Quantitative genomics-enabled selection for simultaneous improvement of lint yield and seed traits in cotton (Gossypium hirsutum L.).

KEY MESSAGE: A Bayesian linkage disequilibrium-based multiple-locus mixed model identified QTLs for fibre, seed and oil traits and predicted breeding worthiness of test lines, enabling their simultaneous improvement in cotton. Improving cotton seed and oil yields has become increasingly important while continuing to breed for higher lint yield. In this study, a novel Bayesian linkage disequilibrium-based multiple-locus mixed model was developed for QTL identification and genomic prediction (GP). A multi-parent population consisting of 256 recombinant inbred lines, derived from four elite cultivars with distinct combinations of traits, was used in the analysis of QTLs for lint percentage, seed index, lint index and seed oil content and their interrelations. All four traits were moderately heritable and correlated but with no large influence of genotype × environment interactions across multiple seasons. Seven to ten major QTLs were identified for each trait with many being adjacent or overlapping for different trait pairs. A fivefold cross-validation of the model indicated prediction accuracies of 0.46-0.62. GP results based on any two-season phenotypes were strongly correlated with phenotypic means of a pooled analysis of three-season experiments (r = 0.83-0.92). When used for selection of improvement in lint, seed and oil yields, GP captured 40-100% of individuals with comparable lint yields of those selected based on the three-season phenotypic results. Thus, this quantitative genomics-enabled approach can not only decipher the genomic variation underlying lint, seed and seed oil traits and their interrelations, but can provide predictions for their simultaneous improvement. We discuss future breeding strategies in cotton that will enhance the entire value of the crop, not just its fibre.

Genetic Mapping and Characterization of Verticillium Wilt Resistance in a Recombinant Inbred Population of Upland Cotton.

Verticillium wilt (VW) is an important and widespread disease of cotton and once established is long-lived and difficult to manage. In Australia, the non-defoliating pathotype of Verticillium dahliae is the most common, and extremely virulent. Breeding cotton varieties with increased VW resistance is the most economical and effective method of controlling this disease and is greatly aided by understanding the genetics of resistance. This study aimed to investigate VW resistance in 240 F7 recombinant inbred lines (RIL) derived from a cross between MCU-5, which has good resistance, and Siokra 1-4, which is susceptible. Using a controlled environment bioassay, we found that resistance based on plant survival or shoot biomass was complex but with major contributions from chromosomes D03 and D09, with genomic prediction analysis estimating a prediction accuracy of 0.73 based on survival scores compared to 0.36 for shoot biomass. Transcriptome analysis of MCU-5 and Siokra 1-4 roots uninfected or infected with V. dahliae revealed that the two cultivars displayed very different root transcriptomes and responded differently to V. dahliae infection. Ninety-nine differentially expressed genes were located in the two mapped resistance regions and so are potential candidates for further identifying the genes responsible for VW resistance.

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.

Characterization and Genetic Mapping of Black Root Rot Resistance in Gossypium arboreum L.

Black root rot (BRR) is an economically important disease of cotton and other crops, especially in cooler regions with short growing seasons. Symptoms include black discoloration of the roots, reduced number of lateral roots and stunted or slow plant growth. The cultivated tetraploid Gossypium species are susceptible to BRR. Resistance to BRR was identified in G. arboreum accession BM13H and is associated with reduced and restricted hyphal growth and less sporulation. Transcriptome analysis indicates that BM13H responds to infection at early time points 2- and 3-days post-inoculation, but by day 5, few differentially expressed genes are observed between infected and uninfected roots. Inheritance of BM13H resistance to BRR was evaluated in an F6 recombinant inbred population and shows a single semi-dominant locus conferring resistance that was fine mapped to a region on chromosome 1, containing ten genes including five putative resistance-like genes.

Improved lint yield under field conditions in cotton over-expressing transcription factors regulating fibre initiation.

Only a few transcription factors (TFs) regulating which cells of the ovule epidermis differentiate into lint fibres have been identified in cotton (Gossypium hirsutum L.). In this study, the effect on lint yield and fibre quality of over-expressing three TFs in cotton, GhHD-1, GhMYB25 and GhMYB25Like, and their double and triple combinations, were evaluated in field experiments over two seasons. The expression of single or stacked TFs were all driven either by an ovule-specific promoter, FBP 7, or a constitutive promoter, Stunt 7, in a Coker 315 background. TF type, either singly or in combination, was found to be the most significant factor affecting lint yield. Among 64 transgenic lines tested, seven were higher yielding than null segregant lines in one or both seasons and were all from the sets with single and double over-expressed TF combinations. A reduced yield was associated with the set of triple combinations. The two most stable high yielding lines across the seasons recorded 12-22% higher yields than the nulls, although were not competitive to locally adapted commercial controls. Over-expression of TFs singly or in combination did not significantly alter fibre length and strength, but sometimes increased fibre micronaire. There were positive relationships between lint yield and lint percentage and lint yield and fibre density amongst the transgenic lines. Our preliminary results suggest that manipulating TF expression, either singly or in pairs, can increase the density of fibres initiated on developing seeds and fibre yields under field conditions while maintaining overall fibre quality.

Correction to: Improved lint yield under field conditions in cotton over-expressing transcription factors regulating fibre initiation.

Due to an unfortunate misunderstanding, an extra middle initial erroneously appeared in the original publication and the full name of the first author should read Shi Ming Liu.

Genetic Identification and Transcriptome Analysis of Lintless and Fuzzless Traits in Gossypium arboreum L.

Cotton fibres, as single cells arising from the seed coat, can be classified as lint and fuzz according to their final length. Gossypium arboreum is a cultivated diploid cotton species and a potential donor of the A subgenome of the more widely grown tetraploid cottons. In this study, we performed genetic studies on one lintless and seven fuzzless G. arboreum accessions. Through association and genetic linkage analyses, a recessive locus on Chr06 containing GaHD-1 was found to be the likely gene underlying the lintless trait. GaHD-1 carried a mutation at a splicing acceptor site that resulted in alternative splicing and a deletion of 247 amino acid from the protein. The regions containing GaGIR1 and GaMYB25-like were found to be associated with fuzz development in G. arboreum, with the former being the major contributor. Comparative transcriptome analyses using 0-5 days post-anthesis (dpa) ovules from lintless, fuzzless, and normal fuzzy seed G. arboreum accessions revealed gene modules and hub genes potentially important for lint and fuzz initiation and development. Three significant modules and 26 hub genes associated with lint fibre initiation were detected by weighted gene co-expression network analysis. Similar analyses identified three vital modules and 10 hub genes to be associated with fuzz development. The findings in this study contribute to understanding the complex molecular mechanism(s) regulating fibre initiation and development and indicate that G. arboreum may have fibre developmental pathways different from tetraploid cotton. It also provides candidate genes for further investigation into modifying fibre development in G. arboreum.

Genetic dissection of the fuzzless seed trait in Gossypium barbadense.

Cotton fibres are single-celled trichomes arising from the epidermal cells of the seed coat and may be either long (lint) or very short (fuzz). The dominant fuzzless N1 of Gossypium hirsutum is a defective allele of the At-subgenome homoeolog of MYB25-like, but the genetic components underlying the recessive fuzzless trait from G. barbadense (Gb) are unknown. We have identified five genetic loci, including a major contributing locus containing MYB25-like_Dt, associated with Gb fuzzless seeds based on genotyping of fuzzy and fuzzless near isogenic lines (NILs) from an interspecies cross (G. barbadense × G. hirsutum). At 3 d post-anthesis when fuzz fibres are initiating, expression of MYB25-like_Dt was significantly lower in fuzzless NILs than in fuzzy seeded NILs, while higher MYB25-like_Dt expression was associated with more seed fuzz across different cotton genotypes. Phenotypic and genotypic analysis of MYB25-like homoeoalleles in cottons showing different fibre phenotypes and their crossing progeny indicated that both MYB25-like_At and MYB25-like_Dt are associated with lint development, and that fuzz development is mainly determined by the expression level of MYB25-like_Dt at ~3 d post-anthesis. Expression of Gb fuzzless seeds depends on genetic background and interactions amongst the multiple loci identified. MYB25-like_Dt is one of the best candidates for N2.

Integrated mapping and characterization of the gene underlying the okra leaf trait in Gossypium hirsutum L.

Diverse leaf morphology has been observed among accessions of Gossypium hirsutum, including okra leaf, which has advantages and disadvantages in cotton production. The okra leaf locus has been mapped to chromosome 15 of the Dt subgenome, but the underlying gene has yet to be identified. In this study, we used a combination of targeted association analysis, F2 population-based fine mapping, and comparative sequencing of orthologues to identify a candidate gene underlying the okra leaf trait in G. hirsutum. The okra leaf gene identified, GhOKRA, encoded a homeodomain leucine-zipper class I protein, whose closely related genes in several other plant species have been shown to be involved in regulating leaf morphology. The transcript levels of GhOKRA in shoot apices were positively correlated with the phenotypic expression of the okra leaf trait. Of the multiple sequence variations observed in the coding region among GrOKRA of Gossypium raimondii and GhOKRA-Dt of normal and okra/superokra leaf G. hirsutum accessions, a non-synonymous substitution near the N terminus and the variable protein sequences at the C terminus may be related to the leaf shape difference. Our results suggest that both transcription and protein activity of GhOKRA may be involved in regulating leaf shape. Furthermore, we found that non-reciprocal homoeologous recombination, or gene conversion, may have played a role in the origin of the okra leaf allele. Our results provided tools for further investigating and understanding the fundamental biological processes that are responsible for the cotton leaf shape variation and will help in the design of cotton plants with an ideal leaf shape for enhanced cotton production.

Dissecting the major genetic components underlying cotton lint development.

Numerous genetic loci and several functionally characterized genes have been linked to determination of lint percentage (lint%), one of the most important cotton yield components, but we still know little about the major genetic components underlying lint%. Here, we first linked the genetic loci containing MYB25-like_At and HD1_At to the fiberless seed trait of 'SL1-7-1' and found that MYB25-like_At and HD1_At were very lowly expressed in 'SL1-7-1' ovules during fiber initiation. We then dissected the genetic components involved in determination of lint% using segregating populations derived from crosses of fuzzless mutants and intermediate segregants with different lint%, which not only confirmed the HD1_At locus but identified the HD1_Dt locus as being the major genetic components contributing to fiber initiation and lint%. The segregating populations also allowed us to evaluate the relative contributions of MYB25-like_At, MYB25-like_Dt, HD1_At, and HD1_Dt to lint%. Haplotype analysis of an Upland cotton (Gossypium hirsutum) population with 723 accessions (including 81 fuzzless seed accessions) showed that lint% of the accessions with the LP allele (higher lint%) at MYB25-like_At, MYB25-like_Dt, or HD1_At was significantly higher than that with the lp allele (lower lint%). The lint% of the Upland cotton accessions with 3 or 4 LP alleles at MYB25-like and HD1 was significantly higher than that with 2 LP alleles. The results prompted us to propose a strategy for breeding high-yielding cotton varieties, i.e. pyramiding the LP alleles of MYB25-like and HD1 with new lint% LP alleles without negative impact on seed size and fiber quality.

HairNet2: deep learning to quantify cotton leaf hairiness, a complex genetic and environmental trait.

BACKGROUND: Cotton accounts for 80% of the global natural fibre production. Its leaf hairiness affects insect resistance, fibre yield, and economic value. However, this phenotype is still qualitatively assessed by visually attributing a Genotype Hairiness Score (GHS) to a leaf/plant, or by using the HairNet deep-learning model which also outputs a GHS. Here, we introduce HairNet2, a quantitative deep-learning model which detects leaf hairs (trichomes) from images and outputs a segmentation mask and a Leaf Trichome Score (LTS). RESULTS: Trichomes of 1250 images were annotated (AnnCoT) and a combination of six Feature Extractor modules and five Segmentation modules were tested alongside a range of loss functions and data augmentation techniques. HairNet2 was further validated on the dataset used to build HairNet (CotLeaf-1), a similar dataset collected in two subsequent seasons (CotLeaf-2), and a dataset collected on two genetically diverse populations (CotLeaf-X). The main findings of this study are that (1) leaf number, environment and image position did not significantly affect results, (2) although GHS and LTS mostly correlated for individual GHS classes, results at the genotype level revealed a strong LTS heterogeneity within a given GHS class, (3) LTS correlated strongly with expert scoring of individual images. CONCLUSIONS: HairNet2 is the first quantitative and scalable deep-learning model able to measure leaf hairiness. Results obtained with HairNet2 concur with the qualitative values used by breeders at both extremes of the scale (GHS 1-2, and 5-5+), but interestingly suggest a reordering of genotypes with intermediate values (GHS 3-4+). Finely ranking mild phenotypes is a difficult task for humans. In addition to providing assistance with this task, HairNet2 opens the door to selecting plants with specific leaf hairiness characteristics which may be associated with other beneficial traits to deliver better varieties.

Genome resources for three modern cotton lines guide future breeding efforts.

Cotton (Gossypium hirsutum L.) is the key renewable fibre crop worldwide, yet its yield and fibre quality show high variability due to genotype-specific traits and complex interactions among cultivars, management practices and environmental factors. Modern breeding practices may limit future yield gains due to a narrow founding gene pool. Precision breeding and biotechnological approaches offer potential solutions, contingent on accurate cultivar-specific data. Here we address this need by generating high-quality reference genomes for three modern cotton cultivars ('UGA230', 'UA48' and 'CSX8308') and updating the 'TM-1' cotton genetic standard reference. Despite hypothesized genetic uniformity, considerable sequence and structural variation was observed among the four genomes, which overlap with ancient and ongoing genomic introgressions from 'Pima' cotton, gene regulatory mechanisms and phenotypic trait divergence. Differentially expressed genes across fibre development correlate with fibre production, potentially contributing to the distinctive fibre quality traits observed in modern cotton cultivars. These genomes and comparative analyses provide a valuable foundation for future genetic endeavours to enhance global cotton yield and sustainability.

The Past, Present, and Future of Host Plant Resistance in Cotton: An Australian Perspective.

Cotton is a key global fiber crop. However, yield potential is limited by the presence of endemic and introduced pests and diseases. The introduction of host plant resistance (HPR), defined as the purposeful use of resistant crop cultivars to reduce the impact of pests and diseases, has been a key breeding target for the Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program. The program has seen success in releasing cultivars resistant to Bacterial blight, Verticillium wilt, Fusarium wilt, and Cotton bunchy top. However, emerging biotic threats such as Black root rot and secondary pests, are becoming more frequent in Australian cotton production systems. The uptake of tools and breeding methods, such as genomic selection, high throughput phenomics, gene editing, and landscape genomics, paired with the continued utilization of sources of resistance from Gossypium germplasm, will be critical for the future of cotton breeding. This review celebrates the success of HPR breeding activities in the CSIRO cotton breeding program and maps a pathway for the future in developing resistant cultivars.

Core Collections: Is There Any Value for Cotton Breeding?

Global plant breeding activities are reliant on the available genetic variation held in extant varieties and germplasm collections. Throughout the mid- to late 1900s, germplasm collecting efforts were prioritized for breeding programs to archive precious material before it disappeared and led to the development of the numerous large germplasm resources now available in different countries. In recent decades, however, the maintenance and particularly the expansion of these germplasm resources have come under threat, and there has been a significant decline in investment in further collecting expeditions, an increase in global biosecurity restrictions, and restrictions placed on the open exchange of some commercial germplasm between breeders. The large size of most genebank collections, as well as constraints surrounding the availability and reliability of accurate germplasm passport data and physical or genetic characterization of the accessions in collections, limits germplasm utilization by plant breeders. To overcome these constraints, core collections, defined as a representative subset of the total germplasm collection, have gained popularity. Core collections aim to increase germplasm utilization by containing highly characterized germplasm that attempts to capture the majority of the variation in a whole collection. With the recent availability of many new genetic tools, the potential to unlock the value of these resources can now be realized. The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program supplies 100% of the cotton cultivars grown in Australia. The program is reliant on the use of plant genetic resources for the development of improved cotton varieties to address emerging challenges in pest and disease resistance as well as the global changes occurring in the climate. Currently, the CSIRO germplasm collection is actively maintained but underutilized by plant breeders. This review presents an overview of the Australian cotton germplasm resources and discusses the appropriateness of a core collection for cotton breeding programs.

HairNet: a deep learning model to score leaf hairiness, a key phenotype for cotton fibre yield, value and insect resistance.

BACKGROUND: Leaf hairiness (pubescence) is an important plant phenotype which regulates leaf transpiration, affects sunlight penetration, and provides increased resistance or susceptibility against certain insects. Cotton accounts for 80% of global natural fibre production, and in this crop leaf hairiness also affects fibre yield and value. Currently, this key phenotype is measured visually which is slow, laborious and operator-biased. Here, we propose a simple, high-throughput and low-cost imaging method combined with a deep-learning model, HairNet, to classify leaf images with great accuracy. RESULTS: A dataset of [Formula: see text] 13,600 leaf images from 27 genotypes of Cotton was generated. Images were collected from leaves at two different positions in the canopy (leaf 3 & leaf 4), from genotypes grown in two consecutive years and in two growth environments (glasshouse & field). This dataset was used to build a 4-part deep learning model called HairNet. On the whole dataset, HairNet achieved accuracies of 89% per image and 95% per leaf. The impact of leaf selection, year and environment on HairNet accuracy was then investigated using subsets of the whole dataset. It was found that as long as examples of the year and environment tested were present in the training population, HairNet achieved very high accuracy per image (86-96%) and per leaf (90-99%). Leaf selection had no effect on HairNet accuracy, making it a robust model. CONCLUSIONS: HairNet classifies images of cotton leaves according to their hairiness with very high accuracy. The simple imaging methodology presented in this study and the high accuracy on a single image per leaf achieved by HairNet demonstrates that it is implementable at scale. We propose that HairNet replaces the current visual scoring of this trait. The HairNet code and dataset can be used as a baseline to measure this trait in other species or to score other microscopic but important phenotypes.

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.

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.

Historical Datasets Support Genomic Selection Models for the Prediction of Cotton Fiber Quality Phenotypes Across Multiple Environments.

Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there has not been a study to evaluate GS prediction models that may be used for predicting cotton breeding lines across multiple environments. In this study, we evaluated the performance of Bayes Ridge Regression, BayesA, BayesB, BayesC and Reproducing Kernel Hilbert Spaces regression models. We then extended the single-site GS model to accommodate genotype × environment interaction (G×E) in order to assess the merits of multi- over single-environment models in a practical breeding and selection context in cotton, a crop for which this has not previously been evaluated. Our study was based on a population of 215 upland cotton (Gossypium hirsutum) breeding lines which were evaluated for fiber length and strength at multiple locations in Australia and genotyped with 13,330 single nucleotide polymorphic (SNP) markers. BayesB, which assumes unique variance for each marker and a proportion of markers to have large effects, while most other markers have zero effect, was the preferred model. GS accuracy for fiber length based on a single-site model varied across sites, ranging from 0.27 to 0.77 (mean = 0.38), while that of fiber strength ranged from 0.19 to 0.58 (mean = 0.35) using randomly selected sub-populations as the training population. Prediction accuracies from the M×E model were higher than those for single-site and across-site models, with an average accuracy of 0.71 and 0.59 for fiber length and strength, respectively. The use of the M×E model could therefore identify which breeding lines have effects that are stable across environments and which ones are responsible for G×E and so reduce the amount of phenotypic screening required in cotton breeding programs to identify adaptable genotypes.

Enhancing Integrated Pest Management in GM Cotton Systems Using Host Plant Resistance.

Cotton has lost many ancestral defensive traits against key invertebrate pests. This is suggested by the levels of resistance to some pests found in wild cotton genotypes as well as in cultivated landraces and is a result of domestication and a long history of targeted breeding for yield and fiber quality, along with the capacity to control pests with pesticides. Genetic modification (GM) allowed integration of toxins from a bacteria into cotton to control key Lepidopteran pests. Since the mid-1990s, use of GM cotton cultivars has greatly reduced the amount of pesticides used in many cotton systems. However, pests not controlled by the GM traits have usually emerged as problems, especially the sucking bug complex. Control of this complex with pesticides often causes a reduction in beneficial invertebrate populations, allowing other secondary pests to increase rapidly and require control. Control of both sucking bug complex and secondary pests is problematic due to the cost of pesticides and/or high risk of selecting for pesticide resistance. Deployment of host plant resistance (HPR) provides an opportunity to manage these issues in GM cotton systems. Cotton cultivars resistant to the sucking bug complex and/or secondary pests would require fewer pesticide applications, reducing costs and risks to beneficial invertebrate populations and pesticide resistance. Incorporation of HPR traits into elite cotton cultivars with high yield and fiber quality offers the potential to further reduce pesticide use and increase the durability of pest management in GM cotton systems. We review the challenges that the identification and use of HPR against invertebrate pests brings to cotton breeding. We explore sources of resistance to the sucking bug complex and secondary pests, the mechanisms that control them and the approaches to incorporate these defense traits to commercial cultivars.

Lack of adaptation to a new host in a generalist herbivore: implications for host plant resistance to twospotted spider mites in cotton.

BACKGROUND: The twospotted spider mite (Tetranychus urticae Koch) is an important pest of cotton. This pest has a broad host range, but when changing between hosts an initial decline in fitness often occurs. This is usually followed by an increase in fitness after rapid adaptation to the new host, usually within five generations. RESULTS: The generality of this adaptive response was tested by assessing elements of fitness when mites were reared on a host to which they were adapted (Gossypium hirsutum L. cv. Sicot 71) or on a new host, Gossypium arboreum L. (accession BM13H). In a first experiment, mites reared on the new host for ten generations showed declining immature survival compared with those reared on the adapted host. In a second experiment, the intrinsic capacity for increase of mites cultured on the new host for six generations was significantly lower than that of mites cultured on the adapted host for six generations and then transferred to the new host. Hence, exposure to the new host for six or ten generations resulted in declining fitness. CONCLUSION: This 'negative adaptation' indicates robust antibiosis traits in G. arboreum accession BM13H, which therefore have value in developing mite-resistant G. hirsutum cultivars.