Integrating Molecular Markers and Environmental Covariates To Interpret Genotype by Environment Interaction in Rice (Oryza sativa L.) Grown in Subtropical Areas

G3: Genes, Genomes, Genetics

article

Eliana Monteverde¹  Lucía Gutierrez²  Pedro Blanco³  Fernando Pérez de Vida³  Juan E. Rosas³  Victoria Bonnecarrère⁴  Gastón Quero⁵  Susan McCouch¹ 

[1] Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca NY 14853;Department of Agronomy, University of Wisconsin - Madison WI 53706;Programa Nacional de Investigación en arroz, Instituto Nacional de Investigación Agropecuaria (INIA), INIA Treinta y Tres 33000, Uruguay;Unidad de Biotecnología, Instituto Nacional de Investigación Agropecuaria (INIA), Estación Experimental Wilson Ferreira Aldunate 90200, Uruguay;Department of Plant Biology, College of Agriculture, Universidad de la República, Montevideo, Uruguay

关键词: rice;    genotype-by-environment interaction;    genomic prediction;    QTL by environment interaction;    environmental covariates;   

DOI  :  10.1534/g3.119.400064

学科分类：社会科学、人文和艺术（综合）

来源: Genetics Society of America

【 摘 要 】

Understanding the genetic and environmental basis of genotype × environment interaction (G×E) is of fundamental importance in plant breeding. If we consider G×E in the context of genotype × year interactions (G×Y), predicting which lines will have stable and superior performance across years is an important challenge for breeders. A better understanding of the factors that contribute to the overall grain yield and quality of rice ( Oryza sativa L.) will lay the foundation for developing new breeding and selection strategies for combining high quality, with high yield. In this study, we used molecular marker data and environmental covariates (EC) simultaneously to predict rice yield, milling quality traits and plant height in untested environments (years), using both reaction norm models and partial least squares (PLS), in two rice breeding populations ( indica and tropical japonica ). We also sought to explain G×E by differential quantitative trait loci (QTL) expression in relation to EC. Our results showed that PLS models trained with both molecular markers and EC gave better prediction accuracies than reaction norm models when predicting future years. We also detected milling quality QTL that showed a differential expression conditional on humidity and solar radiation, providing insight for the main environmental factors affecting milling quality in subtropical and temperate rice growing areas.

【 授权许可】

CC BY|CC BY-NC