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Gu Q, Lv X, Zhang D, Zhang Y, Wang X, Ke H, Yang J, Chen B, Wu L, Zhang G, Wang X, Sun Z, Ma Z. Deepening genomic sequences of 1081 Gossypium hirsutum accessions reveals novel SNPs and haplotypes relevant for practical breeding utility. Genomics 2024; 116:110848. [PMID: 38663523 DOI: 10.1016/j.ygeno.2024.110848] [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: 01/27/2024] [Revised: 04/16/2024] [Accepted: 04/21/2024] [Indexed: 06/03/2024]
Abstract
Fiber quality is a major breeding goal in cotton, but phenotypically direct selection is often hindered. In this study, we identified fiber quality and yield related loci using GWAS based on 2.97 million SNPs obtained from 10.65× resequencing data of 1081 accessions. The results showed that 585 novel fiber loci, including two novel stable SNP peaks associated with fiber length on chromosomes At12 and Dt05 and one novel genome regions linked with fiber strength on chromosome Dt12 were identified. Furthermore, by means of gene expression analysis, GhM_A12G0090, GhM_D05G1692, GhM_D12G3135 were identified and GhM_D11G2208 function was identified in Arabidopsis. Additionally, 14 consistent and stable superior haplotypes were identified, and 25 accessions were detected as possessing these 14 superior haplotype in breeding. This study providing fundamental insight relevant to identification of genes associated with fiber quality and yield will enhance future efforts toward improvement of upland cotton.
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Affiliation(s)
- Qishen Gu
- State Key Laboratory of North China Crop Improvement and Regulation / North China Key Laboratory for Crop Germplasm Resources of Education Ministry / Key Laboratory for Crop Germplasm Resources of Hebei Province / Hebei Agricultural University, Baoding, China
| | - Xing Lv
- State Key Laboratory of North China Crop Improvement and Regulation / North China Key Laboratory for Crop Germplasm Resources of Education Ministry / Key Laboratory for Crop Germplasm Resources of Hebei Province / Hebei Agricultural University, Baoding, China
| | - Dongmei Zhang
- State Key Laboratory of North China Crop Improvement and Regulation / North China Key Laboratory for Crop Germplasm Resources of Education Ministry / Key Laboratory for Crop Germplasm Resources of Hebei Province / Hebei Agricultural University, Baoding, China
| | - Yan Zhang
- State Key Laboratory of North China Crop Improvement and Regulation / North China Key Laboratory for Crop Germplasm Resources of Education Ministry / Key Laboratory for Crop Germplasm Resources of Hebei Province / Hebei Agricultural University, Baoding, China
| | - Xingyi Wang
- State Key Laboratory of North China Crop Improvement and Regulation / North China Key Laboratory for Crop Germplasm Resources of Education Ministry / Key Laboratory for Crop Germplasm Resources of Hebei Province / Hebei Agricultural University, Baoding, China
| | - Huifeng Ke
- State Key Laboratory of North China Crop Improvement and Regulation / North China Key Laboratory for Crop Germplasm Resources of Education Ministry / Key Laboratory for Crop Germplasm Resources of Hebei Province / Hebei Agricultural University, Baoding, China
| | - Jun Yang
- State Key Laboratory of North China Crop Improvement and Regulation / North China Key Laboratory for Crop Germplasm Resources of Education Ministry / Key Laboratory for Crop Germplasm Resources of Hebei Province / Hebei Agricultural University, Baoding, China
| | - Bin Chen
- State Key Laboratory of North China Crop Improvement and Regulation / North China Key Laboratory for Crop Germplasm Resources of Education Ministry / Key Laboratory for Crop Germplasm Resources of Hebei Province / Hebei Agricultural University, Baoding, China
| | - Liqiang Wu
- State Key Laboratory of North China Crop Improvement and Regulation / North China Key Laboratory for Crop Germplasm Resources of Education Ministry / Key Laboratory for Crop Germplasm Resources of Hebei Province / Hebei Agricultural University, Baoding, China
| | - Guiyin Zhang
- State Key Laboratory of North China Crop Improvement and Regulation / North China Key Laboratory for Crop Germplasm Resources of Education Ministry / Key Laboratory for Crop Germplasm Resources of Hebei Province / Hebei Agricultural University, Baoding, China
| | - Xingfen Wang
- State Key Laboratory of North China Crop Improvement and Regulation / North China Key Laboratory for Crop Germplasm Resources of Education Ministry / Key Laboratory for Crop Germplasm Resources of Hebei Province / Hebei Agricultural University, Baoding, China
| | - Zhengwen Sun
- State Key Laboratory of North China Crop Improvement and Regulation / North China Key Laboratory for Crop Germplasm Resources of Education Ministry / Key Laboratory for Crop Germplasm Resources of Hebei Province / Hebei Agricultural University, Baoding, China.
| | - Zhiying Ma
- State Key Laboratory of North China Crop Improvement and Regulation / North China Key Laboratory for Crop Germplasm Resources of Education Ministry / Key Laboratory for Crop Germplasm Resources of Hebei Province / Hebei Agricultural University, Baoding, China.
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Yuan X, Jiang X, Zhang M, Wang L, Jiao W, Chen H, Mao J, Ye W, Song Q. Integrative omics analysis elucidates the genetic basis underlying seed weight and oil content in soybean. THE PLANT CELL 2024; 36:2160-2175. [PMID: 38412459 PMCID: PMC11132872 DOI: 10.1093/plcell/koae062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 01/29/2024] [Accepted: 02/22/2024] [Indexed: 02/29/2024]
Abstract
Synergistic optimization of key agronomic traits by traditional breeding has dramatically enhanced crop productivity in the past decades. However, the genetic basis underlying coordinated regulation of yield- and quality-related traits remains poorly understood. Here, we dissected the genetic architectures of seed weight and oil content by combining genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) using 421 soybean (Glycine max) accessions. We identified 26 and 33 genetic loci significantly associated with seed weight and oil content by GWAS, respectively, and detected 5,276 expression quantitative trait loci (eQTLs) regulating expression of 3,347 genes based on population transcriptomes. Interestingly, a gene module (IC79), regulated by two eQTL hotspots, exhibited significant correlation with both seed weigh and oil content. Twenty-two candidate causal genes for seed traits were further prioritized by TWAS, including Regulator of Weight and Oil of Seed 1 (GmRWOS1), which encodes a sodium pump protein. GmRWOS1 was verified to pleiotropically regulate seed weight and oil content by gene knockout and overexpression. Notably, allelic variations of GmRWOS1 were strongly selected during domestication of soybean. This study uncovers the genetic basis and network underlying regulation of seed weight and oil content in soybean and provides a valuable resource for improving soybean yield and quality by molecular breeding.
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Affiliation(s)
- Xiaobo Yuan
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
| | - Xinyu Jiang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
| | - Mengzhu Zhang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
| | - Longfei Wang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
| | - Wu Jiao
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
| | - Huatao Chen
- Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling, Nanjing, Jiangsu 210014, China
| | - Junrong Mao
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
| | - Wenxue Ye
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
| | - Qingxin Song
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, No. 1 Weigang, Nanjing, Jiangsu 210095, China
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Liu S, Cheng H, Zhang Y, He M, Zuo D, Wang Q, Lv L, Lin Z, Liu J, Song G. Cotton transposon-related variome reveals roles of transposon-related variations in modern cotton cultivation. J Adv Res 2024:S2090-1232(24)00209-1. [PMID: 38810909 DOI: 10.1016/j.jare.2024.05.019] [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: 12/27/2023] [Revised: 03/26/2024] [Accepted: 05/18/2024] [Indexed: 05/31/2024] Open
Abstract
INTRODUCTION Transposon plays a vital role in cotton genome evolution, contributing to the expansion and divergence of genomes within the Gossypium genus. However, knowledge of transposon activity in modern cotton cultivation is limited. OBJECTIVES In this study, we aimed to construct transposon-related variome within Gossypium genus and reveal role of transposon-related variations during cotton cultivation. In addition, we try to identify valuable transposon-related variations for cotton breeding. METHODS We utilized graphical genome construction to build up the graphical transposon-related variome. Based on the graphical variome, we integrated t-test, eQTL analysis and Mendelian Randomization (MR) to identify valuable transposon activities and elite genes. In addition, a convolutional neural network (CNN) model was constructed to evaluate epigenomic effects of transposon-related variations. RESULTS We identified 35,980 transposon activities among 10 cotton genomes, and the diversity of genomic and epigenomic features was observed among 21 transposon categories. The graphical cotton transposon-related variome was constructed, and 9,614 transposon-related variations with plasticity in the modern cotton cohort were used for eQTL, phenotypic t-test and Mendelian Randomization. 128 genes were identified as gene resources improving fiber length and strength simultaneously. 4 genes were selected from 128 genes to construct the elite gene panel whose utility has been validated in a natural cotton cohort and 2 accessions with phenotypic divergence. Based on the eQTL analysis results, we identified transposon-related variations involved in cotton's environmental adaption and human domestication, providing evidence of their role in cotton's adaption-domestication cooperation. CONCLUSIONS The cotton transposon-related variome revealed the role of transposon-related variations in modern cotton cultivation, providing genomic resources for cotton molecular breeding.
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Affiliation(s)
- Shang Liu
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Hailiang Cheng
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; Zhengzhou Research Base, Zhengzhou University, Zhengzhou 450001, China
| | - Youping Zhang
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Man He
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Dongyun Zuo
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Qiaolian Wang
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Limin Lv
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Zhongxv Lin
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Ji Liu
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China.
| | - Guoli Song
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; Zhengzhou Research Base, Zhengzhou University, Zhengzhou 450001, China.
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Han Z, Si Z, Rahman MU, He L, Li Y, Khan AQ, Mao Y, Zulfiqar S, Ishfaq S, Mohsan M, Iqbal MA, Zafar S, Hu Y, Zhang T. Genomic insights into local adaptation of upland cotton in China and Pakistan. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:136. [PMID: 38764078 DOI: 10.1007/s00122-024-04624-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 04/14/2024] [Indexed: 05/21/2024]
Abstract
KEY MESSAGE Different kinship and resistance to cotton leaf curl disease (CLCuD) and heat were found between upland cotton cultivars from China and Pakistan. 175 SNPs and 82 InDels loci related to yield, fiber quality, CLCuD, and heat resistance were identified. Elite alleles found in Pakistani accessions aided local adaptation to climatic condition of two countries. Adaptation of upland cotton (Gossypium hirsutum) beyond its center of origin is expected to be driven by tailoring of the genome and genes to enhance yield and quality in new ecological niches. Here, resequencing of 456 upland cotton accessions revealed two distinct kinships according to the associated country. Fiber quality and lint percentage were consistent across kinships, but resistance to cotton leaf curl disease (CLCuD) and heat was distinctly exhibited by accessions from Pakistan, illustrating highly local adaption. A total of 175 SNP and 82 InDel loci related to yield, fiber quality, CLCuD and heat resistance were identified; among them, only two overlapped between Pakistani and Chinese accessions underscoring the divergent domestication and improvement targets in each country. Loci associated with resistance alleles to leaf curl disease and high temperature were largely found in Pakistani accessions to counter these stresses prevalent in Pakistan. These results revealed that breeding activities led to the accumulation of unique alleles and helped upland cotton become adapted to the respective climatic conditions, which will contribute to elucidating the genetic mechanisms that underlie resilience traits and help develop climate-resilient cotton cultivars for use worldwide.
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Affiliation(s)
- Zegang 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, China
| | - Zhanfeng Si
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Mehboob-Ur Rahman
- Plant Genomics and Molecular Breeding Laboratory, National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences, (NIBGE-C, PIEAS), Faisalabad, Pakistan
| | - Lu He
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Yiqian Li
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Ali Qaiser Khan
- Plant Genomics and Molecular Breeding Laboratory, National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences, (NIBGE-C, PIEAS), Faisalabad, Pakistan
| | - Yun Mao
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Sana Zulfiqar
- Plant Genomics and Molecular Breeding Laboratory, National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences, (NIBGE-C, PIEAS), Faisalabad, Pakistan
| | - Shumila Ishfaq
- Plant Genomics and Molecular Breeding Laboratory, National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences, (NIBGE-C, PIEAS), Faisalabad, Pakistan
| | - Muhammad Mohsan
- Plant Genomics and Molecular Breeding Laboratory, National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences, (NIBGE-C, PIEAS), Faisalabad, Pakistan
| | - Muhammad Atif Iqbal
- Plant Genomics and Molecular Breeding Laboratory, National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences, (NIBGE-C, PIEAS), Faisalabad, Pakistan
| | - Saba Zafar
- Plant Genomics and Molecular Breeding Laboratory, National Institute for Biotechnology and Genetic Engineering College, Pakistan Institute of Engineering and Applied Sciences, (NIBGE-C, PIEAS), Faisalabad, Pakistan
| | - Yan Hu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 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, China.
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Wu H, Liu X, Zong Y, Yang L, Wang J, Tong C, Li H. Leaf morphology related genes revealed by integrating Pan-transcriptome, GWAS and eQTL analyses in a Liriodendron population. PHYSIOLOGIA PLANTARUM 2024; 176:e14392. [PMID: 38887911 DOI: 10.1111/ppl.14392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/15/2024] [Accepted: 05/28/2024] [Indexed: 06/20/2024]
Abstract
Leaf plays an indispensable role in plant development and growth. Although many known genes related to leaf morphology development have been identified, elucidating the complex genetic basis of leaf morphological traits remains a challenge. Liriodendron plants are common ornamental trees due to their unique leaf shapes, while the molecular mechanism underlying Liriodendron leaf morphogenesis has remained unknown. Herein, we firstly constructed a population-level pan-transcriptome of Liriodendron from 81 accessions to explore the expression presence or absence variations (ePAVs), global expression differences at the population level, as well as differentially expressed genes (DEGs) between the Liriodendron chinense and Liriodendron tulipifera accessions. Subsequently, we integrated a genome-wide association study (GWAS), expression quantitative trait loci (eQTL), and transcriptome-wide association study (TWAS) to identify candidate genes related to leaf morphology. Through GWAS analysis, we identified 18 and 17 significant allelic loci in the leaf size and leaf shape modules, respectively. In addition, we discerned 16 candidate genes in relation to leaf morphological traits via TWAS. Further, integrating the co-localization results of GWAS and eQTL, we determined two regulatory hotspot regions, hot88 and hot758, related to leaf size and leaf shape, respectively. Finally, co-expression analysis, eQTL, and linkage mapping together demonstrated that Lchi_4g10795 regulate their own expression levels through cis-eQTL to affect the expression of downstream genes and cooperatively participate in the development of Liriodendron leaf morphology. These findings will improve our understanding of the molecular regulatory mechanism of Liriodendron leaf morphogenesis and will also accelerate molecular breeding of Liriodendron.
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Affiliation(s)
- Hainan Wu
- State Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing, China
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, China
| | - Xiao Liu
- State Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing, China
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, China
| | - Yaxian Zong
- State Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing, China
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, China
| | - Lichun Yang
- State Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing, China
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, China
| | - Jing Wang
- State Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing, China
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, China
| | - Chunfa Tong
- State Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing, China
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, China
| | - Huogen Li
- State Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing, China
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, China
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Wei H, Wang X, Zhang Z, Yang L, Zhang Q, Li Y, He H, Chen D, Zhang B, Zheng C, Leng Y, Cao X, Cui Y, Shi C, Liu Y, Lv Y, Ma J, He W, Liu X, Xu Q, Yuan Q, Yu X, Wang T, Qian H, Li X, Zhang B, Zhang H, Chen W, Guo M, Dai X, Wang Y, Zheng X, Guo L, Xie X, Qian Q, Shang L. Uncovering key salt-tolerant regulators through a combined eQTL and GWAS analysis using the super pan-genome in rice. Natl Sci Rev 2024; 11:nwae043. [PMID: 38650829 PMCID: PMC11034615 DOI: 10.1093/nsr/nwae043] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/21/2024] [Accepted: 01/24/2024] [Indexed: 04/25/2024] Open
Abstract
For sessile plants, gene expression plays a pivotal role in responding to salinity stress by activating or suppressing specific genes. However, our knowledge of genetic variations governing gene expression in response to salt stress remains limited in natural germplasm. Through transcriptome analysis of the Global Mini-Core Rice Collection consisting of a panel of 202 accessions, we identified 22 345 and 27 610 expression quantitative trait loci associated with the expression of 7787 and 9361 eGenes under normal and salt-stress conditions, respectively, leveraging the super pan-genome map. Notably, combined with genome-wide association studies, we swiftly pinpointed the potential candidate gene STG5-a major salt-tolerant locus known as qSTS5. Intriguingly, STG5 is required for maintaining Na+/K+ homeostasis by directly regulating the transcription of multiple members of the OsHKT gene family. Our study sheds light on how genetic variants influence the dynamic changes in gene expression responding to salinity stress and provides a valuable resource for the mining of salt-tolerant genes in the future.
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Affiliation(s)
- Hua Wei
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Xianmeng Wang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Zhipeng Zhang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Longbo Yang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Qianqian Zhang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Yilin Li
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Huiying He
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Dandan Chen
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Bin Zhang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Chongke Zheng
- Institute of Wetland Agriculture and Ecology, Shandong Academy of Agricultural Sciences, Jinan 250100, China
| | - Yue Leng
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Xinglan Cao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Yan Cui
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Chuanlin Shi
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Yifan Liu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Yang Lv
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China
| | - Jie Ma
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China
| | - Wenchuang He
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Xiangpei Liu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Qiang Xu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Qiaoling Yuan
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Xiaoman Yu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Tianyi Wang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Hongge Qian
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Xiaoxia Li
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Bintao Zhang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Hong Zhang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Wu Chen
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Mingliang Guo
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Xiaofan Dai
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Yuexing Wang
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China
| | - Xiaoming Zheng
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Longbiao Guo
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China
| | - Xianzhi Xie
- Institute of Wetland Agriculture and Ecology, Shandong Academy of Agricultural Sciences, Jinan 250100, China
| | - Qian Qian
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China
- Yazhouwan National Laboratory, Sanya 572024, China
| | - Lianguang Shang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
- Yazhouwan National Laboratory, Sanya 572024, China
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7
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Liu S, Cheng H, Zhang Y, He M, Zuo D, Wang Q, Lv L, Lin Z, Song G. Fingerprint Finder: Identifying Genomic Fingerprint Sites in Cotton Cohorts for Genetic Analysis and Breeding Advancement. Genes (Basel) 2024; 15:378. [PMID: 38540437 PMCID: PMC10970022 DOI: 10.3390/genes15030378] [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: 02/28/2024] [Revised: 03/17/2024] [Accepted: 03/18/2024] [Indexed: 06/14/2024] Open
Abstract
Genomic data in Gossypium provide numerous data resources for the cotton genomics community. However, to fill the gap between genomic analysis and breeding field work, detecting the featured genomic items of a subset cohort is essential for geneticists. We developed FPFinder v1.0 software to identify a subset of the cohort's fingerprint genomic sites. The FPFinder was developed based on the term frequency-inverse document frequency algorithm. With the short-read sequencing of an elite cotton pedigree, we identified 453 pedigree fingerprint genomic sites and found that these pedigree-featured sites had a role in cotton development. In addition, we applied FPFinder to evaluate the geographical bias of fiber-length-related genomic sites from a modern cotton cohort consisting of 410 accessions. Enriching elite sites in cultivars from the Yangtze River region resulted in the longer fiber length of Yangze River-sourced accessions. Apart from characterizing functional sites, we also identified 12,536 region-specific genomic sites. Combining the transcriptome data of multiple tissues and samples under various abiotic stresses, we found that several region-specific sites contributed to environmental adaptation. In this research, FPFinder revealed the role of the cotton pedigree fingerprint and region-specific sites in cotton development and environmental adaptation, respectively. The FPFinder can be applied broadly in other crops and contribute to genetic breeding in the future.
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Affiliation(s)
- Shang Liu
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Hailiang Cheng
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
- Zhengzhou Research Base, Zhengzhou University, Zhengzhou 450001, China
| | - Youping Zhang
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
| | - Man He
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
| | - Dongyun Zuo
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
| | - Qiaolian Wang
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
| | - Limin Lv
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
| | - Zhongxv Lin
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Guoli Song
- National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China; (S.L.); (Y.Z.); (M.H.); (D.Z.); (Q.W.); (L.L.)
- Zhengzhou Research Base, Zhengzhou University, Zhengzhou 450001, China
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8
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Yang G, Pan Y, Pan W, Song Q, Zhang R, Tong W, Cui L, Ji W, Song W, Song B, Deng P, Nie X. Combined GWAS and eGWAS reveals the genetic basis underlying drought tolerance in emmer wheat (Triticum turgidum L.). THE NEW PHYTOLOGIST 2024. [PMID: 38358006 DOI: 10.1111/nph.19589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 01/25/2024] [Indexed: 02/16/2024]
Abstract
Drought is one of the major environmental constraints for wheat production world-wide. As the progenitor and genetic reservoir of common wheat, emmer wheat is considered as an invaluable gene pool for breeding drought-tolerant wheat. Combining GWAS and eGWAS analysis of 107 accessions, we identified 86 QTLs, 105 462 eQTLs as well as 68 eQTL hotspots associating with drought tolerance (DT) in emmer wheat. A complex regulatory network composed of 185 upstream regulator and 2432 downstream drought-responsive candidates was developed, of which TtOTS1 was found to play a negative effect in determining DT through affecting root development. This study sheds light on revealing the genetic basis underlying DT, which will provide the indispensable genes and germplasm resources for elite drought tolerance wheat improvement and breeding.
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Affiliation(s)
- Guang Yang
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
- National Key Laboratory of Wheat Improvement, Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agriculture Sciences in Weifang, Weifang, 261325, Shandong, China
| | - Yan Pan
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Wenqiu Pan
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Qingting Song
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Ruoyu Zhang
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Wei Tong
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Licao Cui
- College of Biological Science and Engineering, Jiangxi Agricultural University, Nanchang, 330045, Jiangxi, China
| | - Wanquan Ji
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Weining Song
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Baoxing Song
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
- National Key Laboratory of Wheat Improvement, Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agriculture Sciences in Weifang, Weifang, 261325, Shandong, China
| | - Pingchuan Deng
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Xiaojun Nie
- State Key Laboratory of Crop Stress Biology in Arid Areas, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
- Pioneering Innovation Center for Wheat Stress Tolerance Improvement, State Key Laboratory of Crop Stress Biology for Arid Areas, Yangling, 712100, Shaanxi, China
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9
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Liu Y, Ma X, Li Y, Yang X, Cheng W. Zinc Finger Protein8 ( GhZFP8) Regulates the Initiation of Trichomes in Arabidopsis and the Development of Fiber in Cotton. PLANTS (BASEL, SWITZERLAND) 2024; 13:492. [PMID: 38498441 PMCID: PMC10892670 DOI: 10.3390/plants13040492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/01/2024] [Accepted: 02/05/2024] [Indexed: 03/20/2024]
Abstract
Cotton is one of the most important natural fibers used in the textile industry worldwide. It is important to identify the key factors involved in cotton fiber development. In this study, zinc finger protein8 (GhZFP8) encoding a C2H2 transcription factor (TF) was cloned from cotton. qPCR showed that the transcripts of GhZFP8 in cotton were detected in the leaves and fibers at 3, 6, and 30 days post-anthesis (DPA), but not in the roots, stems, or flowers. The overexpression of GhZFP8 increased the trichome number on the siliques, leaves, and inflorescence, but inhibited the growth. The expression of trichome development and cell-elongation-related genes decreased obviously in GhZFP8 overexpressor Arabidopsis. Indole-3-acetic acid (IAA) and 1-Aminocyclopropanecarboxylic acid (ACC) contents were much higher in GhZFP8 overexpressors than that found in the wild type, but the gibberellin (GA) content was lower. The interference of GhZFP8 in cotton caused smaller bolls and shorter fibers than that of the control. The results of DNA affinity purification (DAP)-seq showed that GhZFP8 could bind to the promoter, exon, intron, and intergenic region of the target genes, which are involved in photosynthesis, signal transduction, synthesis of biomass, etc. Our findings implied that GhZFP8 processed multiple biological functions and regulated the development of cotton fiber.
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Affiliation(s)
- Yongchang Liu
- College of Bioengineering, Jingchu University of Technology, Jingmen 448000, China; (Y.L.); (X.Y.)
| | - Xiaomei Ma
- Cotton Research Institute, Xinjiang Science Academy of Agriculture and Reclaimation, Shihezi 832000, China;
| | - Ying Li
- College of Bioengineering, Jingchu University of Technology, Jingmen 448000, China; (Y.L.); (X.Y.)
| | - Xiaoyu Yang
- College of Bioengineering, Jingchu University of Technology, Jingmen 448000, China; (Y.L.); (X.Y.)
| | - Wenhan Cheng
- College of Bioengineering, Jingchu University of Technology, Jingmen 448000, China; (Y.L.); (X.Y.)
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10
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Wang J, Liang Y, Gong Z, Zheng J, Li Z, Zhou G, Xu Y, Li X. Genomic and epigenomic insights into the mechanism of cold response in upland cotton (Gossypium hirsutum). PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2024; 206:108206. [PMID: 38029617 DOI: 10.1016/j.plaphy.2023.108206] [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: 08/16/2023] [Revised: 11/15/2023] [Accepted: 11/16/2023] [Indexed: 12/01/2023]
Abstract
Functional genome research, including gene transcriptional and posttranslational modifications of histones, can benefit greatly from a high-quality genome assembly. Histone modification plays a significant role in modulating the responses to abiotic stress in plants. However, there are limited reports on the involvement of dynamic changes in histone modification in cold stress response in upland cotton. In this study, the genome of an elite accession, YM11, with considerable cold stress tolerance was de novo assembled, which yielded a genome of 2343.06 Mb with a contig N50 of 88.96 Mb, and a total of 73,821 protein-coding gene models were annotated. Comparisons among YM11 and five Gossypium allopolyploid cotton assemblies highlighted a large amount of structural variations and presence/absence variations. We analyzed transcriptome and metabolome changes in YM11 seedlings subjected to cold stress. Using the CUT&Tag method, genome-wide H3K4me3 and H3K9ac modification patterns and effect of histone changes on gene expression were profiled during cold stress. Significant and consistently changing histone modifications and the gene expressions were screened, of which transcription factors (TFs) were highlighted. Our results suggest a positive correlation between the changes in H3K4me3, H3K9ac modifications and cold stress-responsive gene activation. This genome assembly and comprehensive analysis of genome-wide histone modifications and gene expression provide insights into the genomic variation and epigenetic responses to cold stress in upland cotton.
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Affiliation(s)
- Junduo Wang
- Xinjiang Academy of Agricultural Science, Urumqi, 830091, Xinjiang, China
| | - Yajun Liang
- Xinjiang Academy of Agricultural Science, Urumqi, 830091, Xinjiang, China
| | - Zhaolong Gong
- Xinjiang Academy of Agricultural Science, Urumqi, 830091, Xinjiang, China
| | - Juyun Zheng
- Xinjiang Academy of Agricultural Science, Urumqi, 830091, Xinjiang, China
| | - Zhiqiang Li
- Adsen Biotechnology Co., Ltd., Urumqi, 830022, Xinjiang, China
| | - Guohui Zhou
- Adsen Biotechnology Co., Ltd., Urumqi, 830022, Xinjiang, China
| | - Yuhui Xu
- Adsen Biotechnology Co., Ltd., Urumqi, 830022, Xinjiang, China.
| | - Xueyuan Li
- Xinjiang Academy of Agricultural Science, Urumqi, 830091, Xinjiang, China.
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11
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Li B, Feng C, Zhang W, Sun S, Yue D, Zhang X, Yang X. Comprehensive non-coding RNA analysis reveals specific lncRNA/circRNA-miRNA-mRNA regulatory networks in the cotton response to drought stress. Int J Biol Macromol 2023; 253:126558. [PMID: 37659489 DOI: 10.1016/j.ijbiomac.2023.126558] [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: 03/06/2023] [Revised: 07/29/2023] [Accepted: 08/20/2023] [Indexed: 09/04/2023]
Abstract
Root and leaf are essential organs of plants in sensing and responding to drought stress. However, comparative knowledge of non-coding RNAs (ncRNAs) of root and leaf tissues in the regulation of drought response in cotton is limited. Here, we used deep sequencing data of leaf and root tissues of drought-resistant and drought-sensitive cotton varieties for identifying miRNAs, lncRNAs and circRNAs. A total of 1531 differentially expressed (DE) ncRNAs was identified, including 77 DE miRNAs, 1393 DE lncRNAs and 61 DE circRNAs. The tissue-specific and variety-specific competing endogenous RNA (ceRNA) networks of DE lncRNA-miRNA-mRNA response to drought were constructed. Furthermore, the novel drought-responsive lncRNA 1 (DRL1), specifically and differentially expressed in root, was verified to positively affect phenotypes of cotton seedlings under drought stress, competitively binding to miR477b with GhNAC1 and GhSCL3. In addition, we also constructed another ceRNA network consisting of 18 DE circRNAs, 26 DE miRNAs and 368 DE mRNAs. Fourteen circRNA were characterized, and a novel molecular regulatory system of circ125- miR7484b/miR7450b was proposed under drought stress. Our findings revealed the specificity of ncRNA expression in tissue- and variety-specific patterns involved in the response to drought stress, and uncovered novel regulatory pathways and potentially effective molecules in genetic improvement for crop drought resistance.
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Affiliation(s)
- Baoqi Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China.
| | - Cheng Feng
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Wenhao Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Simin Sun
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Dandan Yue
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Xianlong Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Xiyan Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China.
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12
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Chen L, Liu L, Yang G, Li X, Dai X, Xue L, Yin T. Expression Quantitative Trait Locus of Wood Formation-Related Genes in Salix suchowensis. Int J Mol Sci 2023; 25:247. [PMID: 38203430 PMCID: PMC10778782 DOI: 10.3390/ijms25010247] [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: 11/14/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
Shrub willows are widely planted for landscaping, soil remediation, and biomass production, due to their rapid growth rates. Identification of regulatory genes in wood formation would provide clues for genetic engineering of willows for improved growth traits on marginal lands. Here, we conducted an expression quantitative trait locus (eQTL) analysis, using a full sibling F1 population of Salix suchowensis, to explore the genetic mechanisms underlying wood formation. Based on variants identified from simplified genome sequencing and gene expression data from RNA sequencing, 16,487 eQTL blocks controlling 5505 genes were identified, including 2148 cis-eQTLs and 16,480 trans-eQTLs. eQTL hotspots were identified, based on eQTL frequency in genomic windows, revealing one hotspot controlling genes involved in wood formation regulation. Regulatory networks were further constructed, resulting in the identification of key regulatory genes, including three transcription factors (JAZ1, HAT22, MYB36) and CLV1, BAM1, CYCB2;4, CDKB2;1, associated with the proliferation and differentiation activity of cambium cells. The enrichment of genes in plant hormone pathways indicates their critical roles in the regulation of wood formation. Our analyses provide a significant groundwork for a comprehensive understanding of the regulatory network of wood formation in S. suchowensis.
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Affiliation(s)
| | | | | | | | | | - Liangjiao Xue
- State Key Laboratory of Tree Genetics and Breeding, Jiangsu Key Laboratory for Poplar Germplasm Enhancement and Variety Improvement, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
| | - Tongming Yin
- State Key Laboratory of Tree Genetics and Breeding, Jiangsu Key Laboratory for Poplar Germplasm Enhancement and Variety Improvement, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
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13
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Liu S, Zuo D, Cheng H, He M, Wang Q, Lv L, Zhang Y, Ashraf J, Liu J, Song G. Cotton pedigree genome reveals restriction of cultivar-driven strategy in cotton breeding. Genome Biol 2023; 24:282. [PMID: 38066616 PMCID: PMC10704732 DOI: 10.1186/s13059-023-03124-3] [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: 06/06/2023] [Accepted: 11/26/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Many elite genes have been identified from the available cotton genomic data, providing various genetic resources for gene-driven breeding. However, backbone cultivar-driven breeding is the most widely applied strategy. Revealing the genetic basis of cultivar-driven strategy's restriction is crucial for transition of cotton breeding strategy. RESULT CRI12 is a backbone cultivar in cultivar-driven breeding. Here we sequence the pedigree of CRI12 using Nanopore long-read sequencing. We construct a graphical pedigree genome using the high-quality CRI12 genome and 13,138 structural variations within 20 different pedigree members. We find that low hereditary stability of elite segments in backbone cultivars is a drawback of cultivar-driven strategy. We also identify 623 functional segments in CRI12 for multiple agronomic traits in presence and absence variation-based genome-wide association study on three cohorts. We demonstrate that 25 deleterious segments are responsible for the geographical divergence of cotton in pathogen resistance. We also characterize an elite pathogen-resistant gene (GhKHCP) utilized in modern cotton breeding. In addition, we identify 386 pedigree fingerprint segments by comparing the segments of the CRI12 pedigree with those of a large cotton population. CONCLUSION We characterize the genetic patterns of functional segments in the pedigree of CRI12 using graphical genome method, revealing restrictions of cultivar-driven strategies in cotton breeding. These findings provide theoretical support for transitioning from cultivar-driven to gene-driven strategy in cotton breeding.
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Affiliation(s)
- Shang Liu
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China
| | - Dongyun Zuo
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China
| | - Hailiang Cheng
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China
| | - Man He
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Qiaolian Wang
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China
| | - Limin Lv
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China
| | - Youping Zhang
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China
| | - Javaria Ashraf
- Department of Plant Breeding and Genetics, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Ji Liu
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
| | - Guoli Song
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China.
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14
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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.
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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.
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15
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Huang Y, Qi Z, Li J, You J, Zhang X, Wang M. Genetic interrogation of phenotypic plasticity informs genome-enabled breeding in cotton. J Genet Genomics 2023; 50:971-982. [PMID: 37211312 DOI: 10.1016/j.jgg.2023.05.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 04/19/2023] [Accepted: 05/04/2023] [Indexed: 05/23/2023]
Abstract
Phenotypic plasticity, or the ability to adapt to and thrive in changing climates and variable environments, is essential for developmental programs in plants. Despite its importance, the genetic underpinnings of phenotypic plasticity for key agronomic traits remain poorly understood in many crops. In this study, we aim to fill this gap by using genome-wide association studies to identify genetic variations associated with phenotypic plasticity in upland cotton (Gossypium hirsutum L.). We identified 73 additive quantitative trait loci (QTLs), 32 dominant QTLs, and 6799 epistatic QTLs associated with 20 traits. We also identified 117 additive QTLs, 28 dominant QTLs, and 4691 epistatic QTLs associated with phenotypic plasticity in 19 traits. Our findings reveal new genetic factors, including additive, dominant, and epistatic QTLs, that are linked to phenotypic plasticity and agronomic traits. Meanwhile, we find that the genetic factors controlling the mean phenotype and phenotypic plasticity are largely independent in upland cotton, indicating the potential for simultaneous improvement. Additionally, we envision a genomic design strategy by utilizing the identified QTLs to facilitate cotton breeding. Taken together, our study provides new insights into the genetic basis of phenotypic plasticity in cotton, which should be valuable for future breeding.
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Affiliation(s)
- Yuefan Huang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, China
| | - Zhengyang Qi
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, China
| | - Jianying Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, China
| | - Jiaqi You
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, China
| | - Xianlong Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, China.
| | - Maojun Wang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, China.
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16
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Ming L, Fu D, Wu Z, Zhao H, Xu X, Xu T, Xiong X, Li M, Zheng Y, Li G, Yang L, Xia C, Zhou R, Liao K, Yu Q, Chai W, Li S, Liu Y, Wu X, Mao J, Wei J, Li X, Wang L, Wu C, Xie W. Transcriptome-wide association analyses reveal the impact of regulatory variants on rice panicle architecture and causal gene regulatory networks. Nat Commun 2023; 14:7501. [PMID: 37980346 PMCID: PMC10657423 DOI: 10.1038/s41467-023-43077-6] [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: 02/17/2023] [Accepted: 10/30/2023] [Indexed: 11/20/2023] Open
Abstract
Panicle architecture is a key determinant of rice grain yield and is mainly determined at the 1-2 mm young panicle stage. Here, we investigated the transcriptome of the 1-2 mm young panicles from 275 rice varieties and identified thousands of genes whose expression levels were associated with panicle traits. Multimodel association studies suggested that many small-effect genetic loci determine spikelet per panicle (SPP) by regulating the expression of genes associated with panicle traits. We found that alleles at cis-expression quantitative trait loci of SPP-associated genes underwent positive selection, with a strong preference for alleles increasing SPP. We further developed a method that integrates the associations of cis- and trans-expression components of genes with traits to identify causal genes at even small-effect loci and construct regulatory networks. We identified 36 putative causal genes of SPP, including SDT (MIR156j) and OsMADS17, and inferred that OsMADS17 regulates SDT expression, which was experimentally validated. Our study reveals the impact of regulatory variants on rice panicle architecture and provides new insights into the gene regulatory networks of panicle traits.
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Affiliation(s)
- Luchang Ming
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Debao Fu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Zhaona Wu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Hu Zhao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xingbing Xu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Tingting Xu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xiaohu Xiong
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Mu Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Yi Zheng
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Ge Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Ling Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Chunjiao Xia
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Rongfang Zhou
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Keyan Liao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Qian Yu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Wenqi Chai
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Sijia Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Yinmeng Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xiaokun Wu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Jianquan Mao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Julong Wei
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, USA
| | - Xu Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Lei Wang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Changyin Wu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
| | - Weibo Xie
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China.
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China.
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17
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Bharati R, Sen MK, Severová L, Svoboda R, Fernández-Cusimamani E. Polyploidization and genomic selection integration for grapevine breeding: a perspective. FRONTIERS IN PLANT SCIENCE 2023; 14:1248978. [PMID: 38034577 PMCID: PMC10684766 DOI: 10.3389/fpls.2023.1248978] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023]
Abstract
Grapevines are economically important woody perennial crops widely cultivated for their fruits that are used for making wine, grape juice, raisins, and table grapes. However, grapevine production is constantly facing challenges due to climate change and the prevalence of pests and diseases, causing yield reduction, lower fruit quality, and financial losses. To ease the burden, continuous crop improvement to develop superior grape genotypes with desirable traits is imperative. Polyploidization has emerged as a promising tool to generate genotypes with novel genetic combinations that can confer desirable traits such as enhanced organ size, improved fruit quality, and increased resistance to both biotic and abiotic stresses. While previous studies have shown high polyploid induction rates in Vitis spp., rigorous screening of genotypes among the produced polyploids to identify those exhibiting desired traits remains a major bottleneck. In this perspective, we propose the integration of the genomic selection approach with omics data to predict genotypes with desirable traits among the vast unique individuals generated through polyploidization. This integrated approach can be a powerful tool for accelerating the breeding of grapevines to develop novel and improved grapevine varieties.
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Affiliation(s)
- Rohit Bharati
- Department of Crop Sciences and Agroforestry, The Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Suchdol, Czechia
| | - Madhab Kumar Sen
- Department of Agroecology and Crop Production, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Suchdol, Czechia
| | - Lucie Severová
- Department of Economic Theories, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czechia
| | - Roman Svoboda
- Department of Economic Theories, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czechia
| | - Eloy Fernández-Cusimamani
- Department of Crop Sciences and Agroforestry, The Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Suchdol, Czechia
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18
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You J, Liu Z, Qi Z, Ma Y, Sun M, Su L, Niu H, Peng Y, Luo X, Zhu M, Huang Y, Chang X, Hu X, Zhang Y, Pi R, Liu Y, Meng Q, Li J, Zhang Q, Zhu L, Lin Z, Min L, Yuan D, Grover CE, Fang DD, Lindsey K, Wendel JF, Tu L, Zhang X, Wang M. Regulatory controls of duplicated gene expression during fiber development in allotetraploid cotton. Nat Genet 2023; 55:1987-1997. [PMID: 37845354 PMCID: PMC10632151 DOI: 10.1038/s41588-023-01530-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 09/14/2023] [Indexed: 10/18/2023]
Abstract
Polyploidy complicates transcriptional regulation and increases phenotypic diversity in organisms. The dynamics of genetic regulation of gene expression between coresident subgenomes in polyploids remains to be understood. Here we document the genetic regulation of fiber development in allotetraploid cotton Gossypium hirsutum by sequencing 376 genomes and 2,215 time-series transcriptomes. We characterize 1,258 genes comprising 36 genetic modules that control staged fiber development and uncover genetic components governing their partitioned expression relative to subgenomic duplicated genes (homoeologs). Only about 30% of fiber quality-related homoeologs show phenotypically favorable allele aggregation in cultivars, highlighting the potential for subgenome additivity in fiber improvement. We envision a genome-enabled breeding strategy, with particular attention to 48 favorable alleles related to fiber phenotypes that have been subjected to purifying selection during domestication. Our work delineates the dynamics of gene regulation during fiber development and highlights the potential of subgenomic coordination underpinning phenotypes in polyploid plants.
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Affiliation(s)
- Jiaqi You
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Zhenping Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Zhengyang Qi
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Yizan Ma
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Mengling Sun
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Ling Su
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Hao Niu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Yabing Peng
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xuanxuan Luo
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Mengmeng Zhu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Yuefan Huang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xing Chang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xiubao Hu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Yuqi Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Ruizhen Pi
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Yuqi Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Qingying Meng
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Jianying Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Qinghua Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Longfu Zhu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Zhongxu Lin
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Ling Min
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Daojun Yuan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Corrinne E Grover
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA
| | - David D Fang
- Cotton Fiber Bioscience Research Unit, USDA-ARS, Southern Regional Research Center, New Orleans, LA, USA
| | - Keith Lindsey
- Department of Biosciences, Durham University, Durham, UK
| | - Jonathan F Wendel
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA
| | - Lili Tu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
| | - Xianlong Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
| | - Maojun Wang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
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Li S, Kong L, Xiao X, Li P, Liu A, Li J, Gong J, Gong W, Ge Q, Shang H, Pan J, Chen H, Peng Y, Zhang Y, Lu Q, Shi Y, Yuan Y. Genome-wide artificial introgressions of Gossypium barbadense into G. hirsutum reveal superior loci for simultaneous improvement of cotton fiber quality and yield traits. J Adv Res 2023; 53:1-16. [PMID: 36460274 PMCID: PMC10658236 DOI: 10.1016/j.jare.2022.11.009] [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: 04/18/2022] [Revised: 10/31/2022] [Accepted: 11/24/2022] [Indexed: 12/02/2022] Open
Abstract
INTRODUCTION The simultaneous improvement of fiber quality and yield for cotton is strongly limited by the narrow genetic backgrounds of Gossypium hirsutum (Gh) and the negative genetic correlations among traits. An effective way to overcome the bottlenecks is to introgress the favorable alleles of Gossypium barbadense (Gb) for fiber quality into Gh with high yield. OBJECTIVES This study was to identify superior loci for the improvement of fiber quality and yield. METHODS Two sets of chromosome segment substitution lines (CSSLs) were generated by crossing Hai1 (Gb, donor-parent) with cultivar CCRI36 (Gh) and CCRI45 (Gh) as genetic backgrounds, and cultivated in 6 and 8 environments, respectively. The kmer genotyping strategy was improved and applied to the population genetic analysis of 743 genomic sequencing data. A progeny segregating population was constructed to validate genetic effects of the candidate loci. RESULTS A total of 68,912 and 83,352 genome-wide introgressed kmers were identified in the CCRI36 and CCRI45 populations, respectively. Over 90 % introgressions were homologous exchanges and about 21 % were reverse insertions. In total, 291 major introgressed segments were identified with stable genetic effects, of which 66(22.98 %), 64(21.99 %), 35(12.03 %), 31(10.65 %) and 18(6.19 %) were beneficial for the improvement of fiber length (FL), strength (FS), micronaire, lint-percentage (LP) and boll-weight, respectively. Thirty-nine introgression segments were detected with stable favorable additive effects for simultaneous improvement of 2 or more traits in Gh genetic background, including 6 could increase FL/FS and LP. The pyramiding effects of 3 pleiotropic segments (A07:C45Clu-081, D06:C45Clu-218, D02:C45Clu-193) were further validated in the segregating population. CONCLUSION The combining of genome-wide introgressions and kmer genotyping strategy showed significant advantages in exploring genetic resources. Through the genome-wide comprehensive mining, a total of 11 clusters (segments) were discovered for the stable simultaneous improvement of FL/FS and LP, which should be paid more attention in the future.
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Affiliation(s)
- Shaoqi Li
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Linglei Kong
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Xianghui Xiao
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Pengtao Li
- School of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang 455000, China
| | - Aiying Liu
- 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
| | - Juwu Gong
- 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
| | - Qun Ge
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Haihong Shang
- 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
| | - Hong Chen
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China
| | - Yan Peng
- Third Division of the Xinjiang Production and Construction Corps Agricultural Research Institute, Tumushuke 843900, China
| | - Yuanming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Quanwei Lu
- School of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang 455000, China.
| | - Yuzhen Shi
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China.
| | - Youlu Yuan
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
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20
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Wen X, Chen Z, Yang Z, Wang M, Jin S, Wang G, Zhang L, Wang L, Li J, Saeed S, He S, Wang Z, Wang K, Kong Z, Li F, Zhang X, Chen X, Zhu Y. A comprehensive overview of cotton genomics, biotechnology and molecular biological studies. SCIENCE CHINA. LIFE SCIENCES 2023; 66:2214-2256. [PMID: 36899210 DOI: 10.1007/s11427-022-2278-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/09/2023] [Indexed: 03/12/2023]
Abstract
Cotton is an irreplaceable economic crop currently domesticated in the human world for its extremely elongated fiber cells specialized in seed epidermis, which makes it of high research and application value. To date, numerous research on cotton has navigated various aspects, from multi-genome assembly, genome editing, mechanism of fiber development, metabolite biosynthesis, and analysis to genetic breeding. Genomic and 3D genomic studies reveal the origin of cotton species and the spatiotemporal asymmetric chromatin structure in fibers. Mature multiple genome editing systems, such as CRISPR/Cas9, Cas12 (Cpf1) and cytidine base editing (CBE), have been widely used in the study of candidate genes affecting fiber development. Based on this, the cotton fiber cell development network has been preliminarily drawn. Among them, the MYB-bHLH-WDR (MBW) transcription factor complex and IAA and BR signaling pathway regulate the initiation; various plant hormones, including ethylene, mediated regulatory network and membrane protein overlap fine-regulate elongation. Multistage transcription factors targeting CesA 4, 7, and 8 specifically dominate the whole process of secondary cell wall thickening. And fluorescently labeled cytoskeletal proteins can observe real-time dynamic changes in fiber development. Furthermore, research on the synthesis of cotton secondary metabolite gossypol, resistance to diseases and insect pests, plant architecture regulation, and seed oil utilization are all conducive to finding more high-quality breeding-related genes and subsequently facilitating the cultivation of better cotton varieties. This review summarizes the paramount research achievements in cotton molecular biology over the last few decades from the above aspects, thereby enabling us to conduct a status review on the current studies of cotton and provide strong theoretical support for the future direction.
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Affiliation(s)
- Xingpeng Wen
- Institute for Advanced Studies, Wuhan University, Wuhan, 430072, China
- College of Life Sciences, Wuhan University, Wuhan, 430072, China
| | - Zhiwen Chen
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, University of CAS, Chinese Academy of Sciences, Shanghai, 200032, China
- Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China
| | - Zuoren Yang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Maojun Wang
- Hubei Hongshan Laboratory, National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Shuangxia Jin
- Hubei Hongshan Laboratory, National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Guangda Wang
- State Key Laboratory of Plant Genomics, Institute of Microbiology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Li Zhang
- Institute for Advanced Studies, Wuhan University, Wuhan, 430072, China
| | - Lingjian Wang
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, University of CAS, Chinese Academy of Sciences, Shanghai, 200032, China
| | - Jianying Li
- Hubei Hongshan Laboratory, National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Sumbul Saeed
- Hubei Hongshan Laboratory, National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Shoupu He
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Zhi Wang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Kun Wang
- College of Life Sciences, Wuhan University, Wuhan, 430072, China
| | - Zhaosheng Kong
- State Key Laboratory of Plant Genomics, Institute of Microbiology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China.
- Shanxi Agricultural University, Jinzhong, 030801, China.
| | - Fuguang Li
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
| | - Xianlong Zhang
- Hubei Hongshan Laboratory, National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.
| | - Xiaoya Chen
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, University of CAS, Chinese Academy of Sciences, Shanghai, 200032, China.
- Hainan Yazhou Bay Seed Laboratory, Sanya, 572025, China.
| | - Yuxian Zhu
- Institute for Advanced Studies, Wuhan University, Wuhan, 430072, China.
- College of Life Sciences, Wuhan University, Wuhan, 430072, China.
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21
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Chien PS, Chen PH, Lee CR, Chiou TJ. Transcriptome-wide association study coupled with eQTL analysis reveals the genetic connection between gene expression and flowering time in Arabidopsis. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:5653-5666. [PMID: 37419660 DOI: 10.1093/jxb/erad262] [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: 12/07/2022] [Accepted: 07/06/2023] [Indexed: 07/09/2023]
Abstract
Genome-wide association study (GWAS) has improved our understanding of complex traits, but challenges exist in distinguishing causation versus association caused by linkage disequilibrium. Instead, transcriptome-wide association studies (TWAS) detect direct associations between expression levels and phenotypic variations, providing an opportunity to better prioritize candidate genes. To assess the feasibility of TWAS, we investigated the association between transcriptomes, genomes, and various traits in Arabidopsis, including flowering time. The associated genes formerly known to regulate growth allometry or metabolite production were first identified by TWAS. Next, for flowering time, six TWAS-newly identified genes were functionally validated. Analysis of the expression quantitative trait locus (eQTL) further revealed a trans-regulatory hotspot affecting the expression of several TWAS-identified genes. The hotspot covers the FRIGIDA (FRI) gene body, which possesses multiple haplotypes differentially affecting the expression of downstream genes, such as FLOWERING LOCUS C (FLC) and SUPPRESSOR OF OVEREXPRESSION OF CO 1 (SOC1). We also revealed multiple independent paths towards the loss of function of FRI in natural accessions. Altogether, this study demonstrates the potential of combining TWAS with eQTL analysis to identify important regulatory modules of FRI-FLC-SOC1 for quantitative traits in natural populations.
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Affiliation(s)
- Pei-Shan Chien
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
| | - Pin-Hua Chen
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
| | - Cheng-Ruei Lee
- Institute of Ecology and Evolutionary Biology, National Taiwan University, Taipei, Taiwan
- Institute of Plant Biology, National Taiwan University, Taipei, Taiwan
| | - Tzyy-Jen Chiou
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
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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.
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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
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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.
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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.
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Naveed S, Gandhi N, Billings G, Jones Z, Campbell BT, Jones M, Rustgi S. Alterations in Growth Habit to Channel End-of-Season Perennial Reserves towards Increased Yield and Reduced Regrowth after Defoliation in Upland Cotton ( Gossypium hirsutum L.). Int J Mol Sci 2023; 24:14174. [PMID: 37762483 PMCID: PMC10532291 DOI: 10.3390/ijms241814174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/03/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Cotton (Gossypium spp.) is the primary source of natural textile fiber in the U.S. and a major crop in the Southeastern U.S. Despite constant efforts to increase the cotton fiber yield, the yield gain has stagnated. Therefore, we undertook a novel approach to improve the cotton fiber yield by altering its growth habit from perennial to annual. In this effort, we identified genotypes with high-expression alleles of five floral induction and meristem identity genes (FT, SOC1, FUL, LFY, and AP1) from an Upland cotton mini-core collection and crossed them in various combinations to develop cotton lines with annual growth habit, optimal flowering time, and enhanced productivity. To facilitate the characterization of genotypes with the desired combinations of stacked alleles, we identified molecular markers associated with the gene expression traits via genome-wide association analysis using a 63 K SNP Array. Over 14,500 SNPs showed polymorphism and were used for association analysis. A total of 396 markers showed associations with expression traits. Of these 396 markers, 159 were mapped to genes, 50 to untranslated regions, and 187 to random genomic regions. Biased genomic distribution of associated markers was observed where more trait-associated markers mapped to the cotton D sub-genome. Many quantitative trait loci coincided at specific genomic regions. This observation has implications as these traits could be bred together. The analysis also allowed the identification of candidate regulators of the expression patterns of these floral induction and meristem identity genes whose functions will be validated.
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Affiliation(s)
- Salman Naveed
- Department of Plant and Environmental Sciences, Clemson University Pee Dee Research and Education Center, Florence, SC 29506, USA; (S.N.); (M.J.)
| | - Nitant Gandhi
- Department of Plant and Environmental Sciences, Clemson University Pee Dee Research and Education Center, Florence, SC 29506, USA; (S.N.); (M.J.)
| | - Grant Billings
- Department of Crop & Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Zachary Jones
- Department of Plant and Environmental Sciences, Clemson University Pee Dee Research and Education Center, Florence, SC 29506, USA; (S.N.); (M.J.)
| | - B. Todd Campbell
- USDA-ARS Coastal Plains Soil, Water, and Plant Research Center, Florence, SC 29501, USA;
| | - Michael Jones
- Department of Plant and Environmental Sciences, Clemson University Pee Dee Research and Education Center, Florence, SC 29506, USA; (S.N.); (M.J.)
| | - Sachin Rustgi
- Department of Plant and Environmental Sciences, Clemson University Pee Dee Research and Education Center, Florence, SC 29506, USA; (S.N.); (M.J.)
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25
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Wang S, Wu H, Zhao Y, Wang L, Guan X, Zhao T. Mapping intron retention events contributing to complex traits using splice quantitative trait locus. PLANT METHODS 2023; 19:72. [PMID: 37480119 PMCID: PMC10362629 DOI: 10.1186/s13007-023-01048-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 07/03/2023] [Indexed: 07/23/2023]
Abstract
BACKGROUND Alternative splicing (AS) of mRNA plays an important roles in transcriptome diversity, involving regulation of plant growth and stress response. Understanding the variation of AS events underlying GWAS loci in a crop population can provide insight into the molecular mechanisms of complex agronomic traits. To date, genome-wide association studies relating AS events to agronomic traits have rarely been conducted at the population level in crops. RESULTS Here, a pipeline was constructed to identify candidate AS events related to complex traits. Firstly, ovule transcriptome data were used to characterize intron retention (IR), the predominant type of AS in plants, on a genome-wide scale. This was done in a natural population consisting of 279 upland cotton lines. Secondly, splice quantitative trait locus (sQTL) analysis was carried out, which yielded a total of 2295 sQTLs involving 1607 genes. Of these, 14.25% (n = 427) were cis-regulatory loci. Integration with expression quantitative trait loci (eQTL) revealed that 53 (21.4%) cis-sGenes were regulated by both cis-sQTLs and cis-eQTLs. Finally, co-localization analysis integrated with GWAS loci in this population showed 32 cis-QTLs to be co-located with genetic regulatory loci related to fiber yield and quality traits, indicating that sQTLs are likely to participate in regulating cotton fiber yield and quality. An in-depth evaluation confirmed that differences in the IR rates of sQTL-regulated candidate genes such as GhLRRK1 and GhGC1 are associated with lint percentage (LP), which has potential in breeding applications. CONCLUSION This study provides a clue that AS of mRNA has an impact on crop yield, along with functional sQTLs are new genetic resources for cotton precision breeding.
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Affiliation(s)
- Siyuan Wang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 300058, China
| | - Hongyu Wu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 300058, China
| | - Yongyan Zhao
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, 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, Hainan, China
| | - Luyao Wang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, 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, Hainan, China
| | - Xueying Guan
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, 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, Hainan, China.
| | - Ting Zhao
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, 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, Hainan, China.
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Yang J, Zhang H, Chen H, Sun Z, Ke H, Wang G, Meng C, Wu L, Zhang Y, Wang X, Ma Z. Genome-wide association study reveals novel SNPs and genes in Gossypium hirsutum underlying Aphis gossypii resistance. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:171. [PMID: 37420143 DOI: 10.1007/s00122-023-04415-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 06/23/2023] [Indexed: 07/09/2023]
Abstract
A. gossypii resistance showed great variability in G. hirsutum varieties. One hundred and seventy-six SNPs associated with A. gossypii resistance were identified using GWAS. Four candidate resistance genes were functionally validated. Aphis gossypii is an economically important sap-feeding pest and is widely distributed in the world's cotton-producing regions. Identification of cotton genotypes and developing cultivars with improved A. gossypii resistance (AGR) is essential and desirable for sustainable agriculture. In the present study, A. gossypii was offered no choice but to propagate on 200 Gossypium hirsutum accessions. A relative aphid reproduction index (RARI) was used to evaluate the AGR, which showed large variability in cotton accessions and was classified into 6 grades. A significantly positive correlation was found between AGR and Verticillium wilt resistance. A total of 176 SNPs significantly associated with the RARI were identified using GWAS. Of these, 21 SNPs could be repeatedly detected in three replicates. Cleaved amplified polymorphic sequence, a restriction digestion-based genotyping assay, was developed using SNP1 with the highest observed -log10(P-value). Four genes within the 650 kb region of SNP1 were further identified, including GhRem (remorin-like), GhLAF1 (long after far-red light 1), GhCFIm25 (pre-mRNA cleavage factor Im 25 kDa subunit) and GhPMEI (plant invertase/pectin methylesterase inhibitor superfamily protein). The aphid infection could induce their expression and showed a significant difference between resistant and susceptible cotton varieties. Silencing of GhRem, GhLAF1 or GhCFIm25 could significantly increase aphid reproduction on cotton seedlings. Silencing of GhRem significantly reduced callose deposition, which is reasonably believed to be the cause for the higher AGR. Our results provide insights into understanding the genetic regulation of AGR in cotton and suggest candidate germplasms, SNPs and genes for developing cultivars with improved AGR.
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Affiliation(s)
- Jun Yang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, 071001, Hebei, China
| | - Huimin Zhang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, 071001, Hebei, China
| | - Haonan Chen
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, 071001, Hebei, China
| | - Zhengwen Sun
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, 071001, Hebei, China
| | - Huifeng Ke
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, 071001, Hebei, China
| | - Guoning Wang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, 071001, Hebei, China
| | - Chengsheng Meng
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, 071001, Hebei, China
| | - Liqiang Wu
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, 071001, Hebei, China
| | - Yan Zhang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, 071001, Hebei, China
| | - Xingfen Wang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, 071001, Hebei, China
| | - Zhiying Ma
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, 071001, Hebei, China.
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Peng L, Li Y, Tan W, Wu S, Hao Q, Tong N, Wang Z, Liu Z, Shu Q. Combined genome-wide association studies and expression quantitative trait locus analysis uncovers a genetic regulatory network of floral organ number in a tree peony ( Paeonia suffruticosa Andrews) breeding population. HORTICULTURE RESEARCH 2023; 10:uhad110. [PMID: 37577399 PMCID: PMC10419549 DOI: 10.1093/hr/uhad110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 05/16/2023] [Indexed: 08/15/2023]
Abstract
Great progress has been made in our understanding of floral organ identity determination and its regulatory network in many species; however, the quantitative genetic basis of floral organ number variation is far less well understood for species-specific traits from the perspective of population variation. Here, using a tree peony (Paeonia suffruticosa Andrews, Paeoniaceae) cultivar population as a model, the phenotypic polymorphism and genetic variation based on genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) analysis were analyzed. Based on 24 phenotypic traits of 271 representative cultivars, the transcript profiles of 119 cultivars were obtained, which indicated abundant genetic variation in tree peony. In total, 86 GWAS-related cis-eQTLs and 3188 trans-eQTL gene pairs were found to be associated with the numbers of petals, stamens, and carpels. In addition, 19 floral organ number-related hub genes with 121 cis-eQTLs were obtained by weighted gene co-expression network analysis, among which five hub genes belonging to the ABCE genes of the MADS-box family and their spatial-temporal co-expression and regulatory network were constructed. These results not only help our understanding of the genetic basis of floral organ number variation during domestication, but also pave the way to studying the quantitative genetics and evolution of flower organ number and their regulatory network within populations.
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Affiliation(s)
- Liping Peng
- Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China
- China National Botanical Garden, Beijing 100093, China
| | - Yang Li
- Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China
- China National Botanical Garden, Beijing 100093, China
| | - Wanqing Tan
- Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China
- China National Botanical Garden, Beijing 100093, China
- College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shangwei Wu
- Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China
- China National Botanical Garden, Beijing 100093, China
- College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qing Hao
- College of Landscape Architecture and Forestry, Qingdao Agricultural University, Qingdao 266109, China
| | - Ningning Tong
- Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China
- China National Botanical Garden, Beijing 100093, China
| | - Zhanying Wang
- Peony Research Institute, Luoyang Academy of Agricultural and Forestry Sciences, Luoyang 471000, China
| | - Zheng’an Liu
- Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China
- China National Botanical Garden, Beijing 100093, China
- College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qingyan Shu
- Key Laboratory of Plant Resources, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China
- China National Botanical Garden, Beijing 100093, China
- College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
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Li L, Tian Z, Chen J, Tan Z, Zhang Y, Zhao H, Wu X, Yao X, Wen W, Chen W, Guo L. Characterization of novel loci controlling seed oil content in Brassica napus by marker metabolite-based multi-omics analysis. Genome Biol 2023; 24:141. [PMID: 37337206 DOI: 10.1186/s13059-023-02984-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 06/08/2023] [Indexed: 06/21/2023] Open
Abstract
BACKGROUND Seed oil content is an important agronomic trait of Brassica napus (B. napus), and metabolites are considered as the bridge between genotype and phenotype for physical traits. RESULTS Using a widely targeted metabolomics analysis in a natural population of 388 B. napus inbred lines, we quantify 2172 metabolites in mature seeds by liquid chromatography mass spectrometry, in which 131 marker metabolites are identified to be correlated with seed oil content. These metabolites are then selected for further metabolite genome-wide association study and metabolite transcriptome-wide association study. Combined with weighted correlation network analysis, we construct a triple relationship network, which includes 21,000 edges and 4384 nodes among metabolites, metabolite quantitative trait loci, genes, and co-expression modules. We validate the function of BnaA03.TT4, BnaC02.TT4, and BnaC05.UK, three candidate genes predicted by multi-omics analysis, which show significant impacts on seed oil content through regulating flavonoid metabolism in B. napus. CONCLUSIONS This study demonstrates the advantage of utilizing marker metabolites integrated with multi-omics analysis to dissect the genetic basis of agronomic traits in crops.
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Affiliation(s)
- Long Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Zhitao Tian
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Jie Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Zengdong Tan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Yuting Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Hu Zhao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Xiaowei Wu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Xuan Yao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Weiwei Wen
- Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wei Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.
- Hubei Hongshan Laboratory, Wuhan, China.
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China.
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
| | - Liang Guo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.
- Hubei Hongshan Laboratory, Wuhan, China.
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China.
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
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Li D, Zhang Z, Gao X, Zhang H, Bai D, Wang Q, Zheng T, Li YH, Qiu LJ. The elite variations in germplasms for soybean breeding. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:37. [PMID: 37312749 PMCID: PMC10248635 DOI: 10.1007/s11032-023-01378-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/03/2023] [Indexed: 06/15/2023]
Abstract
The genetic base of soybean cultivars (Glycine max (L.) Merr.) has been narrowed through selective domestication and specific breeding improvement, similar to other crops. This presents challenges in breeding new cultivars with improved yield and quality, reduced adaptability to climate change, and increased susceptibility to diseases. On the other hand, the vast collection of soybean germplasms offers a potential source of genetic variations to address those challenges, but it has yet to be fully leveraged. In recent decades, rapidly improved high-throughput genotyping technologies have accelerated the harness of elite variations in soybean germplasm and provided the important information for solving the problem of a narrowed genetic base in breeding. In this review, we will overview the situation of maintenance and utilization of soybean germplasms, various solutions provided for different needs in terms of the number of molecular markers, and the omics-based high-throughput strategies that have been used or can be used to identify elite alleles. We will also provide an overall genetic information generated from soybean germplasms in yield, quality traits, and pest resistance for molecular breeding.
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Affiliation(s)
- Delin Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Zhengwei Zhang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Xinyue Gao
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Hao Zhang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Dong Bai
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Qi Wang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
- College of Agriculture, Northeast Agricultural University, Harbin, 150030 China
| | - Tianqing Zheng
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Ying-Hui Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Li-Juan Qiu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
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Xiao X, Liu R, Gong J, Li P, Li Z, Gong W, Liu A, Ge Q, Deng X, Li S, Chen Q, Zhang H, Peng R, Peng Y, Shang H, Pan J, Shi Y, Lu Q, Yuan Y. Fine mapping and candidate gene analysis of qFL-A12-5: a fiber length-related QTL introgressed from Gossypium barbadense into Gossypium hirsutum. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:48. [PMID: 36912959 DOI: 10.1007/s00122-023-04247-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 10/21/2022] [Indexed: 06/18/2023]
Abstract
The fiber length-related qFL-A12-5 identified in CSSLs introgressed from Gossypium barbadense into Gossypium hirsutum was fine-mapped to an 18.8 kb region on chromosome A12, leading to the identification of the GhTPR gene as a potential regulator of cotton fiber length. Fiber length is a key determinant of fiber quality in cotton, and it is a key target of artificial selection for breeding and domestication. Although many fiber length-related quantitative trait loci have been identified, there are few reports on their fine mapping or candidate gene validation, thus hampering efforts to understand the mechanistic basis of cotton fiber development. Our previous study identified the qFL-A12-5 associated with superior fiber quality on chromosome A12 in the chromosome segment substitution line (CSSL) MBI7747 (BC4F3:5). A single segment substitution line (CSSL-106) screened from BC6F2 was backcrossed to construct a larger segregation population with its recurrent parent CCRI45, thus enabling the fine mapping of 2852 BC7F2 individuals using denser simple sequence repeat markers to narrow the qFL-A12-5 to an 18.8 kb region of the genome, in which six annotated genes were identified in Gossypium hirsutum. Quantitative real-time PCR and comparative analyses led to the identification of GH_A12G2192 (GhTPR) encoding a tetratricopeptide repeat-like superfamily protein as a promising candidate gene for qFL-A12-5. A comparative analysis of the protein-coding regions of GhTPR among Hai1, MBI7747, and CCRI45 revealed two non-synonymous mutations. The overexpression of GhTPR resulted in longer roots in Arabidopsis, suggesting that GhTPR may regulate cotton fiber development. These results provide a foundation for future efforts to improve cotton fiber length.
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Affiliation(s)
- Xianghui Xiao
- Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi, 830052, China
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Ruixian Liu
- Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi, 830052, China
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Juwu Gong
- Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi, 830052, China
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Pengtao Li
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- School of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang, 455000, China
| | - Ziyin Li
- Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi, 830052, China
- 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
| | - 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
| | - 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
- Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi, 830052, China
| | - Hua Zhang
- Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi, 830052, China
| | - Renhai Peng
- School of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang, 455000, China
| | - Yan Peng
- Third Division of the Xinjiang Production and Construction Corps Agricultural Research Institute, Tumushuke, 843900, Xinjiang, China
| | - Haihong Shang
- 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
| | - Yuzhen Shi
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
| | - Quanwei Lu
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
- School of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang, 455000, China.
| | - Youlu Yuan
- Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi, 830052, China.
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
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Jareczek JJ, Grover CE, Wendel JF. Cotton fiber as a model for understanding shifts in cell development under domestication. FRONTIERS IN PLANT SCIENCE 2023; 14:1146802. [PMID: 36938017 PMCID: PMC10017751 DOI: 10.3389/fpls.2023.1146802] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 02/21/2023] [Indexed: 05/27/2023]
Abstract
Cotton fiber provides the predominant plant textile in the world, and it is also a model for plant cell wall biosynthesis. The development of the single-celled cotton fiber takes place across several overlapping but discrete stages, including fiber initiation, elongation, the transition from elongation to secondary cell wall formation, cell wall thickening, and maturation and cell death. During each stage, the developing fiber undergoes a complex restructuring of genome-wide gene expression change and physiological/biosynthetic processes, which ultimately generate a strikingly elongated and nearly pure cellulose product that forms the basis of the global cotton industry. Here, we provide an overview of this developmental process focusing both on its temporal as well as evolutionary dimensions. We suggest potential avenues for further improvement of cotton as a crop plant.
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Affiliation(s)
- Josef J. Jareczek
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, United States
- Biology Department, Bellarmine University, Louisville, KY, United States
| | - Corrinne E. Grover
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, United States
| | - Jonathan F. Wendel
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, United States
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Gui S, Martinez-Rivas FJ, Wen W, Meng M, Yan J, Usadel B, Fernie AR. Going broad and deep: sequencing-driven insights into plant physiology, evolution, and crop domestication. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 113:446-459. [PMID: 36534120 DOI: 10.1111/tpj.16070] [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: 10/26/2022] [Revised: 12/12/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
Deep sequencing is a term that has become embedded in the plant genomic literature in recent years and with good reason. A torrent of (largely) high-quality genomic and transcriptomic data has been collected and most of this has been publicly released. Indeed, almost 1000 plant genomes have been reported (www.plabipd.de) and the 2000 Plant Transcriptomes Project has long been completed. The EarthBioGenome project will dwarf even these milestones. That said, massive progress in understanding plant physiology, evolution, and crop domestication has been made by sequencing broadly (across a species) as well as deeply (within a single individual). We will outline the current state of the art in genome and transcriptome sequencing before we briefly review the most visible of these broad approaches, namely genome-wide association and transcriptome-wide association studies, as well as the compilation of pangenomes. This will include both (i) the most commonly used methods reliant on single nucleotide polymorphisms and short InDels and (ii) more recent examples which consider structural variants. We will subsequently present case studies exemplifying how their application has brought insight into either plant physiology or evolution and crop domestication. Finally, we will provide conclusions and an outlook as to the perspective for the extension of such approaches to different species, tissues, and biological processes.
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Affiliation(s)
- Songtao Gui
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | | | - Weiwei Wen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Minghui Meng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Björn Usadel
- IBG-4 Bioinformatics, Forschungszentrum Jülich, Wilhelm Johnen Str, BioSc, 52428, Jülich, Germany
- Institute for Biological Data Science, CEPLAS, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm, 14476, Germany
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Acién JM, Cañizares E, Candela H, González-Guzmán M, Arbona V. From Classical to Modern Computational Approaches to Identify Key Genetic Regulatory Components in Plant Biology. Int J Mol Sci 2023; 24:ijms24032526. [PMID: 36768850 PMCID: PMC9916757 DOI: 10.3390/ijms24032526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/19/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
The selection of plant genotypes with improved productivity and tolerance to environmental constraints has always been a major concern in plant breeding. Classical approaches based on the generation of variability and selection of better phenotypes from large variant collections have improved their efficacy and processivity due to the implementation of molecular biology techniques, particularly genomics, Next Generation Sequencing and other omics such as proteomics and metabolomics. In this regard, the identification of interesting variants before they develop the phenotype trait of interest with molecular markers has advanced the breeding process of new varieties. Moreover, the correlation of phenotype or biochemical traits with gene expression or protein abundance has boosted the identification of potential new regulators of the traits of interest, using a relatively low number of variants. These important breakthrough technologies, built on top of classical approaches, will be improved in the future by including the spatial variable, allowing the identification of gene(s) involved in key processes at the tissue and cell levels.
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Affiliation(s)
- Juan Manuel Acién
- Departament de Biologia, Bioquímica i Ciències Naturals, Universitat Jaume I, 12071 Castelló de la Plana, Spain
| | - Eva Cañizares
- Departament de Biologia, Bioquímica i Ciències Naturals, Universitat Jaume I, 12071 Castelló de la Plana, Spain
| | - Héctor Candela
- Instituto de Bioingeniería, Universidad Miguel Hernández, 03202 Elche, Spain
| | - Miguel González-Guzmán
- Departament de Biologia, Bioquímica i Ciències Naturals, Universitat Jaume I, 12071 Castelló de la Plana, Spain
- Correspondence: (M.G.-G.); (V.A.); Tel.: +34-964-72-9415 (M.G.-G.); +34-964-72-9401 (V.A.)
| | - Vicent Arbona
- Departament de Biologia, Bioquímica i Ciències Naturals, Universitat Jaume I, 12071 Castelló de la Plana, Spain
- Correspondence: (M.G.-G.); (V.A.); Tel.: +34-964-72-9415 (M.G.-G.); +34-964-72-9401 (V.A.)
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Gemeinholzer B, Rupp O, Becker A, Strickert M, Müller CM. Genotyping by sequencing and a newly developed mRNA-GBS approach to link population genetic and transcriptome analyses reveal pattern differences between sites and treatments in red clover (Trifolium pratense L.). Front Ecol Evol 2023. [DOI: 10.3389/fevo.2022.1003057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The important worldwide forage crop red clover (Trifolium pratense L.) is widely cultivated as cattle feed and for soil improvement. Wild populations and landraces have great natural diversity that could be used to improve cultivated red clover. However, to date, there is still insufficient knowledge about the natural genetic and phenotypic diversity of the species. Here, we developed a low-cost complexity reduced mRNA analysis (mRNA-GBS) and compared the results with population genetic (GBS) and previously published mRNA-Seq data, to assess whether analysis of intraspecific variation within and between populations and transcriptome responses is possible simultaneously. The mRNA-GBS approach was successful. SNP analyses from the mRNA-GBS approach revealed comparable patterns to the GBS results, but due to site-specific multifactorial influences of environmental responses as well as conceptual and methodological limitations of mRNA-GBS, it was not possible to link transcriptome analyses with reduced complexity and sequencing depth to previously published greenhouse and field expression studies. Nevertheless, the use of short sequences upstream of the poly(A) tail of mRNA to reduce complexity are promising approaches that combine population genetics and expression profiling to analyze many individuals with trait differences simultaneously and cost-effectively, even in non-model species. Nevertheless, our study design across different regions in Germany was also challenging. The use of reduced complexity differential expression analyses most likely overlays site-specific patterns due to highly complex plant responses under natural conditions.
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Wang N, Li Y, Shen C, Yang Y, Wang H, Yao T, Zhang X, Lindsey K, Lin Z. High-resolution sequencing of nine elite upland cotton cultivars uncovers genic variations and breeding improvement targets. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 113:145-159. [PMID: 36453190 DOI: 10.1111/tpj.16041] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/14/2022] [Accepted: 11/20/2022] [Indexed: 06/17/2023]
Abstract
Structural variations (SVs) are critical factors affecting genome evolution and important traits. However, identification results and functional analyses of SVs in upland cotton are rare. Here, based on the genetic relationships, breeding history and cumulative planting area of upland cotton in China, nine predominant cultivars from the past 60 years (1950s-2010s) were selected for long read sequencing to uncover genic variations and breeding improvement targets for this crop. Based on the ZM24 reference genome, 0.88-1.47 × 104 SVs per cultivar were identified, and an SV set was constructed. SVs affected the expression of a large number of genes during fiber elongation, and a transposable element insertion resulted in the glandless phenotype in upland cotton. Six widespread inversions were identified based on nine draft genomes and high-throughput chromosome conformation capture data. Multiple haplotype blocks that were always associated with aggregated SVs were demonstrated to play a pivotal role in the agronomic traits of upland cotton and drove its adaptation to the northern planting region. Exotic introgression was the source of these haplotype blocks and increased the genetic diversity of upland cotton. Our results enrich the genome resources of upland cotton, and the identified SVs will promote genetic and breeding research in cotton.
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Affiliation(s)
- Nian Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yuanxue Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chao Shen
- College of Biological and Food Engineering, Guangdong University of Petrochemical Technology, Maoming, 525000, Guangdong, China
| | - Yang Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Hongya Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Tian Yao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xianlong Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Keith Lindsey
- Department of Biosciences, Durham University, Durham, DH1 3LE, UK
| | - Zhongxu Lin
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
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Mahmood U, Li X, Fan Y, Chang W, Niu Y, Li J, Qu C, Lu K. Multi-omics revolution to promote plant breeding efficiency. FRONTIERS IN PLANT SCIENCE 2022; 13:1062952. [PMID: 36570904 PMCID: PMC9773847 DOI: 10.3389/fpls.2022.1062952] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Crop production is the primary goal of agricultural activities, which is always taken into consideration. However, global agricultural systems are coming under increasing pressure from the rising food demand of the rapidly growing world population and changing climate. To address these issues, improving high-yield and climate-resilient related-traits in crop breeding is an effective strategy. In recent years, advances in omics techniques, including genomics, transcriptomics, proteomics, and metabolomics, paved the way for accelerating plant/crop breeding to cope with the changing climate and enhance food production. Optimized omics and phenotypic plasticity platform integration, exploited by evolving machine learning algorithms will aid in the development of biological interpretations for complex crop traits. The precise and progressive assembly of desire alleles using precise genome editing approaches and enhanced breeding strategies would enable future crops to excel in combating the changing climates. Furthermore, plant breeding and genetic engineering ensures an exclusive approach to developing nutrient sufficient and climate-resilient crops, the productivity of which can sustainably and adequately meet the world's food, nutrition, and energy needs. This review provides an overview of how the integration of omics approaches could be exploited to select crop varieties with desired traits.
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Affiliation(s)
- Umer Mahmood
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Xiaodong Li
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Yonghai Fan
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Wei Chang
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Yue Niu
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Jiana Li
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
- Academy of Agricultural Sciences, Southwest University, Chongqing, China
- Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, China
| | - Cunmin Qu
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
- Academy of Agricultural Sciences, Southwest University, Chongqing, China
- Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, China
| | - Kun Lu
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
- Academy of Agricultural Sciences, Southwest University, Chongqing, China
- Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, China
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Liu C, Zhu X, Zhang J, Shen M, Chen K, Fu X, Ma L, Liu X, Zhou C, Zhou D, Wang G. eQTLs play critical roles in regulating gene expression and identifying key regulators in rice. PLANT BIOTECHNOLOGY JOURNAL 2022; 20:2357-2371. [PMID: 36087348 PMCID: PMC9674320 DOI: 10.1111/pbi.13912] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 08/11/2022] [Accepted: 08/13/2022] [Indexed: 05/28/2023]
Abstract
The regulation of gene expression plays an essential role in both the phenotype and adaptation of plants. Transcriptome sequencing enables simultaneous identification of exonic variants and quantification of gene expression. Here, we sequenced the leaf transcriptomes of 287 rice accessions from around the world and obtained a total of 177 853 high-quality single nucleotide polymorphisms after filtering. Genome-wide association study identified 44 354 expression quantitative trait loci (eQTLs), which regulate the expression of 13 201 genes, as well as 17 local eQTL hotspots and 96 distant eQTL hotspots. Furthermore, a transcriptome-wide association study screened 21 candidate genes for starch content in the flag leaves at the heading stage. HS002 was identified as a significant distant eQTL hotspot with five downstream genes enriched for diterpene antitoxin synthesis. Co-expression analysis, eQTL analysis, and linkage mapping together demonstrated that bHLH026 acts as a key regulator to activate the expression of downstream genes. The transgenic assay revealed that bHLH026 is an important regulator of diterpenoid antitoxin synthesis and enhances the disease resistance of rice. These findings improve our knowledge of the regulatory mechanisms of gene expression variation and complex regulatory networks of the rice genome and will facilitate genetic improvement of cultivated rice varieties.
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Affiliation(s)
- Chang Liu
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan Laboratory, Huazhong Agricultural UniversityWuhanChina
| | - Xiya Zhu
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan Laboratory, Huazhong Agricultural UniversityWuhanChina
| | - Jin Zhang
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan Laboratory, Huazhong Agricultural UniversityWuhanChina
| | - Meng Shen
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan Laboratory, Huazhong Agricultural UniversityWuhanChina
| | - Kai Chen
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan Laboratory, Huazhong Agricultural UniversityWuhanChina
| | - Xiangkui Fu
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan Laboratory, Huazhong Agricultural UniversityWuhanChina
| | - Lian Ma
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan Laboratory, Huazhong Agricultural UniversityWuhanChina
| | - Xuelin Liu
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan Laboratory, Huazhong Agricultural UniversityWuhanChina
| | - Chang Zhou
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan Laboratory, Huazhong Agricultural UniversityWuhanChina
| | - Dao‐Xiu Zhou
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan Laboratory, Huazhong Agricultural UniversityWuhanChina
- Institute of Plant Science Paris‐Saclay (IPS2)CNRS, INRAE, University Paris‐SaclayOrsayFrance
| | - Gongwei Wang
- National Key Laboratory of Crop Genetic ImprovementHubei Hongshan Laboratory, Huazhong Agricultural UniversityWuhanChina
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Tan Z, Peng Y, Xiong Y, Xiong F, Zhang Y, Guo N, Tu Z, Zong Z, Wu X, Ye J, Xia C, Zhu T, Liu Y, Lou H, Liu D, Lu S, Yao X, Liu K, Snowdon RJ, Golicz AA, Xie W, Guo L, Zhao H. Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus. Genome Biol 2022; 23:233. [PMID: 36345039 PMCID: PMC9639296 DOI: 10.1186/s13059-022-02801-z] [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: 05/16/2022] [Accepted: 10/22/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Regulation of gene expression plays an essential role in controlling the phenotypes of plants. Brassica napus (B. napus) is an important source for the vegetable oil in the world, and the seed oil content is an important trait of B. napus. RESULTS We perform a comprehensive analysis of the transcriptional variability in the seeds of B. napus at two developmental stages, 20 and 40 days after flowering (DAF). We detect 53,759 and 53,550 independent expression quantitative trait loci (eQTLs) for 79,605 and 76,713 expressed genes at 20 and 40 DAF, respectively. Among them, the local eQTLs are mapped to the adjacent genes more frequently. The adjacent gene pairs are regulated by local eQTLs with the same open chromatin state and show a stronger mode of expression piggybacking. Inter-subgenomic analysis indicates that there is a feedback regulation for the homoeologous gene pairs to maintain partial expression dosage. We also identify 141 eQTL hotspots and find that hotspot87-88 co-localizes with a QTL for the seed oil content. To further resolve the regulatory network of this eQTL hotspot, we construct the XGBoost model using 856 RNA-seq datasets and the Basenji model using 59 ATAC-seq datasets. Using these two models, we predict the mechanisms affecting the seed oil content regulated by hotspot87-88 and experimentally validate that the transcription factors, NAC13 and SCL31, positively regulate the seed oil content. CONCLUSIONS We comprehensively characterize the gene regulatory features in the seeds of B. napus and reveal the gene networks regulating the seed oil content of B. napus.
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Affiliation(s)
- Zengdong Tan
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Yan Peng
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Yao Xiong
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Feng Xiong
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Yuting Zhang
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Ning Guo
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Zhuo Tu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Zhanxiang Zong
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Xiaokun Wu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Jiang Ye
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Chunjiao Xia
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Tao Zhu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Yinmeng Liu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Hongxiang Lou
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Dongxu Liu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Shaoping Lu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Xuan Yao
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Kede Liu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Rod J. Snowdon
- grid.8664.c0000 0001 2165 8627Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Agnieszka A. Golicz
- grid.8664.c0000 0001 2165 8627Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Weibo Xie
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China ,grid.35155.370000 0004 1790 4137Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Liang Guo
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China ,grid.35155.370000 0004 1790 4137Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China ,grid.488316.00000 0004 4912 1102Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Hu Zhao
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
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Chen H, Han Z, Ma Q, Dong C, Ning X, Li J, Lin H, Xu S, Li Y, Hu Y, Si Z, Song Q. Identification of elite fiber quality loci in upland cotton based on the genotyping-by-target-sequencing technology. FRONTIERS IN PLANT SCIENCE 2022; 13:1027806. [PMID: 36407612 PMCID: PMC9669494 DOI: 10.3389/fpls.2022.1027806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
Genome-wide association studies (GWAS) of fiber quality traits of upland cotton were conducted to identify the single-nucleotide polymorphic (SNP) loci associated with cotton fiber quality, which lays the foundation for the mining of elite] cotton fiber gene resources and its application in molecular breeding. A total of 612 upland cotton accessions were genotyped using the ZJU Cotton Chip No. 1 40K chip array via the liquid-phase probe hybridization-based genotyping-by-target-sequencing (GBTS) technology. In the present study, five fiber quality traits, namely fiber length, fiber strength, micronaire, uniformity and elongation, showed different degrees of variation in different environments. The average coefficient of variation of fiber strength was the greatest, whereas the average coefficient of variation of uniformity was the least. Significant or extremely significant correlations existed among the five fiber quality traits, especially fiber length, strength, uniformity and elongation all being significantly negative correlated with micronaire. Population cluster analysis divided the 612 accessions into four groups: 73 assigned to group I, 226 to group II, 220 to group III and 93 to group IV. Genome-wide association studies of five fiber quality traits in five environments was performed and a total of 42 SNP loci associated with target traits was detected, distributed on 19 chromosomes, with eight loci associated with fiber length, five loci associated with fiber strength, four loci associated with micronaire, twelve loci associated with fiber uniformity and thirteen loci associated with fiber elongation. Of them, seven loci were detected in more than two environments. Nine SNP loci related to fiber length, fiber strength, uniformity and elongation were found on chromosome A07, seven loci related to fiber length, fiber strength, micronaire and elongation were detected on chromosome D01, and five loci associated with fiber length, uniformity and micronaire were detected on chromosome D11. The results from this study could provide more precise molecular markers and genetic resources for cotton breeding for better fiber quality in the future.
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Affiliation(s)
- Hong Chen
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Northwest Inland Region Key Laboratory of Cotton Biology and Genetic Breeding of Ministry of Agriculture, Shihezi, China
| | - Zegang Han
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Research Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Qi Ma
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Northwest Inland Region Key Laboratory of Cotton Biology and Genetic Breeding of Ministry of Agriculture, Shihezi, China
| | - Chengguang Dong
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Northwest Inland Region Key Laboratory of Cotton Biology and Genetic Breeding of Ministry of Agriculture, Shihezi, China
| | - Xinzhu Ning
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Northwest Inland Region Key Laboratory of Cotton Biology and Genetic Breeding of Ministry of Agriculture, Shihezi, China
| | - Jilian Li
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Northwest Inland Region Key Laboratory of Cotton Biology and Genetic Breeding of Ministry of Agriculture, Shihezi, China
| | - Hai Lin
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Northwest Inland Region Key Laboratory of Cotton Biology and Genetic Breeding of Ministry of Agriculture, Shihezi, China
| | - Shouzhen Xu
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Northwest Inland Region Key Laboratory of Cotton Biology and Genetic Breeding of Ministry of Agriculture, Shihezi, China
| | - Yiqian Li
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Research Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Yan Hu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Research Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Zhanfeng Si
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Research Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Qingping Song
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Northwest Inland Region Key Laboratory of Cotton Biology and Genetic Breeding of Ministry of Agriculture, Shihezi, China
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40
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Fan Z, Tieman DM, Knapp SJ, Zerbe P, Famula R, Barbey CR, Folta KM, Amadeu RR, Lee M, Oh Y, Lee S, Whitaker VM. A multi-omics framework reveals strawberry flavor genes and their regulatory elements. THE NEW PHYTOLOGIST 2022; 236:1089-1107. [PMID: 35916073 PMCID: PMC9805237 DOI: 10.1111/nph.18416] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
Flavor is essential to consumer preference of foods and is an increasing focus of plant breeding programs. In fruit crops, identifying genes underlying volatile organic compounds has great promise to accelerate flavor improvement, but polyploidy and heterozygosity in many species have slowed progress. Here we use octoploid cultivated strawberry to demonstrate how genomic heterozygosity, transcriptomic intricacy and fruit metabolomic diversity can be treated as strengths and leveraged to uncover fruit flavor genes and their regulatory elements. Multi-omics datasets were generated including an expression quantitative trait loci map with 196 diverse breeding lines, haplotype-phased genomes of a highly-flavored breeding selection, a genome-wide structural variant map using five haplotypes, and volatile genome-wide association study (GWAS) with > 300 individuals. Overlaying regulatory elements, structural variants and GWAS-linked allele-specific expression of numerous genes to variation in volatile compounds important to flavor. In one example, the functional role of anthranilate synthase alpha subunit 1 in methyl anthranilate biosynthesis was supported via fruit transient gene expression assays. These results demonstrate a framework for flavor gene discovery in fruit crops and a pathway to molecular breeding of cultivars with complex and desirable flavor.
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Affiliation(s)
- Zhen Fan
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| | - Denise M. Tieman
- Horticultural Sciences DepartmentUniversity of FloridaGainesvilleFL32611USA
| | - Steven J. Knapp
- Department of Plant SciencesUniversity of CaliforniaDavisDavisCA95616USA
| | - Philipp Zerbe
- Department of Plant BiologyUniversity of California DavisDavisCA95616USA
| | - Randi Famula
- Department of Plant SciencesUniversity of CaliforniaDavisDavisCA95616USA
| | - Christopher R. Barbey
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| | - Kevin M. Folta
- Horticultural Sciences DepartmentUniversity of FloridaGainesvilleFL32611USA
| | - Rodrigo R. Amadeu
- Horticultural Sciences DepartmentUniversity of FloridaGainesvilleFL32611USA
| | - Manbo Lee
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| | - Youngjae Oh
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| | - Seonghee Lee
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
| | - Vance M. Whitaker
- Horticultural Sciences DepartmentUniversity of Florida, IFAS Gulf Coast Research and Education CenterWimaumaFL33597USA
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Zhang R, Zhang C, Yu C, Dong J, Hu J. Integration of multi-omics technologies for crop improvement: Status and prospects. FRONTIERS IN BIOINFORMATICS 2022; 2:1027457. [PMID: 36438626 PMCID: PMC9689701 DOI: 10.3389/fbinf.2022.1027457] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 09/28/2022] [Indexed: 08/03/2023] Open
Abstract
With the rapid development of next-generation sequencing (NGS), multi-omics techniques have been emerging as effective approaches for crop improvement. Here, we focus mainly on addressing the current status and future perspectives toward omics-related technologies and bioinformatic resources with potential applications in crop breeding. Using a large amount of omics-level data from the functional genome, transcriptome, proteome, epigenome, metabolome, and microbiome, clarifying the interaction between gene and phenotype formation will become possible. The integration of multi-omics datasets with pan-omics platforms and systems biology could predict the complex traits of crops and elucidate the regulatory networks for genetic improvement. Different scales of trait predictions and decision-making models will facilitate crop breeding more intelligent. Potential challenges that integrate the multi-omics data with studies of gene function and their network to efficiently select desirable agronomic traits are discussed by proposing some cutting-edge breeding strategies for crop improvement. Multi-omics-integrated approaches together with other artificial intelligence techniques will contribute to broadening and deepening our knowledge of crop precision breeding, resulting in speeding up the breeding process.
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42
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Yang Z, Wang J, Huang Y, Wang S, Wei L, Liu D, Weng Y, Xiang J, Zhu Q, Yang Z, Nie X, Yu Y, Yang Z, Yang QY. CottonMD: a multi-omics database for cotton biological study. Nucleic Acids Res 2022; 51:D1446-D1456. [PMID: 36215030 PMCID: PMC9825545 DOI: 10.1093/nar/gkac863] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/08/2022] [Accepted: 09/24/2022] [Indexed: 01/30/2023] Open
Abstract
Cotton is an important economic crop, and many loci for important traits have been identified, but it remains challenging and time-consuming to identify candidate or causal genes/variants and clarify their roles in phenotype formation and regulation. Here, we first collected and integrated the multi-omics datasets including 25 genomes, transcriptomes in 76 tissue samples, epigenome data of five species and metabolome data of 768 metabolites from four tissues, and genetic variation, trait and transcriptome datasets from 4180 cotton accessions. Then, a cotton multi-omics database (CottonMD, http://yanglab.hzau.edu.cn/CottonMD/) was constructed. In CottonMD, multiple statistical methods were applied to identify the associations between variations and phenotypes, and many easy-to-use analysis tools were provided to help researchers quickly acquire the related omics information and perform multi-omics data analysis. Two case studies demonstrated the power of CottonMD for identifying and analyzing the candidate genes, as well as the great potential of integrating multi-omics data for cotton genetic breeding and functional genomics research.
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Affiliation(s)
| | | | | | - Shengbo Wang
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China,Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Lulu Wei
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China,Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Dongxu Liu
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China,Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Yonglin Weng
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jinhai Xiang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Qiang Zhu
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China,Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Zhaoen Yang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Xinhui Nie
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Bingtuan, Agricultural College, Shihezi University, Shihezi, Xinjiang 832000, China
| | - Yu Yu
- Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, Xinjiang 832000, China
| | - Zuoren Yang
- Correspondence may also be addressed to Zuoren Yang. Tel: +86 371 55912660;
| | - Qing-Yong Yang
- To whom correspondence should be addressed. Tel: +86 27 87288509;
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Ma J, Jiang Y, Pei W, Wu M, Ma Q, Liu J, Song J, Jia B, Liu S, Wu J, Zhang J, Yu J. Expressed genes and their new alleles identification during fibre elongation reveal the genetic factors underlying improvements of fibre length in cotton. PLANT BIOTECHNOLOGY JOURNAL 2022; 20:1940-1955. [PMID: 35718938 PMCID: PMC9491459 DOI: 10.1111/pbi.13874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 05/29/2022] [Accepted: 06/11/2022] [Indexed: 05/27/2023]
Abstract
Interspecific breeding in cotton takes advantage of genetic recombination among desirable genes from different parental lines. However, the expression new alleles (ENAs) from crossovers within genic regions and their significance in fibre length (FL) improvement are currently not understood. Here, we generated resequencing genomes of 191 interspecific backcross inbred lines derived from CRI36 (Gossypium hirsutum) × Hai7124 (Gossypium barbadense) and 277 dynamic fibre transcriptomes to identify the ENAs and extremely expressed genes (eGenes) potentially influencing FL, and uncovered the dynamic regulatory network of fibre elongation. Of 35 420 eGenes in developing fibres, 10 366 ENAs were identified and preferentially distributed in chromosomes subtelomeric regions. In total, 1056-1255 ENAs showed transgressive expression in fibres at 5-15 dpa (days post-anthesis) of some BILs, 520 of which were located in FL-quantitative trait locus (QTLs) and GhFLA9 (recombination allele) was identified with a larger effect for FL than GhFLA9 of CRI36 allele. Using ENAs as a type of markers, we identified three novel FL-QTLs. Additionally, 456 extremely eGenes were identified that were preferentially distributed in recombination hotspots. Importantly, 34 of them were significantly associated with FL. Gene expression quantitative trait locus analysis identified 1286, 1089 and 1059 eGenes that were colocalized with the FL trait at 5, 10 and 15 dpa, respectively. Finally, we verified the Ghir_D10G011050 gene linked to fibre elongation by the CRISPR-cas9 system. This study provides the first glimpse into the occurrence, distribution and expression of the developing fibres genes (especially ENAs) in an introgression population, and their possible biological significance in FL.
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Affiliation(s)
- Jianjiang Ma
- State Key Laboratory of Cotton BiologyInstitute of Cotton Research of Chinese Academy of Agricultural SciencesKey Laboratory of Cotton Genetic ImprovementMinistry of AgricultureAnyangChina
- Zhengzhou Research Base, State Key Laboratory of Cotton BiologyZhengzhou UniversityZhengzhouChina
| | - Yafei Jiang
- Novogene Bioinformatics InstituteBeijingChina
| | - Wenfeng Pei
- State Key Laboratory of Cotton BiologyInstitute of Cotton Research of Chinese Academy of Agricultural SciencesKey Laboratory of Cotton Genetic ImprovementMinistry of AgricultureAnyangChina
| | - Man Wu
- State Key Laboratory of Cotton BiologyInstitute of Cotton Research of Chinese Academy of Agricultural SciencesKey Laboratory of Cotton Genetic ImprovementMinistry of AgricultureAnyangChina
| | - Qifeng Ma
- State Key Laboratory of Cotton BiologyInstitute of Cotton Research of Chinese Academy of Agricultural SciencesKey Laboratory of Cotton Genetic ImprovementMinistry of AgricultureAnyangChina
| | - Ji Liu
- State Key Laboratory of Cotton BiologyInstitute of Cotton Research of Chinese Academy of Agricultural SciencesKey Laboratory of Cotton Genetic ImprovementMinistry of AgricultureAnyangChina
| | - Jikun Song
- State Key Laboratory of Cotton BiologyInstitute of Cotton Research of Chinese Academy of Agricultural SciencesKey Laboratory of Cotton Genetic ImprovementMinistry of AgricultureAnyangChina
| | - Bing Jia
- State Key Laboratory of Cotton BiologyInstitute of Cotton Research of Chinese Academy of Agricultural SciencesKey Laboratory of Cotton Genetic ImprovementMinistry of AgricultureAnyangChina
| | - Shang Liu
- State Key Laboratory of Cotton BiologyInstitute of Cotton Research of Chinese Academy of Agricultural SciencesKey Laboratory of Cotton Genetic ImprovementMinistry of AgricultureAnyangChina
| | - Jianyong Wu
- State Key Laboratory of Cotton BiologyInstitute of Cotton Research of Chinese Academy of Agricultural SciencesKey Laboratory of Cotton Genetic ImprovementMinistry of AgricultureAnyangChina
- Zhengzhou Research Base, State Key Laboratory of Cotton BiologyZhengzhou UniversityZhengzhouChina
| | - Jinfa Zhang
- Department of Plant and Environmental SciencesNew Mexico State UniversityLas CrucesNew MexicoUSA
| | - Jiwen Yu
- State Key Laboratory of Cotton BiologyInstitute of Cotton Research of Chinese Academy of Agricultural SciencesKey Laboratory of Cotton Genetic ImprovementMinistry of AgricultureAnyangChina
- Zhengzhou Research Base, State Key Laboratory of Cotton BiologyZhengzhou UniversityZhengzhouChina
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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.
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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
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45
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Han X, Gao C, Liu L, Zhang Y, Jin Y, Yan Q, Yang L, Li F, Yang Z. Integration of eQTL Analysis and GWAS Highlights Regulation Networks in Cotton under Stress Condition. Int J Mol Sci 2022; 23:ijms23147564. [PMID: 35886912 PMCID: PMC9324452 DOI: 10.3390/ijms23147564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/02/2022] [Accepted: 07/05/2022] [Indexed: 12/04/2022] Open
Abstract
The genus Gossypium is one of the most economically important crops in the world. Here, we used RNA-seq to quantify gene expression in a collection of G. arboreum seedlings and performed eGWAS on 28,382 expressed genes. We identified a total of 30,089 eQTLs in 10,485 genes, of which >90% were trans-regulate target genes. Using luciferase assays, we confirmed that different cis-eQTL haplotypes could affect promoter activity. We found ~6600 genes associated with ~1300 eQTL hotspots. Moreover, hotspot 309 regulates the expression of 325 genes with roles in stem length, fresh weight, seed germination rate, and genes related to cell wall biosynthesis and salt stress. Transcriptome-wide association study (TWAS) identified 19 candidate genes associated with the cotton growth and salt stress response. The variation in gene expression across the population played an essential role in population differentiation. Only a small number of the differentially expressed genes between South China, the Yangtze River region, and the Yellow River region sites were located in different chromosomal regions. The eQTLs found across the duplicated gene pairs showed conservative cis- or trans- regulation and that the expression levels of gene pairs were correlated. This study provides new insights into the evolution of gene expression regulation in cotton, and identifies eQTLs in stress-related genes for use in breeding improved cotton varieties.
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Affiliation(s)
- Xiao Han
- State Key Laboratory of Cotton Biology, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (X.H.); (L.L.); (Y.Z.); (Y.J.); (Q.Y.); (L.Y.)
| | - Chenxu Gao
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou 450001, China;
| | - Lisen Liu
- State Key Laboratory of Cotton Biology, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (X.H.); (L.L.); (Y.Z.); (Y.J.); (Q.Y.); (L.Y.)
| | - Yihao Zhang
- State Key Laboratory of Cotton Biology, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (X.H.); (L.L.); (Y.Z.); (Y.J.); (Q.Y.); (L.Y.)
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou 450001, China;
| | - Yuying Jin
- State Key Laboratory of Cotton Biology, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (X.H.); (L.L.); (Y.Z.); (Y.J.); (Q.Y.); (L.Y.)
| | - Qingdi Yan
- State Key Laboratory of Cotton Biology, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (X.H.); (L.L.); (Y.Z.); (Y.J.); (Q.Y.); (L.Y.)
| | - Lan Yang
- State Key Laboratory of Cotton Biology, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (X.H.); (L.L.); (Y.Z.); (Y.J.); (Q.Y.); (L.Y.)
| | - Fuguang Li
- State Key Laboratory of Cotton Biology, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (X.H.); (L.L.); (Y.Z.); (Y.J.); (Q.Y.); (L.Y.)
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou 450001, China;
- Correspondence: (F.L.); (Z.Y.)
| | - Zhaoen Yang
- State Key Laboratory of Cotton Biology, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (X.H.); (L.L.); (Y.Z.); (Y.J.); (Q.Y.); (L.Y.)
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou 450001, China;
- Correspondence: (F.L.); (Z.Y.)
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Egan LM, Stiller WN. The Past, Present, and Future of Host Plant Resistance in Cotton: An Australian Perspective. FRONTIERS IN PLANT SCIENCE 2022; 13:895877. [PMID: 35873986 PMCID: PMC9297922 DOI: 10.3389/fpls.2022.895877] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/06/2022] [Indexed: 05/24/2023]
Abstract
Cotton is a key global fiber crop. However, yield potential is limited by the presence of endemic and introduced pests and diseases. The introduction of host plant resistance (HPR), defined as the purposeful use of resistant crop cultivars to reduce the impact of pests and diseases, has been a key breeding target for the Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program. The program has seen success in releasing cultivars resistant to Bacterial blight, Verticillium wilt, Fusarium wilt, and Cotton bunchy top. However, emerging biotic threats such as Black root rot and secondary pests, are becoming more frequent in Australian cotton production systems. The uptake of tools and breeding methods, such as genomic selection, high throughput phenomics, gene editing, and landscape genomics, paired with the continued utilization of sources of resistance from Gossypium germplasm, will be critical for the future of cotton breeding. This review celebrates the success of HPR breeding activities in the CSIRO cotton breeding program and maps a pathway for the future in developing resistant cultivars.
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Guo X, Wang Y, Hou Y, Zhou Z, Sun R, Qin T, Wang K, Liu F, Wang Y, Huang Z, Xu Y, Cai X. Genome-Wide Dissection of the Genetic Basis for Drought Tolerance in Gossypium hirsutum L. Races. FRONTIERS IN PLANT SCIENCE 2022; 13:876095. [PMID: 35837453 PMCID: PMC9274165 DOI: 10.3389/fpls.2022.876095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
Drought seriously threats the growth and development of Gossypium hirsutum L. To dissect the genetic basis for drought tolerance in the G. hirsutum L. germplasm, a population, consisting of 188 accessions of G. hirsutum races and a cultivar (TM-1), was genotyped using the Cotton80KSNP biochip, and 51,268 high-quality single-nucleotide polymorphisms (SNPs) were obtained. Based on the phenotypic data of eight drought relative traits from four environments, we carried out association mapping with five models using GAPIT software. In total, thirty-six SNPs were detected significantly associated at least in two environments or two models. Among these SNPs, 8 and 28 (including 24 SNPs in 5 peak regions) were distributed in the A and D subgenome, respectively; eight SNPs were found to be distributed within separate genes. An SNP, TM73079, located on chromosome D10, was simultaneously associated with leaf fresh weight, leaf wilted weight, and leaf dry weight. Another nine SNPs, TM47696, TM33865, TM40383, TM10267, TM59672, TM59675, TM59677, TM72359, and TM72361, on chromosomes A13, A10, A12, A5, D6, and D9, were localized within or near previously reported quantitative trait loci for drought tolerance. Moreover, 520 genes located 200 kb up- and down-stream of 36 SNPs were obtained and analyzed based on gene annotation and transcriptome sequencing. The results showed that three candidate genes, Gh_D08G2462, Gh_A03G0043, and Gh_A12G0369, may play important roles in drought tolerance. The current GWAS represents the first investigation into mapping QTL for drought tolerance in G. hirsutum races and provides important information for improving cotton cultivars.
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Affiliation(s)
- Xinlei Guo
- Henan Institute of Science and Technology, Collaborative Innovation Center of Modern Biological Breeding of Henan Province, Henan Key Laboratory Molecular Ecology and Germplasm Innovation of Cotton and Wheat, Xinxiang, China
- Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yuanyuan Wang
- Henan Institute of Science and Technology, Collaborative Innovation Center of Modern Biological Breeding of Henan Province, Henan Key Laboratory Molecular Ecology and Germplasm Innovation of Cotton and Wheat, Xinxiang, China
| | - Yuqing Hou
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, China
| | - Zhongli Zhou
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, China
| | - Runrun Sun
- Henan Institute of Science and Technology, Collaborative Innovation Center of Modern Biological Breeding of Henan Province, Henan Key Laboratory Molecular Ecology and Germplasm Innovation of Cotton and Wheat, Xinxiang, China
| | - Tengfei Qin
- Henan Institute of Science and Technology, Collaborative Innovation Center of Modern Biological Breeding of Henan Province, Henan Key Laboratory Molecular Ecology and Germplasm Innovation of Cotton and Wheat, Xinxiang, China
| | - Kunbo Wang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, China
| | - Fang Liu
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, China
| | - Yuhong Wang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, China
| | - Zhongwen Huang
- Henan Institute of Science and Technology, Collaborative Innovation Center of Modern Biological Breeding of Henan Province, Henan Key Laboratory Molecular Ecology and Germplasm Innovation of Cotton and Wheat, Xinxiang, China
| | - Yanchao Xu
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, China
| | - Xiaoyan Cai
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, China
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Mei H, Zhao T, Dong Z, Han J, Xu B, Chen R, Zhang J, Zhang J, Hu Y, Zhang T, Fang L. Population-Scale Polymorphic Short Tandem Repeat Provides an Alternative Strategy for Allele Mining in Cotton. FRONTIERS IN PLANT SCIENCE 2022; 13:916830. [PMID: 35599867 PMCID: PMC9120961 DOI: 10.3389/fpls.2022.916830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 04/20/2022] [Indexed: 06/15/2023]
Abstract
Short tandem repeats (STRs), which vary in size due to featuring variable numbers of repeat units, are present throughout most eukaryotic genomes. To date, few population-scale studies identifying STRs have been reported for crops. Here, we constructed a high-density polymorphic STR map by investigating polymorphic STRs from 911 Gossypium hirsutum accessions. In total, we identified 556,426 polymorphic STRs with an average length of 21.1 bp, of which 69.08% were biallelic. Moreover, 7,718 (1.39%) were identified in the exons of 6,021 genes, which were significantly enriched in transcription, ribosome biogenesis, and signal transduction. Only 5.88% of those exonic STRs altered open reading frames, of which 97.16% were trinucleotide. An alternative strategy STR-GWAS analysis revealed that 824 STRs were significantly associated with agronomic traits, including 491 novel alleles that undetectable by previous SNP-GWAS methods. For instance, a novel polymorphic STR consisting of GAACCA repeats was identified in GH_D06G1697, with its (GAACCA)5 allele increasing fiber length by 1.96-4.83% relative to the (GAACCA)4 allele. The database CottonSTRDB was further developed to facilitate use of STR datasets in breeding programs. Our study provides functional roles for STRs in influencing complex traits, an alternative strategy STR-GWAS for allele mining, and a database serving the cotton community as a valuable resource.
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Affiliation(s)
- Huan Mei
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Ting Zhao
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Zeyu Dong
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Jin Han
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Biyu Xu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Rui Chen
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Jun Zhang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Juncheng Zhang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Yan Hu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
- Hainan Institute of Zhejiang University, Sanya, China
| | - Tianzhen Zhang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
- Hainan Institute of Zhejiang University, Sanya, China
| | - Lei Fang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
- Hainan Institute of Zhejiang University, Sanya, China
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Zhang Y, Zhang H, Zhao H, Xia Y, Zheng X, Fan R, Tan Z, Duan C, Fu Y, Li L, Ye J, Tang S, Hu H, Xie W, Yao X, Guo L. Multi-omics analysis dissects the genetic architecture of seed coat content in Brassica napus. Genome Biol 2022; 23:86. [PMID: 35346318 PMCID: PMC8962237 DOI: 10.1186/s13059-022-02647-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/07/2022] [Indexed: 01/01/2023] Open
Abstract
Background Brassica napus is an important vegetable oil source worldwide. Seed coat content is a complex quantitative trait that negatively correlates with the seed oil content in B. napus. Results Here we provide insights into the genetic basis of natural variation of seed coat content by transcriptome-wide association studies (TWAS) and genome-wide association studies (GWAS) using 382 B. napus accessions. By population transcriptomic analysis, we identify more than 700 genes and four gene modules that are significantly associated with seed coat content. We also characterize three reliable quantitative trait loci (QTLs) controlling seed coat content by GWAS. Combining TWAS and correlation networks of seed coat content-related gene modules, we find that BnaC07.CCR-LIKE (CCRL) and BnaTT8s play key roles in the determination of the trait by modulating lignin biosynthesis. By expression GWAS analysis, we identify a regulatory hotspot on chromosome A09, which is involved in controlling seed coat content through BnaC07.CCRL and BnaTT8s. We then predict the downstream genes regulated by BnaTT8s using multi-omics datasets. We further experimentally validate that BnaCCRL and BnaTT8 positively regulate seed coat content and lignin content. BnaCCRL represents a novel identified gene involved in seed coat development. Furthermore, we also predict the key genes regulating carbon allocation between phenylpropane compounds and oil during seed development in B. napus. Conclusions This study helps us to better understand the complex machinery of seed coat development and provides a genetic resource for genetic improvement of seed coat content in B. napus breeding. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02647-5.
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Li L, Sun Z, Zhang Y, Ke H, Yang J, Li Z, Wu L, Zhang G, Wang X, Ma Z. Development and Utilization of Functional Kompetitive Allele-Specific PCR Markers for Key Genes Underpinning Fiber Length and Strength in Gossypium hirsutum L. FRONTIERS IN PLANT SCIENCE 2022; 13:853827. [PMID: 35360312 PMCID: PMC8964280 DOI: 10.3389/fpls.2022.853827] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 02/15/2022] [Indexed: 05/16/2023]
Abstract
Fiber length (FL) and fiber strength (FS) are the important indicators of fiber quality in cotton. Longer and stronger fibers are preferred for manufacturing finer yarns in the textile industry. Functional markers (FMs) designed from polymorphic sites within gene sequences attributing to phenotypic variation are highly efficient when used for marker-assisted selection (MAS) in breeding superior varieties with longer FL and higher FS. The aims of this study were to develop FMs via kompetitive allele-specific PCR (KASP) assays and to validate the efficacy of the FMs for allele discrimination and the potential value in practice application. We used four single-nucleotide polymorphism markers and 360 cotton accessions and found that two FMs, namely, D11_24030087 and A07_72204443, could effectively differentiate accessions of different genotypes with higher consistency to phenotype. The appeared frequencies of varieties harbored Hap2 (elite alleles G and T) with longer FL (> the mean of accessions with non-elite allele, 28.50 mm) and higher FS (> the mean of accessions with non-elite allele, 29.06 cN•tex-1) were 100 and 72.7%, respectively, which was higher than that of varieties harbored only on a single elite allele (G or T, 77.9 or 61.9%), suggesting a favorable haplotype for selecting varieties with superior FL and FS. These FMs could be valuable for the high-throughput selection of superior materials by providing genotypic information in cotton breeding programs.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Xingfen Wang
- State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Baoding, China
| | - Zhiying Ma
- State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Key Laboratory for Crop Germplasm Resources of Hebei, Hebei Agricultural University, Baoding, China
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