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Roy Choudhury D, Maurya A, Singh NK, Singh GP, Singh R. Discovering New QTNs and Candidate Genes Associated with Rice-Grain-Related Traits within a Collection of Northeast Core Set and Rice Landraces. PLANTS (BASEL, SWITZERLAND) 2024; 13:1707. [PMID: 38931139 PMCID: PMC11207502 DOI: 10.3390/plants13121707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 06/05/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024]
Abstract
Grain-related traits are pivotal in rice cultivation, influencing yield and consumer preference. The complex inheritance of these traits, involving multiple alleles contributing to their expression, poses challenges in breeding. To address these challenges, a multi-locus genome-wide association study (ML-GWAS) utilizing 35,286 high-quality single-nucleotide polymorphisms (SNPs) was conducted. Our study utilized an association panel comprising 483 rice genotypes sourced from a northeast core set and a landraces set collected from various regions in India. Forty quantitative trait nucleotides (QTNs) were identified, associated with four grain-related traits: grain length (GL), grain width (GW), grain aroma (Aro), and length-width ratio (LWR). Notably, 16 QTNs were simultaneously identified using two ML-GWAS methods, distributed across multiple chromosomes. Nearly 258 genes were found near the 16 significant QTNs. Gene annotation study revealed that sixty of these genes exhibited elevated expression levels in specific tissues and were implicated in pathways influencing grain quality. Gene ontology (GO), trait ontology (TO), and enrichment analysis pinpointed 60 candidate genes (CGs) enriched in relevant GO terms. Among them, LOC_Os05g06470, LOC_Os06g06080, LOC_Os08g43470, and LOC_Os03g53110 were confirmed as key contributors to GL, GW, Aro, and LWR. Insights from QTNs and CGs illuminate rice trait regulation and genetic connections, offering potential targets for future studies.
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Affiliation(s)
- Debjani Roy Choudhury
- Division of Genomic Resources, ICAR—National Bureau of Plant Genetic Resources, New Delhi 110012, India; (D.R.C.); (A.M.)
| | - Avantika Maurya
- Division of Genomic Resources, ICAR—National Bureau of Plant Genetic Resources, New Delhi 110012, India; (D.R.C.); (A.M.)
| | | | | | - Rakesh Singh
- Division of Genomic Resources, ICAR—National Bureau of Plant Genetic Resources, New Delhi 110012, India; (D.R.C.); (A.M.)
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Kaur N, Lozada DN, Bhatta M, Barchenger DW, Khokhar ES, Nourbakhsh SS, Sanogo S. Insights into the genetic architecture of Phytophthora capsici root rot resistance in chile pepper (Capsicum spp.) from multi-locus genome-wide association study. BMC PLANT BIOLOGY 2024; 24:416. [PMID: 38760676 PMCID: PMC11100198 DOI: 10.1186/s12870-024-05097-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 05/02/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Phytophthora root rot, a major constraint in chile pepper production worldwide, is caused by the soil-borne oomycete, Phytophthora capsici. This study aimed to detect significant regions in the Capsicum genome linked to Phytophthora root rot resistance using a panel consisting of 157 Capsicum spp. genotypes. Multi-locus genome wide association study (GWAS) was conducted using single nucleotide polymorphism (SNP) markers derived from genotyping-by-sequencing (GBS). Individual plants were separately inoculated with P. capsici isolates, 'PWB-185', 'PWB-186', and '6347', at the 4-8 leaf stage and were scored for disease symptoms up to 14-days post-inoculation. Disease scores were used to calculate disease parameters including disease severity index percentage, percent of resistant plants, area under disease progress curve, and estimated marginal means for each genotype. RESULTS Most of the genotypes displayed root rot symptoms, whereas five accessions were completely resistant to all the isolates and displayed no symptoms of infection. A total of 55,117 SNP markers derived from GBS were used to perform multi-locus GWAS which identified 330 significant SNP markers associated with disease resistance. Of these, 56 SNP markers distributed across all the 12 chromosomes were common across the isolates, indicating association with more durable resistance. Candidate genes including nucleotide-binding site leucine-rich repeat (NBS-LRR), systemic acquired resistance (SAR8.2), and receptor-like kinase (RLKs), were identified within 0.5 Mb of the associated markers. CONCLUSIONS Results will be used to improve resistance to Phytophthora root rot in chile pepper by the development of Kompetitive allele-specific markers (KASP®) for marker validation, genomewide selection, and marker-assisted breeding.
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Affiliation(s)
- Navdeep Kaur
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, 88003, USA
- Current address: Department of Horticultural Sciences, Texas A&M University, College Station, TX, 77843, USA
| | - Dennis N Lozada
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, 88003, USA.
- Chile Pepper Institute, New Mexico State University, Las Cruces, NM, 88003, USA.
| | | | | | - Ehtisham S Khokhar
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, 88003, USA
| | - Seyed Shahabeddin Nourbakhsh
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, 88003, USA
- Department of Extension Plant Sciences, New Mexico State University, Las Cruces, NM, 88003, USA
| | - Soum Sanogo
- Department of Entomology, Plant Pathology and Weed Science, New Mexico State University, Las Cruces, NM, 88003, USA
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Zhao X, Zhan Y, Li K, Zhang Y, Zhou C, Yuan M, Liu M, Li Y, Zuo P, Han Y, Zhao X. Multi-omics analysis reveals novel loci and a candidate regulatory gene of unsaturated fatty acids in soybean (Glycine max (L.) Merr). BIOTECHNOLOGY FOR BIOFUELS AND BIOPRODUCTS 2024; 17:43. [PMID: 38493136 PMCID: PMC10944593 DOI: 10.1186/s13068-024-02489-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 03/07/2024] [Indexed: 03/18/2024]
Abstract
BACKGROUND Soybean is a major oil crop; the nutritional components of soybean oil are mainly controlled by unsaturated fatty acids (FA). Unsaturated FAs mainly include oleic acid (OA, 18:1), linoleic acid (LLA, 18:2), and linolenic acid (LNA, 18:3). The genetic architecture of unsaturated FAs in soybean seeds has not been fully elucidated, although many independent studies have been conducted. A 3 V multi-locus random single nucleotide polymorphism (SNP)-effect mixed linear model (3VmrMLM) was established to identify quantitative trait loci (QTLs) and QTL-by-environment interactions (QEIs) for complex traits. RESULTS In this study, 194 soybean accessions with 36,981 SNPs were calculated using the 3VmrMLM model. As a result, 94 quantitative trait nucleotides (QTNs) and 19 QEIs were detected using single-environment (QTN) and multi-environment (QEI) methods. Three significant QEIs, namely rs4633292, rs39216169, and rs14264702, overlapped with a significant single-environment QTN. CONCLUSIONS For QTNs and QEIs, further haplotype analysis of candidate genes revealed that the Glyma.03G040400 and Glyma.17G236700 genes were beneficial haplotypes that may be associated with unsaturated FAs. This result provides ideas for the identification of soybean lipid-related genes and provides insights for breeding high oil soybean varieties in the future.
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Affiliation(s)
- Xunchao Zhao
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
| | - Yuhang Zhan
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
| | - Kaiming Li
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
| | - Yan Zhang
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
| | - Changjun Zhou
- Daqing Branch, Heilongjiang Academy of Agricultural Science, Daqing, China
| | - Ming Yuan
- Qiqihar Branch, Heilongjiang Academy of Agricultural Science, Qiqihar, China
| | - Miao Liu
- Crop Tillage and Cultivation Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Yongguang Li
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
| | - Peng Zuo
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China.
| | - Yingpeng Han
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China.
| | - Xue Zhao
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China.
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Fattah A, Idaryani, Herniwati, Yasin M, Suriani S, Salim, Nappu MB, Mulia S, Irawan Hannan MF, Wulanningtyas HS, Saenong S, Dewayani W, Suriany, Winanda E, Manwan SW, Asaad M, Warda, Nurjanani, Nurhafsah, Gaffar A, Sunanto, Fadwiwati AY, Nurdin M, Dahya, Ella A. Performance and morphology of several soybean varieties and responses to pests and diseases in South Sulawesi. Heliyon 2024; 10:e25507. [PMID: 38434367 PMCID: PMC10907540 DOI: 10.1016/j.heliyon.2024.e25507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 01/17/2024] [Accepted: 01/29/2024] [Indexed: 03/05/2024] Open
Abstract
Soybeans are a commodity that is widely grown by farmers in rainfed rice fields in South Sulawesi. One of the determining factors in increasing soybean productivity in South Sulawesi is the type of variety. The aim of this research was to determine the characteristics, morphology and response to pests and diseases in several soybean varieties planted in rainfed rice fields in South Sulawesi. This research was carried out in Allepolea Village, Maros Regency in 2022 using a Randomized Block Design with 13 treatments and 3 replications. Varieties tested as treatments include: 1) Derap-1, 2) Devon-2, 3) Deja-1, 4) Anjasmoro, 5) Dena-2, 6) Dena-1, 7) Gepak Kuning, 8) Grobogan, 9) Devon-1, 10) Dega-1, 11) Deja-2, 12) Demas-1, and 13) Detap-1. The results showed that of the 13 varieties tested, the highest height was found in Devon-2 (33.67 cm) and Detap-1 (31.67 cm) in the vegetative phase and in the generative phase in Detap-1 (75.53 cm) and Gepak Yellow (74.67 cm). The largest number of branches is in Dena-1 (3.13 branches). The highest nitrogen content was found in Devon-1 (12.64 m2 per g). The largest leaf area was Detap-1 (4.15 cm2) and Gepak Kuning (4.15 cm2). The highest number of stomata was in Dena-1 (42.80 μm) and Deja-1 (44.00 μm). The highest stomata width was found in Gepak Kuning (2.76 μm). The lowest level of leaf damage due to attacks by Valanga sp (Acrididae) occurred in Grobogan (6.89 %) and Dega-1 (7.35 %). The lowest level of pod damage due to Nezara viridula attack was in Devon-2 (3.56 %) and Dena-2 (3.64 %). The lowest level of leaf damage due to Phaedonia inclusa attack occurred in Dega-1 (4.37 %), Dena-2 (4, 12 %), and Grobogan (4.69 %). Seed damage due to Cercospora sp attack was lowest on Dena-2 (0.81 %). The highest seed yield was in Dena-2 (3.78 t ha-1) and the lowest in Anjasmoro (1.93 t ha-1) and Deja-2 (2.02 t ha-1).
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Affiliation(s)
- Abdul Fattah
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java, 16911, Indonesia
| | - Idaryani
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java, 16911, Indonesia
| | - Herniwati
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java, 16911, Indonesia
| | - M. Yasin
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java, 16911, Indonesia
| | - Suriani Suriani
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java, 16911, Indonesia
| | - Salim
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java, 16911, Indonesia
| | - M. Basir Nappu
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java, 16911, Indonesia
| | - Sahardi Mulia
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java, 16911, Indonesia
| | - Muh Fitrah Irawan Hannan
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java, 16911, Indonesia
| | - Heppy Suci Wulanningtyas
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java, 16911, Indonesia
| | - Sudjak Saenong
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java, 16911, Indonesia
| | - Wanti Dewayani
- Research Center for Agroindustry, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Puspitek, Tangerang Selatan, Banten, Indonesia
| | - Suriany
- Research Center for Agroindustry, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Puspitek, Tangerang Selatan, Banten, Indonesia
| | - Elisa Winanda
- Research Center for Agroindustry, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Puspitek, Tangerang Selatan, Banten, Indonesia
| | - Sri Wahyuni Manwan
- Research Center for Horticultural and Estate Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java 16911, Indonesia
| | - Muh Asaad
- Research Center for Horticultural and Estate Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java 16911, Indonesia
| | - Warda
- Research Center for Horticultural and Estate Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java 16911, Indonesia
| | - Nurjanani
- Research Center for Horticultural and Estate Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java 16911, Indonesia
| | - Nurhafsah
- Research Center for Agroindustry, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Puspitek, Tangerang Selatan, Banten, Indonesia
| | - Abdul Gaffar
- Research Organization for Governance, Economy, and Community Welfare, Jl.Gatot Subroto,No.10. Indonesia
| | - Sunanto
- Research Organization for Governance, Economy, and Community Welfare, Jl.Gatot Subroto,No.10. Indonesia
| | - Andi Yulyani Fadwiwati
- Research Organization for Governance, Economy, and Community Welfare, Jl.Gatot Subroto,No.10. Indonesia
| | - Maryam Nurdin
- Research Organization for Governance, Economy, and Community Welfare, Jl.Gatot Subroto,No.10. Indonesia
| | - Dahya
- Research Organization for Governance, Economy, and Community Welfare, Jl.Gatot Subroto,No.10. Indonesia
| | - Andi Ella
- Research Center for Animal Husbandry, Research Organization for Agriculture and Food, National Research and Innovation Agency, Jl. Raya Jakarta-Bogor, Km 46, Cibinong, Bogor, West Java, 16911, Indonesia
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5
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Sachdeva S, Singh R, Maurya A, Singh VK, Singh UM, Kumar A, Singh GP. New insights into QTNs and potential candidate genes governing rice yield via a multi-model genome-wide association study. BMC PLANT BIOLOGY 2024; 24:124. [PMID: 38373874 PMCID: PMC10877931 DOI: 10.1186/s12870-024-04810-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 02/08/2024] [Indexed: 02/21/2024]
Abstract
BACKGROUND Rice (Oryza sativa L.) is one of the globally important staple food crops, and yield-related traits are prerequisites for improved breeding efficiency in rice. Here, we used six different genome-wide association study (GWAS) models for 198 accessions, with 553,229 single nucleotide markers (SNPs) to identify the quantitative trait nucleotides (QTNs) and candidate genes (CGs) governing rice yield. RESULTS Amongst the 73 different QTNs in total, 24 were co-localized with already reported QTLs or loci in previous mapping studies. We obtained fifteen significant QTNs, pathway analysis revealed 10 potential candidates within 100kb of these QTNs that are predicted to govern plant height, days to flowering, and plot yield in rice. Based on their superior allelic information in 20 elite and 6 inferior genotypes, we found a higher percentage of superior alleles in the elite genotypes in comparison to inferior genotypes. Further, we implemented expression analysis and enrichment analysis enabling the identification of 73 candidate genes and 25 homologues of Arabidopsis, 19 of which might regulate rice yield traits. Of these candidate genes, 40 CGs were found to be enriched in 60 GO terms of the studied traits for instance, positive regulator metabolic process (GO:0010929), intracellular part (GO:0031090), and nucleic acid binding (GO:0090079). Haplotype and phenotypic variation analysis confirmed that LOC_OS09G15770, LOC_OS02G36710 and LOC_OS02G17520 are key candidates associated with rice yield. CONCLUSIONS Overall, we foresee that the QTNs, putative candidates elucidated in the study could summarize the polygenic regulatory networks controlling rice yield and be useful for breeding high-yielding varieties.
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Grants
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR32853/AGIII/103/1159/2019 Department of Biotechnology, Ministry of Science and Technology, India
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Affiliation(s)
- Supriya Sachdeva
- Division of Genomic Resources, ICAR-NBPGR, Pusa, New Delhi, India
| | - Rakesh Singh
- Division of Genomic Resources, ICAR-NBPGR, Pusa, New Delhi, India.
| | - Avantika Maurya
- Division of Genomic Resources, ICAR-NBPGR, Pusa, New Delhi, India
| | - Vikas K Singh
- International Rice Research Institute (IRRI), South Asia Hub, ICRISAT, Hyderabad, India
| | - Uma Maheshwar Singh
- International Rice Research Institute (IRRI), South Asia Regional Centre (ISARC), Varanasi, India
| | - Arvind Kumar
- International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Telangana, India
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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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7
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Wang T, He W, Li X, Zhang C, He H, Yuan Q, Zhang B, Zhang H, Leng Y, Wei H, Xu Q, Shi C, Liu X, Guo M, Wang X, Chen W, Zhang Z, Yang L, Lv Y, Qian H, Zhang B, Yu X, Liu C, Cao X, Cui Y, Zhang Q, Dai X, Guo L, Wang Y, Zhou Y, Ruan J, Qian Q, Shang L. A rice variation map derived from 10 548 rice accessions reveals the importance of rare variants. Nucleic Acids Res 2023; 51:10924-10933. [PMID: 37843097 PMCID: PMC10639064 DOI: 10.1093/nar/gkad840] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 09/08/2023] [Accepted: 09/21/2023] [Indexed: 10/17/2023] Open
Abstract
Detailed knowledge of the genetic variations in diverse crop populations forms the basis for genetic crop improvement and gene functional studies. In the present study, we analyzed a large rice population with a total of 10 548 accessions to construct a rice super-population variation map (RSPVM), consisting of 54 378 986 single nucleotide polymorphisms, 11 119 947 insertion/deletion mutations and 184 736 presence/absence variations. Assessment of variation detection efficiency for different population sizes revealed a sharp increase of all types of variation as the population size increased and a gradual saturation of that after the population size reached 10 000. Variant frequency analysis indicated that ∼90% of the obtained variants were rare, and would therefore likely be difficult to detect in a relatively small population. Among the rare variants, only 2.7% were predicted to be deleterious. Population structure, genetic diversity and gene functional polymorphism of this large population were evaluated based on different subsets of RSPVM, demonstrating the great potential of RSPVM for use in downstream applications. Our study provides both a rich genetic basis for understanding natural rice variations and a powerful tool for exploiting great potential of rare variants in future rice research, including population genetics and functional genomics.
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Affiliation(s)
- Tianyi Wang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
- State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng 475004, China
- Shenzhen Research Institute of Henan university, Shenzhen 518000, China
| | - Wenchuang He
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Xiaoxia Li
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Chao Zhang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Huiying He
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Qiaoling Yuan
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Bin Zhang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Hong Zhang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Yue Leng
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Hua Wei
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Qiang Xu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Chuanlin Shi
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Xiangpei Liu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Mingliang Guo
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Xianmeng Wang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Wu Chen
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Zhipeng Zhang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Longbo Yang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Yang Lv
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Hongge Qian
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Bintao Zhang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Xiaoman Yu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Congcong Liu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Xinglan Cao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Yan Cui
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Qianqian Zhang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Xiaofan Dai
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Longbiao Guo
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China
| | - Yuexing Wang
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China
| | - Yongfeng Zhou
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Jue Ruan
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Qian Qian
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China
- Yazhouwan National Laboratory, No. 8 Huanjin Road, Yazhou District, Sanya City, Hainan Province 572024, China
| | - Lianguang Shang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
- Yazhouwan National Laboratory, No. 8 Huanjin Road, Yazhou District, Sanya City, Hainan Province 572024, China
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Peringottillam M, Sundaram KT, Manickavelu A. Genetic potential of grain-related traits in rice landraces: phenomics and multi-locus association analyses. Mol Biol Rep 2023; 50:9323-9334. [PMID: 37815669 DOI: 10.1007/s11033-023-08807-5] [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: 06/23/2023] [Accepted: 09/07/2023] [Indexed: 10/11/2023]
Abstract
BACKGROUND Grain length, width, weight, and the number of grains per panicle are crucial determinants contributing to yield in cereal crops. Understanding the genetic basis of grain-related traits has been the main research object in crop science. METHODS AND RESULTS Kerala has a collection of different rice landraces. Characterization of these valuable genetic resources for 39 distinct agro-morphological traits was carried out in two seasons from 2017 to 2019 directly in farmers field. Most characteristics were polymorphic except ligule shape, leaf angle, and panicle axis. The results of principal component analysis implied that leaf length, plant height, culm length, flag leaf length, and grain-related traits were the principal discriminatory characteristics of rice landraces. For identifying the genetic basis of key grain traits of rice, three multi locus GWAS models were performed based on 1,47,994 SNPs in 73 rice accessions. As a result, 48 quantitative trait nucleotides (QTNs) were identified to be associated with these traits. After characterization of their function and expression, 15 significant candidate genes involved in regulating grain width, number of grains per panicle, and yield were identified. CONCLUSIONS The detected QTNs and candidate genes in this study could be further used for marker-assisted high-quality breeding of rice.
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Affiliation(s)
- Maya Peringottillam
- Department of Genomic Science, Central University of Kerala, Tejaswini Hills, Periye, Kasaragod, 671316, Kerala, India
| | - Krishna T Sundaram
- Department of Genomic Science, Central University of Kerala, Tejaswini Hills, Periye, Kasaragod, 671316, Kerala, India
- International Rice Research Institute (IRRI), South Asia hub, Patancheru, India
| | - Alagu Manickavelu
- Department of Genomic Science, Central University of Kerala, Tejaswini Hills, Periye, Kasaragod, 671316, Kerala, India.
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9
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Lei X, Li H, Li P, Zhang H, Han Z, Yang B, Duan Y, Njeri NS, Yang D, Zheng J, Ma Y, Zhu X, Fang W. Genome-Wide Association Studies of Biluochun Tea Plant Populations in Dongting Mountain and Comprehensive Identification of Candidate Genes Associated with Core Agronomic Traits by Four Analysis Models. PLANTS (BASEL, SWITZERLAND) 2023; 12:3719. [PMID: 37960075 PMCID: PMC10650078 DOI: 10.3390/plants12213719] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/20/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023]
Abstract
The elite germplasm resources are key to the beautiful appearance and pleasant flavor of Biluochun tea. We collected and measured the agronomic traits of 95 tea plants to reveal the trait diversity and breeding value of Biluochun tea plant populations. The results revealed that the agronomic traits of Biluochun tea plant populations were diverse and had high breeding value. Additionally, we resequenced these tea plant populations to reveal genetic diversity, population structure, and selection pressure. The Biluochun tea plant populations contained two groups and were least affected by natural selection based on the results of population structure and selection pressure. More importantly, four non-synonymous single nucleotide polymorphisms (nsSNPs) and candidate genes associated with (-)-gallocatechin gallate (GCG), (-)-gallocatechin (GC), and caffeine (CAF) were detected using at least two GWAS models. The results will promote the development and application of molecular markers and the utilization of elite germplasm from Biluochun populations.
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Affiliation(s)
- Xiaogang Lei
- College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (P.L.); (H.Z.); (Z.H.); (B.Y.); (Y.D.); (N.S.N.); (Y.M.); (X.Z.)
| | - Haoyu Li
- Dongshan Agriculture and Forestry Service Station, Suzhou 215100, China; (H.L.); (D.Y.); (J.Z.)
| | - Pingping Li
- College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (P.L.); (H.Z.); (Z.H.); (B.Y.); (Y.D.); (N.S.N.); (Y.M.); (X.Z.)
| | - Huan Zhang
- College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (P.L.); (H.Z.); (Z.H.); (B.Y.); (Y.D.); (N.S.N.); (Y.M.); (X.Z.)
| | - Zhaolan Han
- College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (P.L.); (H.Z.); (Z.H.); (B.Y.); (Y.D.); (N.S.N.); (Y.M.); (X.Z.)
| | - Bin Yang
- College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (P.L.); (H.Z.); (Z.H.); (B.Y.); (Y.D.); (N.S.N.); (Y.M.); (X.Z.)
| | - Yu Duan
- College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (P.L.); (H.Z.); (Z.H.); (B.Y.); (Y.D.); (N.S.N.); (Y.M.); (X.Z.)
| | - Ndombi Salome Njeri
- College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (P.L.); (H.Z.); (Z.H.); (B.Y.); (Y.D.); (N.S.N.); (Y.M.); (X.Z.)
| | - Daqiang Yang
- Dongshan Agriculture and Forestry Service Station, Suzhou 215100, China; (H.L.); (D.Y.); (J.Z.)
| | - Junhua Zheng
- Dongshan Agriculture and Forestry Service Station, Suzhou 215100, China; (H.L.); (D.Y.); (J.Z.)
| | - Yuanchun Ma
- College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (P.L.); (H.Z.); (Z.H.); (B.Y.); (Y.D.); (N.S.N.); (Y.M.); (X.Z.)
| | - Xujun Zhu
- College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (P.L.); (H.Z.); (Z.H.); (B.Y.); (Y.D.); (N.S.N.); (Y.M.); (X.Z.)
| | - Wanping Fang
- College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China; (X.L.); (P.L.); (H.Z.); (Z.H.); (B.Y.); (Y.D.); (N.S.N.); (Y.M.); (X.Z.)
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10
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He L, Sui Y, Che Y, Wang H, Rashid KY, Cloutier S, You FM. Genome-wide association studies using multi-models and multi-SNP datasets provide new insights into pasmo resistance in flax. FRONTIERS IN PLANT SCIENCE 2023; 14:1229457. [PMID: 37954993 PMCID: PMC10634603 DOI: 10.3389/fpls.2023.1229457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 07/24/2023] [Indexed: 11/14/2023]
Abstract
Introduction Flax (Linum usitatissimum L.) is an economically important crop due to its oil and fiber. However, it is prone to various diseases, including pasmo caused by the fungus Septoria linicola. Methods In this study, we conducted field evaluations of 445 flax accessions over a five-year period (2012-2016) to assess their resistance to pasmo A total of 246,035 single nucleotide polymorphisms (SNPs) were used for genetic analysis. Four statistical models, including the single-locus model GEMMA and the multi-locus models FarmCPU, mrMLM, and 3VmrMLM, were assessed to identify quantitative trait nucleotides (QTNs) associated with pasmo resistance. Results We identified 372 significant QTNs or 132 tag QTNs associated with pasmo resistance from five pasmo resistance datasets (PAS2012-PAS2016 and the 5-year average, namely PASmean) and three genotypic datasets (the all SNPs/ALL, the gene-based SNPs/GB and the RGA-based SNPs/RGAB). The tag QTNs had R2 values of 0.66-16.98% from the ALL SNP dataset, 0.68-20.54%from the GB SNP dataset, and 0.52-22.42% from the RGAB SNP dataset. Of these tag QTNs, 93 were novel. Additionally, 37 resistance gene analogs (RGAs)co-localizing with 39 tag QTNs were considered as potential candidates for controlling pasmo resistance in flax and 50 QTN-by-environment interactions(QEIs) were identified to account for genes by environmental interactions. Nine RGAs were predicted as candidate genes for ten QEIs. Discussion Our results suggest that pasmo resistance in flax is polygenic and potentially influenced by environmental factors. The identified QTNs provide potential targets for improving pasmo resistance in flax breeding programs. This study sheds light on the genetic basis of pasmo resistance and highlights the importance of considering both genetic and environmental factors in breeding programs for flax.
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Affiliation(s)
- Liqiang He
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
- School of Tropical Agriculture and Forestry, School of Tropical Crops, Hainan University, Haikou, China
| | - Yao Sui
- School of Tropical Agriculture and Forestry, School of Tropical Crops, Hainan University, Haikou, China
| | - Yanru Che
- School of Tropical Agriculture and Forestry, School of Tropical Crops, Hainan University, Haikou, China
| | - Huixian Wang
- School of Tropical Agriculture and Forestry, School of Tropical Crops, Hainan University, Haikou, China
| | - Khalid Y. Rashid
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Sylvie Cloutier
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Frank M. You
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
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11
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Zhang Q, Ye Z, Wang Y, Zhang X, Kong W. Haplotype-Resolution Transcriptome Analysis Reveals Important Responsive Gene Modules and Allele-Specific Expression Contributions under Continuous Salt and Drought in Camellia sinensis. Genes (Basel) 2023; 14:1417. [PMID: 37510320 PMCID: PMC10379978 DOI: 10.3390/genes14071417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 06/29/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
The tea plant, Camellia sinensis (L.) O. Kuntze, is one of the most important beverage crops with significant economic and cultural value. Global climate change and population growth have led to increased salt and drought stress, negatively affecting tea yield and quality. The response mechanism of tea plants to these stresses remains poorly understood due to the lack of reference genome-based transcriptional descriptions. This study presents a high-quality genome-based transcriptome dynamic analysis of C. sinensis' response to salt and drought stress. A total of 2244 upregulated and 2164 downregulated genes were identified under salt and drought stress compared to the control sample. Most of the differentially expression genes (DEGs) were found to involve divergent regulation processes at different time points under stress. Some shared up- and downregulated DEGs related to secondary metabolic and photosynthetic processes, respectively. Weighted gene co-expression network analysis (WGCNA) revealed six co-expression modules significantly positively correlated with C. sinensis' response to salt or drought stress. The MEpurple module indicated crosstalk between the two stresses related to ubiquitination and the phenylpropanoid metabolic regulation process. We identified 1969 salt-responsive and 1887 drought-responsive allele-specific expression (ASE) genes in C. sinensis. Further comparison between these ASE genes and tea plant heterosis-related genes suggests that heterosis likely contributes to the adversity and stress resistance of C. sinensis. This work offers new insight into the underlying mechanisms of C. sinensis' response to salt and drought stress and supports the improved breeding of tea plants with enhanced salt and drought tolerance.
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Affiliation(s)
- Qing Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Ziqi Ye
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Yinghao Wang
- Center for Genomics and Biotechnology, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Xingtan Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Weilong Kong
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
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12
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Wang N, Chen H, Qian Y, Liang Z, Zheng G, Xiang J, Feng T, Li M, Zeng W, Bao Y, Liu E, Zhang C, Xu J, Shi Y. Genome-Wide Association Study of Rice Grain Shape and Chalkiness in a Worldwide Collection of Xian Accessions. PLANTS (BASEL, SWITZERLAND) 2023; 12:419. [PMID: 36771503 PMCID: PMC9919668 DOI: 10.3390/plants12030419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/11/2023] [Accepted: 01/13/2023] [Indexed: 06/18/2023]
Abstract
Rice (Oryza sativa L.) appearance quality, which is mainly defined by grain shape and chalkiness, is an important target in rice breeding. In this study, we first re-sequenced 137 indica accessions and then conducted a genome-wide association study (GWAS) for six agronomic traits with the 2,998,034 derived single nucleotide polymorphisms (SNPs) by using the best linear unbiased prediction (BLUP) values for each trait. The results revealed that 195 SNPs had significant associations with the six agronomic traits. Based on the genome-wide linkage disequilibrium (LD) blocks, candidate genes for the target traits were detected within 100 kb upstream and downstream of the relevant SNP loci. Results indicate that six quantitative trait loci (QTLs) significantly associated with six traits (qTGW4.1, qTGW4.2, qGL4.1, qGL12.1, qGL12.2, qGW2.1, qGW4.1, qGW6.1, qGW8.1, qGW8.2, qGW9.1, qGW11.1, qGLWR2.1, qGLWR2.2, qGLWR4.2, qPGWC5.1 and qDEC6.1) were identified for haplotype analysis. Among these QTLs, two (qTGW4.2 and qGW6.1), were overlapped with FLO19 and OsbZIP47, respectively, and the remaining four were novel QTLs. These candidate genes were further validated by haplotype block construction.
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Affiliation(s)
- Nansheng Wang
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Huguang Chen
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yingzhi Qian
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Zhaojie Liang
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Guiqiang Zheng
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Jun Xiang
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Ting Feng
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Min Li
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Wei Zeng
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Yaling Bao
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Erbao Liu
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Chaopu Zhang
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
| | - Jianlong Xu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yingyao Shi
- College of Agronomy, Anhui Agricultural University, Hefei 230036, China
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Zhang C, Gong R, Zhong H, Dai C, Zhang R, Dong J, Li Y, Liu S, Hu J. Integrated multi-locus genome-wide association studies and transcriptome analysis for seed yield and yield-related traits in Brassica napus. FRONTIERS IN PLANT SCIENCE 2023; 14:1153000. [PMID: 37123841 PMCID: PMC10140536 DOI: 10.3389/fpls.2023.1153000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 03/21/2023] [Indexed: 05/03/2023]
Abstract
Rapeseed (Brassica napus L.), the third largest oil crop, is an important source of vegetable oil and biofuel for the world. Although the breeding and yield has been improved, rapeseed still has the lowest yield compared with other major crops. Thus, increasing rapeseed yield is essential for the high demand of vegetable oil and high-quality protein for live stocks. Silique number per plant (SN), seed per pod (SP), and 1000-seed weight (SW) are the three important factors for seed yield in rapeseed. Some yield-related traits, including plant height (PH), flowering time (FT), primary branch number (BN) and silique number per inflorescence (SI) also affect the yield per plant (YP). Using six multi-locus genome-wide association study (ML-GWAS) approaches, a total of 908 yield-related quantitative trait nucleotides (QTNs) were identified in a panel consisting of 403 rapeseed core accessions based on whole-genome sequencing. Integration of ML-GWAS with transcriptome analysis, 79 candidate genes, including BnaA09g39790D (RNA helicase), BnaA09g39950D (Lipase) and BnaC09g25980D (SWEET7), were further identified and twelve genes were validated by qRT-PCRs to affect the SW or SP in rapeseed. The distribution of superior alleles from nineteen stable QTNs in 20 elite rapeseed accessions suggested that the high-yielding accessions contained more superior alleles. These results would contribute to a further understanding of the genetic basis of yield-related traits and could be used for crop improvement in B. napus.
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Affiliation(s)
- Cuiping Zhang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Ruolin Gong
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Hua Zhong
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Chunyan Dai
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Ru Zhang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Jungang Dong
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
| | - Yangsheng Li
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, China
| | - Shuai Liu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii at Manoa, Honolulu, HI, United States
- *Correspondence: Jihong Hu, ; Shuai Liu,
| | - Jihong Hu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Yangling, China
- *Correspondence: Jihong Hu, ; Shuai Liu,
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Zhang M, Lai L, Liu X, Liu J, Liu R, Wang Y, Liu J, Chen J. Overexpression of Nitrate Transporter 1/Peptide Gene OsNPF7.6 Increases Rice Yield and Nitrogen Use Efficiency. LIFE (BASEL, SWITZERLAND) 2022; 12:life12121981. [PMID: 36556346 PMCID: PMC9786031 DOI: 10.3390/life12121981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/09/2022] [Accepted: 11/11/2022] [Indexed: 11/29/2022]
Abstract
Overuse of nitrogen fertilizer in fields has raised production costs, and caused environmental problems. Improving nitrogen use efficiency (NUE) of rice is essential for sustainable agriculture. Here we report the cloning, characterization and roles for rice of OsNPF7.6, a member of the nitrate transporter 1/peptide transporter family (NPF). The OsNPF7.6 protein is located in the plasma membrane, expressed in each tissue at all stages and is significantly regulated by nitrate in rice. Our study shows that the overexpression of OsNPF7.6 can increase the nitrate uptake rate of rice. Additionally, field experiments showed that OsNPF7.6 overexpression increased the total tiller number per plant and the grain weight per panicle, thereby improving grain yield and agronomic NUE in rice. Thus, OsNPF7.6 can be applied to be a novel target gene for breeding rice varieties with high NUE, and provide a reference for breeding higher yielding rice.
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Affiliation(s)
- Min Zhang
- College of Pharmacy, Henan University of Chinese Medicine, Zhengzhou 450046, China
| | - Liuru Lai
- School of Agriculture, Shenzhen Campus, Sun Yat-sen University, Shenzhen 518107, China
| | - Xintong Liu
- School of Agriculture, Shenzhen Campus, Sun Yat-sen University, Shenzhen 518107, China
| | - Jiajia Liu
- Shandong Jinchunyu Seed Technology Co., Ltd., Jining 272200, China
| | - Ruifang Liu
- The High School Affiliated to Renmin University of China, Shenzhen 518119, China
| | - Yamei Wang
- School of Agriculture, Shenzhen Campus, Sun Yat-sen University, Shenzhen 518107, China
| | - Jindong Liu
- Institute of Crop Sciences, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun South Street, Beijing 100081, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
- Correspondence: (J.L.); (J.C.)
| | - Jingguang Chen
- School of Agriculture, Shenzhen Campus, Sun Yat-sen University, Shenzhen 518107, China
- Correspondence: (J.L.); (J.C.)
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15
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Yasir M, Kanwal HH, Hussain Q, Riaz MW, Sajjad M, Rong J, Jiang Y. Status and prospects of genome-wide association studies in cotton. FRONTIERS IN PLANT SCIENCE 2022; 13:1019347. [PMID: 36330239 PMCID: PMC9623101 DOI: 10.3389/fpls.2022.1019347] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 09/08/2022] [Indexed: 06/16/2023]
Abstract
Over the last two decades, the use of high-density SNP arrays and DNA sequencing have allowed scientists to uncover the majority of the genotypic space for various crops, including cotton. Genome-wide association study (GWAS) links the dots between a phenotype and its underlying genetics across the genomes of populations. It was first developed and applied in the field of human disease genetics. Many areas of crop research have incorporated GWAS in plants and considerable literature has been published in the recent decade. Here we will provide a comprehensive review of GWAS studies in cotton crop, which includes case studies on biotic resistance, abiotic tolerance, fiber yield and quality traits, current status, prospects, bottlenecks of GWAS and finally, thought-provoking question. This review will serve as a catalog of GWAS in cotton and suggest new frontiers of the cotton crop to be studied with this important tool.
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Affiliation(s)
- Muhammad Yasir
- The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Hangzhou, China
| | - Hafiza Hamrah Kanwal
- School of Computer Science, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Quaid Hussain
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, China
| | - Muhammad Waheed Riaz
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, China
| | - Muhammad Sajjad
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, China
| | - Junkang Rong
- The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Hangzhou, China
| | - Yurong Jiang
- The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Hangzhou, China
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Zhang J, Wang S, Wu X, Han L, Wang Y, Wen Y. Identification of QTNs, QTN-by-environment interactions and genes for yield-related traits in rice using 3VmrMLM. FRONTIERS IN PLANT SCIENCE 2022; 13:995609. [PMID: 36325550 PMCID: PMC9618716 DOI: 10.3389/fpls.2022.995609] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
Rice, which supports more than half the population worldwide, is one of the most important food crops. Thus, potential yield-related quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs) have been used to develop efficient rice breeding strategies. In this study, a compressed variance component mixed model, 3VmrMLM, in genome-wide association studies was used to detect QTNs for eight yield-related traits of 413 rice accessions with 44,000 single nucleotide polymorphisms. These traits include florets per panicle, panicle fertility, panicle length, panicle number per plant, plant height, primary panicle branch number, seed number per panicle, and flowering time. Meanwhile, QTNs and QEIs were identified for flowering times in three different environments and five subpopulations. In the detections, a total of 7~23 QTNs were detected for each trait, including the three single-environment flowering time traits. In the detection of QEIs for flowering time in the three environments, 21 QTNs and 13 QEIs were identified. In the five subpopulation analyses, 3~9 QTNs and 2~4 QEIs were detected for each subpopulation. Based on previous studies, we identified 87 known genes around the significant/suggested QTNs and QEIs, such as LOC_Os06g06750 (OsMADS5) and LOC_Os07g47330 (FZP). Further differential expression analysis and functional enrichment analysis identified 30 candidate genes. Of these candidate genes, 27 genes had high expression in specific tissues, and 19 of these 27 genes were homologous to known genes in Arabidopsis. Haplotype difference analysis revealed that LOC_Os04g53210 and LOC_Os07g42440 are possibly associated with yield, and LOC_Os04g53210 may be useful around a QEI for flowering time. These results provide insights for future breeding for high quality and yield in rice.
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Affiliation(s)
- Jin Zhang
- College of Science, Nanjing Agricultural University, Nanjing, China
- Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Shengmeng Wang
- College of Science, Nanjing Agricultural University, Nanjing, China
| | - Xinyi Wu
- College of Science, Nanjing Agricultural University, Nanjing, China
| | - Le Han
- College of Science, Nanjing Agricultural University, Nanjing, China
| | - Yuan Wang
- College of Science, Nanjing Agricultural University, Nanjing, China
| | - Yangjun Wen
- College of Science, Nanjing Agricultural University, Nanjing, China
- Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
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Liu H, Zou M, Zhang B, Yang X, Yuan P, Ding G, Xu F, Shi L. Genome-wide association study identifies candidate genes and favorable haplotypes for seed yield in Brassica napus. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:61. [PMID: 37313016 PMCID: PMC10248642 DOI: 10.1007/s11032-022-01332-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 09/19/2022] [Indexed: 06/15/2023]
Abstract
Oilseed rape (Brassica napus L.) is one of the most essential oil crops. Genetic improvement of seed yield (SY) is a major aim of B. napus breeding. Several studies have been reported on the genetic mechanisms of SY of B. napus. Here, a genome-wide association study (GWAS) of SY was conducted using a panel of 403 natural accessions of B. napus, with more than five million high-quality single-nucleotide polymorphisms (SNPs). A total of 1773 significant SNPs were detected associated with SY, and 783 significant SNPs were co-located with previously reported QTLs. The lead SNPs chrA01__8920351 and chrA02__4555979 were jointly detected in Trial 2_2 and Trial 2_mean value, and in Trial 1_2 and Trial 1_mean value, respectively. Subsequently, two candidate genes of BnaA01g17200D and BnaA02g08680D were identified through combining transcriptome, candidate gene association analysis, and haplotype analysis. BnaA09g10430D detected through lead SNP chrA09__5160639 was associated with SY of B. napus. Our results provide valuable information for studying the genetic control of seed yield in B. napus and valuable genes, haplotypes, and cultivars resources for the breeding of high seed yield B. napus cultivars. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01332-6.
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Affiliation(s)
- Haijiang Liu
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
| | - Maoyan Zou
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
| | - Bingbing Zhang
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
| | - Xinyu Yang
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
| | - Pan Yuan
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
| | - Guangda Ding
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
| | - Fangsen Xu
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
| | - Lei Shi
- National Key Lab of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
- Key Lab of Cultivated Land Conservation, Ministry of Agriculture and Rural Affairs/Microelement Research Centre, Huazhong Agricultural University, Wuhan, 430070 Hubei Province China
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Gupta PK. GWAS for genetics of complex quantitative traits: Genome to pangenome and SNPs to SVs and k-mers. Bioessays 2021; 43:e2100109. [PMID: 34486143 DOI: 10.1002/bies.202100109] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/21/2021] [Accepted: 08/23/2021] [Indexed: 12/22/2022]
Abstract
The development of improved methods for genome-wide association studies (GWAS) for genetics of quantitative traits has been an active area of research during the last 25 years. This activity initially started with the use of mixed linear model (MLM), which was variously modified. During the last decade, however, with the availability of high throughput next generation sequencing (NGS) technology, development and use of pangenomes and novel markers including structural variations (SVs) and k-mers for GWAS has taken over as a new thrust area of research. Pangenomes and SVs are now available in humans, livestock, and a number of plant species, so that these resources along with k-mers are being used in GWAS for exploring additional genetic variation that was hitherto not available for analysis. These developments have resulted in significant improvement in GWAS methodology for detection of marker-trait associations (MTAs) that are relevant to human healthcare and crop improvement.
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Affiliation(s)
- Pushpendra K Gupta
- Department of Genetics and Plant Breeding, Ch. Charan Singh University Meerut, Meerut, Uttar Pradesh, India
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