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Wei S, Yu Z, Du F, Cao F, Yang M, Liu C, Qi Z, Chen Q, Zou J, Wang J. Integrated Transcriptomic and Proteomic Characterization of a Chromosome Segment Substitution Line Reveals the Regulatory Mechanism Controlling the Seed Weight in Soybean. PLANTS (BASEL, SWITZERLAND) 2024; 13:908. [PMID: 38592937 PMCID: PMC10975824 DOI: 10.3390/plants13060908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 03/13/2024] [Accepted: 03/18/2024] [Indexed: 04/11/2024]
Abstract
Soybean is the major global source of edible oils and vegetable proteins. Seed size and weight are crucial traits determining the soybean yield. Understanding the molecular regulatory mechanism underlying the seed weight and size is helpful for improving soybean genetic breeding. The molecular regulatory pathways controlling the seed weight and size were investigated in this study. The 100-seed weight, seed length, seed width, and seed weight per plant of a chromosome segment substitution line (CSSL) R217 increased compared with those of its recurrent parent 'Suinong14' (SN14). Transcriptomic and proteomic analyses of R217 and SN14 were performed at the seed developmental stages S15 and S20. In total, 2643 differentially expressed genes (DEGs) and 208 differentially accumulated proteins (DAPs) were detected at S15, and 1943 DEGs and 1248 DAPs were detected at S20. Furthermore, integrated transcriptomic and proteomic analyses revealed that mitogen-activated protein kinase signaling and cell wall biosynthesis and modification were potential pathways associated with seed weight and size control. Finally, 59 candidate genes that might control seed weight and size were identified. Among them, 25 genes were located on the substituted segments of R217. Two critical pathways controlling seed weight were uncovered in our work. These findings provided new insights into the seed weight-related regulatory network in soybean.
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Affiliation(s)
- Siming Wei
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.W.); (F.D.); (F.C.); (M.Y.); (C.L.); (Z.Q.)
| | - Zhenhai Yu
- Heilongjiang Province Green Food Science Institute, Harbin 150028, China;
| | - Fangfang Du
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.W.); (F.D.); (F.C.); (M.Y.); (C.L.); (Z.Q.)
| | - Fubin Cao
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.W.); (F.D.); (F.C.); (M.Y.); (C.L.); (Z.Q.)
| | - Mingliang Yang
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.W.); (F.D.); (F.C.); (M.Y.); (C.L.); (Z.Q.)
| | - Chunyan Liu
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.W.); (F.D.); (F.C.); (M.Y.); (C.L.); (Z.Q.)
| | - Zhaoming Qi
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.W.); (F.D.); (F.C.); (M.Y.); (C.L.); (Z.Q.)
| | - Qingshan Chen
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.W.); (F.D.); (F.C.); (M.Y.); (C.L.); (Z.Q.)
| | - Jianan Zou
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.W.); (F.D.); (F.C.); (M.Y.); (C.L.); (Z.Q.)
| | - Jinhui Wang
- National Key Laboratory of Smart Farm Technology and System, Key Laboratory of Soybean Biology in Chinese Ministry of Education, College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (S.W.); (F.D.); (F.C.); (M.Y.); (C.L.); (Z.Q.)
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2
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Wu C, Luo J, Xiao Y. Multi-omics assists genomic prediction of maize yield with machine learning approaches. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2024; 44:14. [PMID: 38343399 PMCID: PMC10853138 DOI: 10.1007/s11032-024-01454-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/19/2024] [Indexed: 02/28/2024]
Abstract
With the improvement of high-throughput technologies in recent years, large multi-dimensional plant omics data have been produced, and big-data-driven yield prediction research has received increasing attention. Machine learning offers promising computational and analytical solutions to interpret the biological meaning of large amounts of data in crops. In this study, we utilized multi-omics datasets from 156 maize recombinant inbred lines, containing 2496 single nucleotide polymorphisms (SNPs), 46 image traits (i-traits) from 16 developmental stages obtained through an automatic phenotyping platform, and 133 primary metabolites. Based on benchmark tests with different types of prediction models, some machine learning methods, such as Partial Least Squares (PLS), Random Forest (RF), and Gaussian process with Radial basis function kernel (GaussprRadial), achieved better prediction for maize yield, albeit slight difference for method preferences among i-traits, genomic, and metabolic data. We found that better yield prediction may be caused by various capabilities in ranking and filtering data features, which is found to be linked with biological meaning such as photosynthesis-related or kernel development-related regulations. Finally, by integrating multiple omics data with the RF machine learning approach, we can further improve the prediction accuracy of grain yield from 0.32 to 0.43. Our research provides new ideas for the application of plant omics data and artificial intelligence approaches to facilitate crop genetic improvements. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-024-01454-z.
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Affiliation(s)
- Chengxiu Wu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China
| | - Jingyun Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China
| | - Yingjie Xiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China
- Hubei Hongshan Laboratory, Wuhan, 430070 China
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3
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Zhang Y, Zhang M, Ye J, Xu Q, Feng Y, Xu S, Hu D, Wei X, Hu P, Yang Y. Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice ( Oryza sativa L.). MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:81. [PMID: 37965378 PMCID: PMC10641074 DOI: 10.1007/s11032-023-01423-y] [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: 07/11/2023] [Accepted: 10/09/2023] [Indexed: 11/16/2023]
Abstract
Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01423-y.
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Affiliation(s)
- Yuanyuan Zhang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Mengchen Zhang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Junhua Ye
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Qun Xu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Yue Feng
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Siliang Xu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Dongxiu Hu
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Xinghua Wei
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
| | - Peisong Hu
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
| | - Yaolong Yang
- Zhejiang Lab, Hangzhou, 311121 China
- CNRRI-Zhejiang Lab Computational Breeding Joint Laboratory, China National Rice Research Institute, Hangzhou, China
- National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China
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Montesinos-López OA, Crossa J, Saint Pierre C, Gerard G, Valenzo-Jiménez MA, Vitale P, Valladares-Cellis PE, Buenrostro-Mariscal R, Montesinos-López A, Crespo-Herrera L. Multivariate Genomic Hybrid Prediction with Kernels and Parental Information. Int J Mol Sci 2023; 24:13799. [PMID: 37762107 PMCID: PMC10531250 DOI: 10.3390/ijms241813799] [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: 07/22/2023] [Revised: 08/28/2023] [Accepted: 09/01/2023] [Indexed: 09/29/2023] Open
Abstract
Genomic selection (GS) plays a pivotal role in hybrid prediction. It can enhance the selection of parental lines, accurately predict hybrid performance, and harness hybrid vigor. Likewise, it can optimize breeding strategies by reducing field trial requirements, expediting hybrid development, facilitating targeted trait improvement, and enhancing adaptability to diverse environments. Leveraging genomic information empowers breeders to make informed decisions and significantly improve the efficiency and success rate of hybrid breeding programs. In order to improve the genomic ability performance, we explored the incorporation of parental phenotypic information as covariates under a multi-trait framework. Approach 1, referred to as Pmean, directly utilized parental phenotypic information without any preprocessing. While approach 2, denoted as BV, replaced the direct use of phenotypic values of both parents with their respective breeding values. While an improvement in prediction performance was observed in both approaches, with a minimum 4.24% reduction in the normalized root mean square error (NRMSE), the direct incorporation of parental phenotypic information in the Pmean approach slightly outperformed the BV approach. We also compared these two approaches using linear and nonlinear kernels, but no relevant gain was observed. Finally, our results increase empirical evidence confirming that the integration of parental phenotypic information helps increase the prediction performance of hybrids.
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Affiliation(s)
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco 52640, México, Mexico; (J.C.); (C.S.P.); (G.G.); (P.V.)
- Colegio de Postgraduados, Montecillos 56230, México, Mexico
| | - Carolina Saint Pierre
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco 52640, México, Mexico; (J.C.); (C.S.P.); (G.G.); (P.V.)
| | - Guillermo Gerard
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco 52640, México, Mexico; (J.C.); (C.S.P.); (G.G.); (P.V.)
| | - Marco Alberto Valenzo-Jiménez
- Universidad Michoacana de San Nicolas de Hidalgo (UMSNH), Avenida Francisco J. Mujica S/N Ciudad Universitaria, Morelia 58030, Michoacán, Mexico
| | - Paolo Vitale
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco 52640, México, Mexico; (J.C.); (C.S.P.); (G.G.); (P.V.)
| | | | | | - Abelardo Montesinos-López
- Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara 44430, Jalisco, Mexico
| | - Leonardo Crespo-Herrera
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco 52640, México, Mexico; (J.C.); (C.S.P.); (G.G.); (P.V.)
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5
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Heilmann PG, Frisch M, Abbadi A, Kox T, Herzog E. Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP. FRONTIERS IN PLANT SCIENCE 2023; 14:1178902. [PMID: 37546247 PMCID: PMC10401275 DOI: 10.3389/fpls.2023.1178902] [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: 03/03/2023] [Accepted: 06/26/2023] [Indexed: 08/08/2023]
Abstract
Testcross factorials in newly established hybrid breeding programs are often highly unbalanced, incomplete, and characterized by predominance of special combining ability (SCA) over general combining ability (GCA). This results in a low efficiency of GCA-based selection. Machine learning algorithms might improve prediction of hybrid performance in such testcross factorials, as they have been successfully applied to find complex underlying patterns in sparse data. Our objective was to compare the prediction accuracy of machine learning algorithms to that of GCA-based prediction and genomic best linear unbiased prediction (GBLUP) in six unbalanced incomplete factorials from hybrid breeding programs of rapeseed, wheat, and corn. We investigated a range of machine learning algorithms with three different types of predictor variables: (a) information on parentage of hybrids, (b) in addition hybrid performance of crosses of the parental lines with other crossing partners, and (c) genotypic marker data. In two highly incomplete and unbalanced factorials from rapeseed, in which the SCA variance contributed considerably to the genetic variance, stacked ensembles of gradient boosting machines based on parentage information outperformed GCA prediction. The stacked ensembles increased prediction accuracy from 0.39 to 0.45, and from 0.48 to 0.54 compared to GCA prediction. The prediction accuracy reached by stacked ensembles without marker data reached values comparable to those of GBLUP that requires marker data. We conclude that hybrid prediction with stacked ensembles of gradient boosting machines based on parentage information is a promising approach that is worth further investigations with other data sets in which SCA variance is high.
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Affiliation(s)
| | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
| | | | | | - Eva Herzog
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
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6
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Samal I, Bhoi TK, Raj MN, Majhi PK, Murmu S, Pradhan AK, Kumar D, Paschapur AU, Joshi DC, Guru PN. Underutilized legumes: nutrient status and advanced breeding approaches for qualitative and quantitative enhancement. Front Nutr 2023; 10:1110750. [PMID: 37275642 PMCID: PMC10232757 DOI: 10.3389/fnut.2023.1110750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/02/2023] [Indexed: 06/07/2023] Open
Abstract
Underutilized/orphan legumes provide food and nutritional security to resource-poor rural populations during periods of drought and extreme hunger, thus, saving millions of lives. The Leguminaceae, which is the third largest flowering plant family, has approximately 650 genera and 20,000 species and are distributed globally. There are various protein-rich accessible and edible legumes, such as soybean, cowpea, and others; nevertheless, their consumption rate is far higher than production, owing to ever-increasing demand. The growing global urge to switch from an animal-based protein diet to a vegetarian-based protein diet has also accelerated their demand. In this context, underutilized legumes offer significant potential for food security, nutritional requirements, and agricultural development. Many of the known legumes like Mucuna spp., Canavalia spp., Sesbania spp., Phaseolus spp., and others are reported to contain comparable amounts of protein, essential amino acids, polyunsaturated fatty acids (PUFAs), dietary fiber, essential minerals and vitamins along with other bioactive compounds. Keeping this in mind, the current review focuses on the potential of discovering underutilized legumes as a source of food, feed and pharmaceutically valuable chemicals, in order to provide baseline data for addressing malnutrition-related problems and sustaining pulse needs across the globe. There is a scarcity of information about underutilized legumes and is restricted to specific geographical zones with local or traditional significance. Around 700 genera and 20,000 species remain for domestication, improvement, and mainstreaming. Significant efforts in research, breeding, and development are required to transform existing local landraces of carefully selected, promising crops into types with broad adaptability and economic viability. Different breeding efforts and the use of biotechnological methods such as micro-propagation, molecular markers research and genetic transformation for the development of underutilized crops are offered to popularize lesser-known legume crops and help farmers diversify their agricultural systems and boost their profitability.
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Affiliation(s)
- Ipsita Samal
- Department of Entomology, Faculty of Agriculture, Sri Sri University, Cuttack, Odisha, India
| | - Tanmaya Kumar Bhoi
- Forest Protection Division, ICFRE-Arid Forest Research Institute, Jodhpur, India
| | - M. Nikhil Raj
- Division of Entomology, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Prasanta Kumar Majhi
- Regional Research and Technology Transfer Station, Odisha University of Agriculture and Technology, Keonjhar, Odisha, India
| | - Sneha Murmu
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Dilip Kumar
- ICAR-National Institute of Agricultural Economics and Policy Research, New Delhi, India
| | | | | | - P. N. Guru
- ICAR-Central Institute of Post-Harvest Engineering and Technology, Ludhiana, India
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7
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Integrating Parental Phenotypic Data Enhances Prediction Accuracy of Hybrids in Wheat Traits. Genes (Basel) 2023; 14:genes14020395. [PMID: 36833322 PMCID: PMC9957193 DOI: 10.3390/genes14020395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
Genomic selection (GS) is a methodology that is revolutionizing plant breeding because it can select candidate genotypes without phenotypic evaluation in the field. However, its practical implementation in hybrid prediction remains challenging since many factors affect its accuracy. The main objective of this study was to research the genomic prediction accuracy of wheat hybrids by adding covariates with the hybrid parental phenotypic information to the model. Four types of different models (MA, MB, MC, and MD) with one covariate (same trait to be predicted) (MA_C, MB_C, MC_C, and MD_C) or several covariates (of the same trait and other correlated traits) (MA_AC, MB_AC, MC_AC, and MD_AC) were studied. We found that the four models with parental information outperformed models without parental information in terms of mean square error by at least 14.1% (MA vs. MA_C), 5.5% (MB vs. MB_C), 51.4% (MC vs. MC_C), and 6.4% (MD vs. MD_C) when parental information of the same trait was used and by at least 13.7% (MA vs. MA_AC), 5.3% (MB vs. MB_AC), 55.1% (MC vs. MC_AC), and 6.0% (MD vs. MD_AC) when parental information of the same trait and other correlated traits were used. Our results also show a large gain in prediction accuracy when covariates were considered using the parental phenotypic information, as opposed to marker information. Finally, our results empirically demonstrate that a significant improvement in prediction accuracy was gained by adding parental phenotypic information as covariates; however, this is expensive since, in many breeding programs, the parental phenotypic information is unavailable.
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Gong X, Qi K, Chen J, Zhao L, Xie Z, Yan X, Khanizadeh S, Zhang S, Tao S. Multi-omics analyses reveal stone cell distribution pattern in pear fruit. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 113:626-642. [PMID: 36546867 DOI: 10.1111/tpj.16073] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 12/09/2022] [Accepted: 12/16/2022] [Indexed: 06/17/2023]
Abstract
Stone cells are the brachysclereid cells in pear (Pyrus) fruit, consisting almost entirely of lignified secondary cell walls. They are distributed mainly near the fruit core and spread radially in the whole fruit. However, the development of stone cells has not been comprehensively characterized, and little is known about the regulation of stone cell formation at the transcriptomic, proteomic, and metabolomic levels. In the present study, we performed phenomic analysis on the stone cells and their associated vascular bundles distributed near the fruit cores. Transcriptomic, proteomic, and metabolomic analyses revealed a significant positive regulation of biological processes which contribute to the lignification and lignin deposition in stone cells near the fruit core, including sucrose metabolism and phenylalanine, tyrosine, tryptophan, and phenylalanine biosynthesis. We found many metabolites generated from the phenylpropanoid pathway contributing to the cell wall formation of stone cells near the fruit core. Furthermore, we identified a key transcription factor, PbbZIP48, which was highly expressed near the fruit core and was shown to regulate lignin biosynthesis in stone cells. In conclusion, the present study provides insight into the mechanism of lignified stone cell formation near the pear fruit core at multiple levels.
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Affiliation(s)
- Xin Gong
- College of Horticulture, Nanjing Agricultural University, Nanjing, China
| | - Kaijie Qi
- College of Horticulture, Nanjing Agricultural University, Nanjing, China
| | - Juanli Chen
- College of Horticulture, Nanjing Agricultural University, Nanjing, China
| | - Liangyi Zhao
- College of Horticulture, Nanjing Agricultural University, Nanjing, China
| | - Zhihua Xie
- College of Horticulture, Nanjing Agricultural University, Nanjing, China
| | - Xin Yan
- College of Horticulture, Nanjing Agricultural University, Nanjing, China
| | - Shahrokh Khanizadeh
- Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, Ottawa, Canada
| | - Shaoling Zhang
- College of Horticulture, Nanjing Agricultural University, Nanjing, China
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Shutian Tao
- College of Horticulture, Nanjing Agricultural University, Nanjing, China
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
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9
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Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen MS, Varshney RK, Prasanna BM, Qian Q. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. MOLECULAR PLANT 2022; 15:1664-1695. [PMID: 36081348 DOI: 10.1016/j.molp.2022.09.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 05/12/2023]
Abstract
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; CIMMYT-China Tropical Maize Research Center, School of Food Science and Engineering, Foshan University, Foshan, Guangdong 528231, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China.
| | - Xingping Zhang
- Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China
| | - Huihui Li
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, Hainan 572024, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Jianan Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang, Hebei 050035, China
| | - Michael S Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Rajeev K Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Australia
| | - Boddupalli M Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Qian Qian
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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10
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Zhou D, Zhou X, Sun C, Tang G, Liu L, Chen L, He H, Xiong Q. Transcriptome and Metabolome Analysis Provides Insights into the Heterosis of Yield and Quality Traits in Two Hybrid Rice Varieties (Oryza sativa L.). Int J Mol Sci 2022; 23:ijms232112934. [PMID: 36361748 PMCID: PMC9654843 DOI: 10.3390/ijms232112934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/18/2022] [Accepted: 10/20/2022] [Indexed: 11/24/2022] Open
Abstract
Heterosis is a common biological phenomenon that is useful for breeding superior lines. Using heterosis to increase the yield and quality of crops is one of the main achievements of modern agricultural science. In this study, we analysed the transcriptome and metabolome of two three-line hybrid rice varieties, Taiyou 871 (TY871), and Taiyou 398 (TY398) and the parental grain endosperm using RNA-seq (three biological repeats per variety) and untargeted metabolomic (six biological repeats per variety) methods. TY871 and TY398 showed specific heterosis in yield and quality. Transcriptome analysis of the hybrids revealed 638 to 4059 differentially expressed genes in the grain when compared to the parents. Metabolome analysis of the hybrids revealed 657 to 3714 differential grain metabolites when compared to the parents. The honeydew1 and grey60 module core genes Os04g0350700 and Os05g0154700 are involved in the regulation of awn development, grain size, and grain number, as well as the regulation of grain length and plant height, respectively. Rice grain length may be an important indicator for improving the quality of three-line hybrid rice. In addition, the rice quality-related metabolite NEG_M341T662 was highly connected to the module core genes Os06g0254300 and Os03g0168100. The functions of Os06g0254300 and Os03g0168100 are EF-hand calcium binding protein and late embroideries absolute protein repeat containing protein, respectively. These genes may play a role in the formation of rice quality. We constructed a gene and metabolite coexpression network, which provides a scientific basis for the utilization of heterosis in producing high-yield and high-quality hybrid rice.
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Affiliation(s)
- Dahu Zhou
- Key Laboratory of Crop Physiology, Ecology and Genetic Breeding, Ministry of Education, College of Agronomy, Jiangxi Agricultural University, Nanchang 330045, China
| | - Xinyi Zhou
- Key Laboratory of Crop Physiology, Ecology and Genetic Breeding, Ministry of Education, College of Agronomy, Jiangxi Agricultural University, Nanchang 330045, China
| | - Changhui Sun
- Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
| | - Guoping Tang
- Jiangxi Academy of Agricultural Sciences Rice Research Institute, Nanchang 330200, China
| | - Lin Liu
- Key Laboratory of Crop Physiology, Ecology and Genetic Breeding, Ministry of Education, College of Agronomy, Jiangxi Agricultural University, Nanchang 330045, China
| | - Le Chen
- Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
| | - Haohua He
- Key Laboratory of Crop Physiology, Ecology and Genetic Breeding, Ministry of Education, College of Agronomy, Jiangxi Agricultural University, Nanchang 330045, China
- Correspondence: (H.H.); (Q.X.)
| | - Qiangqiang Xiong
- Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
- Correspondence: (H.H.); (Q.X.)
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11
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Lu Y, Chuan M, Wang H, Chen R, Tao T, Zhou Y, Xu Y, Li P, Yao Y, Xu C, Yang Z. Genetic and molecular factors in determining grain number per panicle of rice. FRONTIERS IN PLANT SCIENCE 2022; 13:964246. [PMID: 35991390 PMCID: PMC9386260 DOI: 10.3389/fpls.2022.964246] [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: 06/09/2022] [Accepted: 07/15/2022] [Indexed: 06/15/2023]
Abstract
It was suggested that the most effective way to improve rice grain yield is to increase the grain number per panicle (GN) through the breeding practice in recent decades. GN is a representative quantitative trait affected by multiple genetic and environmental factors. Understanding the mechanisms controlling GN has become an important research field in rice biotechnology and breeding. The regulation of rice GN is coordinately controlled by panicle architecture and branch differentiation, and many GN-associated genes showed pleiotropic effect in regulating tillering, grain size, flowering time, and other domestication-related traits. It is also revealed that GN determination is closely related to vascular development and the metabolism of some phytohormones. In this review, we summarize the recent findings in rice GN determination and discuss the genetic and molecular mechanisms of GN regulators.
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Affiliation(s)
- Yue Lu
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Mingli Chuan
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Hanyao Wang
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Rujia Chen
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Tianyun Tao
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Yong Zhou
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education, Yangzhou University, Yangzhou, China
| | - Yang Xu
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Pengcheng Li
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Youli Yao
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Chenwu Xu
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education, Yangzhou University, Yangzhou, China
| | - Zefeng Yang
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education, Yangzhou University, Yangzhou, China
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12
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Ferrão LFV, Sater H, Lyrene P, Amadeu RR, Sims CA, Tieman DM, Munoz PR. Terpene volatiles mediates the chemical basis of blueberry aroma and consumer acceptability. Food Res Int 2022; 158:111468. [DOI: 10.1016/j.foodres.2022.111468] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 05/27/2022] [Accepted: 06/01/2022] [Indexed: 11/04/2022]
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13
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Melandri G, Monteverde E, Riewe D, AbdElgawad H, McCouch SR, Bouwmeester H. Can biochemical traits bridge the gap between genomics and plant performance? A study in rice under drought. PLANT PHYSIOLOGY 2022; 189:1139-1152. [PMID: 35166848 PMCID: PMC9157150 DOI: 10.1093/plphys/kiac053] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 01/17/2022] [Indexed: 05/13/2023]
Abstract
The possibility of introducing metabolic/biochemical phenotyping to complement genomics-based predictions in breeding pipelines has been considered for years. Here we examine to what extent and under what environmental conditions metabolic/biochemical traits can effectively contribute to understanding and predicting plant performance. In this study, multivariable statistical models based on flag leaf central metabolism and oxidative stress status were used to predict grain yield (GY) performance for 271 indica rice (Oryza sativa) accessions grown in the field under well-watered and reproductive stage drought conditions. The resulting models displayed significantly higher predictability than multivariable models based on genomic data for the prediction of GY under drought (Q2 = 0.54-0.56 versus 0.35) and for stress-induced GY loss (Q2 = 0.59-0.64 versus 0.03-0.06). Models based on the combined datasets showed predictabilities similar to metabolic/biochemical-based models alone. In contrast to genetic markers, models with enzyme activities and metabolite values also quantitatively integrated the effect of physiological differences such as plant height on GY. The models highlighted antioxidant enzymes of the ascorbate-glutathione cycle and a lipid oxidation stress marker as important predictors of rice GY stability under drought at the reproductive stage, and these stress-related variables were more predictive than leaf central metabolites. These findings provide evidence that metabolic/biochemical traits can integrate dynamic cellular and physiological responses to the environment and can help bridge the gap between the genome and the phenome of crops as predictors of GY performance under drought.
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Affiliation(s)
- Giovanni Melandri
- Laboratory of Plant Physiology, Wageningen University and Research, Wageningen, the Netherlands
- School of Integrative Plant Sciences, Plant Breeding and Genetics Section, Cornell University, Ithaca, New York, USA
| | - Eliana Monteverde
- School of Integrative Plant Sciences, Plant Breeding and Genetics Section, Cornell University, Ithaca, New York, USA
- Departamento de Biología Vegetal, Facultad de Agronomía, Laboratorio de Evolución y Domesticación de las Plantas, Universidad de La República, Montevideo, Uruguay
| | - David Riewe
- Julius Kühn-Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Ecological Chemistry, Plant Analysis and Stored Product Protection, Berlin, Germany
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
| | - Hamada AbdElgawad
- Laboratory for Integrated Molecular Plant Physiology Research, University of Antwerp, Antwerp, Belgium
- Department of Botany, Faculty of Science, Beni-Suef University, Beni Suef, Egypt
| | - Susan R McCouch
- School of Integrative Plant Sciences, Plant Breeding and Genetics Section, Cornell University, Ithaca, New York, USA
| | - Harro Bouwmeester
- Laboratory of Plant Physiology, Wageningen University and Research, Wageningen, the Netherlands
- Plant Hormone Biology group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands
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14
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Sakurai N. Recent applications of metabolomics in plant breeding. BREEDING SCIENCE 2022; 72:56-65. [PMID: 36045891 PMCID: PMC8987846 DOI: 10.1270/jsbbs.21065] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 12/19/2021] [Indexed: 05/27/2023]
Abstract
Metabolites play a central role in maintaining organismal life and in defining crop phenotypes, such as nutritional value, fragrance, color, and stress resistance. Among the 'omes' in biology, the metabolome is the closest to the phenotype. Consequently, metabolomics has been applied to crop improvement as method for monitoring changes in chemical compositions, clarifying the mechanisms underlying cellular functions, discovering markers and diagnostics, and phenotyping for mQTL, mGWAS, and metabolite-genome predictions. In this review, 359 reports of the most recent applications of metabolomics to plant breeding-related studies were examined. In addition to the major crops, more than 160 other crops including rare medicinal plants were considered. One bottleneck associated with using metabolomics is the wide array of instruments that are used to obtain data and the ambiguity associated with metabolite identification and quantification. To further the application of metabolomics to plant breeding, the features and perspectives of the technology are discussed.
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Affiliation(s)
- Nozomu Sakurai
- Bioinformation and DDBJ Center, National Institute of Genetics, 1111 Yata, Mishima, Shizuoka 411-8540, Japan
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15
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Bartholomé J, Prakash PT, Cobb JN. Genomic Prediction: Progress and Perspectives for Rice Improvement. Methods Mol Biol 2022; 2467:569-617. [PMID: 35451791 DOI: 10.1007/978-1-0716-2205-6_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
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Affiliation(s)
- Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, Montpellier, France.
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.
- Rice Breeding Platform, International Rice Research Institute, Manila, Philippines.
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16
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Hu H, Campbell MT, Yeats TH, Zheng X, Runcie DE, Covarrubias-Pazaran G, Broeckling C, Yao L, Caffe-Treml M, Gutiérrez LA, Smith KP, Tanaka J, Hoekenga OA, Sorrells ME, Gore MA, Jannink JL. Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021. [PMID: 34643760 DOI: 10.25739/8p1e-0931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.
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Affiliation(s)
- Haixiao Hu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.
| | - Malachy T Campbell
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Trevor H Yeats
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Xuying Zheng
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Daniel E Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA, 95616, USA
| | - Giovanny Covarrubias-Pazaran
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, 56130, Texcoco, Edo. de México, México
| | - Corey Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Linxing Yao
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Melanie Caffe-Treml
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Lucı A Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Kevin P Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - James Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Owen A Hoekenga
- Cayuga Genetics Consulting Group LLC, Ithaca, NY, 14850, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
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17
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Hu H, Campbell MT, Yeats TH, Zheng X, Runcie DE, Covarrubias-Pazaran G, Broeckling C, Yao L, Caffe-Treml M, Gutiérrez LA, Smith KP, Tanaka J, Hoekenga OA, Sorrells ME, Gore MA, Jannink JL. Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:4043-4054. [PMID: 34643760 PMCID: PMC8580906 DOI: 10.1007/s00122-021-03946-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/05/2021] [Indexed: 05/26/2023]
Abstract
Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.
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Affiliation(s)
- Haixiao Hu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.
| | - Malachy T Campbell
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Trevor H Yeats
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Xuying Zheng
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Daniel E Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA, 95616, USA
| | - Giovanny Covarrubias-Pazaran
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, 56130, Texcoco, Edo. de México, México
| | - Corey Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Linxing Yao
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Melanie Caffe-Treml
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Lucı A Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Kevin P Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - James Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Owen A Hoekenga
- Cayuga Genetics Consulting Group LLC, Ithaca, NY, 14850, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
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18
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Michel S, Wagner C, Nosenko T, Steiner B, Samad-Zamini M, Buerstmayr M, Mayer K, Buerstmayr H. Merging Genomics and Transcriptomics for Predicting Fusarium Head Blight Resistance in Wheat. Genes (Basel) 2021; 12:114. [PMID: 33477759 PMCID: PMC7832326 DOI: 10.3390/genes12010114] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/14/2021] [Accepted: 01/16/2021] [Indexed: 01/13/2023] Open
Abstract
Genomic selection with genome-wide distributed molecular markers has evolved into a well-implemented tool in many breeding programs during the last decade. The resistance against Fusarium head blight (FHB) in wheat is probably one of the most thoroughly studied systems within this framework. Aside from the genome, other biological strata like the transcriptome have likewise shown some potential in predictive breeding strategies but have not yet been investigated for the FHB-wheat pathosystem. The aims of this study were thus to compare the potential of genomic with transcriptomic prediction, and to assess the merit of blending incomplete transcriptomic with complete genomic data by the single-step method. A substantial advantage of gene expression data over molecular markers has been observed for the prediction of FHB resistance in the studied diversity panel of breeding lines and released cultivars. An increase in prediction ability was likewise found for the single-step predictions, although this can mostly be attributed to an increased accuracy among the RNA-sequenced genotypes. The usage of transcriptomics can thus be seen as a complement to already established predictive breeding pipelines with pedigree and genomic data, particularly when more cost-efficient multiplexing techniques for RNA-sequencing will become more accessible in the future.
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Affiliation(s)
- Sebastian Michel
- Institute of Biotechnology in Plant Production (IFA-Tulln), University of Natural Resources and Life Sciences Vienna, 3430 Tulln, Austria; (C.W.); (B.S.); (M.S.-Z.); (M.B.); (H.B.)
| | - Christian Wagner
- Institute of Biotechnology in Plant Production (IFA-Tulln), University of Natural Resources and Life Sciences Vienna, 3430 Tulln, Austria; (C.W.); (B.S.); (M.S.-Z.); (M.B.); (H.B.)
| | - Tetyana Nosenko
- PGSB Plant Genome and Systems Biology, Helmholtz Center Munich, German Research Center for Environmental Health, 85764 Neuherberg, Germany; (T.N.); (K.M.)
- Research Unit Environmental Simulation (EUS) at the Institute of Biochemical Plant Pathology (BIOP), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Barbara Steiner
- Institute of Biotechnology in Plant Production (IFA-Tulln), University of Natural Resources and Life Sciences Vienna, 3430 Tulln, Austria; (C.W.); (B.S.); (M.S.-Z.); (M.B.); (H.B.)
| | - Mina Samad-Zamini
- Institute of Biotechnology in Plant Production (IFA-Tulln), University of Natural Resources and Life Sciences Vienna, 3430 Tulln, Austria; (C.W.); (B.S.); (M.S.-Z.); (M.B.); (H.B.)
- Saatzucht Edelhof GmbH, 3910 Zwettl, Austria
| | - Maria Buerstmayr
- Institute of Biotechnology in Plant Production (IFA-Tulln), University of Natural Resources and Life Sciences Vienna, 3430 Tulln, Austria; (C.W.); (B.S.); (M.S.-Z.); (M.B.); (H.B.)
| | - Klaus Mayer
- PGSB Plant Genome and Systems Biology, Helmholtz Center Munich, German Research Center for Environmental Health, 85764 Neuherberg, Germany; (T.N.); (K.M.)
| | - Hermann Buerstmayr
- Institute of Biotechnology in Plant Production (IFA-Tulln), University of Natural Resources and Life Sciences Vienna, 3430 Tulln, Austria; (C.W.); (B.S.); (M.S.-Z.); (M.B.); (H.B.)
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