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de Ocampo MP, Tam BP, Egdane JA, Chebotarov D, Doi K, Yamauchi A, Ismail AM, Henry A, Mitsuya S. Leaf Na+ effects and multi-trait GWAS point to salt exclusion as the key mechanism for reproductive stage salinity tolerance in rice. ANNALS OF BOTANY 2025; 135:949-962. [PMID: 39731213 PMCID: PMC12064422 DOI: 10.1093/aob/mcae227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2024] [Accepted: 12/26/2024] [Indexed: 12/29/2024]
Abstract
BACKGROUND AND AIMS Since salinity stress may occur across stages of rice (Oryza sativa) crop growth, understanding the effects of salinity at the reproductive stage is important, although it has been much less studied than at the seedling stage. METHODS Lines from the Rice Diversity Panel 1 (RDP1) and the 3000 Rice Genomes (3KRG) were used to screen morphological and physiological traits, map loci controlling salinity tolerance through genome-wide association studies (GWAS), and identify favourable haplotypes associated with reproductive stage salinity tolerance. KEY RESULTS Salt exclusion was identified as the key tolerance mechanism in this study, based on reduced panicle length as flag leaf Na+ increased and a lack of effect of trimming the leaves on genotypic rankings in the salinity treatment. Since larger biomass showed a negative effect on the number of filled grains in multiple experiments, future studies should investigate the effect of whole-plant transpiration levels on salt uptake. In addition to genome-wide significant peaks identified in the single-trait GWAS analyses, six loci showed colocations for multiple traits across experiments. Among these colocating loci, three candidate loci that exhibited favourable haplotypes were also characterized to be involved in co-expression networks, among which apoplast and cell wall functions had been annotated, further highlighting the role of salt exclusion. CONCLUSION The loci identified here could be considered as potential sources for improving reproductive stage salinity tolerance in rice.
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Affiliation(s)
- Marjorie P de Ocampo
- International Rice Research Institute, Pili Drive, Los Baños, Laguna 4031,Philippines
| | - Bui Phuoc Tam
- Loc Troi Agricultural Research Institute, Hoa Tan Village, Dinh Thanh Commune, Thoai Son District, An Giang Province, Vietnam
| | - James A Egdane
- International Rice Research Institute, Pili Drive, Los Baños, Laguna 4031,Philippines
| | - Dmytro Chebotarov
- International Rice Research Institute, Pili Drive, Los Baños, Laguna 4031,Philippines
| | - Kazuyuki Doi
- Graduate School of Bioagricultural Sciences, Nagoya University, Chikusa, Nagoya 464-8601, Japan
| | - Akira Yamauchi
- Graduate School of Bioagricultural Sciences, Nagoya University, Chikusa, Nagoya 464-8601, Japan
| | - Abdelbagi M Ismail
- International Rice Research Institute, Pili Drive, Los Baños, Laguna 4031,Philippines
| | - Amelia Henry
- International Rice Research Institute, Pili Drive, Los Baños, Laguna 4031,Philippines
| | - Shiro Mitsuya
- Graduate School of Bioagricultural Sciences, Nagoya University, Chikusa, Nagoya 464-8601, Japan
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2
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Zhao H, MacLeod IM, Keeble-Gagnere G, Barbulescu DM, Tibbits JF, Kaur S, Hayden M. Using genotype imputation to integrate Canola populations for genome-wide association and genomic prediction of blackleg resistance. BMC Genomics 2025; 26:215. [PMID: 40038585 PMCID: PMC11877698 DOI: 10.1186/s12864-025-11250-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 01/16/2025] [Indexed: 03/06/2025] Open
Abstract
BACKGROUND Integrating germplasm populations genotyped by different genotyping platforms via genotype imputation is a way to utilize accumulated genetic resources. In this study, we used 278 canola samples genotyped via whole-genome sequencing (WGS) at 10× coverage to evaluate the imputation accuracy of three imputation approaches. The optimal imputation methods were used to impute and integrate two Canola genotype datasets: a diverse canola collection genotyped by genotyping-by-sequencing via transcriptome (GBS-t) and a double haploid (DH) line collection genotyped with low-coverage WGS (skim-WGS). The genomic predictive ability (GP) and detection power of marker‒trait association (GWAS) of the combined population for blackleg resistance were evaluated. RESULTS The empirical imputation accuracy (r2) measured as the squared correlation between observed and imputed genotypes was moderate for Minimac3 when imputing from the GBS-t density to the WGS. The accuracy dramatically improved from 0.64 to 0.82 by removing SNPs with poor Minimac3-reported Rsq (Rsq < 0.2) quality statistics. The r2 for GLIMPSE was higher than that for Beagle when imputing from different low-coverage to full-coverage WGS. We imputed and integrated the diverse canola collection and the DH lines, and the combined population showed similar or slightly greater predictive ability (PA) for blackleg resistance traits than did each of the single populations with ~ 921 K SNPs. Higher marker-trait association (MTA) detection powers were indicated with the combined population; however, similar numbers of MTAs were discovered when each single population was combined in a meta-GWAS. CONCLUSION It is feasible to impute and integrate germplasms from different sequencing platforms for downstream analyses. However, genetic heterogeneity across populations could add complexity to the analysis. Increasing the sample size by combining datasets showed slightly greater predictive ability and greater detection power in GWASs in the present study.
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Affiliation(s)
- Huanhuan Zhao
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.
| | - Iona M MacLeod
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
| | - Gabriel Keeble-Gagnere
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Denise M Barbulescu
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Josquin F Tibbits
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Sukhjiwan Kaur
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Matthew Hayden
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia.
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Zhu M, Yong K, Xu K, Cong J, Zhou X, Liu K, Wang X, Fan L, Olsen KM, Huang X, Zhou X, Qiu J. Landrace introgression contributed to the recent feralization of weedy rice in East China. PLANT COMMUNICATIONS 2024; 5:101066. [PMID: 39169627 PMCID: PMC11589283 DOI: 10.1016/j.xplc.2024.101066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 07/25/2024] [Accepted: 08/18/2024] [Indexed: 08/23/2024]
Affiliation(s)
- Min Zhu
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Kaicheng Yong
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Kai Xu
- Crop Production Center, Bright Food Group Co., Ltd., Shanghai 224100, China
| | - Jia Cong
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xiaofang Zhou
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Keyue Liu
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xuechen Wang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Longjiang Fan
- Institute of Crop Science & Institute of Bioinformatics, College of Agriculture & Biotechnology, Zhejiang University, Hangzhou 310058, China
| | - Kenneth M Olsen
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Xuehui Huang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xiaoyi Zhou
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China.
| | - Jie Qiu
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China.
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4
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Sivabharathi RC, Rajagopalan VR, Suresh R, Sudha M, Karthikeyan G, Jayakanthan M, Raveendran M. Haplotype-based breeding: A new insight in crop improvement. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2024; 346:112129. [PMID: 38763472 DOI: 10.1016/j.plantsci.2024.112129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/09/2024] [Accepted: 05/15/2024] [Indexed: 05/21/2024]
Abstract
Haplotype-based breeding (HBB) is one of the cutting-edge technologies in the realm of crop improvement due to the increasing availability of Single Nucleotide Polymorphisms identified by Next Generation Sequencing technologies. The complexity of the data can be decreased with fewer statistical tests and a lower probability of spurious associations by combining thousands of SNPs into a few hundred haplotype blocks. The presence of strong genomic regions in breeding lines of most crop species facilitates the use of haplotypes to improve the efficiency of genomic and marker-assisted selection. Haplotype-based breeding as a Genomic Assisted Breeding (GAB) approach harnesses the genome sequence data to pinpoint the allelic variation used to hasten the breeding cycle and circumvent the challenges associated with linkage drag. This review article demonstrates ways to identify candidate genes, superior haplotype identification, haplo-pheno analysis, and haplotype-based marker-assisted selection. The crop improvement strategies that utilize superior haplotypes will hasten the breeding progress to safeguard global food security.
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Affiliation(s)
- R C Sivabharathi
- Department of Genetics and Plant breeding, CPBG, Tamil Nadu Agricultural University, Coimbatore 641003, India
| | - Veera Ranjani Rajagopalan
- Department of Plant Biotechnology, Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore, 641003, India
| | - R Suresh
- Department of Rice, CPBG, Tamil Nadu Agricultural University, Coimbatore 641003, India
| | - M Sudha
- Department of Plant Biotechnology, Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore, 641003, India.
| | - G Karthikeyan
- Department of Plant Pathology, CPPS, Tamil Nadu Agricultural University, Coimbatore 641003, India
| | - M Jayakanthan
- Department of Plant Molecular Biology and Bioinformatics, Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore 641003, India
| | - M Raveendran
- Directorate of research, Tamil Nadu Agricultural University, Coimbatore 641003, India.
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5
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Wei X, Chen M, Zhang Q, Gong J, Liu J, Yong K, Wang Q, Fan J, Chen S, Hua H, Luo Z, Zhao X, Wang X, Li W, Cong J, Yu X, Wang Z, Huang R, Chen J, Zhou X, Qiu J, Xu P, Murray J, Wang H, Xu Y, Xu C, Xu G, Yang J, Han B, Huang X. Genomic investigation of 18,421 lines reveals the genetic architecture of rice. Science 2024; 385:eadm8762. [PMID: 38963845 DOI: 10.1126/science.adm8762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/29/2024] [Indexed: 07/06/2024]
Abstract
Understanding how numerous quantitative trait loci (QTL) shape phenotypic variation is an important question in genetics. To address this, we established a permanent population of 18,421 (18K) rice lines with reduced population structure. We generated reference-level genome assemblies of the founders and genotyped all 18K-rice lines through whole-genome sequencing. Through high-resolution mapping, 96 high-quality candidate genes contributing to variation in 16 traits were identified, including OsMADS22 and OsFTL1 verified as causal genes for panicle number and heading date, respectively. We identified epistatic QTL pairs and constructed a genetic interaction network with 19 genes serving as hubs. Overall, 170 masking epistasis pairs were characterized, serving as an important factor contributing to genetic background effects across diverse varieties. The work provides a basis to guide grain yield and quality improvements in rice.
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Affiliation(s)
- Xin Wei
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Mengjiao Chen
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Qi Zhang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Junyi Gong
- State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310006, China
| | - Jie Liu
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Kaicheng Yong
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Qin Wang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jiongjiong Fan
- State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310006, China
| | - Suhui Chen
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Hua Hua
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Zhaowei Luo
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xiaoyan Zhao
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xuan Wang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Wei Li
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jia Cong
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xiting Yu
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Zhihan Wang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Ruipeng Huang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jiaxin Chen
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xiaoyi Zhou
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jie Qiu
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Ping Xu
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jeremy Murray
- CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200233, China
| | - Hai Wang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
| | - Yang Xu
- Key Laboratory of Plant Functional Genomics of Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou 225009, China
| | - Chenwu Xu
- Key Laboratory of Plant Functional Genomics of Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou 225009, China
| | - Gen Xu
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Jinliang Yang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Bin Han
- CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200233, China
| | - Xuehui Huang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
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6
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Zhou Y, Kathiresan N, Yu Z, Rivera LF, Yang Y, Thimma M, Manickam K, Chebotarov D, Mauleon R, Chougule K, Wei S, Gao T, Green CD, Zuccolo A, Xie W, Ware D, Zhang J, McNally KL, Wing RA. A high-performance computational workflow to accelerate GATK SNP detection across a 25-genome dataset. BMC Biol 2024; 22:13. [PMID: 38273258 PMCID: PMC10809545 DOI: 10.1186/s12915-024-01820-5] [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: 08/10/2023] [Accepted: 01/09/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Single-nucleotide polymorphisms (SNPs) are the most widely used form of molecular genetic variation studies. As reference genomes and resequencing data sets expand exponentially, tools must be in place to call SNPs at a similar pace. The genome analysis toolkit (GATK) is one of the most widely used SNP calling software tools publicly available, but unfortunately, high-performance computing versions of this tool have yet to become widely available and affordable. RESULTS Here we report an open-source high-performance computing genome variant calling workflow (HPC-GVCW) for GATK that can run on multiple computing platforms from supercomputers to desktop machines. We benchmarked HPC-GVCW on multiple crop species for performance and accuracy with comparable results with previously published reports (using GATK alone). Finally, we used HPC-GVCW in production mode to call SNPs on a "subpopulation aware" 16-genome rice reference panel with ~ 3000 resequenced rice accessions. The entire process took ~ 16 weeks and resulted in the identification of an average of 27.3 M SNPs/genome and the discovery of ~ 2.3 million novel SNPs that were not present in the flagship reference genome for rice (i.e., IRGSP RefSeq). CONCLUSIONS This study developed an open-source pipeline (HPC-GVCW) to run GATK on HPC platforms, which significantly improved the speed at which SNPs can be called. The workflow is widely applicable as demonstrated successfully for four major crop species with genomes ranging in size from 400 Mb to 2.4 Gb. Using HPC-GVCW in production mode to call SNPs on a 25 multi-crop-reference genome data set produced over 1.1 billion SNPs that were publicly released for functional and breeding studies. For rice, many novel SNPs were identified and were found to reside within genes and open chromatin regions that are predicted to have functional consequences. Combined, our results demonstrate the usefulness of combining a high-performance SNP calling architecture solution with a subpopulation-aware reference genome panel for rapid SNP discovery and public deployment.
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Affiliation(s)
- Yong Zhou
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Arizona Genomics Institute (AGI), School of Plant Sciences, University of Arizona, Tucson, AZ, 85721, USA
| | - Nagarajan Kathiresan
- KAUST Supercomputing Laboratory (KSL), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Zhichao Yu
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Luis F Rivera
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Yujian Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Manjula Thimma
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Keerthana Manickam
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Dmytro Chebotarov
- International Rice Research Institute (IRRI), Los Baños, Laguna, 4031, Philippines
| | - Ramil Mauleon
- International Rice Research Institute (IRRI), Los Baños, Laguna, 4031, Philippines
| | - Kapeel Chougule
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Sharon Wei
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Tingting Gao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Carl D Green
- Information Technology Department, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Andrea Zuccolo
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Crop Science Research Center (CSRC), Scuola Superiore Sant'Anna, Pisa, 56127, Italy
| | - Weibo Xie
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Doreen Ware
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
- USDA ARS NEA Plant, Soil & Nutrition Laboratory Research Unit, Ithaca, NY, 14853, USA
| | - Jianwei Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Kenneth L McNally
- International Rice Research Institute (IRRI), Los Baños, Laguna, 4031, Philippines
| | - Rod A Wing
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
- Arizona Genomics Institute (AGI), School of Plant Sciences, University of Arizona, Tucson, AZ, 85721, USA.
- International Rice Research Institute (IRRI), Los Baños, Laguna, 4031, Philippines.
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Sachdeva S, Singh R, Maurya A, Singh VK, Singh UM, Kumar A, Singh GP. Multi-model genome-wide association studies for appearance quality in rice. FRONTIERS IN PLANT SCIENCE 2024; 14:1304388. [PMID: 38273959 PMCID: PMC10808671 DOI: 10.3389/fpls.2023.1304388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/22/2023] [Indexed: 01/27/2024]
Abstract
Improving the quality of the appearance of rice is critical to meet market acceptance. Mining putative quality-related genes has been geared towards the development of effective breeding approaches for rice. In the present study, two SL-GWAS (CMLM and MLM) and three ML-GWAS (FASTmrEMMA, mrMLM, and FASTmrMLM) genome-wide association studies were conducted in a subset of 3K-RGP consisting of 198 rice accessions with 553,831 SNP markers. A total of 594 SNP markers were identified using the mixed linear model method for grain quality traits. Additionally, 70 quantitative trait nucleotides (QTNs) detected by the ML-GWAS models were strongly associated with grain aroma (AR), head rice recovery (HRR, %), and percentage of grains with chalkiness (PGC, %). Finally, 39 QTNs were identified using single- and multi-locus GWAS methods. Among the 39 reliable QTNs, 20 novel QTNs were identified for the above-mentioned three quality-related traits. Based on annotation and previous studies, four functional candidate genes (LOC_Os01g66110, LOC_Os01g66140, LOC_Os07g44910, and LOC_Os02g14120) were found to influence AR, HRR (%), and PGC (%), which could be utilized in rice breeding to improve grain quality traits.
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Affiliation(s)
- Supriya Sachdeva
- Division of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources (NBPGR), New Delhi, India
| | - Rakesh Singh
- Division of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources (NBPGR), New Delhi, India
| | - Avantika Maurya
- Division of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources (NBPGR), New Delhi, India
| | - Vikas Kumar Singh
- International Rice Research Institute, South Asia Hub, International Crop Reseach Institute for Semi Arid Tropics (ICRISAT), Hyderabad, India
| | - Uma Maheshwar Singh
- International Rice Research Institute, South Asia Regional Centre (ISARC), Varanasi, India
| | - Arvind Kumar
- International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Telangana, India
| | - Gyanendra Pratap Singh
- Indian Council of Agricultural Research (ICAR)-National Bureau of Plant Genetic Resources, New Delhi, India
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8
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Schläppi MR, Jessel AR, Jackson AK, Phan H, Jia MH, Edwards JD, Eizenga GC. Navigating rice seedling cold resilience: QTL mapping in two inbred line populations and the search for genes. FRONTIERS IN PLANT SCIENCE 2023; 14:1303651. [PMID: 38162313 PMCID: PMC10755946 DOI: 10.3389/fpls.2023.1303651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 11/20/2023] [Indexed: 01/03/2024]
Abstract
Due to global climate change resulting in extreme temperature fluctuations, it becomes increasingly necessary to explore the natural genetic variation in model crops such as rice to facilitate the breeding of climate-resilient cultivars. To uncover genomic regions in rice involved in managing cold stress tolerance responses and to identify associated cold tolerance genes, two inbred line populations developed from crosses between cold-tolerant and cold-sensitive parents were used for quantitative trait locus (QTL) mapping of two traits: degree of membrane damage after 1 week of cold exposure quantified as percent electrolyte leakage (EL) and percent low-temperature seedling survivability (LTSS) after 1 week of recovery growth. This revealed four EL QTL and 12 LTSS QTL, all overlapping with larger QTL regions previously uncovered by genome-wide association study (GWAS) mapping approaches. Within the QTL regions, 25 cold-tolerant candidate genes were identified based on genomic differences between the cold-tolerant and cold-sensitive parents. Of those genes, 20% coded for receptor-like kinases potentially involved in signal transduction of cold tolerance responses; 16% coded for transcription factors or factors potentially involved in regulating cold tolerance response effector genes; and 64% coded for protein chaperons or enzymes potentially serving as cold tolerance effector proteins. Most of the 25 genes were cold temperature regulated and had deleterious nucleotide variants in the cold-sensitive parent, which might contribute to its cold-sensitive phenotype.
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Affiliation(s)
- Michael R. Schläppi
- Department of Biological Sciences, Marquette University, Milwaukee, WI, United States
| | - Avery R. Jessel
- Department of Biological Sciences, Marquette University, Milwaukee, WI, United States
| | - Aaron K. Jackson
- Dale Bumpers National Rice Research Center, U.S. Department of Agriculture, Agricultural Research Service (USDA-ARS), Stuttgart, AR, United States
| | - Huy Phan
- Department of Biological Sciences, Marquette University, Milwaukee, WI, United States
| | - Melissa H. Jia
- Dale Bumpers National Rice Research Center, U.S. Department of Agriculture, Agricultural Research Service (USDA-ARS), Stuttgart, AR, United States
| | - Jeremy D. Edwards
- Dale Bumpers National Rice Research Center, U.S. Department of Agriculture, Agricultural Research Service (USDA-ARS), Stuttgart, AR, United States
| | - Georgia C. Eizenga
- Dale Bumpers National Rice Research Center, U.S. Department of Agriculture, Agricultural Research Service (USDA-ARS), Stuttgart, AR, United States
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9
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Sakhale SA, Yadav S, Clark LV, Lipka AE, Kumar A, Sacks EJ. Genome-wide association analysis for emergence of deeply sown rice ( Oryza sativa) reveals novel aus-specific phytohormone candidate genes for adaptation to dry-direct seeding in the field. FRONTIERS IN PLANT SCIENCE 2023; 14:1172816. [PMID: 37377815 PMCID: PMC10291202 DOI: 10.3389/fpls.2023.1172816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023]
Abstract
Dry direct-seeded rice (dry-DSR) is typically sown deeply to circumvent the need for irrigation, and thus seedling emergence is a crucial trait affecting plant stand and yield. To breed elite cultivars that use less water and are climate-resilient, an understanding of the genomic regions and underlying genes that confer emergence in deeply sown dry-DSR would be highly advantageous. A combined diversity panel of 470 rice accessions (RDP1 plus aus subset of 3K RGP) was evaluated with 2.9 million single nucleotide polymorphisms (SNPs) to identify associations with dry-DSR traits in the field and component traits in a controlled-environment experiment. Using genome-wide association study (GWAS) analyses, we identified 18 unique QTLs on chromosomes 1, 2, 4, 5, 6, 7, 9, 10, and 11, explaining phenotypic variance ranging from 2.6% to 17.8%. Three QTLs, namely, qSOE-1.1, qEMERG-AUS-1.2, and qEMERG-AUS-7.1, were co-located with previously reported QTLs for mesocotyl length. Among the identified QTLs, half were associated with the emergence of aus, and six were unique to the aus genetic group. Based on functional annotation, we identified eleven compelling candidate genes that primarily regulate phytohormone pathways such as cytokinin, auxin, gibberellic acid, and jasmonic acid. Prior studies indicated that these phytohormones play a critical role in mesocotyl length under deep sowing. This study provides new insight into the importance of aus and indica as desirable genetic resources to mine favorable alleles for deep-sowing tolerance in rice. The candidate genes and marker-tagged desirable alleles identified in this study should benefit rice breeding programs directly.
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Affiliation(s)
- Sandeep A. Sakhale
- Department of Crop Sciences, University of Illinois Urbana-Champaign, Urbana, IL, United States
- International Rice Research Institute (IRRI), Los Baños, Philippines
- International Rice Research Institute (IRRI), South Asia Regional Centre (ISARC), Varanasi, India
| | - Shailesh Yadav
- International Rice Research Institute (IRRI), Los Baños, Philippines
- Africa Rice Center (AfricaRice), Abidjan, Côte d’Ivoire
| | - Lindsay V. Clark
- Department of Crop Sciences, University of Illinois Urbana-Champaign, Urbana, IL, United States
- Seattle Children’s Research Institute, Seattle, WA, United States
| | - Alexander E. Lipka
- Department of Crop Sciences, University of Illinois Urbana-Champaign, Urbana, IL, United States
| | - Arvind Kumar
- International Rice Research Institute (IRRI), Los Baños, Philippines
- International Rice Research Institute (IRRI), South Asia Regional Centre (ISARC), Varanasi, India
| | - Erik J. Sacks
- Department of Crop Sciences, University of Illinois Urbana-Champaign, Urbana, IL, United States
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10
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Beye A, Billot C, Ronfort J, McNally KL, Diouf D, Glaszmann JC. Traces of Introgression from cAus into Tropical Japonica Observed in African Upland Rice Varieties. RICE (NEW YORK, N.Y.) 2023; 16:12. [PMID: 36853402 PMCID: PMC9975138 DOI: 10.1186/s12284-023-00625-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 02/08/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Asian rice Oryza sativa, first domesticated in East Asia, has considerable success in African fields. When and where this introduction occurred is unclear. Rice varieties of Asian origin may have evolved locally during and after migration to Africa, resulting in unique adaptations, particularly in relation to upland cultivation as frequently practiced in Africa. METHODS We investigated the genetic differentiation between Asian and African varieties using the 3000 Rice Genomes SNP dataset. African upland cultivars were first characterized using principal component analysis among 292 tropical Japonica accessions from Africa and Asia. The particularities of African accessions were then explored using two inference techniques, PCA-KDE for supervised classification and chromosome painting, and ELAI for individual allelic dosage monitoring. KEY RESULTS Ambiguities of local differentiation between Japonica and other groups pointed at genomic segments that potentially resulted from genetic exchange. Those specific to West African upland accessions were concentrated on chromosome 6 and featured several cAus introgression signals, including a large one between 17.9 and 21.7 Mb. We found iHS statistics in support of positive selection in this region and we provide a list of candidate genes enriched in GO terms that have regulatory functions involved in stress responses that could have facilitated adaptation to harsh upland growing conditions.
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Affiliation(s)
- Abdoulaye Beye
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, CIRAD, INRAE, Institut Agro, Université de Montpellier, 34398, Montpellier, France
- Laboratoire Campus de Biotechnologies Végétales, Département de Biologie Végétale, Faculté Des Sciences Et Techniques, Université Cheikh Anta Diop, 10700, Dakar-Fann, Dakar, Senegal
| | - Claire Billot
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, CIRAD, INRAE, Institut Agro, Université de Montpellier, 34398, Montpellier, France
| | - Joëlle Ronfort
- UMR AGAP Institut, CIRAD, INRAE, Institut Agro, Université de Montpellier, 34398, Montpellier, France
| | - Kenneth L McNally
- International Rice Research Institute, DAPO Box 7777, Metro Manila, 1301, The Philippines
| | - Diaga Diouf
- Laboratoire Campus de Biotechnologies Végétales, Département de Biologie Végétale, Faculté Des Sciences Et Techniques, Université Cheikh Anta Diop, 10700, Dakar-Fann, Dakar, Senegal
| | - Jean Christophe Glaszmann
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
- UMR AGAP Institut, CIRAD, INRAE, Institut Agro, Université de Montpellier, 34398, Montpellier, France.
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11
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Hanlon MT, Vejchasarn P, Fonta JE, Schneider HM, McCouch SR, Brown KM. Genome wide association analysis of root hair traits in rice reveals novel genomic regions controlling epidermal cell differentiation. BMC PLANT BIOLOGY 2023; 23:6. [PMID: 36597029 PMCID: PMC9811729 DOI: 10.1186/s12870-022-04026-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Genome wide association (GWA) studies demonstrate linkages between genetic variants and traits of interest. Here, we tested associations between single nucleotide polymorphisms (SNPs) in rice (Oryza sativa) and two root hair traits, root hair length (RHL) and root hair density (RHD). Root hairs are outgrowths of single cells on the root epidermis that aid in nutrient and water acquisition and have also served as a model system to study cell differentiation and tip growth. Using lines from the Rice Diversity Panel-1, we explored the diversity of root hair length and density across four subpopulations of rice (aus, indica, temperate japonica, and tropical japonica). GWA analysis was completed using the high-density rice array (HDRA) and the rice reference panel (RICE-RP) SNP sets. RESULTS We identified 18 genomic regions related to root hair traits, 14 of which related to RHD and four to RHL. No genomic regions were significantly associated with both traits. Two regions overlapped with previously identified quantitative trait loci (QTL) associated with root hair density in rice. We identified candidate genes in these regions and present those with previously published expression data relevant to root hair development. We re-phenotyped a subset of lines with extreme RHD phenotypes and found that the variation in RHD was due to differences in cell differentiation, not cell size, indicating genes in an associated genomic region may influence root hair cell fate. The candidate genes that we identified showed little overlap with previously characterized genes in rice and Arabidopsis. CONCLUSIONS Root hair length and density are quantitative traits with complex and independent genetic control in rice. The genomic regions described here could be used as the basis for QTL development and further analysis of the genetic control of root hair length and density. We present a list of candidate genes involved in root hair formation and growth in rice, many of which have not been previously identified as having a relation to root hair growth. Since little is known about root hair growth in grasses, these provide a guide for further research and crop improvement.
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Affiliation(s)
- Meredith T Hanlon
- Department of Plant Science, The Pennsylvania State University, 102 Tyson Building, University Park, PA, 16802, USA
- Intercollege Graduate Degree Program in Plant Biology, Huck Institutes of the Life Sciences, Penn State University, University Park, PA, 16802, USA
| | - Phanchita Vejchasarn
- Department of Plant Science, The Pennsylvania State University, 102 Tyson Building, University Park, PA, 16802, USA
- Rice Department, Ministry of Agriculture, Ubon Ratchathani Rice Research Center, Ubon Ratchathani, 34000, Thailand
| | - Jenna E Fonta
- Department of Plant Science, The Pennsylvania State University, 102 Tyson Building, University Park, PA, 16802, USA
- Intercollege Graduate Degree Program in Plant Biology, Huck Institutes of the Life Sciences, Penn State University, University Park, PA, 16802, USA
| | - Hannah M Schneider
- Department of Plant Science, The Pennsylvania State University, 102 Tyson Building, University Park, PA, 16802, USA
- Centre for Crop Systems Analysis, Wageningen University & Research, Wageningen, the Netherlands
| | - Susan R McCouch
- Section of Plant Breeding and Genetics, School of Integrated Plant Sciences, Cornell University, Ithaca, NY, 14853-1901, USA
- Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14853-1901, USA
| | - Kathleen M Brown
- Department of Plant Science, The Pennsylvania State University, 102 Tyson Building, University Park, PA, 16802, USA.
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12
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Barrero LS, Willmann MR, Craft EJ, Akther KM, Harrington SE, Garzon‐Martinez GA, Glahn RP, Piñeros MA, McCouch SR. Identifying genes associated with abiotic stress tolerance suitable for CRISPR/Cas9 editing in upland rice cultivars adapted to acid soils. PLANT DIRECT 2022; 6:e469. [PMID: 36514785 PMCID: PMC9737570 DOI: 10.1002/pld3.469] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 06/17/2023]
Abstract
Five genes of large phenotypic effect known to confer abiotic stress tolerance in rice were selected to characterize allelic variation in commercial Colombian tropical japonica upland rice cultivars adapted to drought-prone acid soil environments (cv. Llanura11 and Porvenir12). Allelic variants of the genes ART1, DRO1, SUB1A, PSTOL1, and SPDT were characterized by PCR and/or Sanger sequencing in the two upland cultivars and compared with the Nipponbare and other reference genomes. Two genes were identified as possible targets for gene editing: SUB1A (Submergence 1A), to improve tolerance to flooding, and SPDT (SULTR3;4) (SULTR-like Phosphorus Distribution Transporter), to improve phosphorus utilization efficiency and grain quality. Based on technical and regulatory considerations, SPDT was targeted for editing. The two upland cultivars were shown to carry the SPDT wild-type (nondesirable) allele based on sequencing, RNA expression, and phenotypic evaluations under hydroponic and greenhouse conditions. A gene deletion was designed using the CRISPR/Cas9 system, and specialized reagents were developed for SPDT editing, including vectors targeting the gene and a protoplast transfection transient assay. The desired edits were confirmed in protoplasts and serve as the basis for ongoing plant transformation experiments aiming to improve the P-use efficiency of upland rice grown in acidic soils.
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Affiliation(s)
- Luz S. Barrero
- Corporacion Colombiana de Investigacion AgropecuariaAGROSAVIAMosqueraColombia
- Plant Breeding & Genetics Section, School of Integrative Plant ScienceCornell UniversityIthacaNew YorkUSA
| | - Matthew R. Willmann
- Plant Transformation Facility, School of Integrative Plant ScienceCornell UniversityIthacaNew YorkUSA
- Present address:
USDA‐ARS, Robert W. Holley CenterIthacaNew YorkUSA
| | - Eric J. Craft
- Present address:
USDA‐ARS, Robert W. Holley CenterIthacaNew YorkUSA
| | - Kazi M. Akther
- Plant Breeding & Genetics Section, School of Integrative Plant ScienceCornell UniversityIthacaNew YorkUSA
| | - Sandra E. Harrington
- Plant Breeding & Genetics Section, School of Integrative Plant ScienceCornell UniversityIthacaNew YorkUSA
| | | | - Raymond P. Glahn
- Present address:
USDA‐ARS, Robert W. Holley CenterIthacaNew YorkUSA
| | | | - Susan R. McCouch
- Plant Breeding & Genetics Section, School of Integrative Plant ScienceCornell UniversityIthacaNew YorkUSA
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13
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Huang YH, Ku HM, Wang CA, Chen LY, He SS, Chen S, Liao PC, Juan PY, Kao CF. A multiple phenotype imputation method for genetic diversity and core collection in Taiwanese vegetable soybean. FRONTIERS IN PLANT SCIENCE 2022; 13:948349. [PMID: 36119593 PMCID: PMC9480828 DOI: 10.3389/fpls.2022.948349] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
Establishment of vegetable soybean (edamame) [Glycine max (L.) Merr.] germplasms has been highly valued in Asia and the United States owing to the increasing market demand for edamame. The idea of core collection (CC) is to shorten the breeding program so as to improve the availability of germplasm resources. However, multidimensional phenotypes typically are highly correlated and have different levels of missing rate, often failing to capture the underlying pattern of germplasms and select CC precisely. These are commonly observed on correlated samples. To overcome such scenario, we introduced the "multiple imputation" (MI) method to iteratively impute missing phenotypes for 46 morphological traits and jointly analyzed high-dimensional imputed missing phenotypes (EC impu ) to explore population structure and relatedness among 200 Taiwanese vegetable soybean accessions. An advanced maximization strategy with a heuristic algorithm and PowerCore was used to evaluate the morphological diversity among the EC impu . In total, 36 accessions (denoted as CC impu ) were efficiently selected representing high diversity and the entire coverage of the EC impu . Only 4 (8.7%) traits showed slightly significant differences between the CC impu and EC impu . Compared to the EC impu , 96% traits retained all characteristics or had a slight diversity loss in the CC impu . The CC impu exhibited a small percentage of significant mean difference (4.51%), and large coincidence rate (98.1%), variable rate (138.76%), and coverage (close to 100%), indicating the representativeness of the EC impu . We noted that the CC impu outperformed the CC raw in evaluation properties, suggesting that the multiple phenotype imputation method has the potential to deal with missing phenotypes in correlated samples efficiently and reliably without re-phenotyping accessions. Our results illustrated a significant role of imputed missing phenotypes in support of the MI-based framework for plant-breeding programs.
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Affiliation(s)
- Yen-Hsiang Huang
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan
| | - Hsin-Mei Ku
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan
| | - Chong-An Wang
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan
| | - Ling-Yu Chen
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan
| | - Shan-Syue He
- Department of Agronomy, College of Bioresources and Agriculture, National Taiwan University, Taipei, Taiwan
| | - Shu Chen
- Plant Germplasm Division, Taiwan Agricultural Research Institute, Taichung, Taiwan
| | - Po-Chun Liao
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan
| | - Pin-Yuan Juan
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan
| | - Chung-Feng Kao
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan
- Advanced Plant Biotechnology Center, National Chung Hsing University, Taichung, Taiwan
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14
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Deep polygenic neural network for predicting and identifying yield-associated genes in Indonesian rice accessions. Sci Rep 2022; 12:13823. [PMID: 35970979 PMCID: PMC9378700 DOI: 10.1038/s41598-022-16075-9] [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: 11/17/2021] [Accepted: 07/04/2022] [Indexed: 11/12/2022] Open
Abstract
As the fourth most populous country in the world, Indonesia must increase the annual rice production rate to achieve national food security by 2050. One possible solution comes from the nanoscopic level: a genetic variant called Single Nucleotide Polymorphism (SNP), which can express significant yield-associated genes. The prior benchmark of this study utilized a statistical genetics model where no SNP position information and attention mechanism were involved. Hence, we developed a novel deep polygenic neural network, named the NucleoNet model, to address these obstacles. The NucleoNets were constructed with the combination of prominent components that include positional SNP encoding, the context vector, wide models, Elastic Net, and Shannon’s entropy loss. This polygenic modeling obtained up to 2.779 of Mean Squared Error (MSE) with 47.156% of Symmetric Mean Absolute Percentage Error (SMAPE), while revealing 15 new important SNPs. Furthermore, the NucleoNets reduced the MSE score up to 32.28% compared to the Ordinary Least Squares (OLS) model. Through the ablation study, we learned that the combination of Xavier distribution for weights initialization and Normal distribution for biases initialization sparked more various important SNPs throughout 12 chromosomes. Our findings confirmed that the NucleoNet model was successfully outperformed the OLS model and identified important SNPs to Indonesian rice yields.
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15
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Li D, Zhang F, Pinson SRM, Edwards JD, Jackson AK, Xia X, Eizenga GC. Assessment of Rice Sheath Blight Resistance Including Associations with Plant Architecture, as Revealed by Genome-Wide Association Studies. RICE (NEW YORK, N.Y.) 2022; 15:31. [PMID: 35716230 PMCID: PMC9206596 DOI: 10.1186/s12284-022-00574-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 05/13/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Sheath blight (ShB) disease caused by Rhizoctonia solani Kühn, is one of the most economically damaging rice (Oryza sativa L.) diseases worldwide. There are no known major resistance genes, leaving only partial resistance from small-effect QTL to deploy for cultivar improvement. Many ShB-QTL are associated with plant architectural traits detrimental to yield, including tall plants, late maturity, or open canopy from few or procumbent tillers, which confound detection of physiological resistance. RESULTS To identify QTL for ShB resistance, 417 accessions from the Rice Diversity Panel 1 (RDP1), developed for association mapping studies, were evaluated for ShB resistance, plant height and days to heading in inoculated field plots in Arkansas, USA (AR) and Nanning, China (NC). Inoculated greenhouse-grown plants were used to evaluate ShB using a seedling-stage method to eliminate effects from height or maturity, and tiller (TN) and panicle number (PN) per plant. Potted plants were used to evaluate the RDP1 for TN and PN. Genome-wide association (GWA) mapping with over 3.4 million SNPs identified 21 targeted SNP markers associated with ShB which tagged 18 ShB-QTL not associated with undesirable plant architecture traits. Ten SNPs were associated with ShB among accessions of the Indica subspecies, ten among Japonica subspecies accessions, and one among all RDP1 accessions. Across the 18 ShB QTL, only qShB4-1 was not previously reported in biparental mapping studies and qShB9 was not reported in the GWA ShB studies. All 14 PN QTL overlapped with TN QTL, with 15 total TN QTL identified. Allele effects at the five TN QTL co-located with ShB QTL indicated that increased TN does not inevitably increase disease development; in fact, for four ShB QTL that overlapped TN QTL, the alleles increasing resistance were associated with increased TN and PN, suggesting a desirable coupling of alleles at linked genes. CONCLUSIONS Nineteen accessions identified as containing the most SNP alleles associated with ShB resistance for each subpopulation were resistant in both AR and NC field trials. Rice breeders can utilize these accessions and SNPs to develop cultivars with enhanced ShB resistance along with increased TN and PN for improved yield potential.
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Affiliation(s)
- Danting Li
- Guangxi Key Laboratory of Rice Genetics and Breeding, Rice Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, Guangxi, China
| | - Fantao Zhang
- College of Life Sciences, Jiangxi Normal University, Nanchang, Jiangxi, China
| | - Shannon R M Pinson
- USDA Dale Bumpers National Rice Research Center, 2890 Highway 130 East, Stuttgart, AR, 72160, USA.
| | - Jeremy D Edwards
- USDA Dale Bumpers National Rice Research Center, 2890 Highway 130 East, Stuttgart, AR, 72160, USA
| | - Aaron K Jackson
- USDA Dale Bumpers National Rice Research Center, 2890 Highway 130 East, Stuttgart, AR, 72160, USA
| | - Xiuzhong Xia
- Guangxi Key Laboratory of Rice Genetics and Breeding, Rice Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, Guangxi, China
| | - Georgia C Eizenga
- USDA Dale Bumpers National Rice Research Center, 2890 Highway 130 East, Stuttgart, AR, 72160, USA.
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16
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Wang X, Li J, Sun J, Gu S, Wang J, Su C, Li Y, Ma D, Zhao M, Chen W. Mining Beneficial Genes for Salt Tolerance From a Core Collection of Rice Landraces at the Seedling Stage Through Genome-Wide Association Mapping. FRONTIERS IN PLANT SCIENCE 2022; 13:847863. [PMID: 35557725 PMCID: PMC9087808 DOI: 10.3389/fpls.2022.847863] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 04/08/2022] [Indexed: 06/15/2023]
Abstract
Rice is a salt-sensitive plant. High concentration of salt will hinder the absorption of water and nutrients and ultimately affect the yield. In this study, eight seedling-stage salt-related traits within a core collection of rice landraces were evaluated under salinity stress (100 mM NaCl) and normal conditions in a growth chamber. Genome-wide association study (GWAS) was performed with the genotypic data including 2,487,353 single-nucleotide polymorphisms (SNPs) detected in the core collection. A total of 65 QTLs significantly associated with salt tolerance (ST) were identified by GWAS. Among them, a co-localization QTL qTL4 associated with the SKC, RN/K, and SNC on chromosome 6, which explained 14.38-17.94% of phenotypic variation, was selected for further analysis. According to haplotype analysis, qRT-PCR analysis, and sequence alignment, it was finally determined that 4 candidate genes (LOC_Os06g47720, LOC_Os06g47820, LOC_Os06g47850, LOC_Os06g47970) were related to ST. The results provide useful candidate genes for marker assisted selection for ST in the rice molecular breeding programs.
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Affiliation(s)
- Xiaoliang Wang
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Jinquan Li
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, South China Agricultural University, Guangzhou, China
- Strube Research GmbH & Co. KG, Söllingen, Germany
| | - Jian Sun
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Shuang Gu
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Jingbo Wang
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Chang Su
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Yueting Li
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Dianrong Ma
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Minghui Zhao
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Wenfu Chen
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
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17
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Wu D, Qiu J, Sun J, Song BK, Olsen KM, Fan L. Weedy rice, a hidden gold mine in the paddy field. MOLECULAR PLANT 2022; 15:566-568. [PMID: 35032686 DOI: 10.1016/j.molp.2022.01.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 06/14/2023]
Affiliation(s)
- Dongya Wu
- Zhejiang University City College, Hangzhou 310015, China; College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
| | - Jie Qiu
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jian Sun
- Rice Research Institute, Shenyang Agricultural University, Shenyang 110866, China
| | - Beng-Kah Song
- School of Science, Monash University Malaysia, Bandar Sunway, Selangor 46150, Malaysia
| | - Kenneth M Olsen
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Longjiang Fan
- Zhejiang University City College, Hangzhou 310015, China; College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.
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18
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Bartholomé J, Prakash PT, Cobb JN. Genomic Prediction: Progress and Perspectives for Rice Improvement. Methods Mol Biol 2022; 2467:569-617. [PMID: 35451791 DOI: 10.1007/978-1-0716-2205-6_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
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Affiliation(s)
- Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, Montpellier, France.
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.
- Rice Breeding Platform, International Rice Research Institute, Manila, Philippines.
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19
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Joukhadar R, Daetwyler HD. Data Integration, Imputation, and Meta-analysis for Genome-Wide Association Studies. Methods Mol Biol 2022; 2481:173-183. [PMID: 35641765 DOI: 10.1007/978-1-0716-2237-7_11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Growing genomic and phenotypic datasets require different groups around the world to collaborate and integrate these valuable resources to maximize their benefit and increase reference population sizes for genomic prediction and genome-wide association studies (GWAS). However, different studies use different genotyping techniques which requires a synchronizing step for the genotyped variants called "imputation" before combining them. Optimally, different GWAS datasets can be analysed within a meta-analysis, which recruits summary statistics instead of actual data. This chapter describes the general principles for genotypic imputation and meta-GWAS analysis with a description of study designs and command lines required for such analyses.
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Affiliation(s)
- Reem Joukhadar
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Hans D Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia.
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia.
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20
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Chen R, Deng Y, Ding Y, Guo J, Qiu J, Wang B, Wang C, Xie Y, Zhang Z, Chen J, Chen L, Chu C, He G, He Z, Huang X, Xing Y, Yang S, Xie D, Liu Y, Li J. Rice functional genomics: decades' efforts and roads ahead. SCIENCE CHINA. LIFE SCIENCES 2022. [PMID: 34881420 DOI: 10.1007/s11427-021-2024-2020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
Rice (Oryza sativa L.) is one of the most important crops in the world. Since the completion of rice reference genome sequences, tremendous progress has been achieved in understanding the molecular mechanisms on various rice traits and dissecting the underlying regulatory networks. In this review, we summarize the research progress of rice biology over past decades, including omics, genome-wide association study, phytohormone action, nutrient use, biotic and abiotic responses, photoperiodic flowering, and reproductive development (fertility and sterility). For the roads ahead, cutting-edge technologies such as new genomics methods, high-throughput phenotyping platforms, precise genome-editing tools, environmental microbiome optimization, and synthetic methods will further extend our understanding of unsolved molecular biology questions in rice, and facilitate integrations of the knowledge for agricultural applications.
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Affiliation(s)
- Rongzhi Chen
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China
| | - Yiwen Deng
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology & Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
| | - Yanglin Ding
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Jingxin Guo
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, South China Agricultural University, Guangzhou, 510642, China
| | - Jie Qiu
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China
| | - Bing Wang
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Changsheng Wang
- National Center for Gene Research, Center of Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200233, China
| | - Yongyao Xie
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, South China Agricultural University, Guangzhou, 510642, China
| | - Zhihua Zhang
- College of Plant Science, Jilin University, Changchun, 130062, China
| | - Jiaxin Chen
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China
| | - Letian Chen
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, South China Agricultural University, Guangzhou, 510642, China
| | - Chengcai Chu
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| | - Guangcun He
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China
| | - Zuhua He
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology & Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
| | - Xuehui Huang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China
| | - Yongzhong Xing
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070, China
| | - Shuhua Yang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Daoxin Xie
- MOE Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
| | - Yaoguang Liu
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, South China Agricultural University, Guangzhou, 510642, China.
| | - Jiayang Li
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, 100101, China.
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21
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Rice functional genomics: decades' efforts and roads ahead. SCIENCE CHINA. LIFE SCIENCES 2021; 65:33-92. [PMID: 34881420 DOI: 10.1007/s11427-021-2024-0] [Citation(s) in RCA: 125] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/01/2021] [Indexed: 12/16/2022]
Abstract
Rice (Oryza sativa L.) is one of the most important crops in the world. Since the completion of rice reference genome sequences, tremendous progress has been achieved in understanding the molecular mechanisms on various rice traits and dissecting the underlying regulatory networks. In this review, we summarize the research progress of rice biology over past decades, including omics, genome-wide association study, phytohormone action, nutrient use, biotic and abiotic responses, photoperiodic flowering, and reproductive development (fertility and sterility). For the roads ahead, cutting-edge technologies such as new genomics methods, high-throughput phenotyping platforms, precise genome-editing tools, environmental microbiome optimization, and synthetic methods will further extend our understanding of unsolved molecular biology questions in rice, and facilitate integrations of the knowledge for agricultural applications.
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22
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Iqbal Z, Iqbal MS, Khan MIR, Ansari MI. Toward Integrated Multi-Omics Intervention: Rice Trait Improvement and Stress Management. FRONTIERS IN PLANT SCIENCE 2021; 12:741419. [PMID: 34721467 PMCID: PMC8554098 DOI: 10.3389/fpls.2021.741419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 09/20/2021] [Indexed: 05/04/2023]
Abstract
Rice (Oryza sativa) is an imperative staple crop for nearly half of the world's population. Challenging environmental conditions encompassing abiotic and biotic stresses negatively impact the quality and yield of rice. To assure food supply for the unprecedented ever-growing world population, the improvement of rice as a crop is of utmost importance. In this era, "omics" techniques have been comprehensively utilized to decipher the regulatory mechanisms and cellular intricacies in rice. Advancements in omics technologies have provided a strong platform for the reliable exploration of genetic resources involved in rice trait development. Omics disciplines like genomics, transcriptomics, proteomics, and metabolomics have significantly contributed toward the achievement of desired improvements in rice under optimal and stressful environments. The present review recapitulates the basic and applied multi-omics technologies in providing new orchestration toward the improvement of rice desirable traits. The article also provides a catalog of current scenario of omics applications in comprehending this imperative crop in relation to yield enhancement and various environmental stresses. Further, the appropriate databases in the field of data science to analyze big data, and retrieve relevant information vis-à-vis rice trait improvement and stress management are described.
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Affiliation(s)
- Zahra Iqbal
- Molecular Crop Research Unit, Department of Biochemistry, Chulalongkorn University, Bangkok, Thailand
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23
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A high-resolution genome-wide association study of the grain ionome and agronomic traits in rice Oryza sativa subsp. indica. Sci Rep 2021; 11:19230. [PMID: 34584121 PMCID: PMC8478900 DOI: 10.1038/s41598-021-98573-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/09/2021] [Indexed: 02/08/2023] Open
Abstract
This study presents a comprehensive study of the genetic bases controlling variation in the rice ionome employing genome-wide association studies (GWAS) with a diverse panel of indica accessions, each genotyped with 5.2 million markers. GWAS was performed for twelve elements including B, Ca, Co, Cu, Fe, K, Mg, Mn, Mo, Na, P, and Zn and four agronomic traits including days to 50% flowering, grain yield, plant height and thousand grain weight. GWAS identified 128 loci associated with the grain elements and 57 associated with the agronomic traits. There were sixteen co-localization regions containing QTL for two or more traits. Fourteen grain element quantitative trait loci were stable across growing environments, which can be strong candidates to be used in marker-assisted selection to improve the concentrations of nutritive elements in rice grain. Potential candidate genes were revealed including OsNAS3 linked to the locus that controls the variation of Zn and Co concentrations. The effects of starch synthesis and grain filling on multiple grain elements were elucidated through the likely involvement of OsSUS1 and OsGSSB1 genes. Overall, our study provides crucial insights into the genetic basis of ionomic variations in rice and will facilitate improvement in breeding for trace mineral content.
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24
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Castano-Duque L, Ghosal S, Quilloy FA, Mitchell-Olds T, Dixit S. An epigenetic pathway in rice connects genetic variation to anaerobic germination and seedling establishment. PLANT PHYSIOLOGY 2021; 186:1042-1059. [PMID: 33638990 PMCID: PMC8195528 DOI: 10.1093/plphys/kiab100] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 02/09/2021] [Indexed: 06/12/2023]
Abstract
Rice production is shifting from transplanting seedlings to direct sowing of seeds. Following heavy rains, directly sown seeds may need to germinate under anaerobic environments, but most rice (Oryza sativa) genotypes cannot survive these conditions. To identify the genetic architecture of complex traits, we quantified percentage anaerobic germination (AG) in 2,700 (wet-season) and 1,500 (dry-season) sequenced rice genotypes and performed genome-wide association studies (GWAS) using 693,502 single nucleotide polymorphisms. This was followed by post-GWAS analysis with a generalized SNP-to-gene set analysis, meta-analysis, and network analysis. We determined that percentage AG is intermediate-to-high among indica subpopulations, and AG is a polygenic trait associated with transcription factors linked to ethylene responses or genes involved in metabolic processes that are known to be associated with AG. Our post-GWAS analysis identified several genes involved in a wide variety of metabolic processes. We subsequently performed functional analysis focused on the small RNA and methylation pathways. We selected CLASSY 1 (CLSY1), a gene involved in the RNA-directed DNA methylation (RdDm) pathway, for further analyses under AG and found several lines of evidence that CLSY1 influences AG. We propose that the RdDm pathway plays a role in rice responses to water status during germination and seedling establishment developmental stages.
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Affiliation(s)
| | - Sharmistha Ghosal
- Rice Breeding Platform, International Rice Research Institute. Pili Drive, Los Baños, Laguna 4031, Philippines
| | - Fergie A Quilloy
- Rice Breeding Platform, International Rice Research Institute. Pili Drive, Los Baños, Laguna 4031, Philippines
| | | | - Shalabh Dixit
- Rice Breeding Platform, International Rice Research Institute. Pili Drive, Los Baños, Laguna 4031, Philippines
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25
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Mason GA. Growing under water: DNA methylation may affect rice germination and establishment in anaerobic conditions. PLANT PHYSIOLOGY 2021; 186:824-826. [PMID: 33823033 PMCID: PMC8195502 DOI: 10.1093/plphys/kiab137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 03/12/2021] [Indexed: 06/12/2023]
Affiliation(s)
- Grace Alex Mason
- Department of Plant Biology and Genome Center, University of California, Davis, CA 95616, USA
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26
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Kumar P, Choudhary M, Jat BS, Kumar B, Singh V, Kumar V, Singla D, Rakshit S. Skim sequencing: an advanced NGS technology for crop improvement. J Genet 2021. [DOI: 10.1007/s12041-021-01285-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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27
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Yu H, Du Q, Campbell M, Yu B, Walia H, Zhang C. Genome-wide discovery of natural variation in pre-mRNA splicing and prioritising causal alternative splicing to salt stress response in rice. THE NEW PHYTOLOGIST 2021; 230:1273-1287. [PMID: 33453070 PMCID: PMC8048671 DOI: 10.1111/nph.17189] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 01/04/2021] [Indexed: 05/14/2023]
Abstract
Pre-mRNA splicing is an essential step for the regulation of gene expression. In order to specifically capture splicing variants in plants for genome-wide association studies (GWAS), we developed a software tool to quantify and visualise Variations of Splicing in Population (VaSP). VaSP can quantify splicing variants from short-read RNA-seq datasets and discover genotype-specific splicing (GSS) events, which can be used to prioritise causal pre-mRNA splicing events in GWAS. We applied our method to an RNA-seq dataset with 328 samples from 82 genotypes from a rice diversity panel exposed to optimal and saline growing conditions. In total, 764 significant GSS events were identified in salt stress conditions. GSS events were used as markers for a GWAS with the shoot Na+ accumulation, which identified six GSS events in five genes significantly associated with the shoot Na+ content. Two of these genes, OsNUC1 and OsRAD23 emerged as top candidate genes with splice variants that exhibited significant divergence between the variants for shoot growth under salt stress conditions. VaSP is a versatile tool for alternative splicing analysis in plants and a powerful tool for prioritising candidate causal pre-mRNA splicing and corresponding genomic variations in GWAS.
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Affiliation(s)
- Huihui Yu
- School of Biological SciencesUniversity of NebraskaLincolnNE68588USA
| | - Qian Du
- School of Biological SciencesUniversity of NebraskaLincolnNE68588USA
| | - Malachy Campbell
- Department of Agronomy and HorticultureUniversity of NebraskaLincolnNE68583USA
- Department of Plant BiologyCornell UniversityIthacaNY14850USA
| | - Bin Yu
- School of Biological SciencesUniversity of NebraskaLincolnNE68588USA
- Center for Plant Science and InnovationUniversity of NebraskaLincolnNE68588USA
| | - Harkamal Walia
- Department of Agronomy and HorticultureUniversity of NebraskaLincolnNE68583USA
- Center for Plant Science and InnovationUniversity of NebraskaLincolnNE68588USA
| | - Chi Zhang
- School of Biological SciencesUniversity of NebraskaLincolnNE68588USA
- Center for Plant Science and InnovationUniversity of NebraskaLincolnNE68588USA
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28
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Ruang-Areerate P, Travis AJ, Pinson SRM, Tarpley L, Eizenga GC, Guerinot ML, Salt DE, Douglas A, Price AH, Norton GJ. Genome-wide association mapping for grain manganese in rice (Oryza sativa L.) using a multi-experiment approach. Heredity (Edinb) 2021; 126:505-520. [PMID: 33235293 PMCID: PMC8026592 DOI: 10.1038/s41437-020-00390-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 11/06/2020] [Accepted: 11/06/2020] [Indexed: 11/09/2022] Open
Abstract
Manganese (Mn) is an essential trace element for plants and commonly contributes to human health; however, the understanding of the genes controlling natural variation in Mn in crop plants is limited. Here, the integration of two of genome-wide association study approaches was used to increase the identification of valuable quantitative trait loci (QTL) and candidate genes responsible for the concentration of grain Mn across 389 diverse rice cultivars grown in Arkansas and Texas, USA, in multiple years. Single-trait analysis was initially performed using three different SNP datasets. As a result, significant loci could be detected using the high-density SNP dataset. Based on the 5.2 M SNP dataset, major QTLs were located on chromosomes 3 and 7 for Mn containing six candidate genes. In addition, the phenotypic data of grain Mn concentration were combined from three flooded-field experiments from the two sites and 3 years using multi-experiment analysis based on the 5.2 M SNP dataset. Two previous QTLs on chromosome 3 were identified across experiments, whereas new Mn QTLs were identified that were not found in individual experiments, on chromosomes 3, 4, 9 and 11. OsMTP8.1 was identified in both approaches and is a good candidate gene that could be controlling grain Mn concentration. This work demonstrates the utilisation of multi-experiment analysis to identify constitutive QTLs and candidate genes associated with the grain Mn concentration. Hence, the approach should be advantageous to facilitate genomic breeding programmes in rice and other crops considering QTLs and genes associated with complex traits in natural populations.
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Affiliation(s)
- Panthita Ruang-Areerate
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 3UU, UK.
- National Omics Center, National Science and Technology Development Agency (NSTDA), Pathum Thani, 12120, Thailand.
| | - Anthony J Travis
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 3UU, UK
| | - Shannon R M Pinson
- USDA-ARS Dale Bumpers National Rice Research Center, Stuttgart, AR, 72160, USA
| | - Lee Tarpley
- Texas A&M System AgriLife Research Center, Beaumont, TX, 77713, USA
| | - Georgia C Eizenga
- USDA-ARS Dale Bumpers National Rice Research Center, Stuttgart, AR, 72160, USA
| | - Mary Lou Guerinot
- Department of Biological Sciences, Dartmouth College, Hanover, NH, 03755, USA
| | - David E Salt
- Future Food Beacon of Excellence and the School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, UK
| | - Alex Douglas
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 3UU, UK
| | - Adam H Price
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 3UU, UK
| | - Gareth J Norton
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 3UU, UK
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29
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Wei X, Qiu J, Yong K, Fan J, Zhang Q, Hua H, Liu J, Wang Q, Olsen KM, Han B, Huang X. A quantitative genomics map of rice provides genetic insights and guides breeding. Nat Genet 2021; 53:243-253. [PMID: 33526925 DOI: 10.1038/s41588-020-00769-9] [Citation(s) in RCA: 123] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 12/11/2020] [Indexed: 02/07/2023]
Abstract
Extensive allelic variation in agronomically important genes serves as the basis of rice breeding. Here, we present a comprehensive map of rice quantitative trait nucleotides (QTNs) and inferred QTN effects based on eight genome-wide association study cohorts. Population genetic analyses revealed that domestication, local adaptation and heterosis are all associated with QTN allele frequency changes. A genome navigation system, RiceNavi, was developed for QTN pyramiding and breeding route optimization, and implemented in the improvement of a widely cultivated indica variety. This work presents an efficient platform that bridges ever-increasing genomic knowledge and diverse improvement needs in rice.
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Affiliation(s)
- Xin Wei
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, China
| | - Jie Qiu
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, China
| | - Kaicheng Yong
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, China
| | - Jiongjiong Fan
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, China
| | - Qi Zhang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, China
| | - Hua Hua
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, China
| | - Jie Liu
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, China
| | - Qin Wang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, China
| | - Kenneth M Olsen
- Department of Biology, Washington University in St Louis, St Louis, MO, USA
| | - Bin Han
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xuehui Huang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, China.
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30
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Torkamaneh D, Laroche J, Valliyodan B, O'Donoughue L, Cober E, Rajcan I, Vilela Abdelnoor R, Sreedasyam A, Schmutz J, Nguyen HT, Belzile F. Soybean (Glycine max) Haplotype Map (GmHapMap): a universal resource for soybean translational and functional genomics. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:324-334. [PMID: 32794321 DOI: 10.1101/534578] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 07/24/2020] [Accepted: 08/07/2020] [Indexed: 05/27/2023]
Abstract
Here, we describe a worldwide haplotype map for soybean (GmHapMap) constructed using whole-genome sequence data for 1007 Glycine max accessions and yielding 14.9 million variants as well as 4.3 M tag single-nucleotide polymorphisms (SNPs). When sampling random subsets of these accessions, the number of variants and tag SNPs plateaued beyond approximately 800 and 600 accessions, respectively. This suggests extensive coverage of diversity within the cultivated soybean. GmHapMap variants were imputed onto 21 618 previously genotyped accessions with up to 96% success for common alleles. A local association analysis was performed with the imputed data using markers located in a 1-Mb region known to contribute to seed oil content and enabled us to identify a candidate causal SNP residing in the NPC1 gene. We determined gene-centric haplotypes (407 867 GCHs) for the 55 589 genes and showed that such haplotypes can help to identify alleles that differ in the resulting phenotype. Finally, we predicted 18 031 putative loss-of-function (LOF) mutations in 10 662 genes and illustrated how such a resource can be used to explore gene function. The GmHapMap provides a unique worldwide resource for applied soybean genomics and breeding.
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Affiliation(s)
- Davoud Torkamaneh
- Département de Phytologie, Université Laval, Québec City, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Jérôme Laroche
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada
| | - Babu Valliyodan
- National Center for Soybean Biotechnology and Division of Plant Sciences, University of Missouri, Columbia, MO, USA
| | - Louise O'Donoughue
- CÉROM, Centre de recherche Sur Les Grains Inc., Saint-Mathieu de Beloeil, QC, Canada
| | - Elroy Cober
- Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Ricardo Vilela Abdelnoor
- Brazilian Corporation of Agricultural Research (Embrapa Soja), Warta County, PR, Brazil
- Londrina State University (UEL), Londrina, PR, Brazil
| | | | - Jeremy Schmutz
- Institute for Biotechnology, HudsonAlpha, Huntsville, AL, USA
- Department of Energy, Joint Genome Institute, Walnut Creek, CA, USA
| | - Henry T Nguyen
- National Center for Soybean Biotechnology and Division of Plant Sciences, University of Missouri, Columbia, MO, USA
| | - François Belzile
- Département de Phytologie, Université Laval, Québec City, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada
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31
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Torkamaneh D, Laroche J, Valliyodan B, O’Donoughue L, Cober E, Rajcan I, Vilela Abdelnoor R, Sreedasyam A, Schmutz J, Nguyen HT, Belzile F. Soybean (Glycine max) Haplotype Map (GmHapMap): a universal resource for soybean translational and functional genomics. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:324-334. [PMID: 32794321 PMCID: PMC7868971 DOI: 10.1111/pbi.13466] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 07/24/2020] [Accepted: 08/07/2020] [Indexed: 05/10/2023]
Abstract
Here, we describe a worldwide haplotype map for soybean (GmHapMap) constructed using whole-genome sequence data for 1007 Glycine max accessions and yielding 14.9 million variants as well as 4.3 M tag single-nucleotide polymorphisms (SNPs). When sampling random subsets of these accessions, the number of variants and tag SNPs plateaued beyond approximately 800 and 600 accessions, respectively. This suggests extensive coverage of diversity within the cultivated soybean. GmHapMap variants were imputed onto 21 618 previously genotyped accessions with up to 96% success for common alleles. A local association analysis was performed with the imputed data using markers located in a 1-Mb region known to contribute to seed oil content and enabled us to identify a candidate causal SNP residing in the NPC1 gene. We determined gene-centric haplotypes (407 867 GCHs) for the 55 589 genes and showed that such haplotypes can help to identify alleles that differ in the resulting phenotype. Finally, we predicted 18 031 putative loss-of-function (LOF) mutations in 10 662 genes and illustrated how such a resource can be used to explore gene function. The GmHapMap provides a unique worldwide resource for applied soybean genomics and breeding.
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Affiliation(s)
- Davoud Torkamaneh
- Département de PhytologieUniversité LavalQuébec CityQCCanada
- Institut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébec CityQCCanada
- Department of Plant AgricultureUniversity of GuelphGuelphONCanada
| | - Jérôme Laroche
- Institut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébec CityQCCanada
| | - Babu Valliyodan
- National Center for Soybean Biotechnology and Division of Plant SciencesUniversity of MissouriColumbiaMOUSA
| | - Louise O’Donoughue
- CÉROMCentre de recherche Sur Les Grains Inc.Saint‐Mathieu de BeloeilQCCanada
| | - Elroy Cober
- Agriculture and Agri‐Food CanadaOttawaONCanada
| | - Istvan Rajcan
- Department of Plant AgricultureUniversity of GuelphGuelphONCanada
| | - Ricardo Vilela Abdelnoor
- Brazilian Corporation of Agricultural Research (Embrapa Soja)Warta CountyPRBrazil
- Londrina State University (UEL)LondrinaPRBrazil
| | | | - Jeremy Schmutz
- Institute for BiotechnologyHudsonAlphaHuntsvilleALUSA
- Department of EnergyJoint Genome InstituteWalnut CreekCAUSA
| | - Henry T. Nguyen
- National Center for Soybean Biotechnology and Division of Plant SciencesUniversity of MissouriColumbiaMOUSA
| | - François Belzile
- Département de PhytologieUniversité LavalQuébec CityQCCanada
- Institut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébec CityQCCanada
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32
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Gao Y, Yang Z, Yang W, Yang Y, Gong J, Yang QY, Niu X. Plant-ImputeDB: an integrated multiple plant reference panel database for genotype imputation. Nucleic Acids Res 2021; 49:D1480-D1488. [PMID: 33137192 PMCID: PMC7779032 DOI: 10.1093/nar/gkaa953] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 09/23/2020] [Accepted: 10/08/2020] [Indexed: 12/21/2022] Open
Abstract
Genotype imputation is a process that estimates missing genotypes in terms of the haplotypes and genotypes in a reference panel. It can effectively increase the density of single nucleotide polymorphisms (SNPs), boost the power to identify genetic association and promote the combination of genetic studies. However, there has been a lack of high-quality reference panels for most plants, which greatly hinders the application of genotype imputation. Here, we developed Plant-ImputeDB (http://gong_lab.hzau.edu.cn/Plant_imputeDB/), a comprehensive database with reference panels of 12 plant species for online genotype imputation, SNP and block search and free download. By integrating genotype data and whole-genome resequencing data of plants from various studies and databases, the current Plant-ImputeDB provides high-quality reference panels of 12 plant species, including ∼69.9 million SNPs from 34 244 samples. It also provides an easy-to-use online tool with the option of two popular tools specifically designed for genotype imputation. In addition, Plant-ImputeDB accepts submissions of different types of genomic variations, and provides free and open access to all publicly available data in support of related research worldwide. In general, Plant-ImputeDB may serve as an important resource for plant genotype imputation and greatly facilitate the research on plant genetic research.
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Affiliation(s)
- Yingjie Gao
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Zhiquan Yang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Wenqian Yang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Yanbo Yang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Jing Gong
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China.,College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Qing-Yong Yang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China.,College of Agriculture, Shihezi University, Xinjiang 832003, P.R. China
| | - Xiaohui Niu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
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33
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Kim MS, Lozano R, Kim JH, Bae DN, Kim ST, Park JH, Choi MS, Kim J, Ok HC, Park SK, Gore MA, Moon JK, Jeong SC. The patterns of deleterious mutations during the domestication of soybean. Nat Commun 2021; 12:97. [PMID: 33397978 PMCID: PMC7782591 DOI: 10.1038/s41467-020-20337-3] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 11/25/2020] [Indexed: 01/29/2023] Open
Abstract
Globally, soybean is a major protein and oil crop. Enhancing our understanding of the soybean domestication and improvement process helps boost genomics-assisted breeding efforts. Here we present a genome-wide variation map of 10.6 million single-nucleotide polymorphisms and 1.4 million indels for 781 soybean individuals which includes 418 domesticated (Glycine max), 345 wild (Glycine soja), and 18 natural hybrid (G. max/G. soja) accessions. We describe the enhanced detection of 183 domestication-selective sweeps and the patterns of putative deleterious mutations during domestication and improvement. This predominantly selfing species shows 7.1% reduction of overall deleterious mutations in domesticated soybean relative to wild soybean and a further 1.4% reduction from landrace to improved accessions. The detected domestication-selective sweeps also show reduced levels of deleterious alleles. Importantly, genotype imputation with this resource increases the mapping resolution of genome-wide association studies for seed protein and oil traits in a soybean diversity panel.
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Affiliation(s)
- Myung-Shin Kim
- Bio-Evaluation Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, Chungbuk, 28116, Korea
- Plant Immunity Research Center, Plant Genomics and Breeding Institute, College of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Korea
| | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Ji Hong Kim
- Bio-Evaluation Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, Chungbuk, 28116, Korea
| | - Dong Nyuk Bae
- Bio-Evaluation Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, Chungbuk, 28116, Korea
| | - Sang-Tae Kim
- Department of Life Science, The Catholic University of Korea, Bucheon, 14662, Korea
| | - Jung-Ho Park
- Bio-Evaluation Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, Chungbuk, 28116, Korea
| | - Man Soo Choi
- National Institute of Crop Science, Rural Development Administration, Wanju, Jeonbuk, 55365, Korea
| | - Jaehyun Kim
- National Institute of Crop Science, Rural Development Administration, Wanju, Jeonbuk, 55365, Korea
| | - Hyun-Choong Ok
- National Institute of Crop Science, Rural Development Administration, Wanju, Jeonbuk, 55365, Korea
| | - Soo-Kwon Park
- National Institute of Crop Science, Rural Development Administration, Wanju, Jeonbuk, 55365, Korea
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Jung-Kyung Moon
- National Institute of Crop Science, Rural Development Administration, Wanju, Jeonbuk, 55365, Korea.
- Agricultural Genome Center, National Academy of Agricultural Sciences, Rural Development Administration, Jeonju, Jeonbuk, 55365, Korea.
| | - Soon-Chun Jeong
- Bio-Evaluation Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, Chungbuk, 28116, Korea.
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Al-Tamimi N, Oakey H, Tester M, Negrão S. Assessing Rice Salinity Tolerance: From Phenomics to Association Mapping. Methods Mol Biol 2021; 2238:339-375. [PMID: 33471343 DOI: 10.1007/978-1-0716-1068-8_23] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Rice is the most salt-sensitive cereal, suffering yield losses above 50% with soil salinity of 6 dS/m. Thus, understanding the mechanisms of rice salinity tolerance is key to address food security. In this chapter, we provide guidelines to assess rice salinity tolerance using a high-throughput phenotyping platform (HTP) with digital imaging at seedling/early tillering stage and suggest improved analysis methods using stress indices. The protocols described here also include computer scripts for users to improve their experimental design, run genome-wide association studies (GWAS), perform multi-testing corrections, and obtain the Manhattan plots, enabling the identification of loci associated with salinity tolerance. Notably, the computer scripts provided here can be used for any stress or GWAS experiment and independently of HTP.
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Affiliation(s)
- Nadia Al-Tamimi
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Helena Oakey
- School of Agriculture Food and Wine, University of Adelaide, Urrbrae, SA, Australia
| | - Mark Tester
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Sónia Negrão
- School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland.
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35
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Campbell MT, Du Q, Liu K, Sharma S, Zhang C, Walia H. Characterization of the transcriptional divergence between the subspecies of cultivated rice (Oryza sativa). BMC Genomics 2020; 21:394. [PMID: 32513103 PMCID: PMC7278148 DOI: 10.1186/s12864-020-06786-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 05/19/2020] [Indexed: 01/24/2023] Open
Abstract
Background Cultivated rice consists of two subspecies, Indica and Japonica, that exhibit well-characterized differences at the morphological and genetic levels. However, the differences between these subspecies at the transcriptome level remains largely unexamined. Here, we provide a comprehensive characterization of transcriptome divergence and cis-regulatory variation within rice using transcriptome data from 91 accessions from a rice diversity panel (RDP1). Results The transcriptomes of the two subspecies of rice are highly divergent. Japonica have significantly lower expression and genetic diversity relative to Indica, which is likely a consequence of a population bottleneck during Japonica domestication. We leveraged high-density genotypic data and transcript levels to identify cis-regulatory variants that may explain the genetic divergence between the subspecies. We identified significantly more eQTL that were specific to the Indica subspecies compared to Japonica, suggesting that the observed differences in expression and genetic variability also extends to cis-regulatory variation. Conclusions Using RNA sequencing data for 91diverse rice accessions and high-density genotypic data, we show that the two species are highly divergent with respect to gene expression levels, as well as the genetic regulation of expression. The data generated by this study provide, to date, the largest collection of genome-wide transcriptional levels for rice, and provides a community resource to accelerate functional genomic studies in rice.
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Affiliation(s)
- Malachy T Campbell
- Department of Agronomy and Horticulture, University of Nebraska Lincoln, 1825 N 38th St., Lincoln, 68583, NE, USA. .,Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, 175 West Campus Drive, Blacksburg, 24060, VA, USA.
| | - Qian Du
- School of Biological Sciences, University of Nebraska Lincoln, 1901 Vine St., Lincoln, 68503, NE, USA
| | - Kan Liu
- School of Biological Sciences, University of Nebraska Lincoln, 1901 Vine St., Lincoln, 68503, NE, USA
| | - Sandeep Sharma
- Department of Agronomy and Horticulture, University of Nebraska Lincoln, 1825 N 38th St., Lincoln, 68583, NE, USA.,Marine Biotechnology and Ecology Division, CSIR-CSMCRI, Bhavnagar, Gujarat, India
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska Lincoln, 1901 Vine St., Lincoln, 68503, NE, USA
| | - Harkamal Walia
- Department of Agronomy and Horticulture, University of Nebraska Lincoln, 1825 N 38th St., Lincoln, 68583, NE, USA.
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Wang Q, Tang J, Han B, Huang X. Advances in genome-wide association studies of complex traits in rice. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:1415-1425. [PMID: 31720701 DOI: 10.1007/s00122-019-03473-3] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 11/05/2019] [Indexed: 05/27/2023]
Abstract
Genome-wide association studies (GWAS), genetic surveys of the whole genome to detect variants associated with a trait in natural populations, are a powerful approach for dissecting complex traits. This genetic mapping approach has been applied in rice over the last 10 years. During the last decade, GWAS was used to identify the loci underlying tens of rice traits, and several important genes were detected in GWAS and further confirmed in follow-up functional experiments. In this review, we present an overview of the whole process in a typical GWAS, including population design, genotyping, phenotyping and analysis methods. Recent advances in rice GWAS are also provided, including several examples of the functional characterization of candidate genes. The possible breakthroughs of rice GWAS in the next decade are discussed with regard to their application in breeding, the consideration of epistatic interactions and in-depth functional annotations of DNA elements and genetic variants throughout the rice genome.
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Affiliation(s)
- Qin Wang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China
| | - Jiali Tang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China
| | - Bin Han
- National Center for Gene Research, CAS Center for Excellence of Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200233, China
| | - Xuehui Huang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China.
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37
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Qiu J, Jia L, Wu D, Weng X, Chen L, Sun J, Chen M, Mao L, Jiang B, Ye C, Turra GM, Guo L, Ye G, Zhu QH, Imaizumi T, Song BK, Scarabel L, Merotto A, Olsen KM, Fan L. Diverse genetic mechanisms underlie worldwide convergent rice feralization. Genome Biol 2020; 21:70. [PMID: 32213201 PMCID: PMC7098168 DOI: 10.1186/s13059-020-01980-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 03/02/2020] [Indexed: 11/21/2022] Open
Abstract
Background Worldwide feralization of crop species into agricultural weeds threatens global food security. Weedy rice is a feral form of rice that infests paddies worldwide and aggressively outcompetes cultivated varieties. Despite increasing attention in recent years, a comprehensive understanding of the origins of weedy crop relatives and how a universal feralization process acts at the genomic and molecular level to allow the rapid adaptation to weediness are still yet to be explored. Results We use whole-genome sequencing to examine the origin and adaptation of 524 global weedy rice samples representing all major regions of rice cultivation. Weed populations have evolved multiple times from cultivated rice, and a strikingly high proportion of contemporary Asian weed strains can be traced to a few Green Revolution cultivars that were widely grown in the late twentieth century. Latin American weedy rice stands out in having originated through extensive hybridization. Selection scans indicate that most genomic regions underlying weedy adaptations do not overlap with domestication targets of selection, suggesting that feralization occurs largely through changes at loci unrelated to domestication. Conclusions This is the first investigation to provide detailed genomic characterizations of weedy rice on a global scale, and the results reveal diverse genetic mechanisms underlying worldwide convergent rice feralization. Electronic supplementary material The online version of this article (10.1186/s13059-020-01980-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jie Qiu
- Institute of Crop Sciences and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, China.,Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, 200235, China
| | - Lei Jia
- Institute of Crop Sciences and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Dongya Wu
- Institute of Crop Sciences and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Xifang Weng
- Institute of Crop Sciences and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Lijuan Chen
- Rice Research Institute, Yunnan Agricultural University, Kunming, China
| | - Jian Sun
- Rice Research Institute, Shenyang Agricultural University, Shenyang, China
| | - Meihong Chen
- Institute of Crop Sciences and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Lingfeng Mao
- Institute of Crop Sciences and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Bowen Jiang
- Institute of Crop Sciences and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Chuyu Ye
- Institute of Crop Sciences and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Guilherme Menegol Turra
- Department of Crop Sciences, Agricultural School, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Longbiao Guo
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, 310006, China
| | - Guoyou Ye
- International Rice Research Institute (IRRI), Manila, Philippines
| | - Qian-Hao Zhu
- CSIRO Agriculture and Food, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - Toshiyuki Imaizumi
- National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, 305-8666, Japan
| | - Beng-Kah Song
- School of Science, Monash University Malaysia, 46150, Bandar Sunway, Selangor, Malaysia
| | - Laura Scarabel
- Istituto per la Protezione Sostenibile delle Piante (IPSP), CNR, Viale dell'Università, 16, 35020, Legnaro, PD, Italy
| | - Aldo Merotto
- Department of Crop Sciences, Agricultural School, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Kenneth M Olsen
- Department of Biology, Washington University in St. Louis, St. Louis, MO, 63130, USA.
| | - Longjiang Fan
- Institute of Crop Sciences and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310058, China. .,James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou, 310058, China.
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38
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Melandri G, Prashar A, McCouch SR, van der Linden G, Jones HG, Kadam N, Jagadish K, Bouwmeester H, Ruyter-Spira C. Association mapping and genetic dissection of drought-induced canopy temperature differences in rice. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:1614-1627. [PMID: 31846000 PMCID: PMC7031080 DOI: 10.1093/jxb/erz527] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 12/16/2019] [Indexed: 05/07/2023]
Abstract
Drought-stressed plants display reduced stomatal conductance, which results in increased leaf temperature by limiting transpiration. In this study, thermal imaging was used to quantify the differences in canopy temperature under drought in a rice diversity panel consisting of 293 indica accessions. The population was grown under paddy field conditions and drought stress was imposed for 2 weeks at flowering. The canopy temperature of the accessions during stress negatively correlated with grain yield (r= -0.48) and positively with plant height (r=0.56). Temperature values were used to perform a genome-wide association (GWA) analysis using a 45K single nucleotide polynmorphism (SNP) map. A quantitative trait locus (QTL) for canopy temperature under drought was detected on chromosome 3 and fine-mapped using a high-density imputed SNP map. The candidate genes underlying the QTL point towards differences in the regulation of guard cell solute intake for stomatal opening as the possible source of temperature variation. Genetic variation for the significant markers of the QTL was present only within the tall, low-yielding landraces adapted to drought-prone environments. The absence of variation in the shorter genotypes, which showed lower leaf temperature and higher grain yield, suggests that breeding for high grain yield in rice under paddy conditions has reduced genetic variation for stomatal response under drought.
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Affiliation(s)
- Giovanni Melandri
- Laboratory of Plant Physiology, Wageningen University and Research, Wageningen, The Netherlands
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, USA
| | - Ankush Prashar
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Susan R McCouch
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, USA
| | - Gerard van der Linden
- Wageningen UR Plant Breeding, Wageningen University and Research, Wageningen, The Netherlands
| | - Hamlyn G Jones
- Plant Science Division, University of Dundee at The James Hutton Institute, Invergowrie, Dundee, UK
- School of Plant Biology, University of Western Australia, Perth, Australia
| | - Niteen Kadam
- Centre for Crop Systems Analysis, Wageningen University and Research, Wageningen, The Netherlands
- International Rice Research Institute, Los Baños, Philippines
- Department of Plant Biology and Institute of Genomic Biology, University of Illinois, Urbana, IL, USA
| | - Krishna Jagadish
- International Rice Research Institute, Los Baños, Philippines
- Department of Agronomy, Kansas State University, Manhattan, KS, USA
| | - Harro Bouwmeester
- Laboratory of Plant Physiology, Wageningen University and Research, Wageningen, The Netherlands
- Plant Hormone Biology group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Carolien Ruyter-Spira
- Laboratory of Plant Physiology, Wageningen University and Research, Wageningen, The Netherlands
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Melandri G, Sikirou M, Arbelaez JD, Shittu A, Semwal VK, Konaté KA, Maji AT, Ngaujah SA, Akintayo I, Govindaraj V, Shi Y, Agosto-Peréz FJ, Greenberg AJ, Atlin G, Ramaiah V, McCouch SR. Multiple Small-Effect Alleles of Indica Origin Enhance High Iron-Associated Stress Tolerance in Rice Under Field Conditions in West Africa. FRONTIERS IN PLANT SCIENCE 2020; 11:604938. [PMID: 33584748 PMCID: PMC7874229 DOI: 10.3389/fpls.2020.604938] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/15/2020] [Indexed: 05/03/2023]
Abstract
Understanding the genetics of field-based tolerance to high iron-associated (HIA) stress in rice can accelerate the development of new varieties with enhanced yield performance in West African lowland ecosystems. To date, few field-based studies have been undertaken to rigorously evaluate rice yield performance under HIA stress conditions. In this study, two NERICA × O. sativa bi-parental rice populations and one O.sativa diversity panel consisting of 296 rice accessions were evaluated for grain yield and leaf bronzing symptoms over multiple years in four West African HIA stress and control sites. Mapping of these traits identified a large number of QTLs and single nucleotide polymorphisms (SNPs) associated with stress tolerance in the field. Favorable alleles associated with tolerance to high levels of iron in anaerobic rice soils were rare and almost exclusively derived from the indica subpopulation, including the most favorable alleles identified in NERICA varieties. These findings highlight the complex genetic architecture underlying rice response to HIA stress and suggest that a recurrent selection program focusing on an expanded indica genepool could be productively used in combination with genomic selection to increase the efficiency of selection in breeding programs designed to enhance tolerance to this prevalent abiotic stress in West Africa.
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Affiliation(s)
- Giovanni Melandri
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | - Mouritala Sikirou
- Africa Rice Center, Ibadan, Nigeria
- School of Horticulture and Green Landscaping, Kétou, Bénin
| | - Juan D. Arbelaez
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | | | | | | | | | | | - Inoussa Akintayo
- Central Agricultural Research Institute, Suakoko, Liberia
- Africa Rice Center, Suakoko, Liberia
| | - Vishnu Govindaraj
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | - Yuxin Shi
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
| | | | | | - Gary Atlin
- Bill & Melinda Gates Foundation, Seattle, WA, United States
| | | | - Susan R. McCouch
- Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
- Venuprasad Ramaiah,
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40
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Furbank RT, Jimenez-Berni JA, George-Jaeggli B, Potgieter AB, Deery DM. Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops. THE NEW PHYTOLOGIST 2019; 223:1714-1727. [PMID: 30937909 DOI: 10.1111/nph.15817] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Accepted: 03/02/2019] [Indexed: 05/21/2023]
Abstract
Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high-throughput techniques based on machine vision, robotics, and computing (plant phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning, and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focuses on how field-based plant phenomics can enable next-generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis, and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from 'Green Revolution' traits such as harvest index become exhausted. Application of LiDAR, thermal imaging, leaf and canopy spectral reflectance, Chl fluorescence, and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high-throughput phenotyping could enable the next generation of crop research and breeding is presented.
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Affiliation(s)
- Robert T Furbank
- ARC Centre of Excellence for Translational Photosynthesis, Division of Plant Science, Australian National University, Canberra, 2601, ACT, Australia
- CSIRO Agriculture and Food, Canberra, 2601, ACT, Australia
| | - Jose A Jimenez-Berni
- CSIRO Agriculture and Food, Canberra, 2601, ACT, Australia
- Institute for Sustainable Agriculture (IAS), CSIC, Cordoba, 14004, Spain
| | - Barbara George-Jaeggli
- Queensland Alliance for Agriculture & Food Innovation, Centre for Crop Science, The University of Queensland, Hermitage Research Station, Warwick, 4370, QLD, Australia
- Agri-Science Queensland, Queensland Department of Agriculture & Fisheries, Hermitage Research Facility, Warwick, 4370, QLD, Australia
| | - Andries B Potgieter
- Queensland Alliance for Agriculture & Food Innovation, Centre for Crop Science, The University of Queensland, Tor Street, Toowoomba, 4350, QLD, Australia
| | - David M Deery
- CSIRO Agriculture and Food, Canberra, 2601, ACT, Australia
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41
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Davidson H, Shrestha R, Cornulier T, Douglas A, Travis T, Johnson D, Price AH. Spatial Effects and GWA Mapping of Root Colonization Assessed in the Interaction Between the Rice Diversity Panel 1 and an Arbuscular Mycorrhizal Fungus. FRONTIERS IN PLANT SCIENCE 2019; 10:633. [PMID: 31156686 PMCID: PMC6533530 DOI: 10.3389/fpls.2019.00633] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 04/26/2019] [Indexed: 05/09/2023]
Abstract
If water saving methods of rice management are to be adopted, the interaction between rice plants and arbuscular mycorrhizal (AM) fungi will grow in agronomic significance. As yet there are very few studies on the interaction between rice and AM fungi and none on host genetics. A subset 334 cultivars from the Rice Diversity Panel 1 were grown in 250 L boxes filled with phosphorus (P) deficient aerobic soil without addition, with added rock phosphate and with rock phosphate and the AM fungus Rhizophagus irregularis. Statistical analysis of position of plants revealed a positive effect of their neighbors on their dry weight which was stronger in the presence of rock phosphate and even stronger with rock phosphate and AM fungi. A weak but significant difference in the response of cultivars to AM fungus treatment in terms of shoot dry weight (SDW) was revealed. Neighbor hyphal colonization was positively related to a plant's hyphal colonization, providing insights into the way a network of AM fungi interact with multiple hosts. Hyphal colonization ranged from 21 to 89%, and 42% of the variation was explained by rice genotype. Colonization was slightly lower in aus cultivars than other rice subgroups and high in cultivars from the Philippines. Genome wide association (GWA) mapping for hyphal colonization revealed 23 putative quantitative trait loci (QTLs) indicating there is an opportunity to investigate the impact of allelic variation in rice on AM fungal colonization. Using published transcriptomics data for AM response in rice, some promising candidate genes are revealed under these QTLs being a calcium/calmodulin serine/threonine protein kinase at 4.9 Mbp on chromosome 1, two ammonium transporters genes at 24.6 Mbp on chromosome 2 and a cluster of subtilisin genes at 1.2 Mbp on chromosome 4. Future studies should concentrate on the biological significance of genetic variation in rice for AM colonization.
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Affiliation(s)
| | | | | | | | | | | | - Adam H. Price
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, United Kingdom
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Zhao J, Sauvage C, Zhao J, Bitton F, Bauchet G, Liu D, Huang S, Tieman DM, Klee HJ, Causse M. Meta-analysis of genome-wide association studies provides insights into genetic control of tomato flavor. Nat Commun 2019; 10:1534. [PMID: 30948717 PMCID: PMC6449550 DOI: 10.1038/s41467-019-09462-w] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 03/04/2019] [Indexed: 12/30/2022] Open
Abstract
Tomato flavor has changed over the course of long-term domestication and intensive breeding. To understand the genetic control of flavor, we report the meta-analysis of genome-wide association studies (GWAS) using 775 tomato accessions and 2,316,117 SNPs from three GWAS panels. We discover 305 significant associations for the contents of sugars, acids, amino acids, and flavor-related volatiles. We demonstrate that fruit citrate and malate contents have been impacted by selection during domestication and improvement, while sugar content has undergone less stringent selection. We suggest that it may be possible to significantly increase volatiles that positively contribute to consumer preferences while reducing unpleasant volatiles, by selection of the relevant allele combinations. Our results provide genetic insights into the influence of human selection on tomato flavor and demonstrate the benefits obtained from meta-analysis. Flavor is one of the most important traits for improving tomato sensory quality and consumer acceptability. Here, the authors report meta-analysis of genome-wide association studies of flavor related traits and show genetic insights into the influence of human selection during domestication and improvement.
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Affiliation(s)
- Jiantao Zhao
- INRA, UR1052, Génétique et Amélioration des Fruits et Légumes, Domaine Saint Maurice, 67 Allée des Chênes CS 60094, 84143, Montfavet Cedex, France
| | - Christopher Sauvage
- INRA, UR1052, Génétique et Amélioration des Fruits et Légumes, Domaine Saint Maurice, 67 Allée des Chênes CS 60094, 84143, Montfavet Cedex, France.,Syngenta, 12 Chemin de l'Hobit, Saint Sauveur, 31790, France
| | - Jinghua Zhao
- MRC Epidemiology Unit & Institute of Metabolic Science, University of Cambridge, Addrenbrooke's Hospital, Box 285, Hills Road, Cambridge, CB2 0QQ, UK.,Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Wort's Causeway, Cambridge, CB1 8RN, UK
| | - Frédérique Bitton
- INRA, UR1052, Génétique et Amélioration des Fruits et Légumes, Domaine Saint Maurice, 67 Allée des Chênes CS 60094, 84143, Montfavet Cedex, France
| | - Guillaume Bauchet
- INRA, UR1052, Génétique et Amélioration des Fruits et Légumes, Domaine Saint Maurice, 67 Allée des Chênes CS 60094, 84143, Montfavet Cedex, France.,Boyce Thompson Institute, Cornell University, 533 Tower Rd, Ithaca, NY, 14853, USA
| | - Dan Liu
- Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, 518124, Shenzhen, Guangdong, China
| | - Sanwen Huang
- Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, 518124, Shenzhen, Guangdong, China.,Key Laboratory of Biology and Genetic Improvement of Horticultural Crops of the Ministry of Agriculture, Sino-Dutch Joint Laboratory of Horticultural Genomics, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, 100081, Beijing, China
| | - Denise M Tieman
- Horticultural Sciences, Plant Innovation Center, University of Florida, Post Office Box 110690, Gainesville, FL, 32611, USA
| | - Harry J Klee
- Horticultural Sciences, Plant Innovation Center, University of Florida, Post Office Box 110690, Gainesville, FL, 32611, USA
| | - Mathilde Causse
- INRA, UR1052, Génétique et Amélioration des Fruits et Légumes, Domaine Saint Maurice, 67 Allée des Chênes CS 60094, 84143, Montfavet Cedex, France.
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Hong WJ, Kim YJ, Chandran AKN, Jung KH. Infrastructures of systems biology that facilitate functional genomic study in rice. RICE (NEW YORK, N.Y.) 2019; 12:15. [PMID: 30874968 PMCID: PMC6419666 DOI: 10.1186/s12284-019-0276-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Accepted: 03/06/2019] [Indexed: 05/08/2023]
Abstract
Rice (Oryza sativa L.) is both a major staple food for the worldwide population and a model crop plant for studying the mode of action of agronomically valuable traits, providing information that can be applied to other crop plants. Due to the development of high-throughput technologies such as next generation sequencing and mass spectrometry, a huge mass of multi-omics data in rice has been accumulated. Through the integration of those data, systems biology in rice is becoming more advanced.To facilitate such systemic approaches, we have summarized current resources, such as databases and tools, for systems biology in rice. In this review, we categorize the resources using six omics levels: genomics, transcriptomics, proteomics, metabolomics, integrated omics, and functional genomics. We provide the names, websites, references, working states, and number of citations for each individual database or tool and discuss future prospects for the integrated understanding of rice gene functions.
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Affiliation(s)
- Woo-Jong Hong
- Graduate School of Biotechnology & Crop Biotech Institute, Kyung Hee University, Yongin, 17104, Korea
| | - Yu-Jin Kim
- Graduate School of Biotechnology & Crop Biotech Institute, Kyung Hee University, Yongin, 17104, Korea
| | | | - Ki-Hong Jung
- Graduate School of Biotechnology & Crop Biotech Institute, Kyung Hee University, Yongin, 17104, Korea.
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Cobb JN, Biswas PS, Platten JD. Back to the future: revisiting MAS as a tool for modern plant breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:647-667. [PMID: 30560465 PMCID: PMC6439155 DOI: 10.1007/s00122-018-3266-4] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 12/07/2018] [Indexed: 05/04/2023]
Abstract
KEY MESSAGE New models for integration of major gene MAS with modern breeding approaches stand to greatly enhance the reliability and efficiency of breeding, facilitating the leveraging of traditional genetic diversity. Genetic diversity is well recognised as contributing essential variation to crop breeding processes, and marker-assisted selection is cited as the primary tool to bring this diversity into breeding programs without the associated genetic drag from otherwise poor-quality genomes of donor varieties. However, implementation of marker-assisted selection techniques remains a challenge in many breeding programs worldwide. Many factors contribute to this lack of adoption, such as uncertainty in how to integrate MAS with traditional breeding processes, lack of confidence in MAS as a tool, and the expense of the process. However, developments in genomics tools, locus validation techniques, and new models for how to utilise QTLs in breeding programs stand to address these issues. Marker-assisted forward breeding needs to be enabled through the identification of robust QTLs, the design of reliable marker systems to select for these QTLs, and the delivery of these QTLs into elite genomic backgrounds to enable their use without associated genetic drag. To enhance the adoption and effectiveness of MAS, rice is used as an example of how to integrate new developments and processes into a coherent, efficient strategy for utilising genetic variation. When processes are instituted to address these issues, new genes can be rolled out into a breeding program rapidly and completely with a minimum of expense.
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Affiliation(s)
- Joshua N Cobb
- International Rice Research Institute, National Road, Los Banos, Laguna, Philippines
| | - Partha S Biswas
- International Rice Research Institute, National Road, Los Banos, Laguna, Philippines
- Bangladesh Rice Research Institute, Gazipur, 1701, Bangladesh
| | - J Damien Platten
- International Rice Research Institute, National Road, Los Banos, Laguna, Philippines.
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Borrill P, Harrington SA, Uauy C. Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:56-72. [PMID: 30407665 PMCID: PMC6378701 DOI: 10.1111/tpj.14150] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 10/23/2018] [Accepted: 10/30/2018] [Indexed: 05/10/2023]
Abstract
Improving traits in wheat has historically been challenging due to its large and polyploid genome, limited genetic diversity and in-field phenotyping constraints. However, within recent years many of these barriers have been lowered. The availability of a chromosome-level assembly of the wheat genome now facilitates a step-change in wheat genetics and provides a common platform for resources, including variation data, gene expression data and genetic markers. The development of sequenced mutant populations and gene-editing techniques now enables the rapid assessment of gene function in wheat directly. The ability to alter gene function in a targeted manner will unmask the effects of homoeolog redundancy and allow the hidden potential of this polyploid genome to be discovered. New techniques to identify and exploit the genetic diversity within wheat wild relatives now enable wheat breeders to take advantage of these additional sources of variation to address challenges facing food production. Finally, advances in phenomics have unlocked rapid screening of populations for many traits of interest both in greenhouses and in the field. Looking forwards, integrating diverse data types, including genomic, epigenetic and phenomics data, will take advantage of big data approaches including machine learning to understand trait biology in wheat in unprecedented detail.
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Affiliation(s)
- Philippa Borrill
- School of BiosciencesThe University of BirminghamBirminghamB15 2TTUK
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