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  Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth

Tong, H., Küken, A., & Nikoloski, Z. (2020). Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth. Nature Communications, 11(1): 2410. doi:10.1038/s41467-020-16279-5.

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 Creators:
Tong, H.1, Author              
Küken, A.1, Author              
Nikoloski, Z.1, Author              
Affiliations:
1Mathematical Modelling and Systems Biology - Nikoloski, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753310              

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 Abstract: The current trends of crop yield improvements are not expected to meet the projected rise in demand. Genomic selection uses molecular markers and machine learning to identify superior genotypes with improved traits, such as growth. Plant growth directly depends on rates of metabolic reactions which transform nutrients into the building blocks of biomass. Here, we predict growth of Arabidopsis thaliana accessions by employing genomic prediction of reaction rates estimated from accession-specific metabolic models. We demonstrate that, comparing to classical genomic selection on the available data sets for 67 accessions, our approach improves the prediction accuracy for growth within and across nitrogen environments by 32.6% and 51.4%, respectively, and from optimal nitrogen to low carbon environment by 50.4%. Therefore, integration of molecular markers into metabolic models offers an approach to predict traits directly related to metabolism, and its usefulness in breeding can be examined by gathering matching datasets in crops.

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Language(s): eng - English
 Dates: 2020
 Publication Status: Published in print
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Title: Nature Communications
  Abbreviation : Nat. Commun.
Source Genre: Journal
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Publ. Info: London : Nature Publishing Group
Pages: 2410 Volume / Issue: 11 (1) Sequence Number: 2410 Start / End Page: - Identifier: ISSN: 2041-1723
CoNE: https://pure.mpg.de/cone/journals/resource/2041-1723