1
|
Liú R, Xiāo X, Gōng J, Lǐ J, Yán H, Gě Q, Lú Q, Lǐ P, Pān J, Shāng H, Shí Y, Chén Q, Yuán Y, Gǒng W. Genetic linkage analysis of stable QTLs in Gossypium hirsutum RIL population revealed function of GhCesA4 in fiber development. J Adv Res 2023:S2090-1232(23)00379-X. [PMID: 38065406 DOI: 10.1016/j.jare.2023.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/27/2023] [Accepted: 12/02/2023] [Indexed: 02/12/2024] Open
Abstract
INTRODUCTION Upland cotton is an important allotetrapolyploid crop providing natural fibers for textile industry. Under the present high-level breeding and production conditions, further simultaneous improvement of fiber quality and yield is facing unprecedented challenges due to their complex negative correlations. OBJECTIVES The study was to adequately identify quantitative trait loci (QTLs) and dissect how they orchestrate the formation of fiber quality and yield. METHODS A high-density genetic map (HDGM) based on an intraspecific recombinant inbred line (RIL) population consisting of 231 individuals was used to identify QTLs and QTL clusters of fiber quality and yield traits. The weighted gene correlation network analysis (WGCNA) package in R software was utilized to identify WGCNA network and hub genes related to fiber development. Gene functions were verified via virus-induced gene silencing (VIGS) and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 strategies. RESULTS An HDGM consisting of 8045 markers was constructed spanning 4943.01 cM of cotton genome. A total of 295 QTLs were identified based on multi-environmental phenotypes. Among 139 stable QTLs, including 35 newly identified ones, seventy five were of fiber quality and 64 yield traits. A total of 33 QTL clusters harboring 74 QTLs were identified. Eleven candidate hub genes were identified via WGCNA using genes in all stable QTLs and QTL clusters. The relative expression profiles of these hub genes revealed their correlations with fiber development. VIGS and CRISPR/Cas9 edition revealed that the hub gene cellulose synthase 4 (GhCesA4, GH_D07G2262) positively regulate fiber length and fiber strength formation and negatively lint percentage. CONCLUSION Multiple analyses demonstrate that the hub genes harbored in the QTLs orchestrate the fiber development. The hub gene GhCesA4 has opposite pleiotropic effects in regulating trait formation of fiber quality and yield. The results facilitate understanding the genetic basis of negative correlation between cotton fiber quality and yield.
Collapse
Affiliation(s)
- Ruìxián Liú
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China; Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, Xinjiang, China
| | - Xiànghuī Xiāo
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China; Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, Xinjiang, China; College of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang 455000, Henan, China
| | - Jǔwǔ Gōng
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China; Zhengzhou Research Base, National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Jùnwén Lǐ
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China; Zhengzhou Research Base, National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Hàoliàng Yán
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Qún Gě
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China; Zhengzhou Research Base, National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Quánwěi Lú
- College of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang 455000, Henan, China
| | - Péngtāo Lǐ
- College of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang 455000, Henan, China
| | - Jìngtāo Pān
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Hǎihóng Shāng
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China; Zhengzhou Research Base, National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Yùzhēn Shí
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Qúanjiā Chén
- Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, Xinjiang, China.
| | - Yǒulù Yuán
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China; Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, Xinjiang, China; Zhengzhou Research Base, National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Zhengzhou University, Zhengzhou 450001, Henan, China.
| | - Wànkuí Gǒng
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China.
| |
Collapse
|
2
|
Joshi B, Singh S, Tiwari GJ, Kumar H, Boopathi NM, Jaiswal S, Adhikari D, Kumar D, Sawant SV, Iquebal MA, Jena SN. Genome-wide association study of fiber yield-related traits uncovers the novel genomic regions and candidate genes in Indian upland cotton ( Gossypium hirsutum L.). FRONTIERS IN PLANT SCIENCE 2023; 14:1252746. [PMID: 37941674 PMCID: PMC10630025 DOI: 10.3389/fpls.2023.1252746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 09/11/2023] [Indexed: 11/10/2023]
Abstract
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.
Collapse
Affiliation(s)
- Babita Joshi
- Plant Genetic Resources and Improvement, CSIR-National Botanical Research Institute, Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Sanjay Singh
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Gopal Ji Tiwari
- Plant Genetic Resources and Improvement, CSIR-National Botanical Research Institute, Lucknow, India
| | - Harish Kumar
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Regional Research Station, Faridkot, Punjab, India
| | - Narayanan Manikanda Boopathi
- Department of Plant Biotechnology, Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
| | - Sarika Jaiswal
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Dibyendu Adhikari
- Plant Ecology and Climate Change Science, CSIR-National Botanical Research Institute, Lucknow, India
| | - Dinesh Kumar
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Samir V. Sawant
- Molecular Biology & Biotechnology, CSIR-National Botanical Research Institute, Lucknow, India
| | - Mir Asif Iquebal
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Satya Narayan Jena
- Plant Genetic Resources and Improvement, CSIR-National Botanical Research Institute, Lucknow, India
| |
Collapse
|
3
|
Han Z, Ke H, Li X, Peng R, Zhai D, Xu Y, Wu L, Wang W, Cui Y. Detection of epistasis interaction loci for fiber quality-related trait via 3VmrMLM in upland cotton. FRONTIERS IN PLANT SCIENCE 2023; 14:1250161. [PMID: 37841603 PMCID: PMC10568130 DOI: 10.3389/fpls.2023.1250161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/04/2023] [Indexed: 10/17/2023]
Abstract
Cotton fiber quality-related traits, such as fiber length, fiber strength, and fiber elongation, are affected by complex mechanisms controlled by multiple genes. Determining the QTN-by-QTN interactions (QQIs) associated with fiber quality-related traits is therefore essential for accelerating the genetic enhancement of cotton breeding. In this study, a natural population of 1,245 upland cotton varieties with 1,122,352 SNPs was used for detecting the main-effect QTNs and QQIs using the 3V multi-locus random-SNP-effect mixed linear model (3VmrMLM) method. A total of 171 significant main-effect QTNs and 42 QQIs were detected, of which 22 were both main-effect QTNs and QQIs. Of the detected 42 QQIs, a total of 13 significant loci and 5 candidate genes were reported in previous studies. Among the three interaction types, the AD interaction type has a preference for the trait of FE. Additionally, the QQIs have a substantial impact on the enhancement predictability for fiber quality-related traits. The study of QQIs is crucial for elucidating the genetic mechanism of cotton fiber quality and enhancing breeding efficiency.
Collapse
Affiliation(s)
- Zhimin Han
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Baoding, China
| | - Huifeng Ke
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Baoding, China
| | - Xiaoyu Li
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Baoding, China
| | - Ruoxuan Peng
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Baoding, China
| | - Dongdong Zhai
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Baoding, China
| | - Yang Xu
- Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou, Jiangsu, China
| | - Liqiang Wu
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Baoding, China
| | - Wensheng Wang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, China
| | - Yanru Cui
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Baoding, China
| |
Collapse
|
4
|
Zhao T, Wu H, Wang X, Zhao Y, Wang L, Pan J, Mei H, Han J, Wang S, Lu K, Li M, Gao M, Cao Z, Zhang H, Wan K, Li J, Fang L, Zhang T, Guan X. Integration of eQTL and machine learning to dissect causal genes with pleiotropic effects in genetic regulation networks of seed cotton yield. Cell Rep 2023; 42:113111. [PMID: 37676770 DOI: 10.1016/j.celrep.2023.113111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/19/2023] [Accepted: 08/24/2023] [Indexed: 09/09/2023] Open
Abstract
The dissection of a gene regulatory network (GRN) that complements the genome-wide association study (GWAS) locus and the crosstalk underlying multiple agronomical traits remains a major challenge. In this study, we generate 558 transcriptional profiles of lint-bearing ovules at one day post-anthesis from a selective core cotton germplasm, from which 12,207 expression quantitative trait loci (eQTLs) are identified. Sixty-six known phenotypic GWAS loci are colocalized with 1,090 eQTLs, forming 38 functional GRNs associated predominantly with seed yield. Of the eGenes, 34 exhibit pleiotropic effects. Combining the eQTLs within the seed yield GRNs significantly increases the portion of narrow-sense heritability. The extreme gradient boosting (XGBoost) machine learning approach is applied to predict seed cotton yield phenotypes on the basis of gene expression. Top-ranking eGenes (NF-YB3, FLA2, and GRDP1) derived with pleiotropic effects on yield traits are validated, along with their potential roles by correlation analysis, domestication selection analysis, and transgenic plants.
Collapse
Affiliation(s)
- Ting Zhao
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Hongyu Wu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Xutong Wang
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Yongyan Zhao
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Luyao Wang
- Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Jiaying Pan
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Huan Mei
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Jin Han
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Siyuan Wang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Kening Lu
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Menglin Li
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Mengtao Gao
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Zeyi Cao
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Hailin Zhang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Ke Wan
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Jie Li
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Lei Fang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Tianzhen Zhang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Xueying Guan
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China.
| |
Collapse
|
5
|
Sun F, Yang Y, Wang P, Ma J, Du X. Quantitative trait loci and candidate genes for yield-related traits of upland cotton revealed by genome-wide association analysis under drought conditions. BMC Genomics 2023; 24:531. [PMID: 37679709 PMCID: PMC10485960 DOI: 10.1186/s12864-023-09640-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/30/2023] [Indexed: 09/09/2023] Open
Abstract
BACKGROUND Due to the influence of extreme weather, the environment in China's main cotton-producing areas is prone to drought stress conditions, which affect the growth and development of cotton and lead to a decrease in cotton yield. RESULTS In this study, 188 upland cotton germplasm resources were phenotyped for data of 8 traits (including 3 major yield traits) under drought conditions in three environments for two consecutive years. Correlation analysis revealed significant positive correlations between the three yield traits. Genetic analysis showed that the estimated heritability of the seed cotton index (SC) under drought conditions was the highest (80.81%), followed by that of boll weight (BW) (80.64%) and the lint cotton index (LC) (70.49%) With genome-wide association study (GWAS) analysis, a total of 75 quantitative trait loci (QTLs) were identified, including two highly credible new QTL hotspots. Three candidate genes (Gh_D09G064400, Gh_D10G261000 and Gh_D10G254000) located in the two new QTL hotspots, QTL51 and QTL55, were highly expressed in the early stage of fiber development and showed significant correlations with SC, LC and BW. The expression of three candidate genes in two extreme materials after drought stress was analyzed by qRT-PCR, and the expression of these two materials in fibers at 15, 20 and 25 DPA. The expression of these three candidate genes was significantly upregulated after drought stress and was significantly higher in drought-tolerant materials than in drought-sensitive materials. In addition, the expression levels of the three candidate genes were higher in the early stage of fiber development (15 DPA), and the expression levels in drought-tolerant germplasm were higher than those in drought-sensitive germplasm. These three candidate genes may play an important role in determining cotton yield under drought conditions. CONCLUSIONS This study is helpful for understanding the regulatory genes affecting cotton yield under drought conditions and provides germplasm and candidate gene resources for breeding high-yield cotton varieties under these conditions.
Collapse
Affiliation(s)
- Fenglei Sun
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- Hainan Yazhou Bay Seed Laboratory, Sanya, Hainan, 572000, China
| | - Yanlong Yang
- Research Institute of Economic Crops, Xinjiang Academy of Agricultural Sciences, Urumqi, 830091, China.
| | - Penglong Wang
- Research Institute of Economic Crops, Xinjiang Academy of Agricultural Sciences, Urumqi, 830091, China
| | - Jun Ma
- Research Institute of Economic Crops, Xinjiang Academy of Agricultural Sciences, Urumqi, 830091, China
| | - Xiongming Du
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
| |
Collapse
|
6
|
Yang XQ, Li W, Ren ZY, Zhao JJ, Li XY, Wang XX, Pei XY, Liu YG, He KL, Zhang F, Ma XF, Yang DG. GhSINA1, a SEVEN in ABSENTIA ubiquitin ligase, negatively regulates fiber development in Upland cotton. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2023; 201:107853. [PMID: 37385030 DOI: 10.1016/j.plaphy.2023.107853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 05/29/2023] [Accepted: 06/18/2023] [Indexed: 07/01/2023]
Abstract
Protein ubiquitination is essential for plant growth and responses to the environment. The SEVEN IN ABSENTIA (SINA) ubiquitin ligases have been extensively studied in plants, but information on their roles in fiber development is limited. Here, we identified GhSINA1 in Upland cotton (Gossypium hirsutum), which has a conserved RING finger domain and SINA domain. Quantitative real-time PCR (qRT-PCR) analysis showed that GhSINA1 was preferentially expressed during fiber initiation and elongation, especially during initiation in the fuzzless-lintless cotton mutant. Subcellular localization experiments indicated that GhSINA1 localized to the nucleus. In vitro ubiquitination analysis revealed that GhSINA1 has E3 ubiquitin ligase activity. Ectopic overexpression of GhSINA1 in Arabidopsis thaliana reduced the number and length of root hairs and trichomes. Yeast two-hybrid (Y2H), firefly luciferase complementation imaging (LCI), and bimolecular fluorescence complementation (BiFC) assays demonstrated that the GhSINA1 proteins could interact with each other to form homodimers and heterodimers. Overall, these results suggest that GhSINA1 may act as a negative regulator in cotton fiber development through homodimerization and heterodimerization.
Collapse
Affiliation(s)
- Xiao-Qing Yang
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Wei Li
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China; Zhengzhou Research Base, National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, 450001, China; Western Agricultural Research Center, Chinese Academy of Agricultural Sciences, Changji, 831100, China.
| | - Zhong-Ying Ren
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Jun-Jie Zhao
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Xin-Yang Li
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Xing-Xing Wang
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Xiao-Yu Pei
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Yan-Gai Liu
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Kun-Lun He
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Fei Zhang
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Xiong-Feng Ma
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China; Zhengzhou Research Base, National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, 450001, China; Western Agricultural Research Center, Chinese Academy of Agricultural Sciences, Changji, 831100, China.
| | - Dai-Gang Yang
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China; Zhengzhou Research Base, National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, 450001, China; Western Agricultural Research Center, Chinese Academy of Agricultural Sciences, Changji, 831100, China.
| |
Collapse
|
7
|
Huo WQ, Zhang ZQ, Ren ZY, Zhao JJ, Song CX, Wang XX, Pei XY, Liu YG, He KL, Zhang F, Li XY, Li W, Yang DG, Ma XF. Unraveling genomic regions and candidate genes for multiple disease resistance in upland cotton using meta-QTL analysis. Heliyon 2023; 9:e18731. [PMID: 37576216 PMCID: PMC10412778 DOI: 10.1016/j.heliyon.2023.e18731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/15/2023] [Accepted: 07/25/2023] [Indexed: 08/15/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Wen-Qi Huo
- Zhengzhou Research Base, National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, 450001, China
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Zhi-Qiang Zhang
- Zhengzhou Research Base, National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, 450001, China
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Zhong-Ying Ren
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Jun-Jie Zhao
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Cheng-Xiang Song
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Xing-Xing Wang
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Xiao-Yu Pei
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Yan-Gai Liu
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Kun-Lun He
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Fei Zhang
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Xin-Yang Li
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Wei Li
- Zhengzhou Research Base, National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, 450001, China
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- Western Agricultural Research Center, Chinese Academy of Agricultural Sciences, Changji, 831100, China
| | - Dai-Gang Yang
- Zhengzhou Research Base, National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, 450001, China
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- Western Agricultural Research Center, Chinese Academy of Agricultural Sciences, Changji, 831100, China
| | - Xiong-Feng Ma
- Zhengzhou Research Base, National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, 450001, China
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- Western Agricultural Research Center, Chinese Academy of Agricultural Sciences, Changji, 831100, China
| |
Collapse
|
8
|
Rolling WR, Senalik D, Iorizzo M, Ellison S, Van Deynze A, Simon PW. CarrotOmics: a genetics and comparative genomics database for carrot ( Daucus carota). Database (Oxford) 2022; 2022:6693759. [PMID: 36069936 PMCID: PMC9450951 DOI: 10.1093/database/baac079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 08/19/2022] [Accepted: 09/01/2022] [Indexed: 11/15/2022]
Abstract
Abstract
CarrotOmics (https://carrotomics.org/) is a comprehensive database for carrot (Daucus carota L.) breeding and research. CarrotOmics was developed using resources available at the MainLab Bioinformatics core (https://www.bioinfo.wsu.edu/) and is implemented using Tripal with Drupal modules. The database delivers access to download or visualize the carrot reference genome with gene predictions, gene annotations and sequence assembly. Other genomic resources include information for 11 224 genetic markers from 73 linkage maps or genotyping-by-sequencing and descriptions of 371 mapped loci. There are records for 1601 Apiales species (or subspecies) and descriptions of 9408 accessions from 11 germplasm collections representing more than 600 of these species. Additionally, 204 Apiales species have phenotypic information, totaling 28 517 observations from 10 041 biological samples. Resources on CarrotOmics are freely available, search functions are provided to find data of interest and video tutorials are available to describe the search functions and genomic tools. CarrotOmics is a timely resource for the Apiaceae research community and for carrot geneticists developing improved cultivars with novel traits addressing challenges including an expanding acreage in tropical climates, an evolving consumer interested in sustainably grown vegetables and a dynamic environment due to climate change. Data from CarrotOmics can be applied in genomic-assisted selection and genetic research to improve basic research and carrot breeding efficiency.
Database URL
https://carrotomics.org/
Collapse
Affiliation(s)
- William R Rolling
- Vegetable Crop Research Unit, USDA-ARS , Moore Hall, 1575 Linden Drive, Madison, WI 53706-1514, USA
- Department of Horticulture, University of Wisconsin-Madison , Moore Hall, 1575 Linden Drive, Madison, WI 53706-1514, USA
| | - Douglas Senalik
- Vegetable Crop Research Unit, USDA-ARS , Moore Hall, 1575 Linden Drive, Madison, WI 53706-1514, USA
- Department of Horticulture, University of Wisconsin-Madison , Moore Hall, 1575 Linden Drive, Madison, WI 53706-1514, USA
| | - Massimo Iorizzo
- Department of Horticultural Science and Plants for Human Health Institute, North Carolina State University , NC Research Campus, 600 Laureate Way, Kannapolis, NC 28081, USA
| | - Shelby Ellison
- Department of Horticulture, University of Wisconsin-Madison , Moore Hall, 1575 Linden Drive, Madison, WI 53706-1514, USA
| | - Allen Van Deynze
- College of Agricultural & Environmental Sciences, Seed Biotechnology Center, University of California-Davis , 150 Mrak Hall, One Shields Avenue, Davis, CA 95616, USA
| | - Philipp W Simon
- Vegetable Crop Research Unit, USDA-ARS , Moore Hall, 1575 Linden Drive, Madison, WI 53706-1514, USA
- Department of Horticulture, University of Wisconsin-Madison , Moore Hall, 1575 Linden Drive, Madison, WI 53706-1514, USA
| |
Collapse
|
9
|
Duan Y, Chen Q, Chen Q, Zheng K, Cai Y, Long Y, Zhao J, Guo Y, Sun F, Qu Y. Analysis of transcriptome data and quantitative trait loci enables the identification of candidate genes responsible for fiber strength in Gossypium barbadense. G3 GENES|GENOMES|GENETICS 2022; 12:6650278. [PMID: 35881688 PMCID: PMC9434320 DOI: 10.1093/g3journal/jkac167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/23/2022] [Indexed: 11/13/2022]
Abstract
Gossypium barbadense possesses a superior fiber quality because of its fiber length and strength. An in-depth analysis of the underlying genetic mechanism could aid in filling the gap in research regarding fiber strength and could provide helpful information for Gossypium barbadense breeding. Three quantitative trait loci related to fiber strength were identified from a Gossypium barbadense recombinant inbred line (PimaS-7 × 5917) for further analysis. RNA sequencing was performed in the fiber tissues of PimaS-7 × 5917 0–35 days postanthesis. Four specific modules closely related to the secondary wall-thickening stage were obtained using the weighted gene coexpression network analysis. In total, 55 genes were identified as differentially expressed from 4 specific modules. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used for enrichment analysis, and Gbar_D11G032910, Gbar_D08G020540, Gbar_D08G013370, Gbar_D11G033670, and Gbar_D11G029020 were found to regulate fiber strength by playing a role in the composition of structural constituents of cytoskeleton and microtubules during fiber development. Quantitative real-time PCR results confirmed the accuracy of the transcriptome data. This study provides a quick strategy for exploring candidate genes and provides new insights for improving fiber strength in cotton.
Collapse
Affiliation(s)
- Yajie Duan
- College of Agronomy, Xinjiang Agricultural University , Urumqi, Xinjiang 830052, China
| | - Qin Chen
- College of Agronomy, Xinjiang Agricultural University , Urumqi, Xinjiang 830052, China
| | - Quanjia Chen
- College of Agronomy, Xinjiang Agricultural University , Urumqi, Xinjiang 830052, China
| | - Kai Zheng
- College of Agronomy, Xinjiang Agricultural University , Urumqi, Xinjiang 830052, China
| | - Yongsheng Cai
- College of Agronomy, Xinjiang Agricultural University , Urumqi, Xinjiang 830052, China
| | - Yilei Long
- College of Agronomy, Xinjiang Agricultural University , Urumqi, Xinjiang 830052, China
| | - Jieyin Zhao
- College of Agronomy, Xinjiang Agricultural University , Urumqi, Xinjiang 830052, China
| | - Yaping Guo
- College of Agronomy, Xinjiang Agricultural University , Urumqi, Xinjiang 830052, China
| | - Fenglei Sun
- College of Agronomy, Xinjiang Agricultural University , Urumqi, Xinjiang 830052, China
| | - Yanying Qu
- College of Agronomy, Xinjiang Agricultural University , Urumqi, Xinjiang 830052, China
| |
Collapse
|
10
|
Deep polygenic neural network for predicting and identifying yield-associated genes in Indonesian rice accessions. Sci Rep 2022; 12:13823. [PMID: 35970979 PMCID: PMC9378700 DOI: 10.1038/s41598-022-16075-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 07/04/2022] [Indexed: 11/12/2022] Open
Abstract
As the fourth most populous country in the world, Indonesia must increase the annual rice production rate to achieve national food security by 2050. One possible solution comes from the nanoscopic level: a genetic variant called Single Nucleotide Polymorphism (SNP), which can express significant yield-associated genes. The prior benchmark of this study utilized a statistical genetics model where no SNP position information and attention mechanism were involved. Hence, we developed a novel deep polygenic neural network, named the NucleoNet model, to address these obstacles. The NucleoNets were constructed with the combination of prominent components that include positional SNP encoding, the context vector, wide models, Elastic Net, and Shannon’s entropy loss. This polygenic modeling obtained up to 2.779 of Mean Squared Error (MSE) with 47.156% of Symmetric Mean Absolute Percentage Error (SMAPE), while revealing 15 new important SNPs. Furthermore, the NucleoNets reduced the MSE score up to 32.28% compared to the Ordinary Least Squares (OLS) model. Through the ablation study, we learned that the combination of Xavier distribution for weights initialization and Normal distribution for biases initialization sparked more various important SNPs throughout 12 chromosomes. Our findings confirmed that the NucleoNet model was successfully outperformed the OLS model and identified important SNPs to Indonesian rice yields.
Collapse
|
11
|
Zhang J, Mei H, Lu H, Chen R, Hu Y, Zhang T. Transcriptome Time-Course Analysis in the Whole Period of Cotton Fiber Development. FRONTIERS IN PLANT SCIENCE 2022; 13:864529. [PMID: 35463423 PMCID: PMC9022538 DOI: 10.3389/fpls.2022.864529] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
Gossypium hirsutum and Gossypium barbadense are the widely cultivated tetraploid cottons around the world, which evolved great differences in the fiber yield and quality due to the independent domestication process. To reveal the genetic basis of the difference, we integrated 90 samples from ten time points during the fiber developmental period for investigating the dynamics of gene expression changes associated with fiber in G. hirsutum acc. TM-1 and G. barbadense cv. Hai7124 and acc. 3-79. Globally, 44,484 genes expressed in all three cultivars account for 61.14% of the total genes. About 61.39% (N = 3,412) of the cotton transcription factors were involved in fiber development, which consisted of 58 cotton TF families. The differential analysis of intra- and interspecies showed that 3 DPA had more expression changes. To discover the genes with temporally changed expression profiles during the whole fiber development, 1,850 genes predominantly expressed in G. hirsutum and 1,050 in G. barbadense were identified, respectively. Based on the weighted gene co-expression network and time-course analysis, several candidate genes, mainly involved in the secondary cell wall synthesis and phytohormones, were identified in this study, underlying possibly the transcriptional regulation and molecular mechanisms of the fiber quality differences between G. barbadense and G. hirsutum. The quantitative real-time PCR validation of the candidate genes was consistent with the RNA-seq data. Our study provides a strong rationale for the analysis of gene function and breeding of high-quality cotton.
Collapse
|
12
|
Song X, Zhu G, Hou S, Ren Y, Amjid MW, Li W, Guo W. Genome-Wide Association Analysis Reveals Loci and Candidate Genes Involved in Fiber Quality Traits Under Multiple Field Environments in Cotton ( Gossypium hirsutum). FRONTIERS IN PLANT SCIENCE 2021; 12:695503. [PMID: 34421946 PMCID: PMC8374309 DOI: 10.3389/fpls.2021.695503] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 06/16/2021] [Indexed: 05/17/2023]
Abstract
Fiber length, fiber strength, and fiber micronaire are the main fiber quality parameters in cotton. Thus, mining the elite and stable loci/alleles related to fiber quality traits and elucidating the relationship between the two may accelerate genetic improvement of fiber quality in cotton. Here, genome-wide association analysis (GWAS) was performed for fiber quality parameters based on phenotypic data, and 56,010 high-quality single nucleotide polymorphisms (SNPs) using 242 upland cotton accessions under 12 field environments were obtained. Phenotypic analysis exhibited that fiber length (FL) had a positive correlation with fiber strength (FS) and had a negative correlation with fiber micronaire (Mic). Genetic analysis also indicated that FL, FS, and Mic had high heritability of more than 80%. A total of 67 stable quantitative trait loci (QTLs) were identified through GWAS analysis, including 31 for FL, 21 for FS, and 22 for Mic. Of them, three pairs homologous QTLs were detected between A and D subgenomes, and seven co-located QTLs with two fiber quality parameters were found. Compared with the reported QTLs, 34 co-located with previous studies, and 33 were newly revealed. Integrated with transcriptome analysis, we selected 256, 244, and 149 candidate genes for FL, FS, and Mic, respectively. Gene Ontology (GO) analysis showed that most of the genes located in QTLs interval of the three fiber quality traits were involved in sugar biosynthesis, sugar metabolism, microtubule, and cytoskeleton organization, which played crucial roles in fiber development. Through correlation analysis between haplotypes and phenotypes, three genes (GH_A05G1494, GH_D11G3097, and GH_A05G1082) predominately expressed in fiber development stages were indicated to be potentially responsible for FL, FS, and Mic, respectively. The GH_A05G1494 encoded a protein containing SGS-domain, which is related to tubulin-binding and ubiquitin-protein ligase binding. The GH_D11G3097 encoded 20S proteasome beta subunit G1, and was involved in the ubiquitin-dependent protein catabolic process. The GH_A05G1082 encoded RAN binding protein 1 with a molecular function of GTPase activator activity. These results provide new insights and candidate loci/genes for the improvement of fiber quality in cotton.
Collapse
|
13
|
Kushanov FN, Turaev OS, Ernazarova DK, Gapparov BM, Oripova BB, Kudratova MK, Rafieva FU, Khalikov KK, Erjigitov DS, Khidirov MT, Kholova MD, Khusenov NN, Amanboyeva RS, Saha S, Yu JZ, Abdurakhmonov IY. Genetic Diversity, QTL Mapping, and Marker-Assisted Selection Technology in Cotton ( Gossypium spp.). FRONTIERS IN PLANT SCIENCE 2021; 12:779386. [PMID: 34975965 PMCID: PMC8716771 DOI: 10.3389/fpls.2021.779386] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 11/23/2021] [Indexed: 02/05/2023]
Abstract
Cotton genetic resources contain diverse economically important traits that can be used widely in breeding approaches to create of high-yielding elite cultivars with superior fiber quality and adapted to biotic and abiotic stresses. Nevertheless, the creation of new cultivars using conventional breeding methods is limited by the cost and proved to be time consuming process, also requires a space to make field observations and measurements. Decoding genomes of cotton species greatly facilitated generating large-scale high-throughput DNA markers and identification of QTLs that allows confirmation of candidate genes, and use them in marker-assisted selection (MAS)-based breeding programs. With the advances of quantitative trait loci (QTL) mapping and genome-wide-association study approaches, DNA markers associated with valuable traits significantly accelerate breeding processes by replacing the selection with a phenotype to the selection at the DNA or gene level. In this review, we discuss the evolution and genetic diversity of cotton Gossypium genus, molecular markers and their types, genetic mapping and QTL analysis, application, and perspectives of MAS-based approaches in cotton breeding.
Collapse
Affiliation(s)
- Fakhriddin N. Kushanov
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
- Department of Biology, National University of Uzbekistan, Tashkent, Uzbekistan
- *Correspondence: Fakhriddin N. Kushanov, ;
| | - Ozod S. Turaev
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Dilrabo K. Ernazarova
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
- Department of Biology, National University of Uzbekistan, Tashkent, Uzbekistan
| | - Bunyod M. Gapparov
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Barno B. Oripova
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
- Department of Biology, National University of Uzbekistan, Tashkent, Uzbekistan
| | - Mukhlisa K. Kudratova
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Feruza U. Rafieva
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Kuvandik K. Khalikov
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Doston Sh. Erjigitov
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Mukhammad T. Khidirov
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Madina D. Kholova
- Institute of Genetics and Plant Experimental Biology, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Naim N. Khusenov
- Center of Genomics and Bioinformatics, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| | - Roza S. Amanboyeva
- Department of Biology, National University of Uzbekistan, Tashkent, Uzbekistan
| | - Sukumar Saha
- Crop Science Research Laboratory, USDA-ARS, Washington, DC, United States
| | - John Z. Yu
- Southern Plains Agricultural Research Center, USDA-ARS, Washington, DC, United States
| | - Ibrokhim Y. Abdurakhmonov
- Center of Genomics and Bioinformatics, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
| |
Collapse
|