Meta-analysis of polyploid cotton QTL shows unequal contributions of subgenomes to a complex network of genes and gene clusters implicated in lint fiber development

Identification of favorable alleles from exotic Upland cotton lines for fiber quality improvement using multiple association models

Upland cotton (Gossypium hirsutum) faces the challenge of limited genetic diversity in the elite or improved gene pool. To address this issue, we explored alleles contributed by five 'converted' exotic lines sampling most of the undomesticated botanical races of G. hirsutum, in BC1F2 and F3 populations. Joint analysis of all populations along with population-specific analyses identified 38 unique QTL for six different fiber quality traits. At 15 of these loci, DES56 or the elite allele improved upon all the exotics. For another 15, only a single of the five exotics improved upon the elite allele, suggesting the rare alleles that may not have been sampled in the cotton domestication or improvement. At the remaining 8 QTL, multiple exotic lines contributed the superior allele, suggesting that DES56 (and by extension the elite gene pool) has chronically poor alleles at these loci. Converted strains T1046, T326, and T063 showed the highest potential for contributions to cotton fiber quality breeding programs. Upper Half Mean Length and Fiber Strength showed multiple QTL regions affecting both traits simultaneously, while the Uniformity Index showed the smallest heritability values. The estimation of pairwise genetic distances for six parental lines indicates that DES56 has a higher genetic similarity with each exotic line than the exotic lines have with each other. Most of the detected QTL were 'minor' (explaining less than 10% of variance) supporting the implementation of genomic selection techniques to utilize the cumulative effects of most of these QTL distributed genome-wide. Finally, some regions were consistently unfavorable for exotic introgression such as on chromosomes A13 and D09, indicating the possible genome-wide haplotypes that may combine the benefits of a history of scientific breeding of the elite gene pool.

GWAS and eQTL analyses reveal genetic components influencing the key fiber yield trait lint percentage in upland cotton

Lint percentage is an important component of cotton yield traits and an important economic indicator of cotton production. The initial stage of fiber development is a critical developmental period that affects the lint percentage trait, but the genetic regulation of the initial stage of fiber development needs to be resolved. In this study, we used a genomewide association study (GWAS) to identify 11 quantitative trait loci (QTLs) related to lint percentage and identified a total of 13 859 expression QTL (eQTLs) through transcriptome sequencing of 312 upland cotton accessions. Candidate genes for improving the lint percentage trait were identified through transcriptome-wide association study (TWAS), colocalization analysis, and differentially expressed gene analysis. We located nine candidate genes through the TWAS, and prioritized two key candidate genes (Ghir_A12G025980 and Ghir_A12G025990) related to lint percentage through colocalization and differential expression analysis. We showed that two eQTL hotspots (Hot26 and Hot28) synergistically participate in regulating the biological pathways of fiber initiation and development. Additionally, we unlocked the potential of genomic variants in improving the lint percentage by aggregating favorable alleles in accessions. New accessions suitable for improving lint percentage were excavated.

Natural variation in GhROPGEF5 contributes to longer and stronger cotton fibers

Length and strength are key parameters impacting the quality of textiles that can be produced from cotton fibers, and therefore are important considerations in cotton breeding. Through map-based cloning and function analysis, we demonstrated that GhROPGEF5, encoding a ROP guanine nucleotide exchange factor, was the gene controlling fiber length and strength at qFSA10.1. Evolutionary analysis revealed that a base deletion in the third exon of GhROPGEF5 resulting in superior fiber length and strength was a rare mutation occurring in a tiny percentage of Upland cottons, with reduced fiber yield hindering its spread. GhROPGEF5 interacted with and activated GhROP10. Knockout or mutation of GhROPGEF5 resulted a loss of the ability to activate GhROP10. Knockout of GhROPGEF5 or GhROP10 affected the expression of many downstream genes associated with fiber elongation and secondary wall deposition, prolonged fiber elongation and delayed secondary wall deposition, producing denser fiber helices and increasing fiber length and strength. These results revealed new molecular aspects of fiber development and revealed a rare favorable allele for improving fiber quality in cotton breeding.

Comparative transcriptome analysis and meta-QTLs mapping reveal the regulatory mechanism of cold tolerance in rice at the budding stage

Rice (Oryza sativa L.) is one of the most extensively farmed food crops, but its development and productivity are significantly impacted by cold stress during the budding period. In this study, transcriptome sequencing was conducted on two types of rice: the cold-sensitive indica rice A117 and the substantially cold-tolerant japonica rice B106 under control and cold treatments. Differentially expressed genes between the two materials under cold conditions were analyzed using GO and KEGG enrichment analyses. The results revealed that processes such as the TCA cycle, glycolysis/glycogenesis, oxidative phosphorylation, and glutathione metabolism contribute to B106's cold tolerance. Additionally, an enrichment analysis of cold-induced genes in each material and shared genes identified significant enrichment in pathways such as glutathione metabolism, phenylpropanoid biosynthesis, and photosynthesis-antenna proteins. Initial cold tolerance QTLs at the rice bud stage were collected from published literature, and meta-QTL mapping identified 9 MQTLs. Gene expression profiling led to the identification of 75 potential DEGs within the 9 MQTLs region, from which four candidate genes (Os02g0194100, Os03g0802500, Os05g0129000, and Os07g0462000) were selected using qRT-PCR and gene annotation. These findings provide genetic resources for further research on the molecular mechanisms underlying rice's response to cold stress during the bud stage.

Hotspot Regions of Quantitative Trait Loci and Candidate Genes for Ear-Related Traits in Maize: A Literature Review

In this study, hotspot regions, QTL clusters, and candidate genes for eight ear-related traits of maize (ear length, ear diameter, kernel row number, kernel number per row, kernel length, kernel width, kernel thickness, and 100-kernel weight) were summarized and analyzed over the past three decades. This review aims to (1) comprehensively summarize and analyze previous studies on QTLs associated with these eight ear-related traits and identify hotspot bin regions located on maize chromosomes and key candidate genes associated with the ear-related traits and (2) compile major and stable QTLs and QTL clusters from various mapping populations and mapping methods and techniques providing valuable insights for fine mapping, gene cloning, and breeding for high-yield and high-quality maize. Previous research has demonstrated that QTLs for ear-related traits are distributed across all ten chromosomes in maize, and the phenotypic variation explained by a single QTL ranged from 0.40% to 36.76%. In total, 23 QTL hotspot bins for ear-related traits were identified across all ten chromosomes. The most prominent hotspot region is bin 4.08 on chromosome 4 with 15 QTLs related to eight ear-related traits. Additionally, this study identified 48 candidate genes associated with ear-related traits. Out of these, five have been cloned and validated, while twenty-eight candidate genes located in the QTL hotspots were defined by this study. This review offers a deeper understanding of the advancements in QTL mapping and the identification of key candidates associated with eight ear-related traits. These insights will undoubtedly assist maize breeders in formulating strategies to develop higher-yield maize varieties, contributing to global food security.

Meta-QTL analysis in wheat: progress, challenges and opportunities

Wheat, an important cereal crop globally, faces major challenges due to increasing global population and changing climates. The production and productivity are challenged by several biotic and abiotic stresses. There is also a pressing demand to enhance grain yield and quality/nutrition to ensure global food and nutritional security. To address these multifaceted concerns, researchers have conducted numerous meta-QTL (MQTL) studies in wheat, resulting in the identification of candidate genes that govern these complex quantitative traits. MQTL analysis has successfully unraveled the complex genetic architecture of polygenic quantitative traits in wheat. Candidate genes associated with stress adaptation have been pinpointed for abiotic and biotic traits, facilitating targeted breeding efforts to enhance stress tolerance. Furthermore, high-confidence candidate genes (CGs) and flanking markers to MQTLs will help in marker-assisted breeding programs aimed at enhancing stress tolerance, yield, quality and nutrition. Functional analysis of these CGs can enhance our understanding of intricate trait-related genetics. The discovery of orthologous MQTLs shared between wheat and other crops sheds light on common evolutionary pathways governing these traits. Breeders can leverage the most promising MQTLs and CGs associated with multiple traits to develop superior next-generation wheat cultivars with improved trait performance. This review provides a comprehensive overview of MQTL analysis in wheat, highlighting progress, challenges, validation methods and future opportunities in wheat genetics and breeding, contributing to global food security and sustainable agriculture.

Genome-wide association study of fiber yield-related traits uncovers the novel genomic regions and candidate genes in Indian upland cotton (Gossypium hirsutum L.)

Upland cotton (Gossypium hirsutum L.) is a major fiber crop that is cultivated worldwide and has significant economic importance. India harbors the largest area for cotton cultivation, but its fiber yield is still compromised and ranks 22nd in terms of productivity. Genetic improvement of cotton fiber yield traits is one of the major goals of cotton breeding, but the understanding of the genetic architecture underlying cotton fiber yield traits remains limited and unclear. To better decipher the genetic variation associated with fiber yield traits, we conducted a comprehensive genome-wide association mapping study using 117 Indian cotton germplasm for six yield-related traits. To accomplish this, we generated 2,41,086 high-quality single nucleotide polymorphism (SNP) markers using genotyping-by-sequencing (GBS) methods. Population structure, PCA, kinship, and phylogenetic analyses divided the germplasm into two sub-populations, showing weak relatedness among the germplasms. Through association analysis, 205 SNPs and 134 QTLs were identified to be significantly associated with the six fiber yield traits. In total, 39 novel QTLs were identified in the current study, whereas 95 QTLs overlapped with existing public domain data in a comparative analysis. Eight QTLs, qGhBN_SCY_D6-1, qGhBN_SCY_D6-2, qGhBN_SCY_D6-3, qGhSI_LI_A5, qGhLI_SI_A13, qGhLI_SI_D9, qGhBW_SCY_A10, and qGhLP_BN_A8 were identified. Gene annotation of these fiber yield QTLs revealed 2,509 unique genes. These genes were predominantly enriched for different biological processes, such as plant cell wall synthesis, nutrient metabolism, and vegetative growth development in the gene ontology (GO) enrichment study. Furthermore, gene expression analysis using RNAseq data from 12 diverse cotton tissues identified 40 candidate genes (23 stable and 17 novel genes) to be transcriptionally active in different stages of fiber, ovule, and seed development. These findings have revealed a rich tapestry of genetic elements, including SNPs, QTLs, and candidate genes, and may have a high potential for improving fiber yield in future breeding programs for Indian cotton.

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

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

Meta-analysis of seed weight QTLome using a consensus and highly dense genetic map in Brassica napus L

KEY MESSAGE

We report here the discovery of high-confidence MQTL regions and of putative candidate genes associated with seed weight in B. napus using a highly dense consensus genetic map and by comparing various large-scale multiomics datasets. Seed weight (SW) is a direct determinant of seed yield in Brassica napus and is controlled by many loci. To unravel the main genomic regions associated with this complex trait, we used 13 available genetic maps to construct a consensus and highly dense map, comprising 40,401 polymorphic markers and 9191 genetic bins, harboring a cumulative length of 3047.8 cM. Then, we performed a meta-analysis using 639 projected SW quantitative trait loci (QTLs) obtained from studies conducted since 1999, enabling the identification of 57 meta-QTLS (MQTLs). The confidence intervals of our MQTLs were 9.8 and 4.3 times lower than the average CIs of the original QTLs for the A and C subgenomes, respectively, resulting in the detection of some key genes and several putative novel candidate genes associated with SW. By comparing the genes identified in MQTL intervals with multiomics datasets and coexpression analyses of common genes, we defined a more reliable and shorter list of putative candidate genes potentially involved in the regulation of seed maturation and SW. As an example, we provide a list of promising genes with high expression levels in seeds and embryos (e.g., BnaA03g04230D, BnaC03g08840D, BnaA10g29580D and BnaA03g27410D) that can be more finely studied through functional genetics experiments or that may be useful for MQTL-assisted breeding for SW. The high-density genetic consensus map and the single nucleotide polymorphism (SNP) physical map generated from the latest B. napus cv. Darmor-bzh v10 assembly will be a valuable resource for further mapping and map-based cloning of other important traits.

A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice

Improving grain yield potential in rice is an important step toward addressing global food security challenges. The meta-QTL analysis offers stable and robust QTLs irrespective of the genetic background of mapping populations and phenotype environment and effectively narrows confidence intervals (CI) for candidate gene (CG) mining and marker-assisted selection improvement. To achieve these aims, a comprehensive bibliographic search for grain yield traits (spikelet fertility, number of grains per panicle, panicles number per plant, and 1000-grain weight) QTLs was conducted, and 462 QTLs were retrieved from 47 independent QTL research published between 2002 and 2022. QTL projection was performed using a reference map with a cumulative length of 2,945.67 cM, and MQTL analysis was conducted on 313 QTLs. Consequently, a total of 62 MQTLs were identified with reduced mean CI (up to 3.40 fold) compared to the mean CI of original QTLs. However, 10 of these MQTLs harbored at least six of the initial QTLs from diverse genetic backgrounds and environments and were considered the most stable and robust MQTLs. Also, MQTLs were compared with GWAS studies and resulted in the identification of 16 common significant loci modulating the evaluated traits. Gene annotation, gene ontology (GO) enrichment, and RNA-seq analyses of chromosome regions of the stable MQTLs detected 52 potential CGs including those that have been cloned in previous studies. These genes encode proteins known to be involved in regulating grain yield including cytochrome P450, zinc fingers, MADs-box, AP2/ERF domain, F-box, ubiquitin ligase domain protein, homeobox domain, DEAD-box ATP domain, and U-box domain. This study provides the framework for molecular dissection of grain yield in rice. Moreover, the MQTLs and CGs identified could be useful for fine mapping, gene cloning, and marker-assisted selection to improve rice productivity.

Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus

BACKGROUND

Regulation of gene expression plays an essential role in controlling the phenotypes of plants. Brassica napus (B. napus) is an important source for the vegetable oil in the world, and the seed oil content is an important trait of B. napus.

RESULTS

We perform a comprehensive analysis of the transcriptional variability in the seeds of B. napus at two developmental stages, 20 and 40 days after flowering (DAF). We detect 53,759 and 53,550 independent expression quantitative trait loci (eQTLs) for 79,605 and 76,713 expressed genes at 20 and 40 DAF, respectively. Among them, the local eQTLs are mapped to the adjacent genes more frequently. The adjacent gene pairs are regulated by local eQTLs with the same open chromatin state and show a stronger mode of expression piggybacking. Inter-subgenomic analysis indicates that there is a feedback regulation for the homoeologous gene pairs to maintain partial expression dosage. We also identify 141 eQTL hotspots and find that hotspot87-88 co-localizes with a QTL for the seed oil content. To further resolve the regulatory network of this eQTL hotspot, we construct the XGBoost model using 856 RNA-seq datasets and the Basenji model using 59 ATAC-seq datasets. Using these two models, we predict the mechanisms affecting the seed oil content regulated by hotspot87-88 and experimentally validate that the transcription factors, NAC13 and SCL31, positively regulate the seed oil content.

CONCLUSIONS

We comprehensively characterize the gene regulatory features in the seeds of B. napus and reveal the gene networks regulating the seed oil content of B. napus.

Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification

Quantitative trait loci (QTL) for rice grain weight identified using bi-parental populations in various environments were found inconsistent and have a modest role in marker assisted breeding and map-based cloning programs. Thus, the identification of a consistent consensus QTL region across populations is critical to deploy in marker aided breeding programs. Using the QTL meta-analysis technique, we collated rice grain weight QTL information from numerous studies done across populations and in diverse environments to find constitutive QTL for grain weight. Using information from 114 original QTL in meta-analysis, we discovered three significant Meta-QTL (MQTL) for grain weight on chromosome 3. According to gene ontology, these three MQTL have 179 genes, 25 of which have roles in developmental functions. Amino acid sequence BLAST of these genes indicated their orthologue conservation among core cereals with similar functions. MQTL3.1 includes the OsAPX1, PDIL, SAUR, and OsASN1 genes, which are involved in grain development and have been discovered to play a key role in asparagine biosynthesis and metabolism, which is crucial for source-sink regulation. Five potential candidate genes were identified and their expression analysis indicated a significant role in early grain development. The gene sequence information retrieved from the 3 K rice genome project revealed the deletion of six bases coding for serine and alanine in the last exon of OsASN1 led to an interruption in the synthesis of α-helix of the protein, which negatively affected the asparagine biosynthesis pathway in the low grain weight genotypes. Further, the MQTL3.1 was validated using linked marker RM7197 on a set of genotypes with extreme phenotypes. MQTL that have been identified and validated in our study have significant scope in MAS breeding and map-based cloning programs for improving rice grain weight.

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

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

Identification of candidate genes involved in salt stress response at germination and seedling stages by QTL mapping in upland cotton

Salinity is a major abiotic stress at critical stages of seed germination and seedling establishment. Germination rate (GR) and field emergence rate (FER) are the key traits that determine the basic number of plants stand under field conditions. To explore molecular mechanisms in upland cotton under salt stress, a population of 177 recombinant inbred lines, and their parents were evaluated for seed germination traits (GP, germination potential; GR; FW, fresh weight; DW, dry weight; GL, germinal length) and seedling traits (FER; SH, seedling height; NL, number of main stem leaves) in 2016–2018. Based on the linkage map contained 2,859 single nucleotide polymorphism and simple sequence repeat markers, traits under salt stress (E1) and normal conditions (E2), and in the converted relative index (R-value) dataset of 3 years’ trials were used to map quantitative trait loci (QTL). A total of 3 QTL and 2 clusters were detected as salt-tolerant QTL. Three QTL (qGR-Chr4-3, qFER-Chr12-3, and qFER-Chr15-1) were detected under salt stress conditions and R-value dataset, which explained variance of phenotype 9.62–13.67%, and 4.2–4.72%, 4.75–8.96%, respectively. Two clusters (Loci-Chr4-2 and Loci-Chr5-4) harboring the QTL for 4 germination traits (GR, FER, GL, and NL) and 6 seedling traits (GR, FER, DW, FW, SH, and NL) were detected related under salt stress. A total of 691 genes were found in the candidate QTL or clusters. Among them, 4 genes (Gh_A04G1106, Gh_A05G3246, Gh_A05G3177, and Gh_A05G3266) showed expression differences between salt-sensitive and -tolerant lines under salt stress conditions, and were assigned as candidate genes in response to salt stress. The consistent salt-tolerance QTL identified in both germination and seedling stages will facilitate novel insights into effective utilization of cotton genetic resources.

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

Background

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

Result

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

Conclusion

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

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.

Unravelling consensus genomic regions conferring leaf rust resistance in wheat via meta-QTL analysis

Leaf rust, caused by the fungus Puccinia triticina Erikss (Pt), is a destructive disease affecting wheat (Triticum aestivum L.) and a threat to food security. Developing resistant cultivars represents a useful method of disease control, and thus, understanding the genetic basis for leaf rust resistance is required. To this end, a comprehensive bibliographic search for leaf rust resistance quantitative trait loci (QTL) was performed, and 393 QTL were collected from 50 QTL mapping studies. Afterward, a consensus map with a total length of 4,567 cM consisting of different types of markers (simple sequence repeat [SSR], diversity arrays technology [DArT], chip-based single-nucleotide polymorphism [SNP] markers, and SNP markers from genotyping-by-sequencing) was used for QTL projection, and meta-QTL (MQTL) analysis was performed on 320 QTL. A total of 75 MQTL were discovered and refined to 15 high-confidence MQTL (hcmQTL). The candidate genes discovered within the hcmQTL interval were then checked for differential expression using data from three transcriptome studies, resulting in 92 differentially expressed genes (DEGs). The expression of these genes in various leaf tissues during wheat development was explored. This study provides insight into leaf rust resistance in wheat and thereby provides an avenue for developing resistant cultivars by incorporating the most important hcmQTL.

Comparative transcriptome analysis reveals the regulation network for fiber strength in cotton

OBJECTIVE

Determine the effect of secondary cell wall (SCW) thickness and microcrystalline cellulose content (MCC) on mature fiber strength (FS) and reveal through comparative transcriptome analysis the molecular regulation network governing FS in cotton.

RESULTS

Transmission electron microscope (TEM) analysis of two parent varieties, Prema with elite FS and 86-1 with weak fiber, revealed significant difference in the SCW but not in MCC. Transcriptome analysis revealed that genes differentially expressed during SCW thickening (20 DPA) are highly related to FS; in particular, up-regulated genes such as UDPG, CESA2, and NAC83 were important in SCW thickening, likely contributing to higher FS. GO and KEGG enrichment analysis revealed the common up-regulated genes to be enriched in carbon metabolism and terms relating to the cell wall.

CONCLUSIONS

We developed two recombinant inbred lines with elite FS, selected from the filial generation of Prema and 86-1. By comparing transcriptomic data, we revealed the gene expression network governing SCW thickness in mature fiber. Our results provide solid insights into the relationship of the SCW and FS.

Pinpointing genomic regions associated with root system architecture in rice through an integrative meta-analysis approach

Applying an integrated meta-analysis approach led to identification of meta-QTLs/ candidate genes associated with rice root system architecture, which can be used in MQTL-assisted breeding/ genetic engineering of root traits. Root system architecture (RSA) is an important factor for facilitating water and nutrient uptake from deep soils and adaptation to drought stress conditions. In the present research, an integrated meta-analysis approach was employed to find candidate genes and genomic regions involved in rice RSA traits. A whole-genome meta-analysis was performed for 425 initial QTLs reported in 34 independent experiments controlling RSA traits under control and drought stress conditions in the previous twenty years. Sixty-four consensus meta-QTLs (MQTLs) were detected, unevenly distributed on twelve rice chromosomes. The confidence interval (CI) of the identified MQTLs was obtained as 0.11-14.23 cM with an average of 3.79 cM, which was 3.88 times narrower than the mean CI of the original QTLs. Interestingly, 52 MQTLs were co-located with SNP peak positions reported in rice genome-wide association studies (GWAS) for root morphological traits. The genes located in these RSA-related MQTLs were detected and explored to find the drought-responsive genes in the rice root based on the RNA-seq and microarray data. Multiple RSA and drought tolerance-associated genes were found in the MQTLs including the genes involved in auxin biosynthesis or signaling (e.g. YUCCA, WOX, AUX/IAA, ARF), root angle (DRO1-related genes), lateral root development (e.g. DSR, WRKY), root diameter (e.g. OsNAC5), plant cell wall (e.g. EXPA), and lignification (e.g. C4H, PAL, PRX and CAD). The genes located within both the SNP peak positions and the QTL-overview peaks for RSA are suggested as novel candidate genes for further functional analysis. The promising candidate genes and MQTLs can be used as basis for genetic engineering and MQTL-assisted breeding of root phenotypes to improve yield potential, stability and performance in a water-stressed environment.

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.

Genetic Architecture of Novel Sources for Reproductive Cold Tolerance in Sorghum

Enhancements in reproductive cold tolerance of sorghum are essential to expand growing areas into both high-latitude temperate areas and tropical high-altitude environments. Here we present first insights into the genetic architecture of this trait via genome-wide association studies in a broad genetic diversity set (n = 330) phenotyped in multi-location field trials including high-altitude tropical (Mexico) and high-latitude temperate (Germany) environments. We observed a high degree of phenotypic variation and identified several novel, temperate-adapted accessions with superior and environmentally stable cold tolerance. Good heritability indicates strong potential for implementation of reproductive cold tolerance in breeding. Although the trait was found to be strongly quantitative, promising genomic regions with multiple-trait associations were found, including hotspots on chromosomes 3 and 10 which contain candidate genes implicated in different developmental and survival processes under abiotic stress conditions.

GhUBX controlling helical growth results in production of stronger cotton fiber

Cotton fiber is an excellent model for studying plant cell elongation and cell wall biogenesis as well because they are highly polarized and use conserved polarized diffuse growth mechanism. Fiber strength is an important trait among cotton fiber qualities due to ongoing changes in spinning technology. However, the molecular mechanism of fiber strength forming is obscure. Through map-based cloning, we identified the fiber strength gene GhUBX. Increasing its expression, the fiber strength of the transgenic cotton was significantly enhanced compared to the receptor W0 and the helices number of the transgenic fiber was remarkably increased. Additionally, we proved that GhUBX regulates the fiber helical growth by degrading the GhSPL1 via the ubiquitin 26S–proteasome pathway. Taken together, we revealed the internal relationship between fiber helices and fiber stronger. It will be useful for improving the fiber quality in cotton breeding and illustrating the molecular mechanism for plant twisted growth.

•

Isolation of the first fiber strength gene GhUBX using map-based cloning strategy

•

Verification of the function of GhUBX experimentally in transgenic cotton

•

Link helices to the cotton fiber strength, that more helices make fiber stronger

•

An ubiquitin–proteasome system regulating the development of cotton fiber

Cotton Fiber Development Requires the Pentatricopeptide Repeat Protein GhIm for Splicing of Mitochondrial nad7 mRNA

Pentatricopeptide repeat (PPR) proteins encoded by nuclear genomes can bind to organellar RNA and are involved in the regulation of RNA metabolism. However, the functions of many PPR proteins remain unknown in plants, especially in polyploidy crops. Here, through a map-based cloning strategy and Clustered regularly interspaced short palindromic repeats/cas9 (CRISPR/cas9) gene editing technology, we cloned and verified an allotetraploid cotton immature fiber (im) mutant gene (GhImA) encoding a PPR protein in chromosome A03, that is associated with the non-fluffy fiber phenotype. GhImA protein targeted mitochondrion and could bind to mitochondrial nad7 mRNA, which encodes the NAD7 subunit of Complex I. GhImA and its homolog GhImD had the same function and were dosage-dependent. GhImA in the im mutant was a null allele with a 22 bp deletion in the coding region. Null GhImA resulted in the insufficient GhIm dosage, affected mitochondrial nad7 pre-mRNA splicing, produced less mature nad7 transcripts, and eventually reduced Complex I activities, up-regulated alternative oxidase metabolism, caused reactive oxygen species (ROS) burst and activation of stress or hormone response processes. This study indicates that the GhIm protein participates in mitochondrial nad7 splicing, affects respiratory metabolism, and further regulates cotton fiber development via ATP supply and ROS balance.

Identification of hub genes through co-expression network of major QTLs of fiber length and strength traits in multiple RIL populations of cotton

The present study demonstrated a de novo correlation among fiber quality genes in multiple RIL populations including sGK9708 × 0-153, LMY22 × LY343 and Lumianyan28 × Xinluzao24. The current study was conducted to identify the major common QTLs including fiber length and strength, and to identify the co-expression networks of fiber length and strength QTLs harbored genes to target the hub genes. The RNA-seq data of sGK9708 × 0-153 population highlighted 50 and 48 candidate genes of fiber length and fiber strength QTLs. A total of 29 and 21 hub genes were identified in fiber length and strength co-expression network modules. The absolute values of correlation coefficient close to 1 resulted highly positive correlation among hub genes. Results also suggested that the gene correlation significantly influence the gene expression at different fiber development stages. These results might provide useful reference for further experiments in multiple RIL populations and suggest potential candidate genes for functional studies in cotton.

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

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

Meta-analysis of QTL reveals the genetic control of yield-related traits and seed protein content in pea

Pea is one of the most important grain legume crops in temperate regions worldwide. Improving pea yield is a critical breeding target. Nine inter-connected pea recombinant inbred line populations were evaluated in nine environments at INRAE Dijon, France and genotyped using the GenoPea 13.2 K SNP array. Each population has been evaluated in two to four environments. A multi-population Quantitative Trait Loci (QTL) analysis for seed weight per plant (SW), seed number per plant (SN), thousand seed weight (TSW) and seed protein content (SPC) was done. QTL were then projected on the multi-population consensus map and a meta-analysis of QTL was performed. This analysis identified 17 QTL for SW, 16 QTL for SN, 35 QTL for TSW and 21 QTL for SPC, shedding light on trait relationships. These QTL were resolved into 27 metaQTL. Some of them showed small confidence intervals of less than 2 cM encompassing less than one hundred underlying candidate genes. The precision of metaQTL and the potential candidate genes reported in this study enable their use for marker-assisted selection and provide a foundation towards map-based identification of causal polymorphisms.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

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

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

Meta-QTL analysis and identification of candidate genes for drought tolerance in bread wheat (Triticum aestivum L.)

Meta-QTL (MQTL) analysis for drought tolerance was undertaken in bread wheat to identify consensus and robust MQTLs using 340 known QTLs from 11 earlier studies; 13 MQTLs located on 6 chromosomes (1D, 3B, 5A, 6D, 7A and 7D) were identified, with maximum of 4 MQTLs on chromosome 5A. Mean confidence intervals for MQTLs were much narrower (mean, 6.01 cM; range 2.07-19.46 cM), relative to those in original QTLs (mean, 13.6 cM; range, 1.0-119.1 cM). Two MQTLs, namely MQTL4 and MQTL12, were major MQTLs with potential for use in marker-assisting breeding. As many as 228 candidate genes (CGs) were also identified using 6 of the 13 MQTLs. In-silico expression analysis of these 228 CGs allowed identification of 14 important CGs, with + 3 to - 8 fold change in expression under drought (relative to normal conditions) in a tolerant cv. named TAM107. These CGs encoded proteins belonging to the following families: NAD-dependent epimerase/dehydratase, protein kinase, NAD(P)-binding domain protein, heat shock protein 70 (Hsp70), glycosyltransferase 2-like, etc. Important MQTLs and CGs identified in the present study should prove useful for future molecular breeding and for the study of molecular basis of drought tolerance in cereals in general and wheat in particular.

The Ligon lintless -2 Short Fiber Mutation Is Located within a Terminal Deletion of Chromosome 18 in Cotton

Extreme elongation distinguishes about one-fourth of cotton (Gossypium sp.) seed epidermal cells as "lint" fibers, useful for the textile industry, from "fuzz" fibers (<5 mm). Ligon lintless-2 (Li ₂ ), a dominant mutation that results in no lint fiber but normal fuzz fiber, offers insight into pathways and mechanisms that differentiate spinnable cotton from its progenitors. A genetic map developed using 1,545 F2 plants showed that marker CISP15 was 0.4 cM from Li ₂ , and "dominant" simple sequence repeat (SSR) markers (i.e. with null alleles in the Li ₂ genotype) SSR7 and SSR18 showed complete linkage with Li ₂ Nonrandom distribution of markers with null alleles suggests that the Li ₂ phenotype results from a 176- to 221-kb deletion of the terminal region of chromosome 18 that may have been masked in prior pooled-sample mapping strategies. The deletion includes 10 genes with putative roles in fiber development. Two Glycosyltransferase Family 1 genes showed striking expression differences during elongation of wild-type versus Li ₂ fiber, and virus-induced silencing of these genes in the wild type induced Li ₂ -like phenotypes. Further, at least 7 of the 10 putative fiber development genes in the deletion region showed higher expression in the wild type than in Li ₂ mutants during fiber development stages, suggesting coordinated regulation of processes in cell wall development and cell elongation, consistent with the hypothesis that some fiber-related quantitative trait loci comprise closely spaced groups of functionally diverse but coordinately regulated genes.

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.

Genetic Analysis of the Transition from Wild to Domesticated Cotton (Gossypium hirsutum L.)

The evolution and domestication of cotton is of great interest from both economic and evolutionary standpoints. Although many genetic and genomic resources have been generated for cotton, the genetic underpinnings of the transition from wild to domesticated cotton remain poorly known. Here we generated an intraspecific QTL mapping population specifically targeting domesticated cotton phenotypes. We used 466 F₂ individuals derived from an intraspecific cross between the wild Gossypium hirsutum var. yucatanense (TX2094) and the elite cultivar G. hirsutum cv. Acala Maxxa, in two environments, to identify 120 QTL associated with phenotypic changes under domestication. While the number of QTL recovered in each subpopulation was similar, only 22 QTL were considered coincident (i.e., shared) between the two locations, eight of which shared peak markers. Although approximately half of QTL were located in the A-subgenome, many key fiber QTL were detected in the D-subgenome, which was derived from a species with unspinnable fiber. We found that many QTL are environment-specific, with few shared between the two environments, indicating that QTL associated with G. hirsutum domestication are genomically clustered but environmentally labile. Possible candidate genes were recovered and are discussed in the context of the phenotype. We conclude that the evolutionary forces that shape intraspecific divergence and domestication in cotton are complex, and that phenotypic transformations likely involved multiple interacting and environmentally responsive factors.

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.

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.

QTL mapping and genetic effect of chromosome segment substitution lines with excellent fiber quality from Gossypium hirsutum × Gossypium barbadense

Chromosome segment substitution lines (CSSLs) are ideal materials for identifying genetic effects. In this study, CSSL MBI7561 with excellent fiber quality that was selected from BC₄F_3:5 of CCRI45 (Gossypium hirsutum) × Hai1 (Gossypium barbadense) was used to construct 3 secondary segregating populations with 2 generations (BC₅F₂ and BC₅F_2:3). Eighty-one polymorphic markers related to 33 chromosome introgressive segments on 18 chromosomes were finally screened using 2292 SSR markers which covered the whole tetraploid cotton genome. A total of 129 quantitative trait loci (QTL) associated with fiber quality (103) and yield-related traits (26) were detected on 17 chromosomes, explaining 0.85-30.35% of the phenotypic variation; 39 were stable (30.2%), 53 were common (41.1%), 76 were new (58.9%), and 86 had favorable effects on the related traits. More QTL were distributed in the Dt subgenome than in the At subgenome. Twenty-five stable QTL clusters (with stable or common QTL) were detected on 22 chromosome introgressed segments. Finally, the 6 important chromosome introgressed segments (Seg-A02-1, Seg-A06-1, Seg-A07-2, Seg-A07-3, Seg-D07-3, and Seg-D06-2) were identified as candidate chromosome regions for fiber quality, which should be given more attention in future QTL fine mapping, gene cloning, and marker-assisted selection (MAS) breeding.

Dissecting the genetic basis of fiber quality and yield traits in interspecific backcross populations of Gossypium hirsutum × Gossypium barbadense

Fiber quality and yield are important traits of cotton. Quantitative trait locus (QTL) mapping is a prerequisite for marker-assisted selection (MAS) in cotton breeding. To identify QTLs for fiber quality and yield traits, 4 backcross-generation populations (BC₁F₁, BC₁S₁, BC₂F₁, and BC₃F₀) were developed from an interspecific cross between CCRI36 (Gossypium hirsutum L.) and Hai1 (G. barbadense L.). A total of 153 QTLs for fiber quality and yield traits were identified based on data from the BC₁F₁, BC₁S₁, BC₂F₁ and BC₃F₀ populations in the field and from the BC₂F₁ population in an artificial disease nursery using a high-density genetic linkage map with 2292 marker loci covering 5115.16 centimorgans (cM) from the BC₁F₁ population. These QTLs were located on 24 chromosomes, and each could explain 4.98-19.80% of the observed phenotypic variations. Among the 153 QTLs, 30 were consistent with those identified previously. Specifically, 23 QTLs were stably detected in 2 or 3 environments or generations, 6 of which were consistent with those identified previously and the other 17 of which were stable and novel. Ten QTL clusters for different traits were found and 9 of them were novel, which explained the significant correlations among some phenotypic traits in the populations. The results including these stable or consensus QTLs provide valuable information for marker-assisted selection (MAS) in cotton breeding and will help better understand the genetic basis of fiber quality and yield traits, which can then be used in QTL cloning.

QTL mapping and genetic effect of chromosome segment substitution lines with excellent fiber quality from Gossypium hirsutum × Gossypium barbadense

Chromosome segment substitution lines (CSSLs) are ideal materials for identifying genetic effects. In this study, CSSL MBI7561 with excellent fiber quality that was selected from BC₄F_3:5 of CCRI45 (Gossypium hirsutum) × Hai1 (Gossypium barbadense) was used to construct 3 secondary segregating populations with 2 generations (BC₅F₂ and BC₅F_2:3). Eighty-one polymorphic markers related to 33 chromosome introgressive segments on 18 chromosomes were finally screened using 2292 SSR markers which covered the whole tetraploid cotton genome. A total of 129 quantitative trait loci (QTL) associated with fiber quality (103) and yield-related traits (26) were detected on 17 chromosomes, explaining 0.85-30.35% of the phenotypic variation; 39 were stable (30.2%), 53 were common (41.1%), 76 were new (58.9%), and 86 had favorable effects on the related traits. More QTL were distributed in the Dt subgenome than in the At subgenome. Twenty-five stable QTL clusters (with stable or common QTL) were detected on 22 chromosome introgressed segments. Finally, the 6 important chromosome introgressed segments (Seg-A02-1, Seg-A06-1, Seg-A07-2, Seg-A07-3, Seg-D07-3, and Seg-D06-2) were identified as candidate chromosome regions for fiber quality, which should be given more attention in future QTL fine mapping, gene cloning, and marker-assisted selection (MAS) breeding.

Genome-Wide Association Study of Major Agronomic Traits in Foxtail Millet (Setaria italica L.) Using ddRAD Sequencing

Foxtail millet (Setaria italica), the second largest cultivated millet crop after pearl millet, is utilized for food and forage globally. Further, it is also considered as a model crop for studying agronomic, nutritional and biofuel traits. In the present study, a genome-wide association study (GWAS) was performed for ten important agronomic traits in 142 foxtail millet core eco-geographically diverse genotypes using 10 K SNPs developed through GBS-ddRAD approach. Number of SNPs on individual chromosome ranged from 844 (chromosome 5) to 2153 (chromosome 8) with an average SNP frequency of 25.9 per Mb. The pairwise linkage disequilibrium (LD) estimated using the squared-allele frequency correlations was found to decay rapidly with the genetic distance of 177 Kb. However, for individual chromosome, LD decay distance ranged from 76 Kb (chromosome 6) to 357 Kb (chromosome 4). GWAS identified 81 MTAs (marker-trait associations) for ten traits across the genome. High confidence MTAs for three important agronomic traits including FLW (flag leaf width), GY (grain yield) and TGW (thousand-grain weight) were identified. Significant pyramiding effect of identified MTAs further supplemented its importance in breeding programs. Desirable alleles and superior genotypes identified in the present study may prove valuable for foxtail millet improvement through marker-assisted selection.

QTL analysis for yield and fibre quality traits using three sets of introgression lines developed from three Gossypium hirsutum race stocks

Upland cotton (Gossypium hirsutum L.) race stocks may possess desirable traits for the genetic improvement of cotton. Quantitative trait locus (QTL) analysis can assist in uncovering new alleles from unadapted race stocks. In this study, three sets of chromosome segment introgression lines (ILs) were developed from three backcrosses (BC₃) between three race stocks, G. hirsutum races latifolium accs. TX-34 and TX-48 and punctatum acc. TX-114, as donor parents and Texas Marker-1 (TM-1) as the recurrent parent. Based on a total of 452 polymorphic simple sequence repeat (SSR) markers in BC₃F₂ genotyping, 149, 150 and 184 ILs were obtained from TM-1 × TX-34, TM-1 × TX-48 and TM-1 × TX-114, respectively. The average introgressed chromosomal segment length was 12.7 cM, and the total genetic distance was 3268 cM covering approximately 73.4% of the Upland cotton genome. The BC₃F₂, BC₃F_2:3 and BC₃F_2:4 progeny, which produced the ILs, were evaluated for yield and fibre quality traits. A total of 128 QTLs were detected, each of which explained 1.6-13.0% of the phenotypic variation. Thirty-five common QTLs related to eight traits were detected. Six QTL clusters were found on five chromosomes. Thirty-eight QTLs were previously unreported, and they may be footprints of cotton domestication. Domestication or artificial selection by humans successfully eliminated most unfavourable QTLs (21/38); however, some favourable QTLs (17/38) are not present in modern cultivars, demonstrating the importance of race stocks for improving cotton cultivars. The 26 elite ILs developed could be used to improve the yield and fibre quality components simultaneously. These results provide information on desirable QTLs for cotton improvement.

Identification of Loci and Candidate Genes Responsible for Fiber Length in Upland Cotton (Gossypium hirsutum L.) via Association Mapping and Linkage Analyses

Fiber length (FL) is an important fiber quality trait in cotton. Although many fiber quality quantitative trait loci (QTL) responsible for FL have been identified, most cannot be applied to breeding programs, mainly due to unstable environments or large confidence intervals. In this study, we combined a genome-wide association study (GWAS) and linkage mapping to identify and validate high-quality QTLs responsible for FL. For the GWAS, we developed 93,250 high-quality single-nucleotide polymorphism (SNP) markers based on 355 accessions, and the FL was measured in eight different environments. For the linkage mapping, we constructed an F ₂ population from two extreme accessions. The high-density linkage maps spanned 3,848.29 cM, with an average marker interval of 1.41 cM. In total, 14 and 13 QTLs were identified in the association and linkage mapping analyses, respectively. Most importantly, a major QTL on chromosome D03 identified in both populations explained more than 10% of the phenotypic variation (PV). Furthermore, we found that a sucrose synthesis-related gene (Gh_D03G1338) was associated with FL in this QTL region. The RNA-seq data showed that Gh_D03G1338 was highly expressed during the fiber development stage, and the qRT-PCR analysis showed significant expression differences between the long fiber and short fiber varieties. These results suggest that Gh_D03G1338 may determine cotton fiber elongation by regulating the synthesis of sucrose. Favorable QTLs and candidate genes should be useful for increasing fiber quality in cotton breeding.

Characterization and Identification of Methods for Phenotyping Soybean Populations With Tolerance to the Soybean Aphid (Hemiptera: Aphididae)

The development of soybeans tolerant to the soybean aphid [Aphis glycines Matsumura (Hemiptera: Aphididae)] remains unexplored. The objectives of this research were to determine the susceptibility of two high-yielding soybean [Glycine max (L.) Merrill (Fabales: Fabaceae)] genotypes involved in a breeding platform to develop aphid-tolerant recombinant inbred lines (RILs); characterize the peroxidase activity and relative expression of peroxidase transcripts in the parents of RILs; and identify an assay to phenotype aphid-tolerant RILs. Enzyme kinetic assays documented the total peroxidase activity for tolerant (KS4202), susceptible (SD76R), and two high-yielding (U09-105007 and U11-611112) soybeans during two vegetative stages (V1 and V3) at three sampling days (D4, D6, and D8 after aphid introduction). Enzyme kinetic assays showed that V3 infested tolerant and U11-611112 plants had significantly higher peroxidase activity than their respective control plants at D4, and infested tolerant plants were also higher than control plants at D6. There were no apparent trends when comparing the expression of peroxidase-specific transcripts in the absence of aphids (basal levels) in both V1 and V3. Relative expression analyses of two peroxidase transcripts (PRX52 and PRX2) performed to compare differences among the soybean genotypes indicated that, despite basal levels being similar for the treatments analyzed, tolerant soybeans had a tendency for a higher expression of PRX52 in the presence of aphids. Based on the different patterns observed and the feasibility of analyses performed in this study, enzyme kinetics using V3 infested plants may be a marker for screening RILs in a breeding program targeting the development of aphid-tolerant soybeans.

A high-density genetic map of extra-long staple cotton (Gossypium barbadense) constructed using genotyping-by-sequencing based single nucleotide polymorphic markers and identification of fiber traits-related QTL in a recombinant inbred line population

Background

Gossypium barbadense (Sea Island, Egyptian or Pima cotton) cotton has high fiber quality, however, few studies have investigated the genetic basis of its traits using molecular markers. Genome complexity reduction approaches such as genotyping-by-sequencing have been utilized to develop abundant markers for the construction of high-density genetic maps to locate quantitative trait loci (QTLs).

Results

The Chinese G. barbadense cultivar 5917 and American Pima S-7 were used to develop a recombinant inbred line (RIL) population with 143 lines. The 143 RILs together with their parents were tested in three replicated field tests for lint yield traits (boll weight and lint percentage) and fiber quality traits (fiber length, fiber elongation, fiber strength, fiber uniformity and micronaire) and then genotyped using GBS to develop single-nucleotide polymorphism (SNP) markers. A high-density genetic map with 26 linkage groups (LGs) was constructed using 3557 GBS SNPs spanning a total genetic distance of 3076.23 cM at an average density of 1.09 cM between adjacent markers. A total of 42 QTLs were identified, including 24 QTLs on 12 LGs for fiber quality and 18 QTLs on 7 LGs for lint yield traits, with LG1 (9 QTLs), LG10 (7 QTLs) and LG14 (6 QTLs) carrying more QTLs. Common QTLs for the same traits and overlapping QTLs for different traits were detected. Each individual QTLs explained 0.97 to 20.7% of the phenotypic variation.

Conclusions

This study represents one of the first genetic mapping studies on the fiber quality and lint yield traits in a RIL population of G. barbadense using GBS-SNPs. The results provide important information for the subsequent fine mapping of QTLs and the prediction of candidate genes towards map-based cloning and marker-assisted selection in cotton.

GBS Mapping and Analysis of Genes Conserved between Gossypium tomentosum and Gossypium hirsutum Cotton Cultivars that Respond to Drought Stress at the Seedling Stage of the BC₂F₂ Generation

Cotton production is on the decline due to ever-changing environmental conditions. Drought and salinity stress contribute to over 30% of total loss in cotton production, the situation has worsened more due to the narrow genetic base of the cultivated upland cotton. The genetic diversity of upland cotton has been eroded over the years due to intense selection and inbreeding. To break the bottleneck, the wild cotton progenitors offer unique traits which can be introgressed into the cultivated cotton, thereby improving their performance. In this research, we developed a BC₂F₂ population between wild male parent, G. tomentosum as the donor, known for its high tolerance to drought and the elite female parent, G. hirsutum as the recurrent parent, which is high yielding but sensitive to drought stress. The population was genotyped through the genotyping by sequencing (GBS) method, in which 10,888 single-nucleotide polymorphism (SNP) s were generated and used to construct a genetic map. The map spanned 4191.3 cM, with average marker distance of 0.3849 cM. The map size of the two sub genomes had a narrow range, 2149 cM and 2042.3 cM for At and Dt_sub genomes respectively. A total of 66,434 genes were mined, with 32,032 (48.2%) and 34,402 (51.8%) genes being obtained within the At and Dt_sub genomes respectively. Pkinase (PF00069) was found to be the dominant domain, with 1069 genes. Analysis of the main sub family, serine threonine protein kinases through gene ontology (GO), cis element and miRNA targets analysis revealed that most of the genes were involved in various functions aimed at enhancing abiotic stress tolerance. Further analysis of the RNA sequence data and qRT-PCR validation revealed 16 putative genes, which were highly up regulated under drought stress condition, and were found to be targeted by ghr-miR169a and ghr-miR164, previously associated with NAC(NAM, ATAF1/2 and CUC2) and myeloblastosis (MYB), the top rank drought stress tolerance genes. These genes can be exploited further to aid in development of more drought tolerant cotton genotypes.

Genetic Analysis of Gossypium Fiber Quality Traits in Reciprocal Advanced Backcross Populations

In mapping populations segregating for many loci, the large amount of variation among genotypes often masks small-effect quantitative trait loci (QTL). This problem can be reduced by development of populations with fewer chromosome segments segregating. Here, we report early QTL detection in reciprocal advanced backcross populations from crosses between elite Gossypium hirsutum L. 'Acala Maxxa' (GH) and G. barbadense L. 'Pima S6' (GB). A total of 297 BCF and BCF progeny rows-127 segregating for GB chromosome segments in GH background and 170 segregating for GH chromosome segments in GB background-were evaluated in three environments. Totals of 3186 and 3026 polymorphic single-nucleotide polymorphisms (SNPs) in GH and GB backgrounds, respectively, were identified and used for trait mapping. Small-effect QTL (<10% variance explained) made up 87 and 100% of QTL in GH and GB backgrounds, respectively. In both species, favorable alleles were found with effects being masked or neutralized by unfavorable alleles, with greater scope for improvement of GH than GB by introgressive breeding. A total of three stable QTL-two in GH background for fiber elongation (ELO) and micronaire (MIC) and one in GB background for upper-half mean length (UHM)-were identified in two out of three environments. Curiously, only four QTL-three for UHM and one for ELO-showed the expected opposite effects in reciprocal backgrounds, perhaps reflecting the combined consequences of epistasis, small phenotypic effects, and low coverage of some genomic regions. Along with new information for marker-assisted breeding, this study adds to knowledge that can be used to unravel complex genetic networks governing fiber quality traits.

QTL Mapping of Fiber Quality and Yield-Related Traits in an Intra-Specific Upland Cotton Using Genotype by Sequencing (GBS)

Fiber quality and yield improvement are crucial for cotton domestication and breeding. With the transformation in spinning techniques and multiplicity needs, the development of cotton fiber quality and yield is of great importance. A genetic map of 5178 Single Nucleotide Polymorphism (SNP) markers were generated using 277 F2:3 population, from an intra-specific cross between two upland cotton accessions, CCRI35 a high fiber quality as female and Nan Dan Ba Di Da Hua (NH), with good yield properties as male parent. The map spanned 4768.098 cM with an average distance of 0.92 cM. A total of 110 Quantitative Traits Loci (QTLs) were identified for 11 traits, but only 30 QTLs were consistent in at least two environments. The highest percentage of phenotypic variance explained by a single QTL was 15.45%. Two major cluster regions were found, cluster 1 (chromosome17-D03) and cluster 2 (chromosome26-D12). Five candidate genes were identified in the two QTL cluster regions. Based on GO functional annotation, all the genes were highly correlated with fiber development, with functions such as protein kinase and phosphorylation. The five genes were associated with various fiber traits as follows: Gh_D03G0889 linked to qFM-D03_cb, Gh_D12G0093, Gh_D12G0410, Gh_D12G0435 associated with qFS-D12_cb and Gh_D12G0969 linked to qFY-D12_cb. Further structural annotation and fine mapping is needed to determine the specific role played by the five identified genes in fiber quality and yield related pathway.

QTL Mapping for Fiber Quality and Yield Traits Based on Introgression Lines Derived from Gossypium hirsutum × G. tomentosum

The tetraploid species Gossypium hirsutum is cultivated widely throughout the world with high yield and moderate fiber quality, but its genetic basis is narrow. A set of 107 introgression lines (ILs) was developed with an interspecific cross using G. hirsutumacc. 4105 as the recurrent parent and G. tomentosum as the donor parent. A specific locus amplified fragment sequencing (SLAF-seq) strategy was used to obtain high-throughput single nucleotide polymorphism (SNP) markers. In total, 3157 high-quality SNP markers were obtained and further used for identification of quantitative trait loci (QTLs) for fiber quality and yield traits evaluated in multiple environments. In total, 74 QTLs were detected that were associated with five fiber quality traits (30 QTLs) and eight yield traits (44 QTLs), with 2.02-30.15% of the phenotypic variance explained (PVE), and 69 markers were found to be associated with these thirteen traits. Eleven chromosomes in the A sub-genome (At) harbored 47 QTLs, and nine chromosomes in the D sub-genome (Dt) harbored 27 QTLs. More than half (44 QTLs = 59.45%) showed positive additive effects for fiber and yield traits. Five QTL clusters were identified, with three in the At, comprised of thirteen QTLs, and two in the Dt comprised of seven QTLs. The ILs developed in this study and the identified QTLs will facilitate further molecular breeding for improvement of Upland cotton in terms of higher yield with enhanced fiber quality.

Identifying Functional Genes Influencing Gossypium hirsutum Fiber Quality

Fiber quality is an important economic index and a major breeding goal in cotton, but direct phenotypic selection is often hindered due to environmental influences and linkage with yield traits. A genome-wide association study (GWAS) is a powerful tool to identify genes associated with phenotypic traits. In this study, we identified fiber quality genes in upland cotton (Gossypium hirsutum L.) using GWAS based on a high-density CottonSNP80K array and multiple environment tests. A total of 30 and 23 significant single nucleotide polymorphisms (SNPs) associated with five fiber quality traits were identified across the 408 cotton accessions in six environments and the best linear unbiased predictions, respectively. Among these SNPs, seven loci were the same, and 128 candidate genes were predicted in a 1-Mb region (±500 kb of the peak SNP). Furthermore, two major genome regions (GR1 and GR2) associated with multiple fiber qualities in multiple environments on chromosomes A07 and A13 were identified, and within them, 22 candidate genes were annotated. Of these, 11 genes were expressed [log₂(1 + FPKM)>1] in the fiber development stages (5, 10, 20, and 25 dpa) using RNA-Seq. This study provides fundamental insight relevant to identification of genes associated with fiber quality and will accelerate future efforts toward improving fiber quality of upland cotton.

Genomic Selection Outperforms Marker Assisted Selection for Grain Yield and Physiological Traits in a Maize Doubled Haploid Population Across Water Treatments

To increase genetic gain for tolerance to drought, we aimed to identify environmentally stable QTL in per se and testcross combination under well-watered (WW) and drought stressed (DS) conditions and evaluate the possible deployment of QTL using marker assisted and/or genomic selection (QTL/GS-MAS). A total of 169 doubled haploid lines derived from the cross between CML495 and LPSC7F64 and 190 testcrosses (tester CML494) were evaluated in a total of 11 treatment-by-population combinations under WW and DS conditions. In response to DS, grain yield (GY) and plant height (PHT) were reduced while time to anthesis and the anthesis silking interval (ASI) increased for both lines and hybrids. Forty-eight QTL were detected for a total of nine traits. The allele derived from CML495 generally increased trait values for anthesis, ASI, PHT, the normalized difference vegetative index (NDVI) and the green leaf area duration (GLAD; a composite trait of NDVI, PHT and senescence) while it reduced trait values for leaf rolling and senescence. The LOD scores for all detected QTL ranged from 2.0 to 7.2 explaining 4.4 to 19.4% of the observed phenotypic variance with R² ranging from 0 (GY, DS, lines) to 37.3% (PHT, WW, lines). Prediction accuracy of the model used for genomic selection was generally higher than phenotypic variance explained by the sum of QTL for individual traits indicative of the polygenic control of traits evaluated here. We therefore propose to use QTL-MAS in forward breeding to enrich the allelic frequency for a few desired traits with strong additive QTL in early selection cycles while GS-MAS could be used in more mature breeding programs to additionally capture alleles with smaller additive effects.

Activity Recognition Using Complex Network Analysis

In this paper, we perform complex network analysis on a connectivity dataset retrieved from a monitoring system in order to classify simple daily activities. The monitoring system is composed of a set of wearable sensing modules positioned on the subject's body and the connectivity data consists of the correlation between each pair of modules. A number of network measures are then computed followed by the application of statistical significance and feature selection methods. These methods were implemented for the purpose of reducing the total number of modules in the monitoring system required to provide accurate activity classification. The obtained results show that an overall accuracy of 84.6% for activity classification is achieved, using a random forest classifier, and when considering a monitoring system composed of only two modules positioned at the neck and thigh of the subject's body.