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Wang H, Cai X, Umer MJ, Xu Y, Hou Y, Zheng J, Liu F, Wang K, Chen M, Ma S, Yu J, Zhou Z. Genetic Analysis of Cotton Fiber Traits in Gossypium Hybrid Lines. PHYSIOLOGIA PLANTARUM 2024; 176:e14442. [PMID: 39030776 DOI: 10.1111/ppl.14442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/25/2024] [Indexed: 07/22/2024]
Abstract
Cotton plays a crucial role in the progress of the textile industry and the betterment of human life by providing natural fibers. In our study, we explored the genetic determinants of cotton architecture and fiber yield and quality by crossbreeding Gossypium hirsutum and Gossypium barbadense, creating a recombinant inbred line (RIL) population. Utilizing SNP markers, we constructed an extensive genetic map encompassing 7,730 markers over 2,784.2 cM. We appraised two architectural and seven fiber traits within six environments, identifying 58 QTLs, of which 49 demonstrated stability across these environments. These encompassed QTLs for traits such as lint percentage (LP), boll weight (BW), fiber strength (STRENGTH), seed index (SI), and micronaire (MIC), primarily located on chromosomes chr-A07, chr-D06, and chr-D07. Notably, chr-D07 houses a QTL region affecting SI, corroborated by multiple studies. Within this region, the genes BZIP043 and SEP2 were identified as pivotal, with SEP2 particularly showing augmented expression in developing ovules. These discoveries contribute significantly to marker-assisted selection, potentially elevating both the yield and quality of cotton fiber production. These findings provide valuable insights into marker-assisted breeding strategies, offering crucial information to enhance fiber yield and quality in cotton production.
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Affiliation(s)
- Heng Wang
- State Key Laboratory of Cotton Bio-breeding and Integrated Utilization/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), Anyang, Henan, China
| | - Xiaoyan Cai
- State Key Laboratory of Cotton Bio-breeding and Integrated Utilization/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), Anyang, Henan, China
- Hainan Yazhou Bay Seed Laboratory, Sanya 572024, China/ National Nanfan Research Institute (Sanya), Chinese Academy of Agriculture Sciences, Sanya, China
- Henan International Joint Laboratory of Cotton Biology, Anyang, Henan, China
| | - Muhammad Jawad Umer
- State Key Laboratory of Cotton Bio-breeding and Integrated Utilization/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), Anyang, Henan, China
| | - Yanchao Xu
- State Key Laboratory of Cotton Bio-breeding and Integrated Utilization/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), Anyang, Henan, China
| | - Yuqing Hou
- State Key Laboratory of Cotton Bio-breeding and Integrated Utilization/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), Anyang, Henan, China
- Henan International Joint Laboratory of Cotton Biology, Anyang, Henan, China
| | - Jie Zheng
- State Key Laboratory of Cotton Bio-breeding and Integrated Utilization/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), Anyang, Henan, China
- Hainan Yazhou Bay Seed Laboratory, Sanya 572024, China/ National Nanfan Research Institute (Sanya), Chinese Academy of Agriculture Sciences, Sanya, China
- Henan International Joint Laboratory of Cotton Biology, Anyang, Henan, China
| | - Fang Liu
- State Key Laboratory of Cotton Bio-breeding and Integrated Utilization/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), Anyang, Henan, China
- Hainan Yazhou Bay Seed Laboratory, Sanya 572024, China/ National Nanfan Research Institute (Sanya), Chinese Academy of Agriculture Sciences, Sanya, China
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou, China
- Henan International Joint Laboratory of Cotton Biology, Anyang, Henan, China
| | - Kunbo Wang
- State Key Laboratory of Cotton Bio-breeding and Integrated Utilization/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), Anyang, Henan, China
| | - Mengshan Chen
- Chinese Academy of Agricultural Science, Beijing, China
| | | | - Jingzhong Yu
- Standing Committee of the People's Congress of Jiangsu Province, Nanjing, China
| | - Zhongli Zhou
- State Key Laboratory of Cotton Bio-breeding and Integrated Utilization/Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), Anyang, Henan, China
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Li G, Che J, Gong J, Duan L, Zhang Z, Jiang X, Xu P, Fan S, Gong W, Shi Y, Liu A, Li J, Li P, Pan J, Deng X, Yuan Y, Shang H. Quantitative Trait Locus Mapping for Plant Height and Branch Number in CCRI70 Recombinant Inbred Line Population of Upland Cotton (Gossypium hirsutum). PLANTS (BASEL, SWITZERLAND) 2024; 13:1509. [PMID: 38891318 PMCID: PMC11174691 DOI: 10.3390/plants13111509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/08/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024]
Abstract
Upland cotton accounts for a high percentage (95%) of the world's cotton production. Plant height (PH) and branch number (BN) are two important agronomic traits that have an impact on improving the level of cotton mechanical harvesting and cotton yield. In this research, a recombinant inbred line (RIL) population with 250 lines developed from the variety CCRI70 was used for constructing a high-density genetic map and identification of quantitative trait locus (QTL). The results showed that the map harbored 8298 single nucleotide polymorphism (SNP) markers, spanning a total distance of 4876.70 centimorgans (cMs). A total of 69 QTLs for PH (9 stable) and 63 for BN (11 stable) were identified and only one for PH was reported in previous studies. The QTLs for PH and BN harbored 495 and 446 genes, respectively. Combining the annotation information, expression patterns and previous studies of these genes, six genes could be considered as potential candidate genes for PH and BN. The results could be helpful for cotton researchers to better understand the genetic mechanism of PH and BN development, as well as provide valuable genetic resources for cotton breeders to manipulate cotton plant architecture to meet future demands.
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Affiliation(s)
- Gangling Li
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, China; (G.L.); (J.C.)
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
| | - Jincan Che
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, China; (G.L.); (J.C.)
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Juwu Gong
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
| | - Li Duan
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Key Laboratory of Plant Stress Biology, College of Life Science, Henan University, Kaifeng 475001, China
| | - Zhen Zhang
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
| | - Xiao Jiang
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
| | - Peng Xu
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
| | - Senmiao Fan
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
| | - Wankui Gong
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
| | - Yuzhen Shi
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
| | - Aiying Liu
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
| | - Junwen Li
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
| | - Pengtao Li
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
| | - Jingtao Pan
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
| | - Xiaoying Deng
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
| | - Youlu Yuan
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, China; (G.L.); (J.C.)
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
| | - Haihong Shang
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, China; (G.L.); (J.C.)
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (J.G.); (L.D.); (X.J.); (P.X.); (S.F.); (W.G.); (A.L.); (J.L.); (P.L.); (J.P.); (X.D.)
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Li Z, Zhu QH, Moncuquet P, Wilson I, Llewellyn D, Stiller W, Liu S. Quantitative genomics-enabled selection for simultaneous improvement of lint yield and seed traits in cotton (Gossypium hirsutum L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:142. [PMID: 38796822 PMCID: PMC11128407 DOI: 10.1007/s00122-024-04645-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 05/04/2024] [Indexed: 05/29/2024]
Abstract
KEY MESSAGE A Bayesian linkage disequilibrium-based multiple-locus mixed model identified QTLs for fibre, seed and oil traits and predicted breeding worthiness of test lines, enabling their simultaneous improvement in cotton. Improving cotton seed and oil yields has become increasingly important while continuing to breed for higher lint yield. In this study, a novel Bayesian linkage disequilibrium-based multiple-locus mixed model was developed for QTL identification and genomic prediction (GP). A multi-parent population consisting of 256 recombinant inbred lines, derived from four elite cultivars with distinct combinations of traits, was used in the analysis of QTLs for lint percentage, seed index, lint index and seed oil content and their interrelations. All four traits were moderately heritable and correlated but with no large influence of genotype × environment interactions across multiple seasons. Seven to ten major QTLs were identified for each trait with many being adjacent or overlapping for different trait pairs. A fivefold cross-validation of the model indicated prediction accuracies of 0.46-0.62. GP results based on any two-season phenotypes were strongly correlated with phenotypic means of a pooled analysis of three-season experiments (r = 0.83-0.92). When used for selection of improvement in lint, seed and oil yields, GP captured 40-100% of individuals with comparable lint yields of those selected based on the three-season phenotypic results. Thus, this quantitative genomics-enabled approach can not only decipher the genomic variation underlying lint, seed and seed oil traits and their interrelations, but can provide predictions for their simultaneous improvement. We discuss future breeding strategies in cotton that will enhance the entire value of the crop, not just its fibre.
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Affiliation(s)
- Zitong Li
- CSIRO Agriculture and Food, Canberra, ACT, 2601, Australia
| | - Qian-Hao Zhu
- CSIRO Agriculture and Food, Canberra, ACT, 2601, Australia
| | | | - Iain Wilson
- CSIRO Agriculture and Food, Canberra, ACT, 2601, Australia
| | | | | | - Shiming Liu
- CSIRO Agriculture and Food, Narrabri, NSW, 2390, Australia.
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Wen T, Zhang X, Zhu J, Zhang S, Rhaman MS, Zeng W. A SLAF-based high-density genetic map construction and genetic architecture of thermotolerant traits in maize ( Zea mays L.). FRONTIERS IN PLANT SCIENCE 2024; 15:1338086. [PMID: 38384753 PMCID: PMC10880447 DOI: 10.3389/fpls.2024.1338086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/05/2024] [Indexed: 02/23/2024]
Abstract
The leaf scorching trait at flowering is a crucial thermosensitive phenotype in maize under high temperature stress (HS), yet the genetic basis of this trait remains poorly understood. In this study, we genotyped a 254 RIL-F2:8 population, derived from the leaf scorch-free parental inbred line Abe2 and the leaf scorching maternal inbred line B73, using the specific-locus amplified fragment sequencing (SLAF-seq) method. A total of 10,112 polymorphic SLAF markers were developed, and a high-density genetic map with a total length of 1,475.88 cM was constructed. The average sequencing depth of the parents was 55.23X, and that of the progeny was 12.53X. Then, we identified a total of 16 QTLs associated with thermotolerant traits at flowering, of which four QTLs of leaf scorching damage (LS) were distributed on chromosomes 1 (qLS1), 2 (qLS2.1, qLS2.2) and 3 (qLS3), which could explain 19.73% of phenotypic variation. Combining one qLS1 locus with QTL-seq results led to the identification of 6 candidate genes. Expression experiments and sequence variation indicated that Zm00001d033328, encoding N-acetyl-gamma-glutamyl-phosphate reductase, was the most likely candidate gene controlling thermotolerant traits at flowering. In summary, the high-density genetic map and genetic basis of thermotolerant traits lay a critical foundation for mapping other complex traits and identifying the genes associated with thermotolerant traits in maize.
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Affiliation(s)
- Tingting Wen
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agriculture Sciences in Weifang, Weifang, China
- Seed Administration Station of Shandong Province, Jinan, China
| | - Xuefei Zhang
- Taian Daiyue District Bureau of Agriculture and Rural Affairs, Taian, China
| | - Jiaojiao Zhu
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agriculture Sciences in Weifang, Weifang, China
| | - Susu Zhang
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agriculture Sciences in Weifang, Weifang, China
| | - Mohammad Saidur Rhaman
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agriculture Sciences in Weifang, Weifang, China
| | - Wei Zeng
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agriculture Sciences in Weifang, Weifang, China
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Alam B, Liu R, Gong J, Li J, Yan H, Ge Q, Xiao X, Pan J, Shang H, Shi Y, Yuan Y, Gong W. Hub Genes in Stable QTLs Orchestrate the Accumulation of Cottonseed Oil in Upland Cotton via Catalyzing Key Steps of Lipid-Related Pathways. Int J Mol Sci 2023; 24:16595. [PMID: 38068920 PMCID: PMC10706765 DOI: 10.3390/ijms242316595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 11/10/2023] [Accepted: 11/17/2023] [Indexed: 12/18/2023] Open
Abstract
Upland cotton is the fifth-largest oil crop in the world, with an average supply of nearly 20% of vegetable oil production. Cottonseed oil is also an ideal alternative raw material to be efficiently converted into biodiesel. However, the improvement in kernel oil content (KOC) of cottonseed has not received sufficient attention from researchers for a long time, due to the fact that the main product of cotton planting is fiber. Previous studies have tagged QTLs and identified individual candidate genes that regulate KOC of cottonseed. The regulatory mechanism of oil metabolism and accumulation of cottonseed are still elusive. In the current study, two high-density genetic maps (HDGMs), which were constructed based on a recombinant inbred line (RIL) population consisting of 231 individuals, were used to identify KOC QTLs. A total of forty-three stable QTLs were detected via these two HDGM strategies. Bioinformatic analysis of all the genes harbored in the marker intervals of the stable QTLs revealed that a total of fifty-one genes were involved in the pathways related to lipid biosynthesis. Functional analysis via coexpression network and RNA-seq revealed that the hub genes in the co-expression network that also catalyze the key steps of fatty acid synthesis, lipid metabolism and oil body formation pathways (ACX4, LACS4, KCR1, and SQD1) could jointly orchestrate oil accumulation in cottonseed. This study will strengthen our understanding of oil metabolism and accumulation in cottonseed and contribute to KOC improvement in cottonseed in the future, enhancing the security and stability of worldwide food supply.
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Affiliation(s)
- Beena Alam
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China (Y.S.)
| | - Ruixian Liu
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China (Y.S.)
| | - Juwu Gong
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China (Y.S.)
- Zhengzhou Research Base, National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Zhengzhou University, Zhengzhou 450001, China
| | - Junwen Li
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China (Y.S.)
- Zhengzhou Research Base, National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Zhengzhou University, Zhengzhou 450001, China
| | - Haoliang Yan
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China (Y.S.)
- Zhengzhou Research Base, National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Zhengzhou University, Zhengzhou 450001, China
| | - Qun Ge
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China (Y.S.)
- Zhengzhou Research Base, National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Zhengzhou University, Zhengzhou 450001, China
| | - Xianghui Xiao
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China (Y.S.)
| | - Jingtao Pan
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China (Y.S.)
| | - Haihong Shang
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China (Y.S.)
- Zhengzhou Research Base, National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Zhengzhou University, Zhengzhou 450001, China
| | - Yuzhen Shi
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China (Y.S.)
| | - Youlu Yuan
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China (Y.S.)
- Zhengzhou Research Base, National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Zhengzhou University, Zhengzhou 450001, China
| | - Wankui Gong
- National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China (Y.S.)
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Ren H, Wei Z, Zhou B, Chen X, Gao Q, Zhang Z. Molecular marker development and genetic diversity exploration in Medicago polymorpha. PeerJ 2023; 11:e14698. [PMID: 36684677 PMCID: PMC9851046 DOI: 10.7717/peerj.14698] [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: 04/26/2022] [Accepted: 12/14/2022] [Indexed: 01/17/2023] Open
Abstract
Medicago polymorpha L. (bur clover), an invasive plant species of the genus Medicago, has been traditionally used in China as an edible vegetable crop because of its high nutritive value. However, few molecular markers for M. polymorpha have been identified. Using the recently published high-quality reference genome of M. polymorpha, we performed a specific-locus amplified fragment sequencing (SLAF-seq) analysis of 10 M. polymorpha accessions to identify molecular markers and explore genetic diversity. A total of 52,237 high-quality single nucleotide polymorphisms (SNPs) were developed. These SNPs were mostly distributed on pseudochromosome 3, least distributed on pseudochromosome 7, and relatively evenly distributed on five other pseudochromosomes of M. polymorpha. Phenotypic analysis showed that there was a great difference in phenotypic traits among different M. polymorpha accessions. Moreover, clustering all M. polymorpha accessions based on their phenotypic traits revealed three groups. Both phylogenetic analysis and principal component analysis (PCA) of all M. polymorpha accessions based on SNP markers consistently indicated that all M. polymorpha accessions could be divided into three distinct groups (I, II, and III). Subsequent genetic diversity analysis for the 10 M. polymorpha accessions validated the effectiveness of the M. polymorpha germplasm molecular markers in China. Additionally, SSR mining analysis was also performed to identify polymorphic SSR motifs, which could provide valuable candidate markers for the further breeding of M. polymorpha. Since M. polymorpha genetics have not been actively studied, the molecular markers generated from our research will be useful for further research on M. polymorpha resource utilization and marker-assisted breeding.
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Affiliation(s)
- Hailong Ren
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China,Guangzhou Academy of Agricultural Sciences, Guangzhou, Guangdong, China,Hainan Sanya Test Center of Crop Breeding, Xinjiang Academy of Agricultural Sciences, Sanya, Hainan, China
| | - Zhenwu Wei
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China
| | - Bo Zhou
- Hainan Sanya Test Center of Crop Breeding, Xinjiang Academy of Agricultural Sciences, Sanya, Hainan, China
| | - Xiang Chen
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China
| | - Qiang Gao
- Hainan Sanya Test Center of Crop Breeding, Xinjiang Academy of Agricultural Sciences, Sanya, Hainan, China
| | - Zhibin Zhang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, Henan, China
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Liu T, He J, Dong K, Wang X, Zhang L, Ren R, Huang S, Sun X, Pan W, Wang W, Yang P, Yang T, Zhang Z. Genome-wide identification of quantitative trait loci for morpho-agronomic and yield-related traits in foxtail millet (Setaria italica) across multi-environments. Mol Genet Genomics 2022; 297:873-888. [PMID: 35451683 PMCID: PMC9130181 DOI: 10.1007/s00438-022-01894-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 03/31/2022] [Indexed: 11/21/2022]
Abstract
Foxtail millet (Setaria italica) is an ideal model of genetic system for functional genomics of the Panicoideae crop. Identification of QTL responsible for morpho-agronomic and yield-related traits facilitates dissection of genetic control and breeding in cereal crops. Here, based on a Yugu1 × Longgu7 RIL population and genome-wide resequencing data, an updated linkage map harboring 2297 bin and 74 SSR markers was constructed, spanning 1315.1 cM with an average distance of 0.56 cM between adjacent markers. A total of 221 QTL for 17 morpho-agronomic and yield-related traits explaining 5.5 ~ 36% of phenotypic variation were identified across multi-environments. Of these, 109 QTL were detected in two to nine environments, including the most stable qLMS6.1 harboring a promising candidate gene Seita.6G250500, of which 70 were repeatedly identified in different trials in the same geographic location, suggesting that foxtail millet has more identical genetic modules under the similar ecological environment. One hundred-thirty QTL with overlapping intervals formed 22 QTL clusters. Furthermore, six superior recombinant inbred lines, RIL35, RIL48, RIL77, RIL80, RIL115 and RIL125 with transgressive inheritance and enrichment of favorable alleles in plant height, tiller, panicle morphology and yield related-traits were screened by hierarchical cluster. These identified QTL, QTL clusters and superior lines lay ground for further gene-trait association studies and breeding practice in foxtail millet.
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Affiliation(s)
- Tianpeng Liu
- College of Agronomy and Biotechnology, Southwest University, Chongqing, 400716, China
- Crop Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, 730070, China
| | - Jihong He
- Crop Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, 730070, China
| | - Kongjun Dong
- Crop Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, 730070, China
| | - Xuewen Wang
- Institute of Plant Breeding, Genetics, and Genomics, University of Georgia, Athens, GA, 30601, USA
| | - Lei Zhang
- Crop Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, 730070, China
| | - Ruiyu Ren
- Crop Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, 730070, China
| | - Sha Huang
- College of Agronomy and Biotechnology, Southwest University, Chongqing, 400716, China
| | - Xiaoting Sun
- College of Agronomy and Biotechnology, Southwest University, Chongqing, 400716, China
| | - Wanxiang Pan
- College of Life Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China
| | - Wenwen Wang
- College of Agronomy and Biotechnology, Southwest University, Chongqing, 400716, China
| | - Peng Yang
- College of Agronomy and Biotechnology, Southwest University, Chongqing, 400716, China
| | - Tianyu Yang
- Crop Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, 730070, China.
| | - Zhengsheng Zhang
- College of Agronomy and Biotechnology, Southwest University, Chongqing, 400716, China.
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Feng L, Su Q, Yue H, Wang L, Gao J, Xing L, Xu M, Zhou C, Yang Y, Zhou B. TIP41L, a putative candidate gene conferring both seed size and boll weight, was fine-mapped in an introgression line of Gossypium hirsutum-Gossypium arboreum. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2022; 317:111197. [PMID: 35193746 DOI: 10.1016/j.plantsci.2022.111197] [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: 09/05/2021] [Revised: 01/12/2022] [Accepted: 01/22/2022] [Indexed: 06/14/2023]
Abstract
QTLs for yield-related traits in tetraploid cotton have been widely mapped, but QTLs introduced from diploid species into tetraploid cotton background remain uninvolved. Here, a stable introgression line with the traits of small boll and seed on Chr. A12, IL197 derived from Gossypium hirsutum (2n = AADD = 52) × Gossypium arboreum (2n = AA = 26), was employed to construct the F2 and F3 secondary populations for fine-mapping QTLs of yield-related traits. QTL analysis showed eight QTLs were detected for three traits, boll weight (BW), seed index (SI, one-hundred-seed weight in g), and lint percentage, with 3.94-28.13 % of the phenotypic variance explained. Of them, a stable major QTL, q(BW + SI)-A12-1 controlling both BW and SI and covering the shortest region in Chr. A12, was further narrowed into a 60.09 kb-interval through substitution mapping. Finally, five candidate genes were detected in the interval. The qRT-PCR analysis revealed only TIP41-like family protein (TIP41L) kept up-regulated expression and significantly lower in TM-1 than that in IL197 from -1 DPA to 15 DPA during cotton boll rapid developmental stage. Therefore, TIP41L gene is speculated as the most likely candidate gene. Comparative analysis with the other four allotetraploid species showed TIP41L gene was probably diverged after the formation of allotetraploid cotton, which may be selected and swept during domestication of modern upland cotton because small boll and seed are detrimental to fibre yield of cotton. This research would lay a solid foundation for further elucidating the molecular mechanism of cotton boll and seed development.
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Affiliation(s)
- Liuchun Feng
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China; Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, 210014, People's Republic of China
| | - Qiao Su
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Haoran Yue
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Liang Wang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Jianbo Gao
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Liangshuai Xing
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Min Xu
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Chenhui Zhou
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Ying Yang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China
| | - Baoliang Zhou
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, People's Republic of China.
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9
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Gong J, Peng Y, Yu J, Pei W, Zhang Z, Fan D, Liu L, Xiao X, Liu R, Lu Q, Li P, Shang H, Shi Y, Li J, Ge Q, Liu A, Deng X, Fan S, Pan J, Chen Q, Yuan Y, Gong W. Linkage and association analyses reveal that hub genes in energy-flow and lipid biosynthesis pathways form a cluster in upland cotton. Comput Struct Biotechnol J 2022; 20:1841-1859. [PMID: 35521543 PMCID: PMC9046884 DOI: 10.1016/j.csbj.2022.04.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 11/25/2022] Open
Abstract
Upland cotton is an important allotetraploid crop that provides both natural fiber for the textile industry and edible vegetable oil for the food or feed industry. To better understand the genetic mechanism that regulates the biosynthesis of storage oil in cottonseed, we identified the genes harbored in the major quantitative trait loci/nucleotides (QTLs/QTNs) of kernel oil content (KOC) in cottonseed via both multiple linkage analyses and genome-wide association studies (GWAS). In ‘CCRI70′ RILs, six stable QTLs were simultaneously identified by linkage analysis of CHIP and SLAF-seq strategies. In ‘0-153′ RILs, eight stable QTLs were detected by consensus linkage analysis integrating multiple strategies. In the natural panel, thirteen and eight loci were associated across multiple environments with two algorithms of GWAS. Within the confidence interval of a major common QTL on chromosome 3, six genes were identified as participating in the interaction network highly correlated with cottonseed KOC. Further observations of gene differential expression showed that four of the genes, LtnD, PGK, LPLAT1, and PAH2, formed hub genes and two of them, FER and RAV1, formed the key genes in the interaction network. Sequence variations in the coding regions of LtnD, FER, PGK, LPLAT1, and PAH2 genes may support their regulatory effects on oil accumulation in mature cottonseed. Taken together, clustering of the hub genes in the lipid biosynthesis interaction network provides new insights to understanding the mechanism of fatty acid biosynthesis and TAG assembly and to further genetic improvement projects for the KOC in cottonseeds.
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Affiliation(s)
- Juwu Gong
- State Key Laboratory of Cotton Biology, 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
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Yan Peng
- Third Division of the Xinjiang Production and Construction Corps Agricultural Research Institute, Tumushuke, Xijiang 843900, China
| | - Jiwen Yu
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Wenfeng Pei
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Zhen Zhang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Daoran Fan
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Linjie Liu
- State Key Laboratory of Cotton Biology, 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
| | - Xianghui Xiao
- State Key Laboratory of Cotton Biology, 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
| | - Ruixian Liu
- State Key Laboratory of Cotton Biology, 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
| | - Quanwei Lu
- College of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang, China
| | - Pengtao Li
- College of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang, China
| | - Haihong Shang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Yuzhen Shi
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Junwen Li
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Qun Ge
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Aiying Liu
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Xiaoying Deng
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Senmiao Fan
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Jingtao Pan
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
| | - Quanjia Chen
- Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, Xinjiang, China
| | - Youlu Yuan
- State Key Laboratory of Cotton Biology, 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
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Wankui Gong
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
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10
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Wu L, Jia B, Pei W, Wang L, Ma J, Wu M, Song J, Yang S, Xin Y, Huang L, Feng P, Zhang J, Yu J. Quantitative Trait Locus Analysis and Identification of Candidate Genes Affecting Seed Size and Shape in an Interspecific Backcross Inbred Line Population of Gossypium hirsutum × Gossypium barbadense. FRONTIERS IN PLANT SCIENCE 2022; 13:837984. [PMID: 35392518 PMCID: PMC8981304 DOI: 10.3389/fpls.2022.837984] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/31/2022] [Indexed: 06/14/2023]
Abstract
Seed size and shape are key agronomic traits affecting seedcotton yield and seed quality in cotton (Gossypium spp.). However, the genetic mechanisms that regulate the seed physical traits in cotton are largely unknown. In this study, an interspecific backcross inbred line (BIL) population of 250 BC1F7 lines, derived from the recurrent parent Upland CRI36 (Gossypium hirsutum) and Hai7124 (Gossypium barbadense), was used to investigate the genetic basis of cotton seed physical traits via quantitative trait locus (QTL) mapping and candidate gene identification. The BILs were tested in five environments, measuring eight seed size and shape-related traits, including 100-kernel weight, kernel length width and their ratio, kernel area, kernel girth, kernel diameter, and kernel roundness. Based on 7,709 single nucleotide polymorphic (SNP) markers, a total of 49 QTLs were detected and each explained 2.91-35.01% of the phenotypic variation, including nine stable QTLs mapped in at least three environments. Based on pathway enrichment, gene annotation, genome sequence, and expression analysis, five genes encoding starch synthase 4, transcription factor PIF7 and MYC4, ubiquitin-conjugating enzyme E27, and THO complex subunit 4A were identified as candidate genes that might be associated with seed size and shape. Our research provides valuable information to improve seed physical traits in cotton breeding.
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Affiliation(s)
- Luyao Wu
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, China
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Bing Jia
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Wenfeng Pei
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Li Wang
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Jianjiang Ma
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Man Wu
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Jikun Song
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Shuxian Yang
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Yue Xin
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Li Huang
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Pan Feng
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Jinfa Zhang
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, United States
| | - Jiwen Yu
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, China
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
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11
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Wu M, Pei W, Wedegaertner T, Zhang J, Yu J. Genetics, Breeding and Genetic Engineering to Improve Cottonseed Oil and Protein: A Review. FRONTIERS IN PLANT SCIENCE 2022; 13:864850. [PMID: 35360295 PMCID: PMC8961181 DOI: 10.3389/fpls.2022.864850] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 02/15/2022] [Indexed: 05/17/2023]
Abstract
Upland cotton (Gossypium hirsutum) is the world's leading fiber crop and one of the most important oilseed crops. Genetic improvement of cotton has primarily focused on fiber yield and quality. However, there is an increased interest and demand for enhanced cottonseed traits, including protein, oil, fatty acids, and amino acids for broad food, feed and biofuel applications. As a byproduct of cotton production, cottonseed is an important source of edible oil in many countries and could also be a vital source of protein for human consumption. The focus of cotton breeding on high yield and better fiber quality has substantially reduced the natural genetic variation available for effective cottonseed quality improvement within Upland cotton. However, genetic variation in cottonseed oil and protein content exists within the genus of Gossypium and cultivated cotton. A plethora of genes and quantitative trait loci (QTLs) (associated with cottonseed oil, fatty acids, protein and amino acids) have been identified, providing important information for genetic improvement of cottonseed quality. Genetic engineering in cotton through RNA interference and insertions of additional genes of other genetic sources, in addition to the more recent development of genome editing technology has achieved considerable progress in altering the relative levels of protein, oil, fatty acid profile, and amino acids composition in cottonseed for enhanced nutritional value and expanded industrial applications. The objective of this review is to summarize and discuss the cottonseed oil biosynthetic pathway and major genes involved, genetic basis of cottonseed oil and protein content, genetic engineering, genome editing through CRISPR/Cas9, and QTLs associated with quantity and quality enhancement of cottonseed oil and protein.
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Affiliation(s)
- Man Wu
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute, Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, China
| | - Wenfeng Pei
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute, Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | | | - Jinfa Zhang
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, United States
| | - Jiwen Yu
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute, Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, China
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12
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Zhang Z, Gong J, Zhang Z, Gong W, Li J, Shi Y, Liu A, Ge Q, Pan J, Fan S, Deng X, Li S, Chen Q, Yuan Y, Shang H. Identification and analysis of oil candidate genes reveals the molecular basis of cottonseed oil accumulation in Gossypium hirsutum L. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:449-460. [PMID: 34714356 DOI: 10.1007/s00122-021-03975-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/15/2021] [Indexed: 05/14/2023]
Abstract
Based on the integration of QTL-mapping and regulatory network analyses, five high-confidence stable QTL regions, six candidate genes and two microRNAs that potentially affect the cottonseed oil content were discovered. Cottonseed oil is increasingly becoming a promising target for edible oil with its high content of unsaturated fatty acids. In this study, a recombinant inbred line (RIL) cotton population was constructed to detect quantitative trait loci (QTLs) for the cottonseed oil content. A total of 39 QTLs were detected across eight different environments, of which five QTLs were stable. Forty-three candidate genes potentially involved in carbon metabolism, fatty acid synthesis and triacylglycerol biosynthesis processes were further obtained in the stable QTL regions. Transcriptome analysis showed that nineteen of these candidate genes expressed during the developing cottonseed ovules and may affect the cottonseed oil content. Besides, transcription factor (TF) and microRNA (miRNA) co-regulatory network analyses based on the nineteen candidate genes suggested that six genes, two core miRNAs (ghr-miR2949b and ghr-miR2949c), and one TF GhHSL1 were considered to be closely associated with the cottonseed oil content. Moreover, four vital genes were validated by quantitative real-time PCR (qRT-PCR). These results provide insights into the oil accumulation mechanism in developing cottonseed ovules through the construction of a detailed oil accumulation model.
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Affiliation(s)
- Zhibin Zhang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450000, China
| | - Juwu Gong
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- Xinjiang Research Base, State Key Laboratory of Cotton Biology, Xinjiang Agricultural University, Ürümqi, 830001, China
| | - Zhen Zhang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Wankui Gong
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Junwen Li
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Yuzhen Shi
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Aiying Liu
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Qun Ge
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Jingtao Pan
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Senmiao Fan
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Xiaoying Deng
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Shaoqi Li
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Quanjia Chen
- Xinjiang Research Base, State Key Laboratory of Cotton Biology, Xinjiang Agricultural University, Ürümqi, 830001, China
| | - Youlu Yuan
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450000, China.
- Xinjiang Research Base, State Key Laboratory of Cotton Biology, Xinjiang Agricultural University, Ürümqi, 830001, China.
| | - Haihong Shang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450000, China.
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13
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Takele A, Feyissa T, Disasa T. Quantitative trait loci mapping of stem sugar content and stem diameter in sorghum recombinant inbred lines using genotyping-by-sequencing. Mol Biol Rep 2022; 49:3045-3054. [PMID: 35076849 DOI: 10.1007/s11033-022-07131-8] [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: 07/20/2021] [Accepted: 01/06/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Sweet sorghum is an important crop with sugary stem that can mainly be used for syrup, fodder and bio-fuel. Many sugar content QTLs have been discovered from different sources through breeding worldwide. Most of these QTLs are detected using exotic germplasm as a mapping population. This study aimed to detect and map QTLs for stem sugar content and stem diameter targeting Ethiopian recombinant inbred lines of sorghum using genotyping-by-sequencing. METHODS AND RESULT Genotyping-by-sequencing and phenotyping using 139 recombinant inbred lines of sorghum as mapping populations were conducted. A total of 1082 polymorphic and high quality SNP markers that are evenly distributed across the ten linkage groups of sorghum were selected to detect and map the trait of interest. A genetic linkage map using 1082 SNP markers was constructed and several QTLs associated with stem sugar content and stem diameter were identified. Phenotypic variation explained by qBrix4-1 and qBrix2-1 ranged from 6.33 to 14%, respectively. Over two seasons, four QTLs for stem sugar content (qBrix1-1, qBrix2-1, qBrix4-1 and qBrix4-2) and three QTLs for stem diameter (qSD1-1, qSD8-1 and qSD9-1) were detected. CONCLUSION QTLs that significantly associated with stem sugar content and stem diameter have been detected and mapped. This will help sorghum breeding program to develop superior sweet sorghum varieties through the use of appropriate crop improvement approaches like marker assisted breeding. This ultimately contributes to the current development plan to considerably improve food, feed and bio-fuel supply in developing countries like Ethiopia.
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Affiliation(s)
- Abera Takele
- SalaleUniversity, P.O Box 245, Fiche, Ethiopia. .,Institute of Biotechnology, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia.
| | - Tileye Feyissa
- Institute of Biotechnology, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia
| | - Tesfaye Disasa
- National Agricultural Biotechnology Research Center, P.O. Box 2003, Addis Ababa, Ethiopia
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14
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Li Y, Mo T, Ran L, Zeng J, Wang C, Liang A, Dai Y, Wu Y, Zhong Z, Xiao Y. Genome resequencing-based high-density genetic map and QTL detection for yield and fiber quality traits in diploid Asiatic cotton (Gossypium arboreum). Mol Genet Genomics 2022; 297:199-212. [PMID: 35048185 DOI: 10.1007/s00438-021-01848-0] [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: 03/11/2021] [Accepted: 12/14/2021] [Indexed: 10/19/2022]
Abstract
Cotton is the most important fiber crop in the world. Asiatic cotton (Gossypium arboreum, genome A2) is a diploid cotton species producing spinnable fibers and important germplasm for cotton breeding and a significant model for fiber biology. However, the genetic map of Asiatic cotton has been lagging behind tetraploid cottons, as well as other stable crops. This study aimed to construct a high-density SNP genetic map and to map QTLs for important yield and fiber quality traits. Using a recombinant inbred line (RIL) population and genome resequencing technology, we constructed a high-density genetic map that covered 1980.17 cM with an average distance of 0.61 cM between adjacent markers. QTL analysis revealed a total of 297 QTLs for 13 yield and fiber quality traits in three environments, explaining 5.0-37.4% of the phenotypic variance, among which 75 were stably detected in two or three environments. Besides, 47 QTL clusters, comprising 131 QTLs for representative traits, were identified. Our works laid solid foundation for fine mapping and cloning of QTL for yield and fiber quality traits in Asiatic cotton.
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Affiliation(s)
- Yaohua Li
- Biotechnology Research Center, Chongqing Key Laboratory of Application and Safety Control of Genetically Modified Crops, Southwest University, Southwest University Southern Campus, Tiansheng Rd No. 2, Beibei, Chongqing, 400716, China
| | - Tong Mo
- Biotechnology Research Center, Chongqing Key Laboratory of Application and Safety Control of Genetically Modified Crops, Southwest University, Southwest University Southern Campus, Tiansheng Rd No. 2, Beibei, Chongqing, 400716, China
| | - Lingfang Ran
- Biotechnology Research Center, Chongqing Key Laboratory of Application and Safety Control of Genetically Modified Crops, Southwest University, Southwest University Southern Campus, Tiansheng Rd No. 2, Beibei, Chongqing, 400716, China
| | - Jianyan Zeng
- Biotechnology Research Center, Chongqing Key Laboratory of Application and Safety Control of Genetically Modified Crops, Southwest University, Southwest University Southern Campus, Tiansheng Rd No. 2, Beibei, Chongqing, 400716, China
| | - Chuannan Wang
- Biotechnology Research Center, Chongqing Key Laboratory of Application and Safety Control of Genetically Modified Crops, Southwest University, Southwest University Southern Campus, Tiansheng Rd No. 2, Beibei, Chongqing, 400716, China
| | - Aimin Liang
- Biotechnology Research Center, Chongqing Key Laboratory of Application and Safety Control of Genetically Modified Crops, Southwest University, Southwest University Southern Campus, Tiansheng Rd No. 2, Beibei, Chongqing, 400716, China
| | - Yonglu Dai
- Biotechnology Research Center, Chongqing Key Laboratory of Application and Safety Control of Genetically Modified Crops, Southwest University, Southwest University Southern Campus, Tiansheng Rd No. 2, Beibei, Chongqing, 400716, China
| | - Yiping Wu
- Biotechnology Research Center, Chongqing Key Laboratory of Application and Safety Control of Genetically Modified Crops, Southwest University, Southwest University Southern Campus, Tiansheng Rd No. 2, Beibei, Chongqing, 400716, China
| | - Ziman Zhong
- Biotechnology Research Center, Chongqing Key Laboratory of Application and Safety Control of Genetically Modified Crops, Southwest University, Southwest University Southern Campus, Tiansheng Rd No. 2, Beibei, Chongqing, 400716, China
| | - Yuehua Xiao
- Biotechnology Research Center, Chongqing Key Laboratory of Application and Safety Control of Genetically Modified Crops, Southwest University, Southwest University Southern Campus, Tiansheng Rd No. 2, Beibei, Chongqing, 400716, China.
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Gao Y, Zhou S, Huang Y, Zhang B, Xu Y, Zhang G, Lakshmanan P, Yang R, Zhou H, Huang D, Liu J, Tan H, He W, Yang C, Duan W. Quantitative Trait Loci Mapping and Development of KASP Marker Smut Screening Assay Using High-Density Genetic Map and Bulked Segregant RNA Sequencing in Sugarcane ( Saccharum spp.). FRONTIERS IN PLANT SCIENCE 2022; 12:796189. [PMID: 35069651 PMCID: PMC8766830 DOI: 10.3389/fpls.2021.796189] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 12/13/2021] [Indexed: 06/02/2023]
Abstract
Sugarcane is one of the most important industrial crops globally. It is the second largest source of bioethanol, and a major crop for biomass-derived electricity and sugar worldwide. Smut, caused by Sporisorium scitamineum, is a major sugarcane disease in many countries, and is managed by smut-resistant varieties. In China, smut remains the single largest constraint for sugarcane production, and consequently it impacts the value of sugarcane as an energy feedstock. Quantitative trait loci (QTLs) associated with smut resistance and linked diagnostic markers are valuable tools for smut resistance breeding. Here, we developed an F1 population (192 progeny) by crossing two sugarcane varieties with contrasting smut resistance and used for genome-wide single nucleotide polymorphism (SNP) discovery and mapping, using a high-throughput genotyping method called "specific locus amplified fragment sequencing (SLAF-seq) and bulked-segregant RNA sequencing (BSR-seq). SLAF-seq generated 148,500 polymorphic SNP markers. Using SNP and previously identified SSR markers, an integrated genetic map with an average 1.96 cM marker interval was produced. With this genetic map and smut resistance scores of the F1 individuals from four crop years, 21 major QTLs were mapped, with a phenotypic variance explanation (PVE) > 8.0%. Among them, 10 QTLs were stable (repeatable) with PVEs ranging from 8.0 to 81.7%. Further, four QTLs were detected based on BSR-seq analysis. aligning major QTLs with the genome of a sugarcane progenitor Saccharum spontaneum, six markers were found co-localized. Markers located in QTLs and functional annotation of BSR-seq-derived unigenes helped identify four disease resistance candidate genes located in major QTLs. 77 SNPs from major QTLs were then converted to Kompetitive Allele-Specific PCR (KASP) markers, of which five were highly significantly linked to smut resistance. The co-localized QTLs, candidate resistance genes, and KASP markers identified in this study provide practically useful tools for marker-assisted sugarcane smut resistance breeding.
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Affiliation(s)
- Yijing Gao
- Guangxi Key Laboratory of Sugarcane Genetic Improvement, Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Center, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Shan Zhou
- Guangxi Key Laboratory of Sugarcane Genetic Improvement, Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Center, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Yuxin Huang
- Guangxi Key Laboratory of Sugarcane Genetic Improvement, Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Center, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Baoqing Zhang
- Guangxi Key Laboratory of Sugarcane Genetic Improvement, Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Center, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Yuhui Xu
- Adsen Biotechnology Co., Ltd., Urumchi, China
| | - Gemin Zhang
- Guangxi Key Laboratory of Sugarcane Genetic Improvement, Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Center, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Prakash Lakshmanan
- Guangxi Key Laboratory of Sugarcane Genetic Improvement, Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Center, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Chinese Academy of Agricultural Sciences, Nanning, China
- Interdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin, College of Resources and Environment, Southwest University, Chongqing, China
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD, Australia
| | - Rongzhong Yang
- Guangxi Key Laboratory of Sugarcane Genetic Improvement, Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Center, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Hui Zhou
- Guangxi Key Laboratory of Sugarcane Genetic Improvement, Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Center, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Dongliang Huang
- Guangxi Key Laboratory of Sugarcane Genetic Improvement, Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Center, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Junxian Liu
- Guangxi Key Laboratory of Sugarcane Genetic Improvement, Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Center, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Hongwei Tan
- Guangxi Academy of Agricultural Sciences, Nanning, China
| | - Weizhong He
- Guangxi Key Laboratory of Sugarcane Genetic Improvement, Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Center, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Cuifang Yang
- Guangxi Key Laboratory of Sugarcane Genetic Improvement, Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Center, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Weixing Duan
- Guangxi Key Laboratory of Sugarcane Genetic Improvement, Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Center, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Chinese Academy of Agricultural Sciences, Nanning, China
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Liu X, Yang L, Wang J, Wang Y, Guo Z, Li Q, Yang J, Wu Y, Chen L, Teng Z, Liu D, Liu D, Guo K, Zhang Z. Analyzing Quantitative Trait Loci for Fiber Quality and Yield-Related Traits From a Recombinant Inbred Line Population With Gossypium hirsutum Race palmeri as One Parent. FRONTIERS IN PLANT SCIENCE 2022; 12:817748. [PMID: 35046989 PMCID: PMC8763314 DOI: 10.3389/fpls.2021.817748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 12/01/2021] [Indexed: 06/14/2023]
Abstract
Fiber quality and yield-related traits are important agronomic traits in cotton breeding. To detect the genetic basis of fiber quality and yield related traits, a recombinant inbred line (RIL) population consisting of 182 lines was established from a cross between Gossypium hirsutum cultivar CCRI35 and G. hirsutum race palmeri accession TX-832. The RIL population was deeply genotyped using SLAF-seq and was phenotyped in six environments. A high-density genetic linkage map with 15,765 SNP markers and 153 SSR markers was constructed, with an average distance of 0.30 cM between adjacent markers. A total of 210 fiber quality quantitative trait loci (QTLs) and 73 yield-related QTLs were identified. Of the detected QTLs, 62 fiber quality QTLs and 10 yield-related QTLs were stable across multiple environments. Twelve and twenty QTL clusters were detected on the At and Dt subgenome, respectively. Twenty-three major QTL clusters were further validated through associated analysis and five candidate genes of four stable fiber quality QTLs were identified. This study revealed elite loci influencing fiber quality and yield and significant phenotypic selection regions during G. hirsutum domestication, and set a stage for future utilization of molecular marker assisted breeding in cotton breeding programs.
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Song X, Yang Q, Bai Y, Gong K, Wu T, Yu T, Pei Q, Duan W, Huang Z, Wang Z, Liu Z, Kang X, Zhao W, Ma X. Comprehensive analysis of SSRs and database construction using all complete gene-coding sequences in major horticultural and representative plants. HORTICULTURE RESEARCH 2021; 8:122. [PMID: 34059664 PMCID: PMC8167114 DOI: 10.1038/s41438-021-00562-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 02/10/2021] [Accepted: 03/14/2021] [Indexed: 05/05/2023]
Abstract
Simple sequence repeats (SSRs) are one of the most important genetic markers and widely exist in most species. Here, we identified 249,822 SSRs from 3,951,919 genes in 112 plants. Then, we conducted a comprehensive analysis of these SSRs and constructed a plant SSR database (PSSRD). Interestingly, more SSRs were found in lower plants than in higher plants, showing that lower plants needed to adapt to early extreme environments. Four specific enriched functional terms in the lower plant Chlamydomonas reinhardtii were detected when it was compared with seven other higher plants. In addition, Guanylate_cyc existed in more genes of lower plants than of higher plants. In our PSSRD, we constructed an interactive plotting function in the chart interface, and users can easily view the detailed information of SSRs. All SSR information, including sequences, primers, and annotations, can be downloaded from our database. Moreover, we developed Web SSR Finder and Batch SSR Finder tools, which can be easily used for identifying SSRs. Our database was developed using PHP, HTML, JavaScript, and MySQL, which are freely available at http://www.pssrd.info/ . We conducted an analysis of the Myb gene families and flowering genes as two applications of the PSSRD. Further analysis indicated that whole-genome duplication and whole-genome triplication played a major role in the expansion of the Myb gene families. These SSR markers in our database will greatly facilitate comparative genomics and functional genomics studies in the future.
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Affiliation(s)
- Xiaoming Song
- School of Life Sciences/Library, North China University of Science and Technology, Tangshan, Hebei, 063210, China.
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, 610054, Chengdu, China.
- Food Science and Technology Department, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA.
| | - Qihang Yang
- School of Life Sciences/Library, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Yun Bai
- School of Life Sciences/Library, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Ke Gong
- School of Life Sciences/Library, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Tong Wu
- School of Life Sciences/Library, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Tong Yu
- School of Life Sciences/Library, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Qiaoying Pei
- School of Life Sciences/Library, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Weike Duan
- College of Life Sciences and Food Engineering, Huaiyin Institute of Technology, 223003, Huai'an, China
| | - Zhinan Huang
- College of Life Sciences and Food Engineering, Huaiyin Institute of Technology, 223003, Huai'an, China
| | - Zhiyuan Wang
- School of Life Sciences/Library, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Zhuo Liu
- School of Life Sciences/Library, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Xi Kang
- School of Life Sciences/Library, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Wei Zhao
- School of Life Sciences/Library, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Xiao Ma
- School of Life Sciences/Library, North China University of Science and Technology, Tangshan, Hebei, 063210, China.
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Wu X, Feng F, Zhu Y, Xie F, Yang J, Gong J, Liu Y, Zhu W, Gao T, Chen D, Li X, Huang J. Construction of High-Density Genetic Map and Identification of QTLs Associated with Seed Vigor after Exposure to Artificial Aging Conditions in Sweet Corn Using SLAF-seq. Genes (Basel) 2019; 11:genes11010037. [PMID: 31905667 PMCID: PMC7016829 DOI: 10.3390/genes11010037] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/24/2019] [Accepted: 12/25/2019] [Indexed: 01/23/2023] Open
Abstract
Seed vigor is a key factor that determines the quality of seeds, which is of great significance for agricultural production, with the potential to promote growth and productivity. However, the underlying molecular mechanisms and genetic basis for seed vigor remain unknown. High-density genetic linkage mapping is an effective method for genomic study and quantitative trait loci (QTL) mapping. In this study, a high-density genetic map was constructed from a 148 BC4F3 population cross between ‘M03’ and ‘M08’ strains based on specific-locus amplified fragment (SLAF) sequencing. The constructed high-density genetic linkage map (HDGM) included 3876 SNP markers on ten chromosomes covering 2413.25 cM in length, with a mean distance between markers of 0.62 cM. QTL analysis was performed on four sweet corn germination traits that are related to seed vigor under artificial aging conditions. A total of 18 QTLs were identified in two seasons. Interestingly, a stable QTL was detected in two seasons on chromosome 10—termed qGR10—within an interval of 1.37 Mb. Within this interval, combined with gene annotation, we found four candidate genes (GRMZM2G074309, GRMZM2G117319, GRMZM2G465812, and GRMZM2G343519) which may be related to seed vigor after artificial aging.
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Affiliation(s)
- Xiaming Wu
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; (X.W.); (F.F.); (Y.Z.); (F.X.); (J.G.); (Y.L.); (W.Z.); (T.G.); (D.C.); (X.L.)
| | - Faqiang Feng
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; (X.W.); (F.F.); (Y.Z.); (F.X.); (J.G.); (Y.L.); (W.Z.); (T.G.); (D.C.); (X.L.)
| | - Yuzhong Zhu
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; (X.W.); (F.F.); (Y.Z.); (F.X.); (J.G.); (Y.L.); (W.Z.); (T.G.); (D.C.); (X.L.)
| | - Fugui Xie
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; (X.W.); (F.F.); (Y.Z.); (F.X.); (J.G.); (Y.L.); (W.Z.); (T.G.); (D.C.); (X.L.)
| | - Jing Yang
- National Engineering Research Center of Plant Space Breeding, South China Agricultural University, Guangzhou 510642, China;
| | - Jie Gong
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; (X.W.); (F.F.); (Y.Z.); (F.X.); (J.G.); (Y.L.); (W.Z.); (T.G.); (D.C.); (X.L.)
| | - Yu Liu
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; (X.W.); (F.F.); (Y.Z.); (F.X.); (J.G.); (Y.L.); (W.Z.); (T.G.); (D.C.); (X.L.)
| | - Wei Zhu
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; (X.W.); (F.F.); (Y.Z.); (F.X.); (J.G.); (Y.L.); (W.Z.); (T.G.); (D.C.); (X.L.)
| | - Tianle Gao
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; (X.W.); (F.F.); (Y.Z.); (F.X.); (J.G.); (Y.L.); (W.Z.); (T.G.); (D.C.); (X.L.)
| | - Danyi Chen
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; (X.W.); (F.F.); (Y.Z.); (F.X.); (J.G.); (Y.L.); (W.Z.); (T.G.); (D.C.); (X.L.)
| | - Xiaoqin Li
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; (X.W.); (F.F.); (Y.Z.); (F.X.); (J.G.); (Y.L.); (W.Z.); (T.G.); (D.C.); (X.L.)
| | - Jun Huang
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou 510642, China; (X.W.); (F.F.); (Y.Z.); (F.X.); (J.G.); (Y.L.); (W.Z.); (T.G.); (D.C.); (X.L.)
- Correspondence: ; Tel.: +86-020-85288311
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