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Bertolini E, Manjunath M, Ge W, Murphy MD, Inaoka M, Fliege C, Eveland AL, Lipka AE. Genomic prediction of cereal crop architectural traits using models informed by gene regulatory circuitries from maize. Genetics 2024:iyae162. [PMID: 39441092 DOI: 10.1093/genetics/iyae162] [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: 05/23/2024] [Accepted: 09/28/2024] [Indexed: 10/25/2024] Open
Abstract
Plant architecture is a major determinant of planting density, which enhances productivity potential for crops per unit area. Genomic prediction is well positioned to expedite genetic gain of plant architectural traits since they are typically highly heritable. Additionally, the adaptation of genomic prediction models to query predictive abilities of markers tagging certain genomic regions could shed light on the genetic architecture of these traits. Here, we leveraged transcriptional networks from a prior study that contextually described developmental progression during tassel and leaf organogenesis in maize (Zea mays) to inform genomic prediction models for architectural traits. Since these developmental processes underlie tassel branching and leaf angle, 2 important agronomic architectural traits, we tested whether genes prioritized from these networks quantitatively contribute to the genetic architecture of these traits. We used genomic prediction models to evaluate the ability of markers in the vicinity of prioritized network genes to predict breeding values of tassel branching and leaf angle traits for 2 diversity panels in maize and diversity panels from sorghum (Sorghum bicolor) and rice (Oryza sativa). Predictive abilities of markers near these prioritized network genes were similar to those using whole-genome marker sets. Notably, markers near highly connected transcription factors from core network motifs in maize yielded predictive abilities that were significantly greater than expected by chance in not only maize but also closely related sorghum. We expect that these highly connected regulators are key drivers of architectural variation that are conserved across closely related cereal crop species.
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Affiliation(s)
| | - Mohith Manjunath
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Weihao Ge
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Matthew D Murphy
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Mirai Inaoka
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Christina Fliege
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | | | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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Kazemzadeh S, Farrokhi N, Ahmadikhah A, Tabar Heydar K, Gilani A, Askari H, Ingvarsson PK. Genome-wide association study and genotypic variation for the major tocopherol content in rice grain. FRONTIERS IN PLANT SCIENCE 2024; 15:1426321. [PMID: 39439508 PMCID: PMC11493719 DOI: 10.3389/fpls.2024.1426321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 09/03/2024] [Indexed: 10/25/2024]
Abstract
Rice tocopherols, vitamin E compounds with antioxidant activity, play essential roles in human health. Even though the key genes involved in vitamin E biosynthetic pathways have been identified in plants, the genetic architecture of vitamin E content in rice grain remains unclear. A genome-wide association study (GWAS) on 179 genotypically diverse rice accessions with 34,323 SNP markers was conducted to detect QTLs that define total and α- tocopherol contents in rice grains. Total and α-tocopherol contents had a strong positive correlation and varied greatly across the accessions, ranging from 0.230-31.76 and 0.011-30.83 (μg/g), respectively. A total of 13 QTLs were identified, which were spread across five of the rice chromosomes. Among the 13 QTLs, 11 were considered major with phenotypic variation explained (PVE) greater than 10%. Twelve transcription factor (TF) genes, one microprotein (miP), and a transposon were found to be associated with the QTLs with putative roles in controlling tocopherol contents. Moreover, intracellular transport proteins, ABC transporters, nonaspanins, and SNARE, were identified as associated genes on chromosomes 1 and 8. In the vicinity of seven QTLs, protein kinases were identified as key signaling factors. Haplotype analysis revealed the QTLs qAlph1.1, qTot1.1, qAlph2.1, qAlph6.1, qTot6.1, and qTot8.3 to have significant haplogroups. Quantitative RT-PCR validated the expression direction and magnitude of WRKY39 (Os02g0265200), PIP5Ks (Os08g0450800), and MADS59 (Os06g0347700) in defining the major tocopherol contents. This study provides insights for ongoing biofortification efforts to breed and/or engineer vitamin E and antioxidant levels in rice and other cereals.
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Affiliation(s)
- Sara Kazemzadeh
- Department of Cell and Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University, Tehran, Iran
| | - Naser Farrokhi
- Department of Cell and Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University, Tehran, Iran
| | - Asadollah Ahmadikhah
- Department of Cell and Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University, Tehran, Iran
| | | | - Abdolali Gilani
- Agricultural and Natural Resources Research Institute of Khuzestan, Ahwaz, Iran
| | - Hossein Askari
- Department of Cell and Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University, Tehran, Iran
| | - Pär K. Ingvarsson
- Department of Plant Biology, Swedish University of Agricultural Sciences, Uppsala, Sweden
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Saballos AI, Brooks MD, Tranel PJ, Williams MM. Mapping of flumioxazin tolerance in a snap bean diversity panel leads to the discovery of a master genomic region controlling multiple stress resistance genes. FRONTIERS IN PLANT SCIENCE 2024; 15:1404889. [PMID: 39015289 PMCID: PMC11250381 DOI: 10.3389/fpls.2024.1404889] [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: 03/21/2024] [Accepted: 06/12/2024] [Indexed: 07/18/2024]
Abstract
Introduction Effective weed management tools are crucial for maintaining the profitable production of snap bean (Phaseolus vulgaris L.). Preemergence herbicides help the crop to gain a size advantage over the weeds, but the few preemergence herbicides registered in snap bean have poor waterhemp (Amaranthus tuberculatus) control, a major pest in snap bean production. Waterhemp and other difficult-to-control weeds can be managed by flumioxazin, an herbicide that inhibits protoporphyrinogen oxidase (PPO). However, there is limited knowledge about crop tolerance to this herbicide. We aimed to quantify the degree of snap bean tolerance to flumioxazin and explore the underlying mechanisms. Methods We investigated the genetic basis of herbicide tolerance using genome-wide association mapping approach utilizing field-collected data from a snap bean diversity panel, combined with gene expression data of cultivars with contrasting response. The response to a preemergence application of flumioxazin was measured by assessing plant population density and shoot biomass variables. Results Snap bean tolerance to flumioxazin is associated with a single genomic location in chromosome 02. Tolerance is influenced by several factors, including those that are indirectly affected by seed size/weight and those that directly impact the herbicide's metabolism and protect the cell from reactive oxygen species-induced damage. Transcriptional profiling and co-expression network analysis identified biological pathways likely involved in flumioxazin tolerance, including oxidoreductase processes and programmed cell death. Transcriptional regulation of genes involved in those processes is possibly orchestrated by a transcription factor located in the region identified in the GWAS analysis. Several entries belonging to the Romano class, including Bush Romano 350, Roma II, and Romano Purpiat presented high levels of tolerance in this study. The alleles identified in the diversity panel that condition snap bean tolerance to flumioxazin shed light on a novel mechanism of herbicide tolerance and can be used in crop improvement.
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Affiliation(s)
- Ana I. Saballos
- Global Change and Photosynthesis Research Unit, United States Department of Agriculture–Agricultural Research Service, Urbana, IL, United States
| | - Matthew D. Brooks
- Global Change and Photosynthesis Research Unit, United States Department of Agriculture–Agricultural Research Service, Urbana, IL, United States
| | - Patrick J. Tranel
- Department of Crop Sciences, University of Illinois, Urbana, IL, United States
| | - Martin M. Williams
- Global Change and Photosynthesis Research Unit, United States Department of Agriculture–Agricultural Research Service, Urbana, IL, United States
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Khan H, Krishnappa G, Kumar S, Devate NB, Rathan ND, Kumar S, Mishra CN, Ram S, Tiwari R, Parkash O, Ahlawat OP, Mamrutha HM, Singh GP, Singh G. Genome-wide association study identifies novel loci and candidate genes for rust resistance in wheat (Triticum aestivum L.). BMC PLANT BIOLOGY 2024; 24:411. [PMID: 38760694 PMCID: PMC11100168 DOI: 10.1186/s12870-024-05124-2] [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: 01/27/2024] [Accepted: 05/09/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Wheat rusts are important biotic stresses, development of rust resistant cultivars through molecular approaches is both economical and sustainable. Extensive phenotyping of large mapping populations under diverse production conditions and high-density genotyping would be the ideal strategy to identify major genomic regions for rust resistance in wheat. The genome-wide association study (GWAS) population of 280 genotypes was genotyped using a 35 K Axiom single nucleotide polymorphism (SNP) array and phenotyped at eight, 10, and, 10 environments, respectively for stem/black rust (SR), stripe/yellow rust (YR), and leaf/brown rust (LR). RESULTS Forty-one Bonferroni corrected marker-trait associations (MTAs) were identified, including 17 for SR and 24 for YR. Ten stable MTAs and their best combinations were also identified. For YR, AX-94990952 on 1A + AX-95203560 on 4A + AX-94723806 on 3D + AX-95172478 on 1A showed the best combination with an average co-efficient of infection (ACI) score of 1.36. Similarly, for SR, AX-94883961 on 7B + AX-94843704 on 1B and AX-94883961 on 7B + AX-94580041 on 3D + AX-94843704 on 1B showed the best combination with an ACI score of around 9.0. The genotype PBW827 have the best MTA combinations for both YR and SR resistance. In silico study identifies key prospective candidate genes that are located within MTA regions. Further, the expression analysis revealed that 18 transcripts were upregulated to the tune of more than 1.5 folds including 19.36 folds (TraesCS3D02G519600) and 7.23 folds (TraesCS2D02G038900) under stress conditions compared to the control conditions. Furthermore, highly expressed genes in silico under stress conditions were analyzed to find out the potential links to the rust phenotype, and all four genes were found to be associated with the rust phenotype. CONCLUSION The identified novel MTAs, particularly stable and highly expressed MTAs are valuable for further validation and subsequent application in wheat rust resistance breeding. The genotypes with favorable MTA combinations can be used as prospective donors to develop elite cultivars with YR and SR resistance.
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Affiliation(s)
- Hanif Khan
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, 132001, India
| | - Gopalareddy Krishnappa
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, 132001, India.
- ICAR-Sugarcane Breeding Institute, Coimbatore, 641007, India.
| | - Sudheer Kumar
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, 132001, India
| | - Narayana Bhat Devate
- International Centre for Agriculture Research in the Dry Area - Food Legume Research Platform, Amlaha, MP, 466113, India
| | | | - Satish Kumar
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, 132001, India
| | | | - Sewa Ram
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, 132001, India
| | - Ratan Tiwari
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, 132001, India
| | - Om Parkash
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, 132001, India
| | - Om Parkash Ahlawat
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, 132001, India
| | | | - Gyanendra Pratap Singh
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, 132001, India
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, 110012, India
| | - Gyanendra Singh
- ICAR-Indian Institute of Wheat and Barley Research, Karnal, 132001, India
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Zhang Z, Gomes Viana JP, Zhang B, Walden KKO, Müller Paul H, Moose SP, Morris GP, Daum C, Barry KW, Shakoor N, Hudson ME. Major impacts of widespread structural variation on sorghum. Genome Res 2024; 34:286-299. [PMID: 38479835 PMCID: PMC10984582 DOI: 10.1101/gr.278396.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 01/22/2024] [Indexed: 03/22/2024]
Abstract
Genetic diversity is critical to crop breeding and improvement, and dissection of the genomic variation underlying agronomic traits can both assist breeding and give insight into basic biological mechanisms. Although recent genome analyses in plants reveal many structural variants (SVs), most current studies of crop genetic variation are dominated by single-nucleotide polymorphisms (SNPs). The extent of the impact of SVs on global trait variation, as well as their utility in genome-wide selection, is not yet understood. In this study, we built an SV data set based on whole-genome resequencing of diverse sorghum lines (n = 363), validated the correlation of photoperiod sensitivity and variety type, and identified SV hotspots underlying the divergent evolution of cellulosic and sweet sorghum. In addition, we showed the complementary contribution of SVs for heritability of traits related to sorghum adaptation. Importantly, inclusion of SV polymorphisms in association studies revealed genotype-phenotype associations not observed with SNPs alone. Three-way genome-wide association studies (GWAS) based on whole-genome SNP, SV, and integrated SNP + SV data sets showed substantial associations between SVs and sorghum traits. The addition of SVs to GWAS substantially increased heritability estimates for some traits, indicating their important contribution to functional allelic variation at the genome level. Our discovery of the widespread impacts of SVs on heritable gene expression variation could render a plausible mechanism for their disproportionate impact on phenotypic variation. This study expands our knowledge of SVs and emphasizes the extensive impacts of SVs on sorghum.
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Affiliation(s)
- Zhihai Zhang
- DOE Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Joao Paulo Gomes Viana
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Bosen Zhang
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Kimberly K O Walden
- High Performance Computing in Biology, Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Hans Müller Paul
- DOE Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Stephen P Moose
- DOE Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Geoffrey P Morris
- Department of Soil and Crop Science, Colorado State University, Fort Collins, Colorado 80523, USA
| | - Chris Daum
- United States Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Kerrie W Barry
- United States Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Nadia Shakoor
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132, USA
| | - Matthew E Hudson
- DOE Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA;
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
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Jiang L, Lyu S, Yu H, Zhang J, Sun B, Liu Q, Mao X, Chen P, Pan D, Chen W, Fan Z, Li C. Transcription factor encoding gene OsC1 regulates leaf sheath color through anthocyanidin metabolism in Oryza rufipogon and Oryza sativa. BMC PLANT BIOLOGY 2024; 24:147. [PMID: 38418937 PMCID: PMC10900563 DOI: 10.1186/s12870-024-04823-0] [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: 07/17/2023] [Accepted: 02/14/2024] [Indexed: 03/02/2024]
Abstract
Carbohydrates, proteins, lipids, minerals and vitamins are nutrient substances commonly seen in rice grains, but anthocyanidin, with benefit for plant growth and animal health, exists mainly in the common wild rice but hardly in the cultivated rice. To screen the rice germplasm with high intensity of anthocyanidins and identify the variations, we used metabolomics technique and detected significant different accumulation of anthocyanidins in common wild rice (Oryza rufipogon, with purple leaf sheath) and cultivated rice (Oryza sativa, with green leaf sheath). In this study, we identified and characterized a well-known MYB transcription factor, OsC1, through phenotypic (leaf sheath color) and metabolic (metabolite profiling) genome-wide association studies (pGWAS and mGWAS) in 160 common wild rice (O. rufipogon) and 151 cultivated (O. sativa) rice varieties. Transgenic experiments demonstrated that biosynthesis and accumulation of cyanidin-3-Galc, cyanidin 3-O-rutinoside and cyanidin O-syringic acid, as well as purple pigmentation in leaf sheath were regulated by OsC1. A total of 25 sequence variations of OsC1 constructed 16 functional haplotypes (higher accumulation of the three anthocyanidin types within purple leaf sheath) and 9 non-functional haplotypes (less accumulation of anthocyanidins within green leaf sheath). Three haplotypes of OsC1 were newly identified in our germplasm, which have potential values in functional genomics and molecular breeding of rice. Gene-to-metabolite analysis by mGWAS and pGWAS provides a useful and efficient tool for functional gene identification and omics-based crop genetic improvement.
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Affiliation(s)
- Liqun Jiang
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Rice Engineering Laboratory, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, No. 3, Jinying East Road, Tianhe, Guangzhou, China
| | - Shuwei Lyu
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Rice Engineering Laboratory, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, No. 3, Jinying East Road, Tianhe, Guangzhou, China
| | - Hang Yu
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Rice Engineering Laboratory, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, No. 3, Jinying East Road, Tianhe, Guangzhou, China
| | - Jing Zhang
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Rice Engineering Laboratory, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, No. 3, Jinying East Road, Tianhe, Guangzhou, China
| | - Bingrui Sun
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Rice Engineering Laboratory, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, No. 3, Jinying East Road, Tianhe, Guangzhou, China
| | - Qing Liu
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Rice Engineering Laboratory, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, No. 3, Jinying East Road, Tianhe, Guangzhou, China
| | - Xingxue Mao
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Rice Engineering Laboratory, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, No. 3, Jinying East Road, Tianhe, Guangzhou, China
| | - Pingli Chen
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Rice Engineering Laboratory, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, No. 3, Jinying East Road, Tianhe, Guangzhou, China
| | - Dajian Pan
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Rice Engineering Laboratory, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, No. 3, Jinying East Road, Tianhe, Guangzhou, China
| | - Wenfeng Chen
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Rice Engineering Laboratory, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, No. 3, Jinying East Road, Tianhe, Guangzhou, China
| | - Zhilan Fan
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Guangdong Rice Engineering Laboratory, No. 3, Jinying East Road, Tianhe, Guangzhou, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, No. 3, Jinying East Road, Tianhe, Guangzhou, China
| | - Chen Li
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, No. 3, Jinying East Road, Tianhe, Guangzhou, China.
- Guangdong Key Laboratory of New Technology in Rice Breeding, No. 3, Jinying East Road, Tianhe, Guangzhou, China.
- Guangdong Rice Engineering Laboratory, No. 3, Jinying East Road, Tianhe, Guangzhou, China.
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, No. 3, Jinying East Road, Tianhe, Guangzhou, China.
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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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Sang Y, Liu X, Yuan C, Yao T, Li Y, Wang D, Zhao H, Wang Y. Genome-wide association study on resistance of cultivated soybean to Fusarium oxysporum root rot in Northeast China. BMC PLANT BIOLOGY 2023; 23:625. [PMID: 38062401 PMCID: PMC10702129 DOI: 10.1186/s12870-023-04646-5] [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: 07/10/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND Fusarium oxysporum is a prevalent fungal pathogen that diminishes soybean yield through seedling disease and root rot. Preventing Fusarium oxysporum root rot (FORR) damage entails on the identification of resistance genes and developing resistant cultivars. Therefore, conducting fine mapping and marker development for FORR resistance genes is of great significance for fostering the cultivation of resistant varieties. In this study, 350 soybean germplasm accessions, mainly from Northeast China, underwent genotyping using the SoySNP50K Illumina BeadChip, which includes 52,041 single nucleotide polymorphisms (SNPs). Their resistance to FORR was assessed in a greenhouse. Genome-wide association studies utilizing the general linear model, mixed linear model, compressed mixed linear model, and settlement of MLM under progressively exclusive relationship models were conducted to identify marker-trait associations while effectively controlling for population structure. RESULTS The results demonstrated that these models effectively managed population structure. Eight SNP loci significantly associated with FORR resistance in soybean were detected, primarily located on Chromosome 6. Notably, there was a strong linkage disequilibrium between the large-effect SNPs ss715595462 and ss715595463, contributing substantially to phenotypic variation. Within the genetic interval encompassing these loci, 28 genes were present, with one gene Glyma.06G088400 encoding a protein kinase family protein containing a leucine-rich repeat domain identified as a potential candidate gene in the reference genome of Williams82. Additionally, quantitative real-time reverse transcription polymerase chain reaction analysis evaluated the gene expression levels between highly resistant and susceptible accessions, focusing on primary root tissues collected at different time points after F. oxysporum inoculation. Among the examined genes, only this gene emerged as the strongest candidate associated with FORR resistance. CONCLUSIONS The identification of this candidate gene Glyma.06G088400 improves our understanding of soybean resistance to FORR and the markers strongly linked to resistance can be beneficial for molecular marker-assisted selection in breeding resistant soybean accessions against F. oxysporum.
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Affiliation(s)
- Yongsheng Sang
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, National Engineering Research Center for Soybean, Changchun, 130118, Jilin, PR China
- College of Agronomy, Jilin Agricultural University, Changchun, 130118, Jilin, PR China
| | - Xiaodong Liu
- Crop Germplasm Institute, Jilin Academy of Agricultural Sciences, Changchun, 130118, Jilin, China
| | - Cuiping Yuan
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, National Engineering Research Center for Soybean, Changchun, 130118, Jilin, PR China
| | - Tong Yao
- College of Agronomy, Jilin Agricultural University, Changchun, 130118, Jilin, PR China
| | - Yuqiu Li
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, National Engineering Research Center for Soybean, Changchun, 130118, Jilin, PR China
| | - Dechun Wang
- Department of Plant, Soil and Microbial Sciences, Michigan State University, 1066 Bogue St., Rm. A384-E, East Lansing, MI, 48824, USA
| | - Hongkun Zhao
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, National Engineering Research Center for Soybean, Changchun, 130118, Jilin, PR China.
| | - Yumin Wang
- Soybean Research Institute, Jilin Academy of Agricultural Sciences, National Engineering Research Center for Soybean, Changchun, 130118, Jilin, PR China.
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Sanchez DL, Samonte SOPB, Wilson LT. Genetic architecture of head rice and rice chalky grain percentages using genome-wide association studies. FRONTIERS IN PLANT SCIENCE 2023; 14:1274823. [PMID: 38046607 PMCID: PMC10691675 DOI: 10.3389/fpls.2023.1274823] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 10/30/2023] [Indexed: 12/05/2023]
Abstract
High head rice and low chalky grain percentages are key grain quality traits selected in developing rice cultivars. The objectives of this research were to characterize the phenotypic variation of head rice and chalky grain percentages in a diverse collection of rice accessions, identify single nucleotide polymorphism (SNP) markers associated with each of these traits using genome-wide association studies (GWAS), and identify putative candidate genes linked to the SNPs identified by GWAS. Diverse rice varieties, landraces, and breeding lines were grown at the Texas A&M AgriLife Research Center in Beaumont. Head rice percentages (HRP) and chalky grain percentages (CGP) of 195 and 199 non-waxy accessions were estimated in 2018 and 2019, respectively. Phenotypic data were analyzed along with 854,832 SNPs using three statistical models: mixed linear model (MLM), multi-locus mixed model (MLMM), and fixed and random model circulating probability unification (FarmCPU). Significant variations in HRP and CGP were observed between rice accessions. Two significant marker-trait associations (MTAs) were detected on chromosomes 1 and 2, respectively, based on best linear unbiased prediction (BLUP) values in 2018, while in 2019, one SNP was significantly associated with HRP in each of chromosomes 6, 8, 9, and 11, and two in chromosome 7. CGP was significantly associated with five SNPs located in chromosomes 2, 4, 6, and 8 in the 2018 study and ten SNPs in chromosomes 1, 2, 3, 4, 7, 8, 11, and 12 in the 2019 study. The SNPs are located within or linked to putative candidate genes involved in HRP and CGP. This study reports five and ten novel MTAs for HRP and CGP, respectively, while three and five MTAs co-located with previously reported quantitative trait loci for HRP and CGP, respectively. The validation of candidate genes for their roles in determining HRP and CGP is necessary to design functional molecular markers that can be used to effectively develop rice cultivars with desirable grain quality.
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10
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Lozano AC, Ding H, Abe N, Lipka AE. Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops. BMC Bioinformatics 2023; 24:399. [PMID: 37884874 PMCID: PMC10604903 DOI: 10.1186/s12859-023-05519-2] [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: 02/04/2022] [Accepted: 10/03/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND We consider two key problems in genomics involving multiple traits: multi-trait genome wide association studies (GWAS), where the goal is to detect genetic variants associated with the traits; and multi-trait genomic selection (GS), where the emphasis is on accurately predicting trait values. Multi-trait linear mixed models build on the linear mixed model to jointly model multiple traits. Existing estimation methods, however, are limited to the joint analysis of a small number of genotypes; in fact, most approaches consider one SNP at a time. Estimating multi-dimensional genetic and environment effects also results in considerable computational burden. Efficient approaches that incorporate regularization into multi-trait linear models (no random effects) have been recently proposed to identify genomic loci associated with multiple traits (Yu et al. in Multitask learning using task clustering with applications to predictive modeling and GWAS of plant varieties. arXiv:1710.01788 , 2017; Yu et al in Front Big Data 2:27, 2019), but these ignore population structure and familial relatedness (Yu et al in Nat Genet 38:203-208, 2006). RESULTS This work addresses this gap by proposing a novel class of regularized multi-trait linear mixed models along with scalable approaches for estimation in the presence of high-dimensional genotypes and a large number of traits. We evaluate the effectiveness of the proposed methods using datasets in maize and sorghum diversity panels, and demonstrate benefits in both achieving high prediction accuracy in GS and in identifying relevant marker-trait associations. CONCLUSIONS The proposed regularized multivariate linear mixed models are relevant for both GWAS and GS. We hope that they will facilitate agronomy-related research in plant biology and crop breeding endeavors.
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Affiliation(s)
- Aurélie C Lozano
- IBM Research AI, IBM T.J. Watson Reseach Center, Yorktown Heights, USA
| | | | - Naoki Abe
- IBM Research AI, IBM T.J. Watson Reseach Center, Yorktown Heights, USA
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois, Urbana-Champaign, USA.
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11
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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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12
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Joshi B, Singh S, Tiwari GJ, Kumar H, Boopathi NM, Jaiswal S, Adhikari D, Kumar D, Sawant SV, Iquebal MA, Jena SN. Genome-wide association study of fiber yield-related traits uncovers the novel genomic regions and candidate genes in Indian upland cotton ( Gossypium hirsutum L.). FRONTIERS IN PLANT SCIENCE 2023; 14:1252746. [PMID: 37941674 PMCID: PMC10630025 DOI: 10.3389/fpls.2023.1252746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 09/11/2023] [Indexed: 11/10/2023]
Abstract
Upland cotton (Gossypium hirsutum L.) is a major fiber crop that is cultivated worldwide and has significant economic importance. India harbors the largest area for cotton cultivation, but its fiber yield is still compromised and ranks 22nd in terms of productivity. Genetic improvement of cotton fiber yield traits is one of the major goals of cotton breeding, but the understanding of the genetic architecture underlying cotton fiber yield traits remains limited and unclear. To better decipher the genetic variation associated with fiber yield traits, we conducted a comprehensive genome-wide association mapping study using 117 Indian cotton germplasm for six yield-related traits. To accomplish this, we generated 2,41,086 high-quality single nucleotide polymorphism (SNP) markers using genotyping-by-sequencing (GBS) methods. Population structure, PCA, kinship, and phylogenetic analyses divided the germplasm into two sub-populations, showing weak relatedness among the germplasms. Through association analysis, 205 SNPs and 134 QTLs were identified to be significantly associated with the six fiber yield traits. In total, 39 novel QTLs were identified in the current study, whereas 95 QTLs overlapped with existing public domain data in a comparative analysis. Eight QTLs, qGhBN_SCY_D6-1, qGhBN_SCY_D6-2, qGhBN_SCY_D6-3, qGhSI_LI_A5, qGhLI_SI_A13, qGhLI_SI_D9, qGhBW_SCY_A10, and qGhLP_BN_A8 were identified. Gene annotation of these fiber yield QTLs revealed 2,509 unique genes. These genes were predominantly enriched for different biological processes, such as plant cell wall synthesis, nutrient metabolism, and vegetative growth development in the gene ontology (GO) enrichment study. Furthermore, gene expression analysis using RNAseq data from 12 diverse cotton tissues identified 40 candidate genes (23 stable and 17 novel genes) to be transcriptionally active in different stages of fiber, ovule, and seed development. These findings have revealed a rich tapestry of genetic elements, including SNPs, QTLs, and candidate genes, and may have a high potential for improving fiber yield in future breeding programs for Indian cotton.
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Affiliation(s)
- Babita Joshi
- Plant Genetic Resources and Improvement, CSIR-National Botanical Research Institute, Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Sanjay Singh
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Gopal Ji Tiwari
- Plant Genetic Resources and Improvement, CSIR-National Botanical Research Institute, Lucknow, India
| | - Harish Kumar
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Regional Research Station, Faridkot, Punjab, India
| | - Narayanan Manikanda Boopathi
- Department of Plant Biotechnology, Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
| | - Sarika Jaiswal
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Dibyendu Adhikari
- Plant Ecology and Climate Change Science, CSIR-National Botanical Research Institute, Lucknow, India
| | - Dinesh Kumar
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Samir V. Sawant
- Molecular Biology & Biotechnology, CSIR-National Botanical Research Institute, Lucknow, India
| | - Mir Asif Iquebal
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Satya Narayan Jena
- Plant Genetic Resources and Improvement, CSIR-National Botanical Research Institute, Lucknow, India
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Suganami M, Kojima S, Wang F, Yoshida H, Miura K, Morinaka Y, Watanabe M, Matsuda T, Yamamoto E, Matsuoka M. Effective use of legacy data in a genome-wide association studies improves the credibility of quantitative trait loci detection in rice. PLANT PHYSIOLOGY 2023; 191:1561-1573. [PMID: 36652387 PMCID: PMC10022637 DOI: 10.1093/plphys/kiad018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 12/16/2022] [Indexed: 06/17/2023]
Abstract
Genome-wide association studies (GWASs) are used to detect quantitative trait loci (QTL) using genomic and phenotypic data as inputs. While genomic data are obtained with high throughput and low cost, obtaining phenotypic data requires a large amount of effort and time. In past breeding programs, researchers and breeders have conducted a large number of phenotypic surveys and accumulated results as legacy data. In this study, we conducted a GWAS using phenotypic data of temperate japonica rice (Oryza sativa) varieties from a public database. The GWAS using the legacy data detected several known agriculturally important genes, indicating reliability of the legacy data for GWAS. By comparing the GWAS using legacy data (L-GWAS) and a GWAS using phenotypic data that we measured (M-GWAS), we detected reliable QTL for agronomically important traits. These results suggest that an L-GWAS is a strong alternative to replicate tests to confirm the reproducibility of QTL detected by an M-GWAS. In addition, because legacy data have often been accumulated for many traits, it is possible to evaluate the pleiotropic effect of the QTL identified for the specific trait that we focused on with respect to various other traits. This study demonstrates the effectiveness of using legacy data for GWASs and proposes the use of legacy data to accelerate genomic breeding.
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Affiliation(s)
- Mao Suganami
- Author for correspondence: (M.S.), (E.Y.), (M.M.)
| | - Soichi Kojima
- Graduate School of Agricultural Science, Tohoku University, Sendai 980-8572, Japan
| | - Fanmiao Wang
- Bioscience and Biotechnology Center, Nagoya University, Nagoya 464-8601, Japan
| | - Hideki Yoshida
- Faculty of Food and Agricultural Sciences, Institute of Fermentation Sciences, Fukushima University, Fukushima 960-1296, Japan
| | - Kotaro Miura
- Faculty of Bioscience and Biotechnology, Fukui Prefectural University, Fukui 910-1195, Japan
| | - Yoichi Morinaka
- Faculty of Bioscience and Biotechnology, Fukui Prefectural University, Fukui 910-1195, Japan
| | - Masao Watanabe
- Graduate School of Life Sciences, Tohoku University, Sendai 980-8577, Japan
| | - Tsukasa Matsuda
- Faculty of Food and Agricultural Sciences, Institute of Fermentation Sciences, Fukushima University, Fukushima 960-1296, Japan
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Bhat JA, Adeboye KA, Ganie SA, Barmukh R, Hu D, Varshney RK, Yu D. Genome-wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield-related traits in soybean ( Glycine max L.). Front Genet 2022; 13:953833. [PMID: 36419833 PMCID: PMC9677453 DOI: 10.3389/fgene.2022.953833] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/22/2022] [Indexed: 11/09/2022] Open
Abstract
Identifying the genetic components underlying yield-related traits in soybean is crucial for improving its production and productivity. Here, 211 soybean genotypes were evaluated across six environments for four yield-related traits, including seed yield per plant (SYP), number of pods per plant number of seeds per plant and 100-seed weight (HSW). Genome-wide association study (GWAS) and genomic prediction (GP) analyses were performed using 12,617 single nucleotide polymorphism markers from NJAU 355K SoySNP Array. A total of 57 SNPs were significantly associated with four traits across six environments and a combined environment using five Genome-wide association study models. Out of these, six significant SNPs were consistently identified in more than three environments using multiple GWAS models. The genomic regions (±670 kb) flanking these six consistent SNPs were considered stable QTL regions. Gene annotation and in silico expression analysis revealed 15 putative genes underlying the stable QTLs that might regulate soybean yield. Haplotype analysis using six significant SNPs revealed various allelic combinations regulating diverse phenotypes for the studied traits. Furthermore, the GP analysis revealed that accurate breeding values for the studied soybean traits is attainable at an earlier generation. Our study paved the way for increasing soybean yield performance within a short breeding cycle.
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Affiliation(s)
- Javaid Akhter Bhat
- Soybean Research Institution, National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
- International Genome Center, Jiangsu University, Zhenjiang, China
| | | | - Showkat Ahmad Ganie
- Plant Molecular Science and Centre of Systems and Synthetic Biology, Department of Biological Sciences, Royal Holloway University of London, Surrey, United Kingdom
| | - Rutwik Barmukh
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Dezhou Hu
- Soybean Research Institution, National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- Murdoch’s Centre for Crop & Food Innovation, State Agricultural Biotechnology Centre, Food Futures Institute, Murdoch University, Perth, WA, Australia
| | - Deyue Yu
- Soybean Research Institution, National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
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15
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Fernandes SB, Casstevens TM, Bradbury PJ, Lipka AE. A multi-trait multi-locus stepwise approach for conducting GWAS on correlated traits. THE PLANT GENOME 2022; 15:e20200. [PMID: 35307964 DOI: 10.1002/tpg2.20200] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 02/14/2022] [Indexed: 06/14/2023]
Abstract
The ability to accurately quantify the simultaneous effect of multiple genomic loci on multiple traits is now possible due to current and emerging high-throughput genotyping and phenotyping technologies. To date, most efforts to quantify these genotype-to-phenotype relationships have focused on either multi-trait models that test a single marker at a time or multi-locus models that quantify associations with a single trait. Therefore, the purpose of this study was to compare the performance of a multi-trait, multi-locus stepwise (MSTEP) model selection procedure we developed to (a) a commonly used multi-trait single-locus model and (b) a univariate multi-locus model. We used real marker data in maize (Zea mays L.) and soybean (Glycine max L.) to simulate multiple traits controlled by various combinations of pleiotropic and nonpleiotropic quantitative trait nucleotides (QTNs). In general, we found that both multi-trait models outperformed the univariate multi-locus model, especially when analyzing a trait of low heritability. For traits controlled by either a combination of pleiotropic and nonpleiotropic QTNs or a large number of QTNs (i.e., 50), our MSTEP model often outperformed at least one of the two alternative models. When applied to the analysis of two tocochromanol-related traits in maize grain, MSTEP identified the same peak-associated marker that has been reported in a previous study. We therefore conclude that MSTEP is a useful addition to the suite of statistical models that are commonly used to gain insight into the genetic architecture of agronomically important traits.
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Affiliation(s)
- Samuel B Fernandes
- Dep. of Crop Sciences, Univ. of Illinois Urbana-Champaign, Urbana, IL, USA
| | | | | | - Alexander E Lipka
- Dep. of Crop Sciences, Univ. of Illinois Urbana-Champaign, Urbana, IL, USA
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Feldmann MJ, Piepho HP, Knapp SJ. Average semivariance directly yields accurate estimates of the genomic variance in complex trait analyses. G3 GENES|GENOMES|GENETICS 2022; 12:6571389. [PMID: 35442424 PMCID: PMC9157152 DOI: 10.1093/g3journal/jkac080] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 03/17/2022] [Indexed: 11/23/2022]
Abstract
Many important traits in plants, animals, and microbes are polygenic and challenging to improve through traditional marker-assisted selection. Genomic prediction addresses this by incorporating all genetic data in a mixed model framework. The primary method for predicting breeding values is genomic best linear unbiased prediction, which uses the realized genomic relationship or kinship matrix (K) to connect genotype to phenotype. Genomic relationship matrices share information among entries to estimate the observed entries’ genetic values and predict unobserved entries’ genetic values. One of the main parameters of such models is genomic variance (σg2), or the variance of a trait associated with a genome-wide sample of DNA polymorphisms, and genomic heritability (hg2); however, the seminal papers introducing different forms of K often do not discuss their effects on the model estimated variance components despite their importance in genetic research and breeding. Here, we discuss the effect of several standard methods for calculating the genomic relationship matrix on estimates of σg2 and hg2. With current approaches, we found that the genomic variance tends to be either overestimated or underestimated depending on the scaling and centering applied to the marker matrix (Z), the value of the average diagonal element of K, and the assortment of alleles and heterozygosity (H) in the observed population. Using the average semivariance, we propose a new matrix, KASV, that directly yields accurate estimates of σg2 and hg2 in the observed population and produces best linear unbiased predictors equivalent to routine methods in plants and animals.
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Affiliation(s)
- Mitchell J Feldmann
- Department of Plant Sciences, University of California , Davis, CA 95616, USA
| | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim , 70593 Stuttgart, Germany
| | - Steven J Knapp
- Department of Plant Sciences, University of California , Davis, CA 95616, USA
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17
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Sanchez DL, Samonte SOP, Alpuerto JBB, Croaker PA, Morales KY, Yang Y, Wilson LT, Tabien RE, Yan Z, Thomson MJ, Septiningsih EM. Phenotypic variation and genome-wide association studies of main culm panicle node number, maximum node production rate, and degree-days to heading in rice. BMC Genomics 2022; 23:390. [PMID: 35606708 PMCID: PMC9125873 DOI: 10.1186/s12864-022-08629-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 05/16/2022] [Indexed: 11/10/2022] Open
Abstract
Background Grain yield is a complex trait that results from interaction between underlying phenotypic traits and climatic, edaphic, and biotic variables. In rice, main culm panicle node number (MCPNN; the node number on which the panicle is borne) and maximum node production rate (MNPR; the number of leaves that emerge per degree-day > 10°C) are primary phenotypic plant traits that have significant positive direct effects on yield-related traits. Degree-days to heading (DDTH), which has a significant positive effect on grain yield, is influenced by the interaction between MCPNN and MNPR. The objective of this research is to assess the phenotypic variation of MCPNN, MNPR, and DDTH in a panel of diverse rice accessions, determine regions in the rice genome associated with these traits using genome-wide association studies (GWAS), and identify putative candidate genes that control these traits. Results Considerable variation was observed for the three traits in a 220-genotype diverse rice population. MCPNN ranged from 8.1 to 20.9 nodes in 2018 and from 9.9 to 21.0 nodes in 2019. MNPR ranged from 0.0097 to 0.0214 nodes/degree day > 10°C in 2018 and from 0.0108 to 0.0193 nodes/degree-day > 10°C in 2019. DDTH ranged from 713 to 2,345 degree-days > 10°C in 2018 and from 778 to 2,404 degree-days > 10°C in 2019. Thirteen significant (P < 2.91 x 10-7) trait-single nucleotide polymorphism (SNP) associations were identified using the multilocus mixed linear model for GWAS. Significant associations between MCPNN and three SNPs in chromosome 2 (S02_12032235, S02_11971745, and S02_12030176) were detected with both the 2018 and best linear unbiased prediction (BLUP) datasets. Nine SNPs in chromosome 6 (S06_1970442, S06_2310856, S06_2550351, S06_1968653, S06_2296852, S06_1968680, S06_1968681, S06_1970597, and S06_1970602) were significantly associated with MNPR in the 2019 dataset. One SNP in chromosome 11 (S11_29358169) was significantly associated with the DDTH in the BLUP dataset. Conclusions This study identifies SNP markers that are putatively associated with MCPNN, MNPR, and DDTH. Some of these SNPs were located within or near gene models, which identify possible candidate genes involved in these traits. Validation of the putative candidate genes through expression and gene editing analyses are necessary to confirm their roles in regulating MCPNN, MNPR, and DDTH. Identifying the underlying genetic basis for primary phenotypic traits MCPNN and MNPR could lead to the development of fast and efficient approaches for their estimation, such as marker-assisted selection and gene editing, which is essential in increasing breeding efficiency and enhancing grain yield in rice. On the other hand, DDTH is a resultant variable that is highly affected by nitrogen and water management, plant density, and several other factors. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08629-y.
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Affiliation(s)
- Darlene L Sanchez
- Texas A&M AgriLife Research Center at Beaumont, Beaumont, Texas, 77713, USA.
| | | | - Jasper Benedict B Alpuerto
- Texas A&M AgriLife Research Center at Beaumont, Beaumont, Texas, 77713, USA.,Bayer Research and Development Services (Bayer Crop Science), Chesterfield, Missouri, 63017, USA
| | - Peyton A Croaker
- Texas A&M AgriLife Research Center at Beaumont, Beaumont, Texas, 77713, USA
| | - Karina Y Morales
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, 77843, USA
| | - Yubin Yang
- Texas A&M AgriLife Research Center at Beaumont, Beaumont, Texas, 77713, USA
| | - Lloyd T Wilson
- Texas A&M AgriLife Research Center at Beaumont, Beaumont, Texas, 77713, USA
| | - Rodante E Tabien
- Texas A&M AgriLife Research Center at Beaumont, Beaumont, Texas, 77713, USA
| | - Zongbu Yan
- Texas A&M AgriLife Research Center at Beaumont, Beaumont, Texas, 77713, USA
| | - Michael J Thomson
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, 77843, USA
| | - Endang M Septiningsih
- Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, 77843, USA
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18
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Genome Wide Association Study Identifies Candidate Genes Related to the Earlywood Tracheid Properties in Picea crassifolia Kom. FORESTS 2022. [DOI: 10.3390/f13020332] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Picea crassifolia Kom. is one of the timber and ecological conifers in China and its wood tracheid traits directly affect wood formation and adaptability under harsh environment. Molecular studies on P. crassifolia remain inadequate because relatively few genes have been associated with these traits. To identify markers and candidate genes that can potentially be used for genetic improvement of wood tracheid traits, we examined 106 clones of P. crassifolia, and investigated phenotypic data for 14 wood tracheid traits before specific-locus amplified fragment sequencing (SLAF-seq) was employed to perform a genome wide association study (GWAS). Subsequently, the results were used to screen single nucleotide polymorphism (SNP) loci and candidate genes that exhibited a significant correlation with the studied traits. We developed 4,058,883 SLAF-tags and 12,275,765 SNP loci, and our analyses identified a total of 96 SNP loci that showed significant correlations with three earlywood tracheid traits using a mixed linear model (MLM). Next, candidate genes were screened in the 100 kb zone (50 kb upstream, 50 kb downstream) of each of the SNP loci, whereby 67 candidate genes were obtained in earlywood tracheid traits, including 34 genes of known function and 33 genes of unknown function. We provide the most significant SNP for each trait-locus combination and candidate genes occurring within the GWAS hits. These resources provide a foundation for the development of markers that could be used in wood traits improvement and candidate genes for the development of earlywood tracheid in P. crassifolia.
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19
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Genome-wide association study of cassava starch paste properties. PLoS One 2022; 17:e0262888. [PMID: 35061844 PMCID: PMC8782291 DOI: 10.1371/journal.pone.0262888] [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: 06/11/2021] [Accepted: 01/09/2022] [Indexed: 11/21/2022] Open
Abstract
An understanding of cassava starch paste properties (CSPP) can contribute to the selection of clones with differentiated starches. This study aimed to identify genomic regions associated with CSPP using different genome-wide association study (GWAS) methods (MLM, MLMM, and Farm-CPU). The GWAS was performed using 23,078 single-nucleotide polymorphisms (SNPs). The rapid viscoanalyzer (RVA) parameters were pasting temperature (PastTemp), peak viscosity (PeakVisc), hot-paste viscosity (Hot-PVisc), cool-paste viscosity (Cold-PVisc), final viscosity (FinalVis), breakdown (BreDow), and setback (Setback). Broad phenotypic and molecular diversity was identified based on the genomic kinship matrix. The broad-sense heritability estimates (h2) ranged from moderate to high magnitudes (0.66 to 0.76). The linkage disequilibrium (LD) declined to between 0.3 and 2.0 Mb (r2 <0.1) for most chromosomes, except chromosome 17, which exhibited an extensive LD. Thirteen SNPs were found to be significantly associated with CSPP, on chromosomes 3, 8, 17, and 18. Only the BreDow trait had no associated SNPs. The regional marker-trait associations on chromosome 18 indicate a LD block between 2907312 and 3567816 bp and that SNP S18_3081635 was associated with SetBack, FinalVis, and Cold-PVisc (all three GWAS methods) and with Hot-PVisc (MLM), indicating that this SNP can track these four traits simultaneously. The variance explained by the SNPs ranged from 0.13 to 0.18 for SetBack, FinalVis, and Cold-PVisc and from 0.06 to 0.09 for PeakVisc and Hot-PVisc. The results indicated additive effects of the genetic control of Cold-PVisc, FinalVis, Hot-PVisc, and SetBack, especially on the large LD block on chromosome 18. One transcript encoding the glycosyl hydrolase family 35 enzymes on chromosome 17 and one encoding the mannose-p-dolichol utilization defect 1 protein on chromosome 18 were the most likely candidate genes for the regulation of CSPP. These results underline the potential for the assisted selection of high-value starches to improve cassava root quality through breeding programs.
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20
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Wang J, Yu J, Lipka AE, Zhang Z. Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies. Methods Mol Biol 2022; 2481:63-80. [PMID: 35641759 DOI: 10.1007/978-1-0716-2237-7_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
With increasing marker density, estimation of recombination rate between a marker and a causal mutation using linkage analysis becomes less important. Instead, linkage disequilibrium (LD) becomes the major indicator for gene mapping through genome-wide association studies (GWAS). In addition to the linkage between the marker and the causal mutation, many other factors may contribute to the LD, including population structure and cryptic relationships among individuals. As statistical methods and software evolve to improve statistical power and computing speed in GWAS, the corresponding outputs must also evolve to facilitate the interpretation of input data, the analytical process, and final association results. In this chapter, our descriptions focus on (1) considerations in creating a Manhattan plot displaying the strength of LD and locations of markers across a genome; (2) criteria for genome-wide significance threshold and the different appearance of Manhattan plots in single-locus and multiple-locus models; (3) exploration of population structure and kinship among individuals; (4) quantile-quantile (QQ) plot; (5) LD decay across the genome and LD between the associated markers and their neighbors; (6) exploration of individual and marker information on Manhattan and QQ plots via interactive visualization using HTML. The ultimate objective of this chapter is to help users to connect input data to GWAS outputs to balance power and false positives, and connect GWAS outputs to the selection of candidate genes using LD extent.
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Affiliation(s)
- Jiabo Wang
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, Sichuan, China.
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois, Urbana, IL, USA
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, USA
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21
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Zhu F, Fernie AR, Scossa F. Preparation and Curation of Omics Data for Genome-Wide Association Studies. Methods Mol Biol 2022; 2481:127-150. [PMID: 35641762 DOI: 10.1007/978-1-0716-2237-7_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
With the development of large-scale molecular phenotyping platforms, genome-wide association studies have greatly developed, being no longer limited to the analysis of classical agronomic traits, such as yield or flowering time, but also embracing the dissection of the genetic basis of molecular traits. Data generated by omics platforms, however, pose some technical and statistical challenges to the classical methodology and assumptions of an association study. Although genotyping data are subject to strict filtering procedures, and several advanced statistical approaches are now available to adjust for population structure, less attention has been instead devoted to the preparation of omics data prior to GWAS. In the present chapter, we briefly present the methods to acquire profiling data from transcripts, proteins, and small molecules, and discuss the tools and possibilities to clean, normalize, and remove the unwanted variation from large datasets of molecular phenotypic traits prior to their use in GWAS.
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Affiliation(s)
- Feng Zhu
- National R&D Center for Citrus Preservation, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan, China
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Federico Scossa
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
- Council for Agricultural Research and Economics (CREA), Research Centre for Genomics and Bioinformatics (CREA-GB), Rome, Italy.
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22
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Zhou X, Muhammad I, Lan H, Xia C. Recent Advances in the Analysis of Cold Tolerance in Maize. FRONTIERS IN PLANT SCIENCE 2022; 13:866034. [PMID: 35498657 PMCID: PMC9039722 DOI: 10.3389/fpls.2022.866034] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 03/21/2022] [Indexed: 05/19/2023]
Abstract
Maize (Zea mays L.) is an annual grass that originated in tropical and subtropical regions of the New World. Maize is highly sensitive to cold stress during seed gemination and the seedling phase, which can lead to reductions in plant vigor and grain production. There are large differences in the morphological and physiological changes caused by cold stress among maize varieties. In general, cold tolerant varieties have a stronger ability to maintain such changes in traits related to seed germination, root phenotypes, and shoot photosynthesis. These morphological and physiological characteristics have been widely used to evaluate the cold tolerance of maize varieties in genetic analyses. In recent years, considerable progress has been made in elucidating the mechanisms of maize in response to cold tolerance. Several QTL, GWAS, and transcriptomic analyses have been conducted on various maize genotypes and populations that show large variations in cold tolerance, resulting in the discovery of hundreds of candidate cold regulation genes. Nevertheless, only a few candidate genes have been functionally characterized. In the present review, we summarize recent progress in molecular, physiological, genetic, and genomic analyses of cold tolerance in maize. We address the advantages of joint analyses that combine multiple genetic and genomic approaches to improve the accuracy of identifying cold regulated genes that can be further used in molecular breeding. We also discuss the involvement of long-distance signaling in plant cold tolerance. These novel insights will provide a better mechanistic understanding of cold tolerance in maize.
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Affiliation(s)
- Xuemei Zhou
- Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Imran Muhammad
- Department of Chemistry, Punjab College of Science, Faisalabad, Pakistan
| | - Hai Lan
- Maize Research Institute, Sichuan Agricultural University, Chengdu, China
- State Key Laboratory of Crop Gene Resource Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu, China
- Hai Lan
| | - Chao Xia
- Maize Research Institute, Sichuan Agricultural University, Chengdu, China
- *Correspondence: Chao Xia
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23
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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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24
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Sorgini CA, Roberts LM, Sullivan M, Cousins AB, Baxter I, Studer AJ. The genetic architecture of leaf stable carbon isotope composition in Zea mays and the effect of transpiration efficiency on leaf elemental accumulation. G3-GENES GENOMES GENETICS 2021; 11:6321231. [PMID: 34544133 PMCID: PMC8661388 DOI: 10.1093/g3journal/jkab222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 06/07/2021] [Indexed: 11/13/2022]
Abstract
With increased demand on freshwater resources for agriculture, it is imperative that more water-use efficient crops are developed. Leaf stable carbon isotope composition, δ13C, is a proxy for transpiration efficiency and a possible tool for breeders, but the underlying mechanisms effecting δ13C in C4 plants are not known. It has been suggested that differences in specific leaf area (SLA), which potentially reflects variation in internal CO2 diffusion, can impact leaf δ13C. Furthermore, although it is known that water movement is important for elemental uptake, it is not clear how manipulation of transpiration for increased water-use efficiency may impact nutrient accumulation. Here, we characterize the genetic architecture of leaf δ13C and test its relationship to SLA and the ionome in five populations of maize. Five significant QTL for leaf δ13C were identified, including novel QTL as well as some that were identified previously in maize kernels. One of the QTL regions contains an Erecta-like gene, the ortholog of which has been shown to regulate transpiration efficiency and leaf δ13C in Arabidopsis. QTL for δ13C were located in the same general chromosome region, but slightly shifted, when comparing data from two different years. Our data does not support a relationship between δ13C and SLA, and of the 19 elements analyzed, only a weak correlation between molybdenum and δ13C was detected. Together these data add to the genetic understanding of leaf δ13C in maize and suggest that improvements to plant water use may be possible without significantly influencing elemental homeostasis.
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Affiliation(s)
- Crystal A Sorgini
- Department of Crop Sciences, University of Illinois, Urbana, IL 61801, USA
| | - Lucas M Roberts
- Department of Crop Sciences, University of Illinois, Urbana, IL 61801, USA
| | - Madsen Sullivan
- Department of Crop Sciences, University of Illinois, Urbana, IL 61801, USA
| | - Asaph B Cousins
- School of Biological Sciences, Washington State University, Pullman, WA 99164, USA
| | - Ivan Baxter
- Donald Danforth Plant Science Center, St. Louis, MO 63132, USA
| | - Anthony J Studer
- Department of Crop Sciences, University of Illinois, Urbana, IL 61801, USA
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25
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Genome-wide association study for deoxynivalenol production and aggressiveness in wheat and rye head blight by resequencing 92 isolates of Fusarium culmorum. BMC Genomics 2021; 22:630. [PMID: 34461830 PMCID: PMC8404269 DOI: 10.1186/s12864-021-07931-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 08/11/2021] [Indexed: 01/15/2023] Open
Abstract
Background Fusarium culmorum is an important pathogen causing head blight of cereals in Europe. This disease is of worldwide importance leading to reduced yield, grain quality, and contamination by mycotoxins. These mycotoxins are harmful for livestock and humans; therefore, many countries have strict regulatory limits for raw materials and processed food. Extensive genetic diversity is described among field populations of F. culmorum isolates for aggressiveness and production of the trichothecene mycotoxin deoxynivalenol (DON). However, the causes for this quantitative variation are not clear, yet. We analyzed 92 isolates sampled from different field populations in Germany, Russia, and Syria together with an international collection for aggressiveness and DON production in replicated field experiments at two locations in two years with two hosts, wheat and rye. The 30x coverage whole-genome resequencing of all isolates resulted in the identification of 130,389 high quality single nucleotide polymorphisms (SNPs) that were used for the first genome-wide association study in this phytopathogenic fungus. Results In wheat, 20 and 27 SNPs were detected for aggressiveness and DON content, respectively, of which 10 overlapped. Additionally, two different SNPs were significantly associated with aggressiveness in rye that were among those SNPs being associated with DON production in wheat. Most of the SNPs explained only a small proportion of genotypic variance (pG), however, four SNPs were associated with major quantitative trait loci (QTLs) with pG ranging from 12 to 48%. The QTL with the highest pG was involved in DON production and associated with a SNP most probably located within the Tri4 gene. Conclusions The diversity of 92 isolates of F. culmorum were captured using a heuristic approach. Key phenotypic traits, SNPs, and candidate genes underlying aggressiveness and DON production were identified. Clearly, many QTLs are responsible for aggressiveness and DON content in wheat, both traits following a quantitative inheritance. Several SNPs involved in DON metabolism, among them the Tri4 gene of the trichothecene pathway, were inferred as important source of variation in fungal aggressiveness. Using this information underlying the phenotypic variation will be of paramount importance in evaluating strategies for successful resistance breeding. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07931-5.
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26
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Wu D, Tanaka R, Li X, Ramstein GP, Cu S, Hamilton JP, Buell CR, Stangoulis J, Rocheford T, Gore MA. High-resolution genome-wide association study pinpoints metal transporter and chelator genes involved in the genetic control of element levels in maize grain. G3-GENES GENOMES GENETICS 2021; 11:6156830. [PMID: 33677522 PMCID: PMC8759812 DOI: 10.1093/g3journal/jkab059] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/21/2021] [Indexed: 12/18/2022]
Abstract
Despite its importance to plant function and human health, the genetics underpinning element levels in maize grain remain largely unknown. Through a genome-wide association study in the maize Ames panel of nearly 2,000 inbred lines that was imputed with ∼7.7 million SNP markers, we investigated the genetic basis of natural variation for the concentration of 11 elements in grain. Novel associations were detected for the metal transporter genes rte2 (rotten ear2) and irt1 (iron-regulated transporter1) with boron and nickel, respectively. We also further resolved loci that were previously found to be associated with one or more of five elements (copper, iron, manganese, molybdenum, and/or zinc), with two metal chelator and five metal transporter candidate causal genes identified. The nas5 (nicotianamine synthase5) gene involved in the synthesis of nicotianamine, a metal chelator, was found associated with both zinc and iron and suggests a common genetic basis controlling the accumulation of these two metals in the grain. Furthermore, moderate predictive abilities were obtained for the 11 elemental grain phenotypes with two whole-genome prediction models: Bayesian Ridge Regression (0.33–0.51) and BayesB (0.33–0.53). Of the two models, BayesB, with its greater emphasis on large-effect loci, showed ∼4–10% higher predictive abilities for nickel, molybdenum, and copper. Altogether, our findings contribute to an improved genotype-phenotype map for grain element accumulation in maize.
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Affiliation(s)
- Di Wu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Ryokei Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Xiaowei Li
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | | | - Suong Cu
- College of Science and Engineering, Flinders University, Bedford Park, SA 5042, Australia
| | - John P Hamilton
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - C Robin Buell
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - James Stangoulis
- College of Science and Engineering, Flinders University, Bedford Park, SA 5042, Australia
| | - Torbert Rocheford
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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27
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Rogers AR, Dunne JC, Romay C, Bohn M, Buckler ES, Ciampitti IA, Edwards J, Ertl D, Flint-Garcia S, Gore MA, Graham C, Hirsch CN, Hood E, Hooker DC, Knoll J, Lee EC, Lorenz A, Lynch JP, McKay J, Moose SP, Murray SC, Nelson R, Rocheford T, Schnable JC, Schnable PS, Sekhon R, Singh M, Smith M, Springer N, Thelen K, Thomison P, Thompson A, Tuinstra M, Wallace J, Wisser RJ, Xu W, Gilmour AR, Kaeppler SM, De Leon N, Holland JB. The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment. G3-GENES GENOMES GENETICS 2021; 11:6062399. [PMID: 33585867 DOI: 10.1093/g3journal/jkaa050] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 11/07/2020] [Indexed: 11/12/2022]
Abstract
High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.
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Affiliation(s)
- Anna R Rogers
- Program in Genetics, North Carolina State University, Raleigh, NC 27695, USA
| | - Jeffrey C Dunne
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Martin Bohn
- Department of Crop Sciences, University of Illinois at Urban-Champaign, Urbana, IL 61801, USA
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA.,USDA-ARS Plant, Soil, and Nutrition Research Unit, Cornell University, Ithaca, NY 14853, USA
| | | | - Jode Edwards
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA.,USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA
| | - David Ertl
- Iowa Corn Promotion Board, Johnston, IA 50131, USA
| | - Sherry Flint-Garcia
- USDA-ARS Plant Genetics Research Unit, University of Missouri, Columbia, MO 65211, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Christopher Graham
- Plant Science Department, West River Agricultural Center, South Dakota State University, Rapid City, SD 57769, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Elizabeth Hood
- College of Agriculture, Arkansas State University, Jonesboro, AR 72467, USA
| | - David C Hooker
- Department of Plant Agriculture, Ridgetown Campus, University of Guelph, Ridgetown, ON N0P 2C0, Canada
| | - Joseph Knoll
- USDA-ARS Crop Genetics and Breeding Research Unit, Tifton, GA 31793, USA
| | - Elizabeth C Lee
- Department of Plant Agriculture, University of Guelph, Guelph N1G 2W1, Canada
| | - Aaron Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Jonathan P Lynch
- Department of Plant Science, Penn State University, University Park, PA 16802, USA
| | - John McKay
- Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO 80523, USA
| | - Stephen P Moose
- Department of Crop Sciences, University of Illinois at Urban-Champaign, Urbana, IL 61801, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Rebecca Nelson
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Torbert Rocheford
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583, USA
| | - Patrick S Schnable
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA.,Plant Sciences Institute, Iowa State University, Ames, IA 50011, USA
| | - Rajandeep Sekhon
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA
| | - Maninder Singh
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Margaret Smith
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nathan Springer
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583, USA
| | - Kurt Thelen
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN 55108, USA
| | - Peter Thomison
- Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH 43210, USA
| | - Addie Thompson
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN 55108, USA
| | - Mitch Tuinstra
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
| | - Jason Wallace
- Department of Crop and Soil Sciences, University of Georgia, Athens GA 30602, USA
| | - Randall J Wisser
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19716, USA
| | - Wenwei Xu
- Texas A& M AgriLife Research, Texas A& M University, Lubbock, TX 79403, USA
| | | | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, WI 53706, USA
| | - Natalia De Leon
- Department of Agronomy, University of Wisconsin, Madison, WI 53706, USA
| | - James B Holland
- Program in Genetics, North Carolina State University, Raleigh, NC 27695, USA.,Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA.,USDA-ARS Plant Science Research Unit, North Carolina State University, Raleigh, NC 27695-7620, USA
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28
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Li X, Guo T, Wang J, Bekele WA, Sukumaran S, Vanous AE, McNellie JP, Tibbs-Cortes LE, Lopes MS, Lamkey KR, Westgate ME, McKay JK, Archontoulis SV, Reynolds MP, Tinker NA, Schnable PS, Yu J. An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops. MOLECULAR PLANT 2021; 14:874-887. [PMID: 33713844 DOI: 10.1016/j.molp.2021.03.010] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/03/2021] [Accepted: 03/09/2021] [Indexed: 05/08/2023]
Abstract
Identifying mechanisms and pathways involved in gene-environment interplay and phenotypic plasticity is a long-standing challenge. It is highly desirable to establish an integrated framework with an environmental dimension for complex trait dissection and prediction. A critical step is to identify an environmental index that is both biologically relevant and estimable for new environments. With extensive field-observed complex traits, environmental profiles, and genome-wide single nucleotide polymorphisms for three major crops (maize, wheat, and oat), we demonstrated that identifying such an environmental index (i.e., a combination of environmental parameter and growth window) enables genome-wide association studies and genomic selection of complex traits to be conducted with an explicit environmental dimension. Interestingly, genes identified for two reaction-norm parameters (i.e., intercept and slope) derived from flowering time values along the environmental index were less colocalized for a diverse maize panel than for wheat and oat breeding panels, agreeing with the different diversity levels and genetic constitutions of the panels. In addition, we showcased the usefulness of this framework for systematically forecasting the performance of diverse germplasm panels in new environments. This general framework and the companion CERIS-JGRA analytical package should facilitate biologically informed dissection of complex traits, enhanced performance prediction in breeding for future climates, and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation.
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Affiliation(s)
- Xianran Li
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Tingting Guo
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Jinyu Wang
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Wubishet A Bekele
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Sivakumar Sukumaran
- International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
| | - Adam E Vanous
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - James P McNellie
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | | | - Marta S Lopes
- International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
| | - Kendall R Lamkey
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Mark E Westgate
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - John K McKay
- Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO 80523, USA
| | | | - Matthew P Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
| | - Nicholas A Tinker
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | | | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA.
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Rice BR, Lipka AE. Diversifying maize genomic selection models. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:33. [PMID: 37309328 PMCID: PMC10236107 DOI: 10.1007/s11032-021-01221-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 03/07/2021] [Indexed: 06/14/2023]
Abstract
Genomic selection (GS) is one of the most powerful tools available for maize breeding. Its use of genome-wide marker data to estimate breeding values translates to increased genetic gains with fewer breeding cycles. In this review, we cover the history of GS and highlight particular milestones during its adaptation to maize breeding. We discuss how GS can be applied to developing superior maize inbreds and hybrids. Additionally, we characterize refinements in GS models that could enable the encapsulation of non-additive genetic effects, genotype by environment interactions, and multiple levels of the biological hierarchy, all of which could ultimately result in more accurate predictions of breeding values. Finally, we suggest the stages in a maize breeding program where it would be beneficial to apply GS. Given the current sophistication of high-throughput phenotypic, genotypic, and other -omic level data currently available to the maize community, now is the time to explore the implications of their incorporation into GS models and thus ensure that genetic gains are being achieved as quickly and efficiently as possible.
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Affiliation(s)
- Brian R. Rice
- Department of Crop Sciences, University of Illinois, Urbana, IL USA
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30
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Adak A, Conrad C, Chen Y, Wilde SC, Murray SC, Anderson S, Subramanian NK. Validation of Functional Polymorphisms Affecting Maize Plant Height by Unoccupied Aerial Systems (UAS) Discovers Novel Temporal Phenotypes. G3-GENES GENOMES GENETICS 2021; 11:6211193. [PMID: 33822935 PMCID: PMC8495742 DOI: 10.1093/g3journal/jkab075] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 02/28/2021] [Indexed: 11/14/2022]
Abstract
Plant height (PHT) in maize (Zea mays L.) has been scrutinized genetically and phenotypically due to relationship with other agronomically valuable traits (e.g. yield). Heritable variation of PHT is determined by many discovered quantitative trait loci (QTLs); however, phenotypic effects of such loci often lack validation across environments and genetic backgrounds, especially in the hybrid state grown by farmers rather than the inbred state more often used by geneticists. A previous genome wide association study using a topcrossed hybrid diversity panel identified two novel quantitative trait variants (QTVs) controlling both PHT and grain yield. Here, heterogeneous inbred families demonstrated that these two loci, characterized by two single nucleotide polymorphisms (SNPs), cause phenotypic variation in inbred lines, but that size of these effects were variable across four different genetic backgrounds, ranging from 1 to 10 cm. Weekly unoccupied aerial system flights demonstrated the two SNPs had larger effects, varying from 10 to 25 cm, in early growth while effects decreased towards the end of the season. These results show that allelic effect sizes of economically valuable loci are both dynamic in temporal growth and dynamic across genetic backgrounds, resulting in informative phenotypic variability overlooked following traditional phenotyping methods. Public genotyping data shows recent favorable allele selection in elite temperate germplasm with little change across tropical backgrounds. As these loci remain rarer in tropical germplasm, with effects most visible early in growth, they are useful for breeding and selection to expand the genetic basis of maize.
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Affiliation(s)
- Alper Adak
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Clarissa Conrad
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yuanyuan Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.,Department of Environmental Horticulture, Institute of Food and Agricultural Sciences, Mid-Florida Research and Education Center, University of Florida, Apopka, FL, 32703, USA
| | - Scott C Wilde
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Seth C Murray
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Steven Anderson
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.,Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
| | - Nithya K Subramanian
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
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31
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Pototskaya IV, Shamanin VP, Shepelev SS, Bhatta M, Morgounov AI. Analysis of the Genome D Polymorphism of Synthetic Wheat Obtained on the Basis of Ae. tauschii L. RUSS J GENET+ 2021. [DOI: 10.1134/s1022795421020083] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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32
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Tibbs Cortes L, Zhang Z, Yu J. Status and prospects of genome-wide association studies in plants. THE PLANT GENOME 2021; 14:e20077. [PMID: 33442955 DOI: 10.1002/tpg2.20077] [Citation(s) in RCA: 148] [Impact Index Per Article: 49.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 11/18/2020] [Indexed: 05/22/2023]
Abstract
Genome-wide association studies (GWAS) have developed into a powerful and ubiquitous tool for the investigation of complex traits. In large part, this was fueled by advances in genomic technology, enabling us to examine genome-wide genetic variants across diverse genetic materials. The development of the mixed model framework for GWAS dramatically reduced the number of false positives compared with naïve methods. Building on this foundation, many methods have since been developed to increase computational speed or improve statistical power in GWAS. These methods have allowed the detection of genomic variants associated with either traditional agronomic phenotypes or biochemical and molecular phenotypes. In turn, these associations enable applications in gene cloning and in accelerated crop breeding through marker assisted selection or genetic engineering. Current topics of investigation include rare-variant analysis, synthetic associations, optimizing the choice of GWAS model, and utilizing GWAS results to advance knowledge of biological processes. Ongoing research in these areas will facilitate further advances in GWAS methods and their applications.
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Affiliation(s)
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, 50010, USA
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33
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Krishnappa G, Savadi S, Tyagi BS, Singh SK, Mamrutha HM, Kumar S, Mishra CN, Khan H, Gangadhara K, Uday G, Singh G, Singh GP. Integrated genomic selection for rapid improvement of crops. Genomics 2021; 113:1070-1086. [PMID: 33610797 DOI: 10.1016/j.ygeno.2021.02.007] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/08/2020] [Accepted: 02/15/2021] [Indexed: 11/15/2022]
Abstract
An increase in the rate of crop improvement is essential for achieving sustained food production and other needs of ever-increasing population. Genomic selection (GS) is a potential breeding tool that has been successfully employed in animal breeding and is being incorporated into plant breeding. GS promises accelerated breeding cycles through a rapid selection of superior genotypes. Numerous empirical and simulation studies on GS and realized impacts on improvement in the crop yields are recently being reported. For a holistic understanding of the technology, we briefly discuss the concept of genetic gain, GS methodology, its current status, advantages of GS over other breeding methods, prediction models, and the factors controlling prediction accuracy in GS. Also, integration of speed breeding and other novel technologies viz. high throughput genotyping and phenotyping technologies for enhancing the efficiency and pace of GS, followed by its prospective applications in varietal development programs is reviewed.
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Affiliation(s)
| | | | | | | | | | - Satish Kumar
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | - Hanif Khan
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | | | - Gyanendra Singh
- Indian Institute of Wheat and Barley Research, Karnal, India
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34
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Wu J, Yu R, Wang H, Zhou C, Huang S, Jiao H, Yu S, Nie X, Wang Q, Liu S, Weining S, Singh RP, Bhavani S, Kang Z, Han D, Zeng Q. A large-scale genomic association analysis identifies the candidate causal genes conferring stripe rust resistance under multiple field environments. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:177-191. [PMID: 32677132 PMCID: PMC7769225 DOI: 10.1111/pbi.13452] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 06/24/2020] [Accepted: 07/09/2020] [Indexed: 05/02/2023]
Abstract
The incorporation of resistance genes into wheat commercial varieties is the ideal strategy to combat stripe or yellow rust (YR). In a search for novel resistance genes, we performed a large-scale genomic association analysis with high-density 660K single nucleotide polymorphism (SNP) arrays to determine the genetic components of YR resistance in 411 spring wheat lines. Following quality control, 371 972 SNPs were screened, covering over 50% of the high-confidence annotated gene space. Nineteen stable genomic regions harbouring 292 significant SNPs were associated with adult-plant YR resistance across nine environments. Of these, 14 SNPs were localized in the proximity of known loci widely used in breeding. Obvious candidate SNP variants were identified in certain confidence intervals, such as the cloned gene Yr18 and the major locus on chromosome 2BL, despite a large extent of linkage disequilibrium. The number of causal SNP variants was refined using an independent validation panel and consideration of the estimated functional importance of each nucleotide polymorphism. Interestingly, four natural polymorphisms causing amino acid changes in the gene TraesCS2B01G513100 that encodes a serine/threonine protein kinase (STPK) were significantly involved in YR responses. Gene expression and mutation analysis confirmed that STPK played an important role in YR resistance. PCR markers were developed to identify the favourable TraesCS2B01G513100 haplotype for marker-assisted breeding. These results demonstrate that high-resolution SNP-based GWAS enables the rapid identification of putative resistance genes and can be used to improve the efficiency of marker-assisted selection in wheat disease resistance breeding.
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Affiliation(s)
- Jianhui Wu
- State Key Laboratory of Crop Stress Biology for Arid AreasCollege of AgronomyNorthwest A&F UniversityYanglingShaanxiChina
| | - Rui Yu
- State Key Laboratory of Crop Stress Biology for Arid AreasCollege of AgronomyNorthwest A&F UniversityYanglingShaanxiChina
| | - Haiying Wang
- State Key Laboratory of Crop Stress Biology for Arid AreasNorthwest A&F UniversityYanglingShaanxiChina
| | - Cai'e Zhou
- State Key Laboratory of Crop Stress Biology for Arid AreasCollege of AgronomyNorthwest A&F UniversityYanglingShaanxiChina
| | - Shuo Huang
- State Key Laboratory of Crop Stress Biology for Arid AreasCollege of AgronomyNorthwest A&F UniversityYanglingShaanxiChina
| | - Hanxuan Jiao
- State Key Laboratory of Crop Stress Biology for Arid AreasCollege of AgronomyNorthwest A&F UniversityYanglingShaanxiChina
| | - Shizhou Yu
- State Key Laboratory of Crop Stress Biology for Arid AreasCollege of AgronomyNorthwest A&F UniversityYanglingShaanxiChina
| | - Xiaojun Nie
- State Key Laboratory of Crop Stress Biology for Arid AreasCollege of AgronomyNorthwest A&F UniversityYanglingShaanxiChina
| | - Qilin Wang
- State Key Laboratory of Crop Stress Biology for Arid AreasCollege of AgronomyNorthwest A&F UniversityYanglingShaanxiChina
| | - Shengjie Liu
- State Key Laboratory of Crop Stress Biology for Arid AreasCollege of AgronomyNorthwest A&F UniversityYanglingShaanxiChina
| | - Song Weining
- State Key Laboratory of Crop Stress Biology for Arid AreasCollege of AgronomyNorthwest A&F UniversityYanglingShaanxiChina
| | - Ravi Prakash Singh
- International Maize and Wheat Improvement Center (CIMMYT)TexcocoEstado de MexicoMexico
| | - Sridhar Bhavani
- International Maize and Wheat Improvement Center (CIMMYT)TexcocoEstado de MexicoMexico
| | - Zhensheng Kang
- State Key Laboratory of Crop Stress Biology for Arid AreasCollege of Plant ProtectionNorthwest A&F UniversityYanglingShaanxiChina
| | - Dejun Han
- State Key Laboratory of Crop Stress Biology for Arid AreasCollege of AgronomyNorthwest A&F UniversityYanglingShaanxiChina
| | - Qingdong Zeng
- State Key Laboratory of Crop Stress Biology for Arid AreasCollege of Plant ProtectionNorthwest A&F UniversityYanglingShaanxiChina
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35
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Merrick LF, Burke AB, Zhang Z, Carter AH. Comparison of Single-Trait and Multi-Trait Genome-Wide Association Models and Inclusion of Correlated Traits in the Dissection of the Genetic Architecture of a Complex Trait in a Breeding Program. FRONTIERS IN PLANT SCIENCE 2021; 12:772907. [PMID: 35154175 PMCID: PMC8831792 DOI: 10.3389/fpls.2021.772907] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 12/29/2021] [Indexed: 05/06/2023]
Abstract
Unknown genetic architecture makes it difficult to characterize the genetic basis of traits and associated molecular markers because of the complexity of small effect quantitative trait loci (QTLs), environmental effects, and difficulty in phenotyping. Seedling emergence of wheat (Triticum aestivum L.) from deep planting, has a poorly understood genetic architecture, is a vital factor affecting stand establishment and grain yield, and is historically correlated with coleoptile length. This study aimed to dissect the genetic architecture of seedling emergence while accounting for correlated traits using one multi-trait genome-wide association study (MT-GWAS) model and three single-trait GWAS (ST-GWAS) models. The ST-GWAS models included one single-locus model [mixed-linear model (MLM)] and two multi-locus models [fixed and random model circulating probability unification (FarmCPU) and Bayesian information and linkage-disequilibrium iteratively nested keyway (BLINK)]. We conducted GWAS using two populations. The first population consisted of 473 varieties from a diverse association mapping panel phenotyped from 2015 to 2019. The second population consisted of 279 breeding lines phenotyped in 2015 in Lind, WA, with 40,368 markers. We also compared the inclusion of coleoptile length and markers associated with reduced height as covariates in our ST-GWAS models. ST-GWAS found 107 significant markers across 19 chromosomes, while MT-GWAS found 82 significant markers across 14 chromosomes. The FarmCPU and BLINK models, including covariates, were able to identify many small effect markers while identifying large effect markers on chromosome 5A. By using multi-locus model breeding, programs can uncover the complex nature of traits to help identify candidate genes and the underlying architecture of a trait, such as seedling emergence.
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36
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Genome-Wide Association Studies in Arabidopsis thaliana: Statistical Analysis and Network-Based Augmentation of Signals. Methods Mol Biol 2020; 2200:187-210. [PMID: 33175379 DOI: 10.1007/978-1-0716-0880-7_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Genome-wide association studies (GWAS) have proven effective at identifying genetic variants and genes that are associated with phenotypes in humans, animals, and plants. Since most phenotypes of plant species are complex traits regulated by many genes and their functional interactions, GWAS are increasing in popularity for genetic dissections of plant phenotypes. For the reference plant, Arabidopsis thaliana, detailed information on genetic variations became available with the completion of the 1001 Genomes Project, enabling highly resolved association mapping between chromosomal loci and complex traits. Improvements have been made in the statistical analysis methods for testing the significance of genotype-to-phenotype associations, thereby substantially reducing the confounding effects of population structures. Furthermore, there have been large efforts toward post-GWAS augmentation of signals via integration with other types of information to overcome the limited statistical power of GWAS. This chapter describes the stepwise procedure of GWAS in Arabidopsis, focusing on data analysis processes including preprocessing of genotype and phenotype data, statistical analysis to identify phenotype-associated chromosomal loci, identification of phenotype-associated genes based on the phenotype-associated loci, and finally network-based augmentation of GWAS signals to identify additional candidate genes for the phenotype.
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37
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Liu JJ, Sniezko RA, Sissons R, Krakowski J, Alger G, Schoettle AW, Williams H, Zamany A, Zitomer RA, Kegley A. Association Mapping and Development of Marker-Assisted Selection Tools for the Resistance to White Pine Blister Rust in the Alberta Limber Pine Populations. FRONTIERS IN PLANT SCIENCE 2020; 11:557672. [PMID: 33042181 PMCID: PMC7522202 DOI: 10.3389/fpls.2020.557672] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 08/25/2020] [Indexed: 06/11/2023]
Abstract
Since its introduction to North America in the early 1900s, white pine blister rust (WPBR) caused by the fungal pathogen Cronartium ribicola has resulted in substantial economic losses and ecological damage to native North American five-needle pine species. The high susceptibility and mortality of these species, including limber pine (Pinus flexilis), creates an urgent need for the development and deployment of resistant germplasm to support recovery of impacted populations. Extensive screening for genetic resistance to WPBR has been underway for decades in some species but has only started recently in limber pine using seed families collected from wild parental trees in the USA and Canada. This study was conducted to characterize Alberta limber pine seed families for WPBR resistance and to develop reliable molecular tools for marker-assisted selection (MAS). Open-pollinated seed families were evaluated for host reaction following controlled infection using C. ribicola basidiospores. Phenotypic segregation for presence/absence of stem symptoms was observed in four seed families. The segregation ratios of these families were consistent with expression of major gene resistance (MGR) controlled by a dominant R locus. Based on linkage disequilibrium (LD)-based association mapping used to detect single nucleotide polymorphism (SNP) markers associated with MGR against C. ribicola, MGR in these seed families appears to be controlled by Cr4 or other R genes in very close proximity to Cr4. These associated SNPs were located in genes involved in multiple molecular mechanisms potentially underlying limber pine MGR to C. ribicola, including NBS-LRR genes for recognition of C. ribicola effectors, signaling components, and a large set of defense-responsive genes with potential functions in plant effector-triggered immunity (ETI). Interactions of associated loci were identified for MGR selection in trees with complex genetic backgrounds. SNPs with tight Cr4-linkage were further converted to TaqMan assays to confirm their effectiveness as MAS tools. This work demonstrates the successful translation and deployment of molecular genetic knowledge into specific MAS tools that can be easily applied in a selection or breeding program to efficiently screen MGR against WPBR in Alberta limber pine populations.
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Affiliation(s)
- Jun-Jun Liu
- Canadian Forest Service, Natural Resources Canada, Victoria, BC, Canada
| | - Richard A. Sniezko
- USDA Forest Service, Dorena Genetic Resource Center, Cottage Grove, OR, United States
| | - Robert Sissons
- Parks Canada, Waterton Lakes National Park, Waterton Park, AB, Canada
| | | | - Genoa Alger
- Parks Canada, Waterton Lakes National Park, Waterton Park, AB, Canada
| | - Anna W. Schoettle
- USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO, United States
| | - Holly Williams
- Canadian Forest Service, Natural Resources Canada, Victoria, BC, Canada
| | - Arezoo Zamany
- Canadian Forest Service, Natural Resources Canada, Victoria, BC, Canada
| | - Rachel A. Zitomer
- USDA Forest Service, Dorena Genetic Resource Center, Cottage Grove, OR, United States
| | - Angelia Kegley
- USDA Forest Service, Dorena Genetic Resource Center, Cottage Grove, OR, United States
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38
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Hernandez J, Meints B, Hayes P. Introgression Breeding in Barley: Perspectives and Case Studies. FRONTIERS IN PLANT SCIENCE 2020; 11:761. [PMID: 32595671 PMCID: PMC7303309 DOI: 10.3389/fpls.2020.00761] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 05/13/2020] [Indexed: 05/04/2023]
Abstract
Changing production scenarios resulting from unstable climatic conditions are challenging crop improvement efforts. A deeper and more practical understanding of plant genetic resources is necessary if these assets are to be used effectively in developing improved varieties. In general, current varieties and potential varieties have a narrow genetic base, making them prone to suffer the consequences of new and different abiotic and biotic stresses that can reduce crop yield and quality. The deployment of genomic technologies and sophisticated statistical analysis procedures has generated a dramatic change in the way we characterize and access genetic diversity in crop plants, including barley. Various mapping strategies can be used to identify the genetic variants that lead to target phenotypes and these variants can be assigned coordinates in reference genomes. In this way, new genes and/or new alleles at known loci present in wild ancestors, germplasm accessions, land races, and un-adapted introductions can be located and targeted for introgression. In principle, the introgression process can now be streamlined and linkage drag reduced. In this review, we present an overview of (1) past and current efforts to identify diversity that can be tapped to improve barley yield and quality, and (2) case studies of our efforts to introgress resistance to stripe and stem rust from un-adapted germplasm. We conclude with a description of a modified Nested Association Mapping (NAM) population strategy that we are implementing for the development of multi-use naked barley for organic systems and share perspectives on the use of genome editing in introgression breeding.
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Affiliation(s)
- Javier Hernandez
- Department Crop and Soil Science, Oregon State University, Corvallis, OR, United States
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39
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Genome-Wide Association Study for Maize Leaf Cuticular Conductance Identifies Candidate Genes Involved in the Regulation of Cuticle Development. G3-GENES GENOMES GENETICS 2020; 10:1671-1683. [PMID: 32184371 PMCID: PMC7202004 DOI: 10.1534/g3.119.400884] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The cuticle, a hydrophobic layer of cutin and waxes synthesized by plant epidermal cells, is the major barrier to water loss when stomata are closed at night and under water-limited conditions. Elucidating the genetic architecture of natural variation for leaf cuticular conductance (gc) is important for identifying genes relevant to improving crop productivity in drought-prone environments. To this end, we conducted a genome-wide association study of gc of adult leaves in a maize inbred association panel that was evaluated in four environments (Maricopa, AZ, and San Diego, CA, in 2016 and 2017). Five genomic regions significantly associated with gc were resolved to seven plausible candidate genes (ISTL1, two SEC14 homologs, cyclase-associated protein, a CER7 homolog, GDSL lipase, and β-D-XYLOSIDASE 4). These candidates are potentially involved in cuticle biosynthesis, trafficking and deposition of cuticle lipids, cutin polymerization, and cell wall modification. Laser microdissection RNA sequencing revealed that all these candidate genes, with the exception of the CER7 homolog, were expressed in the zone of the expanding adult maize leaf where cuticle maturation occurs. With direct application to genetic improvement, moderately high average predictive abilities were observed for whole-genome prediction of gc in locations (0.46 and 0.45) and across all environments (0.52). The findings of this study provide novel insights into the genetic control of gc and have the potential to help breeders more effectively develop drought-tolerant maize for target environments.
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40
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Tanaka N, Shenton M, Kawahara Y, Kumagai M, Sakai H, Kanamori H, Yonemaru J, Fukuoka S, Sugimoto K, Ishimoto M, Wu J, Ebana K. Whole-Genome Sequencing of the NARO World Rice Core Collection (WRC) as the Basis for Diversity and Association Studies. PLANT & CELL PHYSIOLOGY 2020; 61:922-932. [PMID: 32101292 PMCID: PMC7426033 DOI: 10.1093/pcp/pcaa019] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 02/16/2020] [Indexed: 05/12/2023]
Abstract
Genebanks provide access to diverse materials for crop improvement. To utilize and evaluate them effectively, core collections, such as the World Rice Core Collection (WRC) in the Genebank at the National Agriculture and Food Research Organization, have been developed. Because the WRC consists of 69 accessions with a high degree of genetic diversity, it has been used for >300 projects. To allow deeper investigation of existing WRC data and to further promote research using Genebank rice accessions, we performed whole-genome resequencing of these 69 accessions, examining their sequence variation by mapping against the Oryza sativa ssp. japonica Nipponbare genome. We obtained a total of 2,805,329 single nucleotide polymorphisms (SNPs) and 357,639 insertion-deletions. Based on the principal component analysis and population structure analysis of these data, the WRC can be classified into three major groups. We applied TASUKE, a multiple genome browser to visualize the different WRC genome sequences, and classified haplotype groups of genes affecting seed characteristics and heading date. TASUKE thus provides access to WRC genotypes as a tool for reverse genetics. We examined the suitability of the compact WRC population for genome-wide association studies (GWASs). Heading date, affected by a large number of quantitative trait loci (QTLs), was not associated with known genes, but several seed-related phenotypes were associated with known genes. Thus, for QTLs of strong effect, the compact WRC performed well in GWAS. This information enables us to understand genetic diversity in 37,000 rice accessions maintained in the Genebank and to find genes associated with different phenotypes. The sequence data have been deposited in DNA Data Bank of Japan Sequence Read Archive (DRA) (Supplementary Table S1).
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Affiliation(s)
- N Tanaka
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, 305-8518 Japan
| | - M Shenton
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, 305-8518 Japan
| | - Y Kawahara
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, 305-8518 Japan
- Advanced Analysis Center, National Agriculture and Food Research Organization, Tsukuba Ibaraki, 305-8517, Japan
| | - M Kumagai
- Advanced Analysis Center, National Agriculture and Food Research Organization, Tsukuba Ibaraki, 305-8517, Japan
| | - H Sakai
- Advanced Analysis Center, National Agriculture and Food Research Organization, Tsukuba Ibaraki, 305-8517, Japan
| | - H Kanamori
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, 305-8518 Japan
| | - J Yonemaru
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, 305-8518 Japan
| | - S Fukuoka
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, 305-8518 Japan
| | - K Sugimoto
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, 305-8518 Japan
| | - M Ishimoto
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, 305-8518 Japan
| | - J Wu
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, 305-8518 Japan
| | - K Ebana
- Genetic Resources Center, National Agriculture and Food Research Organization, Plant Genetic Diversity Laboratory, Tsukuba, Ibaraki 305-8502, Japan
- Corresponding author: E-mail, ; Fax, +81-29-838-7408
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Lan S, Zheng C, Hauck K, McCausland M, Duguid SD, Booker HM, Cloutier S, You FM. Genomic Prediction Accuracy of Seven Breeding Selection Traits Improved by QTL Identification in Flax. Int J Mol Sci 2020; 21:ijms21051577. [PMID: 32106624 PMCID: PMC7084455 DOI: 10.3390/ijms21051577] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 02/23/2020] [Accepted: 02/23/2020] [Indexed: 01/21/2023] Open
Abstract
Molecular markers are one of the major factors affecting genomic prediction accuracy and the cost of genomic selection (GS). Previous studies have indicated that the use of quantitative trait loci (QTL) as markers in GS significantly increases prediction accuracy compared with genome-wide random single nucleotide polymorphism (SNP) markers. To optimize the selection of QTL markers in GS, a set of 260 lines from bi-parental populations with 17,277 genome-wide SNPs were used to evaluate the prediction accuracy for seed yield (YLD), days to maturity (DTM), iodine value (IOD), protein (PRO), oil (OIL), linoleic acid (LIO), and linolenic acid (LIN) contents. These seven traits were phenotyped over four years at two locations. Identification of quantitative trait nucleotides (QTNs) for the seven traits was performed using three types of statistical models for genome-wide association study: two SNP-based single-locus (SS), seven SNP-based multi-locus (SM), and one haplotype-block-based multi-locus (BM) models. The identified QTNs were then grouped into QTL based on haplotype blocks. For all seven traits, 133, 355, and 1208 unique QTL were identified by SS, SM, and BM, respectively. A total of 1420 unique QTL were obtained by SS+SM+BM, ranging from 254 (OIL, LIO) to 361 (YLD) for individual traits, whereas a total of 427 unique QTL were achieved by SS+SM, ranging from 56 (YLD) to 128 (LIO). SS models alone did not identify sufficient QTL for GS. The highest prediction accuracies were obtained using single-trait QTL identified by SS+SM+BM for OIL (0.929 ± 0.016), PRO (0.893 ± 0.023), YLD (0.892 ± 0.030), and DTM (0.730 ± 0.062), and by SS+SM for LIN (0.837 ± 0.053), LIO (0.835 ± 0.049), and IOD (0.835 ± 0.041). In terms of the number of QTL markers and prediction accuracy, SS+SM outperformed other models or combinations thereof. The use of all SNPs or QTL of all seven traits significantly reduced the prediction accuracy of traits. The results further validated that QTL outperformed high-density genome-wide random markers, and demonstrated that the combined use of single and multi-locus models can effectively identify a comprehensive set of QTL that improve prediction accuracy, but further studies on detection and removal of redundant or false-positive QTL to maximize prediction accuracy and minimize the number of QTL markers in GS are warranted.
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Affiliation(s)
- Samuel Lan
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada; (S.L.); (C.Z.); (K.H.); (M.M.)
- Department of Mathematics and Statistics, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Chunfang Zheng
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada; (S.L.); (C.Z.); (K.H.); (M.M.)
| | - Kyle Hauck
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada; (S.L.); (C.Z.); (K.H.); (M.M.)
- Department of Mathematics and Statistics, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Madison McCausland
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada; (S.L.); (C.Z.); (K.H.); (M.M.)
- Department of Plant Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Scott D. Duguid
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5, Canada;
| | - Helen M. Booker
- Crop Development Centre, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada;
| | - Sylvie Cloutier
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada; (S.L.); (C.Z.); (K.H.); (M.M.)
- Correspondence: (F.M.Y.); (S.C); Tel.: +1-613-759-1539 (F.M.Y.); +1-613-759-1744 (S.C.)
| | - Frank M. You
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada; (S.L.); (C.Z.); (K.H.); (M.M.)
- Correspondence: (F.M.Y.); (S.C); Tel.: +1-613-759-1539 (F.M.Y.); +1-613-759-1744 (S.C.)
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Guo D, Jiang H, Yan W, Yang L, Ye J, Wang Y, Yan Q, Chen J, Gao Y, Duan L, Liu H, Xie L. Resequencing 200 Flax Cultivated Accessions Identifies Candidate Genes Related to Seed Size and Weight and Reveals Signatures of Artificial Selection. FRONTIERS IN PLANT SCIENCE 2020; 10:1682. [PMID: 32010166 PMCID: PMC6976528 DOI: 10.3389/fpls.2019.01682] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 11/29/2019] [Indexed: 05/13/2023]
Abstract
Seed size and weight are key traits determining crop yield, which often undergo strongly artificial selection during crop domestication. Although seed sizes differ significantly between oil flax and fiber flax, the genetic basis of morphological differences and artificial selection characteristics in seed size remains largely unclear. Here we re-sequenced 200 flax cultivated accessions to generate a genome variation map based on chromosome assembly reference genomes. We provide evidence that oil flax group is the ancestor of cultivated flax, and the oil-fiber dual purpose group (OF) is the evolutionary intermediate transition state between oil and fiber flax. Genome-wide association studies (GWAS) were combined with LD Heatmap to identify candidate regions related to seed size and weight, then candidate genes were screened based on detailed functional annotations and estimation of nucleotide polymorphism effects. Using this strategy, we obtained 13 candidate genes related to seed size and weight. Selective sweeps analysis indicates human-involved selection of small seeds during the oil to fiber flax transition. Our study shows the existence of elite alleles for seed size and weight in flax germplasm and provides molecular insights into approaches for further improvement.
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Affiliation(s)
- Dongliang Guo
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Haixia Jiang
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Wenliang Yan
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Liangjie Yang
- Herbal Medicine Innovation Research Center, Agricultural Bureau of Zhaosu County, Yili, China
| | - Jiali Ye
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Yue Wang
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Qingcheng Yan
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Jiaxun Chen
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Yanfang Gao
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Lepeng Duan
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Huiqing Liu
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Liqiong Xie
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
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Exploring genetic architecture of grain yield and quality traits in a 16-way indica by japonica rice MAGIC global population. Sci Rep 2019; 9:19605. [PMID: 31862941 PMCID: PMC6925145 DOI: 10.1038/s41598-019-55357-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 11/18/2019] [Indexed: 12/27/2022] Open
Abstract
Identification of Quantitative Trait Loci (QTL) has been a challenge for complex traits due to the use of populations with narrow genetic base. Most of QTL mapping studies were carried out from crosses made within the subspecies, either indica × indica or japonica × japonica. In this study we report advantages of using Multi-parent Advanced Generation Inter-Crosses global population, derived from a combination of eight indica and eight japonica elite parents, in QTL discovery for yield and grain quality traits. Genome-wide association study and interval mapping identified 38 and 34 QTLs whereas Bayesian networking detected 60 QTLs with 22 marker-marker associations, 32 trait-trait associations and 65 marker-trait associations. Notably, nine known QTLs/genes qPH1/OsGA20ox2, qDF3/OsMADS50, PL, QDg1, qGW-5b, grb7-2, qGL3/GS3, Amy6/Wx gene and OsNAS3 were consistently identified by all approaches for nine traits whereas qDF3/OsMADS50 was co-located for both yield and days-to-flowering traits on chromosome 3. Moreover, we identified a number of candidate QTLs in either one or two analyses but further validations will be needed. The results indicate that this new population has enabled identifications of significant QTLs and interactions for 16 traits through multiple approaches. Pyramided recombinant inbred lines provide a valuable source for integration into future breeding programs.
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Liu Q, Hobbs HA, Domier LL. Genome-wide association study of the seed transmission rate of soybean mosaic virus and associated traits using two diverse population panels. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:3413-3424. [PMID: 31630210 DOI: 10.1007/s00122-019-03434-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 09/17/2019] [Indexed: 06/10/2023]
Abstract
KEY MESSAGE Genome-wide association analyses identified candidates for genes involved in restricting virus movement into embryonic tissues, suppressing virus-induced seed coat mottling and preserving yield in soybean plants infected with soybean mosaic virus. Soybean mosaic virus (SMV) causes significant reductions in soybean yield and seed quality. Because seedborne infections can serve as primary sources of inoculum for SMV infections, resistance to SMV seed transmission provides a means to limit the impacts of SMV. In this study, two diverse population panels, Pop1 and Pop2, composed of 409 and 199 soybean plant introductions, respectively, were evaluated for SMV seed transmission rate, seed coat mottling, and seed yield from SMV-infected plants. The phenotypic data and genotypic data from the SoySNP50K dataset were analyzed using GAPIT and rrBLUP. For SMV seed transmission rate, a single locus was identified on chromosome 9 in Pop1. For SMV-induced seed coat mottling, loci were identified on chromosome 9 in Pop1 and on chromosome 3 in Pop2. For seed yield from SMV-infected plants, a single locus was identified on chromosome 3 in Pop2 that was within the map interval of a previously described quantitative trait locus for seed number. The high linkage disequilibrium regions surrounding the markers on chromosomes 3 and 9 contained a predicted nonsense-mediated RNA decay gene, multiple pectin methylesterase inhibitor genes (involved in restricting virus movement), two chalcone synthase genes, and a homolog of the yeast Rtf1 gene (involved in RNA-mediated transcriptional gene silencing). The results of this study provided additional insight into the genetic architecture of these three important traits, suggested candidate genes for downstream functional validation, and suggested that genomic prediction would outperform marker-assisted selection for two of the four trait-marker associations.
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Affiliation(s)
- Qiong Liu
- Department of Crop Sciences, University of Illinois, Urbana, IL, 61801, USA
| | - Houston A Hobbs
- Department of Crop Sciences, University of Illinois, Urbana, IL, 61801, USA
| | - Leslie L Domier
- Soybean/Maize Germplasm, Pathology, and Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, Urbana, IL, 61801, USA.
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Ladejobi O, Mackay IJ, Poland J, Praud S, Hibberd JM, Bentley AR. Reference Genome Anchoring of High-Density Markers for Association Mapping and Genomic Prediction in European Winter Wheat. FRONTIERS IN PLANT SCIENCE 2019; 10:1278. [PMID: 31781130 PMCID: PMC6857554 DOI: 10.3389/fpls.2019.01278] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 09/12/2019] [Indexed: 05/28/2023]
Abstract
In this study, we anchored genotyping-by-sequencing data to the International Wheat Genome Sequencing Consortium Reference Sequence v1.0 assembly to generate over 40,000 high quality single nucleotide polymorphism markers on a panel of 376 elite European winter wheat varieties released between 1946 and 2007. We compared association mapping and genomic prediction accuracy for a range of productivity traits with previous results based on lower density dominant DArT markers. The results demonstrate that the availability of RefSeq v1.0 supports higher precision trait mapping and provides the density of markers required to obtain accurate predictions of traits controlled by multiple small effect loci, including grain yield.
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Affiliation(s)
- Olufunmilayo Ladejobi
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
- Department of Plant Sciences, The University of Cambridge, Cambridge, United Kingdom
| | - Ian J. Mackay
- The John Bingham Laboratory, NIAB, Cambridge, United Kingdom
- IMplant Consultancy Ltd., Chelmsford, United Kingdom
| | - Jesse Poland
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | | | - Julian M. Hibberd
- Department of Plant Sciences, The University of Cambridge, Cambridge, United Kingdom
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46
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Jiménez-Galindo JC, Malvar RA, Butrón A, Santiago R, Samayoa LF, Caicedo M, Ordás B. Mapping of resistance to corn borers in a MAGIC population of maize. BMC PLANT BIOLOGY 2019; 19:431. [PMID: 31623579 PMCID: PMC6796440 DOI: 10.1186/s12870-019-2052-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 09/24/2019] [Indexed: 05/05/2023]
Abstract
BACKGROUND Corn borers constitute an important pest of maize around the world; in particular Sesamia nonagrioides Lefèbvre, named Mediterranean corn borer (MCB), causes important losses in Southern Europe. Methods of selection can be combined with transgenic approaches to increase the efficiency and durability of the resistance to corn borers. Previous studies of the genetic factors involved in resistance to MCB have been carried out using bi-parental populations that have low resolution or using association inbred panels that have a low power to detect rare alleles. We developed a Multi-parent Advanced Generation InterCrosses (MAGIC) population to map with high resolution the genetic determinants of resistance to MCB. RESULTS We detected multiple single nucleotide polymorphisms (SNPs) of low effect associated with resistance to stalk tunneling by MCB. We dissected a wide region related to stalk tunneling in multiple studies into three smaller regions (at ~ 150, ~ 155, and ~ 165 Mb in chromosome 6) that closely overlap with regions associated with cell wall composition. We also detected regions associated with kernel resistance and agronomic traits, although the co-localization of significant regions between traits was very low. This indicates that it is possible the concurrent improvement of resistance and agronomic traits. CONCLUSIONS We developed a mapping population which allowed a finer dissection of the genetics of maize resistance to corn borers and a solid nomination of candidate genes based on functional information. The population, given its large variability, was also adequate to map multiple traits and study the relationship between them.
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Affiliation(s)
- José Cruz Jiménez-Galindo
- Misión Biológica de Galicia, Spanish National Research Council (CSIC), Apartado 28, 36080 Pontevedra, Spain
- National Institute of Forestry, Agriculture and Livestock Research (INIFAP), Ave. Hidalgo 1213, Cd. Cuauhtémoc, 31500 Chihuahua, Mexico
| | - Rosa Ana Malvar
- Misión Biológica de Galicia, Spanish National Research Council (CSIC), Apartado 28, 36080 Pontevedra, Spain
| | - Ana Butrón
- Misión Biológica de Galicia, Spanish National Research Council (CSIC), Apartado 28, 36080 Pontevedra, Spain
| | - Rogelio Santiago
- Departamento Biología Vegetal y Ciencias del Suelo, Unidad Asociada BVE1-UVIGO y MBG (CSIC), Facultad de Biología, Universidad de Vigo, Campus As Lagoas Marcosende, 36310 Vigo, Spain
| | - Luis Fernando Samayoa
- North Carolina State University, 4210 Williams Hall 101, Derieux Place, Raleigh, NC 27695 USA
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695-7620 USA
| | - Marlon Caicedo
- Instituto Nacional de Investigaciones Agropecuarias (INIAP), 170315 Quito, Ecuador
| | - Bernardo Ordás
- Misión Biológica de Galicia, Spanish National Research Council (CSIC), Apartado 28, 36080 Pontevedra, Spain
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Carlson MO, Montilla-Bascon G, Hoekenga OA, Tinker NA, Poland J, Baseggio M, Sorrells ME, Jannink JL, Gore MA, Yeats TH. Multivariate Genome-Wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat ( Avena sativa L.). G3 (BETHESDA, MD.) 2019; 9:2963-2975. [PMID: 31296616 PMCID: PMC6723141 DOI: 10.1534/g3.119.400228] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 07/08/2019] [Indexed: 01/06/2023]
Abstract
Oat (Avena sativa L.) has a high concentration of oils, comprised primarily of healthful unsaturated oleic and linoleic fatty acids. To accelerate oat plant breeding efforts, we sought to identify loci associated with variation in fatty acid composition, defined as the types and quantities of fatty acids. We genotyped a panel of 500 oat cultivars with genotyping-by-sequencing and measured the concentrations of ten fatty acids in these oat cultivars grown in two environments. Measurements of individual fatty acids were highly correlated across samples, consistent with fatty acids participating in shared biosynthetic pathways. We leveraged these phenotypic correlations in two multivariate genome-wide association study (GWAS) approaches. In the first analysis, we fitted a multivariate linear mixed model for all ten fatty acids simultaneously while accounting for population structure and relatedness among cultivars. In the second, we performed a univariate association test for each principal component (PC) derived from a singular value decomposition of the phenotypic data matrix. To aid interpretation of results from the multivariate analyses, we also conducted univariate association tests for each trait. The multivariate mixed model approach yielded 148 genome-wide significant single-nucleotide polymorphisms (SNPs) at a 10% false-discovery rate, compared to 129 and 73 significant SNPs in the PC and univariate analyses, respectively. Thus, explicit modeling of the correlation structure between fatty acids in a multivariate framework enabled identification of loci associated with variation in seed fatty acid concentration that were not detected in the univariate analyses. Ultimately, a detailed characterization of the loci underlying fatty acid variation can be used to enhance the nutritional profile of oats through breeding.
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Affiliation(s)
- Maryn O Carlson
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | | | | | - Nicholas A Tinker
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Jesse Poland
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, and
| | - Matheus Baseggio
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Mark E Sorrells
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Jean-Luc Jannink
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
- R.W. Holley Center for Agriculture and Health, US Department of Agriculture, Agricultural Research Service, Ithaca, NY 14853
| | - Michael A Gore
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Trevor H Yeats
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
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Aubry S. The Future of Digital Sequence Information for Plant Genetic Resources for Food and Agriculture. FRONTIERS IN PLANT SCIENCE 2019; 10:1046. [PMID: 31543884 PMCID: PMC6728410 DOI: 10.3389/fpls.2019.01046] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 07/29/2019] [Indexed: 05/27/2023]
Abstract
The recent debates on the legal status of "digital sequence information" (DSI) at the international level could have extensive consequences for the future of agriculture and food security. A large majority of recent advances in biology, medicine, or agriculture were achieved by sharing and mining of freely accessible sequencing data. It is most probably because of the tremendous success of modern genomics and advances of synthetic biology that concerns were raised about possible fair and equitable ways of sharing data. The DSI concept is relatively new, and all concerned parties agreed upon the need for a clear definition. For example, the extent to which DSI understanding is limited only to genetic sequence data has to be clarified. In this paper, I focus on a subset of DSI essential to humankind: the DSI originating from plant genetic resources for food and agriculture (PGRFA). Two international agreements shape the conservation and use of plant genetic resources: the Convention on Biodiversity and the International Treaty for Plant Genetic Resources for Food and Agriculture. In an attempt to mobilize DSI users and producers involved in research, breeding, and conservation, I describe here how the increasing amount of genomic data, information, and studies interact with the existing legal framework at the global level. Using possible scenarios, I will emphasize the complexity of the issues surrounding DSI for PGRFA and propose potential ways forward for developing an inclusive governance and fair use of these genetic resources.
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Affiliation(s)
- Sylvain Aubry
- Department of Plant and Microbial Science, University of Zurich, Zurich, Switzerland
- Section Genetic Resources and Technology, Swiss Federal Office for Agriculture, Bern, Switzerland
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49
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Olatoye MO, Hu Z, Aikpokpodion PO. Epistasis Detection and Modeling for Genomic Selection in Cowpea ( Vigna unguiculata L. Walp.). Front Genet 2019; 10:677. [PMID: 31417604 PMCID: PMC6682672 DOI: 10.3389/fgene.2019.00677] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 06/27/2019] [Indexed: 12/24/2022] Open
Abstract
Genetic architecture reflects the pattern of effects and interaction of genes underlying phenotypic variation. Most mapping and breeding approaches generally consider the additive part of variation but offer limited knowledge on the benefits of epistasis which explains in part the variation observed in traits. In this study, the cowpea multiparent advanced generation inter-cross (MAGIC) population was used to characterize the epistatic genetic architecture of flowering time, maturity, and seed size. In addition, consideration for epistatic genetic architecture in genomic-enabled breeding (GEB) was investigated using parametric, semi-parametric, and non-parametric genomic selection (GS) models. Our results showed that large and moderate effect-sized two-way epistatic interactions underlie the traits examined. Flowering time QTL colocalized with cowpea putative orthologs of Arabidopsis thaliana and Glycine max genes like PHYTOCLOCK1 (PCL1 [Vigun11g157600]) and PHYTOCHROME A (PHY A [Vigun01g205500]). Flowering time adaptation to long and short photoperiod was found to be controlled by distinct and common main and epistatic loci. Parametric and semi-parametric GS models outperformed non-parametric GS model, while using known quantitative trait nucleotide(s) (QTNs) as fixed effects improved prediction accuracy when traits were controlled by large effect loci. In general, our study demonstrated that prior understanding of the genetic architecture of a trait can help make informed decisions in GEB.
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Affiliation(s)
- Marcus O. Olatoye
- Department of Crop Sciences, University of Illinois, Urbana-Champaign, IL, United States
| | - Zhenbin Hu
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
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Genomic signatures of seed mass adaptation to global precipitation gradients in sorghum. Heredity (Edinb) 2019; 124:108-121. [PMID: 31316156 PMCID: PMC6906510 DOI: 10.1038/s41437-019-0249-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Revised: 06/07/2019] [Accepted: 06/21/2019] [Indexed: 12/14/2022] Open
Abstract
Seed mass is a key component of adaptation in plants and a determinant of yield in crops. The climatic drivers and genomic basis of seed mass variation remain poorly understood. In the cereal crop Sorghum bicolor, globally-distributed landraces harbor abundant variation in seed mass, which is associated with precipitation in their agroclimatic zones of origin. This study aimed to test the hypothesis that diversifying selection across precipitation gradients, acting on ancestral cereal grain size regulators, underlies seed mass variation in global sorghum germplasm. We tested this hypothesis in a set of 1901 georeferenced and genotyped sorghum landraces, 100-seed mass from common gardens, and bioclimatic precipitation variables. As predicted, 100-seed mass in global germplasm varies significantly among botanical races and is correlated to proxies of the precipitation gradients. With general and mixed linear model genome-wide associations, we identified 29 and 56 of 100 a priori candidate seed size genes with polymorphisms in the top 1% of seed mass association, respectively. Eleven of these genes harbor polymorphisms associated with the precipitation gradient, including orthologs of genes that regulate seed size in other cereals. With FarmCPU, 13 significant SNPs were identified, including one at an a priori candidate gene. Finally, we identified eleven colocalized outlier SNPs associated with seed mass and precipitation that also carry signatures of selection based on FST scans and PCAdapt, which represents a significant enrichment. Our findings suggest that seed mass in sorghum was shaped by diversifying selection on drought stress, and can inform genomics-enabled breeding for climate-resilient cereals.
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