1
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Wei X, Chen M, Zhang Q, Gong J, Liu J, Yong K, Wang Q, Fan J, Chen S, Hua H, Luo Z, Zhao X, Wang X, Li W, Cong J, Yu X, Wang Z, Huang R, Chen J, Zhou X, Qiu J, Xu P, Murray J, Wang H, Xu Y, Xu C, Xu G, Yang J, Han B, Huang X. Genomic investigation of 18,421 lines reveals the genetic architecture of rice. Science 2024; 385:eadm8762. [PMID: 38963845 DOI: 10.1126/science.adm8762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/29/2024] [Indexed: 07/06/2024]
Abstract
Understanding how numerous quantitative trait loci (QTL) shape phenotypic variation is an important question in genetics. To address this, we established a permanent population of 18,421 (18K) rice lines with reduced population structure. We generated reference-level genome assemblies of the founders and genotyped all 18K-rice lines through whole-genome sequencing. Through high-resolution mapping, 96 high-quality candidate genes contributing to variation in 16 traits were identified, including OsMADS22 and OsFTL1 verified as causal genes for panicle number and heading date, respectively. We identified epistatic QTL pairs and constructed a genetic interaction network with 19 genes serving as hubs. Overall, 170 masking epistasis pairs were characterized, serving as an important factor contributing to genetic background effects across diverse varieties. The work provides a basis to guide grain yield and quality improvements in rice.
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Affiliation(s)
- Xin Wei
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Mengjiao Chen
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Qi Zhang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Junyi Gong
- State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310006, China
| | - Jie Liu
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Kaicheng Yong
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Qin Wang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jiongjiong Fan
- State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310006, China
| | - Suhui Chen
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Hua Hua
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Zhaowei Luo
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xiaoyan Zhao
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xuan Wang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Wei Li
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jia Cong
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xiting Yu
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Zhihan Wang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Ruipeng Huang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jiaxin Chen
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xiaoyi Zhou
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jie Qiu
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Ping Xu
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Jeremy Murray
- CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200233, China
| | - Hai Wang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
| | - Yang Xu
- Key Laboratory of Plant Functional Genomics of Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou 225009, China
| | - Chenwu Xu
- Key Laboratory of Plant Functional Genomics of Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou 225009, China
| | - Gen Xu
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Jinliang Yang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Bin Han
- CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200233, China
| | - Xuehui Huang
- Shanghai Key Laboratory of Plant Molecular Sciences, Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
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2
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Garin V, Diallo C, Tékété ML, Théra K, Guitton B, Dagno K, Diallo AG, Kouressy M, Leiser W, Rattunde F, Sissoko I, Touré A, Nébié B, Samaké M, Kholovà J, Berger A, Frouin J, Pot D, Vaksmann M, Weltzien E, Témé N, Rami JF. Characterization of adaptation mechanisms in sorghum using a multireference back-cross nested association mapping design and envirotyping. Genetics 2024; 226:iyae003. [PMID: 38381593 PMCID: PMC10990433 DOI: 10.1093/genetics/iyae003] [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: 10/19/2023] [Accepted: 12/20/2023] [Indexed: 02/23/2024] Open
Abstract
Identifying the genetic factors impacting the adaptation of crops to environmental conditions is of key interest for conservation and selection purposes. It can be achieved using population genomics, and evolutionary or quantitative genetics. Here we present a sorghum multireference back-cross nested association mapping population composed of 3,901 lines produced by crossing 24 diverse parents to 3 elite parents from West and Central Africa-back-cross nested association mapping. The population was phenotyped in environments characterized by differences in photoperiod, rainfall pattern, temperature levels, and soil fertility. To integrate the multiparental and multi-environmental dimension of our data we proposed a new approach for quantitative trait loci (QTL) detection and parental effect estimation. We extended our model to estimate QTL effect sensitivity to environmental covariates, which facilitated the integration of envirotyping data. Our models allowed spatial projections of the QTL effects in agro-ecologies of interest. We utilized this strategy to analyze the genetic architecture of flowering time and plant height, which represents key adaptation mechanisms in environments like West Africa. Our results allowed a better characterization of well-known genomic regions influencing flowering time concerning their response to photoperiod with Ma6 and Ma1 being photoperiod-sensitive and the region of possible candidate gene Elf3 being photoperiod-insensitive. We also accessed a better understanding of plant height genetic determinism with the combined effects of phenology-dependent (Ma6) and independent (qHT7.1 and Dw3) genomic regions. Therefore, we argue that the West and Central Africa-back-cross nested association mapping and the presented analytical approach constitute unique resources to better understand adaptation in sorghum with direct application to develop climate-smart varieties.
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Affiliation(s)
- Vincent Garin
- Crop Physiology Laboratory, International Crops Research Institute for the Semi-Arid Tropics, Patancheru, 502 324, India
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Chiaka Diallo
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
- Département d’Enseignement et de Recherche des Sciences et Techniques Agricoles, Institut polytechnique rural de formation et de recherche appliquée de Katibougou, Koulikoro, BP 06, Mali
| | - Mohamed Lamine Tékété
- Institut d’Economie Rurale, Bamako, BP 262, Mali
- Faculté des Sciences et Techniques, Université des Sciences des Techniques et des Technologies de Bamako, Bamako, BP E 3206, Mali
| | | | - Baptiste Guitton
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Karim Dagno
- Institut d’Economie Rurale, Bamako, BP 262, Mali
| | | | | | - Willmar Leiser
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
| | - Fred Rattunde
- Agronomy Department, University of Wisconsin, Madison, WI 53705, WI, USA
| | - Ibrahima Sissoko
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
| | - Aboubacar Touré
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
| | - Baloua Nébié
- Dryland Crops Program, International Maize and Wheat Improvement Center (CIMMYT-Senegal) U/C CERAAS, Thiès, Po Box 3320, Senegal
| | - Moussa Samaké
- Faculté des Sciences et Techniques, Université des Sciences des Techniques et des Technologies de Bamako, Bamako, BP E 3206, Mali
| | - Jana Kholovà
- Crop Physiology Laboratory, International Crops Research Institute for the Semi-Arid Tropics, Patancheru, 502 324, India
- Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences, Prague, 165 00, Czech Republic
| | - Angélique Berger
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Julien Frouin
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - David Pot
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Michel Vaksmann
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
| | - Eva Weltzien
- Sorghum Program, International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP 320, Mali
- Agronomy Department, University of Wisconsin, Madison, WI 53705, WI, USA
| | - Niaba Témé
- Institut d’Economie Rurale, Bamako, BP 262, Mali
| | - Jean-François Rami
- CIRAD, UMR AGAP Institut, Montpellier, F-34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, F-34398, France
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3
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Crozier D, Winans ND, Hoffmann L, Patil NY, Klein PE, Klein RR, Rooney WL. Evaluating and Predicting the Performance of Sorghum Lines in an Elite by Exotic Backcross-Nested Association Mapping Population. PLANTS (BASEL, SWITZERLAND) 2024; 13:879. [PMID: 38592905 PMCID: PMC10975396 DOI: 10.3390/plants13060879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 03/09/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024]
Abstract
Maintaining or introducing genetic diversity into plant breeding programs is necessary for continual genetic gain; however, diversity at the cost of reduced performance is not something sought by breeders. To this end, backcross-nested association mapping (BC-NAM) populations, in which the recurrent parent is an elite line, can be employed as a strategy to introgress diversity from unadapted accessions while maintaining agronomic performance. This study evaluates (i) the hybrid performance of sorghum lines from 18 BC1-NAM families and (ii) the potential of genomic prediction to screen lines from BC1-NAM families for hybrid performance prior to phenotypic evaluation. Despite the diverse geographical origins and agronomic performance of the unadapted parents for BC1-NAM families, many BC1-derived lines performed significantly better in the hybrid trials than the elite recurrent parent, R.Tx436. The genomic prediction accuracies for grain yield, plant height, and days to mid-anthesis were acceptable, but the prediction accuracies for plant height were lower than expected. While the prediction accuracies increased when including more individuals in the training set, improvements tended to plateau between two and five lines per family, with larger training sets being required for more complex traits such as grain yield. Therefore, genomic prediction models can be optimized in a large BC1-NAM population with a relatively low fraction of individuals needing to be evaluated. These results suggest that genomic prediction is an effective method of pre-screening lines within BC1-NAM families prior to evaluation in extensive hybrid field trials.
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Affiliation(s)
- Daniel Crozier
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Noah D. Winans
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Leo Hoffmann
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
- Department of Horticulture Sciences, University of Florida, Gainesville, FL 32611, USA
| | - Nikhil Y. Patil
- Department of Horticultural Sciences, Texas A&M University, College Station, TX 77845, USA
- Health Sciences Center, University of Oklahoma, Oklahoma City, OK 73104, USA
| | - Patricia E. Klein
- Health Sciences Center, University of Oklahoma, Oklahoma City, OK 73104, USA
| | - Robert R. Klein
- Crop Germplasm Research Unit, United States Department of Agriculture Agricultural Research Service, College Station, TX 77843, USA;
| | - William L. Rooney
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
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4
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Tanaka R, Wu D, Li X, Tibbs-Cortes LE, Wood JC, Magallanes-Lundback M, Bornowski N, Hamilton JP, Vaillancourt B, Li X, Deason NT, Schoenbaum GR, Buell CR, DellaPenna D, Yu J, Gore MA. Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain. THE PLANT GENOME 2023; 16:e20276. [PMID: 36321716 DOI: 10.1002/tpg2.20276] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize (Zea mays L.) grain is low. Several large-effect genes with cis-acting variants affecting messenger RNA (mRNA) expression are mostly responsible for tocochromanol variation in maize grain, with other relevant associated quantitative trait loci (QTL) yet to be fully resolved. Leveraging existing genomic and transcriptomic information for maize inbreds could improve prediction when selecting for higher vitamin E content. Here, we first evaluated a multikernel genomic best linear unbiased prediction (MK-GBLUP) approach for modeling known QTL in the prediction of nine tocochromanol grain phenotypes (12-21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines. On average, MK-GBLUP models improved predictive abilities by 7.0-13.6% when compared with GBLUP. In a second approach with a subset of 545 lines from the larger panel, the highest average improvement in predictive ability relative to GBLUP was achieved with a multi-trait GBLUP model (15.4%) that had a tocochromanol phenotype and transcript abundances in developing grain for a few large-effect candidate causal genes (1-3 genes per trait) as multiple response variables. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes.
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Affiliation(s)
- Ryokei Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Di Wu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Xiaowei Li
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | | | - Joshua C Wood
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | | | - Nolan Bornowski
- Dep. of Plant Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | - John P Hamilton
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Brieanne Vaillancourt
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Xianran Li
- USDA ARS, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA, 99164, USA
| | - Nicholas T Deason
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | | | - C Robin Buell
- Institute for Plant Breeding, Genetics & Genomics, Center for Applied Genetic Technologies, Dep. of Crop & Soil Sciences, Univ. of Georgia, Athens, GA, 30602, USA
| | - Dean DellaPenna
- Dep. of Biochemistry and Molecular Biology, Michigan State Univ., East Lansing, MI, 48824, USA
| | - Jianming Yu
- Dep. of Agronomy, Iowa State Univ., Ames, IA, 50011, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
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5
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Minow MAA, Marand AP, Schmitz RJ. Leveraging Single-Cell Populations to Uncover the Genetic Basis of Complex Traits. Annu Rev Genet 2023; 57:297-319. [PMID: 37562412 PMCID: PMC10775913 DOI: 10.1146/annurev-genet-022123-110824] [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] [Indexed: 08/12/2023]
Abstract
The ease and throughput of single-cell genomics have steadily improved, and its current trajectory suggests that surveying single-cell populations will become routine. We discuss the merger of quantitative genetics with single-cell genomics and emphasize how this synergizes with advantages intrinsic to plants. Single-cell population genomics provides increased detection resolution when mapping variants that control molecular traits, including gene expression or chromatin accessibility. Additionally, single-cell population genomics reveals the cell types in which variants act and, when combined with organism-level phenotype measurements, unveils which cellular contexts impact higher-order traits. Emerging technologies, notably multiomics, can facilitate the measurement of both genetic changes and genomic traits in single cells, enabling single-cell genetic experiments. The implementation of single-cell genetics will advance the investigation of the genetic architecture of complex molecular traits and provide new experimental paradigms to study eukaryotic genetics.
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Affiliation(s)
- Mark A A Minow
- Department of Genetics, University of Georgia, Athens, Georgia, USA;
| | | | - Robert J Schmitz
- Department of Genetics, University of Georgia, Athens, Georgia, USA;
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6
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Chawla R, Poonia A, Samantara K, Mohapatra SR, Naik SB, Ashwath MN, Djalovic IG, Prasad PVV. Green revolution to genome revolution: driving better resilient crops against environmental instability. Front Genet 2023; 14:1204585. [PMID: 37719711 PMCID: PMC10500607 DOI: 10.3389/fgene.2023.1204585] [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: 04/12/2023] [Accepted: 08/11/2023] [Indexed: 09/19/2023] Open
Abstract
Crop improvement programmes began with traditional breeding practices since the inception of agriculture. Farmers and plant breeders continue to use these strategies for crop improvement due to their broad application in modifying crop genetic compositions. Nonetheless, conventional breeding has significant downsides in regard to effort and time. Crop productivity seems to be hitting a plateau as a consequence of environmental issues and the scarcity of agricultural land. Therefore, continuous pursuit of advancement in crop improvement is essential. Recent technical innovations have resulted in a revolutionary shift in the pattern of breeding methods, leaning further towards molecular approaches. Among the promising approaches, marker-assisted selection, QTL mapping, omics-assisted breeding, genome-wide association studies and genome editing have lately gained prominence. Several governments have progressively relaxed their restrictions relating to genome editing. The present review highlights the evolutionary and revolutionary approaches that have been utilized for crop improvement in a bid to produce climate-resilient crops observing the consequence of climate change. Additionally, it will contribute to the comprehension of plant breeding succession so far. Investing in advanced sequencing technologies and bioinformatics will deepen our understanding of genetic variations and their functional implications, contributing to breakthroughs in crop improvement and biodiversity conservation.
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Affiliation(s)
- Rukoo Chawla
- Department of Genetics and Plant Breeding, Maharana Pratap University of Agriculture and Technology, Udaipur, Rajasthan, India
| | - Atman Poonia
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh Haryana Agricultural University, Bawal, Haryana, India
| | - Kajal Samantara
- Institute of Technology, University of Tartu, Tartu, Estonia
| | - Sourav Ranjan Mohapatra
- Department of Forest Biology and Tree Improvement, Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, India
| | - S. Balaji Naik
- Institute of Integrative Biology and Systems, University of Laval, Quebec City, QC, Canada
| | - M. N. Ashwath
- Department of Forest Biology and Tree Improvement, Kerala Agricultural University, Thrissur, Kerala, India
| | - Ivica G. Djalovic
- Institute of Field and Vegetable Crops, National Institute of the Republic of Serbia, Novi Sad, Serbia
| | - P. V. Vara Prasad
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
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7
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Alseekh S, Karakas E, Zhu F, Wijesingha Ahchige M, Fernie AR. Plant biochemical genetics in the multiomics era. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:4293-4307. [PMID: 37170864 PMCID: PMC10433942 DOI: 10.1093/jxb/erad177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 05/09/2023] [Indexed: 05/13/2023]
Abstract
Our understanding of plant biology has been revolutionized by modern genetics and biochemistry. However, biochemical genetics can be traced back to the foundation of Mendelian genetics; indeed, one of Mendel's milestone discoveries of seven characteristics of pea plants later came to be ascribed to a mutation in a starch branching enzyme. Here, we review both current and historical strategies for the elucidation of plant metabolic pathways and the genes that encode their component enzymes and regulators. We use this historical review to discuss a range of classical genetic phenomena including epistasis, canalization, and heterosis as viewed through the lens of contemporary high-throughput data obtained via the array of approaches currently adopted in multiomics studies.
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Affiliation(s)
- Saleh Alseekh
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| | - Esra Karakas
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany
| | - Feng Zhu
- National R&D Center for Citrus Preservation, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, 430070 Wuhan, China
| | | | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
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8
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Marla S, Felderhoff T, Hayes C, Perumal R, Wang X, Poland J, Morris GP. Genomics and phenomics enabled prebreeding improved early-season chilling tolerance in Sorghum. G3 (BETHESDA, MD.) 2023; 13:jkad116. [PMID: 37232400 PMCID: PMC10411554 DOI: 10.1093/g3journal/jkad116] [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: 02/02/2023] [Revised: 05/11/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023]
Abstract
In temperate climates, earlier planting of tropical-origin crops can provide longer growing seasons, reduce water loss, suppress weeds, and escape post-flowering drought stress. However, chilling sensitivity of sorghum, a tropical-origin cereal crop, limits early planting, and over 50 years of conventional breeding has been stymied by coinheritance of chilling tolerance (CT) loci with undesirable tannin and dwarfing alleles. In this study, phenomics and genomics-enabled approaches were used for prebreeding of sorghum early-season CT. Uncrewed aircraft systems (UAS) high-throughput phenotyping platform tested for improving scalability showed moderate correlation between manual and UAS phenotyping. UAS normalized difference vegetation index values from the chilling nested association mapping population detected CT quantitative trait locus (QTL) that colocalized with manual phenotyping CT QTL. Two of the 4 first-generation Kompetitive Allele Specific PCR (KASP) molecular markers, generated using the peak QTL single nucleotide polymorphisms (SNPs), failed to function in an independent breeding program as the CT allele was common in diverse breeding lines. Population genomic fixation index analysis identified SNP CT alleles that were globally rare but common to the CT donors. Second-generation markers, generated using population genomics, were successful in tracking the donor CT allele in diverse breeding lines from 2 independent sorghum breeding programs. Marker-assisted breeding, effective in introgressing CT allele from Chinese sorghums into chilling-sensitive US elite sorghums, improved early-planted seedling performance ratings in lines with CT alleles by up to 13-24% compared to the negative control under natural chilling stress. These findings directly demonstrate the effectiveness of high-throughput phenotyping and population genomics in molecular breeding of complex adaptive traits.
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Affiliation(s)
- Sandeep Marla
- Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
| | - Terry Felderhoff
- Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
| | - Chad Hayes
- USDA-ARS, Plant Stress & Germplasm Development Unit, Cropping Systems Research Laboratory, Lubbock, TX 79415, USA
| | - Ramasamy Perumal
- Western Kansas Agricultural Research Center, Kansas State University, Hays, KS 67601, USA
| | - Xu Wang
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
- Department of Agricultural and Biological Engineering, University of Florida, IFAS Gulf Coast Research and Education Center, Wimauma, FL 33598, USA
| | - Jesse Poland
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
- Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Geoffrey P Morris
- Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA
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9
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Rio S, Charcosset A, Moreau L, Mary-Huard T. Detecting directional and non-directional epistasis in bi-parental populations using genomic data. Genetics 2023; 224:iyad089. [PMID: 37170627 DOI: 10.1093/genetics/iyad089] [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] [Received: 01/16/2023] [Revised: 01/16/2023] [Accepted: 05/01/2023] [Indexed: 05/13/2023] Open
Abstract
Epistasis, commonly defined as interaction effects between alleles of different loci, is an important genetic component of the variation of phenotypic traits in natural and breeding populations. In addition to its impact on variance, epistasis can also affect the expected performance of a population and is then referred to as directional epistasis. Before the advent of genomic data, the existence of epistasis (both directional and non-directional) was investigated based on complex and expensive mating schemes involving several generations evaluated for a trait of interest. In this study, we propose a methodology to detect the presence of epistasis based on simple inbred biparental populations, both genotyped and phenotyped, ideally along with their parents. Thanks to genomic data, parental proportions as well as shared parental proportions between inbred individuals can be estimated. They allow the evaluation of epistasis through a test of the expected performance for directional epistasis or the variance of genetic values. This methodology was applied to two large multiparental populations, i.e. the American maize and soybean nested association mapping populations, evaluated for different traits. Results showed significant epistasis, especially for the test of directional epistasis, e.g. the increase in anthesis to silking interval observed in most maize inbred progenies or the decrease in grain yield observed in several soybean inbred progenies. In general, the effects detected suggested that shuffling allelic associations of both elite parents had a detrimental effect on the performance of their progeny. This methodology is implemented in the EpiTest R-package and can be applied to any bi/multiparental inbred population evaluated for a trait of interest.
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Affiliation(s)
- Simon Rio
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA-Paris, 91120 Palaiseau, France
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10
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Balint‐Kurti P, Wang G. Special issue: Genetics of maize-microbe interactions. MOLECULAR PLANT PATHOLOGY 2023; 24:671-674. [PMID: 37209308 PMCID: PMC10257038 DOI: 10.1111/mpp.13348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 03/17/2023] [Indexed: 05/22/2023]
Affiliation(s)
- Peter Balint‐Kurti
- USDA‐ARSPlant Science Research UnitRaleighNorth CarolinaUSA
- Department of Entomology and Plant PathologyNorth Carolina State UniversityRaleighNorth CarolinaUSA
| | - Guan‐Feng Wang
- The Key Laboratory of Plant Development and Environmental Adaptation Biology, Ministry of Education, School of Life Sciences, Shandong UniversityQingdaoShandongChina
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11
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Dahanayaka BA, Martin A. Multi-parental fungal mapping population study to detect genomic regions associated with Pyrenophora teres f. teres virulence. Sci Rep 2023; 13:9804. [PMID: 37328500 PMCID: PMC10275933 DOI: 10.1038/s41598-023-36963-y] [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] [Received: 03/08/2023] [Accepted: 06/13/2023] [Indexed: 06/18/2023] Open
Abstract
In recent years multi-parental mapping populations (MPPs) have been widely adopted in many crops to detect quantitative trait loci (QTLs) as this method can compensate for the limitations of QTL analyses using bi-parental mapping populations. Here we report the first multi-parental nested association mapping (MP-NAM) population study used to detect genomic regions associated with host-pathogenic interactions. MP-NAM QTL analyses were conducted on 399 Pyrenophora teres f. teres individuals using biallelic, cross-specific and parental QTL effect models. A bi-parental QTL mapping study was also conducted to compare the power of QTL detection between bi-parental and MP-NAM populations. Using MP-NAM with 399 individuals detected a maximum of eight QTLs with a single QTL effect model whilst only a maximum of five QTLs were detected with an individual bi-parental mapping population of 100 individuals. When reducing the number of isolates in the MP-NAM to 200 individuals the number of QTLs detected remained the same for the MP-NAM population. This study confirms that MPPs such as MP-NAM populations can be successfully used in detecting QTLs in haploid fungal pathogens and that the power of QTL detection with MPPs is greater than with bi-parental mapping populations.
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Affiliation(s)
- Buddhika A Dahanayaka
- Centre for Crop Health, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| | - Anke Martin
- Centre for Crop Health, University of Southern Queensland, Toowoomba, QLD, 4350, Australia.
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12
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Bulut M, Alseekh S, Fernie AR. Natural variation of respiration-related traits in plants. PLANT PHYSIOLOGY 2023; 191:2120-2132. [PMID: 36546766 PMCID: PMC10069898 DOI: 10.1093/plphys/kiac593] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
Plant respiration is one of the greatest global metabolic fluxes, but rates of respiration vary massively both within different cell types as well as between different individuals and different species. Whilst this is well known, few studies have detailed population-level variation of respiration until recently. The last 20 years have seen a renaissance in studies of natural variance. In this review, we describe how experimental breeding populations and collections of large populations of accessions can be used to determine the genetic architecture of plant traits. We further detail how these approaches have been used to study the rate of respiration per se as well as traits that are intimately associated with respiration. The review highlights specific breakthroughs in these areas but also concludes that the approach should be more widely adopted in the study of respiration per se as opposed to the more frequently studied respiration-related traits.
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Affiliation(s)
- Mustafa Bulut
- Department of Root Biology and Symbiosis, Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm 14476, Germany
| | - Saleh Alseekh
- Department of Root Biology and Symbiosis, Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm 14476, Germany
- Center for Plant Systems Biology and Biotechnology, Plovdiv 4000, Bulgaria
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13
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Zhou Y, Yu Z, Chebotarov D, Chougule K, Lu Z, Rivera LF, Kathiresan N, Al-Bader N, Mohammed N, Alsantely A, Mussurova S, Santos J, Thimma M, Troukhan M, Fornasiero A, Green CD, Copetti D, Kudrna D, Llaca V, Lorieux M, Zuccolo A, Ware D, McNally K, Zhang J, Wing RA. Pan-genome inversion index reveals evolutionary insights into the subpopulation structure of Asian rice. Nat Commun 2023; 14:1567. [PMID: 36944612 PMCID: PMC10030860 DOI: 10.1038/s41467-023-37004-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 02/27/2023] [Indexed: 03/23/2023] Open
Abstract
Understanding and exploiting genetic diversity is a key factor for the productive and stable production of rice. Here, we utilize 73 high-quality genomes that encompass the subpopulation structure of Asian rice (Oryza sativa), plus the genomes of two wild relatives (O. rufipogon and O. punctata), to build a pan-genome inversion index of 1769 non-redundant inversions that span an average of ~29% of the O. sativa cv. Nipponbare reference genome sequence. Using this index, we estimate an inversion rate of ~700 inversions per million years in Asian rice, which is 16 to 50 times higher than previously estimated for plants. Detailed analyses of these inversions show evidence of their effects on gene expression, recombination rate, and linkage disequilibrium. Our study uncovers the prevalence and scale of large inversions (≥100 bp) across the pan-genome of Asian rice and hints at their largely unexplored role in functional biology and crop performance.
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Affiliation(s)
- Yong Zhou
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Arizona Genomics Institute (AGI), School of Plant Sciences, University of Arizona, Tucson, AZ, 85721, USA
| | - Zhichao Yu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Dmytro Chebotarov
- International Rice Research Institute (IRRI), Los Baños, 4031, Laguna, Philippines
| | - Kapeel Chougule
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Zhenyuan Lu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Luis F Rivera
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Nagarajan Kathiresan
- Supercomputing Core Lab, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Noor Al-Bader
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Nahed Mohammed
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Aseel Alsantely
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Saule Mussurova
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - João Santos
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Manjula Thimma
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | | | - Alice Fornasiero
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Carl D Green
- Information Technology Department, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Dario Copetti
- Arizona Genomics Institute (AGI), School of Plant Sciences, University of Arizona, Tucson, AZ, 85721, USA
| | - David Kudrna
- Arizona Genomics Institute (AGI), School of Plant Sciences, University of Arizona, Tucson, AZ, 85721, USA
| | - Victor Llaca
- Research and Development, Corteva Agriscience, Johnston, IA, 50131, USA
| | - Mathias Lorieux
- DIADE, University of Montpellier, CIRAD, IRD, Montpellier, France
| | - Andrea Zuccolo
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
- Crop Science Research Center (CSRC), Scuola Superiore Sant'Anna, Pisa, 56127, Italy.
| | - Doreen Ware
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.
- USDA ARS NEA Plant, Soil & Nutrition Laboratory Research Unit, Ithaca, NY, 14853, USA.
| | - Kenneth McNally
- International Rice Research Institute (IRRI), Los Baños, 4031, Laguna, Philippines.
| | - Jianwei Zhang
- Arizona Genomics Institute (AGI), School of Plant Sciences, University of Arizona, Tucson, AZ, 85721, USA.
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China.
| | - Rod A Wing
- Center for Desert Agriculture (CDA), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
- Arizona Genomics Institute (AGI), School of Plant Sciences, University of Arizona, Tucson, AZ, 85721, USA.
- International Rice Research Institute (IRRI), Los Baños, 4031, Laguna, Philippines.
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14
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Singh R, Shim S, Telenko DEP, Goodwin SB. Parental Inbred Lines of the Nested Association Mapping (NAM) Population of Corn Show Sources of Resistance to Tar Spot in Northern Indiana. PLANT DISEASE 2023; 107:262-266. [PMID: 35836387 DOI: 10.1094/pdis-02-22-0314-sc] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Tar spot is a major foliar disease of corn caused by the obligate fungal pathogen Phyllachora maydis, first identified in Indiana in 2015. Under conducive weather conditions, P. maydis causes significant yield losses in the United States and other countries, constituting a major threat to corn production. Relatively little is known about resistance to tar spot other than a major quantitative gene that was identified in tropical maize lines. To test for additional sources of resistance against populations of P. maydis in North America, 26 parental inbred lines of the nested associated mapping (NAM) population were evaluated for tar spot resistance in Indiana in replicated field trials under natural infection for 3 years. Tar spot disease severity was scored visually using a 0-to-100% scale. Maximum disease severity (MDS) for tar spot scoring at reproductive growth stage ranged from 0 to 48.3%, with 0% being most resistant and 48.3% being most susceptible. Nine inbred lines were resistant to P. maydis with MDS ranging from 0 to 5.0%, six were moderately resistant (5.2 to 10.6% MDS), two were moderately susceptible (11.7 to 26.0% MDS), and the remaining eight inbred lines were rated as susceptible (30.0 to 48.3% MDS). There was some variability between years, due to higher disease pressure after 2019. Inbred B73, the common parent of the NAM populations, was rated as susceptible, with MDS of 30.0%. The nine highly resistant lines provide a potential source of new genes for genetic analysis and mapping of tar spot resistance in corn.
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Affiliation(s)
- Raksha Singh
- Crop Production and Pest Control Research Unit, United States Department of Agriculture-Agricultural Research Service, West Lafayette, IN 47907-2054
| | - Sujoung Shim
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907-2054
| | - Darcy E P Telenko
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907-2054
| | - Stephen B Goodwin
- Crop Production and Pest Control Research Unit, United States Department of Agriculture-Agricultural Research Service, West Lafayette, IN 47907-2054
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15
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Cagirici HB, Andorf CM, Sen TZ. Co-expression pan-network reveals genes involved in complex traits within maize pan-genome. BMC PLANT BIOLOGY 2022; 22:595. [PMID: 36529716 PMCID: PMC9762053 DOI: 10.1186/s12870-022-03985-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND With the advances in the high throughput next generation sequencing technologies, genome-wide association studies (GWAS) have identified a large set of variants associated with complex phenotypic traits at a very fine scale. Despite the progress in GWAS, identification of genotype-phenotype relationship remains challenging in maize due to its nature with dozens of variants controlling the same trait. As the causal variations results in the change in expression, gene expression analyses carry a pivotal role in unraveling the transcriptional regulatory mechanisms behind the phenotypes. RESULTS To address these challenges, we incorporated the gene expression and GWAS-driven traits to extend the knowledge of genotype-phenotype relationships and transcriptional regulatory mechanisms behind the phenotypes. We constructed a large collection of gene co-expression networks and identified more than 2 million co-expressing gene pairs in the GWAS-driven pan-network which contains all the gene-pairs in individual genomes of the nested association mapping (NAM) population. We defined four sub-categories for the pan-network: (1) core-network contains the highest represented ~ 1% of the gene-pairs, (2) near-core network contains the next highest represented 1-5% of the gene-pairs, (3) private-network contains ~ 50% of the gene pairs that are unique to individual genomes, and (4) the dispensable-network contains the remaining 50-95% of the gene-pairs in the maize pan-genome. Strikingly, the private-network contained almost all the genes in the pan-network but lacked half of the interactions. We performed gene ontology (GO) enrichment analysis for the pan-, core-, and private- networks and compared the contributions of variants overlapping with genes and promoters to the GWAS-driven pan-network. CONCLUSIONS Gene co-expression networks revealed meaningful information about groups of co-regulated genes that play a central role in regulatory processes. Pan-network approach enabled us to visualize the global view of the gene regulatory network for the studied system that could not be well inferred by the core-network alone.
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Affiliation(s)
- H Busra Cagirici
- US Department of Agriculture - Agricultural Research Service, Crop Improvement Genetics Research Unit, Western Regional Research Center, 800 Buchanan St, Albany, CA, 94710, USA
| | - Carson M Andorf
- US Department of Agriculture - Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA, 50011, USA.
- Department of Computer Science, Iowa State University, Ames, IA, 50011, USA.
| | - Taner Z Sen
- US Department of Agriculture - Agricultural Research Service, Crop Improvement Genetics Research Unit, Western Regional Research Center, 800 Buchanan St, Albany, CA, 94710, USA.
- Department of Bioengineering, University of California, Berkeley, CA, 94720, USA.
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16
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Khatun M, Monir MM, Lou X, Zhu J, Xu H. Genome-wide association studies revealed complex genetic architecture and breeding perspective of maize ear traits. BMC PLANT BIOLOGY 2022; 22:537. [PMID: 36397013 PMCID: PMC9673299 DOI: 10.1186/s12870-022-03913-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Maize (Zea Mays) is one of the world's most important crops. Hybrid maize lines resulted a major improvement in corn production in the previous and current centuries. Understanding the genetic mechanisms of the corn production associated traits greatly facilitate the development of superior hybrid varieties. RESULT In this study, four ear traits associated with corn production of Nested Association Mapping (NAM) population were analyzed using a full genetic model, and further, optimal genotype combinations and total genetic effects of current best lines, superior lines, and superior hybrids were predicted for each of the traits at four different locations. The analysis identified 21-34 highly significant SNPs (-log10P > 5), with an estimated total heritability of 37.31-62.34%, while large contributions to variations was due to dominance, dominance-related epistasis, and environmental interaction effects ([Formula: see text] 14.06% ~ 49.28%), indicating these factors contributed significantly to phenotypic variations of the ear traits. Environment-specific genetic effects were also discovered to be crucial for maize ear traits. There were four SNPs found for three ear traits: two for ear length and weight, and two for ear row number and length. Using the Enumeration method and the stepwise tuning technique, optimum multi-locus genotype combinations for superior lines were identified based on the information obtained from GWAS. CONCLUSIONS Predictions of genetic breeding values showed that different genotype combinations in different geographical regions may be better, and hybrid-line variety breeding with homozygote and heterozygote genotype combinations may have a greater potential to improve ear traits.
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Affiliation(s)
- Mita Khatun
- Institute of Crop Science and Institute of Bioinformatics, Zhejiang University, Hangzhou, 310058, China
| | - Md Mamun Monir
- Institute of Crop Science and Institute of Bioinformatics, Zhejiang University, Hangzhou, 310058, China
| | - Xiangyang Lou
- Department of Biostatistics, University of Florida, Gainesville, FL, 32611, USA
| | - Jun Zhu
- Institute of Crop Science and Institute of Bioinformatics, Zhejiang University, Hangzhou, 310058, China
| | - Haiming Xu
- Institute of Crop Science and Institute of Bioinformatics, Zhejiang University, Hangzhou, 310058, China.
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17
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Guche MD, Pilati S, Trenti F, Dalla Costa L, Giorni P, Guella G, Marocco A, Lanubile A. Functional Study of Lipoxygenase-Mediated Resistance against Fusarium verticillioides and Aspergillus flavus Infection in Maize. Int J Mol Sci 2022; 23:ijms231810894. [PMID: 36142806 PMCID: PMC9503958 DOI: 10.3390/ijms231810894] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/05/2022] [Accepted: 09/15/2022] [Indexed: 11/28/2022] Open
Abstract
Mycotoxin contamination of maize kernels by fungal pathogens like Fusarium verticillioides and Aspergillus flavus is a chronic global challenge impacting food and feed security, health, and trade. Maize lipoxygenase genes (ZmLOXs) synthetize oxylipins that play defense roles and govern host-fungal interactions. The current study investigated the involvement of ZmLOXs in maize resistance against these two fungi. A considerable intraspecific genetic and transcript variability of the ZmLOX family was highlighted by in silico analysis comparing publicly available maize pan-genomes and pan-transcriptomes, respectively. Then, phenotyping and expression analysis of ZmLOX genes along with key genes involved in oxylipin biosynthesis were carried out in a maize mutant carrying a Mu transposon insertion in the ZmLOX4 gene (named UFMulox4) together with Tzi18, Mo17, and W22 inbred lines at 3- and 7-days post-inoculation with F. verticillioides and A. flavus. Tzi18 showed the highest resistance to the pathogens coupled with the lowest mycotoxin accumulation, while UFMulox4 was highly susceptible to both pathogens with the most elevated mycotoxin content. F. verticillioides inoculation determined a stronger induction of ZmLOXs and maize allene oxide synthase genes as compared to A. flavus. Additionally, oxylipin analysis revealed prevalent linoleic (18:2) peroxidation by 9-LOXs, the accumulation of 10-oxo-11-phytoenoic acid (10-OPEA), and triglyceride peroxidation only in F. verticillioides inoculated kernels of resistant genotypes.
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Affiliation(s)
- Mikias Damtew Guche
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
- C3A—Centro Agricoltura Alimenti Ambiente, Via Edmund Mach 1, 38098 San Michele all’Adige, Italy
- Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, 38098 San Michele all’Adige, Italy
| | - Stefania Pilati
- Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, 38098 San Michele all’Adige, Italy
| | - Francesco Trenti
- Department of Physics, University of Trento, Via Sommarive 14, 38123 Povo, Italy
| | - Lorenza Dalla Costa
- Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, 38098 San Michele all’Adige, Italy
| | - Paola Giorni
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
| | - Graziano Guella
- Department of Physics, University of Trento, Via Sommarive 14, 38123 Povo, Italy
| | - Adriano Marocco
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
| | - Alessandra Lanubile
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
- Correspondence: ; Tel.: +39-0523-599206
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18
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Fei X, Wang Y, Zheng Y, Shen X, E L, Ding J, Lai J, Song W, Zhao H. Identification of two new QTLs of maize (Zea mays L.) underlying kernel row number using the HNAU-NAM1 population. BMC Genomics 2022; 23:593. [PMID: 35971070 PMCID: PMC9380338 DOI: 10.1186/s12864-022-08793-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 07/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Maize kernel row number (KRN) is one of the most important yield traits and has changed greatly during maize domestication and selection. Elucidating the genetic basis of KRN will be helpful to improve grain yield in maize. RESULTS Here, we measured KRN in four environments using a nested association mapping (NAM) population named HNAU-NAM1 with 1,617 recombinant inbred lines (RILs) that were derived from 12 maize inbred lines with a common parent, GEMS41. Then, five consensus quantitative trait loci (QTLs) distributing on four chromosomes were identified in at least three environments along with the best linear unbiased prediction (BLUP) values by the joint linkage mapping (JLM) method. These QTLs were further validated by the separate linkage mapping (SLM) and genome-wide association study (GWAS) methods. Three KRN genes cloned through the QTL assay were found in three of the five consensus QTLs, including qKRN1.1, qKRN2.1 and qKRN4.1. Two new QTLs of KRN, qKRN4.2 and qKRN9.1, were also identified. On the basis of public RNA-seq and genome annotation data, five genes highly expressed in ear tissue were considered candidate genes contributing to KRN. CONCLUSIONS This study carried out a comprehensive analysis of the genetic architecture of KRN by using a new NAM population under multiple environments. The present results provide solid information for understanding the genetic components underlying KRN and candidate genes in qKRN4.2 and qKRN9.1. Single-nucleotide polymorphisms (SNPs) closely linked to qKRN4.2 and qKRN9.1 could be used to improve inbred yield during molecular breeding in maize.
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Affiliation(s)
- Xiaohong Fei
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China.,Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China.,Longping Agriculture Science Co. Ltd, Beijing, 100004, People's Republic of China
| | - Yifei Wang
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China.,Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China
| | - Yunxiao Zheng
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China.,Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China
| | - Xiaomeng Shen
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China.,Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China
| | - Lizhu E
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China.,Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China
| | - Junqiang Ding
- State Key Laboratory of Wheat and Maize Crop Science and Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou, 450046, People's Republic of China
| | - Jinsheng Lai
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China.,Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China
| | - Weibin Song
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China. .,Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China.
| | - Haiming Zhao
- State Key Laboratory of Plant Physiology and Biochemistry, China Agricultural University, Beijing, 100193, People's Republic of China. .,Department of Plant Genetics and Breeding, National Maize Improvement Center, China Agricultural University, Beijing, 100193, People's Republic of China.
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19
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Yin X, Bi Y, Jiang F, Guo R, Zhang Y, Fan J, Kang MS, Fan X. Fine mapping of candidate quantitative trait loci for plant and ear height in a maize nested-association mapping population. FRONTIERS IN PLANT SCIENCE 2022; 13:963985. [PMID: 35991429 PMCID: PMC9386523 DOI: 10.3389/fpls.2022.963985] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 07/05/2022] [Indexed: 05/31/2023]
Abstract
Plant height (PH) and ear height (EH) are two important traits in maize (Zea mays L.), as they are closely related to lodging resistance and planting density. Our objectives were to (1) investigate single-nucleotide polymorphisms (SNPs) that are associated with PH and EH for detecting quantitative trait loci (QTL) and new gene that determines PH and EH, (2) explore the value of the QTL in maize breeding, and (3) investigate whether the "triangle heterotic group" theory is applicable for lowering PH and EH to increase yield. Seven inbred female parents were crossed with a common founder male parent Ye 107 to create a nested association mapping (NAM) population. The analysis of phenotypic data on PH and EH revealed wide variation among the parents of the NAM population. Genome-wide association study (GWAS) and high-resolution linkage mapping were conducted using the NAM population, which generated 264,694 SNPs by genotyping-by-sequencing. A total of 105 SNPs and 22 QTL were identified by GWAS and found to be significantly associated with PH and EH. A high-confidence QTL for PH, Qtl-chr1-EP, was identified on chromosome 1 via GWAS and confirmed by linkage analysis in two recombinant inbred line (RIL) populations. Results revealed that the SNP variation in the promoter region of the candidate gene Zm00001d031938, located at Qtl-chr1-EP, which encoded UDP-N-acetylglucosamine-peptide N-acetyl-glucosaminyl-transferase, might decrease PH and EH. Furthermore, the triangle heterotic pattern adopted in maize breeding programs by our team is practicable in selecting high-yield crosses based on the low ratio of EH/PH (EP).
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Affiliation(s)
- Xingfu Yin
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Yaqi Bi
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Fuyan Jiang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Ruijia Guo
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Yudong Zhang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Jun Fan
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Manjit S. Kang
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Xingming Fan
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming, China
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20
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Bheemanahalli R, Ramamoorthy P, Poudel S, Samiappan S, Wijewardane N, Reddy KR. Effects of drought and heat stresses during reproductive stage on pollen germination, yield, and leaf reflectance properties in maize ( Zea mays L.). PLANT DIRECT 2022; 6:e434. [PMID: 35959217 PMCID: PMC9360560 DOI: 10.1002/pld3.434] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/28/2022] [Accepted: 07/19/2022] [Indexed: 05/24/2023]
Abstract
Drought and heat stresses are the major abiotic stress factors detrimental to maize (Zea mays L.) production. Much attention has been directed toward plant responses to heat or drought stress. However, maize reproductive stage responses to combined heat and drought remain less explored. Therefore, this study aimed to quantify the impact of optimum daytime (30°C, control) and warmer daytime temperatures (35°C, heat stress) on pollen germination, morpho-physiology, and yield potential using two maize genotypes ("Mo17" and "B73") under contrasting soil moisture content, that is, 100% and 40% irrigation during flowering. Pollen germination of both genotypes decreased under combined stresses (42%), followed by heat stress (30%) and drought stress (19%). Stomatal conductance and transpiration were comparable between control and heat stress but significantly decreased under combined stresses (83% and 72%) and drought stress (52% and 47%) compared with the control. Genotype "Mo17" reduced its green leaf area to minimize the water loss, which appears to be one of the adaptive strategies of "Mo17" under stress conditions. The leaf reflectance of both genotypes varied across treatments. Vegetation indices associated with pigments (chlorophyll index of green, chlorophyll index of red edge, and carotenoid index) and plant health (normalized difference red-edge index) were found to be highly sensitive to drought and combined stressors than heat stress. Combined drought and heat stresses caused a significant reduction in yield and yield components in both Mo17 (49%) and B73 (86%) genotypes. The harvest index of genotype "B73" was extremely low, indicating poor partitioning efficiency. At least when it comes to "B73," the cause of yield reduction appears to be the result of reduced sink number rather than the pollen and source size. To the best of our awareness, this is the first study that showed how the leaf-level spectra, yield, and quality parameters respond to the short duration of independent and combined stresses during flowering in inbred maize. Further studies are required to validate the responses of potential traits involving diverse maize genotypes under field conditions. This study suggests the need to develop maize with improved tolerance to combined stresses to sustain production under increasing temperatures and low rainfall conditions.
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Affiliation(s)
- Raju Bheemanahalli
- Department of Plant and Soil SciencesMississippi State UniversityMississippi StateMSUSA
| | | | - Sadikshya Poudel
- Department of Plant and Soil SciencesMississippi State UniversityMississippi StateMSUSA
| | | | - Nuwan Wijewardane
- Department of Agricultural & Biological EngineeringMississippi State UniversityMississippi StateMSUSA
| | - K. Raja Reddy
- Department of Plant and Soil SciencesMississippi State UniversityMississippi StateMSUSA
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21
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Renzi JP, Coyne CJ, Berger J, von Wettberg E, Nelson M, Ureta S, Hernández F, Smýkal P, Brus J. How Could the Use of Crop Wild Relatives in Breeding Increase the Adaptation of Crops to Marginal Environments? FRONTIERS IN PLANT SCIENCE 2022; 13:886162. [PMID: 35783966 PMCID: PMC9243378 DOI: 10.3389/fpls.2022.886162] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/11/2022] [Indexed: 06/01/2023]
Abstract
Alongside the use of fertilizer and chemical control of weeds, pests, and diseases modern breeding has been very successful in generating cultivars that have increased agricultural production several fold in favorable environments. These typically homogeneous cultivars (either homozygous inbreds or hybrids derived from inbred parents) are bred under optimal field conditions and perform well when there is sufficient water and nutrients. However, such optimal conditions are rare globally; indeed, a large proportion of arable land could be considered marginal for agricultural production. Marginal agricultural land typically has poor fertility and/or shallow soil depth, is subject to soil erosion, and often occurs in semi-arid or saline environments. Moreover, these marginal environments are expected to expand with ongoing climate change and progressive degradation of soil and water resources globally. Crop wild relatives (CWRs), most often used in breeding as sources of biotic resistance, often also possess traits adapting them to marginal environments. Wild progenitors have been selected over the course of their evolutionary history to maintain their fitness under a diverse range of stresses. Conversely, modern breeding for broad adaptation has reduced genetic diversity and increased genetic vulnerability to biotic and abiotic challenges. There is potential to exploit genetic heterogeneity, as opposed to genetic uniformity, in breeding for the utilization of marginal lands. This review discusses the adaptive traits that could improve the performance of cultivars in marginal environments and breeding strategies to deploy them.
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Affiliation(s)
- Juan Pablo Renzi
- Instituto Nacional de Tecnología Agropecuaria, Hilario Ascasubi, Argentina
- CERZOS, Departamento de Agronomía, Universidad Nacional del Sur (CONICET), Bahía Blanca, Argentina
| | | | - Jens Berger
- Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation, Wembley, WA, Australia
| | - Eric von Wettberg
- Department of Plant and Soil Science, Gund Institute for Environment, University of Vermont, Burlington, VT, United States
- Department of Applied Mathematics, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia
| | - Matthew Nelson
- Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation, Wembley, WA, Australia
- The UWA Institute of Agriculture, University of Western Australia, Crawley, WA, Australia
| | - Soledad Ureta
- CERZOS, Departamento de Agronomía, Universidad Nacional del Sur (CONICET), Bahía Blanca, Argentina
| | - Fernando Hernández
- CERZOS, Departamento de Agronomía, Universidad Nacional del Sur (CONICET), Bahía Blanca, Argentina
| | - Petr Smýkal
- Department of Botany, Faculty of Science, Palacký University, Olomouc, Czechia
| | - Jan Brus
- Department of Geoinformatics, Faculty of Sciences, Palacký University, Olomouc, Czechia
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22
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Thomson MJ, Biswas S, Tsakirpaloglou N, Septiningsih EM. Functional Allele Validation by Gene Editing to Leverage the Wealth of Genetic Resources for Crop Improvement. Int J Mol Sci 2022; 23:ijms23126565. [PMID: 35743007 PMCID: PMC9223900 DOI: 10.3390/ijms23126565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/09/2022] [Accepted: 06/10/2022] [Indexed: 02/05/2023] Open
Abstract
Advances in molecular technologies over the past few decades, such as high-throughput DNA marker genotyping, have provided more powerful plant breeding approaches, including marker-assisted selection and genomic selection. At the same time, massive investments in plant genetics and genomics, led by whole genome sequencing, have led to greater knowledge of genes and genetic pathways across plant genomes. However, there remains a gap between approaches focused on forward genetics, which start with a phenotype to map a mutant locus or QTL with the goal of cloning the causal gene, and approaches using reverse genetics, which start with large-scale sequence data and work back to the gene function. The recent establishment of efficient CRISPR-Cas-based gene editing promises to bridge this gap and provide a rapid method to functionally validate genes and alleles identified through studies of natural variation. CRISPR-Cas techniques can be used to knock out single or multiple genes, precisely modify genes through base and prime editing, and replace alleles. Moreover, technologies such as protoplast isolation, in planta transformation, and the use of developmental regulatory genes promise to enable high-throughput gene editing to accelerate crop improvement.
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23
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Gage JL, Mali S, McLoughlin F, Khaipho-Burch M, Monier B, Bailey-Serres J, Vierstra RD, Buckler ES. Variation in upstream open reading frames contributes to allelic diversity in maize protein abundance. Proc Natl Acad Sci U S A 2022; 119:e2112516119. [PMID: 35349347 PMCID: PMC9169109 DOI: 10.1073/pnas.2112516119] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 02/22/2022] [Indexed: 11/18/2022] Open
Abstract
SignificanceProteins are the machinery which execute essential cellular functions. However, measuring their abundance within an organism can be difficult and resource-intensive. Cells use a variety of mechanisms to control protein synthesis from mRNA, including short open reading frames (uORFs) that lie upstream of the main coding sequence. Ribosomes can preferentially translate uORFs instead of the main coding sequence, leading to reduced translation of the main protein. In this study, we show that uORF sequence variation between individuals can lead to different rates of protein translation and thus variable protein abundances. We also demonstrate that natural variation in uORFs occurs frequently and can be linked to whole-plant phenotypes, indicating that uORF sequence variation likely contributes to plant adaptation.
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Affiliation(s)
- Joseph L. Gage
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695
| | - Sujina Mali
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130
| | - Fionn McLoughlin
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130
| | - Merritt Khaipho-Burch
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Brandon Monier
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853
| | - Julia Bailey-Serres
- Department of Botany and Plant Sciences, Center for Plant Cell Biology, University of California, Riverside, CA 92521
| | - Richard D. Vierstra
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130
| | - Edward S. Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
- Agricultural Research Service, US Department of Agriculture, Ithaca, NY 14853
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24
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Zhao S, Li X, Song J, Li H, Zhao X, Zhang P, Li Z, Tian Z, Lv M, Deng C, Ai T, Chen G, Zhang H, Hu J, Xu Z, Chen J, Ding J, Song W, Chang Y. Genetic dissection of maize plant architecture using a novel nested association mapping population. THE PLANT GENOME 2022; 15:e20179. [PMID: 34859966 DOI: 10.1002/tpg2.20179] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 10/25/2021] [Indexed: 06/13/2023]
Abstract
The leaf angle (LA), plant height (PH), and ear height (EH) are key plant architectural traits influencing maize (Zea mays L.) yield. However, their genetic determinants have not yet been well-characterized. Here, we developed a maize advanced backcross-nested association mapping population in Henan Agricultural University (HNAU-NAM1) comprised of 1,625 BC1 F4 /BC2 F4 lines. These were obtained by crossing a diverse set of 12 representative inbred lines with the common GEMS41 line, which were then genotyped using the MaizeSNP9.4K array. Genetic diversity and phenotypic distribution analyses showed considerable levels of genetic variation. We obtained 18-88 quantitative trait loci (QTLs) associated with LA, PH, and EH by using three complementary mapping methods, named as separate linkage mapping, joint linkage mapping, and genome-wide association studies. Our analyses enabled the identification of ten QTL hot-spot regions associated with the three traits, which were distributed on nine different chromosomes. We further selected 13 major QTLs that were simultaneously detected by three methods and deduced the candidate genes, of which eight were not reported before. The newly constructed HNAU-NAM1 population in this study will further broaden our insights into understanding of genetic regulation of plant architecture, thus will help to improve maize yield and provide an invaluable resource for maize functional genomics and breeding research.
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Affiliation(s)
- Sheng Zhao
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Xueying Li
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Junfeng Song
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural Univ., Beijing, 100193, China
| | - Huimin Li
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Xiaodi Zhao
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Peng Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
- College of Life Science and Technology, Guangxi Univ., Nanning, 530004, China
| | - Zhimin Li
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Zhiqiang Tian
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Meng Lv
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Ce Deng
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Tangshun Ai
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Gengshen Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural Univ., Wuhan, 430070, China
| | - Hui Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Jianlin Hu
- Food Crops Institute, Hubei Academy of Agricultural Sciences, Wuhan, 430064, China
| | - Zhijun Xu
- Zhanjiang Experiment Station, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, 524013, China
| | - Jiafa Chen
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Junqiang Ding
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural Univ., Zhengzhou, 450002, China
| | - Weibin Song
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural Univ., Beijing, 100193, China
| | - Yuxiao Chang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
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25
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Back to the wild: mining maize (Zea mays L.) disease resistance using advanced breeding tools. Mol Biol Rep 2022; 49:5787-5803. [PMID: 35064401 DOI: 10.1007/s11033-021-06815-x] [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: 05/28/2021] [Accepted: 10/06/2021] [Indexed: 10/19/2022]
Abstract
Cultivated modern maize (Zea mays L.) originated through the continuous process of domestication from its wild progenitors. Today, maize is considered as the most important cereal crop which is extensively cultivated in all parts of the world. Maize shows remarkable genotypic and phenotypic diversity which makes it an ideal model species for crop genetic research. However, intensive breeding and artificial selection of desired agronomic traits greatly narrow down the genetic bases of maize. This reduction in genetic diversity among cultivated maize led to increase the chance of more attack of biotic stress as climate changes hampering the maize grain production globally. Maize germplasm requires to integrate both durable multiple-diseases and multiple insect-pathogen resistance through tapping the unexplored resources of maize landraces. Revisiting the landraces seed banks will provide effective opportunities to transfer the resistant genes into the modern cultivars. Here, we describe the maize domestication process and discuss the unique genes from wild progenitors which potentially can be utilized for disease resistant in maize. We also focus on the genetics and disease resistance mechanism of various genes against maize biotic stresses and then considered the different molecular breeding tools for gene transfer and advanced high resolution mapping for gene pyramiding in maize lines. At last, we provide an insight for targeting identified key genes through CRISPR/Cas9 genome editing system to enhance the maize resilience towards biotic stress.
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26
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Ebersbach J, Khan NA, McQuillan I, Higgins EE, Horner K, Bandi V, Gutwin C, Vail SL, Robinson SJ, Parkin IAP. Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population. FRONTIERS IN PLANT SCIENCE 2022; 12:780250. [PMID: 35069637 PMCID: PMC8767643 DOI: 10.3389/fpls.2021.780250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.
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Affiliation(s)
| | - Nazifa Azam Khan
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ian McQuillan
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | | | - Kyla Horner
- Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
| | - Venkat Bandi
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Carl Gutwin
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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27
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Andersen EC, Rockman MV. Natural genetic variation as a tool for discovery in Caenorhabditis nematodes. Genetics 2022; 220:iyab156. [PMID: 35134197 PMCID: PMC8733454 DOI: 10.1093/genetics/iyab156] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 09/11/2021] [Indexed: 11/12/2022] Open
Abstract
Over the last 20 years, studies of Caenorhabditis elegans natural diversity have demonstrated the power of quantitative genetic approaches to reveal the evolutionary, ecological, and genetic factors that shape traits. These studies complement the use of the laboratory-adapted strain N2 and enable additional discoveries not possible using only one genetic background. In this chapter, we describe how to perform quantitative genetic studies in Caenorhabditis, with an emphasis on C. elegans. These approaches use correlations between genotype and phenotype across populations of genetically diverse individuals to discover the genetic causes of phenotypic variation. We present methods that use linkage, near-isogenic lines, association, and bulk-segregant mapping, and we describe the advantages and disadvantages of each approach. The power of C. elegans quantitative genetic mapping is best shown in the ability to connect phenotypic differences to specific genes and variants. We will present methods to narrow genomic regions to candidate genes and then tests to identify the gene or variant involved in a quantitative trait. The same features that make C. elegans a preeminent experimental model animal contribute to its exceptional value as a tool to understand natural phenotypic variation.
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Affiliation(s)
- Erik C Andersen
- Department of Molecular Biosciences, Northwestern University, Evanston, IL 60201, USA
| | - Matthew V Rockman
- Department of Biology and Center for Genomics & Systems Biology, New York University, New York, NY 10003, USA
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28
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Xin Z, Wang M, Cuevas HE, Chen J, Harrison M, Pugh NA, Morris G. Sorghum genetic, genomic, and breeding resources. PLANTA 2021; 254:114. [PMID: 34739592 PMCID: PMC8571242 DOI: 10.1007/s00425-021-03742-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 09/28/2021] [Indexed: 05/24/2023]
Abstract
Sorghum research has entered an exciting and fruitful era due to the genetic, genomic, and breeding resources that are now available to researchers and plant breeders. As the world faces the challenges of a rising population and a changing global climate, new agricultural solutions will need to be developed to address the food and fiber needs of the future. To that end, sorghum will be an invaluable crop species as it is a stress-resistant C4 plant that is well adapted for semi-arid and arid regions. Sorghum has already remained as a staple food crop in many parts of Africa and Asia and is critically important for animal feed and niche culinary applications in other regions, such as the United States. In addition, sorghum has begun to be developed into a promising feedstock for forage and bioenergy production. Due to this increasing demand for sorghum and its potential to address these needs, the continuous development of powerful community resources is required. These resources include vast collections of sorghum germplasm, high-quality reference genome sequences, sorghum association panels for genome-wide association studies of traits involved in food and bioenergy production, mutant populations for rapid discovery of causative genes for phenotypes relevant to sorghum improvement, gene expression atlas, and online databases that integrate all resources and provide the sorghum community with tools that can be used in breeding and genomic studies. Used in tandem, these valuable resources will ensure that the rate, quality, and collaborative potential of ongoing sorghum improvement efforts is able to rival that of other major crops.
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Affiliation(s)
- Zhanguo Xin
- Plant Stress and Germplasm Development Unit, Crop Systems Research Laboratory, USDA-ARS, 3810, 4th Street, Lubbock, TX, 79424, USA.
| | - Mingli Wang
- Plant Genetic Resources Conservation Unit, USDA-ARS, Griffin, GA, 30223, USA
| | - Hugo E Cuevas
- Tropical Agriculture Research Station, USDA-ARS, Mayagüez, 00680, Puerto Rico
| | - Junping Chen
- Plant Stress and Germplasm Development Unit, Crop Systems Research Laboratory, USDA-ARS, 3810, 4th Street, Lubbock, TX, 79424, USA
| | - Melanie Harrison
- Plant Genetic Resources Conservation Unit, USDA-ARS, Griffin, GA, 30223, USA
| | - N Ace Pugh
- Plant Stress and Germplasm Development Unit, Crop Systems Research Laboratory, USDA-ARS, 3810, 4th Street, Lubbock, TX, 79424, USA
| | - Geoffrey Morris
- Crop Quantitative Genomics, Soil and Crop Sciences, Colorado State University, Plant Sciences Building, Fort Collins, CO, 80523, USA
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29
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Tonnessen BW, Bossa-Castro AM, Martin F, Leach JE. Intergenic spaces: a new frontier to improving plant health. THE NEW PHYTOLOGIST 2021; 232:1540-1548. [PMID: 34478160 DOI: 10.1111/nph.17706] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 08/09/2021] [Indexed: 06/13/2023]
Abstract
To more sustainably mitigate the impact of crop diseases on plant health and productivity, there is a need for broader spectrum, long-lasting resistance traits. Defense response (DR) genes, located throughout the genome, participate in cellular and system-wide defense mechanisms to stave off infection by diverse pathogens. This multigenic resistance avoids rapid evolution of a pathogen to overcome host resistance. DR genes reside within resistance-associated quantitative trait loci (QTL), and alleles of DR genes in resistant varieties are more active during pathogen attack relative to susceptible haplotypes. Differential expression of DR genes results from polymorphisms in their regulatory regions, that includes cis-regulatory elements such as transcription factor binding sites as well as features that influence epigenetic structural changes to modulate chromatin accessibility during infection. Many of these elements are found in clusters, known as cis-regulatory modules (CRMs), which are distributed throughout the host genome. Regulatory regions involved in plant-pathogen interactions may also contain pathogen effector binding elements that regulate DR gene expression, and that, when mutated, result in a change in the plants' response. We posit that CRMs and the multiple regulatory elements that comprise them are potential targets for marker-assisted breeding for broad-spectrum, durable disease resistance.
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Affiliation(s)
- Bradley W Tonnessen
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, 80523, USA
- Western Colorado Research Center, Colorado State University, 30624 Hwy 92, Hotchkiss, CO, 81419, USA
| | - Ana M Bossa-Castro
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, 80523, USA
- Universidad de los Andes, Bogotá, 111711, Colombia
| | - Federico Martin
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, 80523, USA
| | - Jan E Leach
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, 80523, USA
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30
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Haplotype associated RNA expression (HARE) improves prediction of complex traits in maize. PLoS Genet 2021; 17:e1009568. [PMID: 34606492 PMCID: PMC8516254 DOI: 10.1371/journal.pgen.1009568] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 10/14/2021] [Accepted: 09/07/2021] [Indexed: 11/19/2022] Open
Abstract
Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for predicting many complex traits. However, it usually cannot capture the combination of alleles in haplotypes and it has generated little insight about the biological function of polymorphisms. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism. Therefore, HARE estimates were more transferrable across different tissues and populations compared to measured transcript expression. We also showed that HARE estimates captured one-third of the variation in gene expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels–a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel)–for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues, whereas accuracy by HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations. Genomic marker data is widely used in the prediction of many traits. However, prediction has been primarily carried out within populations and without explicit modeling of RNA or protein expression. In this study, we explored the prediction of field traits within and across populations using estimated RNA expression attributable to only the DNA sequence around a gene. We showed that the estimated RNA expression was more transferable across populations and tissues than measured RNA expression. We improved prediction of field traits up to 15% using estimated gene expression as compared to observed expression or gene sequence alone. Overall, these findings indicate that structural and functional information in the gene sequence is highly transferable.
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31
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Zhang J, Zhang D, Fan Y, Li C, Xu P, Li W, Sun Q, Huang X, Zhang C, Wu L, Yang H, Wang S, Su X, Li X, Song Y, Wu ME, Lian X, Li Y. The identification of grain size genes by RapMap reveals directional selection during rice domestication. Nat Commun 2021; 12:5673. [PMID: 34584089 PMCID: PMC8478914 DOI: 10.1038/s41467-021-25961-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 09/10/2021] [Indexed: 11/09/2022] Open
Abstract
Cloning quantitative trait locus (QTL) is time consuming and laborious, which hinders the understanding of natural variation and genetic diversity. Here, we introduce RapMap, a method for rapid multi-QTL mapping by employing F2 gradient populations (F2GPs) constructed by minor-phenotypic-difference accessions. The co-segregation standard of the single-locus genetic models ensures simultaneous integration of a three-in-one framework in RapMap i.e. detecting a real QTL, confirming its effect, and obtaining its near-isogenic line-like line (NIL-LL). We demonstrate the feasibility of RapMap by cloning eight rice grain-size genes using 15 F2GPs in three years. These genes explain a total of 75% of grain shape variation. Allele frequency analysis of these genes using a large germplasm collection reveals directional selection of the slender and long grains in indica rice domestication. In addition, major grain-size genes have been strongly selected during rice domestication. We think application of RapMap in crops will accelerate gene discovery and genomic breeding.
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Affiliation(s)
- Juncheng Zhang
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Dejian Zhang
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Yawei Fan
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Cuicui Li
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Pengkun Xu
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Wei Li
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Qi Sun
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Xiaodong Huang
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Chunyu Zhang
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Linyue Wu
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Huaizhou Yang
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Shiyu Wang
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Xiaomin Su
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Xingxing Li
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Yingying Song
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Meng-En Wu
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Xingming Lian
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
| | - Yibo Li
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China.
- Hubei Hongshan Laboratory, Wuhan, China.
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32
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Development of an Aus-Derived Nested Association Mapping ( Aus-NAM) Population in Rice. PLANTS 2021; 10:plants10061255. [PMID: 34205511 PMCID: PMC8234321 DOI: 10.3390/plants10061255] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 06/07/2021] [Accepted: 06/15/2021] [Indexed: 12/30/2022]
Abstract
A genetic resource for studying genetic architecture of agronomic traits and environmental adaptation is essential for crop improvements. Here, we report the development of a rice nested association mapping population (aus-NAM) using 7 aus varieties as diversity donors and T65 as the common parent. Aus-NAM showed broad phenotypic variations. To test whether aus-NAM was useful for quantitative trait loci (QTL) mapping, known flowering genes (Ehd1, Hd1, and Ghd7) in rice were characterized using single-family QTL mapping, joint QTL mapping, and the methods based on genome-wide association study (GWAS). Ehd1 was detected in all the seven families and all the methods. On the other hand, Hd1 and Ghd7 were detected in some families, and joint QTL mapping and GWAS-based methods resulted in weaker and uncertain peaks. Overall, the high allelic variations in aus-NAM provide a valuable genetic resource for the rice community.
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33
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Mural RV, Grzybowski M, Miao C, Damke A, Sapkota S, Boyles RE, Salas Fernandez MG, Schnable PS, Sigmon B, Kresovich S, Schnable JC. Meta-Analysis Identifies Pleiotropic Loci Controlling Phenotypic Trade-offs in Sorghum. Genetics 2021; 218:6294935. [PMID: 34100945 PMCID: PMC9335936 DOI: 10.1093/genetics/iyab087] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 06/07/2021] [Indexed: 01/03/2023] Open
Abstract
Community association populations are composed of phenotypically and genetically diverse accessions. Once these populations are genotyped, the resulting marker data can be reused by different groups investigating the genetic basis of different traits. Because the same genotypes are observed and scored for a wide range of traits in different environments, these populations represent a unique resource to investigate pleiotropy. Here we assembled a set of 234 separate trait datasets for the Sorghum Association Panel, a group of 406 sorghum genotypes widely employed by the sorghum genetics community. Comparison of genome wide association studies conducted with two independently generated marker sets for this population demonstrate that existing genetic marker sets do not saturate the genome and likely capture only 35-43% of potentially detectable loci controlling variation for traits scored in this population. While limited evidence for pleiotropy was apparent in cross-GWAS comparisons, a multivariate adaptive shrinkage approach recovered both known pleiotropic effects of existing loci and new pleiotropic effects, particularly significant impacts of known dwarfing genes on root architecture. In addition, we identified new loci with pleiotropic effects consistent with known trade-offs in sorghum development. These results demonstrate the potential for mining existing trait datasets from widely used community association populations to enable new discoveries from existing trait datasets as new, denser genetic marker datasets are generated for existing community association populations.
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Affiliation(s)
- Ravi V Mural
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588 USA
| | - Marcin Grzybowski
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588 USA
| | - Chenyong Miao
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588 USA
| | - Alyssa Damke
- Department of Plant Pathology, University of Nebraska-Lincoln, Lincoln, NE 68588 USA
| | - Sirjan Sapkota
- Advanced Plant Technology Program, Clemson University, Clemson, SC 29634 USA.,Department of Plant and Environment Sciences, Clemson University, Clemson, SC 29634 USA
| | - Richard E Boyles
- Department of Plant and Environment Sciences, Clemson University, Clemson, SC 29634 USA.,Pee Dee Research and Education Center, Clemson University, Florence, SC 29532 USA
| | | | | | - Brandi Sigmon
- Department of Plant Pathology, University of Nebraska-Lincoln, Lincoln, NE 68588 USA
| | - Stephen Kresovich
- Department of Plant and Environment Sciences, Clemson University, Clemson, SC 29634 USA.,Feed the Future Innovation Lab for Crop Improvement Cornell University, Ithaca, NY 14850 USA
| | - James C Schnable
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68588 USA
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Eggels S, Blankenagel S, Schön CC, Avramova V. The carbon isotopic signature of C 4 crops and its applicability in breeding for climate resilience. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1663-1675. [PMID: 33575820 PMCID: PMC8205923 DOI: 10.1007/s00122-020-03761-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Accepted: 12/30/2020] [Indexed: 05/04/2023]
Abstract
KEY MESSAGE Carbon isotope discrimination is a promising trait for indirect screening for improved water use efficiency of C4 crops. In the context of a changing climate, drought is one of the major factors limiting plant growth and yield. Hence, breeding efforts are directed toward improving water use efficiency (WUE) as a key factor in climate resilience and sustainability of crop production. As WUE is a complex trait and its evaluation is rather resource consuming, proxy traits, which are easier to screen and reliably reflect variation in WUE, are needed. In C3 crops, a trait established to be indicative for WUE is the carbon isotopic composition (δ13C) of plant material, which reflects the preferential assimilation of the lighter carbon isotope 12C over 13C during photosynthesis. In C4 crops, carbon fixation is more complex and δ13C thus depends on many more factors than in C3 crops. Recent physiological and genetic studies indicate a correlation between δ13C and WUE also in C4 crops, as well as a colocalization of quantitative trait loci for the two traits. Moreover, significant intraspecific variation as well as a medium to high heritability of δ13C has been shown in some of the main C4 crops, such as maize, sorghum and sugarcane, indicating its potential for indirect selection and breeding. Further research on physiological, genetic and environmental components influencing δ13C is needed to support its application in improving WUE and making C4 crops resilient to climate change.
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Affiliation(s)
- Stella Eggels
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, Liesel-Beckmann-Straße 2, 85354, Freising, Germany
| | - Sonja Blankenagel
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, Liesel-Beckmann-Straße 2, 85354, Freising, Germany
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, Liesel-Beckmann-Straße 2, 85354, Freising, Germany
| | - Viktoriya Avramova
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, Liesel-Beckmann-Straße 2, 85354, Freising, Germany.
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DArTseq-Based High-Throughput SilicoDArT and SNP Markers Applied for Association Mapping of Genes Related to Maize Morphology. Int J Mol Sci 2021; 22:ijms22115840. [PMID: 34072515 PMCID: PMC8198497 DOI: 10.3390/ijms22115840] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/23/2021] [Accepted: 05/26/2021] [Indexed: 01/30/2023] Open
Abstract
Today, agricultural productivity is essential to meet the needs of a growing population, and is also a key tool in coping with climate change. Innovative plant breeding technologies such as molecular markers, phenotyping, genotyping, the CRISPR/Cas method and next-generation sequencing can help agriculture meet the challenges of the 21st century more effectively. Therefore, the aim of the research was to identify single-nucleotide polymorphisms (SNPs) and SilicoDArT markers related to select morphological features determining the yield in maize. The plant material consisted of ninety-four inbred lines of maize of various origins. These lines were phenotyped under field conditions. A total of 14 morphological features was analyzed. The DArTseq method was chosen for genotyping because this technique reduces the complexity of the genome by restriction enzyme digestion. Subsequently, short fragment sequencing was used. The choice of a combination of restrictases allowed the isolation of highly informative low copy fragments of the genome. Thanks to this method, 90% of the obtained DArTseq markers are complementary to the unique sequences of the genome. All the observed features were normally distributed. Analysis of variance indicated that the main effect of lines was statistically significant (p < 0.001) for all 14 traits of study. Thanks to the DArTseq analysis with the use of next-generation sequencing (NGS) in the studied plant material, it was possible to identify 49,911 polymorphisms, of which 33,452 are SilicoDArT markers and the remaining 16,459 are SNP markers. Among those mentioned, two markers associated with four analyzed traits deserved special attention: SNP (4578734) and SilicoDArT (4778900). SNP marker 4578734 was associated with the following features: anthocyanin coloration of cob glumes, number of days from sowing to anthesis, number of days from sowing to silk emergence and anthocyanin coloration of internodes. SilicoDArT marker 4778900 was associated with the following features: number of days from sowing to anthesis, number of days from sowing to silk emergence, tassel: angle between the axis and lateral branches and plant height. Sequences with a length of 71 bp were used for physical mapping. The BLAST and EnsemblPlants databases were searched against the maize genome to identify the positions of both markers. Marker 4578734 was localized on chromosome 7, the closest gene was Zm00001d022467, approximately 55 Kb apart, encoding anthocyanidin 3-O-glucosyltransferase. Marker 4778900 was located on chromosome 7, at a distance of 45 Kb from the gene Zm00001d045261 encoding starch synthase I. The latter observation indicated that these flanking SilicoDArT and SNP markers were not in a state of linkage disequilibrium.
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36
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Rice BR, Lipka AE. Diversifying maize genomic selection models. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:33. [PMID: 37309328 PMCID: PMC10236107 DOI: 10.1007/s11032-021-01221-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 03/07/2021] [Indexed: 06/14/2023]
Abstract
Genomic selection (GS) is one of the most powerful tools available for maize breeding. Its use of genome-wide marker data to estimate breeding values translates to increased genetic gains with fewer breeding cycles. In this review, we cover the history of GS and highlight particular milestones during its adaptation to maize breeding. We discuss how GS can be applied to developing superior maize inbreds and hybrids. Additionally, we characterize refinements in GS models that could enable the encapsulation of non-additive genetic effects, genotype by environment interactions, and multiple levels of the biological hierarchy, all of which could ultimately result in more accurate predictions of breeding values. Finally, we suggest the stages in a maize breeding program where it would be beneficial to apply GS. Given the current sophistication of high-throughput phenotypic, genotypic, and other -omic level data currently available to the maize community, now is the time to explore the implications of their incorporation into GS models and thus ensure that genetic gains are being achieved as quickly and efficiently as possible.
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Affiliation(s)
- Brian R. Rice
- Department of Crop Sciences, University of Illinois, Urbana, IL USA
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37
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Chidzanga C, Fleury D, Baumann U, Mullan D, Watanabe S, Kalambettu P, Pontre R, Edwards J, Forrest K, Wong D, Langridge P, Chalmers K, Garcia M. Development of an Australian Bread Wheat Nested Association Mapping Population, a New Genetic Diversity Resource for Breeding under Dry and Hot Climates. Int J Mol Sci 2021; 22:4348. [PMID: 33919411 PMCID: PMC8122485 DOI: 10.3390/ijms22094348] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/16/2021] [Accepted: 04/20/2021] [Indexed: 12/20/2022] Open
Abstract
Genetic diversity, knowledge of the genetic architecture of the traits of interest and efficient means of transferring the desired genetic diversity into the relevant genetic background are prerequisites for plant breeding. Exotic germplasm is a rich source of genetic diversity; however, they harbor undesirable traits that limit their suitability for modern agriculture. Nested association mapping (NAM) populations are valuable genetic resources that enable incorporation of genetic diversity, dissection of complex traits and providing germplasm to breeding programs. We developed the OzNAM by crossing and backcrossing 73 diverse exotic parents to two Australian elite varieties Gladius and Scout. The NAM parents were genotyped using the iSelect wheat 90K Infinium SNP array, and the progeny were genotyped using a custom targeted genotyping-by-sequencing assay based on molecular inversion probes designed to target 12,179 SNPs chosen from the iSelect wheat 90K Infinium SNP array of the parents. In total, 3535 BC1F4:6 RILs from 125 families with 21 to 76 lines per family were genotyped and we found 4964 polymorphic and multi-allelic haplotype markers that spanned the whole genome. A subset of 530 lines from 28 families were evaluated in multi-environment trials over three years. To demonstrate the utility of the population in QTL mapping, we chose to map QTL for maturity and plant height using the RTM-GWAS approach and we identified novel and known QTL for maturity and plant height.
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Affiliation(s)
- Charity Chidzanga
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
| | - Delphine Fleury
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
| | - Ute Baumann
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
| | - Dan Mullan
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
- Intergrain 19 Ambitious Link, Bibra Lake, WA 6163, Australia;
| | - Sayuri Watanabe
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
| | - Priyanka Kalambettu
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
| | - Robert Pontre
- Intergrain 19 Ambitious Link, Bibra Lake, WA 6163, Australia;
| | - James Edwards
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
- Australian Grain Technologies, 20 Leitch Rd, Roseworthy, SA 5371, Australia
| | - Kerrie Forrest
- Genomics & Cell Sciences, Agriculture Victoria Research, Department of Jobs, Precincts and Regions, Agribio, 5 Ring Rd, Bundoora, VIC 3083, Australia; (K.F.); (D.W.)
| | - Debbie Wong
- Genomics & Cell Sciences, Agriculture Victoria Research, Department of Jobs, Precincts and Regions, Agribio, 5 Ring Rd, Bundoora, VIC 3083, Australia; (K.F.); (D.W.)
| | - Peter Langridge
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
| | - Ken Chalmers
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
| | - Melissa Garcia
- School of Agriculture, Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia; (C.C.); (D.F.); (U.B.); (S.W.); (P.K.); (P.L.); (K.C.)
- ARC Industrial Transformation Research Hub for Wheat in a Hot and Dry Climate, Waite Research Institute, The University of Adelaide, Glen Osmond, SA 5064, Australia; (D.M.); (J.E.)
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Yang N, Yan J. New genomic approaches for enhancing maize genetic improvement. CURRENT OPINION IN PLANT BIOLOGY 2021; 60:101977. [PMID: 33418269 DOI: 10.1016/j.pbi.2020.11.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 11/07/2020] [Accepted: 11/16/2020] [Indexed: 05/13/2023]
Abstract
Maize (Zea mays) is one of the most widely grown crops in the world, with an annual global production of over 1147 million tons. Genomics approaches are thought to be the best solution for accelerating yield improvement to meet the challenges of a growing population and global climate change. Here, we review current approaches to the exploration of novel genetic variation in genomes, DNA modifications, and transcription levels of cultivated maize, landraces, and wild relatives. We discuss applications of genetic engineering to maize yield improvement and highlight future directions for maize genomics studies.
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Affiliation(s)
- Ning Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
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39
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Strable J. Developmental genetics of maize vegetative shoot architecture. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:19. [PMID: 37309417 PMCID: PMC10236122 DOI: 10.1007/s11032-021-01208-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 01/25/2021] [Indexed: 06/13/2023]
Abstract
More than 1.1 billion tonnes of maize grain were harvested across 197 million hectares in 2019 (FAOSTAT 2020). The vast global productivity of maize is largely driven by denser planting practices, higher yield potential per area of land, and increased yield potential per plant. Shoot architecture, the three-dimensional structural arrangement of the above-ground plant body, is critical to maize grain yield and biomass. Structure of the shoot is integral to all aspects of modern agronomic practices. Here, the developmental genetics of the maize vegetative shoot is reviewed. Plant architecture is ultimately determined by meristem activity, developmental patterning, and growth. The following topics are discussed: shoot apical meristem, leaf architecture, axillary meristem and shoot branching, and intercalary meristem and stem activity. Where possible, classical and current studies in maize developmental genetics, as well as recent advances leveraged by "-omics" analyses, are highlighted within these sections. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01208-1.
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Affiliation(s)
- Josh Strable
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853 USA
- Present Address: Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC 27695 USA
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40
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Liu L, Gallagher J, Arevalo ED, Chen R, Skopelitis T, Wu Q, Bartlett M, Jackson D. Enhancing grain-yield-related traits by CRISPR-Cas9 promoter editing of maize CLE genes. NATURE PLANTS 2021; 7:287-294. [PMID: 33619356 DOI: 10.1038/s41477-021-00858-5] [Citation(s) in RCA: 153] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 01/21/2021] [Indexed: 05/04/2023]
Abstract
Several yield-related traits selected during crop domestication and improvement1,2 are associated with increases in meristem size3, which is controlled by CLE peptide signals in the CLAVATA-WUSCHEL pathway4-13. Here, we engineered quantitative variation for yield-related traits in maize by making weak promoter alleles of CLE genes, and a null allele of a newly identified partially redundant compensating CLE gene, using CRISPR-Cas9 genome editing. These strategies increased multiple maize grain-yield-related traits, supporting the enormous potential for genomic editing in crop enhancement.
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Affiliation(s)
- Lei Liu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Joseph Gallagher
- Biology Department, University of Massachusetts Amherst, Amherst, MA, USA
| | | | - Richelle Chen
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Qingyu Wu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Madelaine Bartlett
- Biology Department, University of Massachusetts Amherst, Amherst, MA, USA
| | - David Jackson
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
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Scott MF, Ladejobi O, Amer S, Bentley AR, Biernaskie J, Boden SA, Clark M, Dell'Acqua M, Dixon LE, Filippi CV, Fradgley N, Gardner KA, Mackay IJ, O'Sullivan D, Percival-Alwyn L, Roorkiwal M, Singh RK, Thudi M, Varshney RK, Venturini L, Whan A, Cockram J, Mott R. Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding. Heredity (Edinb) 2020; 125:396-416. [PMID: 32616877 PMCID: PMC7784848 DOI: 10.1038/s41437-020-0336-6] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 06/16/2020] [Accepted: 06/16/2020] [Indexed: 11/21/2022] Open
Abstract
Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm 'package' that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding.
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Affiliation(s)
| | | | - Samer Amer
- University of Reading, Reading, RG6 6AH, UK
- Faculty of Agriculture, Alexandria University, Alexandria, 23714, Egypt
| | - Alison R Bentley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Jay Biernaskie
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Scott A Boden
- School of Agriculture, Food and Wine, University of Adelaide, Glen Osmond, SA, 5064, Australia
| | | | | | - Laura E Dixon
- Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK
| | - Carla V Filippi
- Instituto de Agrobiotecnología y Biología Molecular (IABIMO), INTA-CONICET, Nicolas Repetto y Los Reseros s/n, 1686, Hurlingham, Buenos Aires, Argentina
| | - Nick Fradgley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Keith A Gardner
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Ian J Mackay
- SRUC, West Mains Road, Kings Buildings, Edinburgh, EH9 3JG, UK
| | | | | | - Manish Roorkiwal
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Rakesh Kumar Singh
- International Center for Biosaline Agriculture, Academic City, Dubai, United Arab Emirates
| | - Mahendar Thudi
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Rajeev Kumar Varshney
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | | | - Alex Whan
- CSIRO, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - James Cockram
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Richard Mott
- UCL Genetics Institute, Gower Street, London, WC1E 6BT, UK
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