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

Frontiers in Plant Science

Plant Science

Sofia Ramos-Pulido¹  Abelardo Montesinos-López¹  Brandon Alejandro Mosqueda González²  Felícitas Alejandra Valladares-Anguiano³  Osval A. Montesinos-López⁴  Carlos Moisés Hernández-Suárez⁵  Paolo Vitale⁶  José Crossa⁷ 

[1] Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco, Mexico;Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), México City, Mexico;Centro de Investigación y Formación del Pensamiento Libre en México, Colima, Mexico;Facultad de Telemática, Universidad de Colima, Colima, Colima, Mexico;Instituto de Innovaciónn y Desarrollo, Universidad Francisco Gavidia, San Salvador, El Salvador;International Maize and Wheat Improvement Center (CIMMYT), El Batan, Edo. de México, Mexico;International Maize and Wheat Improvement Center (CIMMYT), El Batan, Edo. de México, Mexico;Colegio de Postgraduados, Montecillos, Edo. de México, Mexico;Centre for Crop & Food Innovation, Food Futures Institute, Murdoch University, Perth, WA, Australia;

关键词: gains in accuracy;    GBLUP;    genomic prediction;    genotype × environment interaction (GE);    novel method;   

DOI  :  10.3389/fpls.2023.1218151

 received in 2023-05-06, accepted in 2023-07-03,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionGenomic selection (GS) has gained global importance due to its potential to accelerate genetic progress and improve the efficiency of breeding programs.Objectives of the researchIn this research we proposed a method to improve the prediction accuracy of tested lines in new (untested) environments.Method-1The 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 methodWe 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 discussionThe 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%.

【 授权许可】

Unknown   
Copyright © 2023 Montesinos-López, Ramos-Pulido, Hernández-Suárez, Mosqueda González, Valladares-Anguiano, Vitale, Montesinos-López and Crossa