A novel method for genomic-enabled prediction of cultivars in new environments

Osval A. Montesinos-López; Sofia Ramos-Pulido; Carlos Moisés Hernández-Suárez; Brandon Alejandro Mosqueda González; Felícitas Alejandra Valladares-Anguiano; Paolo Vitale; Abelardo Montesinos-López; José Crossa

doi:10.3389/fpls.2023.1218151

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A novel method for genomic-enabled prediction of cultivars in new environments

Journal article

Open access

Peer reviewed

A novel method for genomic-enabled prediction of cultivars in new environments

Osval A. Montesinos-López, Sofia Ramos-Pulido, Carlos Moisés Hernández-Suárez, Brandon Alejandro Mosqueda González, Felícitas Alejandra Valladares-Anguiano, Paolo Vitale, Abelardo Montesinos-López and José Crossa

Frontiers in plant science, Vol.14, 1218151

2023

DOI: https://doi.org/10.3389/fpls.2023.1218151

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Abstract

gains in accuracy

GBLUP

genomic prediction

genotype × environment interaction (GE)

novel method

Plant Science

Introduction: Genomic selection (GS) has gained global importance due to its potential to accelerate genetic progress and improve the efficiency of breeding programs. Objectives of the research: In this research we proposed a method to improve the prediction accuracy of tested lines in new (untested) environments. Method-1: The new method trained the model with a modified response variable (a difference of response variables) that decreases the lack of a non-stationary distribution between the training and testing and improved the prediction accuracy. Comparing new and conventional method: We compared the prediction accuracy of the conventional genomic best linear unbiased prediction (GBLUP) model (M1) including (or not) genotype × environment interaction (GE) (M1_GE; M1_NO_GE) versus the proposed method (M2) on several data sets. Results and discussion: The gain in prediction accuracy of M2, versus M1_GE, M1_NO_GE in terms of Pearson´s correlation was of at least 4.3%, while in terms of percentage of top-yielding lines captured when was selected the 10% (Best10) and 20% (Best20) of lines was at least of 19.5%, while in terms of Normalized Root Mean Squared Error (NRMSE) was of at least of 42.29%.

Details

Title: A novel method for genomic-enabled prediction of cultivars in new environments
Authors/Creators: Osval A. Montesinos-López - Universidad de Colima
Sofia Ramos-Pulido - Universidad de Guadalajara
Carlos Moisés Hernández-Suárez - Universidad de Colima
Brandon Alejandro Mosqueda González - , , , , , , , , , , , , , ,
Felícitas Alejandra Valladares-Anguiano - Universidad de Colima
Paolo Vitale - Centro Internacional de Mejoramiento de Maíz Y Trigo
Abelardo Montesinos-López - Universidad de Guadalajara
José Crossa - , , , , , , , , , , , , , ,
Publication Details: Frontiers in plant science, Vol.14, 1218151
Publisher: Frontiers Media S.A
Grant note: Bill and Melinda Gates Foundation ed Livelihoods (AG2MW) / ;
Identifiers: 991005599068907891
Murdoch Affiliation: Centre for Crop and Food Innovation
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 3 Agriculture, Environment & Ecology; 3.51 Dairy & Animal Sciences; 3.51.115 Livestock Reproduction
Web Of Science research areas: Plant Sciences
ESI research areas: Plant & Animal Science