1
|
Joukhadar R, Li Y, Thistlethwaite R, Forrest KL, Tibbits JF, Trethowan R, Hayden MJ. Optimising desired gain indices to maximise selection response. FRONTIERS IN PLANT SCIENCE 2024; 15:1337388. [PMID: 38978519 PMCID: PMC11228337 DOI: 10.3389/fpls.2024.1337388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 05/23/2024] [Indexed: 07/10/2024]
Abstract
Introduction In plant breeding, we often aim to improve multiple traits at once. However, without knowing the economic value of each trait, it is hard to decide which traits to focus on. This is where "desired gain selection indices" come in handy, which can yield optimal gains in each trait based on the breeder's prioritisation of desired improvements when economic weights are not available. However, they lack the ability to maximise the selection response and determine the correlation between the index and net genetic merit. Methods Here, we report the development of an iterative desired gain selection index method that optimises the sampling of the desired gain values to achieve a targeted or a user-specified selection response for multiple traits. This targeted selection response can be constrained or unconstrained for either a subset or all the studied traits. Results We tested the method using genomic estimated breeding values (GEBVs) for seven traits in a bread wheat (Triticum aestivum) reference breeding population comprising 3,331 lines and achieved prediction accuracies ranging between 0.29 and 0.47 across the seven traits. The indices were validated using 3,005 double haploid lines that were derived from crosses between parents selected from the reference population. We tested three user-specified response scenarios: a constrained equal weight (INDEX1), a constrained yield dominant weight (INDEX2), and an unconstrained weight (INDEX3). Our method achieved an equivalent response to the user-specified selection response when constraining a set of traits, and this response was much better than the response of the traditional desired gain selection indices method without iteration. Interestingly, when using unconstrained weight, our iterative method maximised the selection response and shifted the average GEBVs of the selection candidates towards the desired direction. Discussion Our results show that the method is an optimal choice not only when economic weights are unavailable, but also when constraining the selection response is an unfavourable option.
Collapse
Affiliation(s)
- Reem Joukhadar
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
| | - Yongjun Li
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
| | - Rebecca Thistlethwaite
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia
| | - Kerrie L. Forrest
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
| | - Josquin F. Tibbits
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
| | - Richard Trethowan
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Cobbitty, NSW, Australia
| | - Matthew J. Hayden
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| |
Collapse
|
2
|
Balech R, Maalouf F, Kaur S, Jighly A, Joukhadar R, Alsamman AM, Hamwieh A, Khater LA, Rubiales D, Kumar S. Identification of novel genes associated with herbicide tolerance in Lentil (Lens culinaris ssp. culinaris Medik.). Sci Rep 2024; 14:10215. [PMID: 38702403 PMCID: PMC11068770 DOI: 10.1038/s41598-024-59695-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 04/15/2024] [Indexed: 05/06/2024] Open
Abstract
Weeds pose a major constraint in lentil cultivation, leading to decrease farmers' revenues by reducing the yield and increasing the management costs. The development of herbicide tolerant cultivars is essential to increase lentil yield. Even though herbicide tolerant lines have been identified in lentils, breeding efforts are still limited and lack proper validation. Marker assisted selection (MAS) can increase selection accuracy at early generations. Total 292 lentil accessions were evaluated under different dosages of two herbicides, metribuzin and imazethapyr, during two seasons at Marchouch, Morocco and Terbol, Lebanon. Highly significant differences among accessions were observed for days to flowering (DF) and maturity (DM), plant height (PH), biological yield (BY), seed yield (SY), number of pods per plant (NP), as well as the reduction indices (RI) for PH, BY, SY and NP. A total of 10,271 SNPs markers uniformly distributed along the lentil genome were assayed using Multispecies Pulse SNP chip developed at Agriculture Victoria, Melbourne. Meta-GWAS analysis was used to detect marker-trait associations, which detected 125 SNPs markers associated with different traits and clustered in 85 unique quantitative trait loci. These findings provide valuable insights for initiating MAS programs aiming to enhance herbicide tolerance in lentil crop.
Collapse
Affiliation(s)
- Rind Balech
- International Center for Agricultural Research in the Dry Areas (ICARDA), Terbol, Lebanon.
| | - Fouad Maalouf
- International Center for Agricultural Research in the Dry Areas (ICARDA), Terbol, Lebanon.
| | - Sukhjiwan Kaur
- Department of Energy, AgriBio, Environment and Climate Action, Centre for AgriBioscience, 5 Ring Road, Bundoora, VIC, 3083, Australia
| | - Abdulqader Jighly
- Department of Energy, AgriBio, Environment and Climate Action, Centre for AgriBioscience, 5 Ring Road, Bundoora, VIC, 3083, Australia
| | - Reem Joukhadar
- Department of Energy, AgriBio, Environment and Climate Action, Centre for AgriBioscience, 5 Ring Road, Bundoora, VIC, 3083, Australia
| | | | | | - Lynn Abou Khater
- International Center for Agricultural Research in the Dry Areas (ICARDA), Terbol, Lebanon
| | - Diego Rubiales
- Institute for Sustainable Agriculture, CSIC, Córdoba, Spain
| | | |
Collapse
|
3
|
Zhao H, Savin KW, Li Y, Breen EJ, Maharjan P, Tibbits JF, Kant S, Hayden MJ, Daetwyler HD. Genome-wide association studies dissect the G × E interaction for agronomic traits in a worldwide collection of safflowers ( Carthamus tinctorius L.). MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:24. [PMID: 37309464 PMCID: PMC10248593 DOI: 10.1007/s11032-022-01295-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/30/2022] [Indexed: 06/14/2023]
Abstract
Genome-wide association studies were conducted using a globally diverse safflower (Carthamus tinctorius L.) Genebank collection for grain yield (YP), days to flowering (DF), plant height (PH), 500 seed weight (SW), seed oil content (OL), and crude protein content (PR) in four environments (sites) that differed in water availability. Phenotypic variation was observed for all traits. YP exhibited low overall genetic correlations (rGoverall) across sites, while SW and OL had high rGoverall and high pairwise genetic correlations (rGij) across all pairwise sites. In total, 92 marker-trait associations (MTAs) were identified using three methods, single locus genome-wide association studies (GWAS) using a mixed linear model (MLM), the Bayesian multi-locus method (BayesR), and meta-GWAS. MTAs with large effects across all sites were detected for OL, SW, and PR, and MTAs specific for the different water stress sites were identified for all traits. Five MTAs were associated with multiple traits; 4 of 5 MTAs were variously associated with the three traits of SW, OL, and PR. This study provided insights into the phenotypic variability and genetic architecture of important safflower agronomic traits under different environments. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01295-8.
Collapse
Affiliation(s)
- Huanhuan Zhao
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083 Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Keith W. Savin
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Yongjun Li
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Edmond J. Breen
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Pankaj Maharjan
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400 Australia
| | - Josquin F. Tibbits
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Surya Kant
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083 Australia
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400 Australia
| | - Matthew J. Hayden
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083 Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Hans D. Daetwyler
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083 Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| |
Collapse
|
4
|
Transcriptome Characterization of the Roles of Abscisic Acid and Calcium Signaling during Water Deficit in Garlic. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12052440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Garlic (Allium sativum L.) is one of the most important vegetable crops, and breeding drought-tolerant varieties is a vital research goal. However, the underlying molecular mechanisms in response to drought stress in garlic are still limited. In this study, garlic seedlings were subjected to 15% PEG6000 for 0, 1, 4, and 12 h, respectively, to simulate drought stress. Changes of transcriptomes as a result of drought stress in garlic leaves were determined by de novo assembly using the Illumina platform. In total, 96,712 unigenes and 11,936 differentially expressed genes (DEGs) were identified in the presence of drought conditions. Transcriptome profiling revealed that the DEGs were mainly enriched in the biosynthesis of secondary metabolites, MAPK signaling pathway, starch and sucrose metabolism, phenylpropanoid biosynthesis, and plant hormone signal transduction. Genes involved in abscisic acid and calcium signaling were further investigated and discussed. Our results indicated that a coordinated interplay between abscisic acid and calcium is required for drought-induced response in garlic.
Collapse
|
5
|
Joukhadar R, Daetwyler HD. Data Integration, Imputation, and Meta-analysis for Genome-Wide Association Studies. Methods Mol Biol 2022; 2481:173-183. [PMID: 35641765 DOI: 10.1007/978-1-0716-2237-7_11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Growing genomic and phenotypic datasets require different groups around the world to collaborate and integrate these valuable resources to maximize their benefit and increase reference population sizes for genomic prediction and genome-wide association studies (GWAS). However, different studies use different genotyping techniques which requires a synchronizing step for the genotyped variants called "imputation" before combining them. Optimally, different GWAS datasets can be analysed within a meta-analysis, which recruits summary statistics instead of actual data. This chapter describes the general principles for genotypic imputation and meta-GWAS analysis with a description of study designs and command lines required for such analyses.
Collapse
Affiliation(s)
- Reem Joukhadar
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Hans D Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia.
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia.
| |
Collapse
|
6
|
Albert E, Sauvage C. Identification and Validation of Candidate Genes from Genome-Wide Association Studies. Methods Mol Biol 2022; 2481:249-272. [PMID: 35641769 DOI: 10.1007/978-1-0716-2237-7_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Exploiting the statistical associations coming out from a GWAS experiment to identify and validate candidate genes may be potentially difficult and time consuming. To fill the gap between the identification of candidate genes toward their functional validation onto the trait performance, the prioritization of variants underlying the GWAS-associated regions is necessary. In parallel, recent developments in genomics and statistical methods have been achieved notably in human genetic and they are accordingly being adopted in plant breeding toward the study of the genetic architecture of traits to sustain genetic gains. In this chapter, we aim at providing both theoretical and practical aspects underlying three main options including (1) the MetaGWAS analysis, (2) the statistical fine mapping and (3) the integration of functional data toward the identification and validation of candidate genes from a GWAS experiment.
Collapse
|
7
|
Joukhadar R, Thistlethwaite R, Trethowan RM, Hayden MJ, Stangoulis J, Cu S, Daetwyler HD. Genomic selection can accelerate the biofortification of spring wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3339-3350. [PMID: 34254178 DOI: 10.1007/s00122-021-03900-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 06/28/2021] [Indexed: 06/13/2023]
Abstract
KEY MESSAGE Genomic selection enabled accurate prediction for the concentration of 13 nutritional element traits in wheat. Wheat biofortification is one of the most sustainable strategies to alleviate mineral deficiency in human diets. Here, we investigated the potential of genomic selection using BayesR and Bayesian ridge regression (BRR) models to predict grain yield (YLD) and the concentration of 13 nutritional elements in grains (B, Ca, Co, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P and Zn) using a population of 1470 spring wheat lines. The lines were grown in replicated field trials with two times of sowing (TOS) at 3 locations (Narrabri-NSW, all lines; Merredin-WA and Horsham-VIC, 200 core lines). Narrow-sense heritability across environments (locations/TOS) ranged from 0.09 to 0.45. Co, K, Na and Ca showed low to negative genetic correlations with other traits including YLD, while the remaining traits were negatively correlated with YLD. When all environments were included in the reference population, medium to high prediction accuracy was observed for the different traits across environments. BayesR had higher average prediction accuracy for mineral concentrations (r = 0.55) compared to BRR (r = 0.48) across all traits and environments but both methods had comparable accuracies for YLD. We also investigated the utility of one or two locations (reference locations) to predict the remaining location(s), as well as the ability of one TOS to predict the other. Under these scenarios, BayesR and BRR showed comparable performance but with lower prediction accuracy compared to the scenario of predicting reference environments for new lines. Our study demonstrates the potential of genomic selection for enriching wheat grain with nutritional elements in biofortification breeding.
Collapse
Affiliation(s)
- Reem Joukhadar
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia.
| | - Rebecca Thistlethwaite
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia
| | - Richard M Trethowan
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Cobbitty, NSW, Australia
| | - Matthew J Hayden
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | - James Stangoulis
- College of Science and Engineering, Flinders University, Sturt Road, Bedford Park, South Australia, 5042, Australia
| | - Suong Cu
- College of Science and Engineering, Flinders University, Sturt Road, Bedford Park, South Australia, 5042, Australia
| | - Hans D Daetwyler
- Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| |
Collapse
|
8
|
Reynolds MP, Lewis JM, Ammar K, Basnet BR, Crespo-Herrera L, Crossa J, Dhugga KS, Dreisigacker S, Juliana P, Karwat H, Kishii M, Krause MR, Langridge P, Lashkari A, Mondal S, Payne T, Pequeno D, Pinto F, Sansaloni C, Schulthess U, Singh RP, Sonder K, Sukumaran S, Xiong W, Braun HJ. Harnessing translational research in wheat for climate resilience. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:5134-5157. [PMID: 34139769 PMCID: PMC8272565 DOI: 10.1093/jxb/erab256] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 06/14/2021] [Indexed: 05/24/2023]
Abstract
Despite being the world's most widely grown crop, research investments in wheat (Triticum aestivum and Triticum durum) fall behind those in other staple crops. Current yield gains will not meet 2050 needs, and climate stresses compound this challenge. However, there is good evidence that heat and drought resilience can be boosted through translating promising ideas into novel breeding technologies using powerful new tools in genetics and remote sensing, for example. Such technologies can also be applied to identify climate resilience traits from among the vast and largely untapped reserve of wheat genetic resources in collections worldwide. This review describes multi-pronged research opportunities at the focus of the Heat and Drought Wheat Improvement Consortium (coordinated by CIMMYT), which together create a pipeline to boost heat and drought resilience, specifically: improving crop design targets using big data approaches; developing phenomic tools for field-based screening and research; applying genomic technologies to elucidate the bases of climate resilience traits; and applying these outputs in developing next-generation breeding methods. The global impact of these outputs will be validated through the International Wheat Improvement Network, a global germplasm development and testing system that contributes key productivity traits to approximately half of the global wheat-growing area.
Collapse
Affiliation(s)
- Matthew P Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Janet M Lewis
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Karim Ammar
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Bhoja R Basnet
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kanwarpal S Dhugga
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Philomin Juliana
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Hannes Karwat
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Masahiro Kishii
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Margaret R Krause
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Peter Langridge
- School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, PMB1, Glen Osmond SA 5064, Australia
- Wheat Initiative, Julius Kühn-Institute, Königin-Luise-Str. 19, 14195 Berlin, Germany
| | - Azam Lashkari
- CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, 450002, PR China
| | - Suchismita Mondal
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Thomas Payne
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Diego Pequeno
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Francisco Pinto
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Carolina Sansaloni
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Urs Schulthess
- CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, 450002, PR China
| | - Ravi P Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kai Sonder
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Wei Xiong
- CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, 450002, PR China
| | - Hans J Braun
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| |
Collapse
|