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Mondal AK, Gaur M, Advani J, Swaroop A. Epigenome-metabolism nexus in the retina: implications for aging and disease. Trends Genet 2024:S0168-9525(24)00099-4. [PMID: 38782642 DOI: 10.1016/j.tig.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/25/2024]
Abstract
Intimate links between epigenome modifications and metabolites allude to a crucial role of cellular metabolism in transcriptional regulation. Retina, being a highly metabolic tissue, adapts by integrating inputs from genetic, epigenetic, and extracellular signals. Precise global epigenomic signatures guide development and homeostasis of the intricate retinal structure and function. Epigenomic and metabolic realignment are hallmarks of aging and highlight a link of the epigenome-metabolism nexus with aging-associated multifactorial traits affecting the retina, including age-related macular degeneration and glaucoma. Here, we focus on emerging principles of epigenomic and metabolic control of retinal gene regulation, with emphasis on their contribution to human disease. In addition, we discuss potential mitigation strategies involving lifestyle changes that target the epigenome-metabolome relationship for maintaining retinal function.
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Affiliation(s)
- Anupam K Mondal
- Neurobiology, Neurodegeneration, and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mohita Gaur
- Neurobiology, Neurodegeneration, and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jayshree Advani
- Neurobiology, Neurodegeneration, and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Anand Swaroop
- Neurobiology, Neurodegeneration, and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA.
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Ikram M, Rauf A, Rao MJ, Maqsood MFK, Bakhsh MZM, Ullah M, Batool M, Mehran M, Tahira M. CRISPR-Cas9 based molecular breeding in crop plants: a review. Mol Biol Rep 2024; 51:227. [PMID: 38281301 DOI: 10.1007/s11033-023-09086-w] [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: 09/04/2023] [Accepted: 11/30/2023] [Indexed: 01/30/2024]
Abstract
Traditional crop breeding techniques are not quickly boosting yields to fulfill the expanding population needs. Long crop lifespans hinder the ability of plant breeding to develop superior crop varieties. Due to the arduous crossing, selecting, and challenging processes, it can take decades to establish new varieties with desired agronomic traits. Develop new plant varieties instantly to reduce hunger and improve food security. As a result of the adoption of conventional agricultural techniques, crop genetic diversity has decreased over time. Several traditional and molecular techniques, such as genetic selection, mutant breeding, somaclonal variation, genome-wide association studies, and others, have improved agronomic traits associated with agricultural plant productivity, quality, and resistance to biotic and abiotic stresses. In addition, modern genome editing approaches based on programmable nucleases, CRISPR, and Cas9 proteins have escorted an exciting new era of plant breeding. Plant breeders and scientists worldwide rely on cutting-edge techniques like quick breeding, genome editing tools, and high-throughput phenotyping to boost crop breeding output. This review compiles discoveries in numerous areas of crop breeding, such as using genome editing tools to accelerate the breeding process and create yearly crop generations with the desired features, to describe the shift from conventional to modern plant breeding techniques.
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Affiliation(s)
- Muhammad Ikram
- MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Abdul Rauf
- National Key Laboratory of Horticultural Plant Biology, Ministry of Education, Wuhan, 430070, Hubei, China
| | - Muhammad Junaid Rao
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, 100 Daxue Rd., Nanning, 530004, China.
| | | | | | - Maaz Ullah
- MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Maria Batool
- MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Muhammad Mehran
- Key Laboratory of Arable Land Conservation, Huazhong Agricultural University, Ministry of Agriculture, Wuhan, 430070, China
| | - Maryam Tahira
- National Key Laboratory of Horticultural Plant Biology, Ministry of Education, Wuhan, 430070, Hubei, China
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Kho KH, Sukhan ZP, Hossen S, Cho Y, Lee WK, Nou IS. Age-Dependent Growth-Related QTL Variations in Pacific Abalone, Haliotis discus hannai. Int J Mol Sci 2023; 24:13388. [PMID: 37686194 PMCID: PMC10488178 DOI: 10.3390/ijms241713388] [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: 07/26/2023] [Revised: 08/09/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023] Open
Abstract
Pacific abalone is a high-value, commercially important marine invertebrate. It shows low growth as well as individual and yearly growth variation in aquaculture. Marker-assisted selection breeding could potentially resolve the problem of low and variable growth and increase genetic gain. Expression of quantitative trait loci (QTLs) for growth-related traits, viz., body weight, shell length, and shell width were analyzed at the first, second, and third year of age using an F1 cross population. A total of 37 chromosome-wide QTLs were identified in linkage groups 01, 02, 03, 04, 06, 07, 08, 10, 11, 12, and 13 at different ages. None of the QTLs detected at any one age were expressed in all three age groups. This result suggests that growth-related traits at different ages are influenced by different QTLs in each year. However, multiple-trait QTLs (where one QTL affects all three traits) were detected each year that are also age-specific. Eleven multiple-trait QTLs were detected at different ages: two QTLs in the first year; two QTLs in the second year; and seven QTLs in the third year. As abalone hatcheries use three-year-old abalone for breeding, QTL-linked markers that were detected at the third year of age could potentially be used in marker-assisted selection breeding programs.
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Affiliation(s)
- Kang Hee Kho
- Department of Fisheries Science, Chonnam National University, Yeosu 59626, Republic of Korea; (Z.P.S.); (S.H.); (Y.C.); (W.-K.L.)
| | - Zahid Parvez Sukhan
- Department of Fisheries Science, Chonnam National University, Yeosu 59626, Republic of Korea; (Z.P.S.); (S.H.); (Y.C.); (W.-K.L.)
| | - Shaharior Hossen
- Department of Fisheries Science, Chonnam National University, Yeosu 59626, Republic of Korea; (Z.P.S.); (S.H.); (Y.C.); (W.-K.L.)
| | - Yusin Cho
- Department of Fisheries Science, Chonnam National University, Yeosu 59626, Republic of Korea; (Z.P.S.); (S.H.); (Y.C.); (W.-K.L.)
| | - Won-Kyo Lee
- Department of Fisheries Science, Chonnam National University, Yeosu 59626, Republic of Korea; (Z.P.S.); (S.H.); (Y.C.); (W.-K.L.)
| | - Ill-Sup Nou
- Department of Horticulture, Sunchon National University, Suncheon 57922, Republic of Korea;
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Dong Z, Xu M, Sun X, Wang X. Mendelian randomization and transcriptomic analysis reveal an inverse causal relationship between Alzheimer's disease and cancer. J Transl Med 2023; 21:527. [PMID: 37542274 PMCID: PMC10403895 DOI: 10.1186/s12967-023-04357-3] [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: 04/21/2023] [Accepted: 07/14/2023] [Indexed: 08/06/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) and cancer are common age-related diseases, and epidemiological evidence suggests an inverse relationship between them. However, investigating the potential mechanism underlying their relationship remains insufficient. METHODS Based on genome-wide association summary statistics for 42,034 AD patients and 609,951 cancer patients from the GWAS Catalog using the two-sample Mendelian randomization (MR) method. Moreover, we utilized two-step MR to identify metabolites mediating between AD and cancer. Furthermore, we employed colocalization analysis to identify genes whose upregulation is a risk factor for AD and demonstrated the genes' upregulation to be a favorable prognostic factor for cancer by analyzing transcriptomic data for 33 TCGA cancer types. RESULTS Two-sample MR analysis revealed a significant causal influence for increased AD risk on reduced cancer risk. Two-step MR analysis identified very low-density lipoprotein (VLDL) as a key mediator of the negative cause-effect relationship between AD and cancer. Colocalization analysis uncovered PVRIG upregulation to be a risk factor for AD. Transcriptomic analysis showed that PVRIG expression had significant negative correlations with stemness scores, and positive correlations with antitumor immune responses and overall survival in pan-cancer and multiple cancer types. CONCLUSION AD may result in lower cancer risk. VLDL is a significant intermediate variable linking AD with cancer. PVRIG abundance is a risk factor for AD but a protective factor for cancer. This study demonstrates a causal influence for AD on cancer and provides potential molecular connections between both diseases.
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Affiliation(s)
- Zehua Dong
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
- Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
- Big Data Research Institute, China Pharmaceutical University, Nanjing, 211198, China
| | - Mengli Xu
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
- Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China
- Big Data Research Institute, China Pharmaceutical University, Nanjing, 211198, China
| | - Xu Sun
- Department of Pharmacy, Nanjing Luhe People's Hospital, Nanjing, 211500, China.
- Department of Pharmacy, Luhe Hospital Affiliated with Yangzhou University Medical College, Nanjing, 211500, China.
| | - Xiaosheng Wang
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China.
- Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China.
- Big Data Research Institute, China Pharmaceutical University, Nanjing, 211198, China.
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Wang Y, Bi Y, Jiang F, Shaw RK, Sun J, Hu C, Guo R, Fan X. Mapping and Functional Analysis of QTL for Kernel Number per Row in Tropical and Temperate-Tropical Introgression Lines of Maize ( Zea mays L.). Curr Issues Mol Biol 2023; 45:4416-4430. [PMID: 37232750 DOI: 10.3390/cimb45050281] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 05/10/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023] Open
Abstract
Kernel number per row (KNR) is an essential component of maize (Zea mays L.) grain yield (GY), and understanding its genetic mechanism is crucial to improve GY. In this study, two F7 recombinant inbred line (RIL) populations were created using a temperate-tropical introgression line TML418 and a tropical inbred line CML312 as female parents and a backbone maize inbred line Ye107 as the common male parent. Bi-parental quantitative trait locus (QTL) mapping and genome-wide association analysis (GWAS) were then performed on 399 lines of the two maize RIL populations for KNR in two different environments using 4118 validated single nucleotide polymorphism (SNP) markers. This study aimed to: (1) detect molecular markers and/or the genomic regions associated with KNR; (2) identify the candidate genes controlling KNR; and (3) analyze whether the candidate genes are useful in improving GY. The authors reported a total of 7 QTLs tightly linked to KNR through bi-parental QTL mapping and identified 21 SNPs significantly associated with KNR through GWAS. Among these, a highly confident locus qKNR7-1 was detected at two locations, Dehong and Baoshan, with both mapping approaches. At this locus, three novel candidate genes (Zm00001d022202, Zm00001d022168, Zm00001d022169) were identified to be associated with KNR. These candidate genes were primarily involved in the processes related to compound metabolism, biosynthesis, protein modification, degradation, and denaturation, all of which were related to the inflorescence development affecting KNR. These three candidate genes were not reported previously and are considered new candidate genes for KNR. The progeny of the hybrid Ye107 × TML418 exhibited strong heterosis for KNR, which the authors believe might be related to qKNR7-1. This study provides a theoretical foundation for future research on the genetic mechanism underlying KNR in maize and the use of heterotic patterns to develop high-yielding hybrids.
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Affiliation(s)
- Yuling Wang
- Institute of Resource Plants, Yunnan University, Kunming 650504, China
| | - Yaqi Bi
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Fuyan Jiang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Ranjan Kumar Shaw
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Jiachen Sun
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650500, China
| | - Can Hu
- Institute of Resource Plants, Yunnan University, Kunming 650504, China
| | - Ruijia Guo
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Xingming Fan
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
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Identification of quantitative trait loci for growth traits in red swamp crayfish (Procambarus clarkii). AQUACULTURE AND FISHERIES 2023. [DOI: 10.1016/j.aaf.2023.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Zhou Z, Zhang L, Shu J, Wang M, Li H, Shu H, Wang X, Sun Q, Zhang S. Root Breeding in the Post-Genomics Era: From Concept to Practice in Apple. PLANTS (BASEL, SWITZERLAND) 2022; 11:1408. [PMID: 35684181 PMCID: PMC9182997 DOI: 10.3390/plants11111408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/05/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
The development of rootstocks with a high-quality dwarf-type root system is a popular research topic in the apple industry. However, the precise breeding of rootstocks is still challenging, mainly because the root system is buried deep underground, roots have a complex life cycle, and research on root architecture has progressed slowly. This paper describes ideas for the precise breeding and domestication of wild apple resources and the application of key genes. The primary goal of this research is to combine the existing rootstock resources with molecular breeding and summarize the methods of precision breeding. Here, we reviewed the existing rootstock germplasm, high-quality genome, and genetic resources available to explain how wild resources might be used in modern breeding. In particular, we proposed the 'from genotype to phenotype' theory and summarized the difficulties in future breeding processes. Lastly, the genetics governing root diversity and associated regulatory mechanisms were elaborated on to optimize the precise breeding of rootstocks.
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Affiliation(s)
- Zhou Zhou
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai’an 271018, China; (Z.Z.); (L.Z.); (M.W.); (H.L.); (H.S.); (X.W.)
| | - Lei Zhang
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai’an 271018, China; (Z.Z.); (L.Z.); (M.W.); (H.L.); (H.S.); (X.W.)
| | - Jing Shu
- College of Forestry Engineering, Shandong Agriculture and Engineering University, Jinan 250100, China;
| | - Mengyu Wang
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai’an 271018, China; (Z.Z.); (L.Z.); (M.W.); (H.L.); (H.S.); (X.W.)
| | - Han Li
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai’an 271018, China; (Z.Z.); (L.Z.); (M.W.); (H.L.); (H.S.); (X.W.)
| | - Huairui Shu
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai’an 271018, China; (Z.Z.); (L.Z.); (M.W.); (H.L.); (H.S.); (X.W.)
| | - Xiaoyun Wang
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai’an 271018, China; (Z.Z.); (L.Z.); (M.W.); (H.L.); (H.S.); (X.W.)
| | - Qinghua Sun
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai’an 271018, China; (Z.Z.); (L.Z.); (M.W.); (H.L.); (H.S.); (X.W.)
| | - Shizhong Zhang
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai’an 271018, China; (Z.Z.); (L.Z.); (M.W.); (H.L.); (H.S.); (X.W.)
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Wu L, Jia B, Pei W, Wang L, Ma J, Wu M, Song J, Yang S, Xin Y, Huang L, Feng P, Zhang J, Yu J. Quantitative Trait Locus Analysis and Identification of Candidate Genes Affecting Seed Size and Shape in an Interspecific Backcross Inbred Line Population of Gossypium hirsutum × Gossypium barbadense. FRONTIERS IN PLANT SCIENCE 2022; 13:837984. [PMID: 35392518 PMCID: PMC8981304 DOI: 10.3389/fpls.2022.837984] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/31/2022] [Indexed: 06/14/2023]
Abstract
Seed size and shape are key agronomic traits affecting seedcotton yield and seed quality in cotton (Gossypium spp.). However, the genetic mechanisms that regulate the seed physical traits in cotton are largely unknown. In this study, an interspecific backcross inbred line (BIL) population of 250 BC1F7 lines, derived from the recurrent parent Upland CRI36 (Gossypium hirsutum) and Hai7124 (Gossypium barbadense), was used to investigate the genetic basis of cotton seed physical traits via quantitative trait locus (QTL) mapping and candidate gene identification. The BILs were tested in five environments, measuring eight seed size and shape-related traits, including 100-kernel weight, kernel length width and their ratio, kernel area, kernel girth, kernel diameter, and kernel roundness. Based on 7,709 single nucleotide polymorphic (SNP) markers, a total of 49 QTLs were detected and each explained 2.91-35.01% of the phenotypic variation, including nine stable QTLs mapped in at least three environments. Based on pathway enrichment, gene annotation, genome sequence, and expression analysis, five genes encoding starch synthase 4, transcription factor PIF7 and MYC4, ubiquitin-conjugating enzyme E27, and THO complex subunit 4A were identified as candidate genes that might be associated with seed size and shape. Our research provides valuable information to improve seed physical traits in cotton breeding.
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Affiliation(s)
- Luyao Wu
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, China
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Bing Jia
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Wenfeng Pei
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Li Wang
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Jianjiang Ma
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Man Wu
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Jikun Song
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Shuxian Yang
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Yue Xin
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Li Huang
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Pan Feng
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
| | - Jinfa Zhang
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, United States
| | - Jiwen Yu
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, China
- State Key Laboratory of Cotton Biology, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China
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