1
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Makhoul M, Schlichtermann RH, Ugwuanyi S, Weber SE, Voss-Fels KP, Stahl A, Zetzsche H, Wittkop B, Snowdon RJ, Obermeier C. Novel PHOTOPERIOD-1 gene variants associate with yield-related and root-angle traits in European bread wheat. Theor Appl Genet 2024; 137:125. [PMID: 38727862 DOI: 10.1007/s00122-024-04634-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/20/2024] [Indexed: 05/13/2024]
Abstract
KEY MESSAGE PHOTOPERIOD-1 homoeologous gene copies play a pivotal role in regulation of flowering time in wheat. Here, we show that their influence also extends to spike and shoot architecture and even impacts root development. The sequence diversity of three homoeologous copies of the PHOTOPERIOD-1 gene in European winter wheat was analyzed by Oxford Nanopore amplicon-based multiplex sequencing and molecular markers in a panel of 194 cultivars representing breeding progress over the past 5 decades. A strong, consistent association with an average 8% increase in grain yield was observed for the PpdA1-Hap1 haplotype across multiple environments. This haplotype was found to be linked in 51% of cultivars to the 2NS/2AS translocation, originally introduced from Aegilops ventricosa, which leads to an overestimation of its effect. However, even in cultivars without the 2NS/2AS translocation, PpdA1-Hap1 was significantly associated with increased grain yield, kernel per spike and kernel per m2 under optimal growth conditions, conferring a 4% yield advantage compared to haplotype PpdA1-Hap4. In contrast to Ppd-B1 and Ppd-D1, the Ppd-A1 gene exhibits novel structural variations and a high number of SNPs, highlighting the evolutionary changes that have occurred in this region over the course of wheat breeding history. Additionally, cultivars carrying the photoperiod-insensitive Ppd-D1a allele not only exhibit earlier heading, but also deeper roots compared to those with photoperiod-sensitive alleles under German conditions. PCR and KASP assays have been developed that can be effectively employed in marker-assisted breeding programs to introduce these favorable haplotypes.
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Affiliation(s)
- Manar Makhoul
- Department of Plant Breeding, Justus Liebig University Giessen, Giessen, Germany
| | | | - Samson Ugwuanyi
- Department of Plant Breeding, Justus Liebig University Giessen, Giessen, Germany
| | - Sven E Weber
- Department of Plant Breeding, Justus Liebig University Giessen, Giessen, Germany
| | - Kai P Voss-Fels
- Institute for Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Andreas Stahl
- Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany
| | - Holger Zetzsche
- Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany
| | - Benjamin Wittkop
- Department of Plant Breeding, Justus Liebig University Giessen, Giessen, Germany
| | - Rod J Snowdon
- Department of Plant Breeding, Justus Liebig University Giessen, Giessen, Germany
| | - Christian Obermeier
- Department of Plant Breeding, Justus Liebig University Giessen, Giessen, Germany.
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2
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Yadav S, Ross EM, Wei X, Liu S, Nguyen LT, Powell O, Hickey LT, Deomano E, Atkin F, Voss-Fels KP, Hayes BJ. Use of continuous genotypes for genomic prediction in sugarcane. Plant Genome 2024; 17:e20417. [PMID: 38066702 DOI: 10.1002/tpg2.20417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/30/2023] [Accepted: 11/14/2023] [Indexed: 03/22/2024]
Abstract
Genomic selection in sugarcane faces challenges due to limited genomic tools and high genomic complexity, particularly because of its high and variable ploidy. The classification of genotypes for single nucleotide polymorphisms (SNPs) becomes difficult due to the wide range of possible allele dosages. Previous genomic studies in sugarcane used pseudo-diploid genotyping, grouping all heterozygotes into a single class. In this study, we investigate the use of continuous genotypes as a proxy for allele-dosage in genomic prediction models. The hypothesis is that continuous genotypes could better reflect allele dosage at SNPs linked to mutations affecting target traits, resulting in phenotypic variation. The dataset included genotypes of 1318 clones at 58K SNP markers, with about 26K markers filtered using standard quality controls. Predictions for tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and fiber content (Fiber) were made using parametric, non-parametric, and Bayesian methods. Continuous genotypes increased accuracy by 5%-7% for CCS and Fiber. The pseudo-diploid parametrization performed better for TCH. Reproducing kernel Hilbert spaces model with Gaussian kernel and AK4 (arc-cosine kernel with hidden layer 4) kernel outperformed other methods for TCH and CCS, suggesting that non-additive effects might influence these traits. The prevalence of low-dosage markers in the study may have limited the benefits of approximating allele-dosage information with continuous genotypes in genomic prediction models. Continuous genotypes simplify genomic prediction in polyploid crops, allowing additional markers to be used without adhering to pseudo-diploid inheritance. The approach can particularly benefit high ploidy species or emerging crops with unknown ploidy.
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Affiliation(s)
- Seema Yadav
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
| | - Elizabeth M Ross
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
| | - Xianming Wei
- Sugar Research Australia, Mackay, Queensland, Australia
| | - Shouye Liu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Loan To Nguyen
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
| | - Owen Powell
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
| | - Emily Deomano
- Sugar Research Australia, Indooroopilly, Queensland, Australia
| | - Felicity Atkin
- Sugar Research Australia, Meringa Gordonvale, Queensland, Australia
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia
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3
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Yadav S, Ross EM, Wei X, Powell O, Hivert V, Hickey LT, Atkin F, Deomano E, Aitken KS, Voss-Fels KP, Hayes BJ. Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies. Front Plant Sci 2023; 14:1260517. [PMID: 38023905 PMCID: PMC10667552 DOI: 10.3389/fpls.2023.1260517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023]
Abstract
Mate-allocation strategies in breeding programs can improve progeny performance by harnessing non-additive genetic effects. These approaches prioritise predicted progeny merit over parental breeding value, making them particularly appealing for clonally propagated crops such as sugarcane. We conducted a comparative analysis of mate-allocation strategies, exploring utilising non-additive and heterozygosity effects to maximise clonal performance with schemes that solely consider additive effects to optimise breeding value. Using phenotypic and genotypic data from a population of 2,909 clones evaluated in final assessment trials of Australian sugarcane breeding programs, we focused on three important traits: tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and Fibre. By simulating families from all possible crosses (1,225) with 50 progenies each, we predicted the breeding and clonal values of progeny using two models: GBLUP (considering additive effects only) and extended-GBLUP (incorporating additive, non-additive, and heterozygosity effects). Integer linear programming was used to identify the optimal mate-allocation among selected parents. Compared to breeding value-based approaches, mate-allocation strategies based on clonal performance yielded substantial improvements, with predicted progeny values increasing by 57% for TCH, 12% for CCS, and 16% for fibre. Our simulation study highlights the effectiveness of mate-allocation approaches that exploit non-additive and heterozygosity effects, resulting in superior clonal performance. However, there was a notable decline in additive gain, particularly for TCH, likely due to significant epistatic effects. When selecting crosses based on clonal performance for TCH, the inbreeding coefficient of progeny was significantly lower compared to random mating, underscoring the advantages of leveraging non-additive and heterozygosity effects in mitigating inbreeding depression. Thus, mate-allocation strategies are recommended in clonally propagated crops to enhance clonal performance and reduce the negative impacts of inbreeding.
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Affiliation(s)
- Seema Yadav
- Queensland Alliance for Agriculture and Food Science, The University of Queensland, Brisbane, QLD, Australia
| | - Elizabeth M. Ross
- Queensland Alliance for Agriculture and Food Science, The University of Queensland, Brisbane, QLD, Australia
| | - Xianming Wei
- Sugar Research Australia, Mackay, QLD, Australia
| | - Owen Powell
- Queensland Alliance for Agriculture and Food Science, The University of Queensland, Brisbane, QLD, Australia
| | - Valentin Hivert
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Lee T. Hickey
- Queensland Alliance for Agriculture and Food Science, The University of Queensland, Brisbane, QLD, Australia
| | - Felicity Atkin
- Sugar Research Australia, Meringa Gordonvale, QLD, Australia
| | - Emily Deomano
- Sugar Research Australia, Indooroopilly, QLD, Australia
| | - Karen S. Aitken
- Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Brisbane, QlD, Australia
| | - Kai P. Voss-Fels
- Queensland Alliance for Agriculture and Food Science, The University of Queensland, Brisbane, QLD, Australia
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Ben J. Hayes
- Queensland Alliance for Agriculture and Food Science, The University of Queensland, Brisbane, QLD, Australia
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4
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Blois L, de Miguel M, Bert PF, Ollat N, Rubio B, Voss-Fels KP, Schmid J, Marguerit E. Dissecting the genetic architecture of root-related traits in a grafted wild Vitis berlandieri population for grapevine rootstock breeding. Theor Appl Genet 2023; 136:223. [PMID: 37838631 PMCID: PMC10576685 DOI: 10.1007/s00122-023-04472-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 09/25/2023] [Indexed: 10/16/2023]
Abstract
In woody perennial plants, quantitative genetics and association studies remain scarce for root-related traits, due to the time required to obtain mature plants and the complexity of phenotyping. In grapevine, a grafted cultivated plant, most of the rootstocks used are hybrids between American Vitis species (V. rupestris, V. riparia, and V. berlandieri). In this study, we used a wild population of an American Vitis species (V. berlandieri) to analyze the genetic architecture of the root-related traits of rootstocks in a grafted context. We studied a population consisting of 211 genotypes, with one to five replicates each (n = 846 individuals), plus four commercial rootstocks as control genotypes (110R, 5BB, Börner, and SO4). After two independent years of experimentation, the best linear unbiased estimates method revealed root-related traits with a moderate-to-high heritability (0.36-0.82) and coefficient of genetic variation (0.15-0.45). A genome-wide association study was performed with the BLINK model, leading to the detection of 11 QTL associated with four root-related traits (one QTL was associated with the total number of roots, four were associated with the number of small roots (< 1 mm in diameter), two were associated with the number of medium-sized roots (1 mm < diameter < 2 mm), and four were associated with mean diameter) accounting for up to 25.1% of the variance. Three genotypes were found to have better root-related trait performances than the commercial rootstocks and therefore constitute possible new candidates for use in grapevine rootstock breeding programs.
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Affiliation(s)
- Louis Blois
- EGFV, Bordeaux Sciences Agro, INRAE, ISVV, Univ. Bordeaux, 33882, Villenave d'Ornon, France.
- Department of Grapevine Breeding, Geisenheim University, Von Lade Str. 1, 65366, Geisenheim, Germany.
| | - Marina de Miguel
- EGFV, Bordeaux Sciences Agro, INRAE, ISVV, Univ. Bordeaux, 33882, Villenave d'Ornon, France
| | - Pierre-François Bert
- EGFV, Bordeaux Sciences Agro, INRAE, ISVV, Univ. Bordeaux, 33882, Villenave d'Ornon, France
| | - Nathalie Ollat
- EGFV, Bordeaux Sciences Agro, INRAE, ISVV, Univ. Bordeaux, 33882, Villenave d'Ornon, France
| | - Bernadette Rubio
- EGFV, Bordeaux Sciences Agro, INRAE, ISVV, Univ. Bordeaux, 33882, Villenave d'Ornon, France
| | - Kai P Voss-Fels
- Department of Grapevine Breeding, Geisenheim University, Von Lade Str. 1, 65366, Geisenheim, Germany
| | - Joachim Schmid
- Department of Grapevine Breeding, Geisenheim University, Von Lade Str. 1, 65366, Geisenheim, Germany
| | - Elisa Marguerit
- EGFV, Bordeaux Sciences Agro, INRAE, ISVV, Univ. Bordeaux, 33882, Villenave d'Ornon, France
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5
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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. Front Plant Sci 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] [What about the content of this article? (0)] [Affiliation(s)] [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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6
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Fichtl L, Hofmann M, Kahlen K, Voss-Fels KP, Cast CS, Ollat N, Vivin P, Loose S, Nsibi M, Schmid J, Strack T, Schultz HR, Smith J, Friedel M. Towards grapevine root architectural models to adapt viticulture to drought. Front Plant Sci 2023; 14:1162506. [PMID: 36998680 PMCID: PMC10043487 DOI: 10.3389/fpls.2023.1162506] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 02/27/2023] [Indexed: 05/31/2023]
Abstract
To sustainably adapt viticultural production to drought, the planting of rootstock genotypes adapted to a changing climate is a promising means. Rootstocks contribute to the regulation of scion vigor and water consumption, modulate scion phenological development and determine resource availability by root system architecture development. There is, however, a lack of knowledge on spatio-temporal root system development of rootstock genotypes and its interactions with environment and management that prevents efficient knowledge transfer into practice. Hence, winegrowers take only limited advantage of the large variability of existing rootstock genotypes. Models of vineyard water balance combined with root architectural models, using both static and dynamic representations of the root system, seem promising tools to match rootstock genotypes to frequently occurring future drought stress scenarios and address scientific knowledge gaps. In this perspective, we discuss how current developments in vineyard water balance modeling may provide the background for a better understanding of the interplay of rootstock genotypes, environment and management. We argue that root architecture traits are key drivers of this interplay, but our knowledge on rootstock architectures in the field remains limited both qualitatively and quantitatively. We propose phenotyping methods to help close current knowledge gaps and discuss approaches to integrate phenotyping data into different models to advance our understanding of rootstock x environment x management interactions and predict rootstock genotype performance in a changing climate. This could also provide a valuable basis for optimizing breeding efforts to develop new grapevine rootstock cultivars with optimal trait configurations for future growing conditions.
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Affiliation(s)
- Lukas Fichtl
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
| | - Marco Hofmann
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
| | - Katrin Kahlen
- Department of Modeling and Systems Analysis, Hochschule Geisenheim University, Geisenheim, Germany
| | - Kai P. Voss-Fels
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Clément Saint Cast
- EGFV, University of Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, Villenave d’Ornon, France
| | - Nathalie Ollat
- EGFV, University of Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, Villenave d’Ornon, France
| | - Philippe Vivin
- EGFV, University of Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, Villenave d’Ornon, France
| | - Simone Loose
- Department of Wine and Beverage Business, Hochschule Geisenheim University, Geisenheim, Germany
| | - Mariem Nsibi
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Joachim Schmid
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Timo Strack
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Hans Reiner Schultz
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
| | - Jason Smith
- Gulbali Institute for Agriculture, Water and Environment, Charles Sturt University, Orange, NSW, Australia
| | - Matthias Friedel
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
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7
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Villiers K, Dinglasan E, Hayes BJ, Voss-Fels KP. genomicSimulation: fast R functions for stochastic simulation of breeding programs. G3 Genes|Genomes|Genetics 2022; 12:6687129. [PMID: 36053200 PMCID: PMC9526041 DOI: 10.1093/g3journal/jkac216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 08/03/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Simulation tools are key to designing and optimizing breeding programs that are multiyear, high-effort endeavors. Tools that operate on real genotypes and integrate easily with other analysis software can guide users toward crossing decisions that best balance genetic gains and genetic diversity required to maintain gains in the future. Here, we present genomicSimulation, a fast and flexible tool for the stochastic simulation of crossing and selection based on real genotypes. It is fully written in C for high execution speeds, has minimal dependencies, and is available as an R package for the integration with R’s broad range of analysis and visualization tools. Comparisons of a simulated recreation of a breeding program to a real data set demonstrate the simulated offspring from the tool correctly show key population features, such as genomic relationships and approximate linkage disequilibrium patterns. Both versions of genomicSimulation are freely available on GitHub: The R package version at https://github.com/vllrs/genomicSimulation/ and the C library version at https://github.com/vllrs/genomicSimulationC/.
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Affiliation(s)
- Kira Villiers
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland , St Lucia, 4072 QLD, Australia
| | - Eric Dinglasan
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland , St Lucia, 4072 QLD, Australia
| | - Ben J Hayes
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland , St Lucia, 4072 QLD, Australia
| | - Kai P Voss-Fels
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland , St Lucia, 4072 QLD, Australia
- Department of Grapevine Breeding, Hochschule Geisenheim University , Geisenheim 65366, Germany
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8
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Jambuthenne DT, Riaz A, Athiyannan N, Alahmad S, Ng WL, Ziems L, Afanasenko O, Periyannan SK, Aitken E, Platz G, Godwin I, Voss-Fels KP, Dinglasan E, Hickey LT. Mining the Vavilov wheat diversity panel for new sources of adult plant resistance to stripe rust. Theor Appl Genet 2022; 135:1355-1373. [PMID: 35113190 PMCID: PMC9033734 DOI: 10.1007/s00122-022-04037-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 01/12/2022] [Indexed: 06/14/2023]
Abstract
Multi-year evaluation of the Vavilov wheat diversity panel identified new sources of adult plant resistance to stripe rust. Genome-wide association studies revealed the key genomic regions influencing resistance, including seven novel loci. Wheat stripe rust (YR) caused by Puccinia striiformis f. sp. tritici (Pst) poses a significant threat to global food security. Resistance genes commonly found in many wheat varieties have been rendered ineffective due to the rapid evolution of the pathogen. To identify novel sources of adult plant resistance (APR), 292 accessions from the N.I. Vavilov Institute of Plant Genetic Resources, Saint Petersburg, Russia, were screened for known APR genes (i.e. Yr18, Yr29, Yr46, Yr33, Yr39 and Yr59) using linked polymerase chain reaction (PCR) molecular markers. Accessions were evaluated against Pst (pathotype 134 E16 A + Yr17 + Yr27) at seedling and adult plant stages across multiple years (2014, 2015 and 2016) in Australia. Phenotypic analyses identified 132 lines that potentially carry novel sources of APR to YR. Genome-wide association studies (GWAS) identified 68 significant marker-trait associations (P < 0.001) for YR resistance, representing 47 independent quantitative trait loci (QTL) regions. Fourteen genomic regions overlapped with previously reported Yr genes, including Yr29, Yr56, Yr5, Yr43, Yr57, Yr30, Yr46, Yr47, Yr35, Yr36, Yrxy1, Yr59, Yr52 and YrYL. In total, seven QTL (positioned on chromosomes 1D, 2A, 3A, 3D, 5D, 7B and 7D) did not collocate with previously reported genes or QTL, indicating the presence of promising novel resistance factors. Overall, the Vavilov diversity panel provides a rich source of new alleles which could be used to broaden the genetic bases of YR resistance in modern wheat varieties.
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Affiliation(s)
- Dilani T Jambuthenne
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Adnan Riaz
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Naveenkumar Athiyannan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
- Commonwealth Scientific and Industrial Research Organization (CSIRO), Agriculture and Food,, Canberra, ACT, Australia
| | - Samir Alahmad
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Wei Ling Ng
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Laura Ziems
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Olga Afanasenko
- Department of Plant Resistance To Diseases, All Russian Research Institute for Plant Protection, St Petersburg, Russia, 196608
| | - Sambasivam K Periyannan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
- Commonwealth Scientific and Industrial Research Organization (CSIRO), Agriculture and Food,, Canberra, ACT, Australia
| | - Elizabeth Aitken
- School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD, Australia
| | - Greg Platz
- Department of Agriculture and Fisheries, Hermitage Research Facility, Warwick, QLD, Australia
| | - Ian Godwin
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Eric Dinglasan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia.
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia.
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9
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Copley JP, Engle BN, Ross EM, Speight S, Fordyce G, Wood BJ, Voss-Fels KP, Hayes BJ. Environmental variation effects fertility in tropical beef cattle. Transl Anim Sci 2022; 6:txac035. [PMID: 35529039 PMCID: PMC9070491 DOI: 10.1093/tas/txac035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 03/25/2022] [Indexed: 11/30/2022] Open
Abstract
The northern Australia beef cattle industry operates in harsh environmental conditions which consistently suppress female fertility. To better understand the environmental effect on cattle raised extensively in northern Australia, new environmental descriptors were defined for 54 commercial herds located across the region. Three fertility traits, based on the presence of a corpus luteum at 600 d of age, indicating puberty, (CL Presence, n = 25,176), heifer pregnancy (n = 20,989) and first lactation pregnancy (n = 10,072) were recorded. Temperature, humidity, and rainfall were obtained from publicly available data based on herd location. Being pubertal at 600 d (i.e. CL Presence) increased the likelihood of success at heifer pregnancy and first lactation pregnancy (P < 0.05), underscoring the importance of early puberty in reproductive success. A temperature humidity index (THI) of 65–70 had a significant (P < 0.05) negative effect on first lactation pregnancy rate, heifer pregnancy and puberty at 600 d of age. Area under the curve of daily THI was significant (P < 0.05) and reduced the likelihood of pregnancy at first lactation and puberty at 600 days. Deviation from long-term average rainfall was not significant (P < 0.05) for any trait. Average daily weight gain had a significant and positive relationship (P < 0.05) for heifer and first lactation pregnancy. The results indicate that chronic or cumulative heat load is more determinantal to reproductive performance than acute heat stress. The reason for the lack of a clear relationship between acute heat stress and reproductive performance is unclear but may be partially explained by peak THI and peak nutrition coinciding at the same time. Sufficient evidence was found to justify the use of average daily weight gain and chronic heat load as descriptors to define an environmental gradient.
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Affiliation(s)
- James P Copley
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia, QLD 4072, Australia
- Corresponding author:
| | - Bailey N Engle
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia, QLD 4072, Australia
| | - Elizabeth M Ross
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia, QLD 4072, Australia
| | - Shannon Speight
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia, QLD 4072, Australia
- Black Box Co, Mareeba, QLD 4880, Australia
| | - Geoffry Fordyce
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia, QLD 4072, Australia
| | - Benjamin J Wood
- School of Veterinary Science, University of Queensland, Gatton, QLD 4343, Australia
| | - Kai P Voss-Fels
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia, QLD 4072, Australia
- Institute for Grapevine Breeding, Hochschule Geisenheim University, Geisenheim 65366, Germany
| | - Benjamin J Hayes
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia, QLD 4072, Australia
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10
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Rambla C, Van Der Meer S, Voss-Fels KP, Makhoul M, Obermeier C, Snowdon R, Ober ES, Watt M, Alahmad S, Hickey LT. A toolkit to rapidly modify root systems through single plant selection. Plant Methods 2022; 18:2. [PMID: 35012581 PMCID: PMC8750989 DOI: 10.1186/s13007-021-00834-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 12/22/2021] [Indexed: 05/15/2023]
Abstract
BACKGROUND The incorporation of root traits into elite germplasm is typically a slow process. Thus, innovative approaches are required to accelerate research and pre-breeding programs targeting root traits to improve yield stability in different environments and soil types. Marker-assisted selection (MAS) can help to speed up the process by selecting key genes or quantitative trait loci (QTL) associated with root traits. However, this approach is limited due to the complex genetic control of root traits and the limited number of well-characterised large effect QTL. Coupling MAS with phenotyping could increase the reliability of selection. Here we present a useful framework to rapidly modify root traits in elite germplasm. In this wheat exemplar, a single plant selection (SPS) approach combined three main elements: phenotypic selection (in this case for seminal root angle); MAS using KASP markers (targeting a root biomass QTL); and speed breeding to accelerate each cycle. RESULTS To develop a SPS approach that integrates non-destructive screening for seminal root angle and root biomass, two initial experiments were conducted. Firstly, we demonstrated that transplanting wheat seedlings from clear pots (for seminal root angle assessment) into sand pots (for root biomass assessment) did not impact the ability to differentiate genotypes with high and low root biomass. Secondly, we demonstrated that visual scores for root biomass were correlated with root dry weight (r = 0.72), indicating that single plants could be evaluated for root biomass in a non-destructive manner. To highlight the potential of the approach, we applied SPS in a backcrossing program which integrated MAS and speed breeding for the purpose of rapidly modifying the root system of elite bread wheat line Borlaug100. Bi-directional selection for root angle in segregating generations successfully shifted the mean root angle by 30° in the subsequent generation (P ≤ 0.05). Within 18 months, BC2F4:F5 introgression lines were developed that displayed a full range of root configurations, while retaining similar above-ground traits to the recurrent parent. Notably, the seminal root angle displayed by introgression lines varied more than 30° compared to the recurrent parent, resulting in lines with both narrow and wide root angles, and high and low root biomass phenotypes. CONCLUSION The SPS approach enables researchers and plant breeders to rapidly manipulate root traits of future crop varieties, which could help improve productivity in the face of increasing environmental fluctuations. The newly developed elite wheat lines with modified root traits provide valuable materials to study the value of different root systems to support yield in different environments and soil types.
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Affiliation(s)
- Charlotte Rambla
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Sarah Van Der Meer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Manar Makhoul
- 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
| | - Rod Snowdon
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Eric S Ober
- National Institute of Agricultural Botany (NIAB), 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Michelle Watt
- School of BioSciences, Faculty of Science, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Samir Alahmad
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia.
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia.
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11
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Yadav S, Ross EM, Aitken KS, Hickey LT, Powell O, Wei X, Voss-Fels KP, Hayes BJ. A linkage disequilibrium-based approach to position unmapped SNPs in crop species. BMC Genomics 2021; 22:773. [PMID: 34715779 PMCID: PMC8555328 DOI: 10.1186/s12864-021-08116-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 10/19/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND High-density SNP arrays are now available for a wide range of crop species. Despite the development of many tools for generating genetic maps, the genome position of many SNPs from these arrays is unknown. Here we propose a linkage disequilibrium (LD)-based algorithm to allocate unassigned SNPs to chromosome regions from sparse genetic maps. This algorithm was tested on sugarcane, wheat, and barley data sets. We calculated the algorithm's efficiency by masking SNPs with known locations, then assigning their position to the map with the algorithm, and finally comparing the assigned and true positions. RESULTS In the 20-fold cross-validation, the mean proportion of masked mapped SNPs that were placed by the algorithm to a chromosome was 89.53, 94.25, and 97.23% for sugarcane, wheat, and barley, respectively. Of the markers that were placed in the genome, 98.73, 96.45 and 98.53% of the SNPs were positioned on the correct chromosome. The mean correlations between known and new estimated SNP positions were 0.97, 0.98, and 0.97 for sugarcane, wheat, and barley. The LD-based algorithm was used to assign 5920 out of 21,251 unpositioned markers to the current Q208 sugarcane genetic map, representing the highest density genetic map for this species to date. CONCLUSIONS Our LD-based approach can be used to accurately assign unpositioned SNPs to existing genetic maps, improving genome-wide association studies and genomic prediction in crop species with fragmented and incomplete genome assemblies. This approach will facilitate genomic-assisted breeding for many orphan crops that lack genetic and genomic resources.
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Affiliation(s)
- Seema Yadav
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, 306 Carmody Rd., St. Lucia, Brisbane, Queensland, 4067, Australia.
| | - Elizabeth M Ross
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, 306 Carmody Rd., St. Lucia, Brisbane, Queensland, 4067, Australia
| | - Karen S Aitken
- Agriculture and Food, CSIRO, Queensland Bioscience Precinct, St. Lucia, Brisbane, Queensland, 4067, Australia
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, 306 Carmody Rd., St. Lucia, Brisbane, Queensland, 4067, Australia
| | - Owen Powell
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, 306 Carmody Rd., St. Lucia, Brisbane, Queensland, 4067, Australia
| | - Xianming Wei
- Sugar Research Australia, Mackay, QLD, 4741, Australia
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, 306 Carmody Rd., St. Lucia, Brisbane, Queensland, 4067, Australia
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, 306 Carmody Rd., St. Lucia, Brisbane, Queensland, 4067, Australia.
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12
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Dinglasan EG, Peressini T, Marathamuthu KA, See PT, Snyman L, Platz G, Godwin I, Voss-Fels KP, Moffat CS, Hickey LT. Genetic characterization of adult-plant resistance to tan spot (syn, yellow spot) in wheat. Theor Appl Genet 2021; 134:2823-2839. [PMID: 34061222 DOI: 10.1007/s00122-021-03861-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 05/11/2021] [Indexed: 06/12/2023]
Abstract
QTL mapping identified key genomic regions associated with adult-plant resistance to tan spot, which are effective even in the presence of the sensitivity gene Tsn1, thus serving as a new genetic solution to develop disease-resistant wheat cultivars. Improving resistance to tan spot (Pyrenophora tritici-repentis; Ptr) in wheat by eliminating race-specific susceptibility genes is a common breeding approach worldwide. The potential to exploit variation in quantitative forms of resistance, such as adult-plant resistance (APR), offers an alternative approach that could lead to broad-spectrum protection. We previously identified wheat landraces in the Vavilov diversity panel that exhibited high levels of APR despite carrying the sensitivity gene Tsn1. In this study, we characterised the genetic control of APR by developing a recombinant inbred line population fixed for Tsn1, but segregating for the APR trait. Linkage mapping using DArTseq markers and disease response phenotypes identified a QTL associated with APR to Ptr race 1 (producing Ptr ToxA- and Ptr ToxC) on chromosome 2B (Qts.313-2B), which was consistently detected in multiple adult-plant experiments. Additional loci were also detected on chromosomes 2A, 3D, 5A, 5D, 6A, 6B and 7A at the seedling stage, and on chromosomes 1A and 5B at the adult stage. We demonstrate that Qts.313-2B can be combined with other adult-plant QTL (i.e. Qts.313-1A and Qts.313-5B) to strengthen resistance levels. The APR QTL reported in this study provide a new genetic solution to tan spot in Australia and could be deployed in wheat cultivars, even in the presence of Tsn1, to decrease production losses and reduce the application of fungicides.
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Affiliation(s)
- Eric G Dinglasan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia
| | - Tamaya Peressini
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia
| | | | - Pao Theen See
- Centre for Crop and Disease Management, Curtin University, Perth, WA, Australia
| | - Lisle Snyman
- Department of Agriculture and Fisheries, Hermitage Research Facility, Warwick, QLD, Australia
| | - Greg Platz
- Department of Agriculture and Fisheries, Hermitage Research Facility, Warwick, QLD, Australia
| | - Ian Godwin
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia
| | - Caroline S Moffat
- Centre for Crop and Disease Management, Curtin University, Perth, WA, Australia
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia.
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13
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Yadav S, Wei X, Joyce P, Atkin F, Deomano E, Sun Y, Nguyen LT, Ross EM, Cavallaro T, Aitken KS, Hayes BJ, Voss-Fels KP. Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects. Theor Appl Genet 2021; 134:2235-2252. [PMID: 33903985 PMCID: PMC8263546 DOI: 10.1007/s00122-021-03822-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 03/21/2021] [Indexed: 05/29/2023]
Abstract
Non-additive genetic effects seem to play a substantial role in the expression of complex traits in sugarcane. Including non-additive effects in genomic prediction models significantly improves the prediction accuracy of clonal performance. In the recent decade, genetic progress has been slow in sugarcane. One reason might be that non-additive genetic effects contribute substantially to complex traits. Dense marker information provides the opportunity to exploit non-additive effects in genomic prediction. In this study, a series of genomic best linear unbiased prediction (GBLUP) models that account for additive and non-additive effects were assessed to improve the accuracy of clonal prediction. The reproducible kernel Hilbert space model, which captures non-additive genetic effects, was also tested. The models were compared using 3,006 genotyped elite clones measured for cane per hectare (TCH), commercial cane sugar (CCS), and Fibre content. Three forward prediction scenarios were considered to investigate the robustness of genomic prediction. By using a pseudo-diploid parameterization, we found significant non-additive effects that accounted for almost two-thirds of the total genetic variance for TCH. Average heterozygosity also had a major impact on TCH, indicating that directional dominance may be an important source of phenotypic variation for this trait. The extended-GBLUP model improved the prediction accuracies by at least 17% for TCH, but no improvement was observed for CCS and Fibre. Our results imply that non-additive genetic variance is important for complex traits in sugarcane, although further work is required to better understand the variance component partitioning in a highly polyploid context. Genomics-based breeding will likely benefit from exploiting non-additive genetic effects, especially in designing crossing schemes. These findings can help to improve clonal prediction, enabling a more accurate identification of variety candidates for the sugarcane industry.
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Affiliation(s)
- Seema Yadav
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, Carmody Rd., St. Lucia, Brisbane, QLD, 3064067, Australia
| | - Xianming Wei
- Sugar Research Australia, Mackay, QLD, 4741, Australia
| | - Priya Joyce
- Sugar Research Australia, 50 Meiers Road, Indooroopilly, QLD, 4068, Australia
| | - Felicity Atkin
- Sugar Research Australia, Meringa, Gordonvale, QLD, 4865, Australia
| | - Emily Deomano
- Sugar Research Australia, 50 Meiers Road, Indooroopilly, QLD, 4068, Australia
| | - Yue Sun
- Sugar Research Australia, 50 Meiers Road, Indooroopilly, QLD, 4068, Australia
| | - Loan T Nguyen
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, Carmody Rd., St. Lucia, Brisbane, QLD, 3064067, Australia
| | - Elizabeth M Ross
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, Carmody Rd., St. Lucia, Brisbane, QLD, 3064067, Australia
| | - Tony Cavallaro
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, Carmody Rd., St. Lucia, Brisbane, QLD, 3064067, Australia
| | - Karen S Aitken
- Agriculture and Food, CSIRO, QBP, St. Lucia, QLD, 4067, Australia
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, Carmody Rd., St. Lucia, Brisbane, QLD, 3064067, Australia
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, Queensland Bioscience Precinct, Carmody Rd., St. Lucia, Brisbane, QLD, 3064067, Australia.
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14
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Powell OM, Voss-Fels KP, Jordan DR, Hammer G, Cooper M. Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction. Front Plant Sci 2021; 12:663565. [PMID: 34149761 PMCID: PMC8211918 DOI: 10.3389/fpls.2021.663565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/13/2021] [Indexed: 05/26/2023]
Abstract
Genomic prediction of complex traits across environments, breeding cycles, and populations remains a challenge for plant breeding. A potential explanation for this is that underlying non-additive genetic (GxG) and genotype-by-environment (GxE) interactions generate allele substitution effects that are non-stationary across different contexts. Such non-stationary effects of alleles are either ignored or assumed to be implicitly captured by most gene-to-phenotype (G2P) maps used in genomic prediction. The implicit capture of non-stationary effects of alleles requires the G2P map to be re-estimated across different contexts. We discuss the development and application of hierarchical G2P maps that explicitly capture non-stationary effects of alleles and have successfully increased short-term prediction accuracy in plant breeding. These hierarchical G2P maps achieve increases in prediction accuracy by allowing intermediate processes such as other traits and environmental factors and their interactions to contribute to complex trait variation. However, long-term prediction remains a challenge. The plant breeding community should undertake complementary simulation and empirical experiments to interrogate various hierarchical G2P maps that connect GxG and GxE interactions simultaneously. The existing genetic correlation framework can be used to assess the magnitude of non-stationary effects of alleles and the predictive ability of these hierarchical G2P maps in long-term, multi-context genomic predictions of complex traits in plant breeding.
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Affiliation(s)
- Owen M. Powell
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
| | - Kai P. Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
| | - David R. Jordan
- Queensland Alliance for Agriculture and Food Innovation, Hermitage Research Facility, The University of Queensland, Warwick, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
| | - Graeme Hammer
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, The University of Queensland, St Lucia, QLD, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, St Lucia, QLD, Australia
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15
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Ober ES, Alahmad S, Cockram J, Forestan C, Hickey LT, Kant J, Maccaferri M, Marr E, Milner M, Pinto F, Rambla C, Reynolds M, Salvi S, Sciara G, Snowdon RJ, Thomelin P, Tuberosa R, Uauy C, Voss-Fels KP, Wallington E, Watt M. Wheat root systems as a breeding target for climate resilience. Theor Appl Genet 2021; 134:1645-1662. [PMID: 33900415 PMCID: PMC8206059 DOI: 10.1007/s00122-021-03819-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 03/18/2021] [Indexed: 05/08/2023]
Abstract
In the coming decades, larger genetic gains in yield will be necessary to meet projected demand, and this must be achieved despite the destabilizing impacts of climate change on crop production. The root systems of crops capture the water and nutrients needed to support crop growth, and improved root systems tailored to the challenges of specific agricultural environments could improve climate resiliency. Each component of root initiation, growth and development is controlled genetically and responds to the environment, which translates to a complex quantitative system to navigate for the breeder, but also a world of opportunity given the right tools. In this review, we argue that it is important to know more about the 'hidden half' of crop plants and hypothesize that crop improvement could be further enhanced using approaches that directly target selection for root system architecture. To explore these issues, we focus predominantly on bread wheat (Triticum aestivum L.), a staple crop that plays a major role in underpinning global food security. We review the tools available for root phenotyping under controlled and field conditions and the use of these platforms alongside modern genetics and genomics resources to dissect the genetic architecture controlling the wheat root system. To contextualize these advances for applied wheat breeding, we explore questions surrounding which root system architectures should be selected for, which agricultural environments and genetic trait configurations of breeding populations are these best suited to, and how might direct selection for these root ideotypes be implemented in practice.
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Affiliation(s)
- Eric S Ober
- NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK.
| | - Samir Alahmad
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - James Cockram
- NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Cristian Forestan
- Department of Agricultural and Food Sciences, University of Bologna, Viale G Fanin 44, 40127, Bologna, Italy
| | - Lee T Hickey
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Josefine Kant
- Forschungszentrum Jülich, IBG-2, Wilhelm-Johnen-Straße, 52428, Jülich, Germany
| | - Marco Maccaferri
- Department of Agricultural and Food Sciences, University of Bologna, Viale G Fanin 44, 40127, Bologna, Italy
| | - Emily Marr
- NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | | | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Estado de Mexico, Mexico
| | - Charlotte Rambla
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Matthew Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Estado de Mexico, Mexico
| | - Silvio Salvi
- Department of Agricultural and Food Sciences, University of Bologna, Viale G Fanin 44, 40127, Bologna, Italy
| | - Giuseppe Sciara
- Department of Agricultural and Food Sciences, University of Bologna, Viale G Fanin 44, 40127, Bologna, Italy
| | - 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
| | | | - Roberto Tuberosa
- Department of Agricultural and Food Sciences, University of Bologna, Viale G Fanin 44, 40127, Bologna, Italy
| | - Cristobal Uauy
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, NR4 7UH, UK
| | - Kai P Voss-Fels
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | | | - Michelle Watt
- School of BioSciences, University of Melbourne, Parkville, VIC, 3010, Australia
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16
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Cooper M, Voss-Fels KP, Messina CD, Tang T, Hammer GL. Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity. Theor Appl Genet 2021; 134:1625-1644. [PMID: 33738512 PMCID: PMC8206060 DOI: 10.1007/s00122-021-03812-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 03/05/2021] [Indexed: 05/05/2023]
Abstract
KEY MESSAGE Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is "How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?" Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype-Management (G-M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G-M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G-M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G-M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.
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Affiliation(s)
- Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia.
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
| | - Carlos D Messina
- Corteva Agriscience, Research and Development, Johnston, IA, 50131, USA
| | - Tom Tang
- Corteva Agriscience, Research and Development, Johnston, IA, 50131, USA
| | - Graeme L Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
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17
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Voss-Fels KP, Wei X, Ross EM, Frisch M, Aitken KS, Cooper M, Hayes BJ. Strategies and considerations for implementing genomic selection to improve traits with additive and non-additive genetic architectures in sugarcane breeding. Theor Appl Genet 2021; 134:1493-1511. [PMID: 33587151 DOI: 10.1007/s00122-021-03785-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 01/27/2021] [Indexed: 05/14/2023]
Abstract
Simulations highlight the potential of genomic selection to substantially increase genetic gain for complex traits in sugarcane. The success rate depends on the trait genetic architecture and the implementation strategy. Genomic selection (GS) has the potential to increase the rate of genetic gain in sugarcane beyond the levels achieved by conventional phenotypic selection (PS). To assess different implementation strategies, we simulated two different GS-based breeding strategies and compared genetic gain and genetic variance over five breeding cycles to standard PS. GS scheme 1 followed similar routines like conventional PS but included three rapid recurrent genomic selection (RRGS) steps. GS scheme 2 also included three RRGS steps but did not include a progeny assessment stage and therefore differed more fundamentally from PS. Under an additive trait model, both simulated GS schemes achieved annual genetic gains of 2.6-2.7% which were 1.9 times higher compared to standard phenotypic selection (1.4%). For a complex non-additive trait model, the expected annual rates of genetic gain were lower for all breeding schemes; however, the rates for the GS schemes (1.5-1.6%) were still greater than PS (1.1%). Investigating cost-benefit ratios with regard to numbers of genotyped clones showed that substantial benefits could be achieved when only 1500 clones were genotyped per 10-year breeding cycle for the additive genetic model. Our results show that under a complex non-additive genetic model, the success rate of GS depends on the implementation strategy, the number of genotyped clones and the stage of the breeding program, likely reflecting how changes in QTL allele frequencies change additive genetic variance and therefore the efficiency of selection. These results are encouraging and motivate further work to facilitate the adoption of GS in sugarcane breeding.
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Affiliation(s)
- Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4072, Australia
| | - Xianming Wei
- Sugar Research Australia, Mackay, QLD, 4741, Australia
| | - Elizabeth M Ross
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4072, Australia
| | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Giessen, Germany
| | - Karen S Aitken
- Agriculture and Food, CSIRO, QBP, St. Lucia, QLD, 4067, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4072, Australia
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4072, Australia.
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18
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Yao M, Guan M, Yang Q, Huang L, Xiong X, Jan HU, Voss-Fels KP, Werner CR, He X, Qian W, Snowdon RJ, Guan C, Hua W, Qian L. Regional association analysis coupled with transcriptome analyses reveal candidate genes affecting seed oil accumulation in Brassica napus. Theor Appl Genet 2021; 134:1545-1555. [PMID: 33677638 DOI: 10.1007/s00122-021-03788-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 02/03/2021] [Indexed: 06/12/2023]
Abstract
Regional association analysis of 50 re-sequenced Chinese semi-winter rapeseed accessions in combination with co-expression analysis reveal candidate genes affecting oil accumulation in Brassica napus. One of the breeding goals in rapeseed production is to enhance the seed oil content to cater to the increased demand for vegetable oils due to a growing global population. To investigate the genetic basis of variation in seed oil content, we used 60 K Brassica Infinium SNP array along with phenotype data of 203 Chinese semi-winter rapeseed accessions to perform a genome-wide analysis of haplotype blocks associated with the oil content. Nine haplotype regions harbouring lipid synthesis/transport-, carbohydrate metabolism- and photosynthesis-related genes were identified as significantly associated with the oil content and were mapped to chromosomes A02, A04, A05, A07, C03, C04, C05, C08 and C09, respectively. Regional association analysis of 50 re-sequenced Chinese semi-winter rapeseed accessions combined with transcriptome datasets from 13 accessions was further performed on these nine haplotype regions. This revealed natural variation in the BnTGD3-A02 and BnSSE1-A05 gene regions correlated with the phenotypic variation of the oil content within the A02 and A04 chromosome haplotype regions, respectively. Moreover, co-expression network analysis revealed that BnTGD3-A02 and BnSSE1-A05 were directly linked with fatty acid beta-oxidation-related gene BnKAT2-C04, thus forming a molecular network involved in the potential regulation of seed oil accumulation. The results of this study could be used to combine favourable haplotype alleles for further improvement of the seed oil content in rapeseed.
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Affiliation(s)
- Min Yao
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Mei Guan
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Qian Yang
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Luyao Huang
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Xinghua Xiong
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Habib U Jan
- Molecular Biology, Department of Pathology, MTI-LRH, Peshawar, 25000, Pakistan
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Christian R Werner
- The Roslin Institute University of Edinburgh Easter Bush Research Centre Midlothian, Midlothian, EH25 9RG, UK
| | - Xin He
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Wei Qian
- College of Horticulture and Landscape Architecture, Southwest University, Chongqing, 400715, China
| | - Rod J Snowdon
- Department of Plant Breeding, Land Use and Nutrition, IFZ Research Centre for Biosystems, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Chunyun Guan
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Wei Hua
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China.
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Ministry of Agriculture, Wuhan, 430062, China.
| | - Lunwen Qian
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China.
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19
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Hayes BJ, Wei X, Joyce P, Atkin F, Deomano E, Yue J, Nguyen L, Ross EM, Cavallaro T, Aitken KS, Voss-Fels KP. Accuracy of genomic prediction of complex traits in sugarcane. Theor Appl Genet 2021; 134:1455-1462. [PMID: 33590303 DOI: 10.1007/s00122-021-03782-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 01/21/2021] [Indexed: 05/14/2023]
Abstract
Complex traits in sugarcane can be accurately predicted using genome-wide DNA markers. Genomic single-step prediction is an attractive method for genomic selection in commercial breeding programs. Sugarcane breeding programs have achieved up to 1% genetic gain in key traits such as tonnes of cane per hectare (TCH), commercial cane sugar (CCS) and Fibre content over the past decades. Here, we assess the potential of genomic selection to increase the rate of genetic gain for these traits by deriving genomic estimated breeding values (GEBVs) from a reference population of 3984 clones genotyped for 26 K SNP. We evaluated the three different genomic prediction approaches GBLUP, genomic single step (GenomicSS), and BayesR. GenomicSS combining pedigree and SNP information from historic and recent breeding programs achieved the most accurate predictions for most traits (0.3-0.44). This method is attractive for routine genetic evaluation because it requires relatively little modification to the existing evaluation and results in breeding value estimates for all individuals, not only those genotyped. Adding information from early-stage trials added up to 5% accuracy for CCS and Fibre, but 0% for TCH, reflecting the importance of competition effects for TCH. These GEBV accuracies are sufficiently high that, combined with the right breeding strategy, a doubling of the rate of genetic gain could be achieved. We also assessed the flowering traits days to flowering, gender and pollen viability and found high heritabilities of 0.57, 0.78 and 0.72, respectively. The GEBV accuracies indicated that genomic selection could be used to improve these traits. This could open new avenues for breeders to manage their breeding programs, for example, by synchronising flowering time and selecting males with high pollen viability.
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Affiliation(s)
- Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia.
| | - Xianming Wei
- Sugar Research Australia, Mackay, QLD, 4741, Australia
| | - Priya Joyce
- Sugar Research Australia, 50 Meiers Road, Indooroopilly, QLD, 4068, Australia
| | - Felicity Atkin
- Sugar Research Australia, Meringa Gordonvale, QLD, 4865, Australia
| | - Emily Deomano
- Sugar Research Australia, 50 Meiers Road, Indooroopilly, QLD, 4068, Australia
| | - Jenny Yue
- Sugar Research Australia, 50 Meiers Road, Indooroopilly, QLD, 4068, Australia
| | - Loan Nguyen
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Elizabeth M Ross
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Tony Cavallaro
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Karen S Aitken
- Agriculture and Food, CSIRO, QBP, St. Lucia, QLD, 4067, Australia
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
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20
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Bhatta M, Sandro P, Smith MR, Delaney O, Voss-Fels KP, Gutierrez L, Hickey LT. Need for speed: manipulating plant growth to accelerate breeding cycles. Curr Opin Plant Biol 2021; 60:101986. [PMID: 33418268 DOI: 10.1016/j.pbi.2020.101986] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 11/28/2020] [Accepted: 12/04/2020] [Indexed: 05/02/2023]
Abstract
To develop more productive and resilient crops that are capable of feeding 10 billion people by 2050, we must accelerate the rate of genetic improvement in plant breeding programs. Speed breeding manipulates the growing environment by regulating light and temperature for the purpose of rapid generation advance. Protocols are now available for a range of short-day and long-day species and the approach is highly compatible with other cutting-edge breeding tools such as genomic selection. Here, we highlight how speed breeding hijacks biological processes for applied plant breeding outcomes and provide a case study examining wheat growth and development under speed breeding conditions. The establishment of speed breeding facilities worldwide is expected to provide benefits for capacity building, discovery research, pre-breeding, and plant breeding to accelerate the development of productive and robust crops.
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Affiliation(s)
- Madhav Bhatta
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA; Bayer Crop Science, Chesterfield, MO 63017, USA
| | - Pablo Sandro
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Millicent R Smith
- School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD, 4343, Australia; Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Oscar Delaney
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Lucia Gutierrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA.
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia.
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21
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Makhoul M, Rambla C, Voss-Fels KP, Hickey LT, Snowdon RJ, Obermeier C. Overcoming polyploidy pitfalls: a user guide for effective SNP conversion into KASP markers in wheat. Theor Appl Genet 2020; 133:2413-2430. [PMID: 32500260 PMCID: PMC7360542 DOI: 10.1007/s00122-020-03608-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 05/13/2020] [Indexed: 05/11/2023]
Abstract
Conversion of SNP chip assays into locus-specific KASP markers requires adapted strategies in polyploid species with high genome homeology. Procedures are exemplified by QTL-associated SNPs in hexaploid wheat. Kompetitive allele-specific PCR (KASP) markers are commonly used in marker-assisted commercial plant breeding due to their cost-effectiveness and throughput for high sample volumes. However, conversion of trait-linked SNP markers from array-based SNP detection technologies into KASP markers is particularly challenging in polyploid crop species, due to the presence of highly similar homeologous and paralogous genome sequences. We evaluated strategies and identified key requirements for successful conversion of Illumina Infinium assays from the wheat 90 K SNP array into robust locus-specific KASP markers. Numerous examples showed that commonly used software for semiautomated KASP primer design frequently fails to achieve locus-specificity of KASP assays in wheat. Instead, alignment of SNP probes with multiple reference genomes and Sanger sequencing of relevant genotypes, followed by visual KASP primer placement, was critical for locus-specificity. To identify KASP assays resulting in false calling of heterozygous individuals, validation of KASP assays using extended reference genotype sets including heterozygous genotypes is strongly advised for polyploid crop species. Applying this strategy, we developed highly reproducible, stable KASP assays that are predictive for root biomass QTL haplotypes from highly homoeologous wheat chromosome regions. Due to their locus-specificity, these assays predicted root biomass considerably better than the original trait-associated markers from the Illumina array.
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Affiliation(s)
- M Makhoul
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - C Rambla
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Australia
| | - K P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Australia
| | - L T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Australia
| | - R J Snowdon
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - C Obermeier
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany.
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22
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Alahmad S, Kang Y, Dinglasan E, Mazzucotelli E, Voss-Fels KP, Able JA, Christopher J, Bassi FM, Hickey LT. Adaptive Traits to Improve Durum Wheat Yield in Drought and Crown Rot Environments. Int J Mol Sci 2020; 21:ijms21155260. [PMID: 32722187 PMCID: PMC7432628 DOI: 10.3390/ijms21155260] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 07/22/2020] [Accepted: 07/22/2020] [Indexed: 02/07/2023] Open
Abstract
Durum wheat (Triticum turgidum L. ssp. durum) production can experience significant yield losses due to crown rot (CR) disease. Losses are usually exacerbated when disease infection coincides with terminal drought. Durum wheat is very susceptible to CR, and resistant germplasm is not currently available in elite breeding pools. We hypothesize that deploying physiological traits for drought adaptation, such as optimal root system architecture to reduce water stress, might minimize losses due to CR infection. This study evaluated a subset of lines from a nested association mapping population for stay-green traits, CR incidence and yield in field experiments as well as root traits under controlled conditions. Weekly measurements of normalized difference vegetative index (NDVI) in the field were used to model canopy senescence and to determine stay-green traits for each genotype. Genome-wide association studies using DArTseq molecular markers identified quantitative trait loci (QTLs) on chromosome 6B (qCR-6B) associated with CR tolerance and stay-green. We explored the value of qCR-6B and a major QTL for root angle QTL qSRA-6A using yield datasets from six rainfed environments, including two environments with high CR disease pressure. In the absence of CR, the favorable allele for qSRA-6A provided an average yield advantage of 0.57 t·ha−1, whereas in the presence of CR, the combination of favorable alleles for both qSRA-6A and qCR-6B resulted in a yield advantage of 0.90 t·ha−1. Results of this study highlight the value of combining above- and belowground physiological traits to enhance yield potential. We anticipate that these insights will assist breeders to design improved durum varieties that mitigate production losses due to water deficit and CR.
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Affiliation(s)
- Samir Alahmad
- Centre for Crop Science, The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Brisbane, QLD 4072, Australia; (Y.K.); (E.D.); (K.P.V.-F.)
- Correspondence: (S.A.); (L.T.H.)
| | - Yichen Kang
- Centre for Crop Science, The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Brisbane, QLD 4072, Australia; (Y.K.); (E.D.); (K.P.V.-F.)
| | - Eric Dinglasan
- Centre for Crop Science, The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Brisbane, QLD 4072, Australia; (Y.K.); (E.D.); (K.P.V.-F.)
| | - Elisabetta Mazzucotelli
- Council for Agricultural Research and Economics (CREA)—Research Centre for Genomics and Bioinformatics, 29017 Fiorenzuola d’Arda (PC), Italy;
| | - Kai P. Voss-Fels
- Centre for Crop Science, The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Brisbane, QLD 4072, Australia; (Y.K.); (E.D.); (K.P.V.-F.)
| | - Jason A. Able
- School of Agriculture, Food and Wine, Waite Research Institute, The University of Adelaide, Urrbrae, SA 5064, Australia;
| | - Jack Christopher
- Centre for Crop Science, The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Leslie Research Facility, Toowoomba, QLD 4350, Australia;
| | - Filippo M. Bassi
- International Center for the Agricultural Research in the Dry Areas, Rabat 10000, Morocco;
| | - Lee T. Hickey
- Centre for Crop Science, The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Brisbane, QLD 4072, Australia; (Y.K.); (E.D.); (K.P.V.-F.)
- Correspondence: (S.A.); (L.T.H.)
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23
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Yao M, Guan M, Zhang Z, Zhang Q, Cui Y, Chen H, Liu W, Jan HU, Voss-Fels KP, Werner CR, He X, Liu Z, Guan C, Snowdon RJ, Hua W, Qian L. GWAS and co-expression network combination uncovers multigenes with close linkage effects on the oleic acid content accumulation in Brassica napus. BMC Genomics 2020; 21:320. [PMID: 32326904 PMCID: PMC7181522 DOI: 10.1186/s12864-020-6711-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 03/31/2020] [Indexed: 11/19/2022] Open
Abstract
Background Strong artificial and natural selection causes the formation of highly conserved haplotypes that harbor agronomically important genes. GWAS combination with haplotype analysis has evolved as an effective method to dissect the genetic architecture of complex traits in crop species. Results We used the 60 K Brassica Infinium SNP array to perform a genome-wide analysis of haplotype blocks associated with oleic acid (C18:1) in rapeseed. Six haplotype regions were identified as significantly associated with oleic acid (C18:1) that mapped to chromosomes A02, A07, A08, C01, C02, and C03. Additionally, whole-genome sequencing of 50 rapeseed accessions revealed three genes (BnmtACP2-A02, BnABCI13-A02 and BnECI1-A02) in the A02 chromosome haplotype region and two genes (BnFAD8-C02 and BnSDP1-C02) in the C02 chromosome haplotype region that were closely linked to oleic acid content phenotypic variation. Moreover, the co-expression network analysis uncovered candidate genes from these two different haplotype regions with potential regulatory interrelationships with oleic acid content accumulation. Conclusions Our results suggest that several candidate genes are closely linked, which provides us with an opportunity to develop functional haplotype markers for the improvement of the oleic acid content in rapeseed.
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Affiliation(s)
- Min Yao
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Mei Guan
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Zhenqian Zhang
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Qiuping Zhang
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Yixin Cui
- College of Horticulture and Landscape Architecture, Southwest University, Chongqing, 400715, China
| | - Hao Chen
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Wei Liu
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Habib U Jan
- Precision Medicine Lab, Rehman Medical Institute (RMI), Phase 5, Hayatabad, Peshawar, 25000, Pakistan
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Christian R Werner
- The Roslin Institute University of Edinburgh Easter Bush Research Centre Midlothian, Edinburgh, EH25 9RG, UK
| | - Xin He
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Zhongsong Liu
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China
| | - Chunyun Guan
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, 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
| | - Wei Hua
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China. .,Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, 430062, China.
| | - Lunwen Qian
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, China.
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Wurtzel ET, Vickers CE, Hanson AD, Millar AH, Cooper M, Voss-Fels KP, Nikel PI, Erb TJ. Revolutionizing agriculture with synthetic biology. Nat Plants 2019; 5:1207-1210. [PMID: 31740769 DOI: 10.1038/s41477-019-0539-0] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 09/27/2019] [Indexed: 05/26/2023]
Abstract
Synthetic biology is here to stay and will transform agriculture if given the chance. The huge challenges facing food, fuel and chemical production make it vital to give synthetic biology that chance-notwithstanding the shifts in mindset, training and infrastructure investment this demands. Here, we assess opportunities for agricultural synthetic biology and ways to remove barriers to their realization.
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Affiliation(s)
- Eleanore T Wurtzel
- Department of Biological Sciences, Lehman College, City University of New York, New York, NY, USA.
- Graduate School and University Center-CUNY, New York, NY, USA.
| | - Claudia E Vickers
- CSIRO Synthetic Biology Future Science Platform, Canberra, Australia.
- Australian Institute for Bioengineering & Nanotechnology, University of Queensland, Brisbane, Queensland, Australia.
| | - Andrew D Hanson
- Horticultural Sciences Department, University of Florida, Gainesville, FL, USA.
| | - A Harvey Millar
- ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, University of Western Australia, Crawley, Western Australia, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture & Food Innovation, University of Queensland, St. Lucia, Queensland, Australia
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture & Food Innovation, University of Queensland, St. Lucia, Queensland, Australia
| | - Pablo I Nikel
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs Lyngby, Denmark
| | - Tobias J Erb
- Max-Planck-Institute for Terrestrial Microbiology, Department of Biochemistry & Synthetic Metabolism, Marburg, Germany
- LOEWE Center for Synthetic Microbiology, Marburg, Germany
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25
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Voss-Fels KP, Keeble-Gagnère G, Hickey LT, Tibbits J, Nagornyy S, Hayden MJ, Pasam RK, Kant S, Friedt W, Snowdon RJ, Appels R, Wittkop B. High-resolution mapping of rachis nodes per rachis, a critical determinant of grain yield components in wheat. Theor Appl Genet 2019; 132:2707-2719. [PMID: 31254025 DOI: 10.1007/s00122-019-03383-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 06/16/2019] [Indexed: 05/23/2023]
Abstract
Exploring large genomic data sets based on the latest reference genome assembly identifies the rice ortholog APO1 as a key candidate gene for number of rachis nodes per spike in wheat. Increasing grain yield in wheat is a key breeding objective worldwide. Several component traits contribute to grain yield with spike attributes being among the most important. In this study, we performed a genome-wide association analysis for 12 grain yield and component traits measured in field trials with contrasting agrochemical input levels in a panel of 220 hexaploid winter wheats. A highly significant, environmentally consistent QTL was detected for number of rachis nodes per rachis (NRN) on chromosome 7AL. The five most significant SNPs formed a strong linkage disequilibrium (LD) block and tagged a 2.23 Mb region. Using pairwise LD for exome SNPs located across this interval in a large worldwide hexaploid wheat collection, we reduced the genomic region for NRN to a 258 Kb interval containing four of the original SNP and six high-confidence genes. The ortholog of one (TraesCS7A01G481600) of these genes in rice was ABBERANT PANICLE ORGANIZATION1 (APO1), which is known to have significant effects on panicle attributes. The APO1 ortholog was the best candidate for NRN and was associated with a 115 bp promoter deletion and two amino acid (C47F and D384 N) changes. Using a large worldwide collection of tetraploid and hexaploid wheat, we found 12 haplotypes for the NRN QTL and evidence for positive enrichment of two haplotypes in modern germplasm. Comparison of five QTL haplotypes in Australian yield trials revealed their relative, context-dependent contribution to grain yield. Our study provides diagnostic SNPs and value propositions to support deployment of the NRN trait in wheat breeding.
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Affiliation(s)
- Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Gabriel Keeble-Gagnère
- Agriculture Victoria Research, Department of Job, Precincts and Regions (DJPR), AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Josquin Tibbits
- Agriculture Victoria Research, Department of Job, Precincts and Regions (DJPR), AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Sergej Nagornyy
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Matthew J Hayden
- Agriculture Victoria Research, Department of Job, Precincts and Regions (DJPR), AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | - Raj K Pasam
- Agriculture Victoria Research, Department of Job, Precincts and Regions (DJPR), AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Surya Kant
- Agriculture Victoria Research, Department of Job, Precincts and Regions (DJPR), AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
| | - Wolfgang Friedt
- 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
| | - Rudi Appels
- Agriculture Victoria Research, Department of Job, Precincts and Regions (DJPR), AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.
| | - Benjamin Wittkop
- 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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26
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Voss-Fels KP, Stahl A, Wittkop B, Lichthardt C, Nagler S, Rose T, Chen TW, Zetzsche H, Seddig S, Majid Baig M, Ballvora A, Frisch M, Ross E, Hayes BJ, Hayden MJ, Ordon F, Leon J, Kage H, Friedt W, Stützel H, Snowdon RJ. Breeding improves wheat productivity under contrasting agrochemical input levels. Nat Plants 2019; 5:706-714. [PMID: 31209285 DOI: 10.1038/s41477-019-0445-5] [Citation(s) in RCA: 96] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 05/10/2019] [Indexed: 05/22/2023]
Abstract
The world cropping area for wheat exceeds that of any other crop, and high grain yields in intensive wheat cropping systems are essential for global food security. Breeding has raised yields dramatically in high-input production systems; however, selection under optimal growth conditions is widely believed to diminish the adaptive capacity of cultivars to less optimal cropping environments. Here, we demonstrate, in a large-scale study spanning five decades of wheat breeding progress in western Europe, where grain yields are among the highest worldwide, that breeding for high performance in fact enhances cultivar performance not only under optimal production conditions but also in production systems with reduced agrochemical inputs. New cultivars incrementally accumulated genetic variants conferring favourable effects on key yield parameters, disease resistance, nutrient use efficiency, photosynthetic efficiency and grain quality. Combining beneficial, genome-wide haplotypes could help breeders to more efficiently exploit available genetic variation, optimizing future yield potential in more sustainable production systems.
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Affiliation(s)
- Kai P Voss-Fels
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Queensland, Australia
| | - Andreas Stahl
- Department of Plant Breeding, 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
| | - Carolin Lichthardt
- Institute of Horticultural Production Systems, Leibniz University Hannover, Hannover, Germany
| | - Sabrina Nagler
- Department of Agronomy and Crop Science, Christian Albrechts University of Kiel, Kiel, Germany
| | - Till Rose
- Department of Agronomy and Crop Science, Christian Albrechts University of Kiel, Kiel, Germany
| | - Tsu-Wei Chen
- Institute of Horticultural Production Systems, Leibniz University Hannover, Hannover, Germany
| | - Holger Zetzsche
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
| | - Sylvia Seddig
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Sanitz, Germany
| | - Mirza Majid Baig
- Institute of Crop Science and Resource Conservation, Chair of Plant Breeding, University of Bonn, Bonn, Germany
| | - Agim Ballvora
- Institute of Crop Science and Resource Conservation, Chair of Plant Breeding, University of Bonn, Bonn, Germany
| | - Matthias Frisch
- Institute for Agronomy and Plant Breeding II, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | - Elizabeth Ross
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Queensland, Australia
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Queensland, Australia
| | - Matthew J Hayden
- School of Applied Systems Biology, AgriBio, La Trobe University, Melbourne, Victoria, Australia
| | - Frank Ordon
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
| | - Jens Leon
- Institute of Crop Science and Resource Conservation, Chair of Plant Breeding, University of Bonn, Bonn, Germany
- Field Lab Campus Klein-Altendorf, University of Bonn, Rheinbach, Germany
| | - Henning Kage
- Department of Agronomy and Crop Science, Christian Albrechts University of Kiel, Kiel, Germany
| | - Wolfgang Friedt
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany.
| | - Hartmut Stützel
- Institute of Horticultural Production Systems, Leibniz University Hannover, Hannover, 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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27
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Voss-Fels KP, Stahl A, Wittkop B, Lichthardt C, Nagler S, Rose T, Chen TW, Zetzsche H, Seddig S, Majid Baig M, Ballvora A, Frisch M, Ross E, Hayes BJ, Hayden MJ, Ordon F, Leon J, Kage H, Friedt W, Stützel H, Snowdon RJ. Breeding improves wheat productivity under contrasting agrochemical input levels. Nat Plants 2019; 5:706-714. [PMID: 31209285 DOI: 10.5281/zenodo.1316947] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 05/10/2019] [Indexed: 05/27/2023]
Abstract
The world cropping area for wheat exceeds that of any other crop, and high grain yields in intensive wheat cropping systems are essential for global food security. Breeding has raised yields dramatically in high-input production systems; however, selection under optimal growth conditions is widely believed to diminish the adaptive capacity of cultivars to less optimal cropping environments. Here, we demonstrate, in a large-scale study spanning five decades of wheat breeding progress in western Europe, where grain yields are among the highest worldwide, that breeding for high performance in fact enhances cultivar performance not only under optimal production conditions but also in production systems with reduced agrochemical inputs. New cultivars incrementally accumulated genetic variants conferring favourable effects on key yield parameters, disease resistance, nutrient use efficiency, photosynthetic efficiency and grain quality. Combining beneficial, genome-wide haplotypes could help breeders to more efficiently exploit available genetic variation, optimizing future yield potential in more sustainable production systems.
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Affiliation(s)
- Kai P Voss-Fels
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Queensland, Australia
| | - Andreas Stahl
- Department of Plant Breeding, 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
| | - Carolin Lichthardt
- Institute of Horticultural Production Systems, Leibniz University Hannover, Hannover, Germany
| | - Sabrina Nagler
- Department of Agronomy and Crop Science, Christian Albrechts University of Kiel, Kiel, Germany
| | - Till Rose
- Department of Agronomy and Crop Science, Christian Albrechts University of Kiel, Kiel, Germany
| | - Tsu-Wei Chen
- Institute of Horticultural Production Systems, Leibniz University Hannover, Hannover, Germany
| | - Holger Zetzsche
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
| | - Sylvia Seddig
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Sanitz, Germany
| | - Mirza Majid Baig
- Institute of Crop Science and Resource Conservation, Chair of Plant Breeding, University of Bonn, Bonn, Germany
| | - Agim Ballvora
- Institute of Crop Science and Resource Conservation, Chair of Plant Breeding, University of Bonn, Bonn, Germany
| | - Matthias Frisch
- Institute for Agronomy and Plant Breeding II, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | - Elizabeth Ross
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Queensland, Australia
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Queensland, Australia
| | - Matthew J Hayden
- School of Applied Systems Biology, AgriBio, La Trobe University, Melbourne, Victoria, Australia
| | - Frank Ordon
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
| | - Jens Leon
- Institute of Crop Science and Resource Conservation, Chair of Plant Breeding, University of Bonn, Bonn, Germany
- Field Lab Campus Klein-Altendorf, University of Bonn, Rheinbach, Germany
| | - Henning Kage
- Department of Agronomy and Crop Science, Christian Albrechts University of Kiel, Kiel, Germany
| | - Wolfgang Friedt
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany.
| | - Hartmut Stützel
- Institute of Horticultural Production Systems, Leibniz University Hannover, Hannover, 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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28
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Alahmad S, El Hassouni K, Bassi FM, Dinglasan E, Youssef C, Quarry G, Aksoy A, Mazzucotelli E, Juhász A, Able JA, Christopher J, Voss-Fels KP, Hickey LT. A Major Root Architecture QTL Responding to Water Limitation in Durum Wheat. Front Plant Sci 2019; 10:436. [PMID: 31024600 PMCID: PMC6468307 DOI: 10.3389/fpls.2019.00436] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 03/22/2019] [Indexed: 05/21/2023]
Abstract
The optimal root system architecture (RSA) of a crop is context dependent and critical for efficient resource capture in the soil. Narrow root growth angle promoting deeper root growth is often associated with improved access to water and nutrients in deep soils during terminal drought. RSA, therefore is a drought-adaptive trait that could minimize yield losses in regions with limited rainfall. Here, GWAS for seminal root angle (SRA) identified seven marker-trait associations clustered on chromosome 6A, representing a major quantitative trait locus (qSRA-6A) which also displayed high levels of pairwise LD (r 2 = 0.67). Subsequent haplotype analysis revealed significant differences between major groups. Candidate gene analysis revealed loci related to gravitropism, polar growth and hormonal signaling. No differences were observed for root biomass between lines carrying hap1 and hap2 for qSRA-6A, highlighting the opportunity to perform marker-assisted selection for the qSRA-6A locus and directly select for wide or narrow RSA, without influencing root biomass. Our study revealed that the genetic predisposition for deep rooting was best expressed under water-limitation, yet the root system displayed plasticity producing root growth in response to water availability in upper soil layers. We discuss the potential to deploy root architectural traits in cultivars to enhance yield stability in environments that experience limited rainfall.
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Affiliation(s)
- Samir Alahmad
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Khaoula El Hassouni
- Laboratory of Microbiology and Molecular Biology, Faculty of Sciences, Mohammed V University, Rabat, Morocco
- International Center for Agricultural Research in the Dry Areas, Rabat, Morocco
| | - Filippo M. Bassi
- International Center for Agricultural Research in the Dry Areas, Rabat, Morocco
| | - Eric Dinglasan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Chvan Youssef
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Georgia Quarry
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Alpaslan Aksoy
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | | | - Angéla Juhász
- School of Science, Edith Cowan University, Joondalup, WA, Australia
| | - Jason A. Able
- School of Agriculture, Food & Wine, Waite Research Institute, The University of Adelaide, Urrbrae, SA, Australia
| | - Jack Christopher
- Leslie Research Facility, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Kai P. Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Lee T. Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
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29
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Abstract
Farmers around the world have recently experienced significant crop losses due to severe heat and drought. Such extreme weather events and the need to feed a rapidly growing population have raised concerns for global food security. While plant breeding has been very successful and has delivered today’s highly productive crop varieties, the rate of genetic improvement must double to meet the projected future demands. Here we discuss basic principles and features of crop breeding and how modern technologies could efficiently be explored to boost crop improvement in the face of increasingly challenging production conditions.
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Affiliation(s)
- Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Andreas Stahl
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia.
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30
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Dinglasan EG, Singh D, Shankar M, Afanasenko O, Platz G, Godwin ID, Voss-Fels KP, Hickey LT. Discovering new alleles for yellow spot resistance in the Vavilov wheat collection. Theor Appl Genet 2019; 132:149-162. [PMID: 30327845 DOI: 10.1007/s00122-018-3204-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2018] [Accepted: 10/09/2018] [Indexed: 06/08/2023]
Abstract
GWAS detected 11 yellow spot resistance QTL in the Vavilov wheat collection. Promising adult-plant resistance loci could provide a sustainable genetic solution to yellow spot in modern wheat varieties. Yellow spot, caused by the fungal pathogen Pyrenophora tritici-repentis (Ptr), is the most economically damaging foliar disease of wheat in Australia. Genetic resistance is considered to be the most sustainable means for disease management, yet the genomic regions underpinning resistance to Ptr, particularly adult-plant resistance (APR), remain vastly unknown. In this study, we report results of a genome-wide association study using 295 accessions from the Vavilov wheat collection which were extensively tested for response to Ptr infections in glasshouse and field trials at both seedling an adult growth stages. Combining phenotypic datasets from multiple experiments in Australia and Russia with 25,286 genome-wide, high-quality DArTseq markers, we detected a total of 11 QTL, of which 5 were associated with seedling resistance, 3 with all-stage resistance, and 3 with APR. Interestingly, the novel APR QTL were effective even in the presence of host sensitivity gene Tsn1. These genomic regions could offer broad-spectrum yellow spot protection, not just to ToxA but also other pathogenicity or virulence factors. Vavilov wheat accessions carrying APR QTL combinations displayed enhanced levels of resistance highlighting the potential for QTL stacking through breeding. We propose that the APR genetic factors discovered in our study could be used to improve resistance levels in modern wheat varieties and contribute to the sustainable control of yellow spot.
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Affiliation(s)
- Eric G Dinglasan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia
| | - Dharmendra Singh
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia
| | - Manisha Shankar
- Department of Primary Industries and Regional Development, South Perth, WA, Australia
- School of Agriculture and Environment, University of Western Australia, Crawley, WA, Australia
| | - Olga Afanasenko
- Department of Plant Resistance to Diseases, All-Russian Research Institute of Plant Protection, St. Petersburg, Russia
| | - Greg Platz
- Department of Agriculture and Fisheries, Hermitage Research Facility (HRF), Warwick, QLD, Australia
| | - Ian D Godwin
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia
- School of Agriculture and Food Sciences, The University of Queensland, St. Lucia, QLD, Australia
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia.
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia.
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31
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Voss-Fels KP, Snowdon RJ, Hickey LT. Designer Roots for Future Crops. Trends Plant Sci 2018; 23:957-960. [PMID: 30145109 DOI: 10.1016/j.tplants.2018.08.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 08/01/2018] [Accepted: 08/03/2018] [Indexed: 05/21/2023]
Abstract
Despite the importance of roots, they have largely been ignored by modern crop research and breeding. We discuss important progress in crop root research and highlight how the context-dependent optimisation of below- and above-ground plant components provides opportunities to improve future crops in the face of increasing environmental fluctuations.
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Affiliation(s)
- Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD 4072, Australia
| | - 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.
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD 4072, Australia.
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32
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Voss-Fels KP, Qian L, Gabur I, Obermeier C, Hickey LT, Werner CR, Kontowski S, Frisch M, Friedt W, Snowdon RJ, Gottwald S. Genetic insights into underground responses to Fusarium graminearum infection in wheat. Sci Rep 2018; 8:13153. [PMID: 30177750 PMCID: PMC6120866 DOI: 10.1038/s41598-018-31544-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 08/19/2018] [Indexed: 11/16/2022] Open
Abstract
The ongoing global intensification of wheat production will likely be accompanied by a rising pressure of Fusarium diseases. While utmost attention was given to Fusarium head blight (FHB) belowground plant infections of the pathogen have largely been ignored. The current knowledge about the impact of soil borne Fusarium infection on plant performance and the underlying genetic mechanisms for resistance remain very limited. Here, we present the first large-scale investigation of Fusarium root rot (FRR) resistance using a diverse panel of 215 international wheat lines. We obtained data for a total of 21 resistance-related traits, including large-scale Real-time PCR experiments to quantify fungal spread. Association mapping and subsequent haplotype analyses discovered a number of highly conserved genomic regions associated with resistance, and revealed a significant effect of allele stacking on the stembase discoloration. Resistance alleles were accumulated in European winter wheat germplasm, implying indirect prior selection for improved FRR resistance in elite breeding programs. Our results give first insights into the genetic basis of FRR resistance in wheat and demonstrate how molecular parameters can successfully be explored in genomic prediction. Ongoing work will help to further improve our understanding of the complex interactions of genetic factors influencing FRR resistance.
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Affiliation(s)
- Kai P 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.
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia.
| | - 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
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural University, Changsha, 410128, P.R. China
| | - Iulian Gabur
- 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
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Christian R Werner
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Stefan Kontowski
- W. von Borries-Eckendorf GmbH & Co. KG, Hovedisser Str. 92, 33818, Leopoldshöhe, Germany
| | - Matthias Frisch
- Institute for Agronomy and Plant Breeding II, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Wolfgang Friedt
- 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
| | - Sven Gottwald
- 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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33
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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. 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] [What about the content of this article? (0)] [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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34
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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. Theor Appl Genet 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] [What about the content of this article? (0)] [Affiliation(s)] [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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35
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Voss-Fels KP, Robinson H, Mudge SR, Richard C, Newman S, Wittkop B, Stahl A, Friedt W, Frisch M, Gabur I, Miller-Cooper A, Campbell BC, Kelly A, Fox G, Christopher J, Christopher M, Chenu K, Franckowiak J, Mace ES, Borrell AK, Eagles H, Jordan DR, Botella JR, Hammer G, Godwin ID, Trevaskis B, Snowdon RJ, Hickey LT. VERNALIZATION1 Modulates Root System Architecture in Wheat and Barley. Mol Plant 2018; 11:226-229. [PMID: 29056533 DOI: 10.1016/j.molp.2017.10.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 10/09/2017] [Accepted: 10/11/2017] [Indexed: 05/18/2023]
Affiliation(s)
- Kai P 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
| | - Hannah Robinson
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD 4072, Australia
| | - Stephen R Mudge
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Cecile Richard
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD 4072, Australia
| | - Saul Newman
- CSIRO, Agriculture, Canberra, ACT 2601, Australia
| | - Benjamin Wittkop
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Andreas Stahl
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Wolfgang Friedt
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Matthias Frisch
- Department of Biometry and Population Genetics, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Iulian Gabur
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Anika Miller-Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD 4072, Australia
| | - Bradley C Campbell
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Alison Kelly
- Department of Agriculture and Fisheries, Leslie Research Facility, Toowoomba, QLD 4350, Australia
| | - Glen Fox
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Toowoomba, QLD 4350, Australia
| | - Jack Christopher
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Toowoomba, QLD 4350, Australia
| | - Mandy Christopher
- Department of Agriculture and Fisheries, Leslie Research Facility, Toowoomba, QLD 4350, Australia
| | - Karine Chenu
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Toowoomba, QLD 4350, Australia
| | - Jerome Franckowiak
- Department of Agronomy and Plant Genetics, University of Minnesota, St Paul, MN, USA
| | - Emma S Mace
- Department of Agriculture and Fisheries, Hermitage Research Facility, Warwick, QLD 4370, Australia
| | - Andrew K Borrell
- Queensland Alliance for Agriculture and Food Innovation, Hermitage Research Facility, The University of Queensland, Warwick, QLD 4370, Australia
| | | | - David R Jordan
- Queensland Alliance for Agriculture and Food Innovation, Hermitage Research Facility, The University of Queensland, Warwick, QLD 4370, Australia
| | - José R Botella
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Graeme Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD 4072, Australia
| | - Ian D Godwin
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
| | | | - 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.
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD 4072, Australia.
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Alahmad S, Dinglasan E, Leung KM, Riaz A, Derbal N, Voss-Fels KP, Able JA, Bassi FM, Christopher J, Hickey LT. Speed breeding for multiple quantitative traits in durum wheat. Plant Methods 2018; 14:36. [PMID: 29785201 PMCID: PMC5950182 DOI: 10.1186/s13007-018-0302-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 04/26/2018] [Indexed: 05/06/2023]
Abstract
BACKGROUND Plant breeding requires numerous generations to be cycled and evaluated before an improved cultivar is released. This lengthy process is required to introduce and test multiple traits of interest. However, a technology for rapid generation advance named 'speed breeding' was successfully deployed in bread wheat (Triticum aestivum L.) to achieve six generations per year while imposing phenotypic selection for foliar disease resistance and grain dormancy. Here, for the first time the deployment of this methodology is presented in durum wheat (Triticum durum Desf.) by integrating selection for key traits, including above and below ground traits on the same set of plants. This involved phenotyping for seminal root angle (RA), seminal root number (RN), tolerance to crown rot (CR), resistance to leaf rust (LR) and plant height (PH). In durum wheat, these traits are desirable in environments where yield is limited by in-season rainfall with the occurrence of CR and epidemics of LR. To evaluate this multi-trait screening approach, we applied selection to a large segregating F2 population (n = 1000) derived from a bi-parental cross (Outrob4/Caparoi). A weighted selection index (SI) was developed and applied. The gain for each trait was determined by evaluating F3 progeny derived from 100 'selected' and 100 'unselected' F2 individuals. RESULTS Transgressive segregation was observed for all assayed traits in the Outrob4/Caparoi F2 population. Application of the SI successfully shifted the population mean for four traits, as determined by a significant mean difference between 'selected' and 'unselected' F3 families for CR tolerance, LR resistance, RA and RN. No significant shift for PH was observed. CONCLUSIONS The novel multi-trait phenotyping method presents a useful tool for rapid selection of early filial generations or for the characterization of fixed lines out-of-season. Further, it offers efficient use of resources by assaying multiple traits on the same set of plants. Results suggest that when performed in parallel with speed breeding in early generations, selection will enrich recombinant inbred lines with desirable alleles and will reduce the length and number of years required to combine these traits in elite breeding populations and therefore cultivars.
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Affiliation(s)
- Samir Alahmad
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Brisbane, QLD 4072 Australia
| | - Eric Dinglasan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Brisbane, QLD 4072 Australia
| | - Kung Ming Leung
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Brisbane, QLD 4072 Australia
| | - Adnan Riaz
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Brisbane, QLD 4072 Australia
| | - Nora Derbal
- Department of Ecology and Environmental Engineering, The University of 8 Mai 1945, 24000 Guelma, Algeria
| | - Kai P. Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Brisbane, QLD 4072 Australia
| | - Jason A. Able
- School of Agriculture, Food and Wine, Waite Research Institute, The University of Adelaide, Urrbrae, Adelaide, SA 5064 Australia
| | - Filippo M. Bassi
- International Center for the Agricultural Research in the Dry Areas, 10000 Rabat, Morocco
| | - Jack Christopher
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Leslie Research Facility, Toowoomba, 4350 QLD Australia
| | - Lee T. Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Brisbane, QLD 4072 Australia
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Riaz A, Athiyannan N, Periyannan SK, Afanasenko O, Mitrofanova OP, Platz GJ, Aitken EAB, Snowdon RJ, Lagudah ES, Hickey LT, Voss-Fels KP. Unlocking new alleles for leaf rust resistance in the Vavilov wheat collection. Theor Appl Genet 2018; 131:127-144. [PMID: 28980023 DOI: 10.1007/s00122-017-2990-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 09/21/2017] [Indexed: 05/06/2023]
Abstract
Thirteen potentially new leaf rust resistance loci were identified in a Vavilov wheat diversity panel. We demonstrated the potential of allele stacking to strengthen resistance against this important pathogen. Leaf rust (LR) caused by Puccinia triticina is an important disease of wheat (Triticum aestivum L.), and the deployment of genetically resistant cultivars is the most viable strategy to minimise yield losses. In this study, we evaluated a diversity panel of 295 bread wheat accessions from the N. I. Vavilov Institute of Plant Genetic Resources (St Petersburg, Russia) for LR resistance and performed genome-wide association studies (GWAS) using 10,748 polymorphic DArT-seq markers. The diversity panel was evaluated at seedling and adult plant growth stages using three P. triticina pathotypes prevalent in Australia. GWAS was applied to 11 phenotypic data sets which identified a total of 52 significant marker-trait associations representing 31 quantitative trait loci (QTL). Among them, 29 QTL were associated with adult plant resistance (APR). Of the 31 QTL, 13 were considered potentially new loci, whereas 4 co-located with previously catalogued Lr genes and 14 aligned to regions reported in other GWAS and genomic prediction studies. One seedling LR resistance QTL located on chromosome 3A showed pronounced levels of linkage disequilibrium among markers (r 2 = 0.7), suggested a high allelic fixation. Subsequent haplotype analysis for this region found seven haplotype variants, of which two were strongly associated with LR resistance at seedling stage. Similarly, analysis of an APR QTL on chromosome 7B revealed 22 variants, of which 4 were associated with resistance at the adult plant stage. Furthermore, most of the tested lines in the diversity panel carried 10 or more combined resistance-associated marker alleles, highlighting the potential of allele stacking for long-lasting resistance.
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Affiliation(s)
- Adnan Riaz
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Naveenkumar Athiyannan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
- Commonwealth Scientific and Industrial Research Organization, Agriculture and Food, Canberra, ACT, Australia
| | - Sambasivam K Periyannan
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
- Commonwealth Scientific and Industrial Research Organization, Agriculture and Food, Canberra, ACT, Australia
| | - Olga Afanasenko
- Department of Plant Resistance to Diseases, All-Russian Research Institute for Plant Protection, St Petersburg, Russia
| | - Olga P Mitrofanova
- N. I. Vavilov Institute of Plant Genetic Resources, St Petersburg, Russia
| | - Gregory J Platz
- Department of Agriculture and Fisheries, Hermitage Research Facility, Warwick, QLD, Australia
| | - Elizabeth A B Aitken
- School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD, Australia
| | - Rod J Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | - Evans S Lagudah
- Commonwealth Scientific and Industrial Research Organization, Agriculture and Food, Canberra, ACT, Australia
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia.
| | - Kai P Voss-Fels
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia.
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany.
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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. Theor Appl Genet 2017. [PMID: 29080901 DOI: 10.1007/s00122‐017‐3002‐5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Qian L, Hickey LT, Stahl A, Werner CR, Hayes B, Snowdon RJ, Voss-Fels KP. Exploring and Harnessing Haplotype Diversity to Improve Yield Stability in Crops. Front Plant Sci 2017; 8:1534. [PMID: 28928764 PMCID: PMC5591830 DOI: 10.3389/fpls.2017.01534] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Accepted: 08/22/2017] [Indexed: 05/19/2023]
Abstract
In order to meet future food, feed, fiber, and bioenergy demands, global yields of all major crops need to be increased significantly. At the same time, the increasing frequency of extreme weather events such as heat and drought necessitates improvements in the environmental resilience of modern crop cultivars. Achieving sustainably increase yields implies rapid improvement of quantitative traits with a very complex genetic architecture and strong environmental interaction. Latest advances in genome analysis technologies today provide molecular information at an ultrahigh resolution, revolutionizing crop genomic research, and paving the way for advanced quantitative genetic approaches. These include highly detailed assessment of population structure and genotypic diversity, facilitating the identification of selective sweeps and signatures of directional selection, dissection of genetic variants that underlie important agronomic traits, and genomic selection (GS) strategies that not only consider major-effect genes. Single-nucleotide polymorphism (SNP) markers today represent the genotyping system of choice for crop genetic studies because they occur abundantly in plant genomes and are easy to detect. SNPs are typically biallelic, however, hence their information content compared to multiallelic markers is low, limiting the resolution at which SNP-trait relationships can be delineated. An efficient way to overcome this limitation is to construct haplotypes based on linkage disequilibrium, one of the most important features influencing genetic analyses of crop genomes. Here, we give an overview of the latest advances in genomics-based haplotype analyses in crops, highlighting their importance in the context of polyploidy and genome evolution, linkage drag, and co-selection. We provide examples of how haplotype analyses can complement well-established quantitative genetics frameworks, such as quantitative trait analysis and GS, ultimately providing an effective tool to equip modern crops with environment-tailored characteristics.
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Affiliation(s)
- Lunwen Qian
- Collaborative Innovation Center of Grain and Oil Crops in South China, Hunan Agricultural UniversityChangsha, China
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University GiessenGiessen, Germany
| | - Lee T. Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St LuciaQLD, Australia
| | - Andreas Stahl
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University GiessenGiessen, Germany
| | - Christian R. Werner
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University GiessenGiessen, Germany
| | - Ben Hayes
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St LuciaQLD, Australia
| | - Rod J. Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University GiessenGiessen, Germany
| | - Kai P. Voss-Fels
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University GiessenGiessen, Germany
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St LuciaQLD, Australia
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40
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Voss-Fels KP, Qian L, Parra-Londono S, Uptmoor R, Frisch M, Keeble-Gagnère G, Appels R, Snowdon RJ. Linkage drag constrains the roots of modern wheat. Plant Cell Environ 2017; 40:717-725. [PMID: 28036107 DOI: 10.1111/pce.12888] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 12/20/2016] [Accepted: 12/21/2016] [Indexed: 05/19/2023]
Abstract
Roots, the hidden half of crop plants, are essential for resource acquisition. However, knowledge about the genetic control of below-ground plant development in wheat, one of the most important small-grain crops in the world, is very limited. The molecular interactions connecting root and shoot development and growth, and thus modulating the plant's demand for water and nutrients along with its ability to access them, are largely unexplored. Here, we demonstrate that linkage drag in European bread wheat, driven by strong selection for a haplotype variant controlling heading date, has eliminated a specific combination of two flanking, highly conserved haplotype variants whose interaction confers increased root biomass. Reversing this inadvertent consequence of selection could recover root diversity that may prove essential for future food production in fluctuating environments. Highly conserved synteny to rice across this chromosome segment suggests that adaptive selection has shaped the diversity landscape of this locus across different, globally important cereal crops. By mining wheat gene expression data, we identified root-expressed genes within the region of interest that could help breeders to select positive variants adapted to specific target soil environments.
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Affiliation(s)
- Kai P 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
| | - 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
| | - Sebastian Parra-Londono
- Department of Agronomy, University of Rostock, Justus-von-Liebig-Weg 6, 18059, Rostock, Germany
| | - Ralf Uptmoor
- Department of Agronomy, University of Rostock, Justus-von-Liebig-Weg 6, 18059, Rostock, Germany
| | - Matthias Frisch
- Department of Biometry and Population Genetics, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Gabriel Keeble-Gagnère
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Bundoora, Victoria, 3083, Australia
| | - Rudi Appels
- State Agriculture Biotechnology Centre, School of Veterinary and Life Sciences, Murdoch University, Australia Export Grains Innovation Centre (AEGIC), Perth, WA, 6150, Australia
| | - 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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