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Dallinger HG, Löschenberger F, Azrak N, Ametz C, Michel S, Bürstmayr H. Genome-wide association mapping for pre-harvest sprouting in European winter wheat detects novel resistance QTL, pleiotropic effects, and structural variation in multiple genomes. THE PLANT GENOME 2024; 17:e20301. [PMID: 36851839 DOI: 10.1002/tpg2.20301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 11/20/2022] [Indexed: 06/18/2023]
Abstract
Pre-harvest sprouting (PHS), germination of seeds before harvest, is a major problem in global wheat (Triticum aestivum L.) production, and leads to reduced bread-making quality in affected grain. Breeding for PHS resistance can prevent losses under adverse conditions. Selecting resistant lines in years lacking pre-harvest rain, requires challenging of plants in the field or in the laboratory or using genetic markers. Despite the availability of a wheat reference and pan-genome, linking markers, genes, allelic, and structural variation, a complete understanding of the mechanisms underlying various sources of PHS resistance is still lacking. Therefore, we challenged a population of European wheat varieties and breeding lines with PHS conditions and phenotyped them for PHS traits, grain quality, phenological and agronomic traits to conduct genome-wide association mapping. Furthermore, we compared these marker-trait associations to previously reported PHS loci and evaluated their usefulness for breeding. We found markers associated with PHS on all chromosomes, with strong evidence for novel quantitative trait locus/loci (QTL) on chromosome 1A and 5B. The QTL on chromosome 1A lacks pleiotropic effect, for the QTL on 5B we detected pleiotropic effects on phenology and grain quality. Multiple peaks on chromosome 4A co-located with the major resistance locus Phs-A1, for which two causal genes, TaPM19 and TaMKK3, have been proposed. Mapping markers and genes to the pan-genome and chromosomal alignments provide evidence for structural variation around this major PHS-resistance locus. Although PHS is controlled by many loci distributed across the wheat genome, Phs-A1 on chromosome 4A seems to be the most effective and widely deployed source of resistance, in European wheat varieties.
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Affiliation(s)
- Hermann G Dallinger
- Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Straße 20, Tulln, Austria
- Saatzucht Donau GesmbH & Co KG, Saatzuchtstrasse 11, Probstdorf, Austria
| | | | - Naim Azrak
- Saatzucht Donau GesmbH & Co KG, Saatzuchtstrasse 11, Probstdorf, Austria
| | - Christian Ametz
- Saatzucht Donau GesmbH & Co KG, Saatzuchtstrasse 11, Probstdorf, Austria
| | - Sebastian Michel
- Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Straße 20, Tulln, Austria
| | - Hermann Bürstmayr
- Institute of Biotechnology in Plant Production, Department of Agrobiotechnology, IFA-Tulln, University of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Straße 20, Tulln, Austria
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Kumar M, Kumar S, Sandhu KS, Kumar N, Saripalli G, Prakash R, Nambardar A, Sharma H, Gautam T, Balyan HS, Gupta PK. GWAS and genomic prediction for pre-harvest sprouting tolerance involving sprouting score and two other related traits in spring wheat. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:14. [PMID: 37313293 PMCID: PMC10248620 DOI: 10.1007/s11032-023-01357-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 01/26/2023] [Indexed: 06/15/2023]
Abstract
In wheat, a genome-wide association study (GWAS) and genomic prediction (GP) analysis were conducted for pre-harvest sprouting (PHS) tolerance and two of its related traits. For this purpose, an association panel of 190 accessions was phenotyped for PHS (using sprouting score), falling number, and grain color over two years and genotyped with 9904 DArTseq based SNP markers. GWAS for main-effect quantitative trait nucleotides (M-QTNs) using three different models (CMLM, SUPER, and FarmCPU) and epistatic QTNs (E-QTNs) using PLINK were performed. A total of 171 M-QTNs (CMLM, 47; SUPER, 70; FarmCPU, 54) for all three traits, and 15 E-QTNs involved in 20 first-order epistatic interactions were identified. Some of the above QTNs overlapped the previously reported QTLs, MTAs, and cloned genes, allowing delineating 26 PHS-responsive genomic regions that spread over 16 wheat chromosomes. As many as 20 definitive and stable QTNs were considered important for use in marker-assisted recurrent selection (MARS). The gene, TaPHS1, for PHS tolerance (PHST) associated with one of the QTNs was also validated using the KASP assay. Some of the M-QTNs were shown to have a key role in the abscisic acid pathway involved in PHST. Genomic prediction accuracies (based on the cross-validation approach) using three different models ranged from 0.41 to 0.55, which are comparable to the results of previous studies. In summary, the results of the present study improved our understanding of the genetic architecture of PHST and its related traits in wheat and provided novel genomic resources for wheat breeding based on MARS and GP. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01357-5.
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Affiliation(s)
- Manoj Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Sachin Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | | | - Neeraj Kumar
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC USA
| | - Gautam Saripalli
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD USA
| | - Ram Prakash
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Akash Nambardar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Hemant Sharma
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Tinku Gautam
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Harindra Singh Balyan
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
| | - Pushpendra Kumar Gupta
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, UP India
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3
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Li Z, Chen Y, Ou X, Wang M, Wang N, Li W, Deng Y, Diao Y, Sun Z, Luo Q, Li X, Zhao L, Yan T, Peng W, Jiang Q, Fang Y, Ren Z, Tan F, Luo P, Ren T. Identification of a stable major-effect quantitative trait locus for pre-harvest sprouting in common wheat (Triticum aestivum L.) via high-density SNP-based genotyping. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:4183-4195. [PMID: 36068440 DOI: 10.1007/s00122-022-04211-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/29/2022] [Indexed: 06/15/2023]
Abstract
A major and stable QTL cQSGR.sau.3D, which can explain 33.25% of the phenotypic variation in SGR, was mapped and validated, and cQSGR.sau.3D was found to be independent of GI. In this study, a recombinant inbred line (RIL) population containing 304 lines derived from the cross of Chuan-nong17 (CN17) and Chuan-nong11 (CN11) was genotyped using the Wheat55K single-nucleotide polymorphism array. A high-density genetic map consisting of 8329 markers spanning 4131.54 cM and distributed across 21 wheat chromosomes was constructed. QTLs for whole spike germination rate (SGR) were identified in multiple years. Six and fourteen QTLs were identified using the Inclusive Composite Interval Mapping-Biparental Populations and Multi-Environment Trial methods, respectively. A total of 106 digenic epistatic QTLs were also detected in this study. One of the additive QTLs, cQSGR.sau.3D, which was mapped in the region from 3.5 to 4.5 cM from linkage group 3D-2 on chromosome 3D, can explain 33.25% of the phenotypic variation in SGR and be considered a major and stable QTL for SGR. This QTL was independent of the seeds' germination traits, such as germination index. One Kompetitive Allele-Specific PCR (KASP) marker, KASP-AX-110772653, which is tightly linked to cQSGR.sau.3D, was developed. The genetic effect of cQSGR.sau.3D on SGR in the RIL and natural populations was successfully confirmed. Furthermore, within the interval in which cQSGR.sau.3D is located in Chinese Spring reference genomes, thirty-seven genes were found. cQSGR.sau.3D may provide new resources for pre-harvest sprouting resistance breeding of wheat in the future.
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Affiliation(s)
- Zhi Li
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Yongyan Chen
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Xia Ou
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Mengning Wang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Nanxin Wang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Wei Li
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Yawen Deng
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Yixin Diao
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Zixin Sun
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Qinyi Luo
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Xinli Li
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Liqi Zhao
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Tong Yan
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Wanhua Peng
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Qing Jiang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Yi Fang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Zhenglong Ren
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Feiquan Tan
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Peigao Luo
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China
| | - Tianheng Ren
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China.
- College of Agronomy, Sichuan Agricultural University, Wenjiang, Chengdu, 611130, Sichuan, China.
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Tanin MJ, Saini DK, Sandhu KS, Pal N, Gudi S, Chaudhary J, Sharma A. Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding. Sci Rep 2022; 12:13680. [PMID: 35953529 PMCID: PMC9372038 DOI: 10.1038/s41598-022-18149-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 08/05/2022] [Indexed: 12/03/2022] Open
Abstract
In wheat, a meta-analysis was performed using previously identified QTLs associated with drought stress (DS), heat stress (HS), salinity stress (SS), water-logging stress (WS), pre-harvest sprouting (PHS), and aluminium stress (AS) which predicted a total of 134 meta-QTLs (MQTLs) that involved at least 28 consistent and stable MQTLs conferring tolerance to five or all six abiotic stresses under study. Seventy-six MQTLs out of the 132 physically anchored MQTLs were also verified with genome-wide association studies. Around 43% of MQTLs had genetic and physical confidence intervals of less than 1 cM and 5 Mb, respectively. Consequently, 539 genes were identified in some selected MQTLs providing tolerance to 5 or all 6 abiotic stresses. Comparative analysis of genes underlying MQTLs with four RNA-seq based transcriptomic datasets unravelled a total of 189 differentially expressed genes which also included at least 11 most promising candidate genes common among different datasets. The promoter analysis showed that the promoters of these genes include many stress responsiveness cis-regulatory elements, such as ARE, MBS, TC-rich repeats, As-1 element, STRE, LTR, WRE3, and WUN-motif among others. Further, some MQTLs also overlapped with as many as 34 known abiotic stress tolerance genes. In addition, numerous ortho-MQTLs among the wheat, maize, and rice genomes were discovered. These findings could help with fine mapping and gene cloning, as well as marker-assisted breeding for multiple abiotic stress tolerances in wheat.
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Affiliation(s)
- Mohammad Jafar Tanin
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India.
| | - Dinesh Kumar Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India
| | - Karansher Singh Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99163, USA
| | - Neeraj Pal
- Department of Molecular Biology and Genetic Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India
| | - Santosh Gudi
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India
| | - Jyoti Chaudhary
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, Uttar Pradesh, India
| | - Achla Sharma
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India
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5
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Tanin MJ, Saini DK, Sandhu KS, Pal N, Gudi S, Chaudhary J, Sharma A. Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding. Sci Rep 2022; 12:13680. [PMID: 35953529 DOI: 10.1101/2022.06.24.497482] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 08/05/2022] [Indexed: 05/20/2023] Open
Abstract
In wheat, a meta-analysis was performed using previously identified QTLs associated with drought stress (DS), heat stress (HS), salinity stress (SS), water-logging stress (WS), pre-harvest sprouting (PHS), and aluminium stress (AS) which predicted a total of 134 meta-QTLs (MQTLs) that involved at least 28 consistent and stable MQTLs conferring tolerance to five or all six abiotic stresses under study. Seventy-six MQTLs out of the 132 physically anchored MQTLs were also verified with genome-wide association studies. Around 43% of MQTLs had genetic and physical confidence intervals of less than 1 cM and 5 Mb, respectively. Consequently, 539 genes were identified in some selected MQTLs providing tolerance to 5 or all 6 abiotic stresses. Comparative analysis of genes underlying MQTLs with four RNA-seq based transcriptomic datasets unravelled a total of 189 differentially expressed genes which also included at least 11 most promising candidate genes common among different datasets. The promoter analysis showed that the promoters of these genes include many stress responsiveness cis-regulatory elements, such as ARE, MBS, TC-rich repeats, As-1 element, STRE, LTR, WRE3, and WUN-motif among others. Further, some MQTLs also overlapped with as many as 34 known abiotic stress tolerance genes. In addition, numerous ortho-MQTLs among the wheat, maize, and rice genomes were discovered. These findings could help with fine mapping and gene cloning, as well as marker-assisted breeding for multiple abiotic stress tolerances in wheat.
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Affiliation(s)
- Mohammad Jafar Tanin
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India.
| | - Dinesh Kumar Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India
| | - Karansher Singh Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99163, USA
| | - Neeraj Pal
- Department of Molecular Biology and Genetic Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India
| | - Santosh Gudi
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India
| | - Jyoti Chaudhary
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, Uttar Pradesh, India
| | - Achla Sharma
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India
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Ding G, Hu B, Zhou Y, Yang W, Zhao M, Xie J, Zhang F. Development and Characterization of Chromosome Segment Substitution Lines Derived from Oryza rufipogon in the Background of the Oryza sativa indica Restorer Line R974. Genes (Basel) 2022; 13:genes13050735. [PMID: 35627119 PMCID: PMC9140843 DOI: 10.3390/genes13050735] [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: 03/30/2022] [Revised: 04/12/2022] [Accepted: 04/19/2022] [Indexed: 12/04/2022] Open
Abstract
Dongxiang wild rice (DXWR) (O. rufipogon Griff.), which has the northernmost worldwide distribution of a wild rice species, is a valuable genetic resource with respect to improving stress tolerance in cultivated rice (Oryza sativa L.). In the three-line hybrid rice breeding system, restorer lines play important roles in enhancing the tolerance of hybrid rice. However, restorer lines have yet to be used as a genomic background for development of substitution lines carrying DXWR chromosome segments. We developed a set of 84 chromosome segment substitution lines (CSSLs) from a donor parent DXWR × recurrent parent restorer line R974 (Oryza sativa indica) cross. On average, each CSSL carried 6.27 introgressed homozygous segments, with 93.37% total genome coverage. Using these CSSLs, we identified a single QTL, qDYST-1, associated with salt stress tolerance on chromosome 3. Furthermore, five CSSLs showing strong salt stress tolerance were subjected to whole-genome single-nucleotide polymorphism chip analyses, during which we detected a common substitution segment containing qDYST-1 in all five CSSLs, thereby implying the validity and efficacy of qDYST-1. These novel CSSLs could make a significant contribution to detecting valuable DXWR QTLs, and provide important germplasm resources for breeding novel restorer lines for use in hybrid rice breeding systems.
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Affiliation(s)
- Gumu Ding
- College of Life Sciences, Jiangxi Normal University, Nanchang 330022, China; (G.D.); (Y.Z.); (M.Z.)
| | - Biaolin Hu
- Rice National Engineering Laboratory, Rice Research Institute, Jiangxi Academy of Agricultural Sciences, Nanchang 330022, China;
| | - Yi Zhou
- College of Life Sciences, Jiangxi Normal University, Nanchang 330022, China; (G.D.); (Y.Z.); (M.Z.)
| | - Wanling Yang
- Jiangxi Provincial Key Laboratory of Protection and Utilization of Subtropical Plant Resources, Nanchang 330022, China;
| | - Minmin Zhao
- College of Life Sciences, Jiangxi Normal University, Nanchang 330022, China; (G.D.); (Y.Z.); (M.Z.)
| | - Jiankun Xie
- Jiangxi Provincial Key Laboratory of Protection and Utilization of Subtropical Plant Resources, Nanchang 330022, China;
- Correspondence: (J.X.); (F.Z.)
| | - Fantao Zhang
- College of Life Sciences, Jiangxi Normal University, Nanchang 330022, China; (G.D.); (Y.Z.); (M.Z.)
- Correspondence: (J.X.); (F.Z.)
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7
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Sodedji FAK, Ryu D, Choi J, Agbahoungba S, Assogbadjo AE, N’Guetta SPA, Jung JH, Nho CW, Kim HY. Genetic Diversity and Association Analysis for Carotenoid Content among Sprouts of Cowpea ( Vigna unguiculata L. Walp). Int J Mol Sci 2022; 23:ijms23073696. [PMID: 35409065 PMCID: PMC8998333 DOI: 10.3390/ijms23073696] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 02/01/2023] Open
Abstract
The development and promotion of biofortified foods plants are a sustainable strategy for supplying essential micronutrients for human health and nutrition. We set out to identify quantitative trait loci (QTL) associated with carotenoid content in cowpea sprouts. The contents of carotenoids, including lutein, zeaxanthin, and β-carotene in sprouts of 125 accessions were quantified via high-performance liquid chromatography. Significant variation existed in the profiles of the different carotenoids. Lutein was the most abundant (58 ± 12.8 mg/100 g), followed by zeaxanthin (14.7 ± 3.1 mg/100 g) and β-carotene (13.2 ± 2.9 mg/100 g). A strong positive correlation was observed among the carotenoid compounds (r ≥ 0.87), indicating they can be improved concurrently. The accessions were distributed into three groups, following their carotenoid profiles, with accession C044 having the highest sprout carotenoid content in a single cluster. A total of 3120 genome-wide SNPs were tested for association analysis, which revealed that carotenoid biosynthesis in cowpea sprouts is a polygenic trait controlled by genes with additive and dominance effects. Seven loci were significantly associated with the variation in carotenoid content. The evidence of variation in carotenoid content and genomic regions controlling the trait creates an avenue for breeding cowpea varieties with enhanced sprouts carotenoid content.
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Affiliation(s)
- Frejus Ariel Kpedetin Sodedji
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Korea; (F.A.K.S.); (D.R.); (J.C.); (J.H.J.); (C.W.N.)
- Division of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology (UST), Daejeon 34113, Korea
- Non-Timber Forest Products and Orphan Crop Species Unit, Laboratory of Applied Ecology (LEA), University of Abomey-Calavi (UAC), Cotonou 05 BP 1752, Benin; (S.A.); (A.E.A.)
- West Africa Center of Excellence in Climate Change Biodiversity and Sustainable Agriculture (CEA-CCBAD), Biosciences Research Unit, University Felix Houphouet-Boigny, 22 BP 582 Abidjan 22, Abidjan 582, Côte d’Ivoire;
| | - Dahye Ryu
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Korea; (F.A.K.S.); (D.R.); (J.C.); (J.H.J.); (C.W.N.)
- Division of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology (UST), Daejeon 34113, Korea
| | - Jaeyoung Choi
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Korea; (F.A.K.S.); (D.R.); (J.C.); (J.H.J.); (C.W.N.)
| | - Symphorien Agbahoungba
- Non-Timber Forest Products and Orphan Crop Species Unit, Laboratory of Applied Ecology (LEA), University of Abomey-Calavi (UAC), Cotonou 05 BP 1752, Benin; (S.A.); (A.E.A.)
| | - Achille Ephrem Assogbadjo
- Non-Timber Forest Products and Orphan Crop Species Unit, Laboratory of Applied Ecology (LEA), University of Abomey-Calavi (UAC), Cotonou 05 BP 1752, Benin; (S.A.); (A.E.A.)
| | - Simon-Pierre Assanvo N’Guetta
- West Africa Center of Excellence in Climate Change Biodiversity and Sustainable Agriculture (CEA-CCBAD), Biosciences Research Unit, University Felix Houphouet-Boigny, 22 BP 582 Abidjan 22, Abidjan 582, Côte d’Ivoire;
| | - Je Hyeong Jung
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Korea; (F.A.K.S.); (D.R.); (J.C.); (J.H.J.); (C.W.N.)
| | - Chu Won Nho
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Korea; (F.A.K.S.); (D.R.); (J.C.); (J.H.J.); (C.W.N.)
- Division of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology (UST), Daejeon 34113, Korea
| | - Ho-Youn Kim
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Korea; (F.A.K.S.); (D.R.); (J.C.); (J.H.J.); (C.W.N.)
- Division of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology (UST), Daejeon 34113, Korea
- Correspondence: ; Tel.: +82-33-650-3580
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Identification and Validation of Quantitative Trait Loci Mapping for Spike-Layer Uniformity in Wheat. Int J Mol Sci 2022; 23:ijms23031052. [PMID: 35162974 PMCID: PMC8835109 DOI: 10.3390/ijms23031052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 01/14/2022] [Accepted: 01/15/2022] [Indexed: 01/27/2023] Open
Abstract
Spike-layer uniformity (SLU), the consistency of the spike distribution in the vertical space, is an important trait. It directly affects the yield potential and appearance. Revealing the genetic basis of SLU will provide new insights into wheat improvement. To map the SLU-related quantitative trait loci (QTL), 300 recombinant inbred lines (RILs) that were derived from a cross between H461 and Chinese Spring were used in this study. The RILs and parents were tested in fields from two continuous years from two different pilots. Phenotypic analysis showed that H461 was more consistent in the vertical spatial distribution of the spike layer than in Chinese Spring. Based on inclusive composite interval mapping, four QTL were identified for SLU. There were two major QTL on chromosomes 2BL and 2DL and two minor QTL on chromosomes 1BS and 2BL that were identified. The additive effects of QSlu.sicau-1B, Qslu.sicau-2B-2, and QSlu.sicau-2D were all from the parent, H461. The major QTL, QSlu.sicau-2B-2 and QSlu.sicau-2D, were detected in each of the conducted trials. Based on the best linear unbiased prediction values, the two loci explained 23.97% and 15.98% of the phenotypic variation, respectively. Compared with previous studies, the two major loci were potentially novel and the two minor loci were overlapped. Based on the kompetitive allele-specific PCR (KASP) marker, the genetic effects for QSlu.sicau-2B-2 were validated in an additional RIL population. The genetic effects ranged from 26.65% to 32.56%, with an average value of 30.40%. In addition, QSlu.sicau-2B-2 showed a significant (p < 0.01) and positive influence on the spike length, spikelet number, and thousand kernel weight. The identified QTL and the developed KASP marker will be helpful for fine-mapping these loci, finally contributing to wheat breeding programs in a marker-assisted selection way.
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