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Wang Y, Zeng Z, Li J, Zhao D, Zhao Y, Peng C, Lan C, Wang C. Identification and validation of new quantitative trait loci for spike-related traits in two RIL populations. Mol Breed 2023; 43:64. [PMID: 37533603 PMCID: PMC10390419 DOI: 10.1007/s11032-023-01401-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 06/19/2023] [Indexed: 08/04/2023]
Abstract
Wheat (Triticum aestivum L.) is one of the most important cereal crops for ensuring food security worldwide. Identification of major quantitative trait loci (QTL) for spike-related traits is important for improvement of yield potential in wheat breeding. In this study, by using the wheat 55K single nucleotide polymorphism (SNP) array and diversity array technology (DArT), two recombinant inbred line populations derived from crosses avocet/chilero and avocet/huites were used to map QTL for kernel number per spike (KNS), total spikelet number per spike (TSS), fertile spikelet number per spike (FSS), and spike compactness (SC). Forty-two QTLs were identified on chromosomes 2A (4), 2B (3), 3A (2), 3B (7), 5A (11), 6A (4), 6B, and 7A (10), explaining 3.13-21.80% of the phenotypic variances. Twelve QTLs were detected in multi-environments on chromosomes 2A, 3B (2), 5A (4), 6A (3), 6B, and 7A, while four QTL clusters were detected on chromosomes 3A, 3B, 5A, and 7A. Two stable and new QTL clusters, QKns/Tss/Fss/SC.haust-5A and QKns/Tss/Fss.haust-7A, were detected in the physical intervals of 547.49-590.46 Mb and 511.54-516.15 Mb, accounting for 7.53-14.78% and 7.01-20.66% of the phenotypic variances, respectively. High-confidence annotated genes for QKns/Tss/Fss/SC.haust-5A and QKns/Tss/Fss.haust-7A were more highly expressed in spike development. The results provide new QTL and molecular markers for marker-assisted breeding in wheat. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01401-4.
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Affiliation(s)
- Yuying Wang
- College of Agronomy, Henan University of Science and Technology, Luoyang, 471000 Henan China
- The Shennong Laboratory, Zhengzhou, 450002 Henan China
| | - Zhankui Zeng
- College of Agronomy, Henan University of Science and Technology, Luoyang, 471000 Henan China
- The Shennong Laboratory, Zhengzhou, 450002 Henan China
| | - Jiachuang Li
- College of Agronomy, Henan University of Science and Technology, Luoyang, 471000 Henan China
- The Shennong Laboratory, Zhengzhou, 450002 Henan China
| | - Dehui Zhao
- College of Agronomy, Henan University of Science and Technology, Luoyang, 471000 Henan China
- The Shennong Laboratory, Zhengzhou, 450002 Henan China
| | - Yue Zhao
- College of Agronomy, Henan University of Science and Technology, Luoyang, 471000 Henan China
- The Shennong Laboratory, Zhengzhou, 450002 Henan China
| | - Chen Peng
- College of Agronomy, Henan University of Science and Technology, Luoyang, 471000 Henan China
- The Shennong Laboratory, Zhengzhou, 450002 Henan China
| | - Caixia Lan
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070 Hubei China
| | - Chunping Wang
- College of Agronomy, Henan University of Science and Technology, Luoyang, 471000 Henan China
- The Shennong Laboratory, Zhengzhou, 450002 Henan China
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Li S, Feng T, Zhang C, Zhang F, Li H, Chen Y, Liang L, Zhang C, Zeng W, Liu E, Shi Y, Li M, Meng L. Genetic Dissection of Salt Tolerance and Yield Traits of Geng ( japonica) Rice by Selective Subspecific Introgression. Curr Issues Mol Biol 2023; 45:4796-4813. [PMID: 37367054 DOI: 10.3390/cimb45060305] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 05/13/2023] [Accepted: 05/22/2023] [Indexed: 06/28/2023] Open
Abstract
Salinity is a major factor limiting rice productivity, and developing salt-tolerant (ST) varieties is the most efficient approach. Seventy-eight ST introgression lines (ILs), including nine promising lines with improved ST and yield potential (YP), were developed from four BC2F4 populations from inter-subspecific crosses between an elite Geng (japonica) recipient and four Xian (indica) donors at the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences. Genome-wide characterization of donor introgression identified 35 ST QTLs, 25 of which harbor 38 cloned ST genes as the most likely QTL candidates. Thirty-four are Xian-Geng differentiated ones with the donor (Xian) alleles associated with ST, suggesting differentiated responses to salt stress were one of the major phenotypic differences between the two subspecies. At least eight ST QTLs and many others affecting yield traits were identified under salt/non-stress conditions. Our results indicated that the Xian gene pool contains rich 'hidden' genetic variation for developing superior Geng varieties with improved ST and YP, which could be efficiently exploited by selective introgression. The developed ST ILs and their genetic information on the donor alleles for ST and yield traits would provide a useful platform for developing superior ST and high-yield Geng varieties through breeding by design in the future.
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Affiliation(s)
- Simin Li
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Ting Feng
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Chenyang Zhang
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Fanlin Zhang
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Hua Li
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Yanjun Chen
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Lunping Liang
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Chaopu Zhang
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Wei Zeng
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Erbao Liu
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Yingyao Shi
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Min Li
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Lijun Meng
- Kunpeng Institute of Modern Agriculture at Foshan, Foshan 528200, China
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Luo S, Jia J, Liu R, Wei R, Guo Z, Cai Z, Chen B, Liang F, Xia Q, Nian H, Cheng Y. Identification of major QTLs for soybean seed size and seed weight traits using a RIL population in different environments. Front Plant Sci 2023; 13:1094112. [PMID: 36714756 PMCID: PMC9874164 DOI: 10.3389/fpls.2022.1094112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 12/15/2022] [Indexed: 06/18/2023]
Abstract
INTRODUCTION The seed weight of soybean [Glycine max (L.) Merr.] is one of the major traits that determine soybean yield and is closely related to seed size. However, the genetic basis of the synergistic regulation of traits related to soybean yield is unclear. METHODS To understand the molecular genetic basis for the formation of soybean yield traits, the present study focused on QTLs mapping for seed size and weight traits in different environments and target genes mining. RESULTS A total of 85 QTLs associated with seed size and weight traits were identified using a recombinant inbred line (RIL) population developed from Guizao1×B13 (GB13). We also detected 18 environmentally stable QTLs. Of these, qSL-3-1 was a novel QTL with a stable main effect associated with seed length. It was detected in all environments, three of which explained more than 10% of phenotypic variance (PV), with a maximum of 15.91%. In addition, qSW-20-3 was a novel QTL with a stable main effect associated with seed width, which was identified in four environments. And the amount of phenotypic variance explained (PVE) varied from 9.22 to 21.93%. Five QTL clusters associated with both seed size and seed weight were summarized by QTL cluster identification. Fifteen candidate genes that may be involved in regulating soybean seed size and weight were also screened based on gene function annotation and GO enrichment analysis. DISCUSSION The results provide a biologically basic reference for understanding the formation of soybean seed size and weight traits.
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Affiliation(s)
- Shilin Luo
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Jia Jia
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Riqian Liu
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Ruqian Wei
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Zhibin Guo
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Zhandong Cai
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Bo Chen
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Fuwei Liang
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Qiuju Xia
- Rice Molecular Breeding Institute, Granlux Associated Grains, Shenzhen, Guangdong, China
| | - Hai Nian
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
| | - Yanbo Cheng
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, Guangdong, China
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Elattar MA, Karikari B, Li S, Song S, Cao Y, Aslam M, Hina A, Abou-Elwafa SF, Zhao T. Identification and Validation of Major QTLs, Epistatic Interactions, and Candidate Genes for Soybean Seed Shape and Weight Using Two Related RIL Populations. Front Genet 2021; 12:666440. [PMID: 34122518 PMCID: PMC8195344 DOI: 10.3389/fgene.2021.666440] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 03/29/2021] [Indexed: 11/13/2022] Open
Abstract
Understanding the genetic mechanism underlying seed size, shape, and weight is essential for enhancing soybean cultivars. High-density genetic maps of two recombinant inbred line (RIL) populations, LM6 and ZM6, were evaluated across multiple environments to identify and validate M-QTLs as well as identify candidate genes behind major and stable quantitative trait loci (QTLs). A total of 239 and 43 M-QTLs were mapped by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM) approaches, from which 180 and 18, respectively, are novel QTLs. Twenty-two QTLs including four novel major QTLs were validated in the two RIL populations across multiple environments. Moreover, 18 QTLs showed significant AE effects, and 40 pairwise of the identified QTLs exhibited digenic epistatic effects. Thirty-four QTLs associated with seed flatness index (FI) were identified and reported here for the first time. Seven QTL clusters comprising several QTLs for seed size, shape, and weight on genomic regions of chromosomes 3, 4, 5, 7, 9, 17, and 19 were identified. Gene annotations, gene ontology (GO) enrichment, and RNA-seq analyses of the genomic regions of those seven QTL clusters identified 47 candidate genes for seed-related traits. These genes are highly expressed in seed-related tissues and nodules, which might be deemed as potential candidate genes regulating the seed size, weight, and shape traits in soybean. This study provides detailed information on the genetic basis of the studied traits and candidate genes that could be efficiently implemented by soybean breeders for fine mapping and gene cloning, and for marker-assisted selection (MAS) targeted at improving these traits individually or concurrently.
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Affiliation(s)
- Mahmoud A Elattar
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.,Agronomy Department, Faculty of Agriculture, Minia University, Minia, Egypt
| | - Benjamin Karikari
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Shuguang Li
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Shiyu Song
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Yongce Cao
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Muhammed Aslam
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Aiman Hina
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | | | - Tuanjie Zhao
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
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Kumar P, Eriksen RL, Simko I, Mou B. Molecular Mapping of Water-Stress Responsive Genomic Loci in Lettuce ( Lactuca spp.) Using Kinetics Chlorophyll Fluorescence, Hyperspectral Imaging and Machine Learning. Front Genet 2021; 12:634554. [PMID: 33679897 PMCID: PMC7935093 DOI: 10.3389/fgene.2021.634554] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 01/29/2021] [Indexed: 11/23/2022] Open
Abstract
Deep understanding of genetic architecture of water-stress tolerance is critical for efficient and optimal development of water-stress tolerant cultivars, which is the most economical and environmentally sound approach to maintain lettuce production with limited irrigation. Lettuce (Lactuca sativa L.) production in areas with limited precipitation relies heavily on the use of ground water for irrigation. Lettuce plants are highly susceptible to water-stress, which also affects their nutrient uptake efficiency. Water stressed plants show reduced growth, lower biomass, and early bolting and flowering resulting in bitter flavors. Traditional phenotyping methods to evaluate water-stress are labor intensive, time-consuming and prone to errors. High throughput phenotyping platforms using kinetic chlorophyll fluorescence and hyperspectral imaging can effectively attain physiological traits related to photosynthesis and secondary metabolites that can enhance breeding efficiency for water-stress tolerance. Kinetic chlorophyll fluorescence and hyperspectral imaging along with traditional horticultural traits identified genomic loci affected by water-stress. Supervised machine learning models were evaluated for their accuracy to distinguish water-stressed plants and to identify the most important water-stress related parameters in lettuce. Random Forest (RF) had classification accuracy of 89.7% using kinetic chlorophyll fluorescence parameters and Neural Network (NN) had classification accuracy of 89.8% using hyperspectral imaging derived vegetation indices. The top ten chlorophyll fluorescence parameters and vegetation indices selected by sequential forward selection by RF and NN were genetically mapped using a L. sativa × L. serriola interspecific recombinant inbred line (RIL) population. A total of 25 quantitative trait loci (QTL) segregating for water-stress related horticultural traits, 26 QTL for the chlorophyll fluorescence traits and 34 QTL for spectral vegetation indices (VI) were identified. The percent phenotypic variation (PV) explained by the horticultural QTL ranged from 6.41 to 19.5%, PV explained by chlorophyll fluorescence QTL ranged from 6.93 to 13.26% while the PV explained by the VI QTL ranged from 7.2 to 17.19%. Eight QTL clusters harboring co-localized QTL for horticultural traits, chlorophyll fluorescence parameters and VI were identified on six lettuce chromosomes. Molecular markers linked to the mapped QTL clusters can be targeted for marker-assisted selection to develop water-stress tolerant lettuce.
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Affiliation(s)
- Pawan Kumar
- Crop Improvement and Protection Research Unit, USDA-ARS, Salinas, CA, United States
| | - Renee L Eriksen
- Forage Seed and Cereal Research Unit, USDA-ARS, Corvallis, OR, United States
| | - Ivan Simko
- Crop Improvement and Protection Research Unit, USDA-ARS, Salinas, CA, United States
| | - Beiquan Mou
- Crop Improvement and Protection Research Unit, USDA-ARS, Salinas, CA, United States
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Zhang Z, Li J, Jamshed M, Shi Y, Liu A, Gong J, Wang S, Zhang J, Sun F, Jia F, Ge Q, Fan L, Zhang Z, Pan J, Fan S, Wang Y, Lu Q, Liu R, Deng X, Zou X, Jiang X, Liu P, Li P, Iqbal MS, Zhang C, Zou J, Chen H, Tian Q, Jia X, Wang B, Ai N, Feng G, Wang Y, Hong M, Li S, Lian W, Wu B, Hua J, Zhang C, Huang J, Xu A, Shang H, Gong W, Yuan Y. Genome-wide quantitative trait loci reveal the genetic basis of cotton fibre quality and yield-related traits in a Gossypium hirsutum recombinant inbred line population. Plant Biotechnol J 2020; 18:239-253. [PMID: 31199554 PMCID: PMC6920336 DOI: 10.1111/pbi.13191] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 05/30/2019] [Accepted: 06/11/2019] [Indexed: 05/02/2023]
Abstract
Cotton is widely cultivated globally because it provides natural fibre for the textile industry and human use. To identify quantitative trait loci (QTLs)/genes associated with fibre quality and yield, a recombinant inbred line (RIL) population was developed in upland cotton. A consensus map covering the whole genome was constructed with three types of markers (8295 markers, 5197.17 centimorgans (cM)). Six fibre yield and quality traits were evaluated in 17 environments, and 983 QTLs were identified, 198 of which were stable and mainly distributed on chromosomes 4, 6, 7, 13, 21 and 25. Thirty-seven QTL clusters were identified, in which 92.8% of paired traits with significant medium or high positive correlations had the same QTL additive effect directions, and all of the paired traits with significant medium or high negative correlations had opposite additive effect directions. In total, 1297 genes were discovered in the QTL clusters, 414 of which were expressed in two RNA-Seq data sets. Many genes were discovered, 23 of which were promising candidates. Six important QTL clusters that included both fibre quality and yield traits were identified with opposite additive effect directions, and those on chromosome 13 (qClu-chr13-2) could increase fibre quality but reduce yield; this result was validated in a natural population using three markers. These data could provide information about the genetic basis of cotton fibre quality and yield and help cotton breeders to improve fibre quality and yield simultaneously.
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Zhang K, Kuraparthy V, Fang H, Zhu L, Sood S, Jones DC. High-density linkage map construction and QTL analyses for fiber quality, yield and morphological traits using CottonSNP63K array in upland cotton (Gossypium hirsutum L.). BMC Genomics 2019; 20:889. [PMID: 31771502 PMCID: PMC6878679 DOI: 10.1186/s12864-019-6214-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 10/22/2019] [Indexed: 12/14/2022] Open
Abstract
Background Improving fiber quality and yield are the primary research objectives in cotton breeding for enhancing the economic viability and sustainability of Upland cotton production. Identifying the quantitative trait loci (QTL) for fiber quality and yield traits using the high-density SNP-based genetic maps allows for bridging genomics with cotton breeding through marker assisted and genomic selection. In this study, a recombinant inbred line (RIL) population, derived from cross between two parental accessions, which represent broad allele diversity in Upland cotton, was used to construct high-density SNP-based linkage maps and to map the QTLs controlling important cotton traits. Results Molecular genetic mapping using RIL population produced a genetic map of 3129 SNPs, mapped at a density of 1.41 cM. Genetic maps of the individual chromosomes showed good collinearity with the sequence based physical map. A total of 106 QTLs were identified which included 59 QTLs for six fiber quality traits, 38 QTLs for four yield traits and 9 QTLs for two morphological traits. Sub-genome wide, 57 QTLs were mapped in A sub-genome and 49 were mapped in D sub-genome. More than 75% of the QTLs with favorable alleles were contributed by the parental accession NC05AZ06. Forty-six mapped QTLs each explained more than 10% of the phenotypic variation. Further, we identified 21 QTL clusters where 12 QTL clusters were mapped in the A sub-genome and 9 were mapped in the D sub-genome. Candidate gene analyses of the 11 stable QTL harboring genomic regions identified 19 putative genes which had functional role in cotton fiber development. Conclusion We constructed a high-density genetic map of SNPs in Upland cotton. Collinearity between genetic and physical maps indicated no major structural changes in the genetic mapping populations. Most traits showed high broad-sense heritability. One hundred and six QTLs were identified for the fiber quality, yield and morphological traits. Majority of the QTLs with favorable alleles were contributed by improved parental accession. More than 70% of the mapped QTLs shared the similar map position with previously reported QTLs which suggest the genetic relatedness of Upland cotton germplasm. Identification of QTL clusters could explain the correlation among some fiber quality traits in cotton. Stable and major QTLs and QTL clusters of traits identified in the current study could be the targets for map-based cloning and marker assisted selection (MAS) in cotton breeding. The genomic region on D12 containing the major stable QTLs for micronaire, fiber strength and lint percentage could be potential targets for MAS and gene cloning of fiber quality traits in cotton.
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Affiliation(s)
- Kuang Zhang
- Crop & Soil Sciences Department, North Carolina State University, Raleigh, NC, 27695, USA
| | - Vasu Kuraparthy
- Crop & Soil Sciences Department, North Carolina State University, Raleigh, NC, 27695, USA.
| | - Hui Fang
- Crop & Soil Sciences Department, North Carolina State University, Raleigh, NC, 27695, USA
| | - Linglong Zhu
- Crop & Soil Sciences Department, North Carolina State University, Raleigh, NC, 27695, USA
| | - Shilpa Sood
- Crop & Soil Sciences Department, North Carolina State University, Raleigh, NC, 27695, USA.,4 Cityplace drive, The Climate Corporation (Bayer U.S. Crop Science), St. Louis, MO, 63141, USA
| | - Don C Jones
- Cotton Incorporated, 6399 Weston Parkway, Cary, NC, 27513, USA
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Wang J, Dun X, Shi J, Wang X, Liu G, Wang H. Genetic Dissection of Root Morphological Traits Related to Nitrogen Use Efficiency in Brassica napus L. under Two Contrasting Nitrogen Conditions. Front Plant Sci 2017; 8:1709. [PMID: 29033971 PMCID: PMC5626847 DOI: 10.3389/fpls.2017.01709] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 09/19/2017] [Indexed: 05/21/2023]
Abstract
As the major determinant for nutrient uptake, root system architecture (RSA) has a massive impact on nitrogen use efficiency (NUE). However, little is known the molecular control of RSA as related to NUE in rapeseed. Here, a rapeseed recombinant inbred line population (BnaZNRIL) was used to investigate root morphology (RM, an important component for RSA) and NUE-related traits under high-nitrogen (HN) and low-nitrogen (LN) conditions by hydroponics. Data analysis suggested that RM-related traits, particularly root size had significantly phenotypic correlations with plant dry biomass and N uptake irrespective of N levels, but no or little correlation with N utilization efficiency (NUtE), providing the potential to identify QTLs with pleiotropy or specificity for RM- and NUE-related traits. A total of 129 QTLs (including 23 stable QTLs, which were repeatedly detected at least two environments or different N levels) were identified and 83 of them were integrated into 22 pleiotropic QTL clusters. Five RM-NUE, ten RM-specific and three NUE-specific QTL clusters with same directions of additive-effect implied two NUE-improving approaches (RM-based and N utilization-based directly) and provided valuable genomic regions for NUE improvement in rapeseed. Importantly, all of four major QTLs and most of stable QTLs (20 out of 23) detected here were related to RM traits under HN and/or LN levels, suggested that regulating RM to improve NUE would be more feasible than regulating N efficiency directly. These results provided the promising genomic regions for marker-assisted selection on RM-based NUE improvement in rapeseed.
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Lv H, Wang Q, Liu X, Han F, Fang Z, Yang L, Zhuang M, Liu Y, Li Z, Zhang Y. Whole-Genome Mapping Reveals Novel QTL Clusters Associated with Main Agronomic Traits of Cabbage (Brassica oleracea var. capitata L.). Front Plant Sci 2016; 7:989. [PMID: 27458471 PMCID: PMC4933720 DOI: 10.3389/fpls.2016.00989] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Accepted: 06/22/2016] [Indexed: 05/06/2023]
Abstract
We describe a comprehensive quantitative trait locus (QTL) analysis for 24 main agronomic traits of cabbage. Field experiments were performed using a 196-line double haploid population in three seasons in 2011 and 2012 to evaluate important agronomic traits related to plant type, leaf, and head traits. In total, 144 QTLs with LOD threshold >3.0 were detected for the 24 agronomic traits: 25 for four plant-type-related traits, 64 for 10 leaf-related traits, and 55 for 10 head-related traits; each QTL explained 6.0-55.7% of phenotype variation. Of the QTLs, 95 had contribution rates higher than 10%, and 51 could be detected in more than one season. Major QTLs included Ph 3.1 (max R (2) = 55.7, max LOD = 28.2) for plant height, Ll 3.2 (max R (2) = 31.7, max LOD = 13.95) for leaf length, and Htd 3.2 (max R (2) = 28.5, max LOD = 9.49) for head transverse diameter; these could all be detected in more than one season. Twelve QTL clusters were detected on eight chromosomes, and the most significant four included Indel481-scaffold18376 (3.20 Mb), with five QTLs for five traits; Indel64-scaffold35418 (2.22 Mb), six QTLs for six traits; scaffold39782-Indel84 (1.78 Mb), 11 QTLs for 11 traits; and Indel353-Indel245 (9.89 Mb), seven QTLs for six traits. Besides, most traits clustered within the same region were significantly correlated with each other. The candidate genes at these regions were also discussed. Robust QTLs and their clusters obtained in this study should prove useful for marker-assisted selection (MAS) in cabbage breeding and in furthering our understanding of the genetic control of these traits.
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Affiliation(s)
- Honghao Lv
- Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, Ministry of Agriculture, Institute of Vegetables and Flowers, Chinese Academy of Agricultural SciencesBeijing, China
| | - Qingbiao Wang
- Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (North China), Ministry of Agriculture, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry SciencesBeijing, China
| | - Xing Liu
- Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, Ministry of Agriculture, Institute of Vegetables and Flowers, Chinese Academy of Agricultural SciencesBeijing, China
| | - Fengqing Han
- Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, Ministry of Agriculture, Institute of Vegetables and Flowers, Chinese Academy of Agricultural SciencesBeijing, China
| | - Zhiyuan Fang
- Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, Ministry of Agriculture, Institute of Vegetables and Flowers, Chinese Academy of Agricultural SciencesBeijing, China
| | - Limei Yang
- Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, Ministry of Agriculture, Institute of Vegetables and Flowers, Chinese Academy of Agricultural SciencesBeijing, China
| | - Mu Zhuang
- Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, Ministry of Agriculture, Institute of Vegetables and Flowers, Chinese Academy of Agricultural SciencesBeijing, China
| | - Yumei Liu
- Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, Ministry of Agriculture, Institute of Vegetables and Flowers, Chinese Academy of Agricultural SciencesBeijing, China
| | - Zhansheng Li
- Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, Ministry of Agriculture, Institute of Vegetables and Flowers, Chinese Academy of Agricultural SciencesBeijing, China
| | - Yangyong Zhang
- Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, Ministry of Agriculture, Institute of Vegetables and Flowers, Chinese Academy of Agricultural SciencesBeijing, China
- *Correspondence: Yangyong Zhang
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Li P, Chen F, Cai H, Liu J, Pan Q, Liu Z, Gu R, Mi G, Zhang F, Yuan L. A genetic relationship between nitrogen use efficiency and seedling root traits in maize as revealed by QTL analysis. J Exp Bot 2015; 66:3175-88. [PMID: 25873660 PMCID: PMC4449538 DOI: 10.1093/jxb/erv127] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
That root system architecture (RSA) has an essential role in nitrogen acquisition is expected in maize, but the genetic relationship between RSA and nitrogen use efficiency (NUE) traits remains to be elucidated. Here, the genetic basis of RSA and NUE traits was investigated in maize using a recombination inbred line population that was derived from two lines contrasted for both traits. Under high-nitrogen and low-nitrogen conditions, 10 NUE- and 9 RSA-related traits were evaluated in four field environments and three hydroponic experiments, respectively. In contrast to nitrogen utilization efficiency (NutE), nitrogen uptake efficiency (NupE) had significant phenotypic correlations with RSA, particularly the traits of seminal roots (r = 0.15-0.31) and crown roots (r = 0.15-0.18). A total of 331 quantitative trait loci (QTLs) were detected, including 184 and 147 QTLs for NUE- and RSA-related traits, respectively. These QTLs were assigned into 64 distinct QTL clusters, and ~70% of QTLs for nitrogen-efficiency (NUE, NupE, and NutE) coincided in clusters with those for RSA. Five important QTLs clusters at the chromosomal regions bin1.04, 2.04, 3.04, 3.05/3.06, and 6.07/6.08 were found in which QTLs for both traits had favourable effects from alleles coming from the large-rooted and high-NupE parent. Introgression of these QTL clusters in the advanced backcross-derived lines conferred mean increases in grain yield of ~14.8% for the line per se and ~15.9% in the testcross. These results reveal a significant genetic relationship between RSA and NUE traits, and uncover the most promising genomic regions for marker-assisted selection of RSA to improve NUE in maize.
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Affiliation(s)
- Pengcheng Li
- Key Laboratory of Plant-Soil Interaction, MOE, Center for Resources, Environment and Food Security, College Resources and Environmental Sciences, China Agricultural University, Beijing, China 100193
| | - Fanjun Chen
- Key Laboratory of Plant-Soil Interaction, MOE, Center for Resources, Environment and Food Security, College Resources and Environmental Sciences, China Agricultural University, Beijing, China 100193
| | - Hongguang Cai
- Key Laboratory of Plant-Soil Interaction, MOE, Center for Resources, Environment and Food Security, College Resources and Environmental Sciences, China Agricultural University, Beijing, China 100193
| | - Jianchao Liu
- Key Laboratory of Plant-Soil Interaction, MOE, Center for Resources, Environment and Food Security, College Resources and Environmental Sciences, China Agricultural University, Beijing, China 100193
| | - Qingchun Pan
- Key Laboratory of Plant-Soil Interaction, MOE, Center for Resources, Environment and Food Security, College Resources and Environmental Sciences, China Agricultural University, Beijing, China 100193
| | - Zhigang Liu
- Key Laboratory of Plant-Soil Interaction, MOE, Center for Resources, Environment and Food Security, College Resources and Environmental Sciences, China Agricultural University, Beijing, China 100193
| | - Riliang Gu
- Key Laboratory of Plant-Soil Interaction, MOE, Center for Resources, Environment and Food Security, College Resources and Environmental Sciences, China Agricultural University, Beijing, China 100193
| | - Guohua Mi
- Key Laboratory of Plant-Soil Interaction, MOE, Center for Resources, Environment and Food Security, College Resources and Environmental Sciences, China Agricultural University, Beijing, China 100193
| | - Fusuo Zhang
- Key Laboratory of Plant-Soil Interaction, MOE, Center for Resources, Environment and Food Security, College Resources and Environmental Sciences, China Agricultural University, Beijing, China 100193
| | - Lixing Yuan
- Key Laboratory of Plant-Soil Interaction, MOE, Center for Resources, Environment and Food Security, College Resources and Environmental Sciences, China Agricultural University, Beijing, China 100193
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