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New insights into trait introgression with the look-ahead intercrossing strategy.
Ni, Zheng; Moeinizade, Saba; Kusmec, Aaron; Hu, Guiping; Wang, Lizhi; Schnable, Patrick S.
Affiliation
  • Ni Z; Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USA.
  • Moeinizade S; Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USA.
  • Kusmec A; Department of Agronomy, Iowa State University, Ames, IA 50011, USA.
  • Hu G; Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USA.
  • Wang L; Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USA.
  • Schnable PS; Department of Agronomy, Iowa State University, Ames, IA 50011, USA.
G3 (Bethesda) ; 13(4)2023 04 11.
Article de En | MEDLINE | ID: mdl-36821776
ABSTRACT
Trait introgression (TI) can be a time-consuming and costly task that typically requires multiple generations of backcrossing (BC). Usually, the aim is to introduce one or more alleles (e.g. QTLs) from a single donor into an elite recipient, both of which are fully inbred. This article studies the potential advantages of incorporating intercrossing (IC) into TI programs when compared with relying solely on the traditional BC framework. We simulate a TI breeding pipeline using 3 previously proposed selection strategies for the traditional BC scheme and 3 modified strategies that allow IC. Our proposed look-ahead intercrossing method (LAS-IC) combines look-ahead Monte Carlo simulations, intercrossing, and additional selection criteria to improve computational efficiency. We compared the efficiency of the 6 strategies across 5 levels of resource availability considering the generation when the major QTLs have been successfully introduced into the recipient and a desired background recovery rate reached. Simulations demonstrate that the inclusion of intercrossing in a TI program can substantially increase efficiency and the probability of success. The proposed LAS-IC provides the highest probability of success across the different scenarios using fewer resources compared with BC-only strategies.
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Locus de caractère quantitatif Langue: En Journal: G3 (Bethesda) Année: 2023 Type de document: Article Pays d'affiliation: États-Unis d'Amérique

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Locus de caractère quantitatif Langue: En Journal: G3 (Bethesda) Année: 2023 Type de document: Article Pays d'affiliation: États-Unis d'Amérique