Literature DB >> 32502173

Can metabolic prediction be an alternative to genomic prediction in barley?

Mathias Ruben Gemmer1, Chris Richter2, Yong Jiang3, Thomas Schmutzer1, Manish L Raorane2, Björn Junker2, Klaus Pillen1, Andreas Maurer1.   

Abstract

Like other crop species, barley, the fourth most important crop worldwide, suffers from the genetic bottleneck effect, where further improvements in performance through classical breeding methods become difficult. Therefore, indirect selection methods are of great interest. Here, genomic prediction (GP) based on 33,005 SNP markers and, alternatively, metabolic prediction (MP) based on 128 metabolites with sampling at two different time points in one year, were applied to predict multi-year agronomic traits in the nested association mapping (NAM) population HEB-25. We found prediction abilities of up to 0.93 for plant height with SNP markers and of up to 0.61 for flowering time with metabolites. Interestingly, prediction abilities in GP increased after reducing the number of incorporated SNP markers. The estimated effects of GP and MP were highly concordant, indicating MP as an interesting alternative to GP, being able to reflect a stable genotype-specific metabolite profile. In MP, sampling at an early developmental stage outperformed sampling at a later stage. The results confirm the value of GP for future breeding. With MP, an interesting alternative was also applied successfully. However, based on our results, usage of MP alone cannot be recommended in barley. Nevertheless, MP can assist in unravelling physiological pathways for the expression of agronomically important traits.

Entities:  

Year:  2020        PMID: 32502173     DOI: 10.1371/journal.pone.0234052

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  8 in total

1.  Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials.

Authors:  Pauline Robert; Ellen Goudemand; Jérôme Auzanneau; François-Xavier Oury; Bernard Rolland; Emmanuel Heumez; Sophie Bouchet; Antoine Caillebotte; Tristan Mary-Huard; Jacques Le Gouis; Renaud Rincent
Journal:  Theor Appl Genet       Date:  2022-08-08       Impact factor: 5.574

2.  Leveraging a graft collection to develop metabolome-based trait prediction for the selection of tomato rootstocks with enhanced salt tolerance.

Authors:  Chao Song; Tania Acuña; Michal Adler-Agmon; Shimon Rachmilevitch; Simon Barak; Aaron Fait
Journal:  Hortic Res       Date:  2022-03-14       Impact factor: 7.291

3.  Recent applications of metabolomics in plant breeding.

Authors:  Nozomu Sakurai
Journal:  Breed Sci       Date:  2022-02-03       Impact factor: 2.014

4.  Genome-wide association study on metabolite accumulation in a wild barley NAM population reveals natural variation in sugar metabolism.

Authors:  Mathias Ruben Gemmer; Chris Richter; Thomas Schmutzer; Manish L Raorane; Björn Junker; Klaus Pillen; Andreas Maurer
Journal:  PLoS One       Date:  2021-02-16       Impact factor: 3.240

5.  Merging Genomics and Transcriptomics for Predicting Fusarium Head Blight Resistance in Wheat.

Authors:  Sebastian Michel; Christian Wagner; Tetyana Nosenko; Barbara Steiner; Mina Samad-Zamini; Maria Buerstmayr; Klaus Mayer; Hermann Buerstmayr
Journal:  Genes (Basel)       Date:  2021-01-19       Impact factor: 4.096

6.  Metabolomic selection for enhanced fruit flavor.

Authors:  Vincent Colantonio; Luis Felipe V Ferrão; Denise M Tieman; Nikolay Bliznyuk; Charles Sims; Harry J Klee; Patricio Munoz; Marcio F R Resende
Journal:  Proc Natl Acad Sci U S A       Date:  2022-02-15       Impact factor: 11.205

7.  Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction in Hordeum vulgare.

Authors:  Pernille Bjarup Hansen; Anja Karine Ruud; Gustavo de Los Campos; Marta Malinowska; Istvan Nagy; Simon Fiil Svane; Kristian Thorup-Kristensen; Jens Due Jensen; Lene Krusell; Torben Asp
Journal:  Plants (Basel)       Date:  2022-08-24

8.  Improvement of prediction ability by integrating multi-omic datasets in barley.

Authors:  Po-Ya Wu; Benjamin Stich; Marius Weisweiler; Asis Shrestha; Alexander Erban; Philipp Westhoff; Delphine Van Inghelandt
Journal:  BMC Genomics       Date:  2022-03-12       Impact factor: 3.969

  8 in total

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