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Li W, Boer MP, Joosen RVL, Zheng C, Percival-Alwyn L, Cockram J, Van Eeuwijk FA. Modeling QTL-by-environment interactions for multi-parent populations. FRONTIERS IN PLANT SCIENCE 2024; 15:1410851. [PMID: 39145196 PMCID: PMC11322070 DOI: 10.3389/fpls.2024.1410851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 06/27/2024] [Indexed: 08/16/2024]
Abstract
Multi-parent populations (MPPs) are attractive for genetic and breeding studies because they combine genetic diversity with an easy-to-control population structure. Most methods for mapping QTLs in MPPs focus on the detection of QTLs in single environments. Little attention has been given to mapping QTLs in multienvironment trials (METs) and to detecting and modeling QTL-by-environment interactions (QEIs). We present mixed model approaches for the detection and modeling of consistent versus environment-dependent QTLs, i.e., QTL-by-environment interaction (QEI). QTL effects are assumed to be normally distributed with variances expressing consistency or dependence on environments and families. The entries of the corresponding design matrices are functions of identity-by-descent (IBD) probabilities between parents and offspring and follow from the parental origin of offspring DNA. A polygenic effect is added to the models to account for background genetic variation. We illustrate the wide applicability of our method by analyzing several public MPP datasets with observations from METs. The examples include diallel, nested association mapping (NAM), and multi-parent advanced inter-cross (MAGIC) populations. The results of our approach compare favorably with those of previous studies that used tailored methods.
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Affiliation(s)
- Wenhao Li
- Biometris, Wageningen University and Research Center, Wageningen, Netherlands
| | - Martin P. Boer
- Biometris, Wageningen University and Research Center, Wageningen, Netherlands
| | | | - Chaozhi Zheng
- Biometris, Wageningen University and Research Center, Wageningen, Netherlands
| | | | | | - Fred A. Van Eeuwijk
- Biometris, Wageningen University and Research Center, Wageningen, Netherlands
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Skøt L, Nay MM, Grieder C, Frey LA, Pégard M, Öhlund L, Amdahl H, Radovic J, Jaluvka L, Palmé A, Ruttink T, Lloyd D, Howarth CJ, Kölliker R. Including marker x environment interactions improves genomic prediction in red clover ( Trifolium pratense L.). FRONTIERS IN PLANT SCIENCE 2024; 15:1407609. [PMID: 38916032 PMCID: PMC11194335 DOI: 10.3389/fpls.2024.1407609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 05/20/2024] [Indexed: 06/26/2024]
Abstract
Genomic prediction has mostly been used in single environment contexts, largely ignoring genotype x environment interaction, which greatly affects the performance of plants. However, in the last decade, prediction models including marker x environment (MxE) interaction have been developed. We evaluated the potential of genomic prediction in red clover (Trifolium pratense L.) using field trial data from five European locations, obtained in the Horizon 2020 EUCLEG project. Three models were compared: (1) single environment (SingleEnv), (2) across environment (AcrossEnv), (3) marker x environment interaction (MxE). Annual dry matter yield (DMY) gave the highest predictive ability (PA). Joint analyses of DMY from years 1 and 2 from each location varied from 0.87 in Britain and Switzerland in year 1, to 0.40 in Serbia in year 2. Overall, crude protein (CP) was predicted poorly. PAs for date of flowering (DOF), however ranged from 0.87 to 0.67 for Britain and Switzerland, respectively. Across the three traits, the MxE model performed best and the AcrossEnv worst, demonstrating that including marker x environment effects can improve genomic prediction in red clover. Leaving out accessions from specific regions or from specific breeders' material in the cross validation tended to reduce PA, but the magnitude of reduction depended on trait, region and breeders' material, indicating that population structure contributed to the high PAs observed for DMY and DOF. Testing the genomic estimated breeding values on new phenotypic data from Sweden showed that DMY training data from Britain gave high PAs in both years (0.43-0.76), while DMY training data from Switzerland gave high PAs only for year 1 (0.70-0.87). The genomic predictions we report here underline the potential benefits of incorporating MxE interaction in multi-environment trials and could have perspectives for identifying markers with effects that are stable across environments, and markers with environment-specific effects.
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Affiliation(s)
- Leif Skøt
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Michelle M. Nay
- Division of Plant Breeding, Fodder Plant Breeding, Agroscope, Zurich, Switzerland
| | - Christoph Grieder
- Division of Plant Breeding, Fodder Plant Breeding, Agroscope, Zurich, Switzerland
| | - Lea A. Frey
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | | | | | - Helga Amdahl
- Graminor Breeding Ltd., Bjørke Forsøksgård, Norway
| | | | | | - Anna Palmé
- The Nordic Genetic Resource Centre, Plant Section, Alnarp, Sweden
| | - Tom Ruttink
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Melle, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
| | - David Lloyd
- Germinal Horizon, Plas Gogerddan, Aberystwyth, United Kingdom
| | - Catherine J. Howarth
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Roland Kölliker
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
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Sun Z, Zheng Z, Qi F, Wang J, Wang M, Zhao R, Liu H, Xu J, Qin L, Dong W, Huang B, Han S, Zhang X. Development and evaluation of the utility of GenoBaits Peanut 40K for a peanut MAGIC population. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:72. [PMID: 37786866 PMCID: PMC10542084 DOI: 10.1007/s11032-023-01417-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 09/07/2023] [Indexed: 10/04/2023]
Abstract
Population and genotype data are essential for genetic mapping. The multi-parent advanced generation intercross (MAGIC) population is a permanent mapping population used for precisely mapping quantitative trait loci. Moreover, genotyping-by-target sequencing (GBTS) is a robust high-throughput genotyping technology characterized by its low cost, flexibility, and limited requirements for information management and support. In this study, an 8-way MAGIC population was constructed using eight elite founder lines. In addition, GenoBaits Peanut 40K was developed and utilized for the constructed MAGIC population. A subset (297 lines) of the MAGIC population at the S2 stage was genotyped using GenoBaits Peanut 40K. Furthermore, these lines and the eight parents were analyzed in terms of pod length, width, area, and perimeter. A total of 27 single nucleotide polymorphisms (SNPs) were revealed to be significantly associated with peanut pod size-related traits according to a genome-wide association study. The GenoBaits Peanut 40K provided herein and the constructed MAGIC population will be applicable for future research to identify the key genes responsible for important peanut traits. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01417-w.
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Affiliation(s)
- Ziqi Sun
- Institute of Crop Molecular Breeding, Henan Academy of Agricultural Sciences/The Shennong Laboratory/State Industrial Innovation Center of Biological Breeding/Key Laboratory of Oil Crops in Huang-Huai-Hai Plains, Ministry of Agriculture/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, Henan China
| | - Zheng Zheng
- Institute of Crop Molecular Breeding, Henan Academy of Agricultural Sciences/The Shennong Laboratory/State Industrial Innovation Center of Biological Breeding/Key Laboratory of Oil Crops in Huang-Huai-Hai Plains, Ministry of Agriculture/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, Henan China
| | - Feiyan Qi
- Institute of Crop Molecular Breeding, Henan Academy of Agricultural Sciences/The Shennong Laboratory/State Industrial Innovation Center of Biological Breeding/Key Laboratory of Oil Crops in Huang-Huai-Hai Plains, Ministry of Agriculture/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, Henan China
| | - Juan Wang
- Institute of Crop Molecular Breeding, Henan Academy of Agricultural Sciences/The Shennong Laboratory/State Industrial Innovation Center of Biological Breeding/Key Laboratory of Oil Crops in Huang-Huai-Hai Plains, Ministry of Agriculture/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, Henan China
| | - Mengmeng Wang
- Institute of Crop Molecular Breeding, Henan Academy of Agricultural Sciences/The Shennong Laboratory/State Industrial Innovation Center of Biological Breeding/Key Laboratory of Oil Crops in Huang-Huai-Hai Plains, Ministry of Agriculture/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, Henan China
| | - Ruifang Zhao
- Institute of Crop Molecular Breeding, Henan Academy of Agricultural Sciences/The Shennong Laboratory/State Industrial Innovation Center of Biological Breeding/Key Laboratory of Oil Crops in Huang-Huai-Hai Plains, Ministry of Agriculture/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, Henan China
| | - Hua Liu
- Institute of Crop Molecular Breeding, Henan Academy of Agricultural Sciences/The Shennong Laboratory/State Industrial Innovation Center of Biological Breeding/Key Laboratory of Oil Crops in Huang-Huai-Hai Plains, Ministry of Agriculture/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, Henan China
| | - Jing Xu
- Institute of Crop Molecular Breeding, Henan Academy of Agricultural Sciences/The Shennong Laboratory/State Industrial Innovation Center of Biological Breeding/Key Laboratory of Oil Crops in Huang-Huai-Hai Plains, Ministry of Agriculture/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, Henan China
| | - Li Qin
- Institute of Crop Molecular Breeding, Henan Academy of Agricultural Sciences/The Shennong Laboratory/State Industrial Innovation Center of Biological Breeding/Key Laboratory of Oil Crops in Huang-Huai-Hai Plains, Ministry of Agriculture/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, Henan China
| | - Wenzhao Dong
- Institute of Crop Molecular Breeding, Henan Academy of Agricultural Sciences/The Shennong Laboratory/State Industrial Innovation Center of Biological Breeding/Key Laboratory of Oil Crops in Huang-Huai-Hai Plains, Ministry of Agriculture/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, Henan China
| | - Bingyan Huang
- Institute of Crop Molecular Breeding, Henan Academy of Agricultural Sciences/The Shennong Laboratory/State Industrial Innovation Center of Biological Breeding/Key Laboratory of Oil Crops in Huang-Huai-Hai Plains, Ministry of Agriculture/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, Henan China
| | - Suoyi Han
- Institute of Crop Molecular Breeding, Henan Academy of Agricultural Sciences/The Shennong Laboratory/State Industrial Innovation Center of Biological Breeding/Key Laboratory of Oil Crops in Huang-Huai-Hai Plains, Ministry of Agriculture/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, Henan China
| | - Xinyou Zhang
- Institute of Crop Molecular Breeding, Henan Academy of Agricultural Sciences/The Shennong Laboratory/State Industrial Innovation Center of Biological Breeding/Key Laboratory of Oil Crops in Huang-Huai-Hai Plains, Ministry of Agriculture/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, Henan China
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Wankhade AP, Chimote VP, Viswanatha KP, Yadaru S, Deshmukh DB, Gattu S, Sudini HK, Deshmukh MP, Shinde VS, Vemula AK, Pasupuleti J. Genome-wide association mapping for LLS resistance in a MAGIC population of groundnut (Arachis hypogaea L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:43. [PMID: 36897383 DOI: 10.1007/s00122-023-04256-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 12/19/2022] [Indexed: 06/18/2023]
Abstract
The identified 30 functional nucleotide polymorphisms or genic SNP markers would offer essential information for marker-assisted breeding in groundnut. A genome-wide association study (GWAS) on component traits of LLS resistance in an eight-way multiparent advance generation intercross (MAGIC) population of groundnut in the field and in a light chamber (controlled conditions) was performed via an Affymetrix 48 K single-nucleotide polymorphism (SNP) 'Axiom Arachis' array. Multiparental populations with high-density genotyping enable the detection of novel alleles. In total, five quantitative trait loci (QTLs) with marker - log10(p value) scores ranging from 4.25 to 13.77 for the incubation period (IP) and six QTLs with marker - log10(p value) scores ranging from 4.33 to 10.79 for the latent period (LP) were identified across the A- and B-subgenomes. A total of 62 markers‒trait associations (MTAs) were identified across the A- and B-subgenomes. Markers for LLS scores and the area under the disease progression curve (AUDPC) recorded for plants in the light chamber and under field conditions presented - log10 (p value) scores ranging from 4.22 to 27.30. The highest number of MTAs (six) was identified on chromosomes A05, B07 and B09. Out of a total of 73 MTAs, 37 and 36 MTAs were detected in subgenomes A and B, respectively. Taken together, these results suggest that both subgenomes have equal potential genomic regions contributing to LLS resistance. A total of 30 functional nucleotide polymorphisms or genic SNP markers were detected, among which eight genes were found to encode leucine-rich repeat (LRR) receptor-like protein kinases and putative disease resistance proteins. These important SNPs can be used in breeding programmes for the development of cultivars with improved disease resistance.
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Affiliation(s)
- Ankush Purushottam Wankhade
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, 502 324, India
- Mahatma Phule Krishi Vidyapeeth (MPKV), Rahuri, Maharashtra, 413 722, India
| | | | | | - Shasidhar Yadaru
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, 502 324, India
| | - Dnyaneshwar Bandu Deshmukh
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, 502 324, India
| | - Swathi Gattu
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, 502 324, India
| | - Hari Kishan Sudini
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, 502 324, India
| | | | | | - Anil Kumar Vemula
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, 502 324, India
| | - Janila Pasupuleti
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, 502 324, India.
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Cappa EP, Chen C, Klutsch JG, Sebastian-Azcona J, Ratcliffe B, Wei X, Da Ros L, Ullah A, Liu Y, Benowicz A, Sadoway S, Mansfield SD, Erbilgin N, Thomas BR, El-Kassaby YA. Multiple-trait analyses improved the accuracy of genomic prediction and the power of genome-wide association of productivity and climate change-adaptive traits in lodgepole pine. BMC Genomics 2022; 23:536. [PMID: 35870886 PMCID: PMC9308220 DOI: 10.1186/s12864-022-08747-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 07/08/2022] [Indexed: 11/10/2022] Open
Abstract
Background Genomic prediction (GP) and genome-wide association (GWA) analyses are currently being employed to accelerate breeding cycles and to identify alleles or genomic regions of complex traits in forest trees species. Here, 1490 interior lodgepole pine (Pinus contorta Dougl. ex. Loud. var. latifolia Engelm) trees from four open-pollinated progeny trials were genotyped with 25,099 SNPs, and phenotyped for 15 growth, wood quality, pest resistance, drought tolerance, and defense chemical (monoterpenes) traits. The main objectives of this study were to: (1) identify genetic markers associated with these traits and determine their genetic architecture, and to compare the marker detected by single- (ST) and multiple-trait (MT) GWA models; (2) evaluate and compare the accuracy and control of bias of the genomic predictions for these traits underlying different ST and MT parametric and non-parametric GP methods. GWA, ST and MT analyses were compared using a linear transformation of genomic breeding values from the respective genomic best linear unbiased prediction (GBLUP) model. GP, ST and MT parametric and non-parametric (Reproducing Kernel Hilbert Spaces, RKHS) models were compared in terms of prediction accuracy (PA) and control of bias. Results MT-GWA analyses identified more significant associations than ST. Some SNPs showed potential pleiotropic effects. Averaging across traits, PA from the studied ST-GP models did not differ significantly from each other, with generally a slight superiority of the RKHS method. MT-GP models showed significantly higher PA (and lower bias) than the ST models, being generally the PA (bias) of the RKHS approach significantly higher (lower) than the GBLUP. Conclusions The power of GWA and the accuracy of GP were improved when MT models were used in this lodgepole pine population. Given the number of GP and GWA models fitted and the traits assessed across four progeny trials, this work has produced the most comprehensive empirical genomic study across any lodgepole pine population to date. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08747-7.
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Puglisi D, Visioni A, Ozkan H, Kara İ, Lo Piero AR, Rachdad FE, Tondelli A, Valè G, Cattivelli L, Fricano A. High accuracy of genome-enabled prediction of belowground and physiological traits in barley seedlings. G3 GENES|GENOMES|GENETICS 2022; 12:6517783. [PMID: 35099521 PMCID: PMC8895982 DOI: 10.1093/g3journal/jkac022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 01/21/2022] [Indexed: 11/24/2022]
Abstract
In plants, the study of belowground traits is gaining momentum due to their importance on yield formation and the uptake of water and nutrients. In several cereal crops, seminal root number and seminal root angle are proxy traits of the root system architecture at the mature stages, which in turn contributes to modulating the uptake of water and nutrients. Along with seminal root number and seminal root angle, experimental evidence indicates that the transpiration rate response to evaporative demand or vapor pressure deficit is a key physiological trait that might be targeted to cope with drought tolerance as the reduction of the water flux to leaves for limiting transpiration rate at high levels of vapor pressure deficit allows to better manage soil moisture. In the present study, we examined the phenotypic diversity of seminal root number, seminal root angle, and transpiration rate at the seedling stage in a panel of 8-way Multiparent Advanced Generation Inter-Crosses lines of winter barley and correlated these traits with grain yield measured in different site-by-season combinations. Second, phenotypic and genotypic data of the Multiparent Advanced Generation Inter-Crosses population were combined to fit and cross-validate different genomic prediction models for these belowground and physiological traits. Genomic prediction models for seminal root number were fitted using threshold and log-normal models, considering these data as ordinal discrete variable and as count data, respectively, while for seminal root angle and transpiration rate, genomic prediction was implemented using models based on extended genomic best linear unbiased predictors. The results presented in this study show that genome-enabled prediction models of seminal root number, seminal root angle, and transpiration rate data have high predictive ability and that the best models investigated in the present study include first-order additive × additive epistatic interaction effects. Our analyses indicate that beyond grain yield, genomic prediction models might be used to predict belowground and physiological traits and pave the way to practical applications for barley improvement.
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Affiliation(s)
- Damiano Puglisi
- Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università di Catania , 95123 Catania, Italy
| | - Andrea Visioni
- Biodiversity and Crop Improvement Program, International Center for Agricultural Research in the Dry Areas , 6299 Rabat, Morocco
| | - Hakan Ozkan
- Faculty of Agriculture, Department of Field Crops, University of Cukurova , 01330 Adana, Turkey
| | - İbrahim Kara
- Bahri Dagdas International Agricultural Research Institute , Km Karatay/Konya 42020, Turkey
| | - Angela Roberta Lo Piero
- Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università di Catania , 95123 Catania, Italy
| | - Fatima Ezzahra Rachdad
- Biodiversity and Crop Improvement Program, International Center for Agricultural Research in the Dry Areas , 6299 Rabat, Morocco
- Faculty of Sciences Ben M’sik, Department of Biology, Environment and Ecology Laboratory, Hassan II University of Casablanca , 7955 Casablanca, Morocco
| | - Alessandro Tondelli
- Council for Agricultural Research and Economics—Research Centre for Genomics and Bioinformatics , 29017 Fiorenzuola d’Arda (PC), Italy
| | - Giampiero Valè
- DiSIT, Dipartimento di Scienze e Innovazione Tecnologica, Università del Piemonte Orientale , 13100 Vercelli, Italy
| | - Luigi Cattivelli
- Council for Agricultural Research and Economics—Research Centre for Genomics and Bioinformatics , 29017 Fiorenzuola d’Arda (PC), Italy
| | - Agostino Fricano
- Council for Agricultural Research and Economics—Research Centre for Genomics and Bioinformatics , 29017 Fiorenzuola d’Arda (PC), Italy
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