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

An increase in genomic selection (GS) accuracy can accelerate genetic gain by shortening the breeding cycles. Here, the authors introduce a network-based GS method that uses metabolic models and improves the prediction accuracy of Arabidopsis growth within and across environments.

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Detalles Bibliográficos
Autores principales:	Hao Tong, Anika Küken, Zoran Nikoloski
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/c09ac547bc15471191fabea3a6d70bb4
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spelling	oai:doaj.org-article:c09ac547bc15471191fabea3a6d70bb42021-12-02T15:42:58ZIntegrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth10.1038/s41467-020-16279-52041-1723https://doaj.org/article/c09ac547bc15471191fabea3a6d70bb42020-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-16279-5https://doaj.org/toc/2041-1723An increase in genomic selection (GS) accuracy can accelerate genetic gain by shortening the breeding cycles. Here, the authors introduce a network-based GS method that uses metabolic models and improves the prediction accuracy of Arabidopsis growth within and across environments.Hao TongAnika KükenZoran NikoloskiNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Hao Tong Anika Küken Zoran Nikoloski Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth
description	An increase in genomic selection (GS) accuracy can accelerate genetic gain by shortening the breeding cycles. Here, the authors introduce a network-based GS method that uses metabolic models and improves the prediction accuracy of Arabidopsis growth within and across environments.
format	article
author	Hao Tong Anika Küken Zoran Nikoloski
author_facet	Hao Tong Anika Küken Zoran Nikoloski
author_sort	Hao Tong
title	Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth
title_short	Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth
title_full	Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth
title_fullStr	Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth
title_full_unstemmed	Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth
title_sort	integrating molecular markers into metabolic models improves genomic selection for arabidopsis growth
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/c09ac547bc15471191fabea3a6d70bb4
work_keys_str_mv	AT haotong integratingmolecularmarkersintometabolicmodelsimprovesgenomicselectionforarabidopsisgrowth AT anikakuken integratingmolecularmarkersintometabolicmodelsimprovesgenomicselectionforarabidopsisgrowth AT zorannikoloski integratingmolecularmarkersintometabolicmodelsimprovesgenomicselectionforarabidopsisgrowth
_version_	1718385834010345472

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

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