Multienvironment Models Increase Prediction Accuracy of Complex Traits in Advanced Breeding Lines of Rice

Crop Science

Pérez de Vida, Fernando^3¹  Quero, Gastón^5²  Bonnecarrère, Victoria^4³  Rosas, Juan E.^2⁴  Blanco, Pedro^3⁵  Monteverde, Eliana^1⁶ 

[1] Biotechnology Unit, National Institute of Agricultural Research (INIA), Estación Experimental Wilson Ferreira Aldunate, Rincón del Colorado 90200, Uruguay^4;Dep. of Agronomy, Univ. of Wisconsin–Madison, 1575 Linden Dr., Madison, WI 53706^6;Dep. of Plant Biology, College of Agriculture, Univ. de la República, Garzón 809, Montevideo, Uruguay^5;Dep. of Statistics, College of Agriculture, Univ. de la República, Garzón 780, Montevideo, Uruguay National Rice Research Program, National Institute of Agricultural Research (INIA), INIA Treinta y Tres, Villa Sara 33000, Uruguay^2;National Rice Research Program, National Institute of Agricultural Research (INIA), INIA Treinta y Tres, Villa Sara 33000, Uruguay^3;Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca NY 14853^1

关键词: BLUE;    best linear unbiased estimator;    BLUP;    best linear unbiased prediction;    CV;    cross-validation;    GBLUP;    genomic best linear unbiased prediction;    GBS;    genotyping by sequencing;    GC;    grain chalkiness;    GK;    Gaussian kernel;    GS;    genomic selection;    GY;    grain yield;    G × E;    genotype × environment interaction;    G × Y;    genotype × year interaction;    MTM;    multiple-trait model;    MY;    milling yield;    PH;    plant height;    PHR;    head rice percentage;    SNP;    Single-nucleotide polymorphism;   

DOI  :  10.2135/cropsci2017.09.0564

学科分类：农业科学（综合）

来源: Crop Science

【 摘 要 】

Genotype × environment interaction (G × E) is the differential response of genotypes in different environments and represents a major challenge for breeders. Genotype × year-interaction (G × Y) is a relevant component of G × E, and accounting for it is an important strategy for identifying lines with stable and superior performance across years. In this study, we compared the prediction accuracy of modeling G × Y using covariance structures that differ in their ability to accommodate correlation among environments. We present the use of these approaches in two different rice (Oryza sativa L.) breeding populations (indica and tropical japonica) for predicting grain yield, plant height, and three milling quality traits—milling yield, head rice percentage, and grain chalkiness—under different cross-validation (CV) scenarios. We also compared model performance in the context of global predictions (i.e., predictions across years). Most of the benefits of multienvironment models come from modeling genetic correlations between environments when predicting performance of lines that have been tested in some environments but not others (CV2). For predicting the performance of newly developed lines (CV1), modeling between environment correlations has no effect compared with considering environments independently. Response to selection of multienvironment models when modeling covariance structures that accommodate covariances between environments was always beneficial when predicting the performance of lines across years. We also show that, for some traits, high prediction accuracies can be obtained in untested years, which is important for resource allocation in small breeding programs.

【 授权许可】

CC BY