Development of a 63K SNP Array for Cotton and High-Density Mapping of Intraspecific and Interspecific Populations of Gossypium spp

Genetic linkage analysis of stable QTLs in Gossypium hirsutum RIL population revealed function of GhCesA4 in fiber development

INTRODUCTION

Upland cotton is an important allotetrapolyploid crop providing natural fibers for textile industry. Under the present high-level breeding and production conditions, further simultaneous improvement of fiber quality and yield is facing unprecedented challenges due to their complex negative correlations.

OBJECTIVES

The study was to adequately identify quantitative trait loci (QTLs) and dissect how they orchestrate the formation of fiber quality and yield.

METHODS

A high-density genetic map (HDGM) based on an intraspecific recombinant inbred line (RIL) population consisting of 231 individuals was used to identify QTLs and QTL clusters of fiber quality and yield traits. The weighted gene correlation network analysis (WGCNA) package in R software was utilized to identify WGCNA network and hub genes related to fiber development. Gene functions were verified via virus-induced gene silencing (VIGS) and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 strategies.

RESULTS

An HDGM consisting of 8045 markers was constructed spanning 4943.01 cM of cotton genome. A total of 295 QTLs were identified based on multi-environmental phenotypes. Among 139 stable QTLs, including 35 newly identified ones, seventy five were of fiber quality and 64 yield traits. A total of 33 QTL clusters harboring 74 QTLs were identified. Eleven candidate hub genes were identified via WGCNA using genes in all stable QTLs and QTL clusters. The relative expression profiles of these hub genes revealed their correlations with fiber development. VIGS and CRISPR/Cas9 edition revealed that the hub gene cellulose synthase 4 (GhCesA4, GH_D07G2262) positively regulate fiber length and fiber strength formation and negatively lint percentage.

CONCLUSION

Multiple analyses demonstrate that the hub genes harbored in the QTLs orchestrate the fiber development. The hub gene GhCesA4 has opposite pleiotropic effects in regulating trait formation of fiber quality and yield. The results facilitate understanding the genetic basis of negative correlation between cotton fiber quality and yield.

Genetic diversity and population structure analyses and genome-wide association studies of photoperiod sensitivity in cotton (Gossypium hirsutum L.)

KEY MESSAGE

Genetic diversity and population structure analyses showed progressively narrowed diversity in US Upland cotton compared to land races. GWAS identified genomic regions and candidate genes for photoperiod sensitivity in cotton. Six hundred fifty-seven accessions that included elite cotton germplasm (DIV panel), lines of a public cotton breeding program (FB panel), and tropical landrace accessions (TLA panel) of Gossypium hirsutum L. were genotyped with cottonSNP63K array and phenotyped for photoperiod sensitivity under long day-length conditions. The genetic diversity analysis using 26,952 polymorphic SNPs indicated a progressively narrowed diversity from the landraces (0.230) to the DIV panel accessions (0.195) and FB panel (0.116). Structure analysis in the US germplasm identified seven subpopulations representing all four major regions of the US cotton belt. Three subpopulations were identified within the landrace accessions. The highest fixation index (FST) of 0.65 was found between landrace accessions of Guatemala and the Plains-type cultivars from Southwest cotton region while the lowest FST values were between the germplasms of Mid-South and Southeastern regions. Genome wide association studies (GWAS) of photoperiod response using 600 phenotyped accessions identified 14 marker trait associations spread across eight Upland cotton chromosomes. Six of these marker trait associations, on four chromosomes (A10, D04, D05, and D06), showed significant epistatic interactions. Targeted genomic analysis identified regions with 19 candidate genes including Transcription factor Vascular Plant One-Zinc Finger 1 (VOZ1) and Protein Photoperiod-Independent Early Flowering 1 (PIE1) genes. Genetic diversity and genome wide analyses of photoperiod sensitivity in diverse cotton germplasms will enable the use of genomic tools to systematically utilize the tropical germplasm and its beneficial alleles for broadening the genetic base in Upland cotton.

Alterations in Growth Habit to Channel End-of-Season Perennial Reserves towards Increased Yield and Reduced Regrowth after Defoliation in Upland Cotton (Gossypium hirsutum L.)

Cotton (Gossypium spp.) is the primary source of natural textile fiber in the U.S. and a major crop in the Southeastern U.S. Despite constant efforts to increase the cotton fiber yield, the yield gain has stagnated. Therefore, we undertook a novel approach to improve the cotton fiber yield by altering its growth habit from perennial to annual. In this effort, we identified genotypes with high-expression alleles of five floral induction and meristem identity genes (FT, SOC1, FUL, LFY, and AP1) from an Upland cotton mini-core collection and crossed them in various combinations to develop cotton lines with annual growth habit, optimal flowering time, and enhanced productivity. To facilitate the characterization of genotypes with the desired combinations of stacked alleles, we identified molecular markers associated with the gene expression traits via genome-wide association analysis using a 63 K SNP Array. Over 14,500 SNPs showed polymorphism and were used for association analysis. A total of 396 markers showed associations with expression traits. Of these 396 markers, 159 were mapped to genes, 50 to untranslated regions, and 187 to random genomic regions. Biased genomic distribution of associated markers was observed where more trait-associated markers mapped to the cotton D sub-genome. Many quantitative trait loci coincided at specific genomic regions. This observation has implications as these traits could be bred together. The analysis also allowed the identification of candidate regulators of the expression patterns of these floral induction and meristem identity genes whose functions will be validated.

Genetic dissection of lint percentage in short-season cotton using combined QTL mapping and RNA-seq

KEY MESSAGE

In total, 17 QTLs for lint percentage in short-season cotton, including three stable QTLs, were detected. Twenty-eight differentially expressed genes located within the stable QTLs were identified, and two genes were validated by qRT-PCR. The breeding and use of short-season cotton have significant values in addressing the question of occupying farmlands with either cotton or cereals. However, the fiber yields of short-season cotton varieties are significantly lower than those of middle- and late-maturing varieties. How to effectively improve the fiber yield of short-season cotton has become a focus of cotton research. Here, a high-density genetic map was constructed using genome resequencing and an RIL population generated from the hybridization of two short-season cotton accessions, Dong3 and Dong4. The map contained 4960 bin markers across the 26 cotton chromosomes and spanned 3971.08 cM, with an average distance of 0.80 cM between adjacent markers. Based on the genetic map, quantitative trait locus (QTL) mapping for lint percentage (LP, %), an important yield component trait, was performed. In total, 17 QTLs for LP, including three stable QTLs, qLP-A02, qLP-D04, and qLP-D12, were detected. Three out of 11 non-redundant QTLs overlapped with previously reported QTLs, whereas the other eight were novel QTLs. A total of 28 differentially expressed genes associated with the three stable QTLs were identified using RNA-seq of ovules and fibers at different seed developmental stages from the parental materials. The two genes, Ghir_A02G017640 and Ghir_A02G018500, may be related to LP as determined by further qRT-PCR validation. This study provides useful information for the genetic dissection of LP and promotes the molecular breeding of short-season 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.

Haplotyping interspecific hybrids by dual alignment to both parental genomes

Sequencing-based genotyping of heterozygous diploids requires sufficient depth to accurately call heterozygous genotypes. In interspecific hybrids, alignment of reads to both parental genomes simultaneously can generate haploid data, potentially eliminating the problem of heterozygosity. Two populations of interspecific hybrid rootstocks of walnut (Juglans) and pistachio (Pistacia) were genotyped using alignment to the maternal genome, paternal genome, and dual alignment to both genomes simultaneously. Downsampling was used to examine concordance of imputed genotype calls as a function of sequencing depth. Dual alignment resulted in datasets essentially free of heterozygous genotypes, simplifying the identification and removal of cross-contaminated samples. Concordance between full and downsampled genotype calls was always highest after dual alignment. Nearly all single nucleotide polymorphisms (SNPs) in dual alignment datasets were shared with the corresponding single-parent datasets, but 60%-90% of single-parent SNPs were private to that dataset. Private SNPs in single-parent datasets had higher rates of heterozygosity, lower levels of concordance, and were enriched in fixed differences between parental genomes ("homeo-SNPs") compared to shared SNPs in the same dataset. In multi-parental walnut hybrids, the paternal-aligned dataset was ineffective at resolving population structure in the maternal parent. Overall, the dual alignment strategy effectively produced phased, haploid data, increasing data quality and reducing cost.

First insight into the phylogeny of fine‐leaved Festuca in the Altai Mountain Country based on genome‐wide genotyping

Festuca is one of the largest genera within the Poaceae family. Molecular phylogenies demonstrate that Festuca s.l. comprises two groups: broad‐ and fine‐leaved species. The latter is the species‐richest and taxonomically complicated group due to being paraphyletic. Here, we provide the first insight into the phylogeny of 17 fine‐leaved species of Altai fescues. Based on genome‐wide genotyping, the examined taxa were divided into three markedly differentiated clusters. The first cluster comprises species from the F. rubra complex, the second cluster includes the F. brachyphylla complex, and the third cluster contains taxa from the groups F. ovina, F. valesiaca, and F. kryloviana. Importantly, we detected a complex genetic pattern within the groups of F. valesiaca and F. kryloviana. Moreover, our findings underline a discrepancy between morphological and molecular data for some species distributed within the Altai Mountain Country. We suggest that in order to validate the current findings on the fine‐leaved fescues, additional comprehensive research including morphological, karyological, and molecular methods is required. Nonetheless, our work provides a baseline for further investigations on the genus and studies on the floral diversity of Asia.

Detecting Cotton Leaf Curl Virus Resistance Quantitative Trait Loci in Gossypium hirsutum and iCottonQTL a New R/Shiny App to Streamline Genetic Mapping

Cotton leaf curl virus (CLCuV) causes devastating losses to fiber production in Central Asia. Viral spread across Asia in the last decade is causing concern that the virus will spread further before resistant varieties can be bred. Current development depends on screening each generation under disease pressure in a country where the disease is endemic. We utilized quantitative trait loci (QTL) mapping in four crosses with different sources of resistance to identify single nucleotide polymorphism (SNP) markers associated with the resistance trait to allow development of varieties without the need for field screening every generation. To assist in the analysis of multiple populations, a new publicly available R/Shiny App was developed to streamline genetic mapping using SNP arrays and to also provide an easy method to convert and deposit genetic data into the CottonGen database. Results identified several QTL from each cross, indicating possible multiple modes of resistance. Multiple sources of resistance would provide several genetic routes to combat the virus as it evolves over time. Kompetitive allele specific PCR (KASP) markers were developed and validated for a subset of QTL, which can be used in further development of CLCuV-resistant cotton lines.

Recent progression and future perspectives in cotton genomic breeding

Upland cotton is an important global cash crop for its long seed fibers and high edible oil and protein content. Progress in cotton genomics promotes the advancement of cotton genetics, evolutionary studies, functional genetics, and breeding, and has ushered cotton research and breeding into a new era. Here, we summarize high-impact genomics studies for cotton from the last 10 years. The diploid Gossypium arboreum and allotetraploid Gossypium hirsutum are the main focus of most genetic and genomic studies. We next review recent progress in cotton molecular biology and genetics, which builds on cotton genome sequencing efforts, population studies, and functional genomics, to provide insights into the mechanisms shaping abiotic and biotic stress tolerance, plant architecture, seed oil content, and fiber development. We also suggest the application of novel technologies and strategies to facilitate genome-based crop breeding. Explosive growth in the amount of novel genomic data, identified genes, gene modules, and pathways is now enabling researchers to utilize multidisciplinary genomics-enabled breeding strategies to cultivate "super cotton", synergistically improving multiple traits. These strategies must rise to meet urgent demands for a sustainable cotton industry.

Identification of late blight resistance quantitative trait loci in Solanum pimpinellifolium accession PI 270441

Late blight (LB), caused by the oomycete Phytophthora infestans, is one of the most destructive diseases of the cultivated tomato (Solanum lycopersicum L.) and potato (Solanum tuberosum L.) worldwide. Genetic changes in the pathogen have resulted in the emergence of new genotypes, overcoming formerly effective fungicides or host resistance genes. We previously reported the identification of a LB-resistant accession (PI 270441) of the wild tomato species S. pimpinellifolium L. and the high heritability of its resistance. In the present study, an F₂ population (n = 1,209), derived from a cross between PI 270441 and a LB-susceptible tomato breeding line (Fla. 8059), was screened for response to LB infection. Extreme resistant (n = 44) and susceptible (n = 39) F₂ individuals were selected and used in a trait-based marker analysis (TBA; a.k.a selective genotyping) to identify and map quantitative trait loci (QTLs) conferring LB resistance. Reduced representation libraries (RRLs) of Fla. 8059 and PI 270441 were constructed, sequenced, and mapped to the tomato genome. A total of 13,054 single-nucleotide polymorphisms (SNPs) were identified, of which, 200 were used to construct a genetic linkage map and locate QTLs. Four LB resistance QTLs were identified on chromosomes 1, 10, and 11 of PI 270441. The markers associated with these QTLs can be used to transfer LB resistance from PI 270441 into new tomato cultivars and to develop near-isogenic lines for fine mapping of the QTL.

Identification and genomic characterization of major effect bacterial blight resistance locus (BB-13) in Upland cotton (Gossypium hirsutum L.)

Identification and genomic characterization of major resistance locus against cotton bacterial blight (CBB) using GWAS and linkage mapping to enable genomics-based development of durable CBB resistance and gene discovery in cotton. Cotton bacterial leaf blight (CBB), caused by Xanthomonas citri subsp. malvacearum (Xcm), has periodically been a damaging disease in the USA. Identification and deployment of genetic resistance in cotton cultivars is the most economical and efficient means of reducing crop losses due to CBB. In the current study, genome-wide association study (GWAS) of CBB resistance using an elite diversity panel of 380 accessions, genotyped with the cotton single nucleotide polymorphism (SNP) 63 K array, and phenotyped with race-18 of CBB, localized the CBB resistance to a 2.01-Mb region in the long arm of chromosome D02. Molecular genetic mapping using an F₆ recombinant inbred line (RIL) population showed the CBB resistance in cultivar Arkot 8102 was controlled by a single locus (BB-13). The BB-13 locus was mapped within the 0.95-cM interval near the telomeric region in the long arm of chromosome D02. Flanking SNP markers, i04890Gh and i04907Gh of the BB-13 locus, identified from the combined linkage analysis and GWAS, targeted it to a 371-Kb genomic region. Candidate gene analysis identified thirty putative gene sequences in the targeted genomic region. Nine of these putative genes and two NBS-LRR genes adjacent to the targeted region were putatively involved in plant disease resistance and are possible candidate genes for BB-13 locus. Genetic mapping and genomic targeting of the BB13 locus in the current study will help in cloning the CBB-resistant gene and establishing the molecular genetic architecture of the BB-13 locus towards developing durable resistance to CBB in cotton.

Quantitative Trait Locus Mapping for Fusarium Wilt Race 4 Resistance in a Recombinant Inbred Line Population of Pima Cotton (Gossypium Barbadense)

Fusarium oxysporum f. sp. vasinfectum (FOV) race 4 (FOV4) causes seedling death immediately after emergence, in addition to leaf chlorosis and necrosis, vascular discoloration, plant wilting, defoliation, and plant death at late stages. Breeding for FOV4 resistance is the most cost effective management method. In this study, 163 recombinant inbred lines (RILs) of FOV4-resistant Pima S-6 × susceptible 89590, together with the two parents (Gossypium barbadense), were artificially inoculated with FOV4 and assayed for resistance based on foliar disease severity ratings (DSR) at 30 days post inoculation (dpi) in two replicated tests in the greenhouse or controlled conditions. Significant genotypic variations were detected for FOV4 resistance in a combined analysis of variance. Although a significant genotype × test interaction was detected for DSR, the 10 most resistant RILs had significantly and consistently lower DSR than the susceptible parent in both tests. The heritability estimate for DSR was 0.65, indicating that two-thirds of the phenotypic variation for FOV4 resistance in this Pima RIL population was due to genetic factors. Based on 404 polymorphic SSR markers, five and four quantitative trait loci (QTL) on six chromosomes (c14, c17, c19, c21, c24, and c25) were detected in Tests 1 and 2, respectively, and each explained 15 to 29% of the phenotypic variation. Three QTL on c17, c24, and c25 were in common between the two tests, accounting for 60% and 75% of the QTL detected in Tests 1 and 2, respectively. The three QTL were also reported in previous studies and will be useful for marker-assisted selection for FOV4 resistance in Pima cotton.

Insights into the Genomic Regions and Candidate Genes of Senescence-Related Traits in Upland Cotton via GWAS

Senescence is the last stage of plant development and is controlled by both internal and external factors. Premature senescence significantly affects the yield and quality of cotton. However, the genetic architecture underlying cotton senescence remains unclear. In this study, genome-wide association studies (GWAS) were performed based on 3,015,002 high-quality SNP markers from the resequencing data of 355 upland cotton accessions to detect genomic regions for cotton senescence. A total of 977 candidate genes within 55 senescence-related genomic regions (SGRs), SGR1-SGR55, were predicted. Gene ontology (GO) analysis of candidate genes revealed that a set of biological processes was enriched, such as salt stress, ethylene processes, and leaf senescence. Furthermore, in the leaf senescence GO term, one candidate gene was focused on: Gohir.A12G270900 (GhMKK9), located in SGR36, which encodes a protein of the MAP kinase kinase family. Quantitative real-time PCR (qRT-PCR) analysis showed that GhMKK9 was up-regulated in old cotton leaves. Overexpression of GhMKK9 in Arabidopsis accelerated natural leaf senescence. Virus-induced gene silencing (VIGS) of GhMKK9 in cotton increased drought tolerance. These results suggest that GhMKK9 is a positive regulator and might be involved in drought-induced senescence in cotton. The results provide new insights into the genetic basis of cotton senescence and will be useful for improving cotton breeding in the future.

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.

Cotton Breeding in Australia: Meeting the Challenges of the 21st Century

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program is the sole breeding effort for cotton in Australia, developing high performing cultivars for the local industry which is worth∼AU$3 billion per annum. The program is supported by Cotton Breeding Australia, a Joint Venture between CSIRO and the program's commercial partner, Cotton Seed Distributors Ltd. (CSD). While the Australian industry is the focus, CSIRO cultivars have global impact in North America, South America, and Europe. The program is unique compared with many other public and commercial breeding programs because it focuses on diverse and integrated research with commercial outcomes. It represents the full research pipeline, supporting extensive long-term fundamental molecular research; native and genetically modified (GM) trait development; germplasm enhancement focused on yield and fiber quality improvements; integration of third-party GM traits; all culminating in the release of new commercial cultivars. This review presents evidence of past breeding successes and outlines current breeding efforts, in the areas of yield and fiber quality improvement, as well as the development of germplasm that is resistant to pests, diseases and abiotic stressors. The success of the program is based on the development of superior germplasm largely through field phenotyping, together with strong commercial partnerships with CSD and Bayer CropScience. These relationships assist in having a shared focus and ensuring commercial impact is maintained, while also providing access to markets, traits, and technology. The historical successes, current foci and future requirements of the CSIRO cotton breeding program have been used to develop a framework designed to augment our breeding system for the future. This will focus on utilizing emerging technologies from the genome to phenome, as well as a panomics approach with data management and integration to develop, test and incorporate new technologies into a breeding program. In addition to streamlining the breeding pipeline for increased genetic gain, this technology will increase the speed of trait and marker identification for use in genome editing, genomic selection and molecular assisted breeding, ultimately producing novel germplasm that will meet the coming challenges of the 21st Century.

Genomic prediction of cotton fibre quality and yield traits using Bayesian regression methods

Genomic selection or genomic prediction (GP) has increasingly become an important molecular breeding technology for crop improvement. GP aims to utilise genome-wide marker data to predict genomic breeding value for traits of economic importance. Though GP studies have been widely conducted in various crop species such as wheat and maize, its application in cotton, an essential renewable textile fibre crop, is still significantly underdeveloped. We aim to develop a new GP-based breeding system that can improve the efficiency of our cotton breeding program. This article presents a GP study on cotton fibre quality and yield traits using 1385 breeding lines from the Commonwealth Scientific and Industrial Research Organisation (CSIRO, Australia) cotton breeding program which were genotyped using a high-density SNP chip that generated 12,296 informative SNPs. The aim of this study was twofold: (1) to identify the models and data sources (i.e. genomic and pedigree) that produce the highest prediction accuracies; and (2) to assess the effectiveness of GP as a selection tool in the CSIRO cotton breeding program. The prediction analyses were conducted under various scenarios using different Bayesian predictive models. Results highlighted that the model combining genomic and pedigree information resulted in the best cross validated prediction accuracies: 0.76 for fibre length, 0.65 for fibre strength, and 0.64 for lint yield. Overall, this work represents the largest scale genomic selection studies based on cotton breeding trial data. Prediction accuracies reported in our study indicate the potential of GP as a breeding tool for cotton. The study highlighted the importance of incorporating pedigree and environmental factors in GP models to optimise the prediction performance.

Identification of Stable and Multiple Environment Interaction QTLs and Candidate Genes for Fiber Productive Traits Under Irrigated and Water Stress Conditions Using Intraspecific RILs of Gossypium hirsutum var. MCU5 X TCH1218

Cotton productivity under water-stressed conditions is controlled by multiple quantitative trait loci (QTL). Enhancement of these productivity traits under water deficit stress is crucial for the genetic improvement of upland cotton, Gossypium hirsutum. In the present study, we constructed a genetic map with 504 single nucleotide polymorphisms (SNPs) covering a total span length of 4,416 cM with an average inter-marker distance of 8.76 cM. A total of 181 intra-specific recombinant inbred lines (RILs) were derived from a cross between G. hirsutum var. MCU5 and TCH1218 were used. Although 2,457 polymorphic SNPs were detected between the parents using the CottonSNP50K assay, only 504 SNPs were found to be useful for the construction of the genetic map. In the SNP genotyping, a large number of SNPs showed either >20% missing data, duplication, or segregation distortion. However, the mapped SNPs of this study showed collinearity with the physical map of the reference genome (G. hirsutum var.TM-1), indicating that there was no chromosomal rearrangement within the studied mapping population. RILs were evaluated under multi-environments and seasons for which the phenotypic data were acquired. A total of 53 QTL controlling plant height (PH), number of sympodial branches, boll number (BN), and boll weight (BW) were dissected by QTL analysis under irrigated and water stress conditions. Additionally, it was found that nine QTL hot spots not only co-localized for more than one investigated trait but were also stable with major QTL, i.e., with > 10% of phenotypic variation. One QTL hotspot on chromosome 22 flanked by AX-182254626-AX-182264770 with a span length of 89.4 cM co-localized with seven major and stable QTL linked to a number of sympodial branches both under irrigated and water stress conditions. In addition, putative candidate genes associated with water stress in the QTL hotspots were identified. Besides, few QTL from the hotspots were previously reported across various genetic architects in cotton validating the potential applications of these identified QTL for cotton breeding and improvement. Thus, the major and stable QTL identified in the present study would improve the cotton productivity under water-limited environments through marker-assisted selection.

Inheritance, QTLs, and Candidate Genes of Lint Percentage in Upland Cotton

Cotton (Gossypium spp.) is an important natural fiber plant. Lint percentage (LP) is one of the most important determinants of cotton yield and is a typical quantitative trait with high variation and heritability. Many cotton LP genetic linkages and association maps have been reported. This work summarizes the inheritance, quantitative trait loci (QTLs), and candidate genes of LP to facilitate LP genetic study and molecular breeding. More than 1439 QTLs controlling LP have been reported. Excluding replicate QTLs, 417 unique QTLs have been identified on 26 chromosomes, including 243 QTLs identified at LOD >3. More than 60 are stable, major effective QTLs that can be used in marker-assisted selection (MAS). More than 90 candidate genes for LP have been reported. These genes encode MYB, HOX, NET, and other proteins, and most are preferentially expressed during fiber initiation and elongation. A putative molecular regulatory model of LP was constructed and provides the foundation for the genetic study and molecular breeding of LP.

The Pivotal Role of Major Chromosomes of Sub-Genomes A and D in Fiber Quality Traits of Cotton

Lack of precise information about the candidate genes involved in a complex quantitative trait is a major obstacle in the cotton fiber quality improvement, and thus, overall genetic gain in conventional phenotypic selection is low. Recent molecular interventions and advancements in genome sequencing have led to the development of high-throughput molecular markers, quantitative trait locus (QTL) fine mapping, and single nucleotide polymorphisms (SNPs). These advanced tools have resolved the existing bottlenecks in trait-specific breeding. This review demonstrates the significance of chromosomes 3, 7, 9, 11, and 12 of sub-genomes A and D carrying candidate genes for fiber quality. However, chromosome 7 carrying SNPs for stable and potent QTLs related to fiber quality provides great insights for fiber quality-targeted research. This information can be validated by marker-assisted selection (MAS) and transgene in Arabidopsis and subsequently in cotton.

Features of Chromosome Introgression from Gossypium barbadense L. into G. hirsutum L. during the Development of Alien Substitution Lines

The creation of G. barbadense L./G. hirsutum L. chromosome-substitution lines is an important method to transfer agronomically valuable traits from G. barbadense into G. hirsutum. In this study, 30 monosomic lines of G. hirsutum from the Cytogenetic Collection of Uzbekistan, created in the genotypic background of line L-458, were used in crosses with the G. barbadense line Pima 3-79 to create substitution lines. In the course of this work, new monosomic lines were identified for chromosome 12 and monotelodisome 6 of the A_tsubgenome and for chromosomes 17, 21, and 22 of the D_tsubgenome using chromosome-specific SSR markers and a well-defined tester set of cotton translocation lines (USA). Compared to those in the F₁ hybrids, a strong decrease in the crossing and setting rates was found in the BC₁F₁ backcross lines, with the substitution of chromosomes 2, 4, 6, 7, and 12 of the A_tsubgenome and 17, 18, 21, and 22 of the D_tsubgenome. The F₁ and BC₁F₁ offspring from interspecific crosses differed in their transmission of univalents. Despite the regular pairing of chromosomes and the high meiotic index, interspecific aneuploid hybrids were characterized by a decrease in pollen fertility, which may indicate hidden structural variability in these genomes that did not affect meiotic division. The identification of chromosomes using chromosome-specific SSR markers in the early stages of plant development has greatly accelerated the detection of monosomic plants. The analysis of morphobiological traits revealed that monosomic F₁ hybrids were more similar to the donor line, while BC₁F₁ hybrids were more similar to the recurrent parent but also showed previously undetected traits.

A method for low-coverage single-gamete sequence analysis demonstrates adherence to Mendel's first law across a large sample of human sperm

Recently published single-cell sequencing data from individual human sperm (n=41,189; 969-3377 cells from each of 25 donors) offer an opportunity to investigate questions of inheritance with improved statistical power, but require new methods tailored to these extremely low-coverage data (∼0.01× per cell). To this end, we developed a method, named rhapsodi, that leverages sparse gamete genotype data to phase the diploid genomes of the donor individuals, impute missing gamete genotypes, and discover meiotic recombination breakpoints, benchmarking its performance across a wide range of study designs. We then applied rhapsodi to the sperm sequencing data to investigate adherence to Mendel's Law of Segregation, which states that the offspring of a diploid, heterozygous parent will inherit either allele with equal probability. While the vast majority of loci adhere to this rule, research in model and non-model organisms has uncovered numerous exceptions whereby 'selfish' alleles are disproportionately transmitted to the next generation. Evidence of such 'transmission distortion' (TD) in humans remains equivocal in part because scans of human pedigrees have been under-powered to detect small effects. After applying rhapsodi to the sperm data and scanning for evidence of TD, our results exhibited close concordance with binomial expectations under balanced transmission. Together, our work demonstrates that rhapsodi can facilitate novel uses of inferred genotype data and meiotic recombination events, while offering a powerful quantitative framework for testing for TD in other cohorts and study systems.

CottonGen: The Community Database for Cotton Genomics, Genetics, and Breeding Research

Over the last eight years, the volume of whole genome, gene expression, SNP genotyping, and phenotype data generated by the cotton research community has exponentially increased. The efficient utilization/re-utilization of these complex and large datasets for knowledge discovery, translation, and application in crop improvement requires them to be curated, integrated with other types of data, and made available for access and analysis through efficient online search tools. Initiated in 2012, CottonGen is an online community database providing access to integrated peer-reviewed cotton genomic, genetic, and breeding data, and analysis tools. Used by cotton researchers worldwide, and managed by experts with crop-specific knowledge, it continuous to be the logical choice to integrate new data and provide necessary interfaces for information retrieval. The repository in CottonGen contains colleague, gene, genome, genotype, germplasm, map, marker, metabolite, phenotype, publication, QTL, species, transcriptome, and trait data curated by the CottonGen team. The number of data entries housed in CottonGen has increased dramatically, for example, since 2014 there has been an 18-fold increase in genes/mRNAs, a 23-fold increase in whole genomes, and a 372-fold increase in genotype data. New tools include a genetic map viewer, a genome browser, a synteny viewer, a metabolite pathways browser, sequence retrieval, BLAST, and a breeding information management system (BIMS), as well as various search pages for new data types. CottonGen serves as the home to the International Cotton Genome Initiative, managing its elections and serving as a communication and coordination hub for the community. With its extensive curation and integration of data and online tools, CottonGen will continue to facilitate utilization of its critical resources to empower research for cotton crop improvement.

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.

GBS-SNP and SSR based genetic mapping and QTL analysis for drought tolerance in upland cotton

A recombinant inbred line mapping population of intra-species upland cotton was generated from a cross between the drought-tolerant female parent (AS2) and the susceptible male parent (MCU13). A linkage map was constructed deploying 1,116 GBS-based SNPs and public domain-based 782 SSRs spanning a total genetic distance of 28,083.03 cM with an average chromosomal span length of 1,080.12 cM with inter-marker distance of 10.19 cM.A total of 19 quantitative trait loci (QTLs) were identified in nine chromosomes for field drought tolerance traits. Chromosomes 3 and 8 harbored important drought tolerant QTLs for chlorophyll stability index trait while for relative water content trait, three QTLs on chromosome 8 and one QTL each on chromosome 4, 12 were identified. One QTL on each chromosome 8, 5, and 7, and two QTLs on chromosome 15 linking to proline content were identified. For the nitrate reductase activity trait, two QTLs were identified on chromosome 3 and one on each chromosome 8, 13, and 26. To complement our QTL study, a meta-analysis was conducted along with the public domain database and resulted in a consensus map for chromosome 8. Under field drought stress, chromosome 8 harbored a drought tolerance QTL hotspot with two in-house QTLs for chlorophyll stability index (qCSI01, qCSI02) and three public domain QTLs (qLP.FDT_1, qLP.FDT_2, qCC.ST_3). Identified QTL hotspot on chromosome 8 could play a crucial role in exploring abiotic stress-associated genes/alleles for drought trait improvement.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12298-021-01041-y.

Population structure and genetic diversity of the Pee Dee cotton breeding program

Accelerated marker-assisted selection and genomic selection breeding systems require genotyping data to select the best parents for combining beneficial traits. Since 1935, the Pee Dee (PD) cotton germplasm enhancement program has developed an important genetic resource for upland cotton (Gossypium hirsutum L.), contributing alleles for improved fiber quality, agronomic performance, and genetic diversity. To date, a detailed genetic survey of the program's eight historical breeding cycles has yet to be undertaken. The objectives of this study were to evaluate genetic diversity across and within-breeding groups, examine population structure, and contextualize these findings relative to the global upland cotton gene pool. The CottonSNP63K array was used to identify 17,441 polymorphic markers in a panel of 114 diverse PD genotypes. A subset of 4597 markers was selected to decrease marker density bias. Identity-by-state pairwise distance varied substantially, ranging from 0.55 to 0.97. Pedigree-based estimates of relatedness were not very predictive of observed genetic similarities. Few rare alleles were present, with 99.1% of SNP alleles appearing within the first four breeding cycles. Population structure analysis with principal component analysis, discriminant analysis of principal components, fastSTRUCTURE, and a phylogenetic approach revealed an admixed population with moderate substructure. A small core collection (n < 20) captured 99% of the program's allelic diversity. Allele frequency analysis indicated potential selection signatures associated with stress resistance and fiber cell growth. The results of this study will steer future utilization of the program's germplasm resources and aid in combining program-specific beneficial alleles and maintaining genetic diversity.

Finding Needles in a Haystack: Using Geo-References to Enhance the Selection and Utilization of Landraces in Breeding for Climate-Resilient Cultivars of Upland Cotton (Gossypium hirsutum L.)

The genetic uniformity of cultivated cotton as a consequence of domestication and modern breeding makes it extremely vulnerable to abiotic challenges brought about by major climate shifts. To sustain productivity amidst worsening agro-environments, future breeding objectives need to seriously consider introducing new genetic variation from diverse resources into the current germplasm base of cotton. Landraces are genetically heterogeneous, population complexes that have been primarily selected for their adaptability to specific localized or regional environments. This makes them an invaluable genetic resource of novel allelic diversity that can be exploited to enhance the resilience of crops to marginal environments. The utilization of cotton landraces in breeding programs are constrained by the phenology of the plant and the lack of phenotypic information that can facilitate efficient selection of potential donor parents for breeding. In this review, the genetic value of cotton landraces and the major challenges in their utilization in breeding are discussed. Two strategies namely Focused Identification of Germplasm Strategy and Environmental Association Analysis that have been developed to effectively screen large germplasm collections for accessions with adaptive traits using geo-reference-based, mathematical modelling are highlighted. The potential applications of both approaches in mining available cotton landrace collections are also presented.

Genetic mapping and transcriptomic characterization of a new fuzzless-tufted cottonseed mutant

Fiber mutants are unique and valuable resources for understanding the genetic and molecular mechanisms controlling initiation and development of cotton fibers that are extremely elongated single epidermal cells protruding from the seed coat of cottonseeds. In this study, we reported a new fuzzless-tufted cotton mutant (Gossypium hirsutum) and showed that fuzzless-tufted near-isogenic lines (NILs) had similar agronomic traits and a higher ginning efficiency compared to their recurrent parents with normal fuzzy seeds. Genetic analysis revealed that the mutant phenotype is determined by a single incomplete dominant locus, designated N5. The mutation was fine mapped to an approximately 250-kb interval containing 33 annotated genes using a combination of bulked segregant sequencing, SNP chip genotyping, and fine mapping. Comparative transcriptomic analysis using 0-6 days post-anthesis (dpa) ovules from NILs segregating for the phenotypes of fuzzless-tufted (mutant) and normal fuzzy cottonseeds (wild-type) uncovered candidate genes responsible for the mutant phenotype. It also revealed that the flanking region of the N5 locus is enriched with differentially expressed genes (DEGs) between the mutant and wild-type. Several of those DEGs are members of the gene families with demonstrated roles in cell initiation and elongation, such as calcium-dependent protein kinase and expansin. The transcriptome landscape of the mutant was significantly reprogrammed in the 6 dpa ovules and, to a less extent, in the 0 dpa ovules, but not in the 2 and 4 dpa ovules. At both 0 and 6 dpa, the reprogrammed mutant transcriptome was mainly associated with cell wall modifications and transmembrane transportation, while transcription factor activity was significantly altered in the 6 dpa mutant ovules. These results imply a similar molecular basis for initiation of lint and fuzz fibers despite certain differences.

Detection of ploidy and chromosomal aberrations in commercial oil palm using high-throughput SNP markers

Karyotyping using high-density genome-wide SNP markers identified various chromosomal aberrations in oil palm (Elaeis guineensis Jacq.) with supporting evidence from the 2C DNA content measurements (determined using FCM) and chromosome counts. Oil palm produces a quarter of the world's total vegetable oil. In line with its global importance, an initiative to sequence the oil palm genome was carried out successfully, producing huge amounts of sequence information, allowing SNP discovery. High-capacity SNP genotyping platforms have been widely used for marker-trait association studies in oil palm. Besides genotyping, a SNP array is also an attractive tool for understanding aberrations in chromosome inheritance. Exploiting this, the present study utilized chromosome-wide SNP allelic distributions to determine the ploidy composition of over 1,000 oil palms from a commercial F₁ family, including 197 derived from twin-embryo seeds. Our method consisted of an inspection of the allelic intensity ratio using SNP markers. For palms with a shifted or abnormal distribution ratio, the SNP allelic frequencies were plotted along the pseudo-chromosomes. This method proved to be efficient in identifying whole genome duplication (triploids) and aneuploidy. We also detected several loss of heterozygosity regions which may indicate small chromosomal deletions and/or inheritance of identical by descent regions from both parents. The SNP analysis was validated by flow cytometry and chromosome counts. The triploids were all derived from twin-embryo seeds. This is the first report on the efficiency and reliability of SNP array data for karyotyping oil palm chromosomes, as an alternative to the conventional cytogenetic technique. Information on the ploidy composition and chromosomal structural variation can help to better understand the genetic makeup of samples and lead to a more robust interpretation of the genomic data in marker-trait association analyses.

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.

Rapid Mining of Candidate Genes for Verticillium Wilt Resistance in Cotton Based on BSA-Seq Analysis

Cotton is a globally important cash crop. Verticillium wilt (VW) is commonly known as "cancer" of cotton and causes serious loss of yield and fiber quality in cotton production around the world. Here, we performed a BSA-seq analysis using an F_2:3 segregation population to identify the candidate loci involved in VW resistance. Two QTLs (qvw-D05-1 and qvw-D05-2) related to VW resistance in cotton were identified using two resistant/susceptible bulks from the F₂ segregation population constructed by crossing the resistant cultivar ZZM2 with the susceptible cultivar J11. A total of 30stop-lost SNPs and 42 stop-gained SNPs, which included 17 genes, were screened in the qvw-D05-2 region by SnpEff analysis. Further analysis of the transcriptome data and qRT-PCR revealed that the expression level of Ghir_D05G037630 (designated as GhDRP) varied significantly at certain time points after infection with V. dahliae. The virus-induced gene silencing of GhDRP resulted in higher susceptibility of the plants to V. dahliae than the control, suggesting that GhDRP is involved in the resistance to V. dahlia infection. This study provides a method for rapid mining of quantitative trait loci and screening of candidate genes, as well as enriches the genomic information and gene resources for the molecular breeding of disease resistance in cotton.

Quantitative Trait Locus Analysis and Identification of Candidate Genes for Micronaire in an Interspecific Backcross Inbred Line Population of Gossypium hirsutum × Gossypium barbadense

Cotton is the most important fiber crop and provides indispensable natural fibers for the textile industry. Micronaire (MIC) is determined by fiber fineness and maturity and is an important component of fiber quality. Gossypium barbadense L. possesses long, strong and fine fibers, while upland cotton (Gossypium hirsutum L.) is high yielding with high MIC and widely cultivated worldwide. To identify quantitative trait loci (QTLs) and candidate genes for MIC in G. barbadense, a population of 250 backcross inbred lines (BILs), developed from an interspecific cross of upland cotton CRI36 × Egyptian cotton (G. barbadense) Hai7124, was evaluated in 9 replicated field tests. Based on a high-density genetic map with 7709 genotyping-by-sequencing (GBS)-based single-nucleotide polymorphism (SNP) markers, 25 MIC QTLs were identified, including 12 previously described QTLs and 13 new QTLs. Importantly, two stable MIC QTLs (qMIC-D03-2 on D03 and qMIC-D08-1 on D08) were identified. Of a total of 338 genes identified within the two QTL regions, eight candidate genes with differential expression between TM-1 and Hai7124 were identified. Our research provides valuable information for improving MIC in cotton breeding.

High-Temperature and Drought-Resilience Traits among Interspecific Chromosome Substitution Lines for Genetic Improvement of Upland Cotton

Upland cotton (Gossypium hirsutum L.) growth and development during the pre-and post-flowering stages are susceptible to high temperature and drought. We report the field-based characterization of multiple morpho-physiological and reproductive stress resilience traits in 11 interspecific chromosome substitution (CS) lines isogenic to each other and the inbred G. hirsutum line TM-1. Significant genetic variability was detected (p < 0.001) in multiple traits in CS lines carrying chromosomes and chromosome segments from CS-B (G. barbadense) and CS-T (G. tomentosum). Line CS-T15sh had a positive effect on photosynthesis (13%), stomatal conductance (33%), and transpiration (24%), and a canopy 6.8 °C cooler than TM-1. The average pollen germination was approximately 8% greater among the CS-B than CS-T lines. Based on the stress response index, three CS lines are identified as heat- and drought-tolerant (CS-T07, CS-B15sh, and CS-B18). The three lines demonstrated enhanced photosynthesis (14%), stomatal conductance (29%), transpiration (13%), and pollen germination (23.6%) compared to TM-1 under field conditions, i.e., traits that would expectedly enhance performance in stressful environments. The generated phenotypic data and stress-tolerance indices on novel CS lines, along with phenotypic methods, would help in developing new cultivars with improved resilience to the effects of global warming.

Use of Targeted Amplicon Sequencing in Peanut to Generate Allele Information on Allotetraploid Sub-Genomes

The use of molecular markers in plant breeding has become a routine practice, but the cost per accession can be a hindrance to the routine use of Quantitative Trait Loci (QTL) identification in breeding programs. In this study, we demonstrate the use of targeted re-sequencing as a proof of concept of a cost-effective approach to retrieve highly informative allele information, as well as develop a bioinformatics strategy to capture the genome-specific information of a polyploid species. SNPs were identified from alignment of raw transcriptome reads (2 × 50 bp) to a synthetic tetraploid genome using BWA followed by a GATK pipeline. Regions containing high polymorphic SNPs in both A genome and B genomes were selected as targets for the resequencing study. Targets were amplified using multiplex PCR followed by sequencing on an Illumina HiSeq. Eighty-one percent of the SNP calls in diploids and 68% of the SNP calls in tetraploids were confirmed. These results were also confirmed by KASP validation. Based on this study, we find that targeted resequencing technologies have potential for obtaining maximum allele information in allopolyploids at reduced cost.

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.

QTL mapping and GWAS for field kernel water content and kernel dehydration rate before physiological maturity in maize

Kernel water content (KWC) and kernel dehydration rate (KDR) are two main factors affecting maize seed quality and have a decisive influence on the mechanical harvest. It is of great importance to map and mine candidate genes related to KWCs and KDRs before physiological maturity in maize. 120 double-haploid (DH) lines constructed from Si287 with low KWC and JiA512 with high KWC were used as the mapping population. KWCs were measured every 5 days from 10 to 40 days after pollination, and KDRs were calculated. A total of 1702 SNP markers were used to construct a linkage map, with a total length of 1,309.02 cM and an average map distance of 0.77 cM. 10 quantitative trait loci (QTLs) and 27 quantitative trait nucleotides (QTNs) were detected by genome-wide composite interval mapping (GCIM) and multi-locus random-SNP-effect mixed linear model (mrMLM), respectively. One and two QTL hotspot regions were found on Chromosome 3 and 7, respectively. Analysis of the Gene Ontology showed that 2 GO terms of biological processes (BP) were significantly enriched (P ≤ 0.05) and 6 candidate genes were obtained. This study provides theoretical support for marker-assisted breeding of mechanical harvest variety in maize.

Genetic dissection of an allotetraploid interspecific CSSLs guides interspecific genetics and breeding in cotton

Background

The low genetic diversity of Upland cotton limits the potential for genetic improvement. Making full use of the genetic resources of Sea-island cotton will facilitate genetic improvement of widely cultivated Upland cotton varieties. The chromosome segments substitution lines (CSSLs) provide an ideal strategy for mapping quantitative trait loci (QTL) in interspecific hybridization.

Results

In this study, a CSSL population was developed by PCR-based markers assisted selection (MAS), derived from the crossing and backcrossing of Gossypium hirsutum (Gh) and G. barbadense (Gb), firstly. Then, by whole genome re-sequencing, 11,653,661 high-quality single nucleotide polymorphisms (SNPs) were identified which ultimately constructed 1211 recombination chromosome introgression segments from Gb. The sequencing-based physical map provided more accurate introgressions than the PCR-based markers. By exploiting CSSLs with mutant morphological traits, the genes responding for leaf shape and fuzz-less mutation in the Gb were identified. Based on a high-resolution recombination bin map to uncover genetic loci determining the phenotypic variance between Gh and Gb, 64 QTLs were identified for 14 agronomic traits with an interval length of 158 kb to 27 Mb. Surprisingly, multiple alleles of Gb showed extremely high value in enhancing cottonseed oil content (SOC).

Conclusions

This study provides guidance for studying interspecific inheritance, especially breeding researchers, for future studies using the traditional PCR-based molecular markers and high-throughput re-sequencing technology in the study of CSSLs. Available resources include candidate position for controlling cotton quality and quantitative traits, and excellent breeding materials. Collectively, our results provide insights into the genetic effects of Gb alleles on the Gh, and provide guidance for the utilization of Gb alleles in interspecific breeding.

Recommendations for Choosing the Genotyping Method and Best Practices for Quality Control in Crop Genome-Wide Association Studies

High-throughput genotyping boosts genome-wide association studies (GWAS) in crop species, leading to the identification of single-nucleotide polymorphisms (SNPs) associated with economically important traits. Choosing a cost-effective genotyping method for crop GWAS requires careful examination of several aspects, namely, the purpose and the scale of the study, crop-specific genomic features, and technical and economic matters associated with each genotyping option. Once genotypic data have been obtained, quality control (QC) procedures must be applied to avoid bias and false signals in genotype–phenotype association tests. QC for human GWAS has been extensively reviewed; however, QC for crop GWAS may require different actions, depending on the GWAS population type. Here, we review most popular genotyping methods based on next-generation sequencing (NGS) and array hybridization, and report observations that should guide the investigator in the choice of the genotyping method for crop GWAS. We provide recommendations to perform QC in crop species, and deliver an overview of bioinformatics tools that can be used to accomplish all needed tasks. Overall, this work aims to provide guidelines to harmonize those procedures leading to SNP datasets ready for crop GWAS.

Multiple QTL Mapping in Autopolyploids: A Random-Effect Model Approach with Application in a Hexaploid Sweetpotato Full-Sib Population

In developing countries, the sweetpotato, Ipomoea batatas (L.) Lam. [Formula: see text], is an important autopolyploid species, both socially and economically. However, quantitative trait loci (QTL) mapping has remained limited due to its genetic complexity. Current fixed-effect models can fit only a single QTL and are generally hard to interpret. Here, we report the use of a random-effect model approach to map multiple QTL based on score statistics in a sweetpotato biparental population ('Beauregard' × 'Tanzania') with 315 full-sibs. Phenotypic data were collected for eight yield component traits in six environments in Peru, and jointly adjusted means were obtained using mixed-effect models. An integrated linkage map consisting of 30,684 markers distributed along 15 linkage groups (LGs) was used to obtain the genotype conditional probabilities of putative QTL at every centiMorgan position. Multiple interval mapping was performed using our R package QTLpoly and detected a total of 13 QTL, ranging from none to four QTL per trait, which explained up to 55% of the total variance. Some regions, such as those on LGs 3 and 15, were consistently detected among root number and yield traits, and provided a basis for candidate gene search. In addition, some QTL were found to affect commercial and noncommercial root traits distinctly. Further best linear unbiased predictions were decomposed into additive allele effects and were used to compute multiple QTL-based breeding values for selection. Together with quantitative genotyping and its appropriate usage in linkage analyses, this QTL mapping methodology will facilitate the use of genomic tools in sweetpotato breeding as well as in other autopolyploids.

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.

A genome-wide association study uncovers consistent quantitative trait loci for resistance to Verticillium wilt and Fusarium wilt race 4 in the US Upland cotton

A high-resolution GWAS detected consistent QTL for resistance to Verticillium wilt and Fusarium wilt race 4 in 376 U.S. Upland cotton accessions based on six independent replicated greenhouse tests. Verticillium wilt (VW, caused by Verticillium dahliae Kleb.) and Fusarium wilt (FOV, caused by Fusarium oxysporum f.sp. vasinfectum Atk. Sny & Hans) are the most important soil-borne fungal diseases in cotton. To augment and refine resistance quantitative trait loci (QTL), we conducted a genome-wide association study (GWAS) using high-density genotyping with the CottonSNP63K array. Resistance of 376 US Upland cotton accessions to a defoliating VW and virulent FOV4 was evaluated in four and two independent replicated greenhouse tests, respectively. A total of 15 and 13 QTL for VW and FOV4 resistances were anchored by 30 (on five chromosomes) and 56 (on six chromosomes) significant single nucleotide polymorphic (SNPs) markers, respectively. QTL on c8, c10, c16, and c21 were consistent in two or more tests for VW resistance, while two QTL on c8 and c14 were consistent for FOV4 resistance in two tests. Two QTL clusters on c16 and c19 were observed for both VW and FOV4 resistance, suggesting that these genomic regions may harbor genes in response to both diseases. Using BLAST search against the sequenced TM-1 genome, 30 and 35 candidate genes were identified on four QTL for VW resistance and on three QTL for FOV4 resistance, respectively. These genomic regions were rich in NBS-LRR genes presented in clusters. The results create opportunities for further studies to determine the correlations of field resistance with these QTL, molecular examinations of VW and FOV4 resistances, marker-assisted selection (MAS) and eventual cloning of QTL for disease resistance in cotton.

Introgression Leads to Genomic Divergence and Responsible for Important Traits in Upland Cotton

Understanding the genetic diversity and population structure of germplasms is essential when selecting parents for crop breeding. The genomic changes that occurred during the domestication and improvement of Upland cotton (Gossypium hirsutum) remains poorly understood. Besides, the available genetic resources from cotton cultivars are limited. By applying restriction site-associated DNA marker sequencing (RAD-seq) technology to 582 tetraploid cotton accessions, we confirmed distinct genomic regions on chromosomes A06 and A08 in Upland cotton cultivar subgroups. Based on the pedigree, reported QTLs, introgression analyses, and genome-wide association study (GWAS), we suggest that these divergent regions might have resulted from the introgression of exotic lineages of G. hirsutum landraces and their wild relatives. These regions were the typical genomic signatures that might be responsible for maturity and fiber quality on chromosome A06 and chromosome A08, respectively. Moreover, these genomic regions are located in the putative pericentromeric regions, implying that their application will be challenging. In the study, based on high-density SNP markers, we reported two genomic signatures on chromosomes A06 and A08, which might originate from the introgression events in the Upland cotton population. Our study provides new insights for understanding the impact of historic introgressions on population divergence and important agronomic traits of modern Upland cotton cultivars.

Genome-wide quantitative trait loci reveal the genetic basis of cotton fibre quality and yield-related traits in a Gossypium hirsutum recombinant inbred line population

Cotton is widely cultivated globally because it provides natural fibre for the textile industry and human use. To identify quantitative trait loci (QTLs)/genes associated with fibre quality and yield, a recombinant inbred line (RIL) population was developed in upland cotton. A consensus map covering the whole genome was constructed with three types of markers (8295 markers, 5197.17 centimorgans (cM)). Six fibre yield and quality traits were evaluated in 17 environments, and 983 QTLs were identified, 198 of which were stable and mainly distributed on chromosomes 4, 6, 7, 13, 21 and 25. Thirty-seven QTL clusters were identified, in which 92.8% of paired traits with significant medium or high positive correlations had the same QTL additive effect directions, and all of the paired traits with significant medium or high negative correlations had opposite additive effect directions. In total, 1297 genes were discovered in the QTL clusters, 414 of which were expressed in two RNA-Seq data sets. Many genes were discovered, 23 of which were promising candidates. Six important QTL clusters that included both fibre quality and yield traits were identified with opposite additive effect directions, and those on chromosome 13 (qClu-chr13-2) could increase fibre quality but reduce yield; this result was validated in a natural population using three markers. These data could provide information about the genetic basis of cotton fibre quality and yield and help cotton breeders to improve fibre quality and yield simultaneously.

Dissecting the genetic architecture of seed-cotton and lint yields in Upland cotton using genome-wide association mapping

Seed-cotton yield (SY) and lint yield (LY) are the most important yield traits of cotton. Thus, it is critical to dissect their genetic architecture. Upland cotton (Gossypium hirsutum) is widely grown worldwide. In this study, a genome-wide association mapping was performed based on the CottonSNP80K array to dissect the genetic architecture of SY and LY in Upland cotton. Twenty-three significant associations were detected within four environments, including 11 associated with SY and 12 associated with LY. Seven single nucleotide polymorphisms (SNPs), TM234, TM237, TM247, TM255, TM256, TM263, and TM264, were co-associated with the two traits, which may indicate pleiotropy or intergenic tight linkages. Five SNPs, TM13332, TM39771, TM57119, TM81653, and TM81660, were coincided with those of previous reports and could be used in marker-assisted selection. Combining functional annotations with expression analyses of the genes identified within 400 kb of the significantly associated SNPs, we hypothesize that the three genes, Gh_D05G1077 and Gh_D13G1571 for SY, and Gh_A11G0775 for LY, may have the potential to increase cotton yield. The results would provide useful information for understanding the genetic basis of yield traits in Upland cotton and for facilitating its high-yield breeding through molecular design.

High-density linkage map construction and QTL analyses for fiber quality, yield and morphological traits using CottonSNP63K array in upland cotton (Gossypium hirsutum L.)

Background

Improving fiber quality and yield are the primary research objectives in cotton breeding for enhancing the economic viability and sustainability of Upland cotton production. Identifying the quantitative trait loci (QTL) for fiber quality and yield traits using the high-density SNP-based genetic maps allows for bridging genomics with cotton breeding through marker assisted and genomic selection. In this study, a recombinant inbred line (RIL) population, derived from cross between two parental accessions, which represent broad allele diversity in Upland cotton, was used to construct high-density SNP-based linkage maps and to map the QTLs controlling important cotton traits.

Results

Molecular genetic mapping using RIL population produced a genetic map of 3129 SNPs, mapped at a density of 1.41 cM. Genetic maps of the individual chromosomes showed good collinearity with the sequence based physical map. A total of 106 QTLs were identified which included 59 QTLs for six fiber quality traits, 38 QTLs for four yield traits and 9 QTLs for two morphological traits. Sub-genome wide, 57 QTLs were mapped in A sub-genome and 49 were mapped in D sub-genome. More than 75% of the QTLs with favorable alleles were contributed by the parental accession NC05AZ06. Forty-six mapped QTLs each explained more than 10% of the phenotypic variation. Further, we identified 21 QTL clusters where 12 QTL clusters were mapped in the A sub-genome and 9 were mapped in the D sub-genome. Candidate gene analyses of the 11 stable QTL harboring genomic regions identified 19 putative genes which had functional role in cotton fiber development.

Conclusion

We constructed a high-density genetic map of SNPs in Upland cotton. Collinearity between genetic and physical maps indicated no major structural changes in the genetic mapping populations. Most traits showed high broad-sense heritability. One hundred and six QTLs were identified for the fiber quality, yield and morphological traits. Majority of the QTLs with favorable alleles were contributed by improved parental accession. More than 70% of the mapped QTLs shared the similar map position with previously reported QTLs which suggest the genetic relatedness of Upland cotton germplasm. Identification of QTL clusters could explain the correlation among some fiber quality traits in cotton. Stable and major QTLs and QTL clusters of traits identified in the current study could be the targets for map-based cloning and marker assisted selection (MAS) in cotton breeding. The genomic region on D12 containing the major stable QTLs for micronaire, fiber strength and lint percentage could be potential targets for MAS and gene cloning of fiber quality traits in cotton.

Enhancing Upland cotton for drought resilience, productivity, and fiber quality: comparative evaluation and genetic dissection

To provision the world sustainably, modern society must increase overall crop production, while conserving and preserving natural resources. Producing more with diminishing water resources is an especially daunting endeavor. Toward the goal of genetically improving drought resilience of cultivated Upland cotton (Gossypium hirsutum L.), this study addresses the genetics of differential yield components referred to as productivity and fiber quality traits under regular-water versus low-water (LW) field conditions. We used ten traits to assess water stress deficit, which included six productivity and four fiber quality traits on two recombinant inbred line (RIL) populations from reciprocally crossed cultivars, Phytogen 72 and Stoneville 474. To facilitate genetic inferences, we genotyped RILs with the CottonSNP63K array, assembled high-density linkage maps of over 7000 SNPs and then analyzed quantitative trait variations. Analysis of variance revealed significant differences for all traits (p < 0.05) in these RIL populations. Although the LW irrigation regime significantly reduced all traits, except lint percent, the RILs exhibited a broad phenotypic spectrum of heritable differences across the water regimes. Transgressive segregation occurred among the RILs, suggesting the possibility of genetic gain through phenotypic selection for drought resilience and perhaps through marker-based selection. Analyses revealed more than 150 quantitative trait loci (QTLs) associated with productivity and fiber quality traits (p < 0.005) on different genomic regions of the cotton genome. The multiple-QTL models analysis with LOD > 3.0 detected 21 QTLs associated with productivity and 22 QTLs associated with fiber quality. For fiber traits, strong clustering and QTL associations occurred in c08 and its homolog c24 as well as c10, c14, and c21. Using contemporary genome sequence assemblies and bioinformatically related information, the identification of genomic regions associated with responses to plant stress/drought elevates the possibility of using marker-assisted and omics-based selection to enhance breeding for drought resilient cultivars and identifying candidate genes and networks. RILs with different responses to drought indicated that it is possible to maintain high fiber quality under LW conditions or reduce the of LW impact on quality. The heritable variation among elite bi-parental RILs for productivity and quality under field drought conditions, and their association of QTLs, and thus specific genomic regions, indicate opportunities for breeding-based gains in water resource conservation, i.e., enhancing cotton's agricultural sustainability.

Development of an Axiom Sugarcane100K SNP array for genetic map construction and QTL identification

An Axiom Sugarcane100K SNP array has been designed and successfully utilized to construct the sugarcane genetic map and to identify the QTLs associated with SCYLV resistance. To accelerate genetic studies in sugarcane, an Axiom Sugarcane100K single-nucleotide polymorphism (SNP) array was designed and customized in this study. Target enrichment sequencing 300 sugarcane accessions selected from the world collection of sugarcane and related grass species yielded more than four million SNPs, from which a total of 31,449 single-dose (SD) SNPs and 68,648 low-dosage (33,277 SD and 35,371 double dose) SNPs from two datasets, respectively, were selected and tiled on Affymetrix Axiom SNP array. Most of selected SNPs (91.77%) were located within genic regions (12,935 genes), with an average of 7.1 SNPs/gene according to sorghum gene models. This array was used to genotype 469 sugarcane clones, including one F₁ population derived from the cross between Green German and IND81-146, one selfing population derived from CP80-1827, and 11 diverse sugarcane accessions as controls. Results of genotyping revealed a high polymorphic SNP rate (77.04%) among the 469 samples. Three linkage maps were constructed by using SD SNP markers, including a genetic map for Green German with 3482 SD SNP markers spanning 3336 cM, a map for IND81-146 with 1513 SD SNP markers spanning 2615 cM, and a map for CP80-1827 with 536 SD SNP markers spanning 3651 cM. Quantitative trait loci (QTL) analysis identified 18 QTLs controlling Sugarcane yellow leaf virus resistance segregating in the two mapping populations, harboring 27 disease-resistant genes. This study demonstrated the successful development and utilization of a SNP array as an efficient genetic tool for high-throughput genotyping in highly polyploid sugarcane.

PLANET-SNP pipeline: PLants based ANnotation and Establishment of True SNP pipeline

Acute prediction of SNPs (Single Nucleotide Polymorphisms) from high throughput sequencing data is a challenging problem, having potential to explore possible variation within plants species. For the extraction of profitable information from bulk of data, machine learning (ML) could lead to development of accurate model based on the learning of prior information. We performed state of art, in-depth learning on six different plant species. Comparative evaluation of five different algorithms showed that Random Forest substantially outperformed in selection of potential SNPs, with markedly improved prediction accuracy via 10-fold cross validation technique and integrated in system known as PLANET-SNP. We present the accurate method to extract the potential SNPs with user specific customizable parameters. It will facilitate the identification of efficient and functional SNPs in most easy and intuitive way. PLANET-SNP pipeline is very flexible in terms of data input and output formats. PLANET-SNP Pipeline is available at http://www.ncgd.nbri.res.in/PLANET-SNP-Pipeline.aspx.

Genome-wide association study of the oil content in upland cotton (Gossypium hirsutum L.) and identification of GhPRXR1, a candidate gene for a stable QTLqOC-Dt5-1

Cottonseed oil is one of the most important renewable resources for edible oil and biodiesel. To detect QTLs associated with cottonseed oil content (OC) and identify candidate genes that regulate oil biosynthesis, a panel of upland cotton germplasm lines was selected among those previously used to perform GWASs in China. In the present study, 13 QTLs associated with 53 common SNPs on 13 chromosomes were identified in multiple environments based on 15,369 polymorphic SNPs using the Cotton63 KSNP array. Of these, the OC QTL qOC-Dt5-1 delineated by nine SNPs occurred in a confidence interval of 4 SSRs with previously reported OC QTLs. A combined transcriptome and qRT-PCR analysis revealed that a peroxidase gene (GhPRXR1) was predominantly expressed during the middle-late stage (20-35 days post anthesis) of ovule development. The overexpression of GhPRXR1 in yeast significantly increased the OC by 20.01-37.25 %. Suppression of GhPRXR1 gene expression in the virus-induced gene-silenced cotton reduced the OC by 18.11%. Our results contribute to identifying more OC QTLs and verifying a candidate gene that influences cottonseed oil biosynthesis.

Identification of genomewide single-nucleotide polymorphisms associated with presummer, summer and autumn bolls in upland cotton

Presummer, summer, and autumn bolls (PSB, SB and AB, respectively) in cotton are related to both maturity and yield. Therefore, studying their genetic basis is important for breeding purposes. In this study, we developed an association analysis panel consisting of 169 upland cotton accessions. The panel was phenotyped for PSB, SB and AB across four environments and genotyped using a Cotton SNP80K array. Single-nucleotide polymorphisms (SNPs) associated with these three traits were identified by a genomewide association study. A total of 53,848 high-quality SNPs were screened, and 91 significant trait-associated SNPs were detected. Of the 91 SNPs 33 were associated with PSB, 21 with SB and 37 with AB. Three SNPs for PSB (TM10410, TM13158 and TM21762) and five for AB (TM13730, TM13733, TM13834, TM29666 and TM43214) were repeatedly detected in two environments or by two methods. These eight SNPs exhibited high phenotypic variation of more than 10%, thus allowing their use formarker-assisted selection. The candidate genes for target traits were also identified. These findings provide a theoretical basis for the improvement of early maturity and yield in cotton breeding programmes.