Development of High-Yielding Upland Cotton Genotypes with Reduced Regrowth after Defoliation Using a Combination of Molecular and Conventional Approaches.

Cotton is an economically important crop. However, the yield gain in cotton has stagnated over the years, probably due to its narrow genetic base. The introgression of beneficial variations through conventional and molecular approaches has helped broaden its genetic base to some extent. The growth habit of cotton is one of the crucial factors that determine crop maturation time, yield, and management. This study used 44 diverse upland cotton genotypes to develop high-yielding cotton germplasm with reduced regrowth after defoliation and early maturity by altering its growth habit from perennial to somewhat annual. We selected eight top-scoring genotypes based on the gene expression analysis of five floral induction and meristem identity genes (FT, SOC1, LFY, FUL, and AP1) and used them to make a total of 587 genetic crosses in 30 different combinations of these genotypes. High-performance progeny lines were selected based on the phenotypic data on plant height, flower and boll numbers per plant, boll opening date, floral clustering, and regrowth after defoliation as surrogates of annual growth habit, collected over four years (2019 to 2022). Of the selected lines, 8×5-B3, 8×5-B4, 9×5-C1, 8×9-E2, 8×9-E3, and 39×5-H1 showed early maturity, and 20×37-K1, 20×37-K2, and 20×37-D1 showed clustered flowering, reduced regrowth, high quality of fiber, and high lint yield. In 2022, 15 advanced lines (F8/F7) from seven cross combinations were selected and sent for an increase to a Costa Rica winter nursery to be used in advanced testing and for release as germplasm lines. In addition to these breeding lines, we developed molecular resources to breed for reduced regrowth after defoliation and improved yield by converting eight expression-trait-associated SNP markers we identified earlier into a user-friendly allele-specific PCR-based assay and tested them on eight parental genotypes and an F2 population.

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.

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.

The Breeding Information Management System (BIMS): an online resource for crop breeding.

In this era of big data, breeding programs are producing ever larger amounts of data. This necessitates access to efficient management systems to keep track of cross, performance, pedigree, geographical and image-based data, as well as genotyping data. In this article, we report the progress on the Breeding Information Management System (BIMS), a free, secure and online breeding management system that allows breeders to store, manage, archive and analyze their private breeding data. BIMS is the first publicly available database system that enables individual breeders to integrate their private phenotypic and genotypic data with public data and, at the same time, have complete control of their own breeding data along with access to tools such as data import/export, data analysis and data archiving. The integration of breeding data with publicly available genomic and genetic data enhances genetic understanding of important traits and maximizes the marker-assisted breeding utility for breeders and allied scientists. BIMS incorporates the use of the Android App Field Book, open-source phenotype data collection software for phones and tablets that allows breeders to replace hard copy field books, thus alleviating the possibility of transcription errors while providing faster access to the collected data. BIMS comes with training materials and support for individual or small group training and is currently implemented in the Genome Database for Rosaceae, CottonGEN, the Citrus Genome Database, the Pulse Crop Database, and the Genome Database for Vaccinium. Database URLs: (https://www.rosaceae.org/), (https://www.cottongen.org/), (https://www.citrusgenomedb.org/), (https://www.pulsedb.org/) and (https://www.vaccinium.org/).

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.

Whole genome sequencing of a MAGIC population identified genomic loci and candidate genes for major fiber quality traits in upland cotton (Gossypium hirsutum L.).

KEY MESSAGE: Significant associations between candidate genes and six major cotton fiber quality traits were identified in a MAGIC population using GWAS and whole genome sequencing. Upland cotton (Gossypium hirsutum L.) is the world's major renewable source of fibers for textiles. To identify causative genetic variants that influence the major agronomic measures of cotton fiber quality, which are used to set discount or premium prices on each bale of cotton in the USA, we measured six fiber phenotypes from twelve environments, across three locations and 7 years. Our 550 recombinant inbred lines were derived from a multi-parent advanced generation intercross population and were whole-genome-sequenced at 3× coverage, along with the eleven parental cultivars at 20× coverage. The segregation of 473,517 single nucleotide polymorphisms (SNPs) in this population, including 7506 non-synonymous mutations, was combined with phenotypic data to identify seven highly significant fiber quality loci. At these loci, we found fourteen genes with non-synonymous SNPs. Among these loci, some had simple additive effects, while others were only important in a subset of the population. We observed additive effects for elongation and micronaire, when the three most significant loci for each trait were examined. In an informative subset where the major multi-trait locus on chromosome A07:72-Mb was fixed, we unmasked the identity of another significant fiber strength locus in gene Gh_D13G1792 on chromosome D13. The micronaire phenotype only revealed one highly significant genetic locus at one environmental location, demonstrating a significant genetic by environment component. These loci and candidate causative variant alleles will be useful to cotton breeders for marker-assisted selection with minimal linkage drag and potential biotechnological applications.

Genetic diversity and population structure in the US Upland cotton (Gossypium hirsutum L.).

KEY MESSAGE: Genetic diversity and population structure in the US Upland cotton was established and core sets of allelic richness were identified for developing association mapping populations in cotton. Elite plant breeding programs could likely benefit from the unexploited standing genetic variation of obsolete cultivars without the yield drag typically associated with wild accessions. A set of 381 accessions comprising 378 Upland (Gossypium hirsutum L.) and 3 G. barbadense L. accessions of the United States cotton belt were genotyped using 120 genome-wide SSR markers to establish the genetic diversity and population structure in tetraploid cotton. These accessions represent more than 100 years of Upland cotton breeding in the United States. Genetic diversity analysis identified a total of 546 alleles across 141 marker loci. Twenty-two percent of the alleles in Upland accessions were unique, specific to a single accession. Population structure analysis revealed extensive admixture and identified five subgroups corresponding to Southeastern, Midsouth, Southwest, and Western zones of cotton growing areas in the United States, with the three accessions of G. barbadense forming a separate cluster. Phylogenetic analysis supported the subgroups identified by STRUCTURE. Average genetic distance between G. hirsutum accessions was 0.195 indicating low levels of genetic diversity in Upland cotton germplasm pool. The results from both population structure and phylogenetic analysis were in agreement with pedigree information, although there were a few exceptions. Further, core sets of different sizes representing different levels of allelic richness in Upland cotton were identified. Establishment of genetic diversity, population structure, and identification of core sets from this study could be useful for genetic and genomic analysis and systematic utilization of the standing genetic variation in Upland cotton.

RNA-Seq transcriptome profiling of upland cotton (Gossypium hirsutum L.) root tissue under water-deficit stress.

An RNA-Seq experiment was performed using field grown well-watered and naturally rain fed cotton plants to identify differentially expressed transcripts under water-deficit stress. Our work constitutes the first application of the newly published diploid D5 Gossypium raimondii sequence in the study of tetraploid AD1 upland cotton RNA-seq transcriptome analysis. A total of 1,530 transcripts were differentially expressed between well-watered and water-deficit stressed root tissues, in patterns that confirm the accuracy of this technique for future studies in cotton genomics. Additionally, putative sequence based genome localization of differentially expressed transcripts detected A2 genome specific gene expression under water-deficit stress. These data will facilitate efforts to understand the complex responses governing transcriptomic regulatory mechanisms and to identify candidate genes that may benefit applied plant breeding programs.

Genetic dissection of yield and its component traits using high-density composite map of wheat chromosome 3A: bridging gaps between QTLs and underlying genes.

Earlier we identified wheat (Triticum aestivum L.) chromosome 3A as a major determinant of grain yield and its component traits. In the present study, a high-density genetic linkage map of 81 chromosome 3A-specific markers was developed to increase the precision of previously identified yield component QTLs, and to map QTLs for biomass-related traits. Many of the previously identified QTLs for yield and its component traits were confirmed and were localized to narrower intervals. Four novel QTLs one each for shoot biomass (Xcfa2262-Xbcd366), total biomass (wPt2740-Xcfa2076), kernels/spike (KPS) (Xwmc664-Xbarc67), and Pseudocercosporella induced lodging (PsIL) were also detected. The major QTLs identified for grain yield (GY), KPS, grain volume weight (GVWT) and spikes per square meter (SPSM) respectively explained 23.2%, 24.2%, 20.5% and 20.2% of the phenotypic variation. Comparison of the genetic map with the integrated physical map allowed estimation of recombination frequency in the regions of interest and suggested that QTLs for grain yield detected in the marker intervals Xcdo549-Xbarc310 and Xpsp3047-Xbarc356 reside in the high-recombination regions, thus should be amenable to map-based cloning. On the other hand, QTLs for KPS and SPSM flanked by markers Xwmc664 and Xwmc489 mapped in the low-recombination region thus are not suitable for map-based cloning. Comparisons with the rice (Oryza sativa L.) genomic DNA sequence identified 11 candidate genes (CGs) for yield and yield related QTLs of which chromosomal location of two (CKX2 and GID2-like) was confirmed using wheat aneuploids. This study provides necessary information to perform high-resolution mapping for map-based cloning and for CG-based cloning of yield QTLs.

Genome-wide identification of differentially expressed genes under water deficit stress in upland cotton (Gossypium hirsutum L.).

BACKGROUND: Cotton is the world's primary fiber crop and is a major agricultural commodity in over 30 countries. Like many other global commodities, sustainable cotton production is challenged by restricted natural resources. In response to the anticipated increase of agricultural water demand, a major research direction involves developing crops that use less water or that use water more efficiently. In this study, our objective was to identify differentially expressed genes in response to water deficit stress in cotton. A global expression analysis using cDNA-Amplified Fragment Length Polymorphism was conducted to compare root and leaf gene expression profiles from a putative drought resistant cotton cultivar grown under water deficit stressed and well watered field conditions. RESULTS: We identified a total of 519 differentially expressed transcript derived fragments. Of these, 147 transcript derived fragment sequences were functionally annotated according to their gene ontology. Nearly 70 percent of transcript derived fragments belonged to four major categories: 1) unclassified, 2) stress/defense, 3) metabolism, and 4) gene regulation. We found heat shock protein-related and reactive oxygen species-related transcript derived fragments to be among the major parts of functional pathways induced by water deficit stress. Also, twelve novel transcripts were identified as both water deficit responsive and cotton specific. A subset of differentially expressed transcript derived fragments was verified using reverse transcription-polymerase chain reaction. Differential expression analysis also identified five pairs of duplicated transcript derived fragments in which four pairs responded differentially between each of their two homologues under water deficit stress. CONCLUSIONS: In this study, we detected differentially expressed transcript derived fragments from water deficit stressed root and leaf tissues in tetraploid cotton and provided their gene ontology, functional/biological distribution, and possible roles of gene duplication. This discovery demonstrates complex mechanisms involved with polyploid cotton's transcriptome response to naturally occurring field water deficit stress. The genes identified in this study will provide candidate targets to manipulate the water use characteristics of cotton at the molecular level.

Regression-based multi-trait QTL mapping using a structural equation model.

Quantitative trait loci (QTL) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for the correlation among multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. Structural equation modeling (SEM) allows researchers to explicitly characterize the causal structure among the variables and to decompose effects into direct, indirect, and total effects. In this paper, we developed a multi-trait SEM method of QTL mapping that takes into account the causal relationships among traits related to grain yield. Performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait analysis and the multi-trait least-squares analysis, our multi-trait SEM improves statistical power of QTL detection and provides important insight into how QTLs regulate traits by investigating the direct, indirect, and total QTL effects. The approach also helps build biological models that more realistically reflect the complex relationships among QTL and traits and is more precise and efficient in QTL mapping than single trait analysis.

Bayesian mixture structural equation modelling in multiple-trait QTL mapping.

Quantitative trait loci (QTLs) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for correlation among the multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. In this paper, we developed a Bayesian multiple QTL mapping method for causally related traits using a mixture structural equation model (SEM), which allows researchers to decompose QTL effects into direct, indirect and total effects. Parameters are estimated based on their marginal posterior distribution. The posterior distributions of parameters are estimated using Markov Chain Monte Carlo methods such as the Gibbs sampler and the Metropolis-Hasting algorithm. The number of QTLs affecting traits is determined by the Bayes factor. The performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait Bayesian analysis, our proposed method not only improved the statistical power of QTL detection, accuracy and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more biologically sensible model.

Identification of the family of aquaporin genes and their expression in upland cotton (Gossypium hirsutum L.).

BACKGROUND: Cotton (Gossypium spp.) is produced in over 30 countries and represents the most important natural fiber in the world. One of the primary factors affecting both the quantity and quality of cotton production is water. A major facilitator of water movement through cell membranes of cotton and other plants are the aquaporin proteins. Aquaporin proteins are present as diverse forms in plants, where they function as transport systems for water and other small molecules. The plant aquaporins belong to the large major intrinsic protein (MIP) family. In higher plants, they consist of five subfamilies including plasma membrane intrinsic proteins (PIP), tonoplast intrinsic proteins (TIP), NOD26-like intrinsic proteins (NIP), small basic intrinsic proteins (SIP), and the recently discovered X intrinsic proteins (XIP). Although a great deal is known about aquaporins in plants, very little is known in cotton. RESULTS: From a molecular cloning effort, together with a bioinformatic homology search, 71 upland cotton (G. hirsutum) aquaporin genes were identified. The cotton aquaporins consist of 28 PIP and 23 TIP members with high sequence similarity. We also identified 12 NIP and 7 SIP members that showed more divergence. In addition, one XIP member was identified that formed a distinct 5th subfamily. To explore the physiological roles of these aquaporin genes in cotton, expression analyses were performed for a select set of aquaporin genes from each subfamily using semi-quantitative reverse transcription (RT)-PCR. Our results suggest that many cotton aquaporin genes have high sequence similarity and diverse roles as evidenced by analysis of sequences and their expression. CONCLUSION: This study presents a comprehensive identification of 71 cotton aquaporin genes. Phylogenetic analysis of amino acid sequences divided the large and highly similar multi-gene family into the known 5 aquaporin subfamilies. Together with expression and bioinformatic analyses, our results support the idea that the genes identified in this study represent an important genetic resource providing potential targets to modify the water use properties of cotton.

High-density mapping and comparative analysis of agronomically important traits on wheat chromosome 3A.

Bread wheat chromosome 3A has been shown to contain genes/QTLs controlling grain yield and other agronomic traits. The objectives of this study were to generate high-density physical and genetic-linkage maps of wheat homoeologous group 3 chromosomes and reveal the physical locations of genes/QTLs controlling yield and its component traits, as well as agronomic traits, to obtain a precise estimate of recombination for the corresponding regions and to enrich the QTL-containing regions with markers. Physical mapping was accomplished by 179 DNA markers mostly representing expressed genes using 41 single-break deletion lines. Polymorphism survey of cultivars Cheyenne (CNN) and Wichita (WI), and a substitution line of CNN carrying chromosome 3A from WI [CNN(WI3A)], with 142 RFLP probes and 55 SSR markers revealed that the extent of polymorphism is different among various group 3 chromosomal regions as well as among the homoeologs. A genetic-linkage map for chromosome 3A was developed by mapping 17 QTLs for seven agronomic traits relative to 26 RFLP and 15 SSR chromosome 3A-specific markers on 95 single-chromosome recombinant inbred lines. Comparison of the physical maps with the 3A genetic-linkage map localized the QTLs to gene-containing regions and accounted for only about 36% of the chromosome. Two chromosomal regions containing 9 of the 17 QTLs encompassed less than 10% of chromosome 3A but accounted for almost all of the arm recombination. To identify rice chromosomal regions corresponding to the particular QTL-containing wheat regions, 650 physically mapped wheat group 3 sequences were compared with rice genomic sequences. At an E value of E < or = 10(-5), 82% of the wheat group 3 sequences identified rice homologs, of which 54% were on rice chromosome 1. The rice chromosome 1 region collinear with the two wheat regions that contained 9 QTLs was about 6.5 Mb.