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Zhang H, Tang Y, Yue Y, Chen Y. Advances in the evolution research and genetic breeding of peanut. Gene 2024; 916:148425. [PMID: 38575102 DOI: 10.1016/j.gene.2024.148425] [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: 11/29/2023] [Revised: 03/15/2024] [Accepted: 04/01/2024] [Indexed: 04/06/2024]
Abstract
Peanut is an important cash crop used in oil, food and feed in our country. The rapid development of sequencing technology has promoted the research on the related aspects of peanut genetic breeding. This paper reviews the research progress of peanut origin and evolution, genetic breeding, molecular markers and their applications, genomics, QTL mapping and genome selection techniques. The main problems of molecular genetic breeding in peanut research worldwide include: the narrow genetic resources of cultivated species, unstable genetic transformation and unclear molecular mechanism of important agronomic traits. Considering the severe challenges regarding the supply of edible oil, and the main problems in peanut production, the urgent research directions of peanut are put forward: The de novo domestication and the exploitation of excellent genes from wild resources to improve modern cultivars; Integration of multi-omics data to enhance the importance of big data in peanut genetics and breeding; Cloning the important genes related to peanut agronomic traits and analyzing their fine regulation mechanisms; Precision molecular design breeding and using gene editing technology to accurately improve the key traits of peanut.
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Affiliation(s)
- Hui Zhang
- College of Agriculture, South China Agricultural University, Guangzhou 510642, China.
| | - Yueyi Tang
- Shandong Peanut Research Institute, Qingdao 266100, China
| | - Yunlai Yue
- College of Agriculture, South China Agricultural University, Guangzhou 510642, China
| | - Yong Chen
- College of Agriculture, South China Agricultural University, Guangzhou 510642, China.
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2
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Weber SE, Roscher-Ehrig L, Kox T, Abbadi A, Stahl A, Snowdon RJ. Genomic prediction in Brassica napus: evaluating the benefit of imputed whole-genome sequencing data. Genome 2024; 67:210-222. [PMID: 38708850 DOI: 10.1139/gen-2023-0126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Advances in sequencing technology allow whole plant genomes to be sequenced with high quality. Combining genotypic and phenotypic data in genomic prediction helps breeders to select crossing partners in partially phenotyped populations. In plant breeding programs, the cost of sequencing entire breeding populations still exceeds available genotyping budgets. Hence, the method for genotyping is still mainly single nucleotide polymorphism (SNP) arrays; however, arrays are unable to assess the entire genome- and population-wide diversity. A compromise involves genotyping the entire population using an SNP array and a subset of the population with whole-genome sequencing. Both datasets can then be used to impute markers from whole-genome sequencing onto the entire population. Here, we evaluate whether imputation of whole-genome sequencing data enhances genomic predictions, using data from a nested association mapping population of rapeseed (Brassica napus). Employing two cross-validation schemes that mimic scenarios for the prediction of close and distant relatives, we show that imputed marker data do not significantly improve prediction accuracy, likely due to redundancy in relationship estimates and imputation errors. In simulation studies, only small improvements were observed, further corroborating the findings. We conclude that SNP arrays are already equipped with the information that is added by imputation through relationship and linkage disequilibrium.
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Affiliation(s)
- Sven E Weber
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | - Lennard Roscher-Ehrig
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | | | | | - Andreas Stahl
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
| | - Rod J Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
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3
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Bose S, Banerjee S, Kumar S, Saha A, Nandy D, Hazra S. Review of applications of artificial intelligence (AI) methods in crop research. J Appl Genet 2024; 65:225-240. [PMID: 38216788 DOI: 10.1007/s13353-023-00826-z] [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: 08/13/2023] [Revised: 12/23/2023] [Accepted: 12/26/2023] [Indexed: 01/14/2024]
Abstract
Sophisticated and modern crop improvement techniques can bridge the gap for feeding the ever-increasing population. Artificial intelligence (AI) refers to the simulation of human intelligence in machines, which refers to the application of computational algorithms, machine learning (ML) and deep learning (DL) techniques. This is aimed to generalise patterns and relationships from historical data, employing various mathematical optimisation techniques thus making prediction models for facilitating selection of superior genotypes. These techniques are less resource intensive and can solve the problem based on the analysis of large-scale phenotypic datasets. ML for genomic selection (GS) uses high-throughput genotyping technologies to gather genetic information on a large number of markers across the genome. The prediction of GS models is based on the mathematical relation between genotypic and phenotypic data from the training population. ML techniques have emerged as powerful tools for genome editing through analysing large-scale genomic data and facilitating the development of accurate prediction models. Precise phenotyping is a prerequisite to advance crop breeding for solving agricultural production-related issues. ML algorithms can solve this problem through generating predictive models, based on the analysis of large-scale phenotypic datasets. DL models also have the potential reliability of precise phenotyping. This review provides a comprehensive overview on various ML and DL models, their applications, potential to enhance the efficiency, specificity and safety towards advanced crop improvement protocols such as genomic selection, genome editing, along with phenotypic prediction to promote accelerated breeding.
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Affiliation(s)
- Suvojit Bose
- Department of Vegetables and Spice Crops, Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, 736165, West Bengal, India
| | | | - Soumya Kumar
- School of Agricultural Sciences, JIS University, Kolkata, 700109, West Bengal, India
| | - Akash Saha
- School of Agricultural Sciences, JIS University, Kolkata, 700109, West Bengal, India
| | - Debalina Nandy
- School of Agricultural Sciences, JIS University, Kolkata, 700109, West Bengal, India
| | - Soham Hazra
- Department of Agriculture, Brainware University, Barasat, 700125, West Bengal, India.
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4
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Knoch D, Meyer RC, Heuermann MC, Riewe D, Peleke FF, Szymański J, Abbadi A, Snowdon RJ, Altmann T. Integrated multi-omics analyses and genome-wide association studies reveal prime candidate genes of metabolic and vegetative growth variation in canola. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 117:713-728. [PMID: 37964699 DOI: 10.1111/tpj.16524] [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: 02/01/2023] [Revised: 10/17/2023] [Accepted: 10/23/2023] [Indexed: 11/16/2023]
Abstract
Genome-wide association studies (GWAS) identified thousands of genetic loci associated with complex plant traits, including many traits of agronomical importance. However, functional interpretation of GWAS results remains challenging because of large candidate regions due to linkage disequilibrium. High-throughput omics technologies, such as genomics, transcriptomics, proteomics and metabolomics open new avenues for integrative systems biological analyses and help to nominate systems information supported (prime) candidate genes. In the present study, we capitalise on a diverse canola population with 477 spring-type lines which was previously analysed by high-throughput phenotyping of growth-related traits and by RNA sequencing and metabolite profiling for multi-omics-based hybrid performance prediction. We deepened the phenotypic data analysis, now providing 123 time-resolved image-based traits, to gain insight into the complex relations during early vegetative growth and reanalysed the transcriptome data based on the latest Darmor-bzh v10 genome assembly. Genome-wide association testing revealed 61 298 robust quantitative trait loci (QTL) including 187 metabolite QTL, 56814 expression QTL and 4297 phenotypic QTL, many clustered in pronounced hotspots. Combining information about QTL colocalisation across omics layers and correlations between omics features allowed us to discover prime candidate genes for metabolic and vegetative growth variation. Prioritised candidate genes for early biomass accumulation include A06p05760.1_BnaDAR (PIAL1), A10p16280.1_BnaDAR, C07p48260.1_BnaDAR (PRL1) and C07p48510.1_BnaDAR (CLPR4). Moreover, we observed unequal effects of the Brassica A and C subgenomes on early biomass production.
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Affiliation(s)
- Dominic Knoch
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Corrensstrasse 3, Seeland OT, Gatersleben, Germany
| | - Rhonda C Meyer
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Corrensstrasse 3, Seeland OT, Gatersleben, Germany
| | - Marc C Heuermann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Corrensstrasse 3, Seeland OT, Gatersleben, Germany
| | - David Riewe
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Corrensstrasse 3, Seeland OT, Gatersleben, Germany
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Ecological Chemistry, Plant Analysis and Stored Product Protection, 14195, Berlin, Germany
| | - Fritz F Peleke
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Corrensstrasse 3, Seeland OT, Gatersleben, Germany
| | - Jędrzej Szymański
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Corrensstrasse 3, Seeland OT, Gatersleben, Germany
- Institute of Bio- and Geosciences IBG-4: Bioinformatics, Forschungszentrum Jülich, 52428, Jülich, Germany
| | - Amine Abbadi
- NPZ Innovation GmbH, Hohenlieth, 24363, Holtsee, Germany
- Norddeutsche Pflanzenzucht Hans-Georg Lembke KG, Hohenlieth, 24363, Holtsee, Germany
| | - Rod J Snowdon
- Department of Plant Breeding, Research Centre for Biosystems, Land Use and Nutrition (iFZ), Justus-Liebig-University Giessen, 35392, Giessen, Germany
| | - Thomas Altmann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Corrensstrasse 3, Seeland OT, Gatersleben, Germany
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Weber SE, Chawla HS, Ehrig L, Hickey LT, Frisch M, Snowdon RJ. Accurate prediction of quantitative traits with failed SNP calls in canola and maize. FRONTIERS IN PLANT SCIENCE 2023; 14:1221750. [PMID: 37936929 PMCID: PMC10627008 DOI: 10.3389/fpls.2023.1221750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 10/05/2023] [Indexed: 11/09/2023]
Abstract
In modern plant breeding, genomic selection is becoming the gold standard to select superior genotypes in large breeding populations that are only partially phenotyped. Many breeding programs commonly rely on single-nucleotide polymorphism (SNP) markers to capture genome-wide data for selection candidates. For this purpose, SNP arrays with moderate to high marker density represent a robust and cost-effective tool to generate reproducible, easy-to-handle, high-throughput genotype data from large-scale breeding populations. However, SNP arrays are prone to technical errors that lead to failed allele calls. To overcome this problem, failed calls are often imputed, based on the assumption that failed SNP calls are purely technical. However, this ignores the biological causes for failed calls-for example: deletions-and there is increasing evidence that gene presence-absence and other kinds of genome structural variants can play a role in phenotypic expression. Because deletions are frequently not in linkage disequilibrium with their flanking SNPs, permutation of missing SNP calls can potentially obscure valuable marker-trait associations. In this study, we analyze published datasets for canola and maize using four parametric and two machine learning models and demonstrate that failed allele calls in genomic prediction are highly predictive for important agronomic traits. We present two statistical pipelines, based on population structure and linkage disequilibrium, that enable the filtering of failed SNP calls that are likely caused by biological reasons. For the population and trait examined, prediction accuracy based on these filtered failed allele calls was competitive to standard SNP-based prediction, underlying the potential value of missing data in genomic prediction approaches. The combination of SNPs with all failed allele calls or the filtered allele calls did not outperform predictions with only SNP-based prediction due to redundancy in genomic relationship estimates.
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Affiliation(s)
- Sven E. Weber
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | | | - Lennard Ehrig
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Lee T. Hickey
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Matthias Frisch
- Department of Biometry and Population Genetics, Justus Liebig University, Giessen, Germany
| | - Rod J. Snowdon
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
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Hafeez A, Ali B, Javed MA, Saleem A, Fatima M, Fathi A, Afridi MS, Aydin V, Oral MA, Soudy FA. Plant breeding for harmony between sustainable agriculture, the environment, and global food security: an era of genomics-assisted breeding. PLANTA 2023; 258:97. [PMID: 37823963 DOI: 10.1007/s00425-023-04252-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/22/2023] [Indexed: 10/13/2023]
Abstract
MAIN CONCLUSION Genomics-assisted breeding represents a crucial frontier in enhancing the balance between sustainable agriculture, environmental preservation, and global food security. Its precision and efficiency hold the promise of developing resilient crops, reducing resource utilization, and safeguarding biodiversity, ultimately fostering a more sustainable and secure food production system. Agriculture has been seriously threatened over the last 40 years by climate changes that menace global nutrition and food security. Changes in environmental factors like drought, salt concentration, heavy rainfalls, and extremely low or high temperatures can have a detrimental effects on plant development, growth, and yield. Extreme poverty and increasing food demand necessitate the need to break the existing production barriers in several crops. The first decade of twenty-first century marks the rapid development in the discovery of new plant breeding technologies. In contrast, in the second decade, the focus turned to extracting information from massive genomic frameworks, speculating gene-to-phenotype associations, and producing resilient crops. In this review, we will encompass the causes, effects of abiotic stresses and how they can be addressed using plant breeding technologies. Both conventional and modern breeding technologies will be highlighted. Moreover, the challenges like the commercialization of biotechnological products faced by proponents and developers will also be accentuated. The crux of this review is to mention the available breeding technologies that can deliver crops with high nutrition and climate resilience for sustainable agriculture.
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Affiliation(s)
- Aqsa Hafeez
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Baber Ali
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan.
| | - Muhammad Ammar Javed
- Institute of Industrial Biotechnology, Government College University, Lahore, 54000, Pakistan
| | - Aroona Saleem
- Institute of Industrial Biotechnology, Government College University, Lahore, 54000, Pakistan
| | - Mahreen Fatima
- Faculty of Biosciences, Cholistan University of Veterinary and Animal Sciences, Bahawalpur, 63100, Pakistan
| | - Amin Fathi
- Department of Agronomy, Ayatollah Amoli Branch, Islamic Azad University, Amol, 46151, Iran
| | - Muhammad Siddique Afridi
- Department of Plant Pathology, Federal University of Lavras (UFLA), Lavras, MG, 37200-900, Brazil
| | - Veysel Aydin
- Sason Vocational School, Department of Plant and Animal Production, Batman University, Batman, 72060, Turkey
| | - Mükerrem Atalay Oral
- Elmalı Vocational School of Higher Education, Akdeniz University, Antalya, 07058, Turkey
| | - Fathia A Soudy
- Genetics and Genetic Engineering Department, Faculty of Agriculture, Benha University, Moshtohor, 13736, Egypt
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Weber SE, Frisch M, Snowdon RJ, Voss-Fels KP. Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets. FRONTIERS IN PLANT SCIENCE 2023; 14:1217589. [PMID: 37731980 PMCID: PMC10507710 DOI: 10.3389/fpls.2023.1217589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 08/21/2023] [Indexed: 09/22/2023]
Abstract
In modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype data in breeding populations tends to exhibit considerable redundancy. Therefore, interest is growing in the use of haplotype blocks to overcome redundancy by summarizing co-inherited features. Moreover, haplotype blocks can help to capture local epistasis caused by interacting loci. Here, we compared genomic prediction methods that either used single SNPs or haplotype blocks with regards to their prediction accuracy for important traits in crop datasets. We used four published datasets from canola, maize, wheat and soybean. Different approaches to construct haplotype blocks were compared, including blocks based on LD, physical distance, number of adjacent markers and the algorithms implemented in the software "Haploview" and "HaploBlocker". The tested prediction methods included Genomic Best Linear Unbiased Prediction (GBLUP), Extended GBLUP to account for additive by additive epistasis (EGBLUP), Bayesian LASSO and Reproducing Kernel Hilbert Space (RKHS) regression. We found improved prediction accuracy in some traits when using haplotype blocks compared to SNP-based predictions, however the magnitude of improvement was very trait- and model-specific. Especially in settings with low marker density, haplotype blocks can improve genomic prediction accuracy. In most cases, physically large haplotype blocks yielded a strong decrease in prediction accuracy. Especially when prediction accuracy varies greatly across different prediction models, prediction based on haplotype blocks can improve prediction accuracy of underperforming models. However, there is no "best" method to build haplotype blocks, since prediction accuracy varied considerably across methods and traits. Hence, criteria used to define haplotype blocks should not be viewed as fixed biological parameters, but rather as hyperparameters that need to be adjusted for every dataset.
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Affiliation(s)
- Sven E. Weber
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Matthias Frisch
- Department of Biometry and Population Genetics, Justus Liebig University, Giessen, Germany
| | - Rod J. Snowdon
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Kai P. Voss-Fels
- Institute for Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
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Xiong H, Chen Y, Pan YB, Wang J, Lu W, Shi A. A genome-wide association study and genomic prediction for Phakopsora pachyrhizi resistance in soybean. FRONTIERS IN PLANT SCIENCE 2023; 14:1179357. [PMID: 37313252 PMCID: PMC10258334 DOI: 10.3389/fpls.2023.1179357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 04/25/2023] [Indexed: 06/15/2023]
Abstract
Soybean brown rust (SBR), caused by Phakopsora pachyrhizi, is a devastating fungal disease that threatens global soybean production. This study conducted a genome-wide association study (GWAS) with seven models on a panel of 3,082 soybean accessions to identify the markers associated with SBR resistance by 30,314 high quality single nucleotide polymorphism (SNPs). Then five genomic selection (GS) models, including Ridge regression best linear unbiased predictor (rrBLUP), Genomic best linear unbiased predictor (gBLUP), Bayesian least absolute shrinkage and selection operator (Bayesian LASSO), Random Forest (RF), and Support vector machines (SVM), were used to predict breeding values of SBR resistance using whole genome SNP sets and GWAS-based marker sets. Four SNPs, namely Gm18_57,223,391 (LOD = 2.69), Gm16_29,491,946 (LOD = 3.86), Gm06_45,035,185 (LOD = 4.74), and Gm18_51,994,200 (LOD = 3.60), were located near the reported P. pachyrhizi R genes, Rpp1, Rpp2, Rpp3, and Rpp4, respectively. Other significant SNPs, including Gm02_7,235,181 (LOD = 7.91), Gm02_7234594 (LOD = 7.61), Gm03_38,913,029 (LOD = 6.85), Gm04_46,003,059 (LOD = 6.03), Gm09_1,951,644 (LOD = 10.07), Gm10_39,142,024 (LOD = 7.12), Gm12_28,136,735 (LOD = 7.03), Gm13_16,350,701(LOD = 5.63), Gm14_6,185,611 (LOD = 5.51), and Gm19_44,734,953 (LOD = 6.02), were associated with abundant disease resistance genes, such as Glyma.02G084100, Glyma.03G175300, Glyma.04g189500, Glyma.09G023800, Glyma.12G160400, Glyma.13G064500, Glyma.14g073300, and Glyma.19G190200. The annotations of these genes included but not limited to: LRR class gene, cytochrome 450, cell wall structure, RCC1, NAC, ABC transporter, F-box domain, etc. The GWAS based markers showed more accuracies in genomic prediction than the whole genome SNPs, and Bayesian LASSO model was the ideal model in SBR resistance prediction with 44.5% ~ 60.4% accuracies. This study aids breeders in predicting selection accuracy of complex traits such as disease resistance and can shorten the soybean breeding cycle by the identified markers.
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Affiliation(s)
- Haizheng Xiong
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Yilin Chen
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
| | - Yong-Bao Pan
- Sugarcane Research Unit, Untied State Department of Agriculture – Agriculture Research Service (USDA-ARS), Houma, LA, United States
| | - Jinshe Wang
- Henan Academy of Crops Molecular Breeding, National Centre for Plant Breeding, Zhengzhou, China
| | - Weiguo Lu
- Henan Academy of Crops Molecular Breeding, National Centre for Plant Breeding, Zhengzhou, China
| | - Ainong Shi
- Department of Horticulture, University of Arkansas, Fayetteville, AR, United States
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Bootter MB, Li J, Zhou W, Edwards D, Batley J. Diversity of Phytosterols in Leaves of Wild Brassicaceae Species as Compared to Brassica napus Cultivars: Potential Traits for Insect Resistance and Abiotic Stress Tolerance. PLANTS (BASEL, SWITZERLAND) 2023; 12:plants12091866. [PMID: 37176924 PMCID: PMC10180710 DOI: 10.3390/plants12091866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/22/2023] [Accepted: 04/29/2023] [Indexed: 05/15/2023]
Abstract
Phytosterols are natural compounds found in all higher plants that have a wide variety of roles in plant growth regulation and stress tolerance. The phytosterol composition can also influence the development and reproductive rate of strict herbivorous insects and other important agronomic traits such as temperature and drought tolerance in plants. In this study, we analysed the phytosterol composition in 18 Brassica napus (Rapeseed/canola) cultivars and 20 accessions belonging to 10 related wild Brassicaceae species to explore diverse and novel phytosterol profiles. Plants were grown in a controlled phytotron environment and their phytosterols were analysed using a saponification extraction method followed by GC-MS from the leaf samples. The B. napus cultivars showed slight diversity in eight phytosterols (>0.02%) due to the genotypic effect, whereas the wild accessions showed significant variability in their phytosterol profiles. Of interest, a number of wild accessions were found with high levels of campesterol (HIN20, HIN23, HUN27, HIN30, SARS2, and UPM6563), stigmasterol (UPM6813, UPM6563, ALBA17, and ALBA2), and isofucosterol (SARS12, SAR6, and DMU2). These changes in individual phytosterols, or ratios of phytosterols, can have a significant implication in plant tolerance to abiotic stress and plant insect resistance properties, which can be used in breeding for crop improvement.
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Affiliation(s)
| | - Jing Li
- School of Biological Sciences, University of Western Australia, Perth, WA 6009, Australia
| | - Wenxu Zhou
- School of Biological Sciences, University of Western Australia, Perth, WA 6009, Australia
| | - David Edwards
- School of Biological Sciences, University of Western Australia, Perth, WA 6009, Australia
| | - Jacqueline Batley
- School of Biological Sciences, University of Western Australia, Perth, WA 6009, Australia
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10
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Resistance strategies for defense against Albugo candida causing white rust disease. Microbiol Res 2023; 270:127317. [PMID: 36805163 DOI: 10.1016/j.micres.2023.127317] [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: 05/26/2022] [Revised: 12/12/2022] [Accepted: 02/01/2023] [Indexed: 02/11/2023]
Abstract
Albugo candida, the causal organism of white rust, is an oomycete obligate pathogen infecting crops of Brassicaceae family occurred on aerial part, including vegetable and oilseed crops at all growth stages. The disease expression is characterized by local infection appearing on the abaxial region developing white or creamy yellow blister (sori) on leaves and systemic infections cause hypertrophy and hyperplasia leading to stag-head of reproductive organ. To overcome this problem, several disease management strategies like fungicide treatments were used in the field and disease-resistant varieties have also been developed using conventional and molecular breeding. Due to high variability among A. candida isolates, there is no single approach available to understand the diverse spectrum of disease symptoms. In absence of resistance sources against pathogen, repetitive cultivation of genetically-similar varieties locally tends to attract oomycete pathogen causing heavy yield losses. In the present review, a deep insight into the underlying role of the non-host resistance (NHR) defence mechanism available in plants, and the strategies to exploit available gene pools from plant species that are non-host to A. candida could serve as novel sources of resistance. This work summaries the current knowledge pertaining to the resistance sources available in non-host germ plasm, the understanding of defence mechanisms and the advance strategies covers molecular, biochemical and nature-based solutions in protecting Brassica crops from white rust disease.
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Maulana F, Perumal R, Serba DD, Tesso T. Genomic prediction of hybrid performance in grain sorghum ( Sorghum bicolor L.). FRONTIERS IN PLANT SCIENCE 2023; 14:1139896. [PMID: 37180401 PMCID: PMC10167770 DOI: 10.3389/fpls.2023.1139896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 03/22/2023] [Indexed: 05/16/2023]
Abstract
Genomic selection is expected to improve selection efficiency and genetic gain in breeding programs. The objective of this study was to assess the efficacy of predicting the performance of grain sorghum hybrids using genomic information of parental genotypes. One hundred and two public sorghum inbred parents were genotyped using genotyping-by-sequencing. Ninty-nine of the inbreds were crossed to three tester female parents generating a total of 204 hybrids for evaluation at two environments. The hybrids were sorted in to three sets of 77,59 and 68 and evaluated along with two commercial checks using a randomized complete block design in three replications. The sequence analysis generated 66,265 SNP markers that were used to predict the performance of 204 F1 hybrids resulted from crosses between the parents. Both additive (partial model) and additive and dominance (full model) were constructed and tested using various training population (TP) sizes and cross-validation procedures. Increasing TP size from 41 to 163 increased prediction accuracies for all traits. With the partial model, the five-fold cross validated prediction accuracies ranged from 0.03 for thousand kernel weight (TKW) to 0.58 for grain yield (GY) while it ranged from 0.06 for TKW to 0.67 for GY with the full model. The results suggest that genomic prediction could become an effective tool for predicting the performance of sorghum hybrids based on parental genotypes.
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Affiliation(s)
- Frank Maulana
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Ramasamy Perumal
- Kansas State University, Agricultural Research Center, Hays, KS, United States
| | - Desalegn D. Serba
- United States Department of Agriculture-Agricultural Research Service (USDA-ARS), U.S. Arid Land Agricultural Research Center, Maricopa, AZ, United States
| | - Tesfaye Tesso
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
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12
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Gabur I, Simioniuc DP, Snowdon RJ, Cristea D. Machine Learning Applied to the Search for Nonlinear Features in Breeding Populations. Front Artif Intell 2022; 5:876578. [PMID: 35669178 PMCID: PMC9164111 DOI: 10.3389/frai.2022.876578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Large plant breeding populations are traditionally a source of novel allelic diversity and are at the core of selection efforts for elite material. Finding rare diversity requires a deep understanding of biological interactions between the genetic makeup of one genotype and its environmental conditions. Most modern breeding programs still rely on linear regression models to solve this problem, generalizing the complex genotype by phenotype interactions through manually constructed linear features. However, the identification of positive alleles vs. background can be addressed using deep learning approaches that have the capacity to learn complex nonlinear functions for the inputs. Machine learning (ML) is an artificial intelligence (AI) approach involving a range of algorithms to learn from input data sets and predict outcomes in other related samples. This paper describes a variety of techniques that include supervised and unsupervised ML algorithms to improve our understanding of nonlinear interactions from plant breeding data sets. Feature selection (FS) methods are combined with linear and nonlinear predictors and compared to traditional prediction methods used in plant breeding. Recent advances in ML allowed the construction of complex models that have the capacity to better differentiate between positive alleles and the genetic background. Using real plant breeding program data, we show that ML methods have the ability to outperform current approaches, increase prediction accuracies, decrease the computing time drastically, and improve the detection of important alleles involved in qualitative or quantitative traits.
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Affiliation(s)
- Iulian Gabur
- Department of Plant Breeding, Justus-Liebig-University, Giessen, Germany
- Department of Plant Sciences, Iasi University of Life Sciences, Iasi, Romania
- *Correspondence: Iulian Gabur
| | | | - Rod J. Snowdon
- Department of Plant Breeding, Justus-Liebig-University, Giessen, Germany
| | - Dan Cristea
- Institute of Computer Science, Romanian Academy, Iasi Branch, Iasi, Romania
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13
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Shamsabadi EE, Sabouri H, Soughi H, Sajadi SJ. Using of Molecular Markers in Prediction of Wheat (Triticum aestivum L.) Hybrid Grain Yield Based on Artificial Intelligence Methods and Multivariate Statistics. RUSS J GENET+ 2022. [DOI: 10.1134/s102279542205009x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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14
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Budhlakoti N, Kushwaha AK, Rai A, Chaturvedi KK, Kumar A, Pradhan AK, Kumar U, Kumar RR, Juliana P, Mishra DC, Kumar S. Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops. Front Genet 2022; 13:832153. [PMID: 35222548 PMCID: PMC8864149 DOI: 10.3389/fgene.2022.832153] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 01/10/2022] [Indexed: 12/17/2022] Open
Abstract
Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.
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Affiliation(s)
- Neeraj Budhlakoti
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Anil Rai
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - K K Chaturvedi
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anuj Kumar
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Uttam Kumar
- Borlaug Institute for South Asia (BISA), Ludhiana, India
| | | | | | - D C Mishra
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sundeep Kumar
- ICAR- National Bureau of Plant Genetic Resources, New Delhi, India
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15
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Bayer PE, Petereit J, Danilevicz MF, Anderson R, Batley J, Edwards D. The application of pangenomics and machine learning in genomic selection in plants. THE PLANT GENOME 2021; 14:e20112. [PMID: 34288550 DOI: 10.1002/tpg2.20112] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/01/2021] [Indexed: 05/10/2023]
Abstract
Genomic selection approaches have increased the speed of plant breeding, leading to growing crop yields over the last decade. However, climate change is impacting current and future yields, resulting in the need to further accelerate breeding efforts to cope with these changing conditions. Here we present approaches to accelerate plant breeding and incorporate nonadditive effects in genomic selection by applying state-of-the-art machine learning approaches. These approaches are made more powerful by the inclusion of pangenomes, which represent the entire genome content of a species. Understanding the strengths and limitations of machine learning methods, compared with more traditional genomic selection efforts, is paramount to the successful application of these methods in crop breeding. We describe examples of genomic selection and pangenome-based approaches in crop breeding, discuss machine learning-specific challenges, and highlight the potential for the application of machine learning in genomic selection. We believe that careful implementation of machine learning approaches will support crop improvement to help counter the adverse outcomes of climate change on crop production.
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Affiliation(s)
- Philipp E Bayer
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Jakob Petereit
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Monica Furaste Danilevicz
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Robyn Anderson
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
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16
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Amas J, Anderson R, Edwards D, Cowling W, Batley J. Status and advances in mining for blackleg (Leptosphaeria maculans) quantitative resistance (QR) in oilseed rape (Brassica napus). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3123-3145. [PMID: 34104999 PMCID: PMC8440254 DOI: 10.1007/s00122-021-03877-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/29/2021] [Indexed: 05/04/2023]
Abstract
KEY MESSAGE Quantitative resistance (QR) loci discovered through genetic and genomic analyses are abundant in the Brassica napus genome, providing an opportunity for their utilization in enhancing blackleg resistance. Quantitative resistance (QR) has long been utilized to manage blackleg in Brassica napus (canola, oilseed rape), even before major resistance genes (R-genes) were extensively explored in breeding programmes. In contrast to R-gene-mediated qualitative resistance, QR reduces blackleg symptoms rather than completely eliminating the disease. As a polygenic trait, QR is controlled by numerous genes with modest effects, which exerts less pressure on the pathogen to evolve; hence, its effectiveness is more durable compared to R-gene-mediated resistance. Furthermore, combining QR with major R-genes has been shown to enhance resistance against diseases in important crops, including oilseed rape. For these reasons, there has been a renewed interest among breeders in utilizing QR in crop improvement. However, the mechanisms governing QR are largely unknown, limiting its deployment. Advances in genomics are facilitating the dissection of the genetic and molecular underpinnings of QR, resulting in the discovery of several loci and genes that can be potentially deployed to enhance blackleg resistance. Here, we summarize the efforts undertaken to identify blackleg QR loci in oilseed rape using linkage and association analysis. We update the knowledge on the possible mechanisms governing QR and the advances in searching for the underlying genes. Lastly, we lay out strategies to accelerate the genetic improvement of blackleg QR in oilseed rape using improved phenotyping approaches and genomic prediction tools.
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Affiliation(s)
- Junrey Amas
- School of Biological Sciences and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6001 Australia
| | - Robyn Anderson
- School of Biological Sciences and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6001 Australia
| | - David Edwards
- School of Biological Sciences and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6001 Australia
| | - Wallace Cowling
- School of Agriculture and Environment and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009 Australia
| | - Jacqueline Batley
- School of Biological Sciences and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6001 Australia
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17
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Xiao Q, Wang H, Song N, Yu Z, Imran K, Xie W, Qiu S, Zhou F, Wen J, Dai C, Ma C, Tu J, Shen J, Fu T, Yi B. The Bnapus50K array: a quick and versatile genotyping tool for Brassica napus genomic breeding and research. G3-GENES GENOMES GENETICS 2021; 11:6352499. [PMID: 34568935 PMCID: PMC8473974 DOI: 10.1093/g3journal/jkab241] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 07/06/2021] [Indexed: 12/30/2022]
Abstract
Rapeseed is a globally cultivated commercial crop, primarily grown for its oil. High-density single nucleotide polymorphism (SNP) arrays are widely used as a standard genotyping tool for rapeseed research, including for gene mapping, genome-wide association studies, germplasm resource analysis, and cluster analysis. Although considerable rapeseed genome sequencing data have been released, DNA arrays are still an attractive choice for providing additional genetic data in an era of high-throughput whole-genome sequencing. Here, we integrated re-sequencing DNA array data (32,216, 304 SNPs) from 505 inbred rapeseed lines, allowing us to develop a sensitive and efficient genotyping DNA array, Bnapus50K, with a more consistent genetic and physical distribution of probes. A total of 42,090 high-quality probes were filtered and synthesized, with an average distance between adjacent SNPs of 8 kb. To improve the practical application potential of this array in rapeseed breeding, we also added 1,618 functional probes related to important agronomic traits such as oil content, disease resistance, male sterility, and flowering time. The additional probes also included those specifically for detecting genetically modified material. These probes show a good detection efficiency and are therefore useful for gene mapping, along with crop variety improvement and identification. The novel Bnapus50K DNA array developed in this study could prove to be a quick and versatile genotyping tool for B. napus genomic breeding and research.
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Affiliation(s)
- Qing Xiao
- College of plant science and technology; National Key Laboratory of Crop Genetic Improvement; Huazhong Agricultural University, Wuhan, China, 430070
| | - Huadong Wang
- College of plant science and technology; National Key Laboratory of Crop Genetic Improvement; Huazhong Agricultural University, Wuhan, China, 430070
| | - Nuan Song
- College of plant science and technology; National Key Laboratory of Crop Genetic Improvement; Huazhong Agricultural University, Wuhan, China, 430070
| | - Zewen Yu
- College of plant science and technology; National Key Laboratory of Crop Genetic Improvement; Huazhong Agricultural University, Wuhan, China, 430070
| | - Khan Imran
- Department of Biochemistry, School of Dental Medicine; University of Pennsylvania, Philadelphia, USA 19104-6303
| | - Weibo Xie
- College of plant science and technology; National Key Laboratory of Crop Genetic Improvement; Huazhong Agricultural University, Wuhan, China, 430070
| | - Shuqing Qiu
- Greenfafa Institute of Novel Genechip R&D Co. Ltd., Wuhan, China 430010
| | - Fasong Zhou
- Greenfafa Institute of Novel Genechip R&D Co. Ltd., Wuhan, China 430010
| | - Jing Wen
- College of plant science and technology; National Key Laboratory of Crop Genetic Improvement; Huazhong Agricultural University, Wuhan, China, 430070
| | - Cheng Dai
- College of plant science and technology; National Key Laboratory of Crop Genetic Improvement; Huazhong Agricultural University, Wuhan, China, 430070
| | - Chaozhi Ma
- College of plant science and technology; National Key Laboratory of Crop Genetic Improvement; Huazhong Agricultural University, Wuhan, China, 430070
| | - Jinxing Tu
- College of plant science and technology; National Key Laboratory of Crop Genetic Improvement; Huazhong Agricultural University, Wuhan, China, 430070
| | - Jinxiong Shen
- College of plant science and technology; National Key Laboratory of Crop Genetic Improvement; Huazhong Agricultural University, Wuhan, China, 430070
| | - Tingdong Fu
- College of plant science and technology; National Key Laboratory of Crop Genetic Improvement; Huazhong Agricultural University, Wuhan, China, 430070
| | - Bin Yi
- College of plant science and technology; National Key Laboratory of Crop Genetic Improvement; Huazhong Agricultural University, Wuhan, China, 430070
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18
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Hu D, Jing J, Snowdon RJ, Mason AS, Shen J, Meng J, Zou J. Exploring the gene pool of Brassica napus by genomics-based approaches. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:1693-1712. [PMID: 34031989 PMCID: PMC8428838 DOI: 10.1111/pbi.13636] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 05/13/2021] [Accepted: 05/14/2021] [Indexed: 05/08/2023]
Abstract
De novo allopolyploidization in Brassica provides a very successful model for reconstructing polyploid genomes using progenitor species and relatives to broaden crop gene pools and understand genome evolution after polyploidy, interspecific hybridization and exotic introgression. B. napus (AACC), the major cultivated rapeseed species and the third largest oilseed crop in the world, is a young Brassica species with a limited genetic base resulting from its short history of domestication, cultivation, and intensive selection during breeding for target economic traits. However, the gene pool of B. napus has been significantly enriched in recent decades that has been benefit from worldwide effects by the successful introduction of abundant subgenomic variation and novel genomic variation via intraspecific, interspecific and intergeneric crosses. An important question in this respect is how to utilize such variation to breed crops adapted to the changing global climate. Here, we review the genetic diversity, genome structure, and population-level differentiation of the B. napus gene pool in relation to known exotic introgressions from various species of the Brassicaceae, especially those elucidated by recent genome-sequencing projects. We also summarize progress in gene cloning, trait-marker associations, gene editing, molecular marker-assisted selection and genome-wide prediction, and describe the challenges and opportunities of these techniques as molecular platforms to exploit novel genomic variation and their value in the rapeseed gene pool. Future progress will accelerate the creation and manipulation of genetic diversity with genomic-based improvement, as well as provide novel insights into the neo-domestication of polyploid crops with novel genetic diversity from reconstructed genomes.
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Affiliation(s)
- Dandan Hu
- National Key Laboratory of Crop Genetic ImprovementCollege of Plant Science & TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Jinjie Jing
- National Key Laboratory of Crop Genetic ImprovementCollege of Plant Science & TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Rod J. Snowdon
- Department of Plant BreedingIFZ Research Centre for Biosystems, Land Use and NutritionJustus Liebig UniversityGiessenGermany
| | - Annaliese S. Mason
- Department of Plant BreedingIFZ Research Centre for Biosystems, Land Use and NutritionJustus Liebig UniversityGiessenGermany
- Plant Breeding DepartmentINRESThe University of BonnBonnGermany
| | - Jinxiong Shen
- National Key Laboratory of Crop Genetic ImprovementCollege of Plant Science & TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Jinling Meng
- National Key Laboratory of Crop Genetic ImprovementCollege of Plant Science & TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Jun Zou
- National Key Laboratory of Crop Genetic ImprovementCollege of Plant Science & TechnologyHuazhong Agricultural UniversityWuhanChina
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19
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Derbyshire MC, Khentry Y, Severn-Ellis A, Mwape V, Saad NSM, Newman TE, Taiwo A, Regmi R, Buchwaldt L, Denton-Giles M, Batley J, Kamphuis LG. Modeling first order additive × additive epistasis improves accuracy of genomic prediction for sclerotinia stem rot resistance in canola. THE PLANT GENOME 2021; 14:e20088. [PMID: 33629543 DOI: 10.1002/tpg2.20088] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 01/13/2021] [Indexed: 06/12/2023]
Abstract
The fungus Sclerotinia sclerotiorum infects hundreds of plant species including many crops. Resistance to this pathogen in canola (Brassica napus L. subsp. napus) is controlled by numerous quantitative trait loci (QTL). For such polygenic traits, genomic prediction may be useful for breeding as it can capture many QTL at once while also considering nonadditive genetic effects. Here, we test application of common regression models to genomic prediction of S. sclerotiorum resistance in canola in a diverse panel of 218 plants genotyped at 24,634 loci. Disease resistance was scored by infection with an aggressive isolate and monitoring over 3 wk. We found that including first-order additive × additive epistasis in linear mixed models (LMMs) improved accuracy of breeding value estimation between 3 and 40%, depending on method of assessment, and correlation between phenotypes and predicted total genetic values by 14%. Bayesian models performed similarly to or worse than genomic relationship matrix-based models for estimating breeding values or overall phenotypes from genetic values. Bayesian ridge regression, which is most similar to the genomic relationship matrix-based approach in the amount of shrinkage it applies to marker effects, was the most accurate of this family of models. This confirms several studies indicating the highly polygenic nature of sclerotinia stem rot resistance. Overall, our results highlight the use of simple epistasis terms for prediction of breeding values and total genetic values for a complex disease resistance phenotype in canola.
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Affiliation(s)
- Mark C Derbyshire
- Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia
| | - Yuphin Khentry
- Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia
| | - Anita Severn-Ellis
- School of Biological Sciences, University of Western Australia, Perth, Western Australia, Australia
| | - Virginia Mwape
- Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia
| | - Nur Shuhadah Mohd Saad
- School of Biological Sciences, University of Western Australia, Perth, Western Australia, Australia
| | - Toby E Newman
- Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia
| | - Akeem Taiwo
- Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia
| | - Roshan Regmi
- Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia
| | - Lone Buchwaldt
- Agriculture and Agri-Food, Saskatoon, Saskatchewan, Canada
| | | | - Jacqueline Batley
- School of Biological Sciences, University of Western Australia, Perth, Western Australia, Australia
| | - Lars G Kamphuis
- Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia, Australia
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20
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Knoch D, Werner CR, Meyer RC, Riewe D, Abbadi A, Lücke S, Snowdon RJ, Altmann T. Multi-omics-based prediction of hybrid performance in canola. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1147-1165. [PMID: 33523261 PMCID: PMC7973648 DOI: 10.1007/s00122-020-03759-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 12/19/2020] [Indexed: 05/05/2023]
Abstract
Complementing or replacing genetic markers with transcriptomic data and use of reproducing kernel Hilbert space regression based on Gaussian kernels increases hybrid prediction accuracies for complex agronomic traits in canola. In plant breeding, hybrids gained particular importance due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new top performing hybrids requires labour-intensive and costly breeding programmes, including testing of large numbers of experimental hybrids, the prediction of hybrid performance is of utmost interest to plant breeders. In this study, we tested the effectiveness of hybrid prediction models in spring-type oilseed rape (Brassica napus L./canola) employing different omics profiles, individually and in combination. To this end, a population of 950 F1 hybrids was evaluated for seed yield and six other agronomically relevant traits in commercial field trials at several locations throughout Europe. A subset of these hybrids was also evaluated in a climatized glasshouse regarding early biomass production. For each of the 477 parental rapeseed lines, 13,201 single nucleotide polymorphisms (SNPs), 154 primary metabolites, and 19,479 transcripts were determined and used as predictive variables. Both, SNP markers and transcripts, effectively predict hybrid performance using (genomic) best linear unbiased prediction models (gBLUP). Compared to models using pure genetic markers, models incorporating transcriptome data resulted in significantly higher prediction accuracies for five out of seven agronomic traits, indicating that transcripts carry important information beyond genomic data. Notably, reproducing kernel Hilbert space regression based on Gaussian kernels significantly exceeded the predictive abilities of gBLUP models for six of the seven agronomic traits, demonstrating its potential for implementation in future canola breeding programmes.
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Affiliation(s)
- Dominic Knoch
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
| | - Christian R. Werner
- The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG Scotland, UK
| | - Rhonda C. Meyer
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
| | - David Riewe
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
- Institute for Ecological Chemistry, Plant Analysis and Stored Product Protection, Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, 14195 Berlin, Germany
| | - Amine Abbadi
- NPZ Innovation GmbH, Hohenlieth, 24363 Holtsee, Germany
- Norddeutsche Pflanzenzucht Hans-Georg Lembke KG, Hohenlieth, 24363 Holtsee, Germany
| | - Sophie Lücke
- Norddeutsche Pflanzenzucht Hans-Georg Lembke KG, Hohenlieth, 24363 Holtsee, Germany
| | - Rod J. Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Thomas Altmann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
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21
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Zhao H, Li Y, Petkowski J, Kant S, Hayden MJ, Daetwyler HD. Genomic prediction and genomic heritability of grain yield and its related traits in a safflower genebank collection. THE PLANT GENOME 2021; 14:e20064. [PMID: 33140563 DOI: 10.1002/tpg2.20064] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/11/2020] [Accepted: 09/13/2020] [Indexed: 05/28/2023]
Abstract
Safflower, a minor oilseed crop, is gaining increased attention for food and industrial uses. Safflower genebank collections are an important genetic resource for crop enhancement and future breeding programs. In this study, we investigated the population structure of a safflower collection sourced from the Australian Grain Genebank and assessed the potential of genomic prediction (GP) to evaluate grain yield and related traits using single and multi-site models. Prediction accuracies (PA) of genomic best linear unbiased prediction (GBLUP) from single site models ranged from 0.21 to 0.86 for all traits examined and were consistent with estimated genomic heritability (h2 ), which varied from low to moderate across traits. We generally observed a low level of genome × environment interactions (g × E). Multi-site g × E GBLUP models only improved PA for accessions with at least some phenotypes in the training set. We observed that relaxing quality filtering parameters for genotype-by-sequencing (GBS), such as missing genotype call rate, did not affect PA but upwardly biased h2 estimation. Our results indicate that GP is feasible in safflower evaluation and is potentially a cost-effective tool to facilitate fast introgression of desired safflower trait variation from genebank germplasm into breeding lines.
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Affiliation(s)
- Huanhuan Zhao
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC, 3400, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Yongjun Li
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Joanna Petkowski
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Surya Kant
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC, 3400, Australia
- Centre for Agricultural Innovation, The University of Melbourne, Melbourne, VIC, Australia
| | - Matthew J Hayden
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Hans D Daetwyler
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
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22
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Mohd Saad NS, Severn-Ellis AA, Pradhan A, Edwards D, Batley J. Genomics Armed With Diversity Leads the Way in Brassica Improvement in a Changing Global Environment. Front Genet 2021; 12:600789. [PMID: 33679880 PMCID: PMC7930750 DOI: 10.3389/fgene.2021.600789] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 01/15/2021] [Indexed: 12/14/2022] Open
Abstract
Meeting the needs of a growing world population in the face of imminent climate change is a challenge; breeding of vegetable and oilseed Brassica crops is part of the race in meeting these demands. Available genetic diversity constituting the foundation of breeding is essential in plant improvement. Elite varieties, land races, and crop wild species are important resources of useful variation and are available from existing genepools or genebanks. Conservation of diversity in genepools, genebanks, and even the wild is crucial in preventing the loss of variation for future breeding efforts. In addition, the identification of suitable parental lines and alleles is critical in ensuring the development of resilient Brassica crops. During the past two decades, an increasing number of high-quality nuclear and organellar Brassica genomes have been assembled. Whole-genome re-sequencing and the development of pan-genomes are overcoming the limitations of the single reference genome and provide the basis for further exploration. Genomic and complementary omic tools such as microarrays, transcriptomics, epigenetics, and reverse genetics facilitate the study of crop evolution, breeding histories, and the discovery of loci associated with highly sought-after agronomic traits. Furthermore, in genomic selection, predicted breeding values based on phenotype and genome-wide marker scores allow the preselection of promising genotypes, enhancing genetic gains and substantially quickening the breeding cycle. It is clear that genomics, armed with diversity, is set to lead the way in Brassica improvement; however, a multidisciplinary plant breeding approach that includes phenotype = genotype × environment × management interaction will ultimately ensure the selection of resilient Brassica varieties ready for climate change.
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Affiliation(s)
| | | | | | | | - Jacqueline Batley
- School of Biological Sciences Western Australia and UWA Institute of Agriculture, University of Western Australia, Perth, WA, Australia
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23
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Tong H, Nikoloski Z. Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data. JOURNAL OF PLANT PHYSIOLOGY 2021; 257:153354. [PMID: 33385619 DOI: 10.1016/j.jplph.2020.153354] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/14/2020] [Accepted: 12/15/2020] [Indexed: 05/07/2023]
Abstract
Highly efficient and accurate selection of elite genotypes can lead to dramatic shortening of the breeding cycle in major crops relevant for sustaining present demands for food, feed, and fuel. In contrast to classical approaches that emphasize the need for resource-intensive phenotyping at all stages of artificial selection, genomic selection dramatically reduces the need for phenotyping. Genomic selection relies on advances in machine learning and the availability of genotyping data to predict agronomically relevant phenotypic traits. Here we provide a systematic review of machine learning approaches applied for genomic selection of single and multiple traits in major crops in the past decade. We emphasize the need to gather data on intermediate phenotypes, e.g. metabolite, protein, and gene expression levels, along with developments of modeling techniques that can lead to further improvements of genomic selection. In addition, we provide a critical view of factors that affect genomic selection, with attention to transferability of models between different environments. Finally, we highlight the future aspects of integrating high-throughput molecular phenotypic data from omics technologies with biological networks for crop improvement.
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Affiliation(s)
- Hao Tong
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany; Bioinformatics and Mathematical Modeling Department, Centre for Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria; Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany; Bioinformatics and Mathematical Modeling Department, Centre for Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria; Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
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24
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Gupta N, Gupta M, Akhatar J, Goyal A, Kaur R, Sharma S, Goyal P, Mukta A, Kaur N, Mittal M, Singh MP, Bharti B, Sardana VK, Banga SS. Association genetics of the parameters related to nitrogen use efficiency in Brassica juncea L. PLANT MOLECULAR BIOLOGY 2021; 105:161-175. [PMID: 32997301 DOI: 10.1007/s11103-020-01076-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 09/23/2020] [Indexed: 06/11/2023]
Abstract
Genome wide association studies allowed prediction of 17 candidate genes for association with nitrogen use efficiency. Novel information obtained may provide better understanding of genomic controls underlying germplasm variations for this trait in Indian mustard. Nitrogen use efficiency (NUE) of Indian mustard (Brassica juncea (L.) Czern & Coss.) is low and most breeding efforts to combine NUE with crop performance have not succeeded. Underlying genetics also remain unexplored. We tested 92 SNP-genotyped inbred lines for yield component traits, N uptake efficiency (NUPEFF), nitrogen utilization efficiency (NUTEFF), nitrogen harvest index (NHI) and NUE for two years at two nitrogen doses (No without added N and N100 added @100 kg/ha). Genotypes IC-2489-88, M-633, MCP-632, HUJM 1080, GR-325 and DJ-65 recorded high NUE at low N. These also showed improved crop performance under high N. One determinate mustard genotype DJ-113 DT-3 revealed maximum NUTEFF. Genome wide association studies (GWAS) facilitated recognition of 17 quantitative trait loci (QTLs). Environment specificity was high. B-genome chromosomes (B02, B03, B05, B07 and B08) harbored many useful loci. We also used regional association mapping (RAM) to supplement results from GWAS. Annotation of the genomic regions around peak SNPs helped to predict several gene candidates for root architecture, N uptake, assimilation and remobilization. CAT9 (At1g05940) was consistently envisaged for both NUE and NUPEFF. Major N transporter genes, NRT1.8 and NRT3.1 were predicted for explaining variation for NUTEFF and NUPEFF, respectively. Most significant amino acid transporter gene, AAP1 appeared associated with NUE under limited N conditions. All these candidates were predicted in the regions of high linkage disequilibrium. Sequence information of the predicted candidate genes will permit development of molecular markers to aid breeding for high NUE.
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Affiliation(s)
- Neha Gupta
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Punjab, 141004, Ludhiana, India
| | - Mehak Gupta
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Punjab, 141004, Ludhiana, India
| | - Javed Akhatar
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Punjab, 141004, Ludhiana, India
| | - Anna Goyal
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Punjab, 141004, Ludhiana, India
| | - Rimaljeet Kaur
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Punjab, 141004, Ludhiana, India
| | - Sanjula Sharma
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Punjab, 141004, Ludhiana, India
| | - Prinka Goyal
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Punjab, 141004, Ludhiana, India
| | - Archana Mukta
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Punjab, 141004, Ludhiana, India
| | - Navneet Kaur
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Punjab, 141004, Ludhiana, India
| | - Meenakshi Mittal
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Punjab, 141004, Ludhiana, India
| | - Mohini Prabha Singh
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Punjab, 141004, Ludhiana, India
| | - Baudh Bharti
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Punjab, 141004, Ludhiana, India
| | - V K Sardana
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Punjab, 141004, Ludhiana, India
| | - Surinder S Banga
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Punjab, 141004, Ludhiana, India.
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25
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Fikere M, Barbulescu DM, Malmberg MM, Maharjan P, Salisbury PA, Kant S, Panozzo J, Norton S, Spangenberg GC, Cogan NOI, Daetwyler HD. Genomic Prediction and Genetic Correlation of Agronomic, Blackleg Disease, and Seed Quality Traits in Canola ( Brassica napus L.). PLANTS (BASEL, SWITZERLAND) 2020; 9:E719. [PMID: 32517116 PMCID: PMC7356366 DOI: 10.3390/plants9060719] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/29/2020] [Accepted: 06/02/2020] [Indexed: 02/02/2023]
Abstract
Genomic selection accelerates genetic progress in crop breeding through the prediction of future phenotypes of selection candidates based on only their genomic information. Here we report genetic correlations and genomic prediction accuracies in 22 agronomic, disease, and seed quality traits measured across multiple years (2015-2017) in replicated trials under rain-fed and irrigated conditions in Victoria, Australia. Two hundred and two spring canola lines were genotyped for 62,082 Single Nucleotide Polymorphisms (SNPs) using transcriptomic genotype-by-sequencing (GBSt). Traits were evaluated in single trait and bivariate genomic best linear unbiased prediction (GBLUP) models and cross-validation. GBLUP were also expanded to include genotype-by-environment G × E interactions. Genomic heritability varied from 0.31to 0.66. Genetic correlations were highly positive within traits across locations and years. Oil content was positively correlated with most agronomic traits. Strong, not previously documented, negative correlations were observed between average internal infection (a measure of blackleg disease) and arachidic and stearic acids. The genetic correlations between fatty acid traits followed the expected patterns based on oil biosynthesis pathways. Genomic prediction accuracy ranged from 0.29 for emergence count to 0.69 for seed yield. The incorporation of G × E translates into improved prediction accuracy by up to 6%. The genomic prediction accuracies achieved indicate that genomic selection is ready for application in canola breeding.
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Affiliation(s)
- Mulusew Fikere
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3086, Australia; (M.F.); (M.M.M.); (G.C.S.); (N.O.I.C.)
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Denise M. Barbulescu
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia; (D.M.B.); (P.M.); (S.K.); (J.P.); (S.N.)
| | - M. Michelle Malmberg
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3086, Australia; (M.F.); (M.M.M.); (G.C.S.); (N.O.I.C.)
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
| | - Pankaj Maharjan
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia; (D.M.B.); (P.M.); (S.K.); (J.P.); (S.N.)
| | - Phillip A. Salisbury
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
- Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Surya Kant
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia; (D.M.B.); (P.M.); (S.K.); (J.P.); (S.N.)
- Centre for Agricultural Innovation, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Joe Panozzo
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia; (D.M.B.); (P.M.); (S.K.); (J.P.); (S.N.)
- Centre for Agricultural Innovation, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Sally Norton
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia; (D.M.B.); (P.M.); (S.K.); (J.P.); (S.N.)
| | - German C. Spangenberg
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3086, Australia; (M.F.); (M.M.M.); (G.C.S.); (N.O.I.C.)
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
| | - Noel O. I. Cogan
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3086, Australia; (M.F.); (M.M.M.); (G.C.S.); (N.O.I.C.)
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
| | - Hans D. Daetwyler
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3086, Australia; (M.F.); (M.M.M.); (G.C.S.); (N.O.I.C.)
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
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26
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Knoch D, Abbadi A, Grandke F, Meyer RC, Samans B, Werner CR, Snowdon RJ, Altmann T. Strong temporal dynamics of QTL action on plant growth progression revealed through high-throughput phenotyping in canola. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:68-82. [PMID: 31125482 PMCID: PMC6920335 DOI: 10.1111/pbi.13171] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 05/13/2019] [Accepted: 05/15/2019] [Indexed: 05/08/2023]
Abstract
A major challenge of plant biology is to unravel the genetic basis of complex traits. We took advantage of recent technical advances in high-throughput phenotyping in conjunction with genome-wide association studies to elucidate genotype-phenotype relationships at high temporal resolution. A diverse Brassica napus population from a commercial breeding programme was analysed by automated non-invasive phenotyping. Time-resolved data for early growth-related traits, including estimated biovolume, projected leaf area, early plant height and colour uniformity, were established and complemented by fresh and dry weight biomass. Genome-wide SNP array data provided the framework for genome-wide association analyses. Using time point data and relative growth rates, multiple robust main effect marker-trait associations for biomass and related traits were detected. Candidate genes involved in meristem development, cell wall modification and transcriptional regulation were detected. Our results demonstrate that early plant growth is a highly complex trait governed by several medium and many small effect loci, most of which act only during short phases. These observations highlight the importance of taking the temporal patterns of QTL/allele actions into account and emphasize the need for detailed time-resolved analyses to effectively unravel the complex and stage-specific contributions of genes affecting growth processes that operate at different developmental phases.
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Affiliation(s)
- Dominic Knoch
- Molecular Genetics/HeterosisLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)SeelandGermany
| | - Amine Abbadi
- Norddeutsche Pflanzenzucht Innovation GmbH (NPZi)HoltseeGermany
| | - Fabian Grandke
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
| | - Rhonda C. Meyer
- Molecular Genetics/HeterosisLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)SeelandGermany
| | - Birgit Samans
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
- Present address:
Technische Hochschule Mittelhessen (THM), University of Applied SciencesFachbereich Gesundheit35390GiessenGermany
| | - Christian R. Werner
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
- Present address:
The Roslin InstituteUniversity of EdinburghEaster Bush CampusMidlothianEH25 9RGUK
| | - Rod J. Snowdon
- Department of Plant BreedingResearch Centre for BiosystemsLand Use and Nutrition (iFZ)Justus‐Liebig‐University GiessenGiessenGermany
| | - Thomas Altmann
- Molecular Genetics/HeterosisLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)SeelandGermany
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27
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Malmberg MM, Spangenberg GC, Daetwyler HD, Cogan NOI. Assessment of low-coverage nanopore long read sequencing for SNP genotyping in doubled haploid canola (Brassica napus L.). Sci Rep 2019; 9:8688. [PMID: 31213642 PMCID: PMC6582154 DOI: 10.1038/s41598-019-45131-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 05/28/2019] [Indexed: 11/16/2022] Open
Abstract
Despite the high accuracy of short read sequencing (SRS), there are still issues with attaining accurate single nucleotide polymorphism (SNP) genotypes at low sequencing coverage and in highly duplicated genomes due to misalignment. Long read sequencing (LRS) systems, including the Oxford Nanopore Technologies (ONT) minION, have become popular options for de novo genome assembly and structural variant characterisation. The current high error rate often requires substantial post-sequencing correction and would appear to prevent the adoption of this system for SNP genotyping, but nanopore sequencing errors are largely random. Using low coverage ONT minION sequencing for genotyping of pre-validated SNP loci was examined in 9 canola doubled haploids. The minION genotypes were compared to the Illumina sequences to determine the extent and nature of genotype discrepancies between the two systems. The significant increase in read length improved alignment to the genome and the absence of classical SRS biases results in a more even representation of the genome. Sequencing errors are present, primarily in the form of heterozygous genotypes, which can be removed in completely homozygous backgrounds but requires more advanced bioinformatics in heterozygous genomes. Developments in this technology are promising for routine genotyping in the future.
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Affiliation(s)
- M M Malmberg
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3083, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, 3086, Australia
| | - G C Spangenberg
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3083, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, 3086, Australia
| | - H D Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3083, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, 3086, Australia
| | - N O I Cogan
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3083, Australia. .,School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, 3086, Australia.
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28
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Jan HU, Guan M, Yao M, Liu W, Wei D, Abbadi A, Zheng M, He X, Chen H, Guan C, Nichols RA, Snowdon RJ, Hua W, Qian L. Genome-wide haplotype analysis improves trait predictions in Brassica napus hybrids. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 283:157-164. [PMID: 31128685 DOI: 10.1016/j.plantsci.2019.02.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 02/01/2019] [Accepted: 02/09/2019] [Indexed: 05/05/2023]
Abstract
Combining ability is crucial for parent selection in crop hybrid breeding. Many studies have attempted to provide reliable and quick methods to identify genome regions in parental lines correlating with improved hybrid performance. The local haplotype patterns surrounding densely spaced DNA markers include a large amount of genetic information, and analysis of the relationships between haplotypes and hybrid performance can provide insight into the underlying genome regions which might contribute to enhancing combining ability. Here, we generated 24,403 single-copy, genome-wide SNP loci and calculated the general combining ability (GCA) of 950 hybrids from a diverse panel of 475 pollinators of spring-type canola inbred lines crossed with two testers for days to flowering (DTF) and seed glucosinolate content (GSL). We performed a genome-wide analysis of the haplotypes and detected eight and seven haplotype regions that were significantly associated with the GCA values for DTF and seed GSL, respectively. Additionally, two haplotype blocks containing orthologs of flowering time genes FLOWERING LOCUS T (FT) and FLOWERING LOCUS C (FLC) on chromosome A02 showed additive epistatic interactions influencing flowering time. Moreover, two homoeologous haplotype regions on chromosomes A02 and C02 corresponded to major quantitative trait loci (QTL) for GSL which showed additive effects related to reduction of seed GSL in F1 hybrids. Our study showed that haplotype analysis has the potential to substantially improve the efficiency of hybrid breeding programs.
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Affiliation(s)
- Habib U Jan
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, China; Department of Microbiology and Biotechnology, Abasyn University Peshawar, Khyber Pakhtunkhwa, 25000, Pakistan; Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Mei Guan
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, China
| | - Min Yao
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, China
| | - Wei Liu
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, China
| | - Dayong Wei
- College of Horticulture and Landscape Architecture, Southwest University, Chongqing, 400715, China
| | - Amine Abbadi
- NPZ Innovation GmbH, Hohenlieth, 24363, Holtsee, Germany
| | - Ming Zheng
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, China
| | - Xin He
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, China
| | - Hao Chen
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, China
| | - Chunyun Guan
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, China
| | - Richard A Nichols
- School of Biological and Chemical Sciences, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
| | - Rod J Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Wei Hua
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, China.
| | - Lunwen Qian
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, China; Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany.
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29
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Technow F. Use of F2 Bulks in Training Sets for Genomic Prediction of Combining Ability and Hybrid Performance. G3 (BETHESDA, MD.) 2019; 9:1557-1569. [PMID: 30862623 PMCID: PMC6505161 DOI: 10.1534/g3.118.200994] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 03/09/2019] [Indexed: 11/18/2022]
Abstract
Developing training sets for genomic prediction in hybrid crops requires producing hybrid seed for a large number of entries. In autogamous crop species (e.g., wheat, rice, rapeseed, cotton) this requires elaborate hybridization systems to prevent self-pollination and presents a significant impediment to the implementation of hybrid breeding in general and genomic selection in particular. An alternative to F1 hybrids are bulks of F2 seed from selfed F1 plants (F1:2). Seed production for F1:2 bulks requires no hybridization system because the number of F1 plants needed for producing enough F1:2 seed for multi-environment testing can be generated by hand-pollination. This study evaluated the suitability of F1:2 bulks for use in training sets for genomic prediction of F1 level general combining ability and hybrid performance, under different degrees of divergence between heterotic groups and modes of gene action, using quantitative genetic theory and simulation of a genomic prediction experiment. The simulation, backed by theory, showed that F1:2 training sets are expected to have a lower prediction accuracy relative to F1 training sets, particularly when heterotic groups have strongly diverged. The accuracy penalty, however, was only modest and mostly because of a lower heritability, rather than because of a difference in F1 and F1:2 genetic values. It is concluded that resorting to F1:2 bulks is, in theory at least, a promising approach to remove the significant complication of a hybridization system from the breeding process.
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Affiliation(s)
- Frank Technow
- Maize Product Development/Systems and Innovation for Breeding and Seed Products, DuPont Pioneer, Tavistock/Ontario, Canada
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30
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Stahl A, Vollrath P, Samans B, Frisch M, Wittkop B, Snowdon RJ. Effect of breeding on nitrogen use efficiency-associated traits in oilseed rape. JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:1969-1986. [PMID: 30753580 PMCID: PMC6436158 DOI: 10.1093/jxb/erz044] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 01/06/2019] [Indexed: 05/21/2023]
Abstract
Oilseed rape is one of the most important dicotyledonous field crops in the world, where it plays a key role in productive cereal crop rotations. However, its production requires high nitrogen fertilization and its nitrogen footprint exceeds that of most other globally important crops. Hence, increased nitrogen use efficiency (NUE) in this crop is of high priority for sustainable agriculture. We report a comprehensive study of macrophysiological characteristics associated with breeding progress, conducted under contrasting nitrogen fertilization levels in a large panel of elite oilseed rape varieties representing breeding progress over the past 20 years. The results indicate that increased plant biomass at flowering, along with increases in primary yield components, have increased NUE in modern varieties. Nitrogen uptake efficiency has improved through breeding, particularly at high nitrogen. Despite low heritability, the number of seeds per silique is associated positively with increased yield in modern varieties. Seed weight remains unaffected by breeding progress; however, recent selection for high seed oil content and for high seed yields appears to have promoted a negative correlation (r= -0.39 at high and r= -0.49 at low nitrogen) between seed weight and seed oil concentration. Overall, our results reveal valuable breeding targets to improve NUE in oilseed rape.
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Affiliation(s)
- Andreas Stahl
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
- Correspondence:
| | - Paul Vollrath
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | - Birgit Samans
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | - Matthias Frisch
- Department of Biometry and Population Genetics, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | - Benjamin Wittkop
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | - Rod J Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
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31
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Hao Y, Wang H, Yang X, Zhang H, He C, Li D, Li H, Wang G, Wang J, Fu J. Genomic Prediction using Existing Historical Data Contributing to Selection in Biparental Populations: A Study of Kernel Oil in Maize. THE PLANT GENOME 2019; 12. [PMID: 30951098 DOI: 10.3835/plantgenome2018.05.0025] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Maize ( L.) kernel oil provides high-quality nutrition for animal feed and human health. A certain number of maize breeding programs seek to enhance oil concentration and composition. Genomic selection (GS), which entails selection based on genomic estimated breeding values (GEBVs), has proven to be efficient in breeding programs. Here, we estimate the robustness of predictions for the oil traits of maize kernels in biparental recombination inbred lines (RILs) using a GS model built based on an association population. Most statistical models, including ridge regression-best linear unbiased prediction (RR-BLUP), showed high prediction accuracy in the training population through a cross validation procedure. The training population size was more important than marker density and a statistical model for prediction performance. Using the optimized GS model, prediction of the biparental RIL population showed medium-high prediction accuracy (0.68) compared with prediction using only oil associated markers ( = 0.43). The potential to apply the GS model to another RIL population that is genetically less related to the training population was also examined, showing promising prediction accuracy in the top selected lines. Our results proved that genomic prediction using existing data is robust for the prediction of polygenic traits with moderate to high heritability.
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32
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Gabur I, Chawla HS, Liu X, Kumar V, Faure S, von Tiedemann A, Jestin C, Dryzska E, Volkmann S, Breuer F, Delourme R, Snowdon R, Obermeier C. Finding invisible quantitative trait loci with missing data. PLANT BIOTECHNOLOGY JOURNAL 2018; 16:2102-2112. [PMID: 29729219 PMCID: PMC6230954 DOI: 10.1111/pbi.12942] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Revised: 04/26/2018] [Accepted: 04/28/2018] [Indexed: 05/21/2023]
Abstract
Evolutionary processes during plant polyploidization and speciation have led to extensive presence-absence variation (PAV) in crop genomes, and there is increasing evidence that PAV associates with important traits. Today, high-resolution genetic analysis in major crops frequently implements simple, cost-effective, high-throughput genotyping from single nucleotide polymorphism (SNP) hybridization arrays; however, these are normally not designed to distinguish PAV from failed SNP calls caused by hybridization artefacts. Here, we describe a strategy to recover valuable information from single nucleotide absence polymorphisms (SNaPs) by population-based quality filtering of SNP hybridization data to distinguish patterns associated with genuine deletions from those caused by technical failures. We reveal that including SNaPs in genetic analyses elucidate segregation of small to large-scale structural variants in nested association mapping populations of oilseed rape (Brassica napus), a recent polyploid crop with widespread structural variation. Including SNaP markers in genomewide association studies identified numerous quantitative trait loci, invisible using SNP markers alone, for resistance to two major fungal diseases of oilseed rape, Sclerotinia stem rot and blackleg disease. Our results indicate that PAV has a strong influence on quantitative disease resistance in B. napus and that SNaP analysis using cost-effective SNP array data can provide extensive added value from 'missing data'. This strategy might also be applicable for improving the precision of genetic mapping in many important crop species.
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Affiliation(s)
- Iulian Gabur
- Department of Plant BreedingJustus Liebig UniversityGiessenGermany
| | | | - Xiwei Liu
- Department of Plant BreedingJustus Liebig UniversityGiessenGermany
| | - Vinod Kumar
- IGEPP, INRA, AGROCAMPUS OUESTUniv RennesLe RheuFrance
| | | | - Andreas von Tiedemann
- Section of General Plant Pathology and Crop ProtectionGeorg August UniversityGöttingenGermany
| | | | | | | | | | | | - Rod Snowdon
- Department of Plant BreedingJustus Liebig UniversityGiessenGermany
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Fikere M, Barbulescu DM, Malmberg MM, Shi F, Koh JCO, Slater AT, MacLeod IM, Bowman PJ, Salisbury PA, Spangenberg GC, Cogan NOI, Daetwyler HD. Genomic Prediction Using Prior Quantitative Trait Loci Information Reveals a Large Reservoir of Underutilised Blackleg Resistance in Diverse Canola ( Brassica napus L.) Lines. THE PLANT GENOME 2018; 11. [PMID: 30025024 DOI: 10.3835/plantgenome2017.11.0100] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Genomic prediction is becoming a popular plant breeding method to predict the genetic merit of lines. While some genomic prediction results have been reported in canola, none have been evaluated for blackleg disease. Here, we report genomic prediction for seedling emergence, survival rate, and internal infection), using 532 Spring and Winter canola lines. These lines were phenotyped in two replicated blackleg disease nurseries grown at Wickliffe and Green Lake, Victoria, Australia. A transcriptome genotyping-by-sequencing approach revealed 98,054 single nucleotide polymorphisms (SNPs) after quality control. We assessed various genomic prediction scenarios based on Genomic Best Linear Unbiased Prediction (GBLUP), BayesR and BayesRC, which can make use of prior quantitative trait loci information, via cross-validation. Clustering based on genomic relationships showed that Winter and Spring lines were genetically distinct, indicating limited gene flow between sets. Genetic correlations within traits between Spring and Winter lines ranged from 0.68 and 0.90 (mean = 0.76). Based on GBLUP in the whole population, moderate to high genomic prediction accuracies were achieved within environments (0.35-0.74) and were reduced across environments (0.28-0.58). Prediction accuracy within the Spring set ranged from 0.30-0.69, and from 0.19-0.71 within the Winter set. The BayesR model resulted in slightly lower accuracy to GBLUP. The proportion of genetic variance explained by known blackleg quantitative trait loci (QTL) was < 30%, indicating that there is a large reservoir of genetic variation in blackleg traits that remains to be discovered, but can be captured with genomic prediction. However, providing prior information of known QTL in the BayesRC method resulted in an increased prediction accuracy for survival and internal infection, particularly with Spring lines. Overall, these promising results indicate that genomic prediction will be a valuable tool to make use of all genetic variation to improve blackleg resistance in canola.
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Werner CR, Voss-Fels KP, Miller CN, Qian W, Hua W, Guan CY, Snowdon RJ, Qian L. Effective Genomic Selection in a Narrow-Genepool Crop with Low-Density Markers: Asian Rapeseed as an Example. THE PLANT GENOME 2018; 11. [PMID: 30025015 DOI: 10.3835/plantgenome2017.09.0084] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Genomic selection (GS) has revolutionized breeding for quantitative traits in plants, offering potential to optimize resource allocation in breeding programs and increase genetic gain per unit of time. Modern high-density single nucleotide polymorphism (SNP) arrays comprising up to several hundred thousand markers provide a user-friendly technology to characterize the genetic constitution of whole populations and for implementing GS in breeding programs. However, GS does not build upon detailed genotype profiling facilitated by maximum marker density. With extensive genome-wide linkage disequilibrium (LD) being a common characteristic of breeding pools, fewer representative markers from available high-density genotyping platforms could be sufficient to capture the association between a genomic region and a phenotypic trait. To examine the effects of reduced marker density on genomic prediction accuracy, we collected data on three traits across 2 yr in a panel of 203 homozygous Chinese semiwinter rapeseed ( L.) inbred lines, broadly encompassing allelic variability in the Asian genepool. We investigated two approaches to selecting subsets of markers: a trait-dependent strategy based on genome-wide association study (GWAS) significance thresholds and a trait-independent method to detect representative tag SNPs. Prediction accuracies were evaluated using cross-validation with ridge-regression best linear unbiased predictions (rrBLUP). With semiwinter rapeseed as a model species, we demonstrate that low-density marker sets comprising a few hundred to a few thousand markers enable high prediction accuracies in breeding populations with strong LD comparable to those achieved with high-density arrays. Our results are valuable for facilitating routine application of cost-efficient GS in breeding programs.
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Malmberg MM, Pembleton LW, Baillie RC, Drayton MC, Sudheesh S, Kaur S, Shinozuka H, Verma P, Spangenberg GC, Daetwyler HD, Forster JW, Cogan NO. Genotyping-by-sequencing through transcriptomics: implementation in a range of crop species with varying reproductive habits and ploidy levels. PLANT BIOTECHNOLOGY JOURNAL 2018; 16:877-889. [PMID: 28913899 PMCID: PMC5866951 DOI: 10.1111/pbi.12835] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Revised: 08/03/2017] [Accepted: 09/08/2017] [Indexed: 05/09/2023]
Abstract
The application of genomics in crops has the ability to significantly improve genetic gain for agriculture. Many marker-dense tools have been developed, but few have seen broad adoption in plant genomics due to issues of significant variations of genome size, levels of ploidy, single nucleotide polymorphism (SNP) frequency and reproductive habit. When combined with limited breeding activities, small research communities and scant sequence resources, the suitability of popular systems is often suboptimal and routinely fails to effectively balance cost-effectiveness and sample throughput. Genotyping-by-sequencing (GBS) encompasses a range of protocols including resequencing of the transcriptome. This study describes a skim GBS-transcriptomics (GBS-t) approach developed to be broadly applicable, cost-effective and high-throughput while still assaying a significant number of SNP loci. A range of crop species with differing levels of ploidy and degree of inbreeding/outbreeding were chosen, including perennial ryegrass, a diploid outbreeding forage grass; phalaris, a putative segmental allotetraploid outbreeding forage grass; lentil, a diploid inbreeding grain legume; and canola, an allotetraploid partially outbreeding oilseed. GBS-t was validated as a simple and largely automated, cost-effective method which generates sufficient SNPs (from 89 738 to 231 977) with acceptable levels of missing data and even genome coverage from c. 3 million sequence reads per sample. GBS-t is therefore a broadly applicable system suitable for many crops, offering advantages over other systems. The correct choice of subsequent sequence analysis software is important, and the bioinformatics process should be iterative and tailored to the specific challenges posed by ploidy variation and extent of heterozygosity.
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Affiliation(s)
- M. Michelle Malmberg
- Agriculture VictoriaAgriBioCentre for AgriBioscience5 Ring RoadBundooraVictoria 3083Australia
- School of Applied Systems BiologyLa Trobe UniversityBundooraVictoria 3086Australia
| | - Luke W. Pembleton
- Agriculture VictoriaAgriBioCentre for AgriBioscience5 Ring RoadBundooraVictoria 3083Australia
| | - Rebecca C. Baillie
- Agriculture VictoriaAgriBioCentre for AgriBioscience5 Ring RoadBundooraVictoria 3083Australia
| | - Michelle C. Drayton
- Agriculture VictoriaAgriBioCentre for AgriBioscience5 Ring RoadBundooraVictoria 3083Australia
| | - Shimna Sudheesh
- Agriculture VictoriaAgriBioCentre for AgriBioscience5 Ring RoadBundooraVictoria 3083Australia
| | - Sukhjiwan Kaur
- Agriculture VictoriaAgriBioCentre for AgriBioscience5 Ring RoadBundooraVictoria 3083Australia
| | - Hiroshi Shinozuka
- Agriculture VictoriaAgriBioCentre for AgriBioscience5 Ring RoadBundooraVictoria 3083Australia
| | - Preeti Verma
- Agriculture VictoriaAgriBioCentre for AgriBioscience5 Ring RoadBundooraVictoria 3083Australia
| | - German C. Spangenberg
- Agriculture VictoriaAgriBioCentre for AgriBioscience5 Ring RoadBundooraVictoria 3083Australia
- School of Applied Systems BiologyLa Trobe UniversityBundooraVictoria 3086Australia
| | - Hans D. Daetwyler
- Agriculture VictoriaAgriBioCentre for AgriBioscience5 Ring RoadBundooraVictoria 3083Australia
- School of Applied Systems BiologyLa Trobe UniversityBundooraVictoria 3086Australia
| | - John W. Forster
- Agriculture VictoriaAgriBioCentre for AgriBioscience5 Ring RoadBundooraVictoria 3083Australia
- School of Applied Systems BiologyLa Trobe UniversityBundooraVictoria 3086Australia
| | - Noel O.I. Cogan
- Agriculture VictoriaAgriBioCentre for AgriBioscience5 Ring RoadBundooraVictoria 3083Australia
- School of Applied Systems BiologyLa Trobe UniversityBundooraVictoria 3086Australia
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36
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Werner CR, Qian L, Voss-Fels KP, Abbadi A, Leckband G, Frisch M, Snowdon RJ. Genome-wide regression models considering general and specific combining ability predict hybrid performance in oilseed rape with similar accuracy regardless of trait architecture. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2018; 131:299-317. [PMID: 29080901 DOI: 10.1007/s00122-017-3002-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 10/09/2017] [Indexed: 05/02/2023]
Abstract
Genomic prediction using the Brassica 60 k genotyping array is efficient in oilseed rape hybrids. Prediction accuracy is more dependent on trait complexity than on the prediction model. In oilseed rape breeding programs, performance prediction of parental combinations is of fundamental importance. Due to the phenomenon of heterosis, per se performance is not a reliable indicator for F1-hybrid performance, and selection of well-paired parents requires the testing of large quantities of hybrid combinations in extensive field trials. However, the number of potential hybrids, in general, dramatically exceeds breeding capacity and budget. Integration of genomic selection (GS) could substantially increase the number of potential combinations that can be evaluated. GS models can be used to predict the performance of untested individuals based only on their genotypic profiles, using marker effects previously predicted in a training population. This allows for a preselection of promising genotypes, enabling a more efficient allocation of resources. In this study, we evaluated the usefulness of the Illumina Brassica 60 k SNP array for genomic prediction and compared three alternative approaches based on a homoscedastic ridge regression BLUP and three Bayesian prediction models that considered general and specific combining ability (GCA and SCA, respectively). A total of 448 hybrids were produced in a commercial breeding program from unbalanced crosses between 220 paternal doubled haploid lines and five male-sterile testers. Predictive ability was evaluated for seven agronomic traits. We demonstrate that the Brassica 60 k genotyping array is an adequate and highly valuable platform to implement genomic prediction of hybrid performance in oilseed rape. Furthermore, we present first insights into the application of established statistical models for prediction of important agronomical traits with contrasting patterns of polygenic control.
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Affiliation(s)
- Christian R Werner
- Department of Plant Breeding, Justus Liebig University, 35392, Giessen, Germany
| | - Lunwen Qian
- Department of Plant Breeding, Justus Liebig University, 35392, Giessen, Germany
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Kai P Voss-Fels
- Department of Plant Breeding, Justus Liebig University, 35392, Giessen, Germany
| | - Amine Abbadi
- NPZ Innovation GmbH, Hohenlieth, 24363, Holtsee, Germany
| | | | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, 35392, Giessen, Germany
| | - Rod J Snowdon
- Department of Plant Breeding, Justus Liebig University, 35392, Giessen, Germany.
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37
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Malmberg MM, Shi F, Spangenberg GC, Daetwyler HD, Cogan NOI. Diversity and Genome Analysis of Australian and Global Oilseed Brassica napus L. Germplasm Using Transcriptomics and Whole Genome Re-sequencing. FRONTIERS IN PLANT SCIENCE 2018; 9:508. [PMID: 29725344 PMCID: PMC5917405 DOI: 10.3389/fpls.2018.00508] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 04/03/2018] [Indexed: 05/21/2023]
Abstract
Intensive breeding of Brassica napus has resulted in relatively low diversity, such that B. napus would benefit from germplasm improvement schemes that sustain diversity. As such, samples representative of global germplasm pools need to be assessed for existing population structure, diversity and linkage disequilibrium (LD). Complexity reduction genotyping-by-sequencing (GBS) methods, including GBS-transcriptomics (GBS-t), enable cost-effective screening of a large number of samples, while whole genome re-sequencing (WGR) delivers the ability to generate large numbers of unbiased genomic single nucleotide polymorphisms (SNPs), and identify structural variants (SVs). Furthermore, the development of genomic tools based on whole genomes representative of global oilseed diversity and orientated by the reference genome has substantial industry relevance and will be highly beneficial for canola breeding. As recent studies have focused on European and Chinese varieties, a global diversity panel as well as a substantial number of Australian spring types were included in this study. Focusing on industry relevance, 633 varieties were initially genotyped using GBS-t to examine population structure using 61,037 SNPs. Subsequently, 149 samples representative of global diversity were selected for WGR and both data sets used for a side-by-side evaluation of diversity and LD. The WGR data was further used to develop genomic resources consisting of a list of 4,029,750 high-confidence SNPs annotated using SnpEff, and SVs in the form of 10,976 deletions and 2,556 insertions. These resources form the basis of a reliable and repeatable system allowing greater integration between canola genomics studies, with a strong focus on breeding germplasm and industry applicability.
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Affiliation(s)
- M. Michelle Malmberg
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | - Fan Shi
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, Bundoora, VIC, Australia
| | - German C. Spangenberg
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | - Hans D. Daetwyler
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | - Noel O. I. Cogan
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
- *Correspondence: Noel O. I. Cogan,
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38
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Werner CR, Qian L, Voss-Fels KP, Abbadi A, Leckband G, Frisch M, Snowdon RJ. Genome-wide regression models considering general and specific combining ability predict hybrid performance in oilseed rape with similar accuracy regardless of trait architecture. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2017. [PMID: 29080901 DOI: 10.1007/s00122‐017‐3002‐5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
KEY MESSAGE Genomic prediction using the Brassica 60 k genotyping array is efficient in oilseed rape hybrids. Prediction accuracy is more dependent on trait complexity than on the prediction model. In oilseed rape breeding programs, performance prediction of parental combinations is of fundamental importance. Due to the phenomenon of heterosis, per se performance is not a reliable indicator for F1-hybrid performance, and selection of well-paired parents requires the testing of large quantities of hybrid combinations in extensive field trials. However, the number of potential hybrids, in general, dramatically exceeds breeding capacity and budget. Integration of genomic selection (GS) could substantially increase the number of potential combinations that can be evaluated. GS models can be used to predict the performance of untested individuals based only on their genotypic profiles, using marker effects previously predicted in a training population. This allows for a preselection of promising genotypes, enabling a more efficient allocation of resources. In this study, we evaluated the usefulness of the Illumina Brassica 60 k SNP array for genomic prediction and compared three alternative approaches based on a homoscedastic ridge regression BLUP and three Bayesian prediction models that considered general and specific combining ability (GCA and SCA, respectively). A total of 448 hybrids were produced in a commercial breeding program from unbalanced crosses between 220 paternal doubled haploid lines and five male-sterile testers. Predictive ability was evaluated for seven agronomic traits. We demonstrate that the Brassica 60 k genotyping array is an adequate and highly valuable platform to implement genomic prediction of hybrid performance in oilseed rape. Furthermore, we present first insights into the application of established statistical models for prediction of important agronomical traits with contrasting patterns of polygenic control.
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Affiliation(s)
- Christian R Werner
- Department of Plant Breeding, Justus Liebig University, 35392, Giessen, Germany
| | - Lunwen Qian
- Department of Plant Breeding, Justus Liebig University, 35392, Giessen, Germany.,Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Kai P Voss-Fels
- Department of Plant Breeding, Justus Liebig University, 35392, Giessen, Germany
| | - Amine Abbadi
- NPZ Innovation GmbH, Hohenlieth, 24363, Holtsee, Germany
| | | | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, 35392, Giessen, Germany
| | - Rod J Snowdon
- Department of Plant Breeding, Justus Liebig University, 35392, Giessen, Germany.
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Luo Z, Wang M, Long Y, Huang Y, Shi L, Zhang C, Liu X, Fitt BDL, Xiang J, Mason AS, Snowdon RJ, Liu P, Meng J, Zou J. Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2017; 130:1569-1585. [PMID: 28455767 PMCID: PMC5719798 DOI: 10.1007/s00122-017-2911-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Accepted: 04/19/2017] [Indexed: 05/10/2023]
Abstract
A comprehensive linkage atlas for seed yield in rapeseed. Most agronomic traits of interest for crop improvement (including seed yield) are highly complex quantitative traits controlled by numerous genetic loci, which brings challenges for comprehensively capturing associated markers/genes. We propose that multiple trait interactions underlie complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual quantitative trait loci (QTL) effects more effectively and improve yield predictions. Using a segregating rapeseed (Brassica napus) population, we analyzed a large set of trait data generated in 19 independent experiments to investigate correlations between seed yield and other complex traits, and further identified QTL in this population with a SNP-based genetic bin map. A total of 1904 consensus QTL accounting for 22 traits, including 80 QTL directly affecting seed yield, were anchored to the B. napus reference sequence. Through trait association analysis and QTL meta-analysis, we identified a total of 525 indivisible QTL that either directly or indirectly contributed to seed yield, of which 295 QTL were detected across multiple environments. A majority (81.5%) of the 525 QTL were pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting genetic effects, as well as 31 QTL with minor complementary effects. Implementation of the 525 QTL in genomic prediction models improved seed yield prediction accuracy. Dissecting the genetic and phenotypic interrelationships underlying complex quantitative traits using this method will provide valuable insights for genomics-based crop improvement.
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Affiliation(s)
- Ziliang Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China
| | - Meng Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China
| | - Yan Long
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China
| | - Yongju Huang
- School of Life and Medical Sciences, University of Hertfordshire, Hatfield, Hertfordshire AL10 9AB UK
| | - Lei Shi
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China
| | - Chunyu Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China
| | - Xiang Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China
| | - Bruce D. L. Fitt
- School of Life and Medical Sciences, University of Hertfordshire, Hatfield, Hertfordshire AL10 9AB UK
| | - Jinxia Xiang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China
| | - Annaliese S. Mason
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Rod J. Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Peifa Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China
| | - Jinling Meng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China
| | - Jun Zou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China
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40
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Guignard MS, Leitch AR, Acquisti C, Eizaguirre C, Elser JJ, Hessen DO, Jeyasingh PD, Neiman M, Richardson AE, Soltis PS, Soltis DE, Stevens CJ, Trimmer M, Weider LJ, Woodward G, Leitch IJ. Impacts of Nitrogen and Phosphorus: From Genomes to Natural Ecosystems and Agriculture. Front Ecol Evol 2017. [DOI: 10.3389/fevo.2017.00070] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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41
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Würschum T, Maurer HP, Weissmann S, Hahn V, Leiser WL. Accuracy of within- and among-family genomic prediction in triticale. PLANT BREEDING 2017. [PMID: 0 DOI: 10.1111/pbr.12465] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Affiliation(s)
- Tobias Würschum
- State Plant Breeding Institute; University of Hohenheim; 70599 Stuttgart Germany
| | - Hans Peter Maurer
- State Plant Breeding Institute; University of Hohenheim; 70599 Stuttgart Germany
| | | | - Volker Hahn
- State Plant Breeding Institute; University of Hohenheim; 70599 Stuttgart Germany
| | - Willmar L. Leiser
- State Plant Breeding Institute; University of Hohenheim; 70599 Stuttgart Germany
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42
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Mason AS, Higgins EE, Snowdon RJ, Batley J, Stein A, Werner C, Parkin IAP. A user guide to the Brassica 60K Illumina Infinium™ SNP genotyping array. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2017; 130:621-633. [PMID: 28220206 DOI: 10.1007/s00122-016-2849-1] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 09/14/2016] [Indexed: 06/06/2023]
Abstract
The Brassica napus 60K Illumina Infinium™ SNP array has had huge international uptake in the rapeseed community due to the revolutionary speed of acquisition and ease of analysis of this high-throughput genotyping data, particularly when coupled with the newly available reference genome sequence. However, further utilization of this valuable resource can be optimized by better understanding the promises and pitfalls of SNP arrays. We outline how best to analyze Brassica SNP marker array data for diverse applications, including linkage and association mapping, genetic diversity and genomic introgression studies. We present data on which SNPs are locus-specific in winter, semi-winter and spring B. napus germplasm pools, rather than amplifying both an A-genome and a C-genome locus or multiple loci. Common issues that arise when analyzing array data will be discussed, particularly those unique to SNP markers and how to deal with these for practical applications in Brassica breeding applications.
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Affiliation(s)
- Annaliese S Mason
- Department of Plant Breeding, IFZ for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany.
| | - Erin E Higgins
- Agriculture and Agri-Food Canada, 107 Science Place, Saskatoon, S7N0X2, Canada
| | - Rod J Snowdon
- Department of Plant Breeding, IFZ for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Jacqueline Batley
- School of Agriculture and Food Sciences and Centre for Integrative Legume Research, The University of Queensland, Brisbane, 4072, Australia
- School of Plant Biology and The UWA Institute of Agriculture, The University of Western Australia, 35 Stirling Highway, Crawley, 6009, Perth, Australia
| | - Anna Stein
- Department of Plant Breeding, IFZ for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Christian Werner
- Department of Plant Breeding, IFZ for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Isobel A P Parkin
- Agriculture and Agri-Food Canada, 107 Science Place, Saskatoon, S7N0X2, Canada
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43
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Jiang Y, Schulthess AW, Rodemann B, Ling J, Plieske J, Kollers S, Ebmeyer E, Korzun V, Argillier O, Stiewe G, Ganal MW, Röder MS, Reif JC. Validating the prediction accuracies of marker-assisted and genomic selection of Fusarium head blight resistance in wheat using an independent sample. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2017; 130:471-482. [PMID: 27858103 DOI: 10.1007/s00122-016-2827-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 11/12/2016] [Indexed: 06/06/2023]
Abstract
Compared with independent validation, cross-validation simultaneously sampling genotypes and environments provided similar estimates of accuracy for genomic selection, but inflated estimates for marker-assisted selection. Estimates of prediction accuracy of marker-assisted (MAS) and genomic selection (GS) require validations. The main goal of our study was to compare the prediction accuracies of MAS and GS validated in an independent sample with results obtained from fivefold cross-validation using genomic and phenotypic data for Fusarium head blight resistance in wheat. In addition, the applicability of the reliability criterion, a concept originally developed in the context of classic animal breeding and GS, was explored for MAS. We observed that prediction accuracies of MAS were overestimated by 127% using cross-validation sampling genotype and environments in contrast to independent validation. In contrast, prediction accuracies of GS determined in independent samples are similar to those estimated with cross-validation sampling genotype and environments. This can be explained by small population differentiation between the training and validation sets in our study. For European wheat breeding, which is so far characterized by a slow temporal dynamic in allele frequencies, this assumption seems to be realistic. Thus, GS models used to improve European wheat populations are expected to possess a long-lasting validity. Since quantitative trait loci information can be exploited more precisely if the predicted genotype is more related to the training population, the reliability criterion is also a valuable tool to judge the level of prediction accuracy of individual genotypes in MAS.
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Affiliation(s)
- Yong Jiang
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany
| | - Albert Wilhelm Schulthess
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany
| | | | - Jie Ling
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany
| | | | | | | | | | | | | | | | - Marion S Röder
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany
| | - Jochen C Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Gatersleben, Germany.
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Liu P, Zhao Y, Liu G, Wang M, Hu D, Hu J, Meng J, Reif JC, Zou J. Hybrid Performance of an Immortalized F 2 Rapeseed Population Is Driven by Additive, Dominance, and Epistatic Effects. FRONTIERS IN PLANT SCIENCE 2017; 8:815. [PMID: 28572809 PMCID: PMC5435766 DOI: 10.3389/fpls.2017.00815] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2017] [Accepted: 05/01/2017] [Indexed: 05/19/2023]
Abstract
Genomics-based prediction of hybrid performance promises to boost selection gain. The main goal of our study was to investigate the relevance of additive, dominance, and epistatic effects for determining hybrid seed yield in a biparental rapeseed population. We re-analyzed 60,000 SNP array and seed yield data points from an immortalized F2 population comprised of 318 hybrids and 180 parental lines by performing genome-wide QTL mapping and predictions in combination with five-fold cross-validation. Moreover, an additional set of 37 hybrids were genotyped and phenotyped in an independent environment. The decomposition of the phenotypic variance components and the cross-validated results of the QTL mapping and genome-wide predictions revealed that the hybrid performance in rapeseed was driven by a mix of additive, dominance, and epistatic effects. Interestingly, the genome-wide prediction accuracy in the additional 37 hybrids remained high when modeling exclusively additive effects but was severely reduced when dominance or epistatic effects were also included. This loss in accuracy was most likely caused by more pronounced interactions of environments with dominance and epistatic effects than with additive effects. Consequently, the development of robust hybrid prediction models, including dominance and epistatic effects, required much deeper phenotyping in multi-environmental trials.
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Affiliation(s)
- Peifa Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityWuhan, China
| | - Yusheng Zhao
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)Stadt Seeland, Germany
| | - Guozheng Liu
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)Stadt Seeland, Germany
| | - Meng Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityWuhan, China
| | - Dandan Hu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityWuhan, China
| | - Jun Hu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityWuhan, China
| | - Jinling Meng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityWuhan, China
| | - Jochen C. Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)Stadt Seeland, Germany
- *Correspondence: Jochen C. Reif
| | - Jun Zou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural UniversityWuhan, China
- Jun Zou
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45
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Fu YB, Yang MH, Zeng F, Biligetu B. Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding. FRONTIERS IN PLANT SCIENCE 2017; 8:1182. [PMID: 28729875 PMCID: PMC5498511 DOI: 10.3389/fpls.2017.01182] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 06/20/2017] [Indexed: 05/09/2023]
Abstract
Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding.
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Affiliation(s)
- Yong-Bi Fu
- Plant Gene Resources of Canada, Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, SaskatoonSK, Canada
- *Correspondence: Yong-Bi Fu,
| | - Mo-Hua Yang
- Plant Gene Resources of Canada, Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, SaskatoonSK, Canada
- College of Forestry, Central South University of Forestry and TechnologyChangsha, China
| | - Fangqin Zeng
- Plant Gene Resources of Canada, Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, SaskatoonSK, Canada
| | - Bill Biligetu
- Department of Plant Sciences, University of Saskatchewan, SaskatoonSK, Canada
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46
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Qian L, Voss-Fels K, Cui Y, Jan HU, Samans B, Obermeier C, Qian W, Snowdon RJ. Deletion of a Stay-Green Gene Associates with Adaptive Selection in Brassica napus. MOLECULAR PLANT 2016; 9:1559-1569. [PMID: 27825945 DOI: 10.1016/j.molp.2016.10.017] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Revised: 10/19/2016] [Accepted: 10/20/2016] [Indexed: 05/02/2023]
Abstract
Chlorophyll levels provide important information about plant growth and physiological plasticity in response to changing environments. The stay-green gene NON-YELLOWING 1 (NYE1) is believed to regulate chlorophyll degradation during senescence, concomitantly affecting the disassembly of the light-harvesting complex and hence indirectly influencing photosynthesis. We identified Brassica napus accessions carrying an NYE1 deletion associated with increased chlorophyll content, and with upregulated expression of light-harvesting complex and photosynthetic reaction center (PSI and PSII) genes. Comparative analysis of the seed oil content of accessions with related genetic backgrounds revealed that the B. napus NYE1 gene deletion (bnnye1) affected oil accumulation, and linkage disequilibrium signatures suggested that the locus has been subject to artificial selection by breeding in oilseed B. napus forms. Comparative analysis of haplotype diversity groups (haplogroups) between three different ecotypes of the allopolyploid B. napus and its A-subgenome diploid progenitor, Brassica rapa, indicated that introgression of the bnnye1 deletion from Asian B. rapa into winter-type B. napus may have simultaneously improved its adaptation to cooler environments experienced by autumn-sown rapeseed.
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Affiliation(s)
- Lunwen Qian
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Kai Voss-Fels
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Yixin Cui
- College of Agronomy and Biotechnology, Southwest University, 400716 Chongqing, China
| | - Habib U Jan
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Birgit Samans
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Christian Obermeier
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Wei Qian
- College of Agronomy and Biotechnology, Southwest University, 400716 Chongqing, China
| | - Rod J Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany.
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47
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Zou J, Zhao Y, Liu P, Shi L, Wang X, Wang M, Meng J, Reif JC. Seed Quality Traits Can Be Predicted with High Accuracy in Brassica napus Using Genomic Data. PLoS One 2016; 11:e0166624. [PMID: 27880793 PMCID: PMC5120799 DOI: 10.1371/journal.pone.0166624] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 11/01/2016] [Indexed: 11/19/2022] Open
Abstract
Improving seed oil yield and quality are central targets in rapeseed (Brassica napus) breeding. The primary goal of our study was to examine and compare the potential and the limits of marker-assisted selection and genome-wide prediction of six important seed quality traits of B. napus. Our study is based on a bi-parental population comprising 202 doubled haploid lines and a diverse validation set including 117 B. napus inbred lines derived from interspecific crosses between B. rapa and B. carinata. We used phenotypic data for seed oil, protein, erucic acid, linolenic acid, stearic acid, and glucosinolate content. All lines were genotyped with a 60k SNP array. We performed five-fold cross-validations in combination with linkage mapping and four genome-wide prediction approaches in the bi-parental population. Quantitative trait loci (QTL) with large effects were detected for erucic acid, stearic acid, and glucosinolate content, blazing the trail for marker-assisted selection. Despite substantial differences in the complexity of the genetic architecture of the six traits, genome-wide prediction models had only minor impacts on the prediction accuracies. We evaluated the effects of training population size, marker density and phenotyping intensity on the prediction accuracy. The prediction accuracy in the independent and genetically very distinct validation set still amounted to 0.14 for protein content and 0.17 for oil content reflecting the utility of the developed calibration models even in very diverse backgrounds.
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Affiliation(s)
- Jun Zou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Peifa Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Lei Shi
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Xiaohua Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Meng Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Jinling Meng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Jochen Christoph Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
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Slater AT, Cogan NOI, Forster JW, Hayes BJ, Daetwyler HD. Improving Genetic Gain with Genomic Selection in Autotetraploid Potato. THE PLANT GENOME 2016; 9. [PMID: 27902807 DOI: 10.3835/plantgenome2016.02.0021] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Potato ( L.) breeders consider a large number of traits during cultivar development and progress in conventional breeding can be slow. There is accumulating evidence that some of these traits, such as yield, are affected by a large number of genes with small individual effects. Recently, significant efforts have been applied to the development of genomic resources to improve potato breeding, culminating in a draft genome sequence and the identification of a large number of single nucleotide polymorphisms (SNPs). The availability of these genome-wide SNPs is a prerequisite for implementing genomic selection for improvement of polygenic traits such as yield. In this review, we investigate opportunities for the application of genomic selection to potato, including novel breeding program designs. We have considered a number of factors that will influence this process, including the autotetraploid and heterozygous genetic nature of potato, the rate of decay of linkage disequilibrium, the number of required markers, the design of a reference population, and trait heritability. Based on estimates of the effective population size derived from a potato breeding program, we have calculated the expected accuracy of genomic selection for four key traits of varying heritability and propose that it will be reasonably accurate. We compared the expected genetic gain from genomic selection with the expected gain from phenotypic and pedigree selection, and found that genetic gain can be substantially improved by using genomic selection.
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49
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Auinger HJ, Schönleben M, Lehermeier C, Schmidt M, Korzun V, Geiger HH, Piepho HP, Gordillo A, Wilde P, Bauer E, Schön CC. Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2016; 129:2043-2053. [PMID: 27480157 PMCID: PMC5069347 DOI: 10.1007/s00122-016-2756-5] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Accepted: 07/15/2016] [Indexed: 05/02/2023]
Abstract
KEY MESSAGE Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant height and thousand kernel weight within and across four selection cycles of a hybrid rye breeding program. Prediction performance was assessed using genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP). A total of 1040 S2 lines were genotyped with 16 k SNPs and each year testcrosses of 260 S2 lines were phenotyped in seven or eight locations. The performance gap between GBLUP and PBLUP increased significantly for all traits when model calibration was performed on aggregated data from several cycles. Prediction accuracies obtained from cross-validation were in the order of 0.70 for all traits when data from all cycles (N CS = 832) were used for model training and exceeded within-cycle accuracies in all cases. As long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size, aggregating data from several preceding cycles is recommended for predicting genetic values in subsequent cycles despite decreasing relatedness over time.
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Affiliation(s)
- Hans-Jürgen Auinger
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Liesel-Beckmann-Str. 2, 85354, Freising, Germany
| | - Manfred Schönleben
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Liesel-Beckmann-Str. 2, 85354, Freising, Germany
| | - Christina Lehermeier
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Liesel-Beckmann-Str. 2, 85354, Freising, Germany
| | - Malthe Schmidt
- KWS LOCHOW GMBH, Ferdinand-von-Lochow-Straße 5, 29303, Bergen, Germany
| | - Viktor Korzun
- KWS LOCHOW GMBH, Ferdinand-von-Lochow-Straße 5, 29303, Bergen, Germany
| | - Hartwig H Geiger
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Fruwirthstr. 21, 70599, Stuttgart, Germany
| | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstr. 23, 70599, Stuttgart, Germany
| | - Andres Gordillo
- KWS LOCHOW GMBH, Ferdinand-von-Lochow-Straße 5, 29303, Bergen, Germany
| | - Peer Wilde
- KWS LOCHOW GMBH, Ferdinand-von-Lochow-Straße 5, 29303, Bergen, Germany
| | - Eva Bauer
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Liesel-Beckmann-Str. 2, 85354, Freising, Germany
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Liesel-Beckmann-Str. 2, 85354, Freising, Germany.
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