qualitative resistance conferred by major genes (Rdr genes) has been widely studied but quantitative resistance to BSD requires further investigation. In this study, segregating populations connected through the BSD resistant Rosa wichurana male parent were phenotyped for disease resistance over several years and locations. A pseudo-testcross approach was used, resulting in six parental maps across three populations. A total of 45 individual QTLs with significant effect on BSD resistance were mapped on the male maps (on linkage groups (LG) B3, B4, B5 and B6), and 12 on the female maps (on LG A1, A2, A3, A4 and A5). Two major regions linked to BSD resistance were identified on LG B3 and B5 of the male maps and were integrated into a consensus map built from all three of the male maps. A meta-analysis was used to narrow down the confidence intervals of individual QTLs from three populations by generating meta-QTLs. Two 'hot spots' or meta-QTLs were found per LG, enabling reduction of the confidence interval to 10.42 cM for B3 and 11.47 cM for B5. An expert annotation of NBS-LRR encoding genes of the genome assembly of Hibrand et al. was performed and used to explore potential co-localization with R-genes. Co-localization with defense response genes was also investigated.

A Meta-Analysis of Quantitative Trait Loci Associated with Multiple Disease Resistance in Rice (Oryza sativa L.)

Rice blast, sheath blight and bacterial leaf blight are major rice diseases found worldwide. The development of resistant cultivars is generally perceived as the most effective way to combat these diseases. Plant disease resistance is a polygenic trait where a combinatorial effect of major and minor genes affects this trait. To locate the source of this trait, various quantitative trait loci (QTL) mapping studies have been performed in the past two decades. However, investigating the congruency between the reported QTL is a daunting task due to the heterogeneity amongst the QTLs studied. Hence, the aim of our study is to integrate the reported QTLs for resistance against rice blast, sheath blight and bacterial leaf blight and objectively analyze and consolidate the location of QTL clusters in the chromosomes, reducing the QTL intervals and thus identifying candidate genes within the selected meta-QTL. A total of twenty-seven studies for resistance QTLs to rice blast (8), sheath blight (15) and bacterial leaf blight (4) was compiled for QTL projection and analyses. Cumulatively, 333 QTLs associated with rice blast (114), sheath blight (151) and bacterial leaf blight (68) resistance were compiled, where 303 QTLs could be projected onto a consensus map saturated with 7633 loci. Meta-QTL analysis on 294 QTLs yielded 48 meta-QTLs, where QTLs with membership probability lower than 60% were excluded, reducing the number of QTLs within the meta-QTL to 274. Further, three meta-QTL regions (MQTL2.5, MQTL8.1 and MQTL9.1) were selected for functional analysis on the basis that MQTL2.5 harbors the highest number of QTLs; meanwhile, MQTL8.1 and MQTL9.1 have QTLs associated with all three diseases mentioned above. The functional analysis allows for determination of enriched gene ontology and resistance gene analogs (RGAs) and other defense-related genes. To summarize, MQTL2.5, MQTL8.1 and MQTL9.1 have a considerable number of R-genes that account for 10.21%, 4.08% and 6.42% of the total genes found in these meta-QTLs, respectively. Defense genes constitute around 3.70%, 8.16% and 6.42% of the total number of genes in MQTL2.5, MQTL8.1 and MQTL9.1, respectively. This frequency is higher than the total frequency of defense genes in the rice genome, which is 0.0096% (167 defense genes/17,272 total genes). The integration of the QTLs facilitates the identification of QTL hotspots for rice blast, sheath blight and bacterial blight resistance with reduced intervals, which helps to reduce linkage drag in breeding. The candidate genes within the promising regions could be utilized for improvement through genetical engineering.

GWAS reveals consistent QTL for drought and salt tolerance in a MAGIC population of 550 lines derived from intermating of 11 Upland cotton (Gossypium hirsutum) parents

Cotton is grown in arid and semi-arid regions where abiotic stresses such as drought and salt are prevalent. There is a lack of studies that simultaneously address the genetic and genomic basis of tolerance to drought and salt stress. In this study, a multi-parent advanced generation inter-cross (MAGIC) population of 550 recombinant inbred lines (RILs) together with their 11 Upland cotton parents with a total of 473,516 polymorphic SNP markers was used to identify quantitative trait loci (QTL) for drought tolerance (DT) and salt tolerance (ST) at the seedling stage based on two replicated greenhouse tests. Transgressive segregation occurred in the MAGIC-RILs, indicating that tolerant and sensitive alleles recombined for tolerance to the abiotic stress during the intermating process for the population development. A total of 20 QTL were detected for DT including 13 and 7 QTL based on plant height (PH) and dry shoot weight (DSW), respectively; and 23 QTL were detected for ST including 12 and 11 QTL for PH and DSW, respectively. There were several chromosomes with QTL clusters for abiotic stress tolerance including four QTL on chromosome A13 and three QTL on A01 for DT, and four QTL on D08 and three QTL on A11 for ST. Nine QTL (21% of the 43 QTL) detected were in common between DT and ST, indicating a common genetic basis for DT and ST. The narrow chromosomal regions for most of the QTL detected in this study allowed identification of 53 candidate genes associated with responses to salt and drought stress and abiotic stimulus. The QTL identified for both DT and ST have significantly augmented the repertoire of QTL for abiotic stress tolerance that can be used for marker-assisted selection to develop cultivars with resilience to drought and/or salt and further genomic studies towards the identification of drought and salt tolerance genes in cotton.

Genome-wide association study reveals the genetic basis of fiber quality traits in upland cotton (Gossypium hirsutum L.)

BACKGROUND

Fiber quality is an important economic trait of cotton, and its improvement is a major goal of cotton breeding. To better understand the genetic mechanisms responsible for fiber quality traits, we conducted a genome-wide association study to identify and mine fiber-quality-related quantitative trait loci (QTLs) and genes.

RESULTS

In total, 42 single nucleotide polymorphisms (SNPs) and 31 QTLs were identified as being significantly associated with five fiber quality traits. Twenty-five QTLs were identified in previous studies, and six novel QTLs were firstly identified in this study. In the QTL regions, 822 genes were identified and divided into four clusters based on their expression profiles. We also identified two pleiotropic SNPs. The SNP locus i52359Gb was associated with fiber elongation, strength, length and uniformity, while i11316Gh was associated with fiber strength and length. Moreover, these two SNPs were nonsynonymous and located in genes Gh_D09G2376 and Gh_D06G1908, respectively. RT-qPCR analysis revealed that these two genes were preferentially expressed at one or more stages of cotton fiber development, which was consistent with the RNA-seq data. Thus, Gh_D09G2376 and Gh_D06G1908 may be involved in fiber developmental processes.

CONCLUSIONS

The findings of this study provide insights into the genetic bases of fiber quality traits, and the identified QTLs or genes may be applicable in cotton breeding to improve fiber quality.

Conservation and Divergence in Duplicated Fiber Coexpression Networks Accompanying Domestication of the Polyploid Gossypium hirsutum L

Gossypium hirsutum L. (Upland cotton) has an evolutionary history involving inter-genomic hybridization, polyploidization, and subsequent domestication. We analyzed the developmental dynamics of the cotton fiber transcriptome accompanying domestication using gene coexpression networks for both joint and homoeologous networks. Remarkably, most genes exhibited expression for at least one homoeolog, confirming previous reports of widespread gene usage in cotton fibers. Most coexpression modules comprising the joint network are preserved in each subgenomic network and are enriched for similar biological processes, showing a general preservation of network modular structure for the two co-resident genomes in the polyploid. Interestingly, only one fifth of homoeologs co-occur in the same module when separated, despite similar modular structures between the joint and homoeologous networks. These results suggest that the genome-wide divergence between homoeologous genes is sufficient to separate their co-expression profiles at the intermodular level, despite conservation of intramodular relationships within each subgenome. Most modules exhibit D-homoeolog expression bias, although specific modules do exhibit A-homoeolog bias. Comparisons between wild and domesticated coexpression networks revealed a much tighter and denser network structure in domesticated fiber, as evidenced by its fewer modules, 13-fold increase in the number of development-related module member genes, and the poor preservation of the wild network topology. These results demonstrate the amazing complexity that underlies the domestication of cotton fiber.

Examining two sets of introgression lines across multiple environments reveals background-independent and stably expressed quantitative trait loci of fiber quality in cotton

Background-independent (BI) and stably expressed (SE) quantitative trait loci (QTLs) were identified using two sets of introgression lines across multiple environments. Genetic background more greatly affected fiber quality traits than environmental factors. Sixty-one SE-QTLs, including two BI-QTLs, were novel and 48 SE-QTLs, including seven BI-QTLs, were previously reported. Cotton fiber quality traits are controlled by QTLs and are susceptible to environmental influence. Fiber quality improvement is an essential goal in cotton breeding but is hindered by limited knowledge of the genetic basis of fiber quality traits. In this study, two sets of introgression lines of Gossypium hirsutum × G. barbadense were used to dissect the QTL stability of three fiber quality traits (fiber length, strength and micronaire) across environments using 551 simple sequence repeat markers selected from our high-density genetic map. A total of 76 and 120 QTLs were detected in the CCRI36 and CCRI45 backgrounds, respectively. Nine BI-QTLs were found, and 78 (41.71%) of the detected QTLs were reported previously. Thirty-nine and 79 QTLs were SE-QTLs in at least two environments in the CCRI36 and CCRI45 backgrounds, respectively. Forty-eight SE-QTLs, including seven BI-QTLs, were confirmed in previous reports, and 61 SE-QTLs, including two BI-QTLs, were considered novel. These results indicate that genetic background more strongly impacts on fiber quality traits than environmental factors. Twenty-three clusters with BI- and/or SE-QTLs were identified, 19 of which harbored favorable alleles from G. barbadense for two or three fiber quality traits. This study is the first report using two sets of introgression lines to identify fiber quality QTLs across environments in cotton, providing insights into the effect of genetic backgrounds and environments on the QTL expression of fiber quality and important information for the genetic basis underlying fiber quality traits toward QTL cloning and molecular breeding.

ddRADseq-assisted construction of a high-density SNP genetic map and QTL fine mapping for growth-related traits in the spotted scat (Scatophagus argus)

BACKGROUND

Scatophagus argus is a popular farmed fish in several countries of Southeast Asia, including China. Although S. argus has a highly promising economic value, a significant lag of breeding research severely obstructs the sustainable development of aquaculture industry. As one of the most important economic traits, growth traits are controlled by multiple gene loci called quantitative trait loci (QTLs). It is urgently needed to launch a marker assisted selection (MAS) breeding program to improve growth and other pivotal traits. Thus a high-density genetic linkage map is necessary for the fine mapping of QTLs associated with target traits.

RESULTS

Using restriction site-associated DNA sequencing, 6196 single nucleotide polymorphism (SNP) markers were developed from a full-sib mapping population for genetic map construction. A total of 6193 SNPs were grouped into 24 linkage groups (LGs), and the total length reached 2191.65 cM with an average marker interval of 0.35 cM. Comparative genome mapping revealed 23 one-to-one and 1 one-to-two syntenic relationships between S. argus LGs and Larimichthys crocea chromosomes. Based on the high-quality linkage map, a total of 44 QTLs associated with growth-related traits were identified on 11 LGs. Of which, 19 significant QTLs for body weight were detected on 9 LGs, explaining 8.8-19.6% of phenotypic variances. Within genomic regions flanking the SNP markers in QTL intervals, we predicted 15 candidate genes showing potential relationships with growth, such as Hbp1, Vgll4 and Pim3, which merit further functional exploration.

CONCLUSIONS

The first SNP genetic map with a fine resolution of 0.35 cM for S. argus has been developed, which shows a high level of syntenic relationship with L. crocea genomes. This map can provide valuable information for future genetic, genomic and evolutionary studies. The QTLs and SNP markers significantly associated with growth-related traits will act as useful tools in gene mapping, map-based cloning and MAS breeding to speed up the genetic improvement in important traits of S. argus. The interesting candidate genes are promising for further investigations and have the potential to provide deeper insights into growth regulation in the future.

Identification of candidate genes for key fibre-related QTLs and derivation of favourable alleles in Gossypium hirsutum recombinant inbred lines with G. barbadense introgressions

Fine mapping QTLs and identifying candidate genes for cotton fibre-quality and yield traits would be beneficial to cotton breeding. Here, we constructed a high-density genetic map by specific-locus amplified fragment sequencing (SLAF-seq) to identify QTLs associated with fibre-quality and yield traits using 239 recombinant inbred lines (RILs), which was developed from LMY22 (a high-yield Gossypium hirsutumL. cultivar) × LY343 (a superior fibre-quality germplasm with G. barbadenseL. introgressions). The genetic map spanned 3426.57 cM, including 3556 SLAF-based SNPs and 199 SSR marker loci. A total of 104 QTLs, including 67 QTLs for fibre quality and 37 QTLs for yield traits, were identified with phenotypic data collected from 7 environments. Among these, 66 QTLs were co-located in 19 QTL clusters on 12 chromosomes, and 24 QTLs were detected in three or more environments and determined to be stable. We also investigated the genomic components of LY343 and their contributions to fibre-related traits by deep sequencing the whole genome of LY343, and we found that genomic components from G. hirsutum races (which entered LY343 via its G. barbadense parent) contributed more favourable alleles than those from G. barbadense. We further identified six putative candidate genes for stable QTLs, including Gh_A03G1147 (GhPEL6), Gh_D07G1598 (GhCSLC6) and Gh_D13G1921 (GhTBL5) for fibre-length QTLs and Gh_D03G0919 (GhCOBL4), Gh_D09G1659 (GhMYB4) and Gh_D09G1690 (GhMYB85) for lint-percentage QTLs. Our results provide comprehensive insight into the genetic basis of the formation of fibre-related traits and would be helpful for cloning fibre-development-related genes as well as for marker-assisted genetic improvement in cotton.