دورية أكاديمية
أعرض في EDS

Multi-omics-based prediction of hybrid performance in canola.

التفاصيل البيبلوغرافية
العنوان: Multi-omics-based prediction of hybrid performance in canola.
المؤلفون: Knoch D; Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Seeland, OT Gatersleben, Germany. knochd@ipk-gatersleben.de., Werner CR; The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, Scotland, UK., Meyer RC; Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Seeland, OT Gatersleben, Germany., Riewe D; 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., Abbadi A; NPZ Innovation GmbH, Hohenlieth, 24363, Holtsee, Germany.; Norddeutsche Pflanzenzucht Hans-Georg Lembke KG, Hohenlieth, 24363, Holtsee, Germany., Lücke S; Norddeutsche Pflanzenzucht Hans-Georg Lembke KG, Hohenlieth, 24363, Holtsee, Germany., Snowdon RJ; Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany., Altmann T; Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Seeland, OT Gatersleben, Germany.
المصدر: TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik [Theor Appl Genet] 2021 Apr; Vol. 134 (4), pp. 1147-1165. Date of Electronic Publication: 2021 Feb 01.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Springer Country of Publication: Germany NLM ID: 0145600 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-2242 (Electronic) Linking ISSN: 00405752 NLM ISO Abbreviation: Theor Appl Genet Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Berlin, New York, Springer
مواضيع طبية MeSH: Crosses, Genetic* , Genome, Plant* , Hybrid Vigor* , Metabolome* , Polymorphism, Single Nucleotide* , Transcriptome*, Brassica napus/*genetics, Brassica napus/growth & development ; Brassica napus/metabolism ; Hybridization, Genetic ; Models, Genetic ; Phenotype ; Plant Breeding ; Quantitative Trait Loci ; Seeds/genetics ; Seeds/growth & development ; Seeds/metabolism
مستخلص: Key Message: 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 F 1 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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معلومات مُعتمدة: 234585441 Deutsche Forschungsgemeinschaft
فهرسة مساهمة: Keywords: Agronomic traits; Genomic best linear unbiased prediction (gBLUP); Heterosis; Hybrid prediction; Reproducing kernel Hilbert space regression (RKHS); Spring-type brassica napus (canola)
تواريخ الأحداث: Date Created: 20210201 Date Completed: 20210824 Latest Revision: 20220421
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC7973648
DOI: 10.1007/s00122-020-03759-x
PMID: 33523261
قاعدة البيانات: MEDLINE
الوصف
تدمد:1432-2242
DOI:10.1007/s00122-020-03759-x