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Tang B, Yang H, Yin Q, Miao W, Lei Y, Cui Q, Cheng J, Zhang X, Chen Y, Du J, Xie L, Tang S, Wang M, Li J, Cao M, Chen L, Xie F, Li X, Zhu F, Wang Z, Xiong C, Dai X, Zou X, Liu F. Fertility restorer gene CaRf and PepperSNP50K provide a promising breeding system for hybrid pepper. HORTICULTURE RESEARCH 2024; 11:uhae223. [PMID: 39415972 PMCID: PMC11480663 DOI: 10.1093/hr/uhae223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 07/28/2024] [Indexed: 10/19/2024]
Abstract
Cytoplasmic male sterility (CMS) is pivotal in plant breeding and widely employed in various crop hybrids, including pepper. However, the functional validation of the restorer of fertility (Rf) gene in pepper has been lacking until now. This study identifies and characterizes CaRf, a single dominant locus crucial for restoring CMS in the pepper strong recovery inbred line Zhangshugang. The CaRf gene encodes a mitochondria-targeted pentatricopeptide repeat protein, validated through the induction of male sterility upon its silencing in hybrid F1 plants. To enhance pepper breeding efficiency, 176 important pepper breeding parent materials were resequenced, and a PepperSNP50K liquid-phase breeding chip was developed, comprising 51 172 markers. Integration of CaRf functional characterization and PepperSNP50K facilitated the development of a high-quality red pepper hybrid. These findings provide significant insights and practical strategies for advancing molecular-designed breeding in peppers.
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Affiliation(s)
- Bingqian Tang
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Huiping Yang
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Qinbiao Yin
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Wu Miao
- Hunan Xiangyan Seed Industry Co., Ltd, Changsha 410125, China
| | - Yuting Lei
- Higentec Co. Ltd., Changsha, Hunan, 410125, China
| | - Qingzhi Cui
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Jiawen Cheng
- Higentec Co. Ltd., Changsha, Hunan, 410125, China
| | - Xinhao Zhang
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Ying Chen
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Juan Du
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Lingling Xie
- Institute of Vegetable Research, Hunan Academy of Agricultural Science, Changsha 410125, China
| | - Shunxue Tang
- Higentec Co. Ltd., Changsha, Hunan, 410125, China
| | - Meiqi Wang
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Jiayue Li
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Mingyue Cao
- Higentec Co. Ltd., Changsha, Hunan, 410125, China
| | - Li Chen
- Institute of Vegetable Research, Hunan Academy of Agricultural Science, Changsha 410125, China
| | - Fangling Xie
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Xiumin Li
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Fan Zhu
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Zhongyi Wang
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Cheng Xiong
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Xiongze Dai
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Xuexiao Zou
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
| | - Feng Liu
- Engineering Research Center of Education, Ministry for Germplasm Innovation and Breeding New Varieties of Horticultural Crops, Key Laboratory for Vegetable Biology of Hunan Province, College of Horticulture, Hunan Agricultural University, Changsha 410128, China
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2
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Maistriaux LC, Laurent MJ, Jeanguenin L, Prado SA, Nader J, Welcker C, Charcosset A, Tardieu F, Nicolas SD, Chaumont F. Genetic variability of aquaporin expression in maize: From eQTLs to a MITE insertion regulating PIP2;5 expression. PLANT PHYSIOLOGY 2024; 196:368-384. [PMID: 38839061 PMCID: PMC11376376 DOI: 10.1093/plphys/kiae326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 02/28/2024] [Accepted: 05/02/2024] [Indexed: 06/07/2024]
Abstract
Plant aquaporins are involved in numerous physiological processes, such as cellular homeostasis, tissue hydraulics, transpiration, and nutrient supply, and are key players of the response to environmental cues. While varying expression patterns of aquaporin genes have been described across organs, developmental stages, and stress conditions, the underlying regulation mechanisms remain elusive. Hence, this work aimed to shed light on the expression variability of 4 plasma membrane intrinsic protein (PIP) genes in maize (Zea mays) leaves, and its genetic causes, through expression quantitative trait locus (eQTL) mapping across a 252-hybrid diversity panel. Significant genetic variability in PIP transcript abundance was observed to different extents depending on the isoforms. The genome-wide association study mapped numerous eQTLs, both local and distant, thus emphasizing the existing natural diversity of PIP gene expression across the studied panel and the potential to reveal regulatory actors and mechanisms. One eQTL associated with PIP2;5 expression variation was characterized. Genomic sequence comparison and in vivo reporter assay attributed, at least partly, the local eQTL to a transposon-containing polymorphism in the PIP2;5 promoter. This work paves the way to the molecular understanding of PIP gene regulation and its possible integration into larger networks regulating physiological and stress adaptation processes.
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Affiliation(s)
- Laurie C Maistriaux
- Louvain Institute of Biomolecular Science and Technology, UCLouvain, 1348 Louvain-la-Neuve, Belgium
| | - Maxime J Laurent
- Louvain Institute of Biomolecular Science and Technology, UCLouvain, 1348 Louvain-la-Neuve, Belgium
| | - Linda Jeanguenin
- Louvain Institute of Biomolecular Science and Technology, UCLouvain, 1348 Louvain-la-Neuve, Belgium
| | | | - Joseph Nader
- Louvain Institute of Biomolecular Science and Technology, UCLouvain, 1348 Louvain-la-Neuve, Belgium
| | - Claude Welcker
- INRAE, LEPSE, Université de Montpellier, 34060 Montpellier, France
| | - Alain Charcosset
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - François Tardieu
- INRAE, LEPSE, Université de Montpellier, 34060 Montpellier, France
| | - Stéphane D Nicolas
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - François Chaumont
- Louvain Institute of Biomolecular Science and Technology, UCLouvain, 1348 Louvain-la-Neuve, Belgium
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3
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Feng Y, Li X, Qin Y, Li Y, Yang Z, Xiong X, Wan J, Qiu M, Hou Q, Zhang Z, Guo Z, Zhang X, Niu J, Zhou Q, Tang J, Fu Z. Identification of anther thermotolerance genes by the integration of linkage and association analysis in maize. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 119:1953-1966. [PMID: 38943629 DOI: 10.1111/tpj.16900] [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: 02/27/2024] [Revised: 05/24/2024] [Accepted: 06/14/2024] [Indexed: 07/01/2024]
Abstract
Maize is one of the world's most important staple crops, yet its production is increasingly threatened by the rising frequency of high-temperature stress (HTS). To investigate the genetic basis of anther thermotolerance under field conditions, we performed linkage and association analysis to identify HTS response quantitative trait loci (QTL) using three recombinant inbred line (RIL) populations and an association panel containing 375 diverse maize inbred lines. These analyses resulted in the identification of 16 co-located large QTL intervals. Among the 37 candidate genes identified in these QTL intervals, five have rice or Arabidopsis homologs known to influence pollen and filament development. Notably, one of the candidate genes, ZmDUP707, has been subject to selection pressure during breeding. Its expression is suppressed by HTS, leading to pollen abortion and barren seeds. We also identified several additional candidate genes potentially underly QTL previously reported by other researchers. Taken together, our results provide a pool of valuable candidate genes that could be employed by future breeding programs aiming at enhancing maize HTS tolerance.
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Affiliation(s)
- Yijian Feng
- National Key Laboratory of Wheat and Maize Crops Science/Collaborative Innovation Center of Henan Grain Crops/College of Agronomy/The Shennong Laboratory, Henan Agricultural University, Zhengzhou, 450046, China
| | - Xinlong Li
- National Key Laboratory of Wheat and Maize Crops Science/Collaborative Innovation Center of Henan Grain Crops/College of Agronomy/The Shennong Laboratory, Henan Agricultural University, Zhengzhou, 450046, China
| | - Yongtian Qin
- Hebi Academy of Agricultural Sciences, Hebi, 458030, Henan, China
| | - Yibo Li
- National Key Laboratory of Wheat and Maize Crops Science/Collaborative Innovation Center of Henan Grain Crops/College of Agronomy/The Shennong Laboratory, Henan Agricultural University, Zhengzhou, 450046, China
| | - Zeyuan Yang
- National Key Laboratory of Wheat and Maize Crops Science/Collaborative Innovation Center of Henan Grain Crops/College of Agronomy/The Shennong Laboratory, Henan Agricultural University, Zhengzhou, 450046, China
| | - Xuehang Xiong
- National Key Laboratory of Wheat and Maize Crops Science/Collaborative Innovation Center of Henan Grain Crops/College of Agronomy/The Shennong Laboratory, Henan Agricultural University, Zhengzhou, 450046, China
| | - Jiong Wan
- Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou, 571101, China
| | - Meng Qiu
- National Key Laboratory of Wheat and Maize Crops Science/Collaborative Innovation Center of Henan Grain Crops/College of Agronomy/The Shennong Laboratory, Henan Agricultural University, Zhengzhou, 450046, China
| | - Qiuchan Hou
- National Key Laboratory of Wheat and Maize Crops Science/Collaborative Innovation Center of Henan Grain Crops/College of Agronomy/The Shennong Laboratory, Henan Agricultural University, Zhengzhou, 450046, China
| | - Zhanhui Zhang
- National Key Laboratory of Wheat and Maize Crops Science/Collaborative Innovation Center of Henan Grain Crops/College of Agronomy/The Shennong Laboratory, Henan Agricultural University, Zhengzhou, 450046, China
| | - Zhanyong Guo
- National Key Laboratory of Wheat and Maize Crops Science/Collaborative Innovation Center of Henan Grain Crops/College of Agronomy/The Shennong Laboratory, Henan Agricultural University, Zhengzhou, 450046, China
| | - Xuehai Zhang
- National Key Laboratory of Wheat and Maize Crops Science/Collaborative Innovation Center of Henan Grain Crops/College of Agronomy/The Shennong Laboratory, Henan Agricultural University, Zhengzhou, 450046, China
| | - Jishan Niu
- National Key Laboratory of Wheat and Maize Crops Science/Collaborative Innovation Center of Henan Grain Crops/College of Agronomy/The Shennong Laboratory, Henan Agricultural University, Zhengzhou, 450046, China
| | - Qingqian Zhou
- National Key Laboratory of Wheat and Maize Crops Science/Collaborative Innovation Center of Henan Grain Crops/College of Agronomy/The Shennong Laboratory, Henan Agricultural University, Zhengzhou, 450046, China
| | - Jihua Tang
- National Key Laboratory of Wheat and Maize Crops Science/Collaborative Innovation Center of Henan Grain Crops/College of Agronomy/The Shennong Laboratory, Henan Agricultural University, Zhengzhou, 450046, China
| | - Zhiyuan Fu
- National Key Laboratory of Wheat and Maize Crops Science/Collaborative Innovation Center of Henan Grain Crops/College of Agronomy/The Shennong Laboratory, Henan Agricultural University, Zhengzhou, 450046, China
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4
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Ali B, Huguenin-Bizot B, Laurent M, Chaumont F, Maistriaux LC, Nicolas S, Duborjal H, Welcker C, Tardieu F, Mary-Huard T, Moreau L, Charcosset A, Runcie D, Rincent R. High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:175. [PMID: 38958724 DOI: 10.1007/s00122-024-04679-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 06/15/2024] [Indexed: 07/04/2024]
Abstract
KEY MESSAGE Transcriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits. The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plant's response to such climatic factors can be capitalized in prediction models to maximize crop improvement. Implementing multi-omics characterization in field evaluations is challenging due to high costs. It is, however, possible to do it on reference genotypes in controlled conditions. Using omics measured on a platform, we tested different multi-omics-based prediction approaches, using a high dimensional linear mixed model (MegaLMM) to predict genotypes for platform traits and agronomic field traits in a panel of 244 maize hybrids. We considered two prediction scenarios: in the first one, new hybrids are predicted (CV-NH), and in the second one, partially observed hybrids are predicted (CV-POH). For both scenarios, all hybrids were characterized for omics on the platform. We observed that omics can predict both additive and non-additive genetic effects for the platform traits, resulting in much higher predictive abilities than GBLUP. It highlights their efficiency in capturing regulatory processes in relation to growth conditions. For the field traits, we observed that the additive components of omics only slightly improved predictive abilities for predicting new hybrids (CV-NH, model MegaGAO) and for predicting partially observed hybrids (CV-POH, model GAOxW-BLUP) in comparison to GBLUP. We conclude that measuring the omics in the fields would be of considerable interest in predicting productivity if the costs of omics drop significantly.
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Affiliation(s)
- Baber Ali
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Bertrand Huguenin-Bizot
- Laboratoire Reproduction Et Développement Des Plantes, CNRS, ENS de Lyon-46, Allée d'Italie, 69364, Lyon, France
| | - Maxime Laurent
- Louvain Institute of Biomolecular Science and Technology, UCLouvain, Louvain-La-Neuve, Belgium
| | - François Chaumont
- Louvain Institute of Biomolecular Science and Technology, UCLouvain, Louvain-La-Neuve, Belgium
| | - Laurie C Maistriaux
- Louvain Institute of Biomolecular Science and Technology, UCLouvain, Louvain-La-Neuve, Belgium
| | - Stéphane Nicolas
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Hervé Duborjal
- Limagrain, Limagrain Fields Seeds, Research Centre, 63720, Chappes, France
| | | | | | - Tristan Mary-Huard
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Laurence Moreau
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Alain Charcosset
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France
| | - Daniel Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA, USA
| | - Renaud Rincent
- INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, 91190, Gif-Sur-Yvette, France.
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5
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Weber SE, Roscher-Ehrig L, Kox T, Abbadi A, Stahl A, Snowdon RJ. Genomic prediction in Brassica napus: evaluating the benefit of imputed whole-genome sequencing data. Genome 2024; 67:210-222. [PMID: 38708850 DOI: 10.1139/gen-2023-0126] [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: 05/07/2024]
Abstract
Advances in sequencing technology allow whole plant genomes to be sequenced with high quality. Combining genotypic and phenotypic data in genomic prediction helps breeders to select crossing partners in partially phenotyped populations. In plant breeding programs, the cost of sequencing entire breeding populations still exceeds available genotyping budgets. Hence, the method for genotyping is still mainly single nucleotide polymorphism (SNP) arrays; however, arrays are unable to assess the entire genome- and population-wide diversity. A compromise involves genotyping the entire population using an SNP array and a subset of the population with whole-genome sequencing. Both datasets can then be used to impute markers from whole-genome sequencing onto the entire population. Here, we evaluate whether imputation of whole-genome sequencing data enhances genomic predictions, using data from a nested association mapping population of rapeseed (Brassica napus). Employing two cross-validation schemes that mimic scenarios for the prediction of close and distant relatives, we show that imputed marker data do not significantly improve prediction accuracy, likely due to redundancy in relationship estimates and imputation errors. In simulation studies, only small improvements were observed, further corroborating the findings. We conclude that SNP arrays are already equipped with the information that is added by imputation through relationship and linkage disequilibrium.
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Affiliation(s)
- Sven E Weber
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | - Lennard Roscher-Ehrig
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | | | | | - Andreas Stahl
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
| | - Rod J Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
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6
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Balconi C, Galaretto A, Malvar RA, Nicolas SD, Redaelli R, Andjelkovic V, Revilla P, Bauland C, Gouesnard B, Butron A, Torri A, Barata AM, Kravic N, Combes V, Mendes-Moreira P, Murariu D, Šarčević H, Schierscher-Viret B, Vincent M, Zanetto A, Kessel B, Madur D, Mary-Huard T, Pereira A, Placinta DD, Strigens A, Charcosset A, Goritschnig S. Genetic and Phenotypic Evaluation of European Maize Landraces as a Tool for Conservation and Valorization of Agrobiodiversity. BIOLOGY 2024; 13:454. [PMID: 38927334 PMCID: PMC11201045 DOI: 10.3390/biology13060454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 05/30/2024] [Accepted: 06/04/2024] [Indexed: 06/28/2024]
Abstract
The ECPGR European Evaluation Network (EVA) for Maize involves genebanks, research institutions, and private breeding companies from nine countries focusing on the valorization of maize genetic resources across Europe. This study describes a diverse collection of 626 local landraces and traditional varieties of maize (Zea mays L.) from nine European genebanks, including criteria for selection of the collection and its genetic and phenotypic diversity. High-throughput pool genotyping grouped the landraces into nine genetic groups with a threshold of 0.6 admixture, while 277 accessions were designated admixed and likely to have resulted from previous breeding activities. The grouping correlated well with the geographic origins of the collection, also reflecting the various pathways of introduction of maize to Europe. Phenotypic evaluations of 588 accessions for flowering time and plant architecture in multilocation trials over three years confirmed the great diversity within the collection, although phenotypic clusters only partially correlated with the genetic grouping. The EVA approach promotes conservation of genetic resources and opens an opportunity to increase genetic variability for developing improved varieties and populations for farmers, with better adaptation to specific environments and greater tolerance to various stresses. As such, the EVA maize collection provides valuable sources of diversity for facing climate change due to the varieties' local adaptation.
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Affiliation(s)
- Carlotta Balconi
- CREA—Council for Agricultural Research and Economics, Research Centre for Cereal and Industrial Crops, via Stezzano 24, 24126 Bergamo, Italy; (R.R.); (A.T.)
| | - Agustin Galaretto
- INRAE, CNRS, AgroParisTech, GQE—Le Moulon, Université Paris-Saclay, 12 route 128, 91190 Gif-sur-Yvette, France; (A.G.); (S.D.N.); (C.B.); (V.C.); (D.M.); (T.M.-H.); (A.C.)
| | - Rosa Ana Malvar
- Misión Biológica de Galicia Consejo Superior de Investigaciones Científicas, Pazo de Salcedo Carballeira, 8 Salcedo, 36143 Pontevedra, Spain; (R.A.M.)
| | - Stéphane D. Nicolas
- INRAE, CNRS, AgroParisTech, GQE—Le Moulon, Université Paris-Saclay, 12 route 128, 91190 Gif-sur-Yvette, France; (A.G.); (S.D.N.); (C.B.); (V.C.); (D.M.); (T.M.-H.); (A.C.)
| | - Rita Redaelli
- CREA—Council for Agricultural Research and Economics, Research Centre for Cereal and Industrial Crops, via Stezzano 24, 24126 Bergamo, Italy; (R.R.); (A.T.)
| | - Violeta Andjelkovic
- Maize Research Institute Zemun Polje, 11000 Belgrade, Serbia; (V.A.); (N.K.)
| | - Pedro Revilla
- Misión Biológica de Galicia Consejo Superior de Investigaciones Científicas, Pazo de Salcedo Carballeira, 8 Salcedo, 36143 Pontevedra, Spain; (R.A.M.)
| | - Cyril Bauland
- INRAE, CNRS, AgroParisTech, GQE—Le Moulon, Université Paris-Saclay, 12 route 128, 91190 Gif-sur-Yvette, France; (A.G.); (S.D.N.); (C.B.); (V.C.); (D.M.); (T.M.-H.); (A.C.)
| | - Brigitte Gouesnard
- UMR AGAP Institut, CIRAD, INRAE, Institut Agro, University Montpellier, F-34398 Montpellier, France (M.V.); (A.Z.)
| | - Ana Butron
- Misión Biológica de Galicia Consejo Superior de Investigaciones Científicas, Pazo de Salcedo Carballeira, 8 Salcedo, 36143 Pontevedra, Spain; (R.A.M.)
| | - Alessio Torri
- CREA—Council for Agricultural Research and Economics, Research Centre for Cereal and Industrial Crops, via Stezzano 24, 24126 Bergamo, Italy; (R.R.); (A.T.)
| | - Ana Maria Barata
- Banco Português de Germoplasma Vegetal, Quinta de S. José, S.Pedro de Merelim, 4700-859 Braga, Portugal;
| | - Natalija Kravic
- Maize Research Institute Zemun Polje, 11000 Belgrade, Serbia; (V.A.); (N.K.)
| | - Valérie Combes
- INRAE, CNRS, AgroParisTech, GQE—Le Moulon, Université Paris-Saclay, 12 route 128, 91190 Gif-sur-Yvette, France; (A.G.); (S.D.N.); (C.B.); (V.C.); (D.M.); (T.M.-H.); (A.C.)
| | - Pedro Mendes-Moreira
- Coimbra School of Agriculture, Polytechnic University of Coimbra (ESAC-IPC), 3045-093 Coimbra, Portugal; (P.M.-M.); (A.P.)
- CERNAS—Research Centre for Natural Resources, Environment and Society, Bencanta, 3045-601 Coimbra, Portugal
| | - Danela Murariu
- Suceava Genebank, B-Dul. 1 Mai 17, 720224 Suceava, Romania; (D.M.); (D.D.P.)
| | - Hrvoje Šarčević
- Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia;
| | | | - Morgane Vincent
- UMR AGAP Institut, CIRAD, INRAE, Institut Agro, University Montpellier, F-34398 Montpellier, France (M.V.); (A.Z.)
| | - Anne Zanetto
- UMR AGAP Institut, CIRAD, INRAE, Institut Agro, University Montpellier, F-34398 Montpellier, France (M.V.); (A.Z.)
| | - Bettina Kessel
- KWS SAAT SE & Co. KGaA, Grimsehlstr. 31, 37574 Einbeck, Germany;
| | - Delphine Madur
- INRAE, CNRS, AgroParisTech, GQE—Le Moulon, Université Paris-Saclay, 12 route 128, 91190 Gif-sur-Yvette, France; (A.G.); (S.D.N.); (C.B.); (V.C.); (D.M.); (T.M.-H.); (A.C.)
| | - Tristan Mary-Huard
- INRAE, CNRS, AgroParisTech, GQE—Le Moulon, Université Paris-Saclay, 12 route 128, 91190 Gif-sur-Yvette, France; (A.G.); (S.D.N.); (C.B.); (V.C.); (D.M.); (T.M.-H.); (A.C.)
- INRAE, UMR MIA Paris-Saclay, Université Paris-Saclay, AgroParisTech, 91120 Paris, France
| | - André Pereira
- Coimbra School of Agriculture, Polytechnic University of Coimbra (ESAC-IPC), 3045-093 Coimbra, Portugal; (P.M.-M.); (A.P.)
- CERNAS—Research Centre for Natural Resources, Environment and Society, Bencanta, 3045-601 Coimbra, Portugal
| | | | - Alexandre Strigens
- DSP—Delley Semences et Plantes SA, Route de Portalban 40, 1567 Delley, Switzerland;
| | - Alain Charcosset
- INRAE, CNRS, AgroParisTech, GQE—Le Moulon, Université Paris-Saclay, 12 route 128, 91190 Gif-sur-Yvette, France; (A.G.); (S.D.N.); (C.B.); (V.C.); (D.M.); (T.M.-H.); (A.C.)
| | - Sandra Goritschnig
- ECPGR, Alliance of Bioversity International and CIAT, Via di San Domenico 1, 00153 Rome, Italy
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7
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Foster TL, Kloiber-Maitz M, Gilles L, Frei UK, Pfeffer S, Chen YR, Dutta S, Seetharam AS, Hufford MB, Lübberstedt T. Fine mapping of major QTL qshgd1 for spontaneous haploid genome doubling in maize (Zea mays L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:117. [PMID: 38700534 DOI: 10.1007/s00122-024-04615-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 04/04/2024] [Indexed: 05/09/2024]
Abstract
KEY MESSAGE A large-effect QTL was fine mapped, which revealed 79 gene models, with 10 promising candidate genes, along with a novel inversion. In commercial maize breeding, doubled haploid (DH) technology is arguably the most efficient resource for rapidly developing novel, completely homozygous lines. However, the DH strategy, using in vivo haploid induction, currently requires the use of mutagenic agents which can be not only hazardous, but laborious. This study focuses on an alternative approach to develop DH lines-spontaneous haploid genome duplication (SHGD) via naturally restored haploid male fertility (HMF). Inbred lines A427 and Wf9, the former with high HMF and the latter with low HMF, were selected to fine-map a large-effect QTL associated with SHGD-qshgd1. SHGD alleles were derived from A427, with novel haploid recombinant groups having varying levels of the A427 chromosomal region recovered. The chromosomal region of interest is composed of 45 megabases (Mb) of genetic information on chromosome 5. Significant differences between haploid recombinant groups for HMF were identified, signaling the possibility of mapping the QTL more closely. Due to suppression of recombination from the proximity of the centromere, and a newly discovered inversion region, the associated QTL was only confined to a 25 Mb region, within which only a single recombinant was observed among ca. 9,000 BC1 individuals. Nevertheless, 79 gene models were identified within this 25 Mb region. Additionally, 10 promising candidate genes, based on RNA-seq data, are described for future evaluation, while the narrowed down genome region is accessible for straightforward introgression into elite germplasm by BC methods.
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Affiliation(s)
- Tyler L Foster
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA.
| | | | - Laurine Gilles
- Limagrain Europe SAS, Research Centre, 63720, Chappes, France
| | - Ursula K Frei
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
| | - Sarah Pfeffer
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
| | - Yu-Ru Chen
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA
| | - Somak Dutta
- Department of Statistics, Iowa State University, Ames, IA, 50011, USA
| | - Arun S Seetharam
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Matthew B Hufford
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
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8
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Sumithra TG, Sharma SRK, Suresh G, Gop AP, Surya S, Gomathi P, Anil MK, Sajina KA, Reshma KJ, Ebeneezar S, Narasimapallavan I, Gopalakrishnan A. Mechanistic insights into the early life stage microbiota of silver pompano ( Trachinotus blochii). Front Microbiol 2024; 15:1356828. [PMID: 38694807 PMCID: PMC11061439 DOI: 10.3389/fmicb.2024.1356828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 03/13/2024] [Indexed: 05/04/2024] Open
Abstract
Introduction Deep investigations of host-associated microbiota can illuminate microbe-based solutions to improve production in an unprecedented manner. The poor larval survival represents the critical bottleneck in sustainable marine aquaculture practices. However, little is known about the microbiota profiles and their governing eco-evolutionary processes of the early life stages of marine teleost, impeding the development of suitable beneficial microbial management strategies. The study provides first-hand mechanistic insights into microbiota and its governing eco-evolutionary processes in early life stages of a tropical marine teleost model, Trachinotus blochii. Methods The microbiota profiles and their dynamics from the first day of hatching till the end of metamorphosis and that of fingerling's gut during the routine hatchery production were studied using 16S rRNA amplicon-based high-throughput sequencing. Further, the relative contributions of various external factors (rearing water, live feed, microalgae, and formulated feed) to the microbiota profiles at different ontogenies was also analyzed. Results A less diverse but abundant core microbial community (~58% and 54% in the whole microbiota and gut microbiota, respectively) was observed throughout the early life stages, supporting 'core microbiota' hypothesis. Surprisingly, there were two well-differentiated clusters in the whole microbiota profiles, ≤10 DPH (days post-hatching) and > 10 DPH samples. The levels of microbial taxonomic signatures of stress indicated increased stress in the early stages, a possible explanation for increased mortality during early life stages. Further, the results suggested an adaptive mechanism for establishing beneficial strains along the ontogenetic progression. Moreover, the highly transient microbiota in the early life stages became stable along the ontogenetic progression, hypothesizing that the earlier life stages will be the best window to influence the microbiota. The egg microbiota also crucially affected the microbial community. Noteworthily, both water and the feed microbiota significantly contributed to the early microbiota, with the feed microbiota having a more significant contribution to fish microbiota. The results illustrated that rotifer enrichment would be the optimal medium for the early larval microbiota manipulations. Conclusion The present study highlighted the crucial foundations for the microbial ecology of T. blochii during early life stages with implications to develop suitable beneficial microbial management strategies for sustainable mariculture production.
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Affiliation(s)
- T. G. Sumithra
- Marine Biotechnology, Fish Nutrition, and Health Division, ICAR-Central Marine Fisheries Research Institute (CMFRI), Kochi, India
| | - S. R. Krupesha Sharma
- Marine Biotechnology, Fish Nutrition, and Health Division, ICAR-Central Marine Fisheries Research Institute (CMFRI), Kochi, India
| | - Gayathri Suresh
- Marine Biotechnology, Fish Nutrition, and Health Division, ICAR-Central Marine Fisheries Research Institute (CMFRI), Kochi, India
- Cochin University of Science and Technology, Kochi, Kerala, India
| | - Ambarish P. Gop
- Vizhinjam Regional Centre of ICAR-Central Marine Fisheries Research Institute, Thiruvananthapuram, Kerala, India
| | - S. Surya
- Vizhinjam Regional Centre of ICAR-Central Marine Fisheries Research Institute, Thiruvananthapuram, Kerala, India
| | - P. Gomathi
- Vizhinjam Regional Centre of ICAR-Central Marine Fisheries Research Institute, Thiruvananthapuram, Kerala, India
| | - M. K. Anil
- Vizhinjam Regional Centre of ICAR-Central Marine Fisheries Research Institute, Thiruvananthapuram, Kerala, India
| | - K. A. Sajina
- Marine Biotechnology, Fish Nutrition, and Health Division, ICAR-Central Marine Fisheries Research Institute (CMFRI), Kochi, India
| | - K. J. Reshma
- Marine Biotechnology, Fish Nutrition, and Health Division, ICAR-Central Marine Fisheries Research Institute (CMFRI), Kochi, India
| | - Sanal Ebeneezar
- Marine Biotechnology, Fish Nutrition, and Health Division, ICAR-Central Marine Fisheries Research Institute (CMFRI), Kochi, India
| | - Iyyapparaja Narasimapallavan
- Marine Biotechnology, Fish Nutrition, and Health Division, ICAR-Central Marine Fisheries Research Institute (CMFRI), Kochi, India
| | - A. Gopalakrishnan
- Marine Biotechnology, Fish Nutrition, and Health Division, ICAR-Central Marine Fisheries Research Institute (CMFRI), Kochi, India
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9
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Lorenzi A, Bauland C, Pin S, Madur D, Combes V, Palaffre C, Guillaume C, Touzy G, Mary-Huard T, Charcosset A, Moreau L. Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:75. [PMID: 38453705 PMCID: PMC11341662 DOI: 10.1007/s00122-024-04566-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/30/2024] [Indexed: 03/09/2024]
Abstract
KEY MESSAGE We validated the efficiency of genomic predictions calibrated on sparse factorial training sets to predict the next generation of hybrids and tested different strategies for updating predictions along generations. Genomic selection offers new prospects for revisiting hybrid breeding schemes by replacing extensive phenotyping of individuals with genomic predictions. Finding the ideal design for training genomic prediction models is still an open question. Previous studies have shown promising predictive abilities using sparse factorial instead of tester-based training sets to predict single-cross hybrids from the same generation. This study aims to further investigate the use of factorials and their optimization to predict line general combining abilities (GCAs) and hybrid values across breeding cycles. It relies on two breeding cycles of a maize reciprocal genomic selection scheme involving multiparental connected reciprocal populations from flint and dent complementary heterotic groups selected for silage performances. Selection based on genomic predictions trained on a factorial design resulted in a significant genetic gain for dry matter yield in the new generation. Results confirmed the efficiency of sparse factorial training sets to predict candidate line GCAs and hybrid values across breeding cycles. Compared to a previous study based on the first generation, the advantage of factorial over tester training sets appeared lower across generations. Updating factorial training sets by adding single-cross hybrids between selected lines from the previous generation or a random subset of hybrids from the new generation both improved predictive abilities. The CDmean criterion helped determine the set of single-crosses to phenotype to update the training set efficiently. Our results validated the efficiency of sparse factorial designs for calibrating hybrid genomic prediction experimentally and showed the benefit of updating it along generations.
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Affiliation(s)
- Alizarine Lorenzi
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
- RAGT2n, Genetics and Analytics Unit, 12510, Druelle, France
| | - Cyril Bauland
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Sophie Pin
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Delphine Madur
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Valérie Combes
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Carine Palaffre
- UE 0394 SMH, INRAE, 2297 Route de l'INRA, 40390, Saint-Martin-de-Hinx, France
| | | | - Gaëtan Touzy
- RAGT2n, Genetics and Analytics Unit, 12510, Druelle, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France.
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10
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Blancon J, Buet C, Dubreuil P, Tixier MH, Baret F, Praud S. Maize green leaf area index dynamics: genetic basis of a new secondary trait for grain yield in optimal and drought conditions. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:68. [PMID: 38441678 PMCID: PMC10914915 DOI: 10.1007/s00122-024-04572-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/03/2024] [Indexed: 03/07/2024]
Abstract
KEY MESSAGE Green Leaf Area Index dynamics is a promising secondary trait for grain yield and drought tolerance. Multivariate GWAS is particularly well suited to identify the genetic determinants of the green leaf area index dynamics. Improvement of maize grain yield is impeded by important genotype-environment interactions, especially under drought conditions. The use of secondary traits, that are correlated with yield, more heritable and less prone to genotype-environment interactions, can increase breeding efficiency. Here, we studied the genetic basis of a new secondary trait: the green leaf area index (GLAI) dynamics over the maize life cycle. For this, we used an unmanned aerial vehicle to characterize the GLAI dynamics of a diverse panel in well-watered and water-deficient trials in two years. From the dynamics, we derived 24 traits (slopes, durations, areas under the curve), and showed that six of them were heritable traits representative of the panel diversity. To identify the genetic determinants of GLAI, we compared two genome-wide association approaches: a univariate (single-trait) method and a multivariate (multi-trait) method combining GLAI traits, grain yield, and precocity. The explicit modeling of correlation structure between secondary traits and grain yield in the multivariate mixed model led to 2.5 times more associations detected. A total of 475 quantitative trait loci (QTLs) were detected. The genetic architecture of GLAI traits appears less complex than that of yield with stronger-effect QTLs that are more stable between environments. We also showed that a subset of GLAI QTLs explains nearly one fifth of yield variability across a larger environmental network of 11 water-deficient trials. GLAI dynamics is a promising grain yield secondary trait in optimal and drought conditions, and the detected QTLs could help to increase breeding efficiency through a marker-assisted approach.
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Affiliation(s)
- Justin Blancon
- UMR GDEC, INRAE, Université Clermont Auvergne, 63000, Clermont-Ferrand, France.
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France.
| | - Clément Buet
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France
| | - Pierre Dubreuil
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France
| | | | | | - Sébastien Praud
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France
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11
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Zhao B, Li K, Wang M, Liu Z, Yin P, Wang W, Li Z, Li X, Zhang L, Han Y, Li J, Yang X. Genetic basis of maize stalk strength decoded via linkage and association mapping. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 117:1558-1573. [PMID: 38113320 DOI: 10.1111/tpj.16583] [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: 08/27/2023] [Revised: 11/20/2023] [Accepted: 11/26/2023] [Indexed: 12/21/2023]
Abstract
Stalk lodging is a severe problem that limits maize production worldwide, although little attention has been given to its genetic basis. Here we measured rind penetrometer resistance (RPR), an effective index for stalk lodging, in a multi-parent population of 1948 recombinant inbred lines (RILs) and an association population of 508 inbred lines (AMP508). Linkage and association mapping identified 53 and 29 single quantitative trait loci (QTLs) and 50 and 19 pairs of epistatic interactions for RPR in the multi-parent population and AMP508 population, respectively. Phenotypic variation explained by all identified epistatic QTLs (up to ~5%) was much less than that explained by all single additive QTLs (up to ~33% in the multi-parent population and ~ 60% in the AMP508 population). Among all detected QTLs, only eight single QTLs explained >10% of phenotypic variation in single RIL populations. Alleles that increased RPR were enriched in tropical/subtropical (TST) groups from the AMP508 population. Based on genome-wide association studies in both populations, we identified 137 candidate genes affecting RPR, which were assigned to multiple biological processes, such as the biosynthesis of cell wall components. Sixty-six candidate genes were cross-validated by multiple methods or populations. Most importantly, 23 candidate genes were upregulated or downregulated in high-RPR lines relative to low-RPR lines, supporting the associations between candidate genes and RPR. These findings reveal the complex nature of the genetic basis underlying RPR and provide loci or candidate genes for developing elite varieties that are resistant to stalk lodging via molecular breeding.
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Affiliation(s)
- Binghao Zhao
- State Key Laboratory of Plant Environmental Resilience and National Maize Improvement Center of China, China Agricultural University, Beijing, 100193, China
| | - Kun Li
- State Key Laboratory of Plant Environmental Resilience and National Maize Improvement Center of China, China Agricultural University, Beijing, 100193, China
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Min Wang
- State Key Laboratory of Plant Environmental Resilience and National Maize Improvement Center of China, China Agricultural University, Beijing, 100193, China
| | - Zhiyuan Liu
- State Key Laboratory of Plant Environmental Resilience and National Maize Improvement Center of China, China Agricultural University, Beijing, 100193, China
| | - Pengfei Yin
- State Key Laboratory of Plant Environmental Resilience and National Maize Improvement Center of China, China Agricultural University, Beijing, 100193, China
| | - Weidong Wang
- State Key Laboratory of Plant Environmental Resilience and National Maize Improvement Center of China, China Agricultural University, Beijing, 100193, China
| | - Zhigang Li
- State Key Laboratory of Plant Environmental Resilience and National Maize Improvement Center of China, China Agricultural University, Beijing, 100193, China
| | - Xiaowei Li
- State Key Laboratory of Plant Environmental Resilience and National Maize Improvement Center of China, China Agricultural University, Beijing, 100193, China
| | - Lili Zhang
- State Key Laboratory of Plant Environmental Resilience and National Maize Improvement Center of China, China Agricultural University, Beijing, 100193, China
| | - Yingjia Han
- State Key Laboratory of Plant Environmental Resilience and National Maize Improvement Center of China, China Agricultural University, Beijing, 100193, China
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, the Chinese Academy of Sciences, Beijing, 100093, China
| | - Jiansheng Li
- State Key Laboratory of Plant Environmental Resilience and National Maize Improvement Center of China, China Agricultural University, Beijing, 100193, China
| | - Xiaohong Yang
- State Key Laboratory of Plant Environmental Resilience and National Maize Improvement Center of China, China Agricultural University, Beijing, 100193, China
- Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
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12
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Hiraoka Y, Ferrante SP, Wu GA, Federici CT, Roose ML. Development and Assessment of SNP Genotyping Arrays for Citrus and Its Close Relatives. PLANTS (BASEL, SWITZERLAND) 2024; 13:691. [PMID: 38475537 DOI: 10.3390/plants13050691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/13/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024]
Abstract
Rapid advancements in technologies provide various tools to analyze fruit crop genomes to better understand genetic diversity and relationships and aid in breeding. Genome-wide single nucleotide polymorphism (SNP) genotyping arrays offer highly multiplexed assays at a relatively low cost per data point. We report the development and validation of 1.4M SNP Axiom® Citrus HD Genotyping Array (Citrus 15AX 1 and Citrus 15AX 2) and 58K SNP Axiom® Citrus Genotyping Arrays for Citrus and close relatives. SNPs represented were chosen from a citrus variant discovery panel consisting of 41 diverse whole-genome re-sequenced accessions of Citrus and close relatives, including eight progenitor citrus species. SNPs chosen mainly target putative genic regions of the genome and are accurately called in both Citrus and its closely related genera while providing good coverage of the nuclear and chloroplast genomes. Reproducibility of the arrays was nearly 100%, with a large majority of the SNPs classified as the most stringent class of markers, "PolyHighResolution" (PHR) polymorphisms. Concordance between SNP calls in sequence data and array data average 98%. Phylogenies generated with array data were similar to those with comparable sequence data and little affected by 3 to 5% genotyping error. Both arrays are publicly available.
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Affiliation(s)
- Yoko Hiraoka
- Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
| | - Sergio Pietro Ferrante
- Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
| | - Guohong Albert Wu
- US Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA
| | - Claire T Federici
- Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
| | - Mikeal L Roose
- Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
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13
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Ndlovu N, Kachapur RM, Beyene Y, Das B, Ogugo V, Makumbi D, Spillane C, McKeown PC, Prasanna BM, Gowda M. Linkage mapping and genomic prediction of grain quality traits in tropical maize ( Zea mays L.). Front Genet 2024; 15:1353289. [PMID: 38456017 PMCID: PMC10918846 DOI: 10.3389/fgene.2024.1353289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 02/07/2024] [Indexed: 03/09/2024] Open
Abstract
The suboptimal productivity of maize systems in sub-Saharan Africa (SSA) is a pressing issue, with far-reaching implications for food security, nutrition, and livelihood sustainability within the affected smallholder farming communities. Dissecting the genetic basis of grain protein, starch and oil content can increase our understanding of the governing genetic systems, improve the efficacy of future breeding schemes and optimize the end-use quality of tropical maize. Here, four bi-parental maize populations were evaluated in field trials in Kenya and genotyped with mid-density single nucleotide polymorphism (SNP) markers. Genotypic (G), environmental (E) and G×E variations were found to be significant for all grain quality traits. Broad sense heritabilities exhibited substantial variation (0.18-0.68). Linkage mapping identified multiple quantitative trait loci (QTLs) for the studied grain quality traits: 13, 7, 33, 8 and 2 QTLs for oil content, protein content, starch content, grain texture and kernel weight, respectively. The co-localization of QTLs identified in our research suggests the presence of shared genetic factors or pleiotropic effects, implying that specific genomic regions influence the expression of multiple grain quality traits simultaneously. Genomic prediction accuracies were moderate to high for the studied traits. Our findings highlight the polygenic nature of grain quality traits and demonstrate the potential of genomic selection to enhance genetic gains in maize breeding. Furthermore, the identified genomic regions and single nucleotide polymorphism markers can serve as the groundwork for investigating candidate genes that regulate grain quality traits in tropical maize. This, in turn, can facilitate the implementation of marker-assisted selection (MAS) in breeding programs focused on improving grain nutrient levels.
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Affiliation(s)
- Noel Ndlovu
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, Galway, Ireland
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Rajashekar M. Kachapur
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
- University of Agricultural Sciences, Dharwad, Karnataka, India
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Veronica Ogugo
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Dan Makumbi
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Charles Spillane
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, Galway, Ireland
| | - Peter C. McKeown
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, Galway, Ireland
| | | | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
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14
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Mitsuhashi S. Genetic diversity among maize (Zea mays L.) inbred lines adapted to Japanese climates. PLoS One 2024; 19:e0297549. [PMID: 38271395 PMCID: PMC10810424 DOI: 10.1371/journal.pone.0297549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/08/2024] [Indexed: 01/27/2024] Open
Abstract
Understanding the genetic diversity of inbred lines is vital for development of superior F1 varieties. The present study aimed to analyze Japanese maize parental inbred lines and determine their genetic diversity for future breeding. Genetic analyses were conducted using multiple methods. Principal component analysis (PCA), phylogenetic trees, and Bayesian clustering reflected borders between heterotic groups according to the derivation of each inbred line. A self-pollinated line derived from a classic F1 variety and another line from an open-pollinated population from the same derivation were classified as separate components by PCA and Bayesian clustering. The result suggests that open pollination could be essential in modern breeding. Of those classified as dent or flint based on their derivation, some had a combination of all components or clusters. Therefore, the classification of inbred lines should be based on their derivation and DNA markers. The findings will be valuable for breeding and genetic studies in Japan. Additionally, these techniques may be used to obtain a more significant number of SNPs and related phenotypic data.
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Affiliation(s)
- Shohei Mitsuhashi
- Institute of Livestock and Grassland Science, NARO, Nasushiobara, Tochigi, Japan
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15
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Mastrangelo AM, Hartings H, Lanzanova C, Balconi C, Locatelli S, Cassol H, Valoti P, Petruzzino G, Pecchioni N. Genetic Diversity within a Collection of Italian Maize Inbred Lines: A Resource for Maize Genomics and Breeding. PLANTS (BASEL, SWITZERLAND) 2024; 13:336. [PMID: 38337869 PMCID: PMC10857507 DOI: 10.3390/plants13030336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 01/16/2024] [Accepted: 01/17/2024] [Indexed: 02/12/2024]
Abstract
Genetic diversity is fundamental for studying the complex architecture of the traits of agronomic importance, controlled by major and minor loci. Moreover, well-characterized germplasm collections are essential tools for dissecting and analyzing genetic and phenotypic diversity in crops. A panel of 360 entries, a subset of a larger collection maintained within the GenBank at CREA Bergamo, which includes the inbreds derived from traditional Italian maize open-pollinated (OP) varieties and advanced breeding ones (Elite Inbreds), was analyzed to identify SNP markers using the tGBS® genotyping-by-sequencing technology. A total of 797,368 SNPs were found during the initial analysis. Imputation and filtering processes were carried out based on the percentage of missing data, redundant markers, and rarest allele frequencies, resulting in a final dataset of 15,872 SNP markers for which a physical map position was identified. Using this dataset, the inbred panel was characterized for linkage disequilibrium (LD), genetic diversity, population structure, and genetic relationships. LD decay at a genome-wide level indicates that the collection is a suitable resource for association mapping. Population structure analyses, which were carried out with different clustering methods, showed stable grouping statistics for four groups, broadly corresponding to 'Insubria', 'Microsperma', and 'Scagliolino' genotypes, with a fourth group composed prevalently of elite accessions derived from Italian and US breeding programs. Based on these results, the CREA Italian maize collection, genetically characterized in this study, can be considered an important tool for the mapping and characterization of useful traits and associated loci/alleles, to be used in maize breeding programs.
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Affiliation(s)
- Anna Maria Mastrangelo
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, SS 673 Metri 25200, 71122 Foggia, Italy; (G.P.); (N.P.)
| | - Hans Hartings
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, Via Stezzano 24, 24126 Bergamo, Italy; (H.H.); (C.L.); (C.B.); (S.L.); (H.C.); (P.V.)
| | - Chiara Lanzanova
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, Via Stezzano 24, 24126 Bergamo, Italy; (H.H.); (C.L.); (C.B.); (S.L.); (H.C.); (P.V.)
| | - Carlotta Balconi
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, Via Stezzano 24, 24126 Bergamo, Italy; (H.H.); (C.L.); (C.B.); (S.L.); (H.C.); (P.V.)
| | - Sabrina Locatelli
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, Via Stezzano 24, 24126 Bergamo, Italy; (H.H.); (C.L.); (C.B.); (S.L.); (H.C.); (P.V.)
| | - Helga Cassol
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, Via Stezzano 24, 24126 Bergamo, Italy; (H.H.); (C.L.); (C.B.); (S.L.); (H.C.); (P.V.)
| | - Paolo Valoti
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, Via Stezzano 24, 24126 Bergamo, Italy; (H.H.); (C.L.); (C.B.); (S.L.); (H.C.); (P.V.)
| | - Giuseppe Petruzzino
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, SS 673 Metri 25200, 71122 Foggia, Italy; (G.P.); (N.P.)
| | - Nicola Pecchioni
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, SS 673 Metri 25200, 71122 Foggia, Italy; (G.P.); (N.P.)
- CREA-Centro di Ricerca Cerealicoltura e Colture Industriali/Research Centre for Cereal and Industrial Crops, Via Stezzano 24, 24126 Bergamo, Italy; (H.H.); (C.L.); (C.B.); (S.L.); (H.C.); (P.V.)
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16
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Sanchez D, Allier A, Ben Sadoun S, Mary-Huard T, Bauland C, Palaffre C, Lagardère B, Madur D, Combes V, Melkior S, Bettinger L, Murigneux A, Moreau L, Charcosset A. Assessing the potential of genetic resource introduction into elite germplasm: a collaborative multiparental population for flint maize. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:19. [PMID: 38214870 PMCID: PMC10786986 DOI: 10.1007/s00122-023-04509-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 11/18/2023] [Indexed: 01/13/2024]
Abstract
KEY MESSAGE Implementing a collaborative pre-breeding multi-parental population efficiently identifies promising donor x elite pairs to enrich the flint maize elite germplasm. Genetic diversity is crucial for maintaining genetic gains and ensuring breeding programs' long-term success. In a closed breeding program, selection inevitably leads to a loss of genetic diversity. While managing diversity can delay this loss, introducing external sources of diversity is necessary to bring back favorable genetic variation. Genetic resources exhibit greater diversity than elite materials, but their lower performance levels hinder their use. This is the case for European flint maize, for which elite germplasm has incorporated only a limited portion of the diversity available in landraces. To enrich the diversity of this elite genetic pool, we established an original cooperative maize bridging population that involves crosses between private elite materials and diversity donors to create improved genotypes that will facilitate the incorporation of original favorable variations. Twenty donor × elite BC1S2 families were created and phenotyped for hybrid value for yield related traits. Crosses showed contrasted means and variances and therefore contrasted potential in terms of selection as measured by their usefulness criterion (UC). Average expected mean performance gain over the initial elite material was 5%. The most promising donor for each elite line was identified. Results also suggest that one more generation, i.e., 3 in total, of crossing to the elite is required to fully exploit the potential of a donor. Altogether, our results support the usefulness of incorporating genetic resources into elite flint maize. They call for further effort to create fixed diversity donors and identify those most suitable for each elite program.
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Affiliation(s)
- Dimitri Sanchez
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Antoine Allier
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
- Syngenta, 12 Chemin de L'Hobit, 31790, Saint-Sauveur, France
| | - Sarah Ben Sadoun
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA-Paris Saclay, 91120, Palaiseau, France
| | - Cyril Bauland
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Carine Palaffre
- UE 0394 SMH, INRAE, 2297 Route de l'INRA, 40390, Saint-Martin-de-Hinx, France
| | - Bernard Lagardère
- UE 0394 SMH, INRAE, 2297 Route de l'INRA, 40390, Saint-Martin-de-Hinx, France
| | - Delphine Madur
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Valérie Combes
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | | | | | - Alain Murigneux
- Limagrain Europe, 28 Route d'Ennezat, 63720, Chappes, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France.
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17
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Walker PL, Belmonte MF, McCallum BD, McCartney CA, Randhawa HS, Henriquez MA. Dual RNA-sequencing of Fusarium head blight resistance in winter wheat. FRONTIERS IN PLANT SCIENCE 2024; 14:1299461. [PMID: 38239218 PMCID: PMC10794533 DOI: 10.3389/fpls.2023.1299461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 11/29/2023] [Indexed: 01/22/2024]
Abstract
Fusarium head blight (FHB) is a devastating fungal disease responsible for significant yield losses in wheat and other cereal crops across the globe. FHB infection of wheat spikes results in grain contamination with mycotoxins, reducing both grain quality and yield. Breeding strategies have resulted in the production of FHB-resistant cultivars, however, the underlying molecular mechanisms of resistance in the majority of these cultivars are still poorly understood. To improve our understanding of FHB-resistance, we performed a transcriptomic analysis of FHB-resistant AC Emerson, FHB-moderately resistant AC Morley, and FHB-susceptible CDC Falcon in response to Fusarium graminearum. Wheat spikelets located directly below the point of inoculation were collected at 7-days post inoculation (dpi), where dual RNA-sequencing was performed to explore differential expression patterns between wheat cultivars in addition to the challenging pathogen. Differential expression analysis revealed distinct defense responses within FHB-resistant cultivars including the enrichment of physical defense through the lignin biosynthesis pathway, and DON detoxification through the activity of UDP-glycosyltransferases. Nucleotide sequence variants were also identified broadly between these cultivars with several variants being identified within differentially expressed putative defense genes. Further, F. graminearum demonstrated differential expression of mycotoxin biosynthesis pathways during infection, leading to the identification of putative pathogenicity factors.
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Affiliation(s)
- Philip L. Walker
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB, Canada
| | - Mark F. Belmonte
- Department of Biological Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Brent D. McCallum
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB, Canada
| | - Curt A. McCartney
- Department of Plant Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Harpinder S. Randhawa
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, AB, Canada
| | - Maria A. Henriquez
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB, Canada
- Department of Plant Sciences, University of Manitoba, Winnipeg, MB, Canada
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18
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Guo J, Guo J, Li L, Bai X, Huo X, Shi W, Gao L, Dai K, Jing R, Hao C. Combined linkage analysis and association mapping identifies genomic regions associated with yield-related and drought-tolerance traits in wheat (Triticum aestivum L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:250. [PMID: 37982873 DOI: 10.1007/s00122-023-04494-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 10/26/2023] [Indexed: 11/21/2023]
Abstract
KEY MESSAGE Combined linkage analysis and association mapping identified genomic regions associated with yield and drought tolerance, providing information to assist breeding for high yield and drought tolerance in wheat. Wheat (Triticum aestivum L.) is one of the most widely grown food crops and provides adequate amounts of protein to support human health. Drought stress is the most important abiotic stress constraining yield during the flowering and grain development periods. Precise targeting of genomic regions underlying yield- and drought tolerance-responsive traits would assist in breeding programs. In this study, two water treatments (well-watered, WW, and rain-fed water stress, WS) were applied, and five yield-related agronomic traits (plant height, PH; spike length, SL; spikelet number per spike, SNPS; kernel number per spike, KNPS; thousand kernel weight, TKW) and drought response values (DRVs) were used to characterize the drought sensitivity of each accession. Association mapping was performed on an association panel of 304 accessions, and linkage analysis was applied to a doubled haploid (DH) population of 152 lines. Eleven co-localized genomic regions associated with yield traits and DRV were identified in both populations. Many previously cloned key genes were located in these regions. In particular, a TKW-associated region on chromosome 2D was identified using both association mapping and linkage analysis and a key candidate gene, TraesCS2D02G142500, was detected based on gene annotation and differences in expression levels. Exonic SNPs were analyzed by sequencing the full length of TraesCS2D02G142500 in the association panel, and a rare haplotype, Hap-2, which reduced TKW to a lesser extent than Hap-1 under drought stress, and the Hap-2 varieties presented drought-insensitive. Altogether, this study provides fundamental insights into molecular targets for high yield and drought tolerance in wheat.
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Affiliation(s)
- Jie Guo
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China
| | - Jiahui Guo
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China
- College of Agronomy, Yangzhou University, Yangzhou, 225009, Jiangsu, China
| | - Long Li
- State Key Laboratory of Crop Gene Resources and Breeding/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xionghui Bai
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China
| | - Xiaoyu Huo
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China
| | - Weiping Shi
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China
| | - Lifeng Gao
- State Key Laboratory of Crop Gene Resources and Breeding/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Keli Dai
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China.
| | - Ruilian Jing
- State Key Laboratory of Crop Gene Resources and Breeding/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Chenyang Hao
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China.
- State Key Laboratory of Crop Gene Resources and Breeding/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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19
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Lamkey CM, Lorenz AJ. A genomic analysis of the University of Nebraska Replicated Recurrent Selection program. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:243. [PMID: 37950832 DOI: 10.1007/s00122-023-04475-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 09/27/2023] [Indexed: 11/13/2023]
Abstract
The inbred-hybrid system of maize breeding closely resembles a reciprocal full-sib (RFS) selection program. Studying changes in genetic variation as a result of RFS selection can help illuminate long-standing questions regarding the relative roles of selection and genetic drift and help understand the nature of adaptation occurring in selection programs. The University of Nebraska-Lincoln Replicated Recurrent Selection (UNL-RpRS) program underwent eight cycles of replicated RFS and S1-progeny selection, making it a powerful system to study genomic changes accompanying selection for inter-population performance. The objectives of this study were to identify regions of the genome under selection after eight cycles of selection and evaluate the effect eight cycles of selection for inter-population full-sib performance had in expanding genome-wide and localized population structure. We address these questions with a large set of individuals sampled from the UNL-RpRS program with dense genotyping-by-sequence data. We found evidence of parallel selection signatures in the UNL-RpRS program, with a region on chromosome 7 being implicated in three of the four selection systems studied. Regions that displayed selection signatures across independently run selection programs represent regions likely to be capitalizing on standing genetic variation and support a soft sweep model of adaptation. We did not find selection to be a strong force in diverging populations undergoing RFS. This could be due to the nature of adaptation occurring in these populations, underlying gene action, or a result of unstable genetic topographies.
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Affiliation(s)
- Collin M Lamkey
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, 68583, USA
- Corteva Agriscience, 19456 Hwy 22, Mankato, MN, 56001, USA
| | - Aaron J Lorenz
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, 68583, USA.
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA.
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20
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John M, Lencz T. Potential application of elastic nets for shared polygenicity detection with adapted threshold selection. Int J Biostat 2023; 19:417-438. [PMID: 36327464 PMCID: PMC10154439 DOI: 10.1515/ijb-2020-0108] [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: 01/28/2020] [Accepted: 10/05/2022] [Indexed: 11/06/2022]
Abstract
Current research suggests that hundreds to thousands of single nucleotide polymorphisms (SNPs) with small to modest effect sizes contribute to the genetic basis of many disorders, a phenomenon labeled as polygenicity. Additionally, many such disorders demonstrate polygenic overlap, in which risk alleles are shared at associated genetic loci. A simple strategy to detect polygenic overlap between two phenotypes is based on rank-ordering the univariate p-values from two genome-wide association studies (GWASs). Although high-dimensional variable selection strategies such as Lasso and elastic nets have been utilized in other GWAS analysis settings, they are yet to be utilized for detecting shared polygenicity. In this paper, we illustrate how elastic nets, with polygenic scores as the dependent variable and with appropriate adaptation in selecting the penalty parameter, may be utilized for detecting a subset of SNPs involved in shared polygenicity. We provide theory to better understand our approaches, and illustrate their utility using synthetic datasets. Results from extensive simulations are presented comparing the elastic net approaches with the rank ordering approach, in various scenarios. Results from simulations studies exhibit one of the elastic net approaches to be superior when the correlations among the SNPs are high. Finally, we apply the methods on two real datasets to illustrate further the capabilities, limitations and differences among the methods.
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Affiliation(s)
- Majnu John
- Institute of Behavioral Science, Feinstein Institutes of Medical Research, Manhasset, NY
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health System, Glen Oaks, NY
- Departments of Psychiatry and of Mathematics, Hofstra University, Hempstead, NY
| | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes of Medical Research, Manhasset, NY
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health System, Glen Oaks, NY
- Departments of Psychiatry and of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY
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21
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Lanzl T, Melchinger AE, Schön CC. Influence of the mating design on the additive genetic variance in plant breeding populations. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:236. [PMID: 37906322 PMCID: PMC10618341 DOI: 10.1007/s00122-023-04447-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 08/14/2023] [Indexed: 11/02/2023]
Abstract
KEY MESSAGE Mating designs determine the realized additive genetic variance in a population sample. Deflated or inflated variances can lead to reduced or overly optimistic assessment of future selection gains. The additive genetic variance [Formula: see text] inherent to a breeding population is a major determinant of short- and long-term genetic gain. When estimated from experimental data, it is not only the additive variances at individual loci (QTL) but also covariances between QTL pairs that contribute to estimates of [Formula: see text]. Thus, estimates of [Formula: see text] depend on the genetic structure of the data source and vary between population samples. Here, we provide a theoretical framework for calculating the expectation and variance of [Formula: see text] from genotypic data of a given population sample. In addition, we simulated breeding populations derived from different numbers of parents (P = 2, 4, 8, 16) and crossed according to three different mating designs (disjoint, factorial and half-diallel crosses). We calculated the variance of [Formula: see text] and of the parameter b reflecting the covariance component in [Formula: see text] standardized by the genic variance. Our results show that mating designs resulting in large biparental families derived from few disjoint crosses carry a high risk of generating progenies exhibiting strong covariances between QTL pairs on different chromosomes. We discuss the consequences of the resulting deflated or inflated [Formula: see text] estimates for phenotypic and genome-based selection as well as for applying the usefulness criterion in selection. We show that already one round of recombination can effectively break negative and positive covariances between QTL pairs induced by the mating design. We suggest to obtain reliable estimates of [Formula: see text] and its components in a population sample by applying statistical methods differing in their treatment of QTL covariances.
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Affiliation(s)
- Tobias Lanzl
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Albrecht E Melchinger
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany.
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22
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Kimutai C, Ndlovu N, Chaikam V, Ertiro BT, Das B, Beyene Y, Kiplagat O, Spillane C, Prasanna BM, Gowda M. Discovery of genomic regions associated with grain yield and agronomic traits in Bi-parental populations of maize ( Zea mays. L) Under optimum and low nitrogen conditions. Front Genet 2023; 14:1266402. [PMID: 37964777 PMCID: PMC10641019 DOI: 10.3389/fgene.2023.1266402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/12/2023] [Indexed: 11/16/2023] Open
Abstract
Low soil nitrogen levels, compounded by the high costs associated with nitrogen supplementation through fertilizers, significantly contribute to food insecurity, malnutrition, and rural poverty in maize-dependent smallholder communities of sub-Saharan Africa (SSA). The discovery of genomic regions associated with low nitrogen tolerance in maize can enhance selection efficiency and facilitate the development of improved varieties. To elucidate the genetic architecture of grain yield (GY) and its associated traits (anthesis-silking interval (ASI), anthesis date (AD), plant height (PH), ear position (EPO), and ear height (EH)) under different soil nitrogen regimes, four F3 maize populations were evaluated in Kenya and Zimbabwe. GY and all the traits evaluated showed significant genotypic variance and moderate heritability under both optimum and low nitrogen stress conditions. A total of 91 quantitative trait loci (QTL) related to GY (11) and other secondary traits (AD (26), PH (19), EH (24), EPO (7) and ASI (4)) were detected. Under low soil nitrogen conditions, PH and ASI had the highest number of QTLs. Furthermore, some common QTLs were identified between secondary traits under both nitrogen regimes. These QTLs are of significant value for further validation and possible rapid introgression into maize populations using marker-assisted selection. Identification of many QTL with minor effects indicates genomic selection (GS) is more appropriate for their improvement. Genomic prediction within each population revealed low to moderately high accuracy under optimum and low soil N stress management. However, the accuracies were higher for GY, PH and EH under optimum compared to low soil N stress. Our findings indicate that genetic gain can be improved in maize breeding for low N stress tolerance by using GS.
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Affiliation(s)
- Collins Kimutai
- Seed, Crop and Horticultural Sciences, School of Agriculture and Biotechnology, University of Eldoret, Eldoret, Kenya
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Noel Ndlovu
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, Galway, Ireland
| | - Vijay Chaikam
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | | | - Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Oliver Kiplagat
- Seed, Crop and Horticultural Sciences, School of Agriculture and Biotechnology, University of Eldoret, Eldoret, Kenya
| | - Charles Spillane
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, Galway, Ireland
| | | | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
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23
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Weber SE, Chawla HS, Ehrig L, Hickey LT, Frisch M, Snowdon RJ. Accurate prediction of quantitative traits with failed SNP calls in canola and maize. FRONTIERS IN PLANT SCIENCE 2023; 14:1221750. [PMID: 37936929 PMCID: PMC10627008 DOI: 10.3389/fpls.2023.1221750] [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: 05/12/2023] [Accepted: 10/05/2023] [Indexed: 11/09/2023]
Abstract
In modern plant breeding, genomic selection is becoming the gold standard to select superior genotypes in large breeding populations that are only partially phenotyped. Many breeding programs commonly rely on single-nucleotide polymorphism (SNP) markers to capture genome-wide data for selection candidates. For this purpose, SNP arrays with moderate to high marker density represent a robust and cost-effective tool to generate reproducible, easy-to-handle, high-throughput genotype data from large-scale breeding populations. However, SNP arrays are prone to technical errors that lead to failed allele calls. To overcome this problem, failed calls are often imputed, based on the assumption that failed SNP calls are purely technical. However, this ignores the biological causes for failed calls-for example: deletions-and there is increasing evidence that gene presence-absence and other kinds of genome structural variants can play a role in phenotypic expression. Because deletions are frequently not in linkage disequilibrium with their flanking SNPs, permutation of missing SNP calls can potentially obscure valuable marker-trait associations. In this study, we analyze published datasets for canola and maize using four parametric and two machine learning models and demonstrate that failed allele calls in genomic prediction are highly predictive for important agronomic traits. We present two statistical pipelines, based on population structure and linkage disequilibrium, that enable the filtering of failed SNP calls that are likely caused by biological reasons. For the population and trait examined, prediction accuracy based on these filtered failed allele calls was competitive to standard SNP-based prediction, underlying the potential value of missing data in genomic prediction approaches. The combination of SNPs with all failed allele calls or the filtered allele calls did not outperform predictions with only SNP-based prediction due to redundancy in genomic relationship estimates.
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Affiliation(s)
- Sven E. Weber
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | | | - Lennard Ehrig
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Lee T. Hickey
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Matthias Frisch
- Department of Biometry and Population Genetics, Justus Liebig University, Giessen, Germany
| | - Rod J. Snowdon
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
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24
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Djabali Y, Rincent R, Martin ML, Blein-Nicolas M. Plasticity QTLs specifically contribute to the genotype × water availability interaction in maize. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:228. [PMID: 37855950 DOI: 10.1007/s00122-023-04458-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 08/31/2023] [Indexed: 10/20/2023]
Abstract
KEY MESSAGE Multi-trial genome wide association study of plasticity indices allow to detect QTLs specifically involved in the genotype x water availability interaction. Concerns regarding high maize yield losses due to increasing occurrences of drought events are growing, and breeders are still looking for molecular markers for drought tolerance. However, the genetic determinism of traits in response to drought is highly complex and identification of causal regions is a tremendous task. Here, we exploit the phenotypic data obtained from four trials carried out on a phenotyping platform, where a diversity panel of 254 maize hybrids was grown under well-watered and water deficit conditions, to investigate the genetic bases of the drought response in maize. To dissociate drought effect from other environmental factors, we performed multi-trial genome-wide association study on well-watered and water deficit phenotypic means, and on phenotypic plasticity indices computed from measurements made for six ecophysiological traits. We identify 102 QTLs and 40 plasticity QTLs. Most of them were new compared to those obtained from a previous study on the same dataset. Our results show that plasticity QTLs cover genetic regions not identified by QTLs. Furthermore, for all ecophysiological traits, except one, plasticity QTLs are specifically involved in the genotype by water availability interaction, for which they explain between 60 and 100% of the variance. Altogether, QTLs and plasticity QTLs captured more than 75% of the genotype by water availability interaction variance, and allowed to find new genetic regions. Overall, our results demonstrate the importance of considering phenotypic plasticity to decipher the genetic architecture of trait response to stress.
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Affiliation(s)
- Yacine Djabali
- Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91190, Gif-sur-Yvette, France
- Université de Paris Cité, Institute of Plant Sciences Paris-Saclay (IPS2), 91190, Gif-sur-Yvette, France
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Renaud Rincent
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Marie-Laure Martin
- Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91190, Gif-sur-Yvette, France.
- Université de Paris Cité, Institute of Plant Sciences Paris-Saclay (IPS2), 91190, Gif-sur-Yvette, France.
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France.
| | - Mélisande Blein-Nicolas
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190, Gif-Sur-Yvette, France.
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Beugnot A, Mary-Huard T, Bauland C, Combes V, Madur D, Lagardère B, Palaffre C, Charcosset A, Moreau L, Fievet JB. Identifying QTLs involved in hybrid performance and heterotic group complementarity: new GWAS models applied to factorial and admixed diallel maize hybrid panels. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:219. [PMID: 37816986 PMCID: PMC10564676 DOI: 10.1007/s00122-023-04431-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 07/25/2023] [Indexed: 10/12/2023]
Abstract
KEY MESSAGE An original GWAS model integrating the ancestry of alleles was proposed and allowed the detection of background specific additive and dominance QTLs involved in heterotic group complementarity and hybrid performance. Maize genetic diversity is structured into genetic groups selected and improved relative to each other. This process increases group complementarity and differentiation over time and ensures that the hybrids produced from inter-group crosses exhibit high performances and heterosis. To identify loci involved in hybrid performance and heterotic group complementarity, we introduced an original association study model that disentangles allelic effects from the heterotic group origin of the alleles and compared it with a conventional additive/dominance model. This new model was applied on a factorial between Dent and Flint lines and a diallel between Dent-Flint admixed lines with two different layers of analysis: within each environment and in a multiple-environment context. We identified several strong additive QTLs for all traits, including some well-known additive QTLs for flowering time (in the region of Vgt1/2 on chromosome 8). Yield trait displayed significant non-additive effects in the diallel panel. Most of the detected Yield QTLs exhibited overdominance or, more likely, pseudo-overdominance effects. Apparent overdominance at these QTLs contributed to a part of the genetic group complementarity. The comparison between environments revealed a higher stability of additive QTL effects than non-additive ones. Several QTLs showed variations of effects according to the local heterotic group origin. We also revealed large chromosomic regions that display genetic group origin effects. Altogether, our results illustrate how admixed panels combined with dedicated GWAS modeling allow the identification of new QTLs that could not be revealed by a classical hybrid panel analyzed with traditional modeling.
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Affiliation(s)
- Aurélien Beugnot
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France
| | - Cyril Bauland
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | - Valerie Combes
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | - Delphine Madur
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | | | | | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France
| | - Julie B Fievet
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91272, Gif-Sur-Yvette, France.
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Liu Y, Ao M, Lu M, Zheng S, Zhu F, Ruan Y, Guan Y, Zhang A, Cui Z. Genomic selection to improve husk tightness based on genomic molecular markers in maize. FRONTIERS IN PLANT SCIENCE 2023; 14:1252298. [PMID: 37828926 PMCID: PMC10566295 DOI: 10.3389/fpls.2023.1252298] [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: 07/03/2023] [Accepted: 09/04/2023] [Indexed: 10/14/2023]
Abstract
Introduction The husk tightness (HTI) in maize plays a crucial role in regulating the water content of ears during the maturity stage, thereby influencing the quality of mechanical grain harvesting in China. Genomic selection (GS), which employs molecular markers, offers a promising approach for identifying and selecting inbred lines with the desired HTI trait in maize breeding. However, the effectiveness of GS is contingent upon various factors, including the genetic architecture of breeding populations, sequencing platforms, and statistical models. Methods An association panel of maize inbred lines was grown across three sites over two years, divided into four subgroups. GS analysis for HTI prediction was performed using marker data from three sequencing platforms and six marker densities with six statistical methods. Results The findings indicate that a loosely attached husk can aid in the dissipation of water from kernels in temperate maize germplasms across most environments but not nessarily for tropical-origin maize. Considering the balance between GS prediction accuracy and breeding cost, the optimal prediction strategy is the rrBLUP model, the 50K sequencing platform, a 30% proportion of the test population, and a marker density of r2=0.1. Additionally, selecting a specific SS subgroup for sampling the testing set significantly enhances the predictive capacity for husk tightness. Discussion The determination of the optimal GS prediction strategy for HTI provides an economically feasible reference for the practice of molecular breeding. It also serves as a reference method for GS breeding of other agronomic traits.
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Affiliation(s)
- Yuncan Liu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Man Ao
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Ming Lu
- Maize Research Institute, Jilin Academy of Agricultural Sciences, Gongzhuling, China
| | - Shubo Zheng
- Maize Research Institute, Jilin Academy of Agricultural Sciences, Gongzhuling, China
| | - Fangbo Zhu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Yanye Ruan
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Yixin Guan
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Ao Zhang
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Zhenhai Cui
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
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Khan MA, Cowling WA, Banga SS, Barbetti MJ, Cantila AY, Amas JC, Thomas WJ, You MP, Tyagi V, Bharti B, Edwards D, Batley J. Genetic and molecular analysis of stem rot (Sclerotinia sclerotiorum) resistance in Brassica napus (canola type). Heliyon 2023; 9:e19237. [PMID: 37674843 PMCID: PMC10477455 DOI: 10.1016/j.heliyon.2023.e19237] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 08/16/2023] [Accepted: 08/16/2023] [Indexed: 09/08/2023] Open
Abstract
Identifying the molecular and genetic basis of resistance to Sclerotinia stem rot (Sclerotinia sclerotiorum) is critical for developing long-term and cost-effective management of this disease in rapeseed/canola (Brassica napus). Current cultural or chemical management options provide, at best, only partial and/or sporadic control. Towards this, a B. napus breeding population (Mystic x Rainbow), including the parents, F1, F2, BC1P1 and BC1P2, was utilized in a field study to determine the inheritance pattern of Sclerotinia stem rot resistance (based on stem lesion length, SLL). Broad sense heritability was 0.58 for SLL and 0.44 for days to flowering (DTF). There was a significant negative correlation between SLL and stem diameter (SD) (r = -0.39) and between SLL and DTF (r = -0.28), suggesting co-selection of SD and DTF traits, along with SLL, should assist in improving overall resistance. Non-additive genetic variance was evident for SLL, DTF, and SD. In a genome wide association study (GWAS), a significant quantitative trait locus (QTL) was identified for SLL. Several putative candidate marker trait associations (MTA) were located within this QTL region. Overall, this study has provided valuable new understanding of inheritance of resistance to S. sclerotiorum, and has identified QTL, MTAs and transgressive segregants with high-level resistances. Together, these will foster more rapid selection for multiple traits associated with Sclerotinia stem rot resistance, by enabling breeders to make critical choices towards selecting/developing cultivars with enhanced resistance to this devastating pathogen.
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Affiliation(s)
- Muhammad Azam Khan
- UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA, Australia 6009
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia 6009
- Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad, 38000, Pakistan
| | - Wallace A. Cowling
- UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA, Australia 6009
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia 6009
| | - Surinder Singh Banga
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004, Punjab, India
| | - Martin J. Barbetti
- UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA, Australia 6009
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia 6009
| | - Aldrin Y. Cantila
- School of Biological Sciences, The University of Western Australia, Perth, WA, Australia 6009
| | - Junrey C. Amas
- School of Biological Sciences, The University of Western Australia, Perth, WA, Australia 6009
| | - William J.W. Thomas
- School of Biological Sciences, The University of Western Australia, Perth, WA, Australia 6009
| | - Ming Pei You
- UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA, Australia 6009
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia 6009
| | - Vikrant Tyagi
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004, Punjab, India
| | - Baudh Bharti
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004, Punjab, India
| | - David Edwards
- School of Biological Sciences, The University of Western Australia, Perth, WA, Australia 6009
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia 6009
| | - Jacqueline Batley
- School of Biological Sciences, The University of Western Australia, Perth, WA, Australia 6009
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia 6009
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Melchinger AE, Frisch M. Genomic prediction in hybrid breeding: II. Reciprocal recurrent genomic selection with full-sib and half-sib families. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:203. [PMID: 37653062 PMCID: PMC10471712 DOI: 10.1007/s00122-023-04446-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 08/09/2023] [Indexed: 09/02/2023]
Abstract
KEY MESSAGE Genomic prediction of GCA effects based on model training with full-sib rather than half-sib families yields higher short- and long-term selection gain in reciprocal recurrent genomic selection for hybrid breeding, if SCA effects are important. Reciprocal recurrent genomic selection (RRGS) is a powerful tool for ensuring sustainable selection progress in hybrid breeding. For training the statistical model, one can use half-sib (HS) or full-sib (FS) families produced by inter-population crosses of candidates from the two parent populations. Our objective was to compare HS-RRGS and FS-RRGS for the cumulative selection gain ([Formula: see text]), the genetic, GCA and SCA variances ([Formula: see text],[Formula: see text], [Formula: see text]) of the hybrid population, and prediction accuracy ([Formula: see text]) for GCA effects across cycles. Using SNP data from maize and wheat, we simulated RRGS programs over 10 cycles, each consisting of four sub-cycles with genomic selection of [Formula: see text] out of 950 candidates in each parent population. Scenarios differed for heritability [Formula: see text] and the proportion [Formula: see text] of traits, training set (TS) size ([Formula: see text]), and maize vs. wheat. Curves of [Formula: see text] over selection cycles showed no crossing of both methods. If [Formula: see text] was high, [Formula: see text] was generally higher for FS-RRGS than HS-RRGS due to higher [Formula: see text]. In contrast, HS-RRGS was superior or on par with FS-RRGS, if [Formula: see text] or [Formula: see text] and [Formula: see text] were low. [Formula: see text] showed a steeper increase and higher selection limit for scenarios with low [Formula: see text], high [Formula: see text] and large [Formula: see text]. [Formula: see text] and even more so [Formula: see text] decreased rapidly over cycles for both methods due to the high selection intensity and the role of the Bulmer effect for reducing [Formula: see text]. Since the TS for FS-RRGS can additionally be used for hybrid prediction, we recommend this method for achieving simultaneously the two major goals in hybrid breeding: population improvement and cultivar development.
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Affiliation(s)
- Albrecht E. Melchinger
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599 Stuttgart, Germany
| | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, 35392 Gießen, Germany
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Khan N, Zhang J, Islam S, Appels R, Dell B. Wheat Water-Soluble Carbohydrate Remobilisation under Water Deficit by 1-FEH w3. Curr Issues Mol Biol 2023; 45:6634-6650. [PMID: 37623238 PMCID: PMC10453044 DOI: 10.3390/cimb45080419] [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: 07/18/2023] [Revised: 08/03/2023] [Accepted: 08/08/2023] [Indexed: 08/26/2023] Open
Abstract
Fructan 1-exohydrolase (1-FEH) is one of the major enzymes in water-soluble carbohydrate (WSC) remobilisation for grains in wheat. We investigated the functional role of 1-FEH w1, w2, and w3 isoforms in WSC remobilisation under post-anthesis water deficit using mutation lines derived from the Australian wheat variety Chara. F1 seeds, developed by backcrossing the 1-FEH w1, w2, and w3 mutation lines with Chara, were genotyped using the Infinium 90K SNP iSelect platform to characterise the mutated region. Putative deletions were identified in FEH mutation lines encompassing the FEH genomic regions. Mapping analysis demonstrated that mutations affected significantly longer regions than the target FEH gene regions. Functional roles of the non-target genes were carried out utilising bioinformatics and confirmed that the non-target genes were unlikely to confound the effects considered to be due to the influence of 1-FEH gene functions. Glasshouse experiments revealed that the 1-FEH w3 mutation line had a slower degradation and remobilisation of fructans than the 1-FEH w2 and w1 mutation lines and Chara, which reduced grain filling and grain yield. Thus, 1-FEH w3 plays a vital role in reducing yield loss under drought. This insight into the distinct role of the 1-FEH isoforms provides new gene targets for water-deficit-tolerant wheat breeding.
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Affiliation(s)
- Nusrat Khan
- Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, 90 South Street, Murdoch, WA 6163, Australia; (N.K.); (J.Z.); (S.I.)
- Department of Plant Sciences, North Dakota State University, Fargo, ND 58102, USA
| | - Jingjuan Zhang
- Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, 90 South Street, Murdoch, WA 6163, Australia; (N.K.); (J.Z.); (S.I.)
| | - Shahidul Islam
- Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, 90 South Street, Murdoch, WA 6163, Australia; (N.K.); (J.Z.); (S.I.)
- Department of Plant Sciences, North Dakota State University, Fargo, ND 58102, USA
| | - Rudi Appels
- Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia;
| | - Bernard Dell
- Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, 90 South Street, Murdoch, WA 6163, Australia; (N.K.); (J.Z.); (S.I.)
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Melchinger AE, Fernando R, Stricker C, Schön CC, Auinger HJ. Genomic prediction in hybrid breeding: I. Optimizing the training set design. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:176. [PMID: 37532821 PMCID: PMC10397156 DOI: 10.1007/s00122-023-04413-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 06/23/2023] [Indexed: 08/04/2023]
Abstract
KEY MESSAGE Training sets produced by maximizing the number of parent lines, each involved in one cross, had the highest prediction accuracy for H0 hybrids, but lowest for H1 and H2 hybrids. Genomic prediction holds great promise for hybrid breeding but optimum composition of the training set (TS) as determined by the number of parents (nTS) and crosses per parent (c) has received little attention. Our objective was to examine prediction accuracy ([Formula: see text]) of GCA for lines used as parents of the TS (I1 lines) or not (I0 lines), and H0, H1 and H2 hybrids, comprising crosses of type I0 × I0, I1 × I0 and I1 × I1, respectively, as function of nTS and c. In the theory, we developed estimates for [Formula: see text] of GBLUPs for hybrids: (i)[Formula: see text] based on the expected prediction accuracy, and (ii) [Formula: see text] based on [Formula: see text] of GBLUPs of GCA and SCA effects. In the simulation part, hybrid populations were generated using molecular data from two experimental maize data sets. Additive and dominance effects of QTL borrowed from literature were used to simulate six scenarios of traits differing in the proportion (τSCA = 1%, 6%, 22%) of SCA variance in σG2 and heritability (h2 = 0.4, 0.8). Values of [Formula: see text] and [Formula: see text] closely agreed with [Formula: see text] for hybrids. For given size NTS = nTS × c of TS, [Formula: see text] of H0 hybrids and GCA of I0 lines was highest for c = 1. Conversely, for GCA of I1 lines and H1 and H2 hybrids, c = 1 yielded lowest [Formula: see text] with concordant results across all scenarios for both data sets. In view of these opposite trends, the optimum choice of c for maximizing selection response across all types of hybrids depends on the size and resources of the breeding program.
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Affiliation(s)
- Albrecht E Melchinger
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany.
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany.
| | - Rohan Fernando
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Christian Stricker
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Hans-Jürgen Auinger
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
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Galić V, Anđelković V, Kravić N, Grčić N, Ledenčan T, Jambrović A, Zdunić Z, Nicolas S, Charcosset A, Šatović Z, Šimić D. Genetic diversity and selection signatures in a gene bank panel of maize inbred lines from Southeast Europe compared with two West European panels. BMC PLANT BIOLOGY 2023; 23:315. [PMID: 37316827 PMCID: PMC10265872 DOI: 10.1186/s12870-023-04336-2] [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: 01/25/2023] [Accepted: 06/07/2023] [Indexed: 06/16/2023]
Abstract
Southeast Europe (SEE) is a very important maize-growing region, comparable to the Corn belt region of the United States, with similar dent germplasm (dent by dent hybrids). Historically, this region has undergone several genetic material swaps, following the trends in the US, with one of the most significant swaps related to US aid programs after WWII. The imported accessions used to make double-cross hybrids were also mixed with previously adapted germplasm originating from several more distant OPVs, supporting the transition to single cross-breeding. Many of these materials were deposited at the Maize Gene Bank of the Maize Research Institute Zemun Polje (MRIZP) between the 1960s and 1980s. A part of this Gene Bank (572 inbreds) was genotyped with Affymetrix Axiom Maize Genotyping Array with 616,201 polymorphic variants. Data were merged with two other genotyping datasets with mostly European flint (TUM dataset) and dent (DROPS dataset) germplasm. The final pan-European dataset consisted of 974 inbreds and 460,243 markers. Admixture analysis showed seven ancestral populations representing European flint, B73/B14, Lancaster, B37, Wf9/Oh07, A374, and Iodent pools. Subpanel of inbreds with SEE origin showed a lack of Iodent germplasm, marking its historical context. Several signatures of selection were identified at chromosomes 1, 3, 6, 7, 8, 9, and 10. The regions under selection were mined for protein-coding genes and were used for gene ontology (GO) analysis, showing a highly significant overrepresentation of genes involved in response to stress. Our results suggest the accumulation of favorable allelic diversity, especially in the context of changing climate in the genetic resources of SEE.
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Affiliation(s)
- Vlatko Galić
- Agricultural Institute Osijek, Južno predgrađe 17, Osijek, HR31000, Croatia.
- Centre of Excellence for Biodiversity and Molecular Plant Breeding (CroP-BioDiv), Svetošimunska cesta 25, Zagreb, HR10000, Croatia.
| | - Violeta Anđelković
- Maize Research Institute Zemun Polje, Slobodana Bajića 1, Belgrade, 11185, Serbia
| | - Natalija Kravić
- Maize Research Institute Zemun Polje, Slobodana Bajića 1, Belgrade, 11185, Serbia
| | - Nikola Grčić
- Maize Research Institute Zemun Polje, Slobodana Bajića 1, Belgrade, 11185, Serbia
| | - Tatjana Ledenčan
- Agricultural Institute Osijek, Južno predgrađe 17, Osijek, HR31000, Croatia
| | - Antun Jambrović
- Agricultural Institute Osijek, Južno predgrađe 17, Osijek, HR31000, Croatia
- Centre of Excellence for Biodiversity and Molecular Plant Breeding (CroP-BioDiv), Svetošimunska cesta 25, Zagreb, HR10000, Croatia
| | - Zvonimir Zdunić
- Agricultural Institute Osijek, Južno predgrađe 17, Osijek, HR31000, Croatia
- Centre of Excellence for Biodiversity and Molecular Plant Breeding (CroP-BioDiv), Svetošimunska cesta 25, Zagreb, HR10000, Croatia
| | - Stéphane Nicolas
- GQE ‑ Le Moulon, INRAE, Univ. Paris‑Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif‑sur‑Yvette, 91190, France
| | - Alain Charcosset
- GQE ‑ Le Moulon, INRAE, Univ. Paris‑Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif‑sur‑Yvette, 91190, France
| | - Zlatko Šatović
- Centre of Excellence for Biodiversity and Molecular Plant Breeding (CroP-BioDiv), Svetošimunska cesta 25, Zagreb, HR10000, Croatia
- Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, Zagreb, HR10000, Croatia
| | - Domagoj Šimić
- Agricultural Institute Osijek, Južno predgrađe 17, Osijek, HR31000, Croatia
- Centre of Excellence for Biodiversity and Molecular Plant Breeding (CroP-BioDiv), Svetošimunska cesta 25, Zagreb, HR10000, Croatia
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Machado IP, DoVale JC, Sabadin F, Fritsche-Neto R. On the usefulness of mock genomes to define heterotic pools, testers, and hybrid predictions in orphan crops. FRONTIERS IN PLANT SCIENCE 2023; 14:1164555. [PMID: 37332727 PMCID: PMC10272588 DOI: 10.3389/fpls.2023.1164555] [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: 02/13/2023] [Accepted: 05/10/2023] [Indexed: 06/20/2023]
Abstract
The advances in genomics in recent years have increased the accuracy and efficiency of breeding programs for many crops. Nevertheless, the adoption of genomic enhancement for several other crops essential in developing countries is still limited, especially for those that do not have a reference genome. These crops are more often called orphans. This is the first report to show how the results provided by different platforms, including the use of a simulated genome, called the mock genome, can generate in population structure and genetic diversity studies, especially when the intention is to use this information to support the formation of heterotic groups, choice of testers, and genomic prediction of single crosses. For that, we used a method to assemble a reference genome to perform the single-nucleotide polymorphism (SNP) calling without needing an external genome. Thus, we compared the analysis results using the mock genome with the standard approaches (array and genotyping-by-sequencing (GBS)). The results showed that the GBS-Mock presented similar results to the standard methods of genetic diversity studies, division of heterotic groups, the definition of testers, and genomic prediction. These results showed that a mock genome constructed from the population's intrinsic polymorphisms to perform the SNP calling is an effective alternative for conducting genomic studies of this nature in orphan crops, especially those that do not have a reference genome.
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Affiliation(s)
| | - Júlio César DoVale
- Department of Crop Science, Federal University of Ceará, Fortaleza, Brazil
| | - Felipe Sabadin
- School of Plant and Environmental Sciences, Virginia Tech: Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
| | - Roberto Fritsche-Neto
- LSU AgCenter, Louisiana State University Agricultural Center, Baton Rouge, LA, United States
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Arca M, Gouesnard B, Mary-Huard T, Le Paslier MC, Bauland C, Combes V, Madur D, Charcosset A, Nicolas SD. Genotyping of DNA pools identifies untapped landraces and genomic regions to develop next-generation varieties. PLANT BIOTECHNOLOGY JOURNAL 2023; 21:1123-1139. [PMID: 36740649 DOI: 10.1111/pbi.14022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 01/18/2023] [Indexed: 05/27/2023]
Abstract
Landraces, that is, traditional varieties, have a large diversity that is underexploited in modern breeding. A novel DNA pooling strategy was implemented to identify promising landraces and genomic regions to enlarge the genetic diversity of modern varieties. As proof of concept, DNA pools from 156 American and European maize landraces representing 2340 individuals were genotyped with an SNP array to assess their genome-wide diversity. They were compared to elite cultivars produced across the 20th century, represented by 327 inbred lines. Detection of selective footprints between landraces of different geographic origin identified genes involved in environmental adaptation (flowering times, growth) and tolerance to abiotic and biotic stress (drought, cold, salinity). Promising landraces were identified by developing two novel indicators that estimate their contribution to the genome of inbred lines: (i) a modified Roger's distance standardized by gene diversity and (ii) the assignation of lines to landraces using supervised analysis. It showed that most landraces do not have closely related lines and that only 10 landraces, including famous landraces as Reid's Yellow Dent, Lancaster Surecrop and Lacaune, cumulated half of the total contribution to inbred lines. Comparison of ancestral lines directly derived from landraces with lines from more advanced breeding cycles showed a decrease in the number of landraces with a large contribution. New inbred lines derived from landraces with limited contributions enriched more the haplotype diversity of reference inbred lines than those with a high contribution. Our approach opens an avenue for the identification of promising landraces for pre-breeding.
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Affiliation(s)
- Mariangela Arca
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Brigitte Gouesnard
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Tristan Mary-Huard
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | | | - Cyril Bauland
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Valérie Combes
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Delphine Madur
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Alain Charcosset
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Stéphane D Nicolas
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
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Grzybowski MW, Mural RV, Xu G, Turkus J, Yang J, Schnable JC. A common resequencing-based genetic marker data set for global maize diversity. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 113:1109-1121. [PMID: 36705476 DOI: 10.1111/tpj.16123] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/20/2023] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Maize (Zea mays ssp. mays) populations exhibit vast ranges of genetic and phenotypic diversity. As sequencing costs have declined, an increasing number of projects have sought to measure genetic differences between and within maize populations using whole-genome resequencing strategies, identifying millions of segregating single-nucleotide polymorphisms (SNPs) and insertions/deletions (InDels). Unlike older genotyping strategies like microarrays and genotyping by sequencing, resequencing should, in principle, frequently identify and score common genetic variants. However, in practice, different projects frequently employ different analytical pipelines, often employ different reference genome assemblies and consistently filter for minor allele frequency within the study population. This constrains the potential to reuse and remix data on genetic diversity generated from different projects to address new biological questions in new ways. Here, we employ resequencing data from 1276 previously published maize samples and 239 newly resequenced maize samples to generate a single unified marker set of approximately 366 million segregating variants and approximately 46 million high-confidence variants scored across crop wild relatives, landraces as well as tropical and temperate lines from different breeding eras. We demonstrate that the new variant set provides increased power to identify known causal flowering-time genes using previously published trait data sets, as well as the potential to track changes in the frequency of functionally distinct alleles across the global distribution of modern maize.
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Affiliation(s)
- Marcin W Grzybowski
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Plant Molecular Ecophysiology, Institute of Plant Experimental Biology and Biotechnology, Faculty of Biology, University of Warsaw, Warsaw, Poland
| | - Ravi V Mural
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Gen Xu
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Jonathan Turkus
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Jinliang Yang
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - James C Schnable
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
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Sa KJ, Park H, Jang SJ, Lee JK. Association Mapping of Amylose Content in Maize RIL Population Using SSR and SNP Markers. PLANTS (BASEL, SWITZERLAND) 2023; 12:239. [PMID: 36678952 PMCID: PMC9865990 DOI: 10.3390/plants12020239] [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: 11/28/2022] [Revised: 12/27/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
The ratio of amylose to amylopectin in maize kernel starch is important for the appearance, structure, and quality of food products and processing. This study aimed to identify quantitative trait loci (QTLs) controlling amylose content in maize through association mapping with simple sequence repeat (SSR) and single-nucleotide polymorphism (SNP) markers. The average value of amylose content for an 80-recombinant-inbred-line (RIL) population was 8.8 ± 0.7%, ranging from 2.1 to 15.9%. We used two different analyses-Q + K and PCA + K mixed linear models (MLMs)-and found 38 (35 SNP and 3 SSR) and 32 (29 SNP and 3 SSR) marker-trait associations (MTAs) associated with amylose content. A total of 34 (31 SNP and 3 SSR) and 28 (25 SNP and 3 SSR) MTAs were confirmed in the Q + K and PCA + K MLMs, respectively. This study detected some candidate genes for amylose content, such as GRMZM2G118690-encoding BBR/BPC transcription factor, which is used for the control of seed development and is associated with the amylose content of rice. GRMZM5G830776-encoding SNARE-interacting protein (KEULE) and the uncharacterized marker PUT-163a-18172151-1376 were significant with higher R2 value in two difference methods. GRMZM2G092296 were also significantly associated with amylose content in this study. This study focused on amylose content using a RIL population derived from dent and waxy inbred lines using molecular markers. Future studies would be of benefit for investigating the physical linkage between starch synthesis genes using SNP and SSR markers, which would help to build a more detailed genetic map and provide new insights into gene regulation of agriculturally important traits.
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Affiliation(s)
- Kyu Jin Sa
- Department of Applied Plant Sciences, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Hyeon Park
- Department of Applied Plant Sciences, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
- Interdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - So Jung Jang
- Department of Applied Plant Sciences, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
- Interdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Ju Kyong Lee
- Department of Applied Plant Sciences, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
- Interdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon 24341, Republic of Korea
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Marone MP, Singh HC, Pozniak CJ, Mascher M. A technical guide to TRITEX, a computational pipeline for chromosome-scale sequence assembly of plant genomes. PLANT METHODS 2022; 18:128. [PMID: 36461065 PMCID: PMC9719158 DOI: 10.1186/s13007-022-00964-1] [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: 09/13/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND As complete and accurate genome sequences are becoming easier to obtain, more researchers wish to get one or more of them to support their research endeavors. Reliable and well-documented sequence assembly workflows find use in reference or pangenome projects. RESULTS We describe modifications to the TRITEX genome assembly workflow motivated by the rise of fast and easy long-read contig assembly of inbred plant genomes and the routine deployment of the toolchains in pangenome projects. New features include the use as surrogates of or complements to dense genetic maps and the introduction of user-editable tables to make the curation of contig placements easier and more intuitive. CONCLUSION Even maximally contiguous sequence assemblies of the telomere-to-telomere sort, and to a yet greater extent, the fragmented kind require validation, correction, and comparison to reference standards. As pangenomics is burgeoning, these tasks are bound to become more widespread and TRITEX is one tool to get them done. This technical guide is supported by a step-by-step computational tutorial accessible under https://tritexassembly.bitbucket.io/ . The TRITEX source code is hosted under this URL: https://bitbucket.org/tritexassembly .
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Affiliation(s)
- Marina Püpke Marone
- Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Seeland, Germany
- Department of Genetics, Evolution, Microbiology and Immunology, University of Campinas, Campinas, Brazil
| | - Harmeet Chawla Singh
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK, S7N 5A8, Canada
- Department of Plant Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
| | - Curtis J Pozniak
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK, S7N 5A8, Canada
| | - Martin Mascher
- Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Seeland, Germany.
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
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Ren W, Zhao L, Liang J, Wang L, Chen L, Li P, Liu Z, Li X, Zhang Z, Li J, He K, Zhao Z, Ali F, Mi G, Yan J, Zhang F, Chen F, Yuan L, Pan Q. Genome-wide dissection of changes in maize root system architecture during modern breeding. NATURE PLANTS 2022; 8:1408-1422. [PMID: 36396706 DOI: 10.1038/s41477-022-01274-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 10/12/2022] [Indexed: 05/12/2023]
Abstract
Appropriate root system architecture (RSA) can improve maize yields in densely planted fields, but little is known about its genetic basis in maize. Here we performed root phenotyping of 14,301 field-grown plants from an association mapping panel to study the genetic architecture of maize RSA. A genome-wide association study identified 81 high-confidence RSA-associated candidate genes and revealed that 28 (24.3%) of known root-related genes were selected during maize domestication and improvement. We found that modern maize breeding has selected for a steeply angled root system. Favourable alleles related to steep root system angle have continuously accumulated over the course of modern breeding, and our data pinpoint the root-related genes that have been selected in different breeding eras. We confirm that two auxin-related genes, ZmRSA3.1 and ZmRSA3.2, contribute to the regulation of root angle and depth in maize. Our genome-wide identification of RSA-associated genes provides new strategies and genetic resources for breeding maize suitable for high-density planting.
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Affiliation(s)
- Wei Ren
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Longfei Zhao
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Jiaxing Liang
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Lifeng Wang
- Cereal Crops Research Institute, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Limei Chen
- Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, Beijing, China
| | - Pengcheng Li
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Yangzhou University, Yangzhou, China
| | - Zhigang Liu
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Xiaojie Li
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Zhihai Zhang
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Jieping Li
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Kunhui He
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Zheng Zhao
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Farhan Ali
- Cereal Crops Research Institute, Pirsabak, Nowshera, Pakistan
| | - Guohua Mi
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Fusuo Zhang
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China
| | - Fanjun Chen
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China.
- Sanya Institute of China Agricultural University, Sanya, China.
| | - Lixing Yuan
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China.
- Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, Beijing, China.
| | - Qingchun Pan
- College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions, Ministry of Education, China Agricultural University, Beijing, China.
- Sanya Institute of China Agricultural University, Sanya, China.
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Multi-Trait Genomic Prediction Improves Accuracy of Selection among Doubled Haploid Lines in Maize. Int J Mol Sci 2022; 23:ijms232314558. [PMID: 36498886 PMCID: PMC9735914 DOI: 10.3390/ijms232314558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/11/2022] [Accepted: 11/19/2022] [Indexed: 11/24/2022] Open
Abstract
Recent advances in maize doubled haploid (DH) technology have enabled the development of large numbers of DH lines quickly and efficiently. However, testing all possible hybrid crosses among DH lines is a challenge. Phenotyping haploid progenitors created during the DH process could accelerate the selection of DH lines. Based on phenotypic and genotypic data of a DH population and its corresponding haploids, we compared phenotypes and estimated genetic correlations between the two populations, compared genomic prediction accuracy of multi-trait models against conventional univariate models within the DH population, and evaluated whether incorporating phenotypic data from haploid lines into a multi-trait model could better predict performance of DH lines. We found significant phenotypic differences between DH and haploid lines for nearly all traits; however, their genetic correlations between populations were moderate to strong. Furthermore, a multi-trait model taking into account genetic correlations between traits in the single-environment trial or genetic covariances in multi-environment trials can significantly increase genomic prediction accuracy. However, integrating information of haploid lines did not further improve our prediction. Our findings highlight the superiority of multi-trait models in predicting performance of DH lines in maize breeding, but do not support the routine phenotyping and selection on haploid progenitors of DH lines.
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Vidal A, Gauthier F, Rodrigez W, Guiglielmoni N, Leroux D, Chevrolier N, Jasson S, Tourrette E, Martin OC, Falque M. SeSAM: software for automatic construction of order-robust linkage maps. BMC Bioinformatics 2022; 23:499. [PMCID: PMC9675223 DOI: 10.1186/s12859-022-05045-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 11/08/2022] [Indexed: 11/21/2022] Open
Abstract
Background Genotyping and sequencing technologies produce increasingly large numbers of genetic markers with potentially high rates of missing or erroneous data. Therefore, the construction of linkage maps is more and more complex. Moreover, the size of segregating populations remains constrained by cost issues and is less and less commensurate with the numbers of SNPs available. Thus, guaranteeing a statistically robust marker order requires that maps include only a carefully selected subset of SNPs. Results In this context, the SeSAM software allows automatic genetic map construction using seriation and placement approaches, to produce (1) a high-robustness framework map which includes as many markers as possible while keeping the order robustness beyond a given statistical threshold, and (2) a high-density total map including the framework plus almost all polymorphic markers. During this process, care is taken to limit the impact of genotyping errors and of missing data on mapping quality. SeSAM can be used with a wide range of biparental populations including from outcrossing species for which phases are inferred on-the-fly by maximum-likelihood during map elongation. The package also includes functions to simulate data sets, convert data formats, detect putative genotyping errors, visualize data and map quality (including graphical genotypes), and merge several maps into a consensus. SeSAM is also suitable for interactive map construction, by providing lower-level functions for 2-point and multipoint EM analyses. The software is implemented in a R package including functions in C++. Conclusions SeSAM is a fully automatic linkage mapping software designed to (1) produce a framework map as robust as desired by optimizing the selection of a subset of markers, and (2) produce a high-density map including almost all polymorphic markers. The software can be used with a wide range of biparental mapping populations including cases from outcrossing. SeSAM is freely available under a GNU GPL v3 license and works on Linux, Windows, and macOS platforms. It can be downloaded together with its user-manual and quick-start tutorial from ForgeMIA (SeSAM project) at https://forgemia.inra.fr/gqe-acep/sesam/-/releases Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-05045-7.
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Affiliation(s)
- Adrien Vidal
- grid.460789.40000 0004 4910 6535Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
| | - Franck Gauthier
- grid.460789.40000 0004 4910 6535Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
| | - Willy Rodrigez
- grid.460789.40000 0004 4910 6535Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
| | - Nadège Guiglielmoni
- grid.460789.40000 0004 4910 6535Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
| | - Damien Leroux
- grid.507621.7INRAE, Unité de Mathématiques et Informatique Appliquées - Toulouse, Toulouse, France
| | - Nicolas Chevrolier
- grid.460789.40000 0004 4910 6535Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
| | - Sylvain Jasson
- grid.507621.7INRAE, Unité de Mathématiques et Informatique Appliquées - Toulouse, Toulouse, France
| | - Elise Tourrette
- grid.460789.40000 0004 4910 6535Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
| | - Olivier C. Martin
- grid.460789.40000 0004 4910 6535Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France ,grid.503243.3Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif-sur-Yvette, France ,Université Paris Cité, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91190 Gif-sur-Yvette, France
| | - Matthieu Falque
- grid.460789.40000 0004 4910 6535Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
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Qu J, Chassaigne-Ricciulli AA, Fu F, Yu H, Dreher K, Nair SK, Gowda M, Beyene Y, Makumbi D, Dhliwayo T, Vicente FS, Olsen M, Prasanna BM, Li W, Zhang X. Low-Density Reference Fingerprinting SNP Dataset of CIMMYT Maize Lines for Quality Control and Genetic Diversity Analyses. PLANTS (BASEL, SWITZERLAND) 2022; 11:3092. [PMID: 36432819 PMCID: PMC9697014 DOI: 10.3390/plants11223092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 10/31/2022] [Accepted: 11/10/2022] [Indexed: 06/16/2023]
Abstract
CIMMYT maize lines (CMLs), which represent the tropical maize germplasm, are freely available worldwide. All currently released 615 CMLs and fourteen temperate maize inbred lines were genotyped with 180 kompetitive allele-specific PCR single nucleotide polymorphisms to develop a reference fingerprinting SNP dataset that can be used to perform quality control (QC) and genetic diversity analyses. The QC analysis identified 25 CMLs with purity, identity, or mislabeling issues. Further field observation, purification, and re-genotyping of these CMLs are required. The reference fingerprinting SNP dataset was developed for all of the currently released CMLs with 152 high-quality SNPs. The results of principal component analysis and average genetic distances between subgroups showed a clear genetic divergence between temperate and tropical maize, whereas the three tropical subgroups partially overlapped with one another. More than 99% of the pairs of CMLs had genetic distances greater than 0.30, showing their high genetic diversity, and most CMLs are distantly related. The heterotic patterns, estimated with the molecular markers, are consistent with those estimated using pedigree information in two major maize breeding programs at CIMMYT. These research findings are helpful for ensuring the regeneration and distribution of the true CMLs, via QC analysis, and for facilitating the effective utilization of the CMLs, globally.
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Affiliation(s)
- Jingtao Qu
- Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco 56237, Mexico
| | | | - Fengling Fu
- Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
| | - Haoqiang Yu
- Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
| | - Kate Dreher
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco 56237, Mexico
| | - Sudha K. Nair
- Asia Regional Maize Program, International Maize and Wheat Improvement Center (CIMMYT), ICRISAT Campus, Patancheru, Hyderabad 502324, Telangana, India
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P.O. Box 1041, Nairobi 00621, Kenya
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P.O. Box 1041, Nairobi 00621, Kenya
| | - Dan Makumbi
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P.O. Box 1041, Nairobi 00621, Kenya
| | - Thanda Dhliwayo
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco 56237, Mexico
| | - Felix San Vicente
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco 56237, Mexico
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P.O. Box 1041, Nairobi 00621, Kenya
| | - Boddupalli M. Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P.O. Box 1041, Nairobi 00621, Kenya
| | - Wanchen Li
- Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco 56237, Mexico
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Akohoue F, Miedaner T. Meta-analysis and co-expression analysis revealed stable QTL and candidate genes conferring resistances to Fusarium and Gibberella ear rots while reducing mycotoxin contamination in maize. FRONTIERS IN PLANT SCIENCE 2022; 13:1050891. [PMID: 36388551 PMCID: PMC9662303 DOI: 10.3389/fpls.2022.1050891] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
Fusarium (FER) and Gibberella ear rots (GER) are the two most devastating diseases of maize (Zea mays L.) which reduce yield and affect grain quality worldwide, especially by contamination with mycotoxins. Genetic improvement of host resistance to effectively tackle FER and GER diseases requires the identification of stable quantitative trait loci (QTL) to facilitate the application of genomics-assisted breeding for improving selection efficiency in breeding programs. We applied improved meta-analysis algorithms to re-analyze 224 QTL identified in 15 studies based on dense genome-wide single nucleotide polymorphisms (SNP) in order to identify meta-QTL (MQTL) and colocalized genomic loci for fumonisin (FUM) and deoxynivalenol (DON) accumulation, silk (SR) and kernel (KR) resistances of both FER and GER, kernel dry-down rate (KDD) and husk coverage (HC). A high-resolution genetic consensus map with 36,243 loci was constructed and enabled the projection of 164 of the 224 collected QTL. Candidate genes (CG) mining was performed within the most refined MQTL, and identified CG were cross-validated using publicly available transcriptomic data of maize under Fusarium graminearum infection. The meta-analysis revealed 40 MQTL, of which 29 were associated each with 2-5 FER- and/or GER-related traits. Twenty-eight of the 40 MQTL were common to both FER and GER resistances and 19 MQTL were common to silk and kernel resistances. Fourteen most refined MQTL on chromosomes 1, 2, 3, 4, 7 and 9 harbored a total of 2,272 CG. Cross-validation identified 59 of these CG as responsive to FER and/or GER diseases. MQTL ZmMQTL2.2, ZmMQTL9.2 and ZmMQTL9.4 harbored promising resistance genes, of which GRMZM2G011151 and GRMZM2G093092 were specific to the resistant line for both diseases and encoded "terpene synthase21 (tps21)" and "flavonoid O-methyltransferase2 (fomt2)", respectively. Our findings revealed stable refined MQTL harboring promising candidate genes for use in breeding programs for improving FER and GER resistances with reduced mycotoxin accumulation. These candidate genes can be transferred into elite cultivars by integrating refined MQTL into genomics-assisted backcross breeding strategies.
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42
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Richardson AE, Hake S. The power of classic maize mutants: Driving forward our fundamental understanding of plants. THE PLANT CELL 2022; 34:2505-2517. [PMID: 35274692 PMCID: PMC9252469 DOI: 10.1093/plcell/koac081] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/08/2022] [Indexed: 05/12/2023]
Abstract
Since Mendel, maize has been a powerhouse of fundamental genetics research. From testing the Mendelian laws of inheritance, to the first genetic and cytogenetic maps, to the use of whole-genome sequencing data for crop improvement, maize is at the forefront of genetics advances. Underpinning much of this revolutionary work are the classic morphological mutants; the "freaks" that stood out in the field to even the untrained eye. Here we review some of these classic developmental mutants and their importance in the history of genetics, as well as their key role in our fundamental understanding of plant development.
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Affiliation(s)
- Annis E Richardson
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Sarah Hake
- Department of Plant and Microbial Biology, University of California, Berkeley, California, USA
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43
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Schneider M, Casale F, Stich B. Accurate recombination estimation from pooled genotyping and sequencing: a case study on barley. BMC Genomics 2022; 23:468. [PMID: 35752769 PMCID: PMC9233355 DOI: 10.1186/s12864-022-08701-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/15/2022] [Indexed: 11/15/2022] Open
Abstract
Sexual reproduction involves meiotic recombination and the creation of crossing over between homologous chromosomes, which leads to new allele combinations. We present a new approach that uses the allele frequency differences and the physical distance of neighboring polymorphisms to estimate the recombination rate from pool genotyping or sequencing. This allows a considerable cost reduction compared to conventional mapping based on genotyping or sequencing data of single individuals. We evaluated the approach based on computer simulations at various genotyping depths and population sizes as well as applied it to experimental data of 45 barley populations, comprising 4182 RIL. High correlations between the recombination rates from this new pool genetic mapping approach and conventional mapping in simulated and experimental barley populations were observed. The proposed method therefore provides a reliable genetic map position and recombination rate estimation in defined genomic windows.
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Affiliation(s)
- Michael Schneider
- Institute of Quantitative Genetics and Genomics of Plants, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Federico Casale
- Institute of Quantitative Genetics and Genomics of Plants, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Benjamin Stich
- Institute of Quantitative Genetics and Genomics of Plants, Heinrich Heine University, 40225, Düsseldorf, Germany. .,Max Planck Institute for Plant Breeding Research, 50829, Köln, Germany. .,Cluster of Excellence on Plant Sciences, From Complex Traits Towards Synthetic Modules, Universitätsstraße 1, 40225, Düsseldorf, Germany.
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44
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Welcker C, Spencer NA, Turc O, Granato I, Chapuis R, Madur D, Beauchene K, Gouesnard B, Draye X, Palaffre C, Lorgeou J, Melkior S, Guillaume C, Presterl T, Murigneux A, Wisser RJ, Millet EJ, van Eeuwijk F, Charcosset A, Tardieu F. Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions. Nat Commun 2022; 13:3225. [PMID: 35680899 PMCID: PMC9184527 DOI: 10.1038/s41467-022-30872-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 05/23/2022] [Indexed: 11/08/2022] Open
Abstract
Combined phenomic and genomic approaches are required to evaluate the margin of progress of breeding strategies. Here, we analyze 65 years of genetic progress in maize yield, which was similar (101 kg ha-1 year-1) across most frequent environmental scenarios in the European growing area. Yield gains were linked to physiologically simple traits (plant phenology and architecture) which indirectly affected reproductive development and light interception in all studied environments, marked by significant genomic signatures of selection. Conversely, studied physiological processes involved in stress adaptation remained phenotypically unchanged (e.g. stomatal conductance and growth sensitivity to drought) and showed no signatures of selection. By selecting for yield, breeders indirectly selected traits with stable effects on yield, but not physiological traits whose effects on yield can be positive or negative depending on environmental conditions. Because yield stability under climate change is desirable, novel breeding strategies may be needed for exploiting alleles governing physiological adaptive traits.
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Affiliation(s)
- Claude Welcker
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | | | - Olivier Turc
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Italo Granato
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Romain Chapuis
- DIASCOPE, Université de Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Delphine Madur
- GQE-Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | | | - Brigitte Gouesnard
- AGAP institut Univ. Montpellier, INRAE, CIRAD, Institut Agro, Montpellier, France
| | - Xavier Draye
- Catholic Univ. Louvain, Earth & Life Institute, Louvain la Neuve, Belgium
| | | | | | | | | | | | | | - Randall J Wisser
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | | | | | - Alain Charcosset
- GQE-Le Moulon, INRA, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - François Tardieu
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France.
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45
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Haleem A, Klees S, Schmitt AO, Gültas M. Deciphering Pleiotropic Signatures of Regulatory SNPs in Zea mays L. Using Multi-Omics Data and Machine Learning Algorithms. Int J Mol Sci 2022; 23:5121. [PMID: 35563516 PMCID: PMC9100765 DOI: 10.3390/ijms23095121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/28/2022] [Accepted: 05/02/2022] [Indexed: 01/25/2023] Open
Abstract
Maize is one of the most widely grown cereals in the world. However, to address the challenges in maize breeding arising from climatic anomalies, there is a need for developing novel strategies to harness the power of multi-omics technologies. In this regard, pleiotropy is an important genetic phenomenon that can be utilized to simultaneously enhance multiple agronomic phenotypes in maize. In addition to pleiotropy, another aspect is the consideration of the regulatory SNPs (rSNPs) that are likely to have causal effects in phenotypic development. By incorporating both aspects in our study, we performed a systematic analysis based on multi-omics data to reveal the novel pleiotropic signatures of rSNPs in a global maize population. For this purpose, we first applied Random Forests and then Markov clustering algorithms to decipher the pleiotropic signatures of rSNPs, based on which hierarchical network models are constructed to elucidate the complex interplay among transcription factors, rSNPs, and phenotypes. The results obtained in our study could help to understand the genetic programs orchestrating multiple phenotypes and thus could provide novel breeding targets for the simultaneous improvement of several agronomic traits.
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Affiliation(s)
- Ataul Haleem
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany; (A.H.); (S.K.); (A.O.S.)
- Faculty of Agriculture, South Westphalia University of Applied Sciences, Lübecker Ring 2, 59494 Soest, Germany
| | - Selina Klees
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany; (A.H.); (S.K.); (A.O.S.)
- Center for Integrated Breeding Research (CiBreed), Georg-August University, Carl-Sprengel-Weg 1, 37075 Göttingen, Germany
| | - Armin Otto Schmitt
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany; (A.H.); (S.K.); (A.O.S.)
- Center for Integrated Breeding Research (CiBreed), Georg-August University, Carl-Sprengel-Weg 1, 37075 Göttingen, Germany
| | - Mehmet Gültas
- Faculty of Agriculture, South Westphalia University of Applied Sciences, Lübecker Ring 2, 59494 Soest, Germany
- Center for Integrated Breeding Research (CiBreed), Georg-August University, Carl-Sprengel-Weg 1, 37075 Göttingen, Germany
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46
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Meher PK, Rustgi S, Kumar A. Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results. Heredity (Edinb) 2022; 128:519-530. [PMID: 35508540 DOI: 10.1038/s41437-022-00539-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 04/19/2022] [Accepted: 04/19/2022] [Indexed: 11/09/2022] Open
Abstract
We evaluated the performances of three BLUP and five Bayesian methods for genomic prediction by using nine actual and 54 simulated datasets. The genomic prediction accuracy was measured using Pearson's correlation coefficient between the genomic estimated breeding value (GEBV) and the observed phenotypic data using a fivefold cross-validation approach with 100 replications. The Bayesian alphabets performed better for the traits governed by a few genes/QTLs with relatively larger effects. On the contrary, the BLUP alphabets (GBLUP and CBLUP) exhibited higher genomic prediction accuracy for the traits controlled by several small-effect QTLs. Additionally, Bayesian methods performed better for the highly heritable traits and, for other traits, performed at par with the BLUP methods. Further, genomic BLUP (GBLUP) was identified as the least biased method for the GEBV estimation. Among the Bayesian methods, the Bayesian ridge regression and Bayesian LASSO were less biased than other Bayesian alphabets. Nonetheless, genomic prediction accuracy increased with an increase in trait heritability, irrespective of the sample size, marker density, and the QTL type (major/minor effect). In sum, this study provides valuable information regarding the choice of the selection method for genomic prediction in different breeding programs.
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Affiliation(s)
- Prabina Kumar Meher
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-12, India.
| | - Sachin Rustgi
- Department of Plant and Environmental Sciences, Clemson University Pee Dee Research and Education Center, Darlington, SC, USA.
| | - Anuj Kumar
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-12, India
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47
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Yang W, Guo T, Luo J, Zhang R, Zhao J, Warburton ML, Xiao Y, Yan J. Target-oriented prioritization: targeted selection strategy by integrating organismal and molecular traits through predictive analytics in breeding. Genome Biol 2022; 23:80. [PMID: 35292095 PMCID: PMC8922918 DOI: 10.1186/s13059-022-02650-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 03/08/2022] [Indexed: 11/10/2022] Open
Abstract
Genomic prediction in crop breeding is hindered by modeling on limited phenotypic traits. We propose an integrative multi-trait breeding strategy via machine learning algorithm, target-oriented prioritization (TOP). Using a large hybrid maize population, we demonstrate that the accuracy for identifying a candidate that is phenotypically closest to an ideotype, or target variety, achieves up to 91%. The strength of TOP is enhanced when omics level traits are included. We show that TOP enables selection of inbreds or hybrids that outperform existing commercial varieties. It improves multiple traits and accurately identifies improved candidates for new varieties, which will greatly influence breeding.
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Affiliation(s)
- Wenyu Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- College of Science, Huazhong Agricultural University, Wuhan, 430070, China
| | | | - Jingyun Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ruyang Zhang
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Beijing Academy of Agricultural & Forestry Sciences, Beijing, 100097, China
| | - Jiuran Zhao
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Beijing Academy of Agricultural & Forestry Sciences, Beijing, 100097, China
| | - Marilyn L Warburton
- United States Department of Agriculture-Agricultural Research Service, Corn Host Plant Resistance Research Unit, Box 9555, Mississippi State, MS, 39762, USA
| | - Yingjie Xiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
- Hubei Hongshan Laboratory, Wuhan, 430070, China.
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
- Hubei Hongshan Laboratory, Wuhan, 430070, China.
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48
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Medina-Lozano I, Díaz A. Applications of Genomic Tools in Plant Breeding: Crop Biofortification. Int J Mol Sci 2022; 23:3086. [PMID: 35328507 PMCID: PMC8950180 DOI: 10.3390/ijms23063086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/04/2022] [Accepted: 03/10/2022] [Indexed: 12/02/2022] Open
Abstract
Crop breeding has mainly been focused on increasing productivity, either directly or by decreasing the losses caused by biotic and abiotic stresses (that is, incorporating resistance to diseases and enhancing tolerance to adverse conditions, respectively). Quite the opposite, little attention has been paid to improve the nutritional value of crops. It has not been until recently that crop biofortification has become an objective within breeding programs, through either conventional methods or genetic engineering. There are many steps along this long path, from the initial evaluation of germplasm for the content of nutrients and health-promoting compounds to the development of biofortified varieties, with the available and future genomic tools assisting scientists and breeders in reaching their objectives as well as speeding up the process. This review offers a compendium of the genomic technologies used to explore and create biodiversity, to associate the traits of interest to the genome, and to transfer the genomic regions responsible for the desirable characteristics into potential new varieties. Finally, a glimpse of future perspectives and challenges in this emerging area is offered by taking the present scenario and the slow progress of the regulatory framework as the starting point.
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Affiliation(s)
- Inés Medina-Lozano
- Departamento de Ciencia Vegetal, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Universidad de Zaragoza, Avda. Montañana 930, 50059 Zaragoza, Spain;
- Instituto Agroalimentario de Aragón—IA2, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Universidad de Zaragoza, 50013 Zaragoza, Spain
| | - Aurora Díaz
- Departamento de Ciencia Vegetal, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Universidad de Zaragoza, Avda. Montañana 930, 50059 Zaragoza, Spain;
- Instituto Agroalimentario de Aragón—IA2, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Universidad de Zaragoza, 50013 Zaragoza, Spain
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49
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Odell SG, Hudson AI, Praud S, Dubreuil P, Tixier MH, Ross-Ibarra J, Runcie DE. Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci. G3 (BETHESDA, MD.) 2022; 12:6509518. [PMID: 35100382 PMCID: PMC8895984 DOI: 10.1093/g3journal/jkac011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 10/27/2021] [Indexed: 12/02/2022]
Abstract
The search for quantitative trait loci that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as biparental quantitative trait loci mapping and genome-wide association studies each have their own advantages and limitations. Multiparent advanced generation intercross populations contain more recombination events and genetic diversity than biparental mapping populations and are better able to estimate effect sizes of rare alleles than association mapping populations. Here, we discuss the results of using a multiparent advanced generation intercross population of doubled haploid maize lines created from 16 diverse founders to perform quantitative trait loci mapping. We compare 3 models that assume bi-allelic, founder, and ancestral haplotype allelic states for quantitative trait loci. The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multiallelic quantitative trait loci.
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Affiliation(s)
- Sarah G Odell
- Department of Plant Sciences, University of California, Davis, CA 95616, USA.,Department of Evolution and Ecology, University of California, Davis, CA 95616, USA
| | - Asher I Hudson
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA.,Center for Population Biology, University of California, Davis, CA 95616, USA
| | - Sébastien Praud
- Limagrain, Centre de Recherche de Chappes, Chappes 63720, France
| | - Pierre Dubreuil
- Limagrain, Centre de Recherche de Chappes, Chappes 63720, France
| | | | - Jeffrey Ross-Ibarra
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA.,Center for Population Biology, University of California, Davis, CA 95616, USA.,Genome Center, University of California, Davis, CA 95616, USA
| | - Daniel E Runcie
- Department of Plant Sciences, University of California, Davis, CA 95616, USA
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50
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Weiß TM, Zhu X, Leiser WL, Li D, Liu W, Schipprack W, Melchinger AE, Hahn V, Würschum T. Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.). G3 (BETHESDA, MD.) 2022; 12:6509517. [PMID: 35100379 PMCID: PMC8895988 DOI: 10.1093/g3journal/jkab445] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/16/2021] [Indexed: 12/19/2022]
Abstract
Genomic selection is a well-investigated approach that facilitates and supports selection decisions for complex traits and has meanwhile become a standard tool in modern plant breeding. Phenomic selection has only recently been suggested and uses the same statistical procedures to predict the targeted traits but replaces marker data with near-infrared spectroscopy data. It may represent an attractive low-cost, high-throughput alternative but has not been sufficiently studied until now. Here, we used 400 genotypes of maize (Zea mays L.) comprising elite lines of the Flint and Dent heterotic pools as well as 6 Flint landraces, which were phenotyped in multienvironment trials for anthesis-silking-interval, early vigor, final plant height, grain dry matter content, grain yield, and phosphorus concentration in the maize kernels, to compare the predictive abilities of genomic as well as phenomic prediction under different scenarios. We found that both approaches generally achieved comparable predictive abilities within material groups. However, phenomic prediction was less affected by population structure and performed better than its genomic counterpart for predictions among diverse groups of breeding material. We therefore conclude that phenomic prediction is a promising tool for practical breeding, for instance when working with unknown and rather diverse germplasm. Moreover, it may make the highly monopolized sector of plant breeding more accessible also for low-tech institutions by combining well established, widely available, and cost-efficient spectral phenotyping with the statistical procedures elaborated for genomic prediction - while achieving similar or even better results than with marker data.
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Affiliation(s)
- Thea Mi Weiß
- State Plant Breeding Institute, University of Hohenheim, Stuttgart 70593, Germany.,Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart 70593, Germany
| | - Xintian Zhu
- State Plant Breeding Institute, University of Hohenheim, Stuttgart 70593, Germany.,Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart 70593, Germany
| | - Willmar L Leiser
- State Plant Breeding Institute, University of Hohenheim, Stuttgart 70593, Germany
| | - Dongdong Li
- Key Laboratory of Crop Heterosis and Utilization, Ministry of Education, Key Laboratory of Crop Genetic Improvement, Beijing Municipality, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
| | - Wenxin Liu
- Key Laboratory of Crop Heterosis and Utilization, Ministry of Education, Key Laboratory of Crop Genetic Improvement, Beijing Municipality, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
| | - Wolfgang Schipprack
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart 70593, Germany
| | - Albrecht E Melchinger
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart 70593, Germany
| | - Volker Hahn
- State Plant Breeding Institute, University of Hohenheim, Stuttgart 70593, Germany
| | - Tobias Würschum
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart 70593, Germany
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