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Orantes-Bonilla M, Wang H, Lee HT, Golicz AA, Hu D, Li W, Zou J, Snowdon RJ. Transgressive and parental dominant gene expression and cytosine methylation during seed development in Brassica napus hybrids. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:113. [PMID: 37071201 PMCID: PMC10113308 DOI: 10.1007/s00122-023-04345-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 03/12/2023] [Indexed: 05/13/2023]
Abstract
KEY MESSAGE Transcriptomic and epigenomic profiling of gene expression and small RNAs during seed and seedling development reveals expression and methylation dominance levels with implications on early stage heterosis in oilseed rape. The enhanced performance of hybrids through heterosis remains a key aspect in plant breeding; however, the underlying mechanisms are still not fully elucidated. To investigate the potential role of transcriptomic and epigenomic patterns in early expression of hybrid vigor, we investigated gene expression, small RNA abundance and genome-wide methylation in hybrids from two distant Brassica napus ecotypes during seed and seedling developmental stages using next-generation sequencing. A total of 31117, 344, 36229 and 7399 differentially expressed genes, microRNAs, small interfering RNAs and differentially methylated regions were identified, respectively. Approximately 70% of the differentially expressed or methylated features displayed parental dominance levels where the hybrid followed the same patterns as the parents. Via gene ontology enrichment and microRNA-target association analyses during seed development, we found copies of reproductive, developmental and meiotic genes with transgressive and paternal dominance patterns. Interestingly, maternal dominance was more prominent in hypermethylated and downregulated features during seed formation, contrasting to the general maternal gamete demethylation reported during gametogenesis in angiosperms. Associations between methylation and gene expression allowed identification of putative epialleles with diverse pivotal biological functions during seed formation. Furthermore, most differentially methylated regions, differentially expressed siRNAs and transposable elements were in regions that flanked genes without differential expression. This suggests that differential expression and methylation of epigenomic features may help maintain expression of pivotal genes in a hybrid context. Differential expression and methylation patterns during seed formation in an F1 hybrid provide novel insights into genes and mechanisms with potential roles in early heterosis.
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Affiliation(s)
- Mauricio Orantes-Bonilla
- Department of Plant Breeding, Land Use and Nutrition, IFZ Research Centre for Biosystems, Justus Liebig University, Giessen, Germany
| | - Hao Wang
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
| | - Huey Tyng Lee
- Department of Plant Breeding, Land Use and Nutrition, IFZ Research Centre for Biosystems, Justus Liebig University, Giessen, Germany
| | - Agnieszka A Golicz
- Department of Plant Breeding, Land Use and Nutrition, IFZ Research Centre for Biosystems, Justus Liebig University, Giessen, Germany
| | - Dandan Hu
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
| | - Wenwen Li
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
| | - Jun Zou
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
| | - Rod J Snowdon
- Department of Plant Breeding, Land Use and Nutrition, IFZ Research Centre for Biosystems, Justus Liebig University, Giessen, Germany.
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Li R, Tian M, He Q, Zhang L. Correlation between Parental Transcriptome and Field Data for the Characterization of Heterosis in Chinese Cabbage. Genes (Basel) 2023; 14:genes14040776. [PMID: 37107533 PMCID: PMC10137735 DOI: 10.3390/genes14040776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 02/20/2023] [Accepted: 03/20/2023] [Indexed: 04/29/2023] Open
Abstract
In Chinese cabbage breeding, hybrids have made a terrific contribution due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new, top-performing hybrids requires a large scale of human and material resources, the prediction of hybrid performance is of utmost interest to plant breeders. In our research, leaf transcriptome data from eight parents were used to investigate if they might be employed as markers to predict hybrid performance and heterosis. In Chinese cabbage, heterosis of plant growth weight (PGW) and heterosis of head weight (HW) were more obvious than other traits. The number of differential expression genes (DEGs) between parents was related to the PGW, length of the biggest outer leaf (LOL), leaf head height (LHH), leaf head width (LHW), HW, leaf number of head (LNH) and plant height (PH) of hybrids, and up-regulated DEGs number was also associated with these traits. Euclidean and binary distances of parental gene expression levels were significantly correlated with the PGW, LOL, LHH, LHW, HW and PH of hybrids. Additionally, there was a significant correlation between the parental expression levels of multiple genes involved in the ribosomal metabolic pathway and hybrid observations and heterosis in PGW, with the BrRPL23A gene showing the highest correlation with the MPH of PGW(r = 0.75). Therefore, leaf transcriptome data can preliminarily predict the hybrid performance and select parents in Chinese cabbage.
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Affiliation(s)
- Ru Li
- State Key Laboratory of Crop Stress Biology for Arid Area, College of Horticulture, Northwest A&F University, Yangling 712100, China
| | - Min Tian
- State Key Laboratory of Crop Stress Biology for Arid Area, College of Horticulture, Northwest A&F University, Yangling 712100, China
| | - Qiong He
- State Key Laboratory of Crop Stress Biology for Arid Area, College of Horticulture, Northwest A&F University, Yangling 712100, China
| | - Lugang Zhang
- State Key Laboratory of Crop Stress Biology for Arid Area, College of Horticulture, Northwest A&F University, Yangling 712100, China
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Niedziela A, Bednarek PT. Population structure and genetic diversity of a germplasm for hybrid breeding in rye (Secale cereale L.) using high-density DArTseq-based silicoDArT and SNP markers. J Appl Genet 2023; 64:217-229. [PMID: 36595165 PMCID: PMC10076414 DOI: 10.1007/s13353-022-00740-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/25/2022] [Accepted: 12/02/2022] [Indexed: 01/04/2023]
Abstract
Investigating genetic structure and diversity is crucial for the rye hybrid breeding strategy, leading to improved plant productivity and adaptation. The present study elucidated the population structure and genetic diversity of 188 rye accessions, comprising 94 pollen fertility restoration lines (RF) and 94 cytoplasmic male-sterile (CMS) lines with Pampa sterilizing cytoplasm using SNP and silicoDArT markers from the diversity array technology (DArT)-based sequencing platform (DArTseq). Expected heterozygosity (He) and Shanon's diversity (I) indexes varied slightly between marker systems and groups of germplasms (He = 0.34, I = 0.51 for RF and CMS lines genotyped using SNPs; He = 0.31, I = 0.48, and He = 0.35, I = 0.53 for RF and CMS using silicoDArTs, respectively). ANOVA indicated moderate variation (7%) between RF and CMS breeding materials. The same parameter varied when chromosome-assigned markers were used and ranged from 5.8% for 5R to 7.4% for 4R. However, when silicoDArT markers were applied, the respective values varied from 6.4% (1R) to 8.2% (3R and 4R). The model-based (Bayesian) population structure analysis based on the total marker pool identified two major subpopulations for the studied rye germplasm. The first one (P1) encompasses 93 RF accessions, and the second one (P2) encompasses 94 CMS and one RF accession. However, a similar analysis related to markers assigned to selected chromosomes failed to put plant materials into any of the populations in the same way as the total marker pool. Furthermore, the differences in grouping depended on marker types used for analysis.
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Affiliation(s)
- Agnieszka Niedziela
- Plant Breeding and Acclimatization Institute - National Research Institute, 05-870, Błonie, Radzików, Poland
| | - Piotr Tomasz Bednarek
- Plant Breeding and Acclimatization Institute - National Research Institute, 05-870, Błonie, Radzików, Poland.
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4
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Robert P, Goudemand E, Auzanneau J, Oury FX, Rolland B, Heumez E, Bouchet S, Caillebotte A, Mary-Huard T, Le Gouis J, Rincent R. Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:3337-3356. [PMID: 35939074 DOI: 10.1007/s00122-022-04170-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 06/28/2022] [Indexed: 06/15/2023]
Abstract
Phenomic prediction of wheat grain yield and heading date in different multi-environmental trial scenarios is accurate. Modelling the genotype-by-environment interaction effect using phenomic data is a potentially low-cost complement to genomic prediction. The performance of wheat cultivars in multi-environmental trials (MET) is difficult to predict because of the genotype-by-environment interactions (G × E). Phenomic selection is supposed to be efficient for modelling the G × E effect because it accounts for non-additive effects. Here, phenomic data are near-infrared (NIR) spectra obtained from plant material. While phenomic selection has recently been shown to accurately predict wheat grain yield in single environments, its accuracy needs to be investigated for MET. We used four datasets from two winter wheat breeding programs to test and compare the predictive abilities of phenomic and genomic models for grain yield and heading date in different MET scenarios. We also compared different methods to model the G × E using different covariance matrices based on spectra. On average, phenomic and genomic prediction abilities are similar in all different MET scenarios. Better predictive abilities were obtained when G × E effects were modelled with NIR spectra than without them, and it was better to use all the spectra of all genotypes in all environments for modelling the G × E. To facilitate the implementation of phenomic prediction, we tested MET designs where the NIR spectra were measured only on the genotype-environment combinations phenotyped for the target trait. Missing spectra were predicted with a weighted multivariate ridge regression. Intermediate predictive abilities for grain yield were obtained in a sparse testing scenario and for new genotypes, which shows that phenomic selection is an efficient and practicable prediction method for dealing with G × E.
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Affiliation(s)
- Pauline Robert
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
- INRAE - Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, Clermont-Ferrand, France
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - Ellen Goudemand
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - Jérôme Auzanneau
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
| | - François-Xavier Oury
- INRAE - Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, Clermont-Ferrand, France
| | - Bernard Rolland
- INRAE-Agrocampus Ouest-Université Rennes 1, UMR1349, IGEPP, Domaine de la Motte, 35653, Le Rheu, France
| | - Emmanuel Heumez
- INRAE, UE 972, Grandes Cultures Innovation Environnement, 2 Chaussée Brunehaut, 80200, Estrées-Mons, France
| | - Sophie Bouchet
- INRAE - Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, Clermont-Ferrand, France
| | - Antoine Caillebotte
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
- MIA, INRAE, AgroParisTech, Université Paris-Saclay, 75005, Paris, France
| | - Jacques Le Gouis
- INRAE - Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, Clermont-Ferrand, France
| | - Renaud Rincent
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
- INRAE - Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, Clermont-Ferrand, France.
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5
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Predicting hybrid rice performance using AIHIB model based on artificial intelligence. Sci Rep 2022; 12:9709. [PMID: 35690641 PMCID: PMC9188612 DOI: 10.1038/s41598-022-13805-x] [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: 10/12/2021] [Accepted: 05/27/2022] [Indexed: 11/24/2022] Open
Abstract
Hybrid breeding is fast becoming a key instrument in plants' crop productivity. Grain yield performance of hybrids (F1) under different parental genetic features has consequently received considerable attention in the literature. The main objective of this study was to introduce a new method, known as AI_HIB under different parental genetic features using artificial intelligence (AI) techniques. In so doing, the rice cultivars TAM, KHZ, SPD, GHB, IR28, AHM, SHP and their F1 hybrid were used. Having recorded Grain Yield (GY), Unfertile Panicle Number (UFP), Plant Height (HE), Days to Flowering (DF), Panicle Exertion (PE), Panicle Length (PL), Filled Grain Number (FG), Primary Branches Number (PBN), Flag Leaf Length (FLL), Flag Leaf Width (FLW), Flag Leaf Area (FLA), and Plant Biomass (BI) in the field, we include these features in our proposed model. When using the GA and PSO algorithm to select the features, grain yield had the highest frequency at the input of the Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) structure. The AI_HIB_ANN result revealed that the trained neural network with parental data enjoyed a good ability to predict the response of hybrid performance. Findings also reflected that the obtained MSE was low and R2 value was greater than 96%. AI_HIB_SVM and AI_HIB_ANFIS showed that measuring attributes could predict number of primary branches, plant height, days to flowering and grain yield per plant with accuracies of 99%. These findings have significant implications as it presents a new promising prediction method for hybrid rice yield based on the characteristics of the parent lines by AI. These findings contribute to provide a basis for designing a smartphone application in terms of the AI_HIB_SVM and AI_HIB_ANFIS methods to easily predict hybrid performance with a high accuracy rate.
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6
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Song C, Acuña T, Adler-Agmon M, Rachmilevitch S, Barak S, Fait A. Leveraging a graft collection to develop metabolome-based trait prediction for the selection of tomato rootstocks with enhanced salt tolerance. HORTICULTURE RESEARCH 2022; 9:uhac061. [PMID: 35531316 PMCID: PMC9071376 DOI: 10.1093/hr/uhac061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 02/27/2022] [Indexed: 06/14/2023]
Abstract
Grafting has been demonstrated to significantly enhance the salt tolerance of crops. However, breeding efforts to develop enhanced graft combinations are hindered by knowledge-gaps as to how rootstocks mediate scion-response to salt stress. We grafted the scion of cultivated M82 onto rootstocks of 254 tomato accessions and explored the morphological and metabolic responses of grafts under saline conditions (EC = 20 dS m-1) as compared to self-grafted M82 (SG-M82). Correlation analysis and Least Absolute Shrinkage and Selection Operator were performed to address the association between morphological diversification and metabolic perturbation. We demonstrate that grafting the same variety onto different rootstocks resulted in scion phenotypic heterogeneity and emphasized the productivity efficiency of M82 irrespective of the rootstock. Spectrophotometric analysis to test lipid oxidation showed largest variability of malondialdehyde (MDA) equivalents across the population, while the least responsive trait was the ratio of fruit fresh weight to total fresh weight (FFW/TFW). Generally, grafts showed greater values for the traits measured than SG-M82, except for branch number and wild race-originated rootstocks; the latter were associated with smaller scion growth parameters. Highly responsive and correlated metabolites were identified across the graft collection including malate, citrate, and aspartate, and their variance was partly related to rootstock origin. A group of six metabolites that consistently characterized exceptional graft response was observed, consisting of sorbose, galactose, sucrose, fructose, myo-inositol, and proline. The correlation analysis and predictive modelling, integrating phenotype- and leaf metabolite data, suggest a potential predictive relation between a set of leaf metabolites and yield-related traits.
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Affiliation(s)
- Chao Song
- The Albert Katz International School for Desert Studies, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 8499000, Israel
| | - Tania Acuña
- Albert Katz Department of Dryland Biotechnologies, French Associates Institute for Agriculture and Biotechnology of Drylands, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 8499000, Israel
| | | | - Shimon Rachmilevitch
- Albert Katz Department of Dryland Biotechnologies, French Associates Institute for Agriculture and Biotechnology of Drylands, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 8499000, Israel
| | - Simon Barak
- Albert Katz Department of Dryland Biotechnologies, French Associates Institute for Agriculture and Biotechnology of Drylands, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 8499000, Israel
| | - Aaron Fait
- Albert Katz Department of Dryland Biotechnologies, French Associates Institute for Agriculture and Biotechnology of Drylands, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 8499000, Israel
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7
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Shen G, Hu W, Wang X, Zhou X, Han Z, Sherif A, Ayaad M, Xing Y. Assembly of yield heterosis of an elite rice hybrid is promising by manipulating dominant quantitative trait loci. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2022; 64:688-701. [PMID: 34995015 DOI: 10.1111/jipb.13220] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 01/04/2022] [Indexed: 05/27/2023]
Abstract
In the past, rice hybrids with strong heterosis have been obtained empirically, by developing and testing thousands of combinations. Here, we aimed to determine whether heterosis of an elite hybrid could be achieved by manipulating major quantitative trait loci. We used 202 chromosome segment substitution lines from the elite hybrid Shanyou 63 to evaluate single segment heterosis (SSH) of yield per plant and identify heterotic loci. All nine detected heterotic loci acted in a dominant fashion, and no SSH exhibited overdominance. Functional alleles of key yield-related genes Ghd7, Ghd7.1, Hd1, and GS3 were dispersed in both parents. No functional alleles of three investigated genes were expressed at higher levels in the hybrids than in the more desirable parents. A hybrid pyramiding eight heterotic loci in the female parent Zhenshan 97 background had a comparable yield to Shanyou 63 and much higher yield than Zhenshan 97. Five hybrids pyramiding eight or nine heterotic loci in the combined parental genome background showed similar yield performance to that of Shanyou 63. These results suggest that dominance underlying functional complementation is an important contributor to yield heterosis and that heterosis assembly might be successfully promised by manipulating several major dominant heterotic loci.
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Affiliation(s)
- Guojing Shen
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
| | - Wei Hu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
| | - Xianmeng Wang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Xiangchun Zhou
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Zhongming Han
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
| | - Ahmed Sherif
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Mohammed Ayaad
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
- Plant Research Department, Nuclear Research Center, Atomic Energy Authority, Abo-Zaabal, 13759, Egypt
| | - Yongzhong Xing
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
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8
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Robert P, Auzanneau J, Goudemand E, Oury FX, Rolland B, Heumez E, Bouchet S, Le Gouis J, Rincent R. Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:895-914. [PMID: 34988629 DOI: 10.1007/s00122-021-04005-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/23/2021] [Indexed: 05/15/2023]
Abstract
Phenomic selection is a promising alternative or complement to genomic selection in wheat breeding. Models combining spectra from different environments maximise the predictive ability of grain yield and heading date of wheat breeding lines. Phenomic selection (PS) is a recent breeding approach similar to genomic selection (GS) except that genotyping is replaced by near-infrared (NIR) spectroscopy. PS can potentially account for non-additive effects and has the major advantage of being low cost and high throughput. Factors influencing GS predictive abilities have been intensively studied, but little is known about PS. We tested and compared the abilities of PS and GS to predict grain yield and heading date from several datasets of bread wheat lines corresponding to the first or second years of trial evaluation from two breeding companies and one research institute in France. We evaluated several factors affecting PS predictive abilities including the possibility of combining spectra collected in different environments. A simple H-BLUP model predicted both traits with prediction ability from 0.26 to 0.62 and with an efficient computation time. Our results showed that the environments in which lines are grown had a crucial impact on predictive ability based on the spectra acquired and was specific to the trait considered. Models combining NIR spectra from different environments were the best PS models and were at least as accurate as GS in most of the datasets. Furthermore, a GH-BLUP model combining genotyping and NIR spectra was the best model of all (prediction ability from 0.31 to 0.73). We demonstrated also that as for GS, the size and the composition of the training set have a crucial impact on predictive ability. PS could therefore replace or complement GS for efficient wheat breeding programs.
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Affiliation(s)
- Pauline Robert
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - Jérôme Auzanneau
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
| | - Ellen Goudemand
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - François-Xavier Oury
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
| | - Bernard Rolland
- INRAE-Agrocampus Ouest-Université Rennes 1, UMR1349, IGEPP, Domaine de la Motte, 35653, Le Rheu, France
| | - Emmanuel Heumez
- INRAE, UE 972, Grandes Cultures Innovation Environnement, 2 Chaussée Brunehaut, 80200, EstréesMons, France
| | - Sophie Bouchet
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
| | - Jacques Le Gouis
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
| | - Renaud Rincent
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France.
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France.
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9
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Zhou J, Liu R, Shuai M, Yan ZY, Chen X. Comparative transcriptome analyses of different Salvia miltiorrhiza varieties during the accumulation of tanshinones. PeerJ 2021; 9:e12300. [PMID: 34721983 PMCID: PMC8541307 DOI: 10.7717/peerj.12300] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 09/22/2021] [Indexed: 01/07/2023] Open
Abstract
Salvia miltiorrhiza (Labiatae) is an important medicinal plant in traditional Chinese medicine. Tanshinones are one of the main active components of S. miltiorrhiza. It has been found that the intraspecific variation of S. miltiorrhiza is relatively large and the content of tanshinones in its roots of different varieties is also relatively different. To investigate the molecular mechanisms that responsible for the differences among these varieties, the tanshinones content was determined and comparative transcriptomics analysis was carried out during the tanshinones accumulation stage. A total of 52,216 unigenes were obtained from the transcriptome by RNA sequencing among which 23,369 genes were differentially expressed among different varieties, and 2,016 genes including 18 diterpenoid biosynthesis-related genes were differentially expressed during the tanshinones accumulation stage. Functional categorization of the differentially expressed genes (DEGs) among these varieties revealed that the pathway related to photosynthesis, oxidative phosphorylation, secondary metabolite biosynthesis, diterpenoid biosynthesis, terpenoid backbone biosynthesis, sesquiterpenoid and triterpenoid biosynthesis are the most differentially regulated processes in these varieties. The six tanshinone components in these varieties showed different dynamic changes in tanshinone accumulation stage. In addition, combined with the analysis of the dynamic changes, 277 DEGs (including one dehydrogenase, three CYP450 and 24 transcription factors belonging to 12 transcription factor families) related to the accumulation of tanshinones components were obtained. Furthermore, the KEGG pathway enrichment analysis of these 277 DEGs suggested that there might be an interconnection between the primary metabolic processes, signaling processes and the accumulation of tanshinones components. This study expands the vision of intraspecific variation and gene regulation mechanism of secondary metabolite biosynthesis pathways in medicinal plants from the “omics” perspective.
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Affiliation(s)
- Jingwen Zhou
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.,Key Laboratory of Characteristic Chinese Medicinal Resources in Southwest, Chengdu, Sichuan, China
| | - Rui Liu
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.,Key Laboratory of Characteristic Chinese Medicinal Resources in Southwest, Chengdu, Sichuan, China
| | - Min Shuai
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.,Key Laboratory of Characteristic Chinese Medicinal Resources in Southwest, Chengdu, Sichuan, China
| | - Zhu-Yun Yan
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.,Key Laboratory of Characteristic Chinese Medicinal Resources in Southwest, Chengdu, Sichuan, China
| | - Xin Chen
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.,Key Laboratory of Characteristic Chinese Medicinal Resources in Southwest, Chengdu, Sichuan, China
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10
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Fortuny AP, Bueno RA, Pereira da Costa JH, Zanor MI, Rodríguez GR. Tomato fruit quality traits and metabolite content are affected by reciprocal crosses and heterosis. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:5407-5425. [PMID: 34013312 DOI: 10.1093/jxb/erab222] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 05/17/2021] [Indexed: 06/12/2023]
Abstract
Heterosis occurs when the F1s outperform their parental lines for a trait. Reciprocal hybrids are obtained by changing the cross direction of parental genotypes. Both biological phenomena could affect the external and internal attributes of fleshy fruits. This work aimed to detect reciprocal effects and heterosis in tomato (Solanum lycopersicum) fruit quality traits and metabolite content. Twelve agronomic traits and 28 metabolites identified and estimated by 1H-NMR were evaluated in five cultivars grown in two environments. Given that the genotype component was more important than the phenotype, the traits were evaluated following a full diallel mating design among those cultivars, in a greenhouse. Hybrids showed a higher phenotypic diversity than parental lines. Interestingly, the metabolites, mainly amino acids, displayed more reciprocal effects and heterosis. Agronomic traits were more influenced by general combining ability (GCA) and metabolites by specific combining ability (SCA). Furthermore, the genetic distance between parental lines was not causally related to the occurrence of reciprocal effects or heterosis. Hybrids with heterosis and a high content of metabolites linked to tomato flavour and nutritious components were obtained. Our results highlight the impact of selecting a cultivar as male or female in a cross to enhance the variability of fruit attributes through hybrids as well as the possibility to exploit heterosis for fruit composition.
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Affiliation(s)
- Agustina P Fortuny
- Instituto de Biología Molecular y Celular de Rosario (IBR), CONICET-UNR, Rosario, Argentina
- Instituto de Investigaciones en Ciencias Agrarias de Rosario (IICAR-CONICET-UNR), Rosario, Argentina
| | - Rodrigo A Bueno
- Cátedra de Genética, Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Rosario, Argentina
| | - Javier H Pereira da Costa
- Instituto de Investigaciones en Ciencias Agrarias de Rosario (IICAR-CONICET-UNR), Rosario, Argentina
- Cátedra de Genética, Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Rosario, Argentina
| | - María Inés Zanor
- Instituto de Biología Molecular y Celular de Rosario (IBR), CONICET-UNR, Rosario, Argentina
- Departamento de Química Biológica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Rosario, Argentina
| | - Gustavo R Rodríguez
- Instituto de Investigaciones en Ciencias Agrarias de Rosario (IICAR-CONICET-UNR), Rosario, Argentina
- Cátedra de Genética, Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Rosario, Argentina
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11
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Knoch D, Werner CR, Meyer RC, Riewe D, Abbadi A, Lücke S, Snowdon RJ, Altmann T. Multi-omics-based prediction of hybrid performance in canola. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1147-1165. [PMID: 33523261 PMCID: PMC7973648 DOI: 10.1007/s00122-020-03759-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 12/19/2020] [Indexed: 05/05/2023]
Abstract
Complementing or replacing genetic markers with transcriptomic data and use of reproducing kernel Hilbert space regression based on Gaussian kernels increases hybrid prediction accuracies for complex agronomic traits in canola. In plant breeding, hybrids gained particular importance due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new top performing hybrids requires labour-intensive and costly breeding programmes, including testing of large numbers of experimental hybrids, the prediction of hybrid performance is of utmost interest to plant breeders. In this study, we tested the effectiveness of hybrid prediction models in spring-type oilseed rape (Brassica napus L./canola) employing different omics profiles, individually and in combination. To this end, a population of 950 F1 hybrids was evaluated for seed yield and six other agronomically relevant traits in commercial field trials at several locations throughout Europe. A subset of these hybrids was also evaluated in a climatized glasshouse regarding early biomass production. For each of the 477 parental rapeseed lines, 13,201 single nucleotide polymorphisms (SNPs), 154 primary metabolites, and 19,479 transcripts were determined and used as predictive variables. Both, SNP markers and transcripts, effectively predict hybrid performance using (genomic) best linear unbiased prediction models (gBLUP). Compared to models using pure genetic markers, models incorporating transcriptome data resulted in significantly higher prediction accuracies for five out of seven agronomic traits, indicating that transcripts carry important information beyond genomic data. Notably, reproducing kernel Hilbert space regression based on Gaussian kernels significantly exceeded the predictive abilities of gBLUP models for six of the seven agronomic traits, demonstrating its potential for implementation in future canola breeding programmes.
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Affiliation(s)
- Dominic Knoch
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
| | - Christian R. Werner
- The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG Scotland, UK
| | - Rhonda C. Meyer
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
| | - David Riewe
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
- Institute for Ecological Chemistry, Plant Analysis and Stored Product Protection, Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, 14195 Berlin, Germany
| | - Amine Abbadi
- NPZ Innovation GmbH, Hohenlieth, 24363 Holtsee, Germany
- Norddeutsche Pflanzenzucht Hans-Georg Lembke KG, Hohenlieth, 24363 Holtsee, Germany
| | - Sophie Lücke
- Norddeutsche Pflanzenzucht Hans-Georg Lembke KG, Hohenlieth, 24363 Holtsee, Germany
| | - Rod J. Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Thomas Altmann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
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12
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Geng X, Qu Y, Jia Y, He S, Pan Z, Wang L, Du X. Assessment of heterosis based on parental genetic distance estimated with SSR and SNP markers in upland cotton (Gossypium hirsutum L.). BMC Genomics 2021; 22:123. [PMID: 33602146 PMCID: PMC7891138 DOI: 10.1186/s12864-021-07431-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 02/08/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Heterosis has been extensively utilized in different crops and made a significant contribution to global food security. Genetic distance (GD) is one of the valuable criteria for selecting parents in hybrid breeding. The objectives of this study were to estimate the GD between parents using both simple sequence repeat (SSR) markers and single nucleotide polymorphism (SNP) markers and to investigate the efficiency of the prediction of hybrid performance based on GD. The experiment comprised of four male parents, 282 female parents and 1128 F1, derived from NCII mating scheme. The hybrids, their parents and two check cultivars were evaluated for two years. Performance of F1, mid-parent heterosis (MPH), and best parent heterosis (BPH) were evaluated for ten agronomic and fiber quality traits, including plant height, boll weight, boll number, lint percentage, fiber length, fiber strength, fiber uniformity, fiber elongation ratio, micronaire, and spinning consistent index. RESULTS Heterosis was observed in all hybrids and, the traits like plant height, boll number, boll weight and lint percentage exhibited higher heterosis than the fiber quality traits. Correlations were significant between parental and F1 performances. The F1 performances between three hybrid sets (Elite×Elite, Exotic×Elite, and Historic×Elite) showed significant differences in eight traits, including boll number, lint percentage, fiber length, fiber strength, fiber uniformity, fiber elongation ratio, micronaire, and spinning consistent index. The correlation of the GD assessed by both SSR and SNP markers was significantly positive. The cluster analysis based on GD results estimated using SNP showed that all the female parents divided into five groups and the F1 performance between these five groups showed significant differences in four traits, including lint percentage, micronaire, fiber strength, and fiber elongation ratio. The correlation between GD and F1 performance, MPH and BPH were significant for lint percentage and micronaire. CONCLUSIONS Our results suggested that GD between parents could be helpful in heterosis prediction for certain traits. This study reveals that molecular marker analysis can serve as a basis for assigning germplasm into heterotic groups and to provide guidelines for parental selection in hybrid cotton breeding.
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Affiliation(s)
- Xiaoli Geng
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China
| | - Yujie Qu
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Yinhua Jia
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China
| | - Shoupu He
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China
| | - Zhaoe Pan
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Liru Wang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Xiongming Du
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
- Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China.
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13
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Xu Y, Zhao Y, Wang X, Ma Y, Li P, Yang Z, Zhang X, Xu C, Xu S. Incorporation of parental phenotypic data into multi-omic models improves prediction of yield-related traits in hybrid rice. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:261-272. [PMID: 32738177 PMCID: PMC7868986 DOI: 10.1111/pbi.13458] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 06/14/2020] [Accepted: 07/22/2020] [Indexed: 05/15/2023]
Abstract
Hybrid breeding has been shown to effectively increase rice productivity. However, identifying desirable hybrids out of numerous potential combinations is a daunting challenge. Genomic selection holds great promise for accelerating hybrid breeding by enabling early selection before phenotypes are measured. With the recent advances in multi-omic technologies, hybrid prediction based on transcriptomic and metabolomic data has received increasing attention. However, the current omic-based hybrid prediction has ignored parental phenotypic information, which is of fundamental importance in plant breeding. In this study, we integrated parental phenotypic information into various multi-omic prediction models applied in hybrid breeding of rice and compared the predictabilities of 15 combinations from four sets of predictors from the parents, that is genome, transcriptome, metabolome and phenome. The predictability for each combination was evaluated using the best linear unbiased prediction and a modified fast HAT method. We found significant interactions between predictors and traits in predictability, but joint prediction with various combinations of the predictors significantly improved predictability relative to prediction of any single source omic data for each trait investigated. Incorporation of parental phenotypic data into various omic predictors increased the predictability, averagely by 13.6%, 54.5%, 19.9% and 8.3%, for grain yield, number of tillers per plant, number of grains per panicle and 1000 grain weight, respectively. Among nine models of incorporating parental traits, the AD-All model was the most effective one. This novel strategy of incorporating parental phenotypic data into multi-omic prediction is expected to improve hybrid breeding progress, especially with the development of high-throughput phenotyping technologies.
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Affiliation(s)
- Yang Xu
- Jiangsu Key Laboratory of Crop Genetics and PhysiologyKey Laboratory of Plant Functional Genomics of Ministry of EducationJiangsu Key Laboratory of Crop Genomics and Molecular BreedingCo‐Innovation Center for Modern Production Technology of Grain CropsAgricultural College of Yangzhou UniversityYangzhouChina
| | - Yue Zhao
- Jiangsu Key Laboratory of Crop Genetics and PhysiologyKey Laboratory of Plant Functional Genomics of Ministry of EducationJiangsu Key Laboratory of Crop Genomics and Molecular BreedingCo‐Innovation Center for Modern Production Technology of Grain CropsAgricultural College of Yangzhou UniversityYangzhouChina
| | - Xin Wang
- Jiangsu Key Laboratory of Crop Genetics and PhysiologyKey Laboratory of Plant Functional Genomics of Ministry of EducationJiangsu Key Laboratory of Crop Genomics and Molecular BreedingCo‐Innovation Center for Modern Production Technology of Grain CropsAgricultural College of Yangzhou UniversityYangzhouChina
| | - Ying Ma
- Jiangsu Key Laboratory of Crop Genetics and PhysiologyKey Laboratory of Plant Functional Genomics of Ministry of EducationJiangsu Key Laboratory of Crop Genomics and Molecular BreedingCo‐Innovation Center for Modern Production Technology of Grain CropsAgricultural College of Yangzhou UniversityYangzhouChina
| | - Pengcheng Li
- Jiangsu Key Laboratory of Crop Genetics and PhysiologyKey Laboratory of Plant Functional Genomics of Ministry of EducationJiangsu Key Laboratory of Crop Genomics and Molecular BreedingCo‐Innovation Center for Modern Production Technology of Grain CropsAgricultural College of Yangzhou UniversityYangzhouChina
| | - Zefeng Yang
- Jiangsu Key Laboratory of Crop Genetics and PhysiologyKey Laboratory of Plant Functional Genomics of Ministry of EducationJiangsu Key Laboratory of Crop Genomics and Molecular BreedingCo‐Innovation Center for Modern Production Technology of Grain CropsAgricultural College of Yangzhou UniversityYangzhouChina
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT)MexicoDFMexico
| | - Chenwu Xu
- Jiangsu Key Laboratory of Crop Genetics and PhysiologyKey Laboratory of Plant Functional Genomics of Ministry of EducationJiangsu Key Laboratory of Crop Genomics and Molecular BreedingCo‐Innovation Center for Modern Production Technology of Grain CropsAgricultural College of Yangzhou UniversityYangzhouChina
| | - Shizhong Xu
- Department of Botany and Plant SciencesUniversity of CaliforniaRiversideCAUSA
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14
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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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15
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Vendelbo NM, Sarup P, Orabi J, Kristensen PS, Jahoor A. Genetic structure of a germplasm for hybrid breeding in rye (Secale cereale L.). PLoS One 2020; 15:e0239541. [PMID: 33035208 PMCID: PMC7546470 DOI: 10.1371/journal.pone.0239541] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 09/09/2020] [Indexed: 01/11/2023] Open
Abstract
Rye (Secale cereale L.) responds strongly to changes in heterozygosity with hybrids portraying strong heterosis effect on all developmental and yielding characteristics. In order to achieve the highest potential heterosis effect parental lines must originate from genetically distinct gene pools. Here we report the first comprehensive SNP-based population study of an elite germplasm using fertilization control system for hybrid breeding in rye that is genetically different to the predominating P-type. In total 376 inbred lines from Nordic Seed Germany GmbH were genotyped for 4419 polymorphic SNPs. The aim of this study was to confirm and quantify the genetic separation of parental populations, unveil their genetic characteristics and investigate underlying population structures. Through a palette of complimenting analysis, we confirmed a strong genetic differentiation (FST = 0.332) of parental populations validating the germplasms suitability for hybrid breeding. These were, furthermore, found to diverge considerably in several features with the maternal population portraying a strong population structure characterized by a narrow genetic profile, small effective population size and high genome-wise linkage disequilibrium. We propose that the employed male-sterility system putatively constitutes a population determining parameter by influencing the rate of introducing novel genetic variation to the parental populations. Functional analysis of linkage blocks led to identification of a conserved segment on the distal 4RL chromosomal region annotated to the Rfp3 male-fertility restoration 'Pampa' type gene. Findings of our study emphasized the immediate value of comprehensive population studies on elite breeding germplasms as a pre-requisite for application of genomic-based breeding techniques, introgression of novel material and to support breeder decision-making.
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Affiliation(s)
- Nikolaj M. Vendelbo
- Nordic Seed A/S, Odder, Denmark
- Department of Agroecology, Aarhus University, Slagelse, Denmark
| | | | | | | | - Ahmed Jahoor
- Nordic Seed A/S, Odder, Denmark
- Department of Plant Breeding, The Swedish University of Agricultural Sciences, Alnarp, Sweden
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16
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Heterosis Breeding in Eggplant ( Solanum melongena L.): Gains and Provocations. PLANTS 2020; 9:plants9030403. [PMID: 32213925 PMCID: PMC7154857 DOI: 10.3390/plants9030403] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 03/17/2020] [Accepted: 03/20/2020] [Indexed: 01/15/2023]
Abstract
Heterosis (or hybrid vigor) results in a hybrid’s phenotypic superiority over its founder parents for quantitative and qualitative traits. Hybrid vigor is defined by mechanisms such as dominant complementation, over-dominance, and epistasis. Eggplant (Solanum melongena L.) is an essential vegetable crop and a good source of dietary minerals, vitamins, and anthocyanins, with a high oxygen radical absorbance capacity and low caloric value. Given the economic and nutritional significance of eggplants, breeding efforts focus on developing high-yielding varieties—mostly F1 hybrids—with important traits. Studies indicate the successful exploitation of heterosis in the eggplant for a considerable improvement with respect to quantitative traits. In this direction, estimating heterosis for yield-related traits could well be useful for examining the most beneficial hybrid mix with the exploitation of top-quality hybrid. This review examines the current perception of the breeding and molecular aspects of heterosis in eggplants and cites several studies describing the mechanisms. Rendering and combining recent genomics, epigenetic, proteomic, and metabolomics studies present new prospects towards the understanding of the regulatory events of heterosis involved in the evolution and the domestication of the eggplant ideotype.
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17
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Tomkowiak A, Bocianowski J, Kwiatek M, Kowalczewski PŁ. Dependence of the Heterosis Effect on Genetic Distance, Determined using Various Molecular Markers. Open Life Sci 2020; 15:1-11. [PMID: 33987466 PMCID: PMC8114616 DOI: 10.1515/biol-2020-0001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 12/03/2019] [Indexed: 11/15/2022] Open
Abstract
A number of studies have shown that the greater the genetic diversity of parental lines, the greater the heterosis effect. Genetic or phenotypic variation can be estimated by genotype testing on the basis of the observations obtained through prediction (a priori) or the observations and studies (a posteriori). The first method uses data such as the genealogy of a given subject and the information about its geographical origin. The second method is based on the phenotypic observation and studies, as well as on the molecular research. The development of molecular genetics and genotype testing methods at the DNA level has made it possible to rapidly assess the genetic variability regardless of the modifying effect of the environment. The aim of this study was to determine the relationship between the degree of relatedness and the DNA polymorphism (determined using AFLP, RAPD, and SSR markers) of inbred maize lines and the effect of hybrid-form heterosis. Our analysis demonstrated that the parental components for heterosis crosses can be selected on the basis of the genetic similarity determined using the molecular SSR markers and the Jaccard, Kluczyński, Nei, and Rogers coefficients. Molecular AFLP markers proved less useful for selecting the parental components, but may be used to group lines with incomplete origin data. In the case of the RAPD markers, no clear relationship between genetic distance and the heterosis effect was found in this study.
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Affiliation(s)
- Agnieszka Tomkowiak
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, 11 Dojazd Street, 60-632 Poznań, Poland
| | - Jan Bocianowski
- Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, 28 Wojska Polskiego Street, 60-637 Poznań, Poland
| | - Michał Kwiatek
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, 11 Dojazd Street, 60-632 Poznań, Poland
| | - Przemysław Łukasz Kowalczewski
- Institute of Food Technology of Plant Origin, Poznań University of Life Sciences, 31 Wojska Polskiego Street, 60-624 Poznań, Poland
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18
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Liu H, Wang Q, Chen M, Ding Y, Yang X, Liu J, Li X, Zhou C, Tian Q, Lu Y, Fan D, Shi J, Zhang L, Kang C, Sun M, Li F, Wu Y, Zhang Y, Liu B, Zhao XY, Feng Q, Yang J, Han B, Lai J, Zhang XS, Huang X. Genome-wide identification and analysis of heterotic loci in three maize hybrids. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:185-194. [PMID: 31199059 PMCID: PMC6920156 DOI: 10.1111/pbi.13186] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 06/01/2019] [Accepted: 06/04/2019] [Indexed: 05/18/2023]
Abstract
Heterosis, or hybrid vigour, is a predominant phenomenon in plant genetics, serving as the basis of crop hybrid breeding, but the causative loci and genes underlying heterosis remain unclear in many crops. Here, we present a large-scale genetic analysis using 5360 offsprings from three elite maize hybrids, which identifies 628 loci underlying 19 yield-related traits with relatively high mapping resolutions. Heterotic pattern investigations of the 628 loci show that numerous loci, mostly with complete-incomplete dominance (the major one) or overdominance effects (the secondary one) for heterozygous genotypes and nearly equal proportion of advantageous alleles from both parental lines, are the major causes of strong heterosis in these hybrids. Follow-up studies for 17 heterotic loci in an independent experiment using 2225 F2 individuals suggest most heterotic effects are roughly stable between environments with a small variation. Candidate gene analysis for one major heterotic locus (ub3) in maize implies that there may exist some common genes contributing to crop heterosis. These results provide a community resource for genetics studies in maize and new implications for heterosis in plants.
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Affiliation(s)
- Hongjun Liu
- State Key Laboratory of Crop BiologyCollege of Life SciencesShandong Agricultural UniversityTai'anChina
| | - Qin Wang
- Shanghai Key Laboratory of Plant Molecular SciencesCollege of Life SciencesShanghai Normal UniversityShanghaiChina
| | - Mengjiao Chen
- Shanghai Key Laboratory of Plant Molecular SciencesCollege of Life SciencesShanghai Normal UniversityShanghaiChina
| | - Yahui Ding
- State Key Laboratory of Crop BiologyCollege of Life SciencesShandong Agricultural UniversityTai'anChina
| | - Xuerong Yang
- State Key Laboratory of Crop BiologyCollege of Life SciencesShandong Agricultural UniversityTai'anChina
| | - Jie Liu
- Shanghai Key Laboratory of Plant Molecular SciencesCollege of Life SciencesShanghai Normal UniversityShanghaiChina
| | - Xiaohan Li
- State Key Laboratory of Crop BiologyCollege of Life SciencesShandong Agricultural UniversityTai'anChina
| | - Congcong Zhou
- National Center for Gene ResearchCAS Center for Excellence of Molecular Plant SciencesInstitute of Plant Physiology and EcologyShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
| | - Qilin Tian
- National Center for Gene ResearchCAS Center for Excellence of Molecular Plant SciencesInstitute of Plant Physiology and EcologyShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
| | - Yiqi Lu
- National Center for Gene ResearchCAS Center for Excellence of Molecular Plant SciencesInstitute of Plant Physiology and EcologyShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
| | - Danlin Fan
- National Center for Gene ResearchCAS Center for Excellence of Molecular Plant SciencesInstitute of Plant Physiology and EcologyShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
| | - Junpeng Shi
- State Key Laboratory of Agrobiotechnology and National Maize Improvement CenterDepartment of Plant Genetics and BreedingChina Agricultural UniversityBeijingChina
| | - Lin Zhang
- College of AgricultureNortheast Agricultural UniversityHarbinChina
| | - Congbin Kang
- State Key Laboratory of Crop BiologyCollege of Life SciencesShandong Agricultural UniversityTai'anChina
| | - Mingfei Sun
- State Key Laboratory of Crop BiologyCollege of Life SciencesShandong Agricultural UniversityTai'anChina
| | - Fangyuan Li
- State Key Laboratory of Crop BiologyCollege of Life SciencesShandong Agricultural UniversityTai'anChina
| | - Yujian Wu
- State Key Laboratory of Crop BiologyCollege of Life SciencesShandong Agricultural UniversityTai'anChina
| | - Yongzhong Zhang
- State Key Laboratory of Crop BiologyCollege of AgronomyShandong Agricultural UniversityTai'anChina
| | - Baoshen Liu
- State Key Laboratory of Crop BiologyCollege of AgronomyShandong Agricultural UniversityTai'anChina
| | - Xiang Yu Zhao
- State Key Laboratory of Crop BiologyCollege of Life SciencesShandong Agricultural UniversityTai'anChina
| | - Qi Feng
- National Center for Gene ResearchCAS Center for Excellence of Molecular Plant SciencesInstitute of Plant Physiology and EcologyShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
| | - Jinliang Yang
- Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNEUSA
| | - Bin Han
- National Center for Gene ResearchCAS Center for Excellence of Molecular Plant SciencesInstitute of Plant Physiology and EcologyShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
| | - Jinsheng Lai
- State Key Laboratory of Agrobiotechnology and National Maize Improvement CenterDepartment of Plant Genetics and BreedingChina Agricultural UniversityBeijingChina
| | - Xian Sheng Zhang
- State Key Laboratory of Crop BiologyCollege of Life SciencesShandong Agricultural UniversityTai'anChina
| | - Xuehui Huang
- Shanghai Key Laboratory of Plant Molecular SciencesCollege of Life SciencesShanghai Normal UniversityShanghaiChina
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19
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Hu X, Xie W, Wu C, Xu S. A directed learning strategy integrating multiple omic data improves genomic prediction. PLANT BIOTECHNOLOGY JOURNAL 2019; 17:2011-2020. [PMID: 30950198 PMCID: PMC6737184 DOI: 10.1111/pbi.13117] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 02/04/2019] [Accepted: 03/13/2019] [Indexed: 05/05/2023]
Abstract
Genomic prediction (GP) aims to construct a statistical model for predicting phenotypes using genome-wide markers and is a promising strategy for accelerating molecular plant breeding. However, current progress of phenotype prediction using genomic data alone has reached a bottleneck, and previous studies on transcriptomic and metabolomic predictions ignored genomic information. Here, we designed a novel strategy of GP called multilayered least absolute shrinkage and selection operator (MLLASSO) by integrating multiple omic data into a single model that iteratively learns three layers of genetic features (GFs) supervised by observed transcriptome and metabolome. Significantly, MLLASSO learns higher order information of gene interactions, which enables us to achieve a significant improvement of predictability of yield in rice from 0.1588 (GP alone) to 0.2451 (MLLASSO). In the prediction of the first two layers, some genes were found to be genetically predictable genes (GPGs) as their expressions were accurately predicted with genetic markers. Interestingly, we made three dramatic discoveries for the GPGs: (i) GPGs are good predictors for highly complex traits like yield; (ii) GPGs are mostly eQTL genes (cis or trans); and (iii) trait-related transcriptional factor families are enriched in GPGs. These findings support the notion that learned GFs not only are good predictors for traits but also have specific biological implications regarding regulation of gene expressions. To differentiate the new method from conventional GP models, we called MLLASSO a directed learning strategy supervised by intermediate omic data. This new prediction model appears to be more reliable and more robust than conventional GP models.
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Affiliation(s)
- Xuehai Hu
- College of InformaticsAgricultural Bioinformatics Key Laboratory of Hubei ProvinceHuazhong Agricultural UniversityWuhanHubeiChina
- Department of Botany and Plant SciencesUniversity of CaliforniaRiversideCAUSA
| | - Weibo Xie
- College of InformaticsAgricultural Bioinformatics Key Laboratory of Hubei ProvinceHuazhong Agricultural UniversityWuhanHubeiChina
- Department of Botany and Plant SciencesUniversity of CaliforniaRiversideCAUSA
| | - Chengchao Wu
- College of InformaticsAgricultural Bioinformatics Key Laboratory of Hubei ProvinceHuazhong Agricultural UniversityWuhanHubeiChina
| | - Shizhong Xu
- Department of Botany and Plant SciencesUniversity of CaliforniaRiversideCAUSA
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20
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Zheng Y, Wang P, Chen X, Sun Y, Yue C, Ye N. Transcriptome and Metabolite Profiling Reveal Novel Insights into Volatile Heterosis in the Tea Plant ( Camellia Sinensis). Molecules 2019; 24:E3380. [PMID: 31533323 PMCID: PMC6767024 DOI: 10.3390/molecules24183380] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 09/16/2019] [Accepted: 09/16/2019] [Indexed: 12/16/2022] Open
Abstract
Tea aroma is a key indicator for evaluating tea quality. Although notable success in tea aroma improvement has been achieved with heterosis breeding technology, the molecular basis underlying heterosis remains largely unexplored. Thus, the present report studies the tea plant volatile heterosis using a high-throughput next-generation RNA-seq strategy and gas chromatography-mass spectrometry. Phenotypically, we found higher terpenoid volatile and green leaf volatile contents by gas chromatography-mass spectrometry in the F1 hybrids than in their parental lines. Volatile heterosis was obvious in both F1 hybrids. At the molecular level, the comparative transcriptomics analysis revealed that approximately 41% (9027 of 21,995) of the genes showed non-additive expression, whereas only 7.83% (1723 of 21,995) showed additive expression. Among the non-additive genes, 42.1% showed high parental dominance and 17.6% showed over-dominance. Among different expression genes with high parental dominance and over-dominance expression patterns, KEGG and GO analyses found that plant hormone signal transduction, tea plant physiological process related pathways and most pathways associated with tea tree volatiles were enriched. In addition, we identified multiple genes (CsDXS, CsAATC2, CsSPLA2, etc.) and transcription factors (CsMYB1, CsbHLH79, CsWRKY40, etc.) that played important roles in tea volatile heterosis. Based on transcriptome and metabolite profiling, we conclude that non-additive action plays a major role in tea volatile heterosis. Genes and transcription factors involved in tea volatiles showing over-dominance expression patterns can be considered candidate genes and provide novel clues for breeding high-volatile tea varieties.
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Affiliation(s)
- Yucheng Zheng
- College of Horticulture, Key Laboratory of Tea Science, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian, China.
| | - Pengjie Wang
- College of Horticulture, Key Laboratory of Tea Science, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian, China.
| | - Xuejin Chen
- College of Horticulture, Key Laboratory of Tea Science, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian, China.
| | - Yun Sun
- College of Horticulture, Key Laboratory of Tea Science, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian, China.
| | - Chuan Yue
- College of Horticulture, Key Laboratory of Tea Science, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian, China.
| | - Naixing Ye
- College of Horticulture, Key Laboratory of Tea Science, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian, China.
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21
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Wang S, Wei J, Li R, Qu H, Chater JM, Ma R, Li Y, Xie W, Jia Z. Identification of optimal prediction models using multi-omic data for selecting hybrid rice. Heredity (Edinb) 2019; 123:395-406. [PMID: 30911139 DOI: 10.1038/s41437-019-0210-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 02/22/2019] [Accepted: 02/25/2019] [Indexed: 11/09/2022] Open
Abstract
Genomic prediction benefits hybrid rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available for predicting breeding values for agronomically important traits. In this study, the best prediction strategies were determined for yield, 1000 grain weight, number of grains per panicle, and number of tillers per plant of hybrid rice (derived from recombinant inbred lines) by comprehensively evaluating all possible combinations of omic datasets with different prediction methods. It was demonstrated that, in rice, the predictions using a combination of genomic and metabolomic data generally produce better results than single-omics predictions or predictions based on other combined omic data. Best linear unbiased prediction (BLUP) appears to be the most efficient prediction method compared to the other commonly used approaches, including least absolute shrinkage and selection operator (LASSO), stochastic search variable selection (SSVS), support vector machines with radial basis function and epsilon regression (SVM-R(EPS)), support vector machines with radial basis function and nu regression (SVM-R(NU)), support vector machines with polynomial kernel and epsilon regression (SVM-P(EPS)), support vector machines with polynomial kernel and nu regression (SVM-P(NU)) and partial least squares regression (PLS). This study has provided guidelines for selection of hybrid rice in terms of which types of omic datasets and which method should be used to achieve higher trait predictability. The answer to these questions will benefit academic research and will also greatly reduce the operative cost for the industry which specializes in breeding and selection.
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Affiliation(s)
- Shibo Wang
- Department of Botany & Plant Sciences, University of California, Riverside, CA, USA
| | - Julong Wei
- College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Ruidong Li
- Department of Botany & Plant Sciences, University of California, Riverside, CA, USA
| | - Han Qu
- Department of Botany & Plant Sciences, University of California, Riverside, CA, USA
| | - John M Chater
- Department of Botany & Plant Sciences, University of California, Riverside, CA, USA
| | - Renyuan Ma
- Department of Mathematics, Bowdoin College, Brunswick, ME, USA
| | - Yonghao Li
- Department of Neuroscience, University of British Columbia, Vancouver, BC, Canada
| | - Weibo Xie
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Zhenyu Jia
- Department of Botany & Plant Sciences, University of California, Riverside, CA, USA.
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Dissecting Heterosis During the Ear Inflorescence Development Stage in Maize via a Metabolomics-based Analysis. Sci Rep 2019; 9:212. [PMID: 30659214 PMCID: PMC6338801 DOI: 10.1038/s41598-018-36446-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 11/13/2018] [Indexed: 11/08/2022] Open
Abstract
Heterosis can increase the yield of many crops and has been extensively applied in agriculture. In maize, female inflorescence architecture directly determines grain yield. Thus, exploring the relationship between early maize ear inflorescence development and heterosis regarding yield-related traits may be helpful for characterizing the molecular mechanisms underlying heterotic performance. In this study, we fine mapped the overdominant heterotic locus (hlEW2b), associated with ear width, in an approximately 1.98-Mb region based on analyses of chromosome segment substitution lines and the corresponding testcross population. Maize ear inflorescences at the floral meristem stage were collected from two inbred lines, one chromosome segment substitution line that carried hlEW2b (sub-CSSL16), the receptor parent lx9801, and the Zheng58 × sub-CSSL16 and Zheng58 × lx9801 hybrid lines. A total of 256 metabolites were identified, including 31 and 24 metabolites that were differentially accumulated between the two hybrid lines and between the two inbred lines, respectively. Most of these metabolites are involved in complex regulatory mechanisms important for maize ear development. For example, nucleotides are basic metabolites affecting cell composition and carbohydrate synthesis. Additionally, nicotinate and nicotinamide metabolism is important for photosynthesis, plant stress responses, and cell expansion. Moreover, flavonoid and phenolic metabolites regulate auxin transport and cell apoptosis. Meanwhile, phytohormone biosynthesis and distribution influence the cell cycle and cell proliferation. Our results revealed that changes in metabolite contents may affect the heterotic performance related to ear width and yield in maize hybrid lines. This study provides new clues in heterosis at the metabolomics level and implies that differentially accumulated metabolites made distinct contributions to the heterosis at an early stage of ear inflorescences development.
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Da L, Liu Y, Yang J, Tian T, She J, Ma X, Xu W, Su Z. AppleMDO: A Multi-Dimensional Omics Database for Apple Co-Expression Networks and Chromatin States. FRONTIERS IN PLANT SCIENCE 2019; 10:1333. [PMID: 31695717 PMCID: PMC6817610 DOI: 10.3389/fpls.2019.01333] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 09/25/2019] [Indexed: 05/17/2023]
Abstract
As an economically important crop, apple is one of the most cultivated fruit trees in temperate regions worldwide. Recently, a large number of high-quality transcriptomic and epigenomic datasets for apple were made available to the public, which could be helpful in inferring gene regulatory relationships and thus predicting gene function at the genome level. Through integration of the available apple genomic, transcriptomic, and epigenomic datasets, we constructed co-expression networks, identified functional modules, and predicted chromatin states. A total of 112 RNA-seq datasets were integrated to construct a global network and a conditional network (tissue-preferential network). Furthermore, a total of 1,076 functional modules with closely related gene sets were identified to assess the modularity of biological networks and further subjected to functional enrichment analysis. The results showed that the function of many modules was related to development, secondary metabolism, hormone response, and transcriptional regulation. Transcriptional regulation is closely related to epigenetic marks on chromatin. A total of 20 epigenomic datasets, which included ChIP-seq, DNase-seq, and DNA methylation analysis datasets, were integrated and used to classify chromatin states. Based on the ChromHMM algorithm, the genome was divided into 620,122 fragments, which were classified into 24 states according to the combination of epigenetic marks and enriched-feature regions. Finally, through the collaborative analysis of different omics datasets, the online database AppleMDO (http://bioinformatics.cau.edu.cn/AppleMDO/) was established for cross-referencing and the exploration of possible novel functions of apple genes. In addition, gene annotation information and functional support toolkits were also provided. Our database might be convenient for researchers to develop insights into the function of genes related to important agronomic traits and might serve as a reference for other fruit trees.
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24
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Kaushik P, Plazas M, Prohens J, Vilanova S, Gramazio P. Diallel genetic analysis for multiple traits in eggplant and assessment of genetic distances for predicting hybrids performance. PLoS One 2018; 13:e0199943. [PMID: 29949625 PMCID: PMC6021119 DOI: 10.1371/journal.pone.0199943] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 06/15/2018] [Indexed: 11/22/2022] Open
Abstract
Evaluation and prediction of the performance of hybrids is important in eggplant (Solanum melongena) breeding. A set of 10 morphologically highly diverse eggplant parents, including nine inbred S. melongena and one weedy S. insanum accessions, were intercrossed according to a half-diallel mating design without reciprocals to obtain 45 hybrids. Parents and hybrids were evaluated for 14 morphological and agronomic conventional descriptors and 14 fruit morphometric traits using Tomato Analyzer. Genetic distances among parents were estimated with 7,335 polymorphic SNP markers. Wide ranges of variation and significant differences were observed in the set of 55 genotypes for all traits, although the hybrids group had significantly higher vigour and yield than parents. General and specific combining abilities (GCA and SCA) were significant for most (GCA) or all (SCA) traits, although a wide variation was obtained for GCA/SCA ratios. Many relevant traits associated to vigour and yield had low GCA/SCA ratios and narrow-sense heritability (h2) values, while the reverse occurred for most fruit shape descriptors. Broad-sense heritability (H2) values were generally high, irrespective of GCA/SCA ratios. Significant correlations were found between traits related to size of leaf, flower and fruit, as well as among many fruit morphometric traits. Genetic distances (GD) among parents were coherent with their phylogenetic relationships, but few significant and generally low correlations were found between GD and hybrid means, heterosis or SCA. The results provide relevant information for developing appropriate strategies for parent selection and hybrid development in eggplant and suggest that GD among parents have limited value to predict hybrid performance in this crop.
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Affiliation(s)
- Prashant Kaushik
- Instituto de Conservación y Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Valencia, Spain
| | - Mariola Plazas
- Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Científicas-Universitat Politècnica de València, Valencia, Spain
| | - Jaime Prohens
- Instituto de Conservación y Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Valencia, Spain
| | - Santiago Vilanova
- Instituto de Conservación y Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Valencia, Spain
| | - Pietro Gramazio
- Instituto de Conservación y Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Valencia, Spain
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25
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Hallahan BF, Fernandez-Tendero E, Fort A, Ryder P, Dupouy G, Deletre M, Curley E, Brychkova G, Schulz B, Spillane C. Hybridity has a greater effect than paternal genome dosage on heterosis in sugar beet (Beta vulgaris). BMC PLANT BIOLOGY 2018; 18:120. [PMID: 29907096 PMCID: PMC6003118 DOI: 10.1186/s12870-018-1338-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Accepted: 05/31/2018] [Indexed: 05/30/2023]
Abstract
BACKGROUND The phenomenon of heterosis is critical to plant breeding and agricultural productivity. Heterosis occurs when F1 hybrid offspring display quantitative improvements in traits to levels that do not occur in the parents. Increasing the genome dosage (i.e. ploidy level) of F1 offspring can contribute to heterosis effects. Sugar beet (Beta vulgaris) provides a model for investigating the relative effects of genetic hybridity and genome dosage on heterosis. Sugar beet lines of different ploidy levels were crossed to generate diploid and triploid F1 offspring to investigate the effect of; (1) paternal genome dosage increase on F1 heterosis, and; (2) homozygous versus heterozygous tetraploid male parents on F1 triploid heterosis. A range of traits of agronomic and commercial importance were analyzed for the extent of heterosis effects observed in the F1 offspring. RESULTS Comparisons of parental lines to diploid (EA, EB) and triploid (EAA, EBB) F1 hybrids for total yield, root yield, and sugar yield indicated that there was no effect of paternal genome dosage increases on heterosis levels, indicating that hybridity is the main contributor to the heterosis levels observed. For all traits measured (apart from seed viability), F1 triploid hybrids derived from heterozygous tetraploid male parents displayed equivalent levels of heterosis as F1 triploid hybrids generated with homozygous tetraploid male parents, suggesting that heterosis gains in F1 triploids do not arise by simply increasing the extent of multi-locus heterozygosity in sugar beet F1 offspring. CONCLUSIONS Overall, our study indicates that; (1) increasing the paternal genome dosage does not enhance heterosis in F1 hybrids, and; (2) increasing multi-locus heterozygosity using highly heterozygous paternal genomes to generate F1 triploid hybrids does not enhance heterosis. Our findings have implications for the design of future F1 hybrid improvement programs for sugar beet.
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Affiliation(s)
- Brendan F. Hallahan
- Genetics and Biotechnology Laboratory, Plant and AgriBioscience Research Centre (PABC), Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4 Ireland
| | - Eva Fernandez-Tendero
- Genetics and Biotechnology Laboratory, Plant and AgriBioscience Research Centre (PABC), Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4 Ireland
| | - Antoine Fort
- Genetics and Biotechnology Laboratory, Plant and AgriBioscience Research Centre (PABC), Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4 Ireland
| | - Peter Ryder
- Genetics and Biotechnology Laboratory, Plant and AgriBioscience Research Centre (PABC), Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4 Ireland
| | - Gilles Dupouy
- Genetics and Biotechnology Laboratory, Plant and AgriBioscience Research Centre (PABC), Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4 Ireland
| | - Marc Deletre
- Genetics and Biotechnology Laboratory, Plant and AgriBioscience Research Centre (PABC), Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4 Ireland
| | - Edna Curley
- Genetics and Biotechnology Laboratory, Plant and AgriBioscience Research Centre (PABC), Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4 Ireland
| | - Galina Brychkova
- Genetics and Biotechnology Laboratory, Plant and AgriBioscience Research Centre (PABC), Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4 Ireland
| | | | - Charles Spillane
- Genetics and Biotechnology Laboratory, Plant and AgriBioscience Research Centre (PABC), Ryan Institute, National University of Ireland Galway, University Road, Galway, H91 REW4 Ireland
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Seifert F, Thiemann A, Schrag TA, Rybka D, Melchinger AE, Frisch M, Scholten S. Small RNA-based prediction of hybrid performance in maize. BMC Genomics 2018; 19:371. [PMID: 29783940 PMCID: PMC5963143 DOI: 10.1186/s12864-018-4708-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Accepted: 04/22/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Small RNA (sRNA) sequences are known to have a broad impact on gene regulation by various mechanisms. Their performance for the prediction of hybrid traits has not yet been analyzed. Our objective was to analyze the relation of parental sRNA expression with the performance of their hybrids, to develop a sRNA-based prediction approach, and to compare it to more common SNP and mRNA transcript based predictions using a factorial mating scheme of a maize hybrid breeding program. RESULTS Correlation of genomic differences and messenger RNA (mRNA) or sRNA expression differences between parental lines with hybrid performance of their hybrids revealed that sRNAs showed an inverse relationship in contrast to the other two data types. We associated differences for SNPs, mRNA and sRNA expression between parental inbred lines with the performance of their hybrid combinations and developed two prediction approaches using distance measures based on associated markers. Cross-validations revealed parental differences in sRNA expression to be strong predictors for hybrid performance for grain yield in maize, comparable to genomic and mRNA data. The integration of both positively and negatively associated markers in the prediction approaches enhanced the prediction accurary. The associated sRNAs belong predominantly to the canonical size classes of 22- and 24-nt that show specific genomic mapping characteristics. CONCLUSION Expression profiles of sRNA are a promising alternative to SNPs or mRNA expression profiles for hybrid prediction, especially for plant species without reference genome or transcriptome information. The characteristics of the sRNAs we identified suggest that association studies based on breeding populations facilitate the identification of sRNAs involved in hybrid performance.
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Affiliation(s)
- Felix Seifert
- Developmental Biology, Biocenter Klein Flottbek, University of Hamburg, 22609 Hamburg, Germany
| | - Alexander Thiemann
- Developmental Biology, Biocenter Klein Flottbek, University of Hamburg, 22609 Hamburg, Germany
| | - Tobias A. Schrag
- Institute for Plant Breeding, Seed Science and Population Genetics, Quantitative Genetics and Genomics of Crops, University of Hohenheim, Fruwirthstrasse 21, 70599 Stuttgart, Germany
| | - Dominika Rybka
- Developmental Biology, Biocenter Klein Flottbek, University of Hamburg, 22609 Hamburg, Germany
| | - Albrecht E. Melchinger
- Institute for Plant Breeding, Seed Science and Population Genetics, Quantitative Genetics and Genomics of Crops, University of Hohenheim, Fruwirthstrasse 21, 70599 Stuttgart, Germany
| | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, 35392 Giessen, Germany
| | - Stefan Scholten
- Developmental Biology, Biocenter Klein Flottbek, University of Hamburg, 22609 Hamburg, Germany
- Institute for Plant Breeding, Seed Science and Population Genetics, Quantitative Genetics and Genomics of Crops, University of Hohenheim, Fruwirthstrasse 21, 70599 Stuttgart, Germany
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de Abreu e Lima F, Willmitzer L, Nikoloski Z. Classification-driven framework to predict maize hybrid field performance from metabolic profiles of young parental roots. PLoS One 2018; 13:e0196038. [PMID: 29698533 PMCID: PMC5919381 DOI: 10.1371/journal.pone.0196038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Accepted: 04/05/2018] [Indexed: 01/10/2023] Open
Abstract
Maize (Zea mays L.) is a staple food whose production relies on seed stocks that largely comprise hybrid varieties. Therefore, knowledge about the molecular determinants of hybrid performance (HP) in the field can be used to devise better performing hybrids to address the demands for sustainable increase in yield. Here, we propose and test a classification-driven framework that uses metabolic profiles from in vitro grown young roots of parental lines from the Dent × Flint maize heterotic pattern to predict field HP. We identify parental analytes that best predict the metabolic inheritance patterns in 328 hybrids. We then demonstrate that these analytes are also predictive of field HP (0.64 ≥ r ≥ 0.79) and discriminate hybrids of good performance (accuracy of 87.50%). Therefore, our approach provides a cost-effective solution for hybrid selection programs.
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Affiliation(s)
| | - Lothar Willmitzer
- Max-Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zoran Nikoloski
- Max-Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- * E-mail:
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28
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Seifert F, Thiemann A, Grant-Downton R, Edelmann S, Rybka D, Schrag TA, Frisch M, Dickinson HG, Melchinger AE, Scholten S. Parental Expression Variation of Small RNAs Is Negatively Correlated with Grain Yield Heterosis in a Maize Breeding Population. FRONTIERS IN PLANT SCIENCE 2018; 9:13. [PMID: 29441076 PMCID: PMC5797689 DOI: 10.3389/fpls.2018.00013] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 01/04/2018] [Indexed: 05/26/2023]
Abstract
Heterosis refers to a quantitative phenomenon in which F1 hybrid trait values exceed the mean of the parental values in a positive direction. Generally, it is dependent on a high degree of heterozygosity, which is maintained in hybrid breeding by developing parental lines in separate, genetically distinct heterotic groups. The mobility of small RNAs (sRNAs) that mediate epigenetic regulation of gene expression renders them promising candidates for modulating the action of combined diverse genomes in trans-and evidence already indicates their contribution to transgressive phenotypes. By sequencing small RNA libraries of a panel of 21 maize parental inbred lines we found a low overlap of 35% between the sRNA populations from both distinct heterotic groups. Surprisingly, in contrast to genetic or gene expression variation, parental sRNA expression variation is negatively correlated with grain yield (GY) heterosis. Among 0.595 million expressed sRNAs, we identified 9,767, predominantly 22- and 24-nt long sRNAs, which showed an association of their differential expression between parental lines and GY heterosis of the respective hybrids. Of these sRNAs, 3,485 or 6,282 showed an association with high or low GY heterosis, respectively, thus the low heterosis associated group prevailing at 64%. The heterosis associated sRNAs map more frequently to genes that show differential expression between parental lines than reference sets. Together these findings suggest that trans-chromosomal actions of sRNAs in hybrids might add up to a negative contribution in heterosis formation, mediated by unfavorable gene expression regulation. We further revealed an exclusive accumulation of 22-nt sRNAs that are associated with low GY heterosis in pericentromeric genomic regions. That recombinational suppression led to this enrichment is indicated by its close correlation with low recombination rates. The existence of this enrichment, which we hypothesize resulted from the separated breeding of inbred lines within heterotic groups, may have implications for hybrid breeding strategies addressing the recombinational constraints characteristic of complex crop genomes.
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Affiliation(s)
- Felix Seifert
- Biocenter Klein Flottbek, University of Hamburg, Hamburg, Germany
| | | | | | - Susanne Edelmann
- Biocenter Klein Flottbek, University of Hamburg, Hamburg, Germany
| | - Dominika Rybka
- Biocenter Klein Flottbek, University of Hamburg, Hamburg, Germany
| | - Tobias A. Schrag
- Institute for Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany
| | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus-Liebig University, Giessen, Germany
| | - Hugh G. Dickinson
- Department of Plant Sciences, University of Oxford, Oxford, United Kingdom
| | - Albrecht E. Melchinger
- Institute for Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany
| | - Stefan Scholten
- Biocenter Klein Flottbek, University of Hamburg, Hamburg, Germany
- Institute for Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany
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29
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Li Z, Coffey L, Garfin J, Miller ND, White MR, Spalding EP, de Leon N, Kaeppler SM, Schnable PS, Springer NM, Hirsch CN. Genotype-by-environment interactions affecting heterosis in maize. PLoS One 2018; 13:e0191321. [PMID: 29342221 PMCID: PMC5771596 DOI: 10.1371/journal.pone.0191321] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2017] [Accepted: 01/03/2018] [Indexed: 12/17/2022] Open
Abstract
The environment can influence heterosis, the phenomena in which the offspring of two inbred parents exhibits phenotypic performance beyond the inbred parents for specific traits. In this study we measured 25 traits in a set of 47 maize hybrids and their inbred parents grown in 16 different environments with varying levels of average productivity. By quantifying 25 vegetative and reproductive traits across the life cycle we were able to analyze interactions between the environment and multiple distinct instances of heterosis. The magnitude and rank among hybrids for better-parent heterosis (BPH) varied for the different traits and environments. Across the traits, a higher within plot variance was observed for inbred lines compared to hybrids. However, for most traits, variance across environments was not significantly different for inbred lines compared to hybrids. Further, for many traits the correlations of BPH to hybrid performance and BPH to better parent performance were of comparable magnitude. These results indicate that inbred lines and hybrids show similar trends in environmental response and both are contributing to observed genotype-by-environment interactions for heterosis. This study highlights the degree of heterosis is not an inherent trait of a specific hybrid, but varies depending on the trait measured and the environment where that trait is measured. Studies that attempt to correlate molecular processes with heterosis are hindered by the fact that heterosis is not a consistent attribute of a specific hybrid.
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Affiliation(s)
- Zhi Li
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, Minnesota, United States of America
| | - Lisa Coffey
- Department of Agronomy, Iowa State University, Ames, Iowa, United States of America
| | - Jacob Garfin
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, Minnesota, United States of America
| | - Nathan D. Miller
- Department of Botany, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Michael R. White
- Department of Agronomy, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Edgar P. Spalding
- Department of Botany, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Shawn M. Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Patrick S. Schnable
- Department of Agronomy, Iowa State University, Ames, Iowa, United States of America
| | - Nathan M. Springer
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, Minnesota, United States of America
| | - Candice N. Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, Minnesota, United States of America
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30
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Mixão V, Gabaldón T. Hybridization and emergence of virulence in opportunistic human yeast pathogens. Yeast 2017; 35:5-20. [PMID: 28681409 PMCID: PMC5813172 DOI: 10.1002/yea.3242] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 06/27/2017] [Accepted: 06/28/2017] [Indexed: 02/06/2023] Open
Abstract
Hybridization between different species can result in the emergence of new lineages and adaptive phenotypes. Occasionally, hybridization in fungal organisms can drive the appearance of opportunistic lifestyles or shifts to new hosts, resulting in the emergence of novel pathogens. In recent years, an increasing number of studies have documented the existence of hybrids in diverse yeast clades, including some comprising human pathogens. Comparative and population genomics studies performed on these clades are enabling us to understand what roles hybridization may play in the evolution and emergence of a virulence potential towards humans. Here we survey recent genomic studies on several yeast pathogenic clades where hybrids have been identified, and discuss the broader implications of hybridization in the evolution and emergence of pathogenic lineages. © 2017 The Authors. Yeast published by John Wiley & Sons, Ltd.
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Affiliation(s)
- Verónica Mixão
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain.,Universitat Pompeu Fabra, 08003, Barcelona, Spain
| | - Toni Gabaldón
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain.,Universitat Pompeu Fabra, 08003, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats, Pg. Lluís Companys 23, 08010, Barcelona, Spain
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31
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Westhues M, Schrag TA, Heuer C, Thaller G, Utz HF, Schipprack W, Thiemann A, Seifert F, Ehret A, Schlereth A, Stitt M, Nikoloski Z, Willmitzer L, Schön CC, Scholten S, Melchinger AE. Omics-based hybrid prediction in maize. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2017. [PMID: 28647896 DOI: 10.1007/s00122-017-2934-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Complementing genomic data with other "omics" predictors can increase the probability of success for predicting the best hybrid combinations using complex agronomic traits. Accurate prediction of traits with complex genetic architecture is crucial for selecting superior candidates in animal and plant breeding and for guiding decisions in personalized medicine. Whole-genome prediction has revolutionized these areas but has inherent limitations in incorporating intricate epistatic interactions. Downstream "omics" data are expected to integrate interactions within and between different biological strata and provide the opportunity to improve trait prediction. Yet, predicting traits from parents to progeny has not been addressed by a combination of "omics" data. Here, we evaluate several "omics" predictors-genomic, transcriptomic and metabolic data-measured on parent lines at early developmental stages and demonstrate that the integration of transcriptomic with genomic data leads to higher success rates in the correct prediction of untested hybrid combinations in maize. Despite the high predictive ability of genomic data, transcriptomic data alone outperformed them and other predictors for the most complex heterotic trait, dry matter yield. An eQTL analysis revealed that transcriptomic data integrate genomic information from both, adjacent and distant sites relative to the expressed genes. Together, these findings suggest that downstream predictors capture physiological epistasis that is transmitted from parents to their hybrid offspring. We conclude that the use of downstream "omics" data in prediction can exploit important information beyond structural genomics for leveraging the efficiency of hybrid breeding.
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Affiliation(s)
- Matthias Westhues
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany
| | - Tobias A Schrag
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany
| | - Claas Heuer
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel, 24098, Kiel, Germany
- Inguran LLC dba STGenetics, 22575 SH6 South, Navasota, TX, 77868, USA
| | - Georg Thaller
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel, 24098, Kiel, Germany
| | - H Friedrich Utz
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany
| | - Wolfgang Schipprack
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany
| | - Alexander Thiemann
- Biocenter Klein Flottbek, Developmental Biology and Biotechnology, University of Hamburg, 22609, Hamburg, Germany
| | - Felix Seifert
- Biocenter Klein Flottbek, Developmental Biology and Biotechnology, University of Hamburg, 22609, Hamburg, Germany
| | - Anita Ehret
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel, 24098, Kiel, Germany
| | - Armin Schlereth
- Max-Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Mark Stitt
- Max-Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Zoran Nikoloski
- Max-Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Lothar Willmitzer
- Max-Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Chris C Schön
- Plant Breeding, Technische Universität München, 85354, Freising, Germany
| | - Stefan Scholten
- Biocenter Klein Flottbek, Developmental Biology and Biotechnology, University of Hamburg, 22609, Hamburg, Germany.
| | - Albrecht E Melchinger
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany.
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Zhou H, Xia D, Zeng J, Jiang G, He Y. Dissecting combining ability effect in a rice NCII-III population provides insights into heterosis in indica-japonica cross. RICE (NEW YORK, N.Y.) 2017; 10:39. [PMID: 28853048 PMCID: PMC5574824 DOI: 10.1186/s12284-017-0179-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2017] [Accepted: 08/21/2017] [Indexed: 05/26/2023]
Abstract
BACKGROUND Combining ability is a measure for selecting elite parents that make the highest contributions to hybrid performance. However, the genetic bases of combining ability and how they contributed to heterosis is seldomly known. RESULTS We constructed a both NCII and NCIII population derived from an indica-japonica cross to study the relationship among parental performance, combining ability and hybrid performance of 11 agronomic traits. Among them, specific combining ability is more important to grain yield than parental performance and general combining ability. We performed linkage analyses to phenotypic values and combining ability of all 11 traits in Doubled haploid lines and its two backcross populations and identified 108 QTLs in total. Among these QTLs, four known loci, Sd1, Ghd7, Ghd8 and DEP1 contribute a lot to GCA effects of agronomic traits except grain yield and seed setting rate. Three QTLs, Ghd8, S5 and qS12, contribute a lot to SCA effects of grain yield and present overdominace. CONCLUSIONS Our study provides insights into the genetic bases of combining ability and heterosis and will promote the improvements of indica-japonica hybrid breeding.
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Affiliation(s)
- Hao Zhou
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research, Huazhong Agricultural University, Wuhan, China
| | - Duo Xia
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research, Huazhong Agricultural University, Wuhan, China
| | - Jing Zeng
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research, Huazhong Agricultural University, Wuhan, China
| | - Gonghao Jiang
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research, Huazhong Agricultural University, Wuhan, China
| | - Yuqing He
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research, Huazhong Agricultural University, Wuhan, China
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33
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Guo Z, Magwire MM, Basten CJ, Xu Z, Wang D. Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2016; 129:2413-2427. [PMID: 27586153 DOI: 10.1007/s00122-016-2780-5] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Accepted: 08/24/2016] [Indexed: 05/05/2023]
Abstract
Predictive ability derived from gene expression and metabolic information was evaluated using genomic prediction methods based on datasets from a public maize panel. With the rapid development of high throughput biological technologies, information from gene expression and metabolites has received growing attention in plant genetics and breeding. In this study, we evaluated the utility of gene expression and metabolic information for genomic prediction using data obtained from a maize diversity panel. Our results show that, when used as predictor variables, gene expression levels and metabolite abundances provided reasonable predictive abilities relative to those based on genetic markers, although these values were not as large as those with genetic markers. Integrating gene expression levels and metabolite abundances with genetic markers significantly improved predictive abilities in comparison to the benchmark genomic best linear unbiased prediction model using genome-wide markers only. Predictive abilities based on gene expression and metabolites were trait-specific and were affected by the time of measurement and tissue samples as well as the number of genes and metabolites included in the model. In general, our results suggest that, rather than being conventionally used as intermediate phenotypes, gene expression and metabolic information can be used as predictors for genomic prediction and help improve genetic gains for complex traits in breeding programs.
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Affiliation(s)
- Zhigang Guo
- Syngenta Crop Protection, LLC, 3054 E Cornwallis Rd., Research Triangle Park, NC, 27709, USA.
| | - Michael M Magwire
- Syngenta Crop Protection, LLC, 3054 E Cornwallis Rd., Research Triangle Park, NC, 27709, USA
| | - Christopher J Basten
- Syngenta Crop Protection, LLC, 3054 E Cornwallis Rd., Research Triangle Park, NC, 27709, USA
| | - Zhanyou Xu
- Syngenta Crop Protection, LLC, 2369 330th Street, Slater, IA, 50244, USA
| | - Daolong Wang
- Syngenta Crop Protection, LLC, 3054 E Cornwallis Rd., Research Triangle Park, NC, 27709, USA
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34
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Belamkar V, Farmer AD, Weeks NT, Kalberer SR, Blackmon WJ, Cannon SB. Genomics-assisted characterization of a breeding collection of Apios americana, an edible tuberous legume. Sci Rep 2016; 6:34908. [PMID: 27721469 PMCID: PMC5056515 DOI: 10.1038/srep34908] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Accepted: 09/20/2016] [Indexed: 11/16/2022] Open
Abstract
For species with potential as new crops, rapid improvement may be facilitated by new genomic methods. Apios (Apios americana Medik.), once a staple food source of Native American Indians, produces protein-rich tubers, tolerates a wide range of soils, and symbiotically fixes nitrogen. We report the first high-quality de novo transcriptome assembly, an expression atlas, and a set of 58,154 SNP and 39,609 gene expression markers (GEMs) for characterization of a breeding collection. Both SNPs and GEMs identify six genotypic clusters in the collection. Transcripts mapped to the Phaseolus vulgaris genome-another phaseoloid legume with the same chromosome number-provide provisional genetic locations for 46,852 SNPs. Linkage disequilibrium decays within 10 kb (based on the provisional genetic locations), consistent with outcrossing reproduction. SNPs and GEMs identify more than 21 marker-trait associations for at least 11 traits. This study demonstrates a holistic approach for mining plant collections to accelerate crop improvement.
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Affiliation(s)
- Vikas Belamkar
- Interdepartmental Genetics, Iowa State University, Ames, IA 50011, USA
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | | | - Nathan T. Weeks
- United States Department of Agriculture–Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, USA
| | - Scott R. Kalberer
- United States Department of Agriculture–Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, USA
| | - William J. Blackmon
- Department of Horticulture, Louisiana Agricultural Experiment Station, Louisiana State University Agricultural Center, Baton Rouge, LA 70803, USA
| | - Steven B. Cannon
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
- United States Department of Agriculture–Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, USA
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35
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Xu S, Xu Y, Gong L, Zhang Q. Metabolomic prediction of yield in hybrid rice. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2016; 88:219-227. [PMID: 27311694 DOI: 10.1111/tpj.13242] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Revised: 06/10/2016] [Accepted: 06/15/2016] [Indexed: 05/18/2023]
Abstract
Rice (Oryza sativa) provides a staple food source for more than 50% of the world's population. An increase in yield can significantly contribute to global food security. Hybrid breeding can potentially help to meet this goal because hybrid rice often shows a considerable increase in yield when compared with pure-bred cultivars. We recently developed a marker-guided prediction method for hybrid yield and showed a substantial increase in yield through genomic hybrid breeding. We now have transcriptomic and metabolomic data as potential resources for prediction. Using six prediction methods, including least absolute shrinkage and selection operator (LASSO), best linear unbiased prediction (BLUP), stochastic search variable selection, partial least squares, and support vector machines using the radial basis function and polynomial kernel function, we found that the predictability of hybrid yield can be further increased using these omic data. LASSO and BLUP are the most efficient methods for yield prediction. For high heritability traits, genomic data remain the most efficient predictors. When metabolomic data are used, the predictability of hybrid yield is almost doubled compared with genomic prediction. Of the 21 945 potential hybrids derived from 210 recombinant inbred lines, selection of the top 10 hybrids predicted from metabolites would lead to a ~30% increase in yield. We hypothesize that each metabolite represents a biologically built-in genetic network for yield; thus, using metabolites for prediction is equivalent to using information integrated from these hidden genetic networks for yield prediction.
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Affiliation(s)
- Shizhong Xu
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92507, USA
| | - Yang Xu
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92507, USA
| | - Liang Gong
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
| | - Qifa Zhang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
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36
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Zenke-Philippi C, Thiemann A, Seifert F, Schrag T, Melchinger AE, Scholten S, Frisch M. Prediction of hybrid performance in maize with a ridge regression model employed to DNA markers and mRNA transcription profiles. BMC Genomics 2016; 17:262. [PMID: 27025377 PMCID: PMC4812617 DOI: 10.1186/s12864-016-2580-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Accepted: 03/08/2016] [Indexed: 12/15/2022] Open
Abstract
Background Ridge regression models can be used for predicting heterosis and hybrid performance. Their application to mRNA transcription profiles has not yet been investigated. Our objective was to compare the prediction accuracy of models employing mRNA transcription profiles with that of models employing genome-wide markers using a data set of 98 maize hybrids from a breeding program. Results We predicted hybrid performance and mid-parent heterosis for grain yield and grain dry matter content and employed cross validation to assess the prediction accuracy. Prediction with a ridge regression model using random effects for mRNA transcription profiles resulted in similar prediction accuracies than employing the model to DNA markers. For hybrids, of which none of the parental inbred lines was part of the training set, the ridge regression model did not reach the prediction accuracy that was obtained with a model using transcriptome-based distances. Conclusion We conclude that mRNA transcription profiles are a promising alternative to DNA markers for hybrid prediction, but further studies with larger data sets are required to investigate the superiority of alternative prediction models.
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Affiliation(s)
- Carola Zenke-Philippi
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Giessen, 35392, Germany
| | - Alexander Thiemann
- Biocenter Klein Flottbek, Developmental Biology and Biotechnology, University of Hamburg, Hamburg, 22609, Germany
| | - Felix Seifert
- Biocenter Klein Flottbek, Developmental Biology and Biotechnology, University of Hamburg, Hamburg, 22609, Germany
| | - Tobias Schrag
- Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, Stuttgart, 70593, Germany
| | - Albrecht E Melchinger
- Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, Stuttgart, 70593, Germany
| | - Stefan Scholten
- Biocenter Klein Flottbek, Developmental Biology and Biotechnology, University of Hamburg, Hamburg, 22609, Germany.,Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, Stuttgart, 70593, Germany
| | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Giessen, 35392, Germany.
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37
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Dan Z, Hu J, Zhou W, Yao G, Zhu R, Zhu Y, Huang W. Metabolic prediction of important agronomic traits in hybrid rice (Oryza sativa L.). Sci Rep 2016; 6:21732. [PMID: 26907211 PMCID: PMC4764848 DOI: 10.1038/srep21732] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2015] [Accepted: 01/29/2016] [Indexed: 11/09/2022] Open
Abstract
Hybrid crops have contributed greatly to improvements in global food and fodder production over the past several decades. Nevertheless, the growing population and changing climate have produced food crises and energy shortages. Breeding new elite hybrid varieties is currently an urgent task, but present breeding procedures are time-consuming and labour-intensive. In this study, parental metabolic information was utilized to predict three polygenic traits in hybrid rice. A complete diallel cross population consisting of eighteen rice inbred lines was constructed, and the hybrids’ plant height, heading date and grain yield per plant were predicted using 525 metabolites. Metabolic prediction models were built using the partial least square regression method, with predictive abilities ranging from 0.858 to 0.977 for the hybrid phenotypes, relative heterosis, and specific combining ability. Only slight changes in predictive ability were observed between hybrid populations, and nearly no changes were detected between reciprocal hybrids. The outcomes of prediction of the three highly polygenic traits demonstrated that metabolic prediction was an accurate (high predictive abilities) and efficient (unaffected by population genetic structures) strategy for screening promising superior hybrid rice. Exploitation of this pre-hybridization strategy may contribute to rice production improvement and accelerate breeding programs.
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Affiliation(s)
- Zhiwu Dan
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan 430072, China.,Engineering Research Center for Plant Biotechnology and Germplasm Utilization, Ministry of Education, Wuhan University, Wuhan 430072, China
| | - Jun Hu
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan 430072, China.,Engineering Research Center for Plant Biotechnology and Germplasm Utilization, Ministry of Education, Wuhan University, Wuhan 430072, China
| | - Wei Zhou
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan 430072, China.,Engineering Research Center for Plant Biotechnology and Germplasm Utilization, Ministry of Education, Wuhan University, Wuhan 430072, China
| | - Guoxin Yao
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan 430072, China.,Engineering Research Center for Plant Biotechnology and Germplasm Utilization, Ministry of Education, Wuhan University, Wuhan 430072, China
| | - Renshan Zhu
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan 430072, China.,Engineering Research Center for Plant Biotechnology and Germplasm Utilization, Ministry of Education, Wuhan University, Wuhan 430072, China
| | - Yingguo Zhu
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan 430072, China.,Engineering Research Center for Plant Biotechnology and Germplasm Utilization, Ministry of Education, Wuhan University, Wuhan 430072, China
| | - Wenchao Huang
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan 430072, China.,Engineering Research Center for Plant Biotechnology and Germplasm Utilization, Ministry of Education, Wuhan University, Wuhan 430072, China
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38
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Genome-based establishment of a high-yielding heterotic pattern for hybrid wheat breeding. Proc Natl Acad Sci U S A 2015; 112:15624-9. [PMID: 26663911 DOI: 10.1073/pnas.1514547112] [Citation(s) in RCA: 116] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Hybrid breeding promises to boost yield and stability. The single most important element in implementing hybrid breeding is the recognition of a high-yielding heterotic pattern. We have developed a three-step strategy for identifying heterotic patterns for hybrid breeding comprising the following elements. First, the full hybrid performance matrix is compiled using genomic prediction. Second, a high-yielding heterotic pattern is searched based on a developed simulated annealing algorithm. Third, the long-term success of the identified heterotic pattern is assessed by estimating the usefulness, selection limit, and representativeness of the heterotic pattern with respect to a defined base population. This three-step approach was successfully implemented and evaluated using a phenotypic and genomic wheat dataset comprising 1,604 hybrids and their 135 parents. Integration of metabolomic-based prediction was not as powerful as genomic prediction. We show that hybrid wheat breeding based on the identified heterotic pattern can boost grain yield through the exploitation of heterosis and enhance recurrent selection gain. Our strategy represents a key step forward in hybrid breeding and is relevant for self-pollinating crops, which are currently shifting from pure-line to high-yielding and resilient hybrid varieties.
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39
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Thiemann A, Fu J, Seifert F, Grant-Downton RT, Schrag TA, Pospisil H, Frisch M, Melchinger AE, Scholten S. Genome-wide meta-analysis of maize heterosis reveals the potential role of additive gene expression at pericentromeric loci. BMC PLANT BIOLOGY 2014; 14:88. [PMID: 24693880 PMCID: PMC4234143 DOI: 10.1186/1471-2229-14-88] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2013] [Accepted: 03/28/2014] [Indexed: 05/09/2023]
Abstract
BACKGROUND The identification of QTL involved in heterosis formation is one approach to unravel the not yet fully understood genetic basis of heterosis - the improved agronomic performance of hybrid F1 plants compared to their inbred parents. The identification of candidate genes underlying a QTL is important both for developing markers and determining the molecular genetic basis of a trait, but remains difficult owing to the large number of genes often contained within individual QTL. To address this problem in heterosis analysis, we applied a meta-analysis strategy for grain yield (GY) of Zea mays L. as example, incorporating QTL-, hybrid field-, and parental gene expression data. RESULTS For the identification of genes underlying known heterotic QTL, we made use of tight associations between gene expression pattern and the trait of interest, identified by correlation analyses. Using this approach genes strongly associated with heterosis for GY were discovered to be clustered in pericentromeric regions of the complex maize genome. This suggests that expression differences of sequences in recombination-suppressed regions are important in the establishment of heterosis for GY in F1 hybrids and also in the conservation of heterosis for GY across genotypes. Importantly functional analysis of heterosis-associated genes from these genomic regions revealed over-representation of a number of functional classes, identifying key processes contributing to heterosis for GY. Based on the finding that the majority of the analyzed heterosis-associated genes were addtitively expressed, we propose a model referring to the influence of cis-regulatory variation on heterosis for GY by the compensation of fixed detrimental expression levels in parents. CONCLUSIONS The study highlights the utility of a meta-analysis approach that integrates phenotypic and multi-level molecular data to unravel complex traits in plants. It provides prospects for the identification of genes relevant for QTL, and also suggests a model for the potential role of additive expression in the formation and conservation of heterosis for GY via dominant, multigenic quantitative trait loci. Our findings contribute to a deeper understanding of the multifactorial phenomenon of heterosis, and thus to the breeding of new high yielding varieties.
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Affiliation(s)
- Alexander Thiemann
- Biocenter Klein Flottbek, Developmental Biology and Biotechnology, University of Hamburg, Hamburg 22609, Germany
| | - Junjie Fu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China
| | - Felix Seifert
- Biocenter Klein Flottbek, Developmental Biology and Biotechnology, University of Hamburg, Hamburg 22609, Germany
| | | | - Tobias A Schrag
- Institute for Plant Breeding, Seed Science and Population Genetics, Applied Genetics and Plant Breeding, University of Hohenheim, Stuttgart 70599, Germany
| | - Heike Pospisil
- Department of Bioinformatics, Technical University of Applied Sciences Wildau, Wildau 15745, Germany
| | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Biometry and Population Genetics, Justus-Liebig University, Giessen 35392, Germany
| | - Albrecht E Melchinger
- Institute for Plant Breeding, Seed Science and Population Genetics, Applied Genetics and Plant Breeding, University of Hohenheim, Stuttgart 70599, Germany
| | - Stefan Scholten
- Biocenter Klein Flottbek, Developmental Biology and Biotechnology, University of Hamburg, Hamburg 22609, Germany
- Institute for Plant Breeding, Seed Science and Population Genetics, Plant Breeding and Biotechnology, University of Hohenheim, Stuttgart 70599, Germany
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Feher K, Lisec J, Römisch-Margl L, Selbig J, Gierl A, Piepho HP, Nikoloski Z, Willmitzer L. Deducing hybrid performance from parental metabolic profiles of young primary roots of maize by using a multivariate diallel approach. PLoS One 2014; 9:e85435. [PMID: 24409329 PMCID: PMC3883692 DOI: 10.1371/journal.pone.0085435] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2013] [Accepted: 11/26/2013] [Indexed: 01/04/2023] Open
Abstract
Heterosis, the greater vigor of hybrids compared to their parents, has been exploited in maize breeding for more than 100 years to produce ever better performing elite hybrids of increased yield. Despite extensive research, the underlying mechanisms shaping the extent of heterosis are not well understood, rendering the process of selecting an optimal set of parental lines tedious. This study is based on a dataset consisting of 112 metabolite levels in young roots of four parental maize inbred lines and their corresponding twelve hybrids, along with the roots' biomass as a heterotic trait. Because the parental biomass is a poor predictor for hybrid biomass, we established a model framework to deduce the biomass of the hybrid from metabolite profiles of its parental lines. In the proposed framework, the hybrid metabolite levels are expressed relative to the parental levels by incorporating the standard concept of additivity/dominance, which we name the Combined Relative Level (CRL). Our modeling strategy includes a feature selection step on the parental levels which are demonstrated to be predictive of CRL across many hybrid metabolites. We demonstrate that these selected parental metabolites are further predictive of hybrid biomass. Our approach directly employs the diallel structure in a multivariate fashion, whereby we attempt to not only predict macroscopic phenotype (biomass), but also molecular phenotype (metabolite profiles). Therefore, our study provides the first steps for further investigations of the genetic determinants to metabolism and, ultimately, growth. Finally, our success on the small-scale experiments implies a valid strategy for large-scale experiments, where parental metabolite profiles may be used together with profiles of selected hybrids as a training set to predict biomass of all possible hybrids.
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Affiliation(s)
- Kristen Feher
- Max-Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
- Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Jan Lisec
- Max-Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Lilla Römisch-Margl
- Department of Plants Genetics, Technical University München, Freising, Germany
| | - Joachim Selbig
- Max-Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
- Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Alfons Gierl
- Department of Plants Genetics, Technical University München, Freising, Germany
| | - Hans-Peter Piepho
- Institute for Crop Science, University of Hohenheim, Stuttgart, Germany
| | - Zoran Nikoloski
- Max-Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Lothar Willmitzer
- Max-Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
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41
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Whitford R, Fleury D, Reif JC, Garcia M, Okada T, Korzun V, Langridge P. Hybrid breeding in wheat: technologies to improve hybrid wheat seed production. JOURNAL OF EXPERIMENTAL BOTANY 2013; 64:5411-28. [PMID: 24179097 DOI: 10.1093/jxb/ert333] [Citation(s) in RCA: 126] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Global food security demands the development and delivery of new technologies to increase and secure cereal production on finite arable land without increasing water and fertilizer use. There are several options for boosting wheat yields, but most offer only small yield increases. Wheat is an inbred plant, and hybrids hold the potential to deliver a major lift in yield and will open a wide range of new breeding opportunities. A series of technological advances are needed as a base for hybrid wheat programmes. These start with major changes in floral development and architecture to separate the sexes and force outcrossing. Male sterility provides the best method to block self-fertilization, and modifying the flower structure will enhance pollen access. The recent explosion in genomic resources and technologies provides new opportunities to overcome these limitations. This review outlines the problems with existing hybrid wheat breeding systems and explores molecular-based technologies that could improve the hybrid production system to reduce hybrid seed production costs, a prerequisite for a commercial hybrid wheat system.
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Affiliation(s)
- Ryan Whitford
- Australian Centre for Plant Functional Genomics, School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Urrbrae, South Australia 5064, Australia
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Reif JC, Zhao Y, Würschum T, Gowda M, Hahn V. Genomic prediction of sunflower hybrid performance. PLANT BREEDING 2013; 132:107-114. [PMID: 0 DOI: 10.1111/pbr.12007] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Affiliation(s)
- Jochen C. Reif
- State Plant Breeding Institute; University of Hohenheim; 70599; Stuttgart; Germany
| | - Yusheng Zhao
- State Plant Breeding Institute; University of Hohenheim; 70599; Stuttgart; Germany
| | - Tobias Würschum
- State Plant Breeding Institute; University of Hohenheim; 70599; Stuttgart; Germany
| | - Manje Gowda
- State Plant Breeding Institute; University of Hohenheim; 70599; Stuttgart; Germany
| | - Volker Hahn
- State Plant Breeding Institute; University of Hohenheim; 70599; Stuttgart; Germany
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43
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Guo T, Li H, Yan J, Tang J, Li J, Zhang Z, Zhang L, Wang J. Performance prediction of F1 hybrids between recombinant inbred lines derived from two elite maize inbred lines. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2013; 126:189-201. [PMID: 22972201 DOI: 10.1007/s00122-012-1973-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2011] [Accepted: 08/16/2012] [Indexed: 05/04/2023]
Abstract
Selection of recombinant inbred lines (RILs) from elite hybrids is a key method in maize breeding especially in developing countries. The RILs are normally derived by repeated self-pollination and selection. In this study, we first investigated the accuracy of different models in predicting the performance of F(1) hybrids between RILs derived from two elite maize inbred lines Zong3 and 87-1, and then compared these models through simulation using a wider range of genetic models. Results indicated that appropriate prediction models depended on genetic architecture, e.g., combined model using breeding value and genome-wide prediction (BV+GWP) has the highest prediction accuracy for high V(D)/V(A) ratio (>0.5) traits. Theoretical studies demonstrated that different components of genetic variance were captured by different prediction models, which in turn explained the accuracy of these models in predicting the F(1) hybrid performance. Based on genome-wide prediction model (GWP), 114 untested F(1) hybrids possibly having higher grain yield than the original F(1) hybrid Yuyu22 (the single cross between Zong3 and 87-1) have been identified and recommended for further field test.
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Affiliation(s)
- Tingting Guo
- National Maize Improvement Center, China Agricultural University, Beijing 100193, China
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44
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Baranwal VK, Mikkilineni V, Zehr UB, Tyagi AK, Kapoor S. Heterosis: emerging ideas about hybrid vigour. JOURNAL OF EXPERIMENTAL BOTANY 2012; 63:6309-14. [PMID: 23095992 DOI: 10.1093/jxb/ers291] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Perceived by Charles Darwin in many vegetable plants and rediscovered by George H Shull and Edward M East in maize, heterosis or hybrid vigour is one of the most widely utilized phenomena, not only in agriculture but also in animal breeding. Although, numerous studies have been carried out to understand its genetic and/or molecular basis in the past 100 years, our knowledge of the underlying molecular processes that results in hybrid vigour can best be defined as superficial. Even after century long deliberations, there is no consensus on the relative/individual contribution of the genetic/epigenetic factors in the manifestation of heterosis. However, with the recent advancements in functional genomics, transcriptomics, proteomics, and metabolomics-related technologies, the riddle of heterosis is being reinvestigated by adopting systems-level approaches to understand the underlying molecular mechanisms. A number of intriguing hypotheses are converging towards the idea of a cumulative positive effect of the differential expression of a variety of genes, on one or several yield-affecting metabolic pathways or overall energy-use efficiency, as the underlying mechanism for the manifestation of heterosis. Presented here is a brief account of clues gathered from various investigative approaches targeted towards better scientific understanding of this process.
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Affiliation(s)
- Vinay Kumar Baranwal
- Department of Plant Molecular Biology, University of Delhi, South Campus, Benito Juarez Road, New Delhi-110021, India
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Paschold A, Jia Y, Marcon C, Lund S, Larson NB, Yeh CT, Ossowski S, Lanz C, Nettleton D, Schnable PS, Hochholdinger F. Complementation contributes to transcriptome complexity in maize (Zea mays L.) hybrids relative to their inbred parents. Genome Res 2012; 22:2445-54. [PMID: 23086286 PMCID: PMC3514674 DOI: 10.1101/gr.138461.112] [Citation(s) in RCA: 102] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Typically, F1-hybrids are more vigorous than their homozygous, genetically distinct parents, a phenomenon known as heterosis. In the present study, the transcriptomes of the reciprocal maize (Zea mays L.) hybrids B73×Mo17 and Mo17×B73 and their parental inbred lines B73 and Mo17 were surveyed in primary roots, early in the developmental manifestation of heterotic root traits. The application of statistical methods and a suitable experimental design established that 34,233 (i.e., 86%) of all high-confidence maize genes were expressed in at least one genotype. Nearly 70% of all expressed genes were differentially expressed between the two parents and 42%–55% of expressed genes were differentially expressed between one of the parents and one of the hybrids. In both hybrids, ∼10% of expressed genes exhibited nonadditive gene expression. Consistent with the dominance model (i.e., complementation) for heterosis, 1124 genes that were expressed in the hybrids were expressed in only one of the two parents. For 65 genes, it could be shown that this was a consequence of complementation of genomic presence/absence variation. For dozens of other genes, alleles from the inactive inbred were activated in the hybrid, presumably via interactions with regulatory factors from the active inbred. As a consequence of these types of complementation, both hybrids expressed more genes than did either parental inbred. Finally, in hybrids, ∼14% of expressed genes exhibited allele-specific expression (ASE) levels that differed significantly from the parental-inbred expression ratios, providing further evidence for interactions of regulatory factors from one parental genome with target genes from the other parental genome.
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Affiliation(s)
- Anja Paschold
- Institute of Crop Science and Resource Conservation, Division of Crop Functional Genomics, University of Bonn, 53113 Bonn, Germany
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Ding D, Wang Y, Han M, Fu Z, Li W, Liu Z, Hu Y, Tang J. MicroRNA transcriptomic analysis of heterosis during maize seed germination. PLoS One 2012; 7:e39578. [PMID: 22761829 PMCID: PMC3384671 DOI: 10.1371/journal.pone.0039578] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2011] [Accepted: 05/23/2012] [Indexed: 12/13/2022] Open
Abstract
Heterosis has been utilized widely in the breeding of maize and other crops, and plays an important role in increasing yield, improving quality and enhancing stresses resistance, but the molecular mechanism responsible for heterosis is far from clear. To illustrate whether miRNA-dependent gene regulation is responsible for heterosis during maize germination, a deep-sequencing technique was applied to germinating embryos of a maize hybrid, Yuyu22, which is cultivated widely in China and its parental inbred lines, Yu87-1 and Zong3. The target genes of several miRNAs showing significant expression in the hybrid and parental lines were predicted and tested using real-time PCR. A total of 107 conserved maize miRNAs were co-detected in the hybrid and parental lines. Most of these miRNAs were expressed non-additively in the hybrid compared to its parental lines. These results indicated that miRNAs might participate in heterosis during maize germination and exert an influence via the decay of their target genes. Novel miRNAs were predicted follow a rigorous criterion and only the miRNAs detected in all three samples were treated as a novel maize miRNA. In total, 34 miRNAs belonged to 20 miRNA families were predicted in germinating maize seeds. Global repression of miRNAs in the hybrid, which might result in enhanced gene expression, might be one reason why the hybrid showed higher embryo germination vigor compared to its parental lines.
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Affiliation(s)
- Dong Ding
- College of Agronomy, Henan Agricultural University, Zhengzhou, China
| | - Yinju Wang
- College of Agronomy, Henan Agricultural University, Zhengzhou, China
| | - Mingshui Han
- College of Agronomy, Henan Agricultural University, Zhengzhou, China
| | - Zhiyuan Fu
- College of Agronomy, Henan Agricultural University, Zhengzhou, China
| | - Weihua Li
- College of Agronomy, Henan Agricultural University, Zhengzhou, China
| | - Zonghua Liu
- College of Agronomy, Henan Agricultural University, Zhengzhou, China
| | - Yanmin Hu
- College of Agronomy, Henan Agricultural University, Zhengzhou, China
| | - Jihua Tang
- College of Agronomy, Henan Agricultural University, Zhengzhou, China
- * E-mail:
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Fu J, Falke KC, Thiemann A, Schrag TA, Melchinger AE, Scholten S, Frisch M. Partial least squares regression, support vector machine regression, and transcriptome-based distances for prediction of maize hybrid performance with gene expression data. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2012; 124:825-33. [PMID: 22101908 DOI: 10.1007/s00122-011-1747-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2011] [Accepted: 10/28/2011] [Indexed: 05/05/2023]
Abstract
The performance of hybrids can be predicted with gene expression data from their parental inbred lines. Implementing such prediction approaches in breeding programs promises to increase the efficiency of hybrid breeding. The objectives of our study were to compare the accuracy of prediction models employing multiple linear regression (MLR), partial least squares regression (PLS), support vector machine regression (SVM), and transcriptome-based distances (D(B)). For a factorial of 7 flint and 14 dent maize lines, the grain yield of the hybrids was assessed and the gene expression of the parental lines was profiled with a 56k microarray. The accuracy of the prediction models was measured by the correlation between predicted and observed yield employing two cross-validation schemes. The first modeled the prediction of hybrids when testcross data are available for both parental lines (type 2 hybrids), and the second modeled the prediction of hybrids when no testcross data for the parental lines were available (type 0 hybrids). MLR, SVM, and PLS resulted in a high correlation between predicted and observed yield for type 2 hybrids, whereas for type 0 hybrids D(B) had greater prediction accuracy. The regression methods were robust to the choice of the set of profiled genes and required only a few hundred genes. In contrast, for an accurate hybrid prediction with D(B), 1,000-1,500 genes were required, and the prediction accuracy depended strongly on the set of profiled genes. We conclude that for prediction within one set of genetic material MLR is a promising approach, and for transferring prediction models from one set of genetic material to a related one, the transcriptome-based distance D(B) is most promising.
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Affiliation(s)
- Junjie Fu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
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48
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Wang L, Ge H, Hao C, Dong Y, Zhang X. Identifying loci influencing 1,000-kernel weight in wheat by microsatellite screening for evidence of selection during breeding. PLoS One 2012; 7:e29432. [PMID: 22328917 PMCID: PMC3273457 DOI: 10.1371/journal.pone.0029432] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2011] [Accepted: 11/28/2011] [Indexed: 11/19/2022] Open
Abstract
Chinese wheat mini core collection (262 accessions) was genotyped at 531 microsatellite loci representing a mean marker density of 5.1 cM. One-thousand-kernel weights (TKW) of lines were measured in five trials (three environments in four growing seasons). Structure analysis based on 42 unlinked SSR loci indicated that the materials formed two sub-populations, viz., landraces and modern varieties. A large difference in TKW (7.08 g, P<0.001) was found between the two sub-groups. Therefore, TKW is a major yield component that was improved in the past 6 decades; it increased from a mean 31.5 g in the 1940s to 44.64 g in the 2000s, representing a 2.19 g increase in each decade. Analyses based on a mixed linear model (MLM), population structure (Q) and relative kinship (K) revealed 22 SSR loci that were significantly associated with mean TKW (MTKW) of the five trials estimated by the best linear unbiased predictor (BLUP) method. They were mainly distributed on chromosomes of homoeologous groups 1, 2, 3, 5 and 7. Six loci, cfa2234-3A, gwm156-3B, barc56-5A, gwm234-5B, wmc17-7A and cfa2257-7A individually explained more than 11.84% of the total phenotypic variation. Favored alleles for breeding at the 22 loci were inferred according to their estimated effects on MTKW based on mean difference of varieties grouped by genotypes. Statistical simulation showed that these favored alleles have additive genetic effects. Frequency changes of alleles at loci associated with TKW are much more dramatic than those at neutral loci between the sub-groups. The numbers of favored alleles in modern varieties indicate there is still considerable genetic potential for their use as markers for genome selection of TKW in wheat breeding. Alleles that can be used globally to increase TKW were inferred according to their distribution by latitude and frequency of changes between landraces and the modern varieties.
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Affiliation(s)
- Lanfen Wang
- Key Laboratory of Crop Germplasm Resources and Utilization, Ministry of Agriculture, The National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hongmei Ge
- Key Laboratory of Crop Germplasm Resources and Utilization, Ministry of Agriculture, The National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chenyang Hao
- Key Laboratory of Crop Germplasm Resources and Utilization, Ministry of Agriculture, The National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yushen Dong
- Key Laboratory of Crop Germplasm Resources and Utilization, Ministry of Agriculture, The National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xueyong Zhang
- Key Laboratory of Crop Germplasm Resources and Utilization, Ministry of Agriculture, The National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
- * E-mail:
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Yan J, Warburton M, Crouch J. Association Mapping for Enhancing Maize ( Zea maysL.) Genetic Improvement. CROP SCIENCE 2011; 51:433-449. [PMID: 0 DOI: 10.2135/cropsci2010.04.0233] [Citation(s) in RCA: 181] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Affiliation(s)
- Jianbing Yan
- National Maize Improvement Center of China; China Agricultural Univ.; Beijing 100193 China
- International Maize and Wheat Improvement Center (CIMMYT); Apartado Postal 6-640 06600 Mexico DF Mexico
| | - Marilyn Warburton
- USDA-ARS; Corn Host Plant Resistance Research Unit; Box 9555 Mississippi State MS 39762
| | - Jonathan Crouch
- International Maize and Wheat Improvement Center (CIMMYT); Apartado Postal 6-640 06600 Mexico DF Mexico
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50
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Goff SA. A unifying theory for general multigenic heterosis: energy efficiency, protein metabolism, and implications for molecular breeding. THE NEW PHYTOLOGIST 2011; 189:923-937. [PMID: 21166808 DOI: 10.1111/j.1469-8137.2010.03574.x] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Hybrids between genetically diverse varieties display enhanced growth, and increased total biomass, stress resistance and grain yield. Gene expression and metabolic studies in maize, rice and other species suggest that protein metabolism plays a role in the growth differences between hybrids and inbreds. Single trait heterosis can be explained by the existing theories of dominance, overdominance and epistasis. General multigenic heterosis is observed in a wide variety of different species and is likely to share a common underlying biological mechanism. This review presents a model to explain differences in growth and yield caused by general multigenic heterosis. The model describes multigenic heterosis in terms of energy-use efficiency and faster cell cycle progression where hybrids have more efficient growth than inbreds because of differences in protein metabolism. The proposed model is consistent with the observed variation of gene expression in different pairs of inbred lines and hybrid offspring as well as growth differences in polyploids and aneuploids. It also suggests an approach to enhance yield gains in both hybrid and inbred crops via the creation of an appropriate computational analysis pipeline coupled to an efficient molecular breeding program.
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Affiliation(s)
- Stephen A Goff
- iPlant Collaborative, BIO5 Institute, University of Arizona, Tucson, AZ 85721, USA
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