Genomic Prediction of Breeding Values when Modeling Genotype × Environment Interaction using Pedigree and Dense Molecular Markers

• Published in: Crop science Vol. 52; no. 2; pp. 707 - 719
• Main Authors: Burgueño, Juan, Campos, Gustavo de los, Weigel, Kent, Crossa, José
• Format: Journal Article
• Language: English
• Published: Madison, WI Crop Science Society of America 01.03.2012; The Crop Science Society of America, Inc; American Society of Agronomy
• Subjects: Agronomy. Soil science and plant productions; Animal breeding; Biological and medical sciences; breeding value; Fundamental and applied biological sciences. Psychology; genetic markers; Genetics and breeding of economic plants; genotype; Genotypes; information sources; marker-assisted selection; pedigree; plant breeders; Plant breeding; prediction; Triticum aestivum; Varietal selection. Specialized plant breeding, plant breeding aims; Wheat; Genotype environment interaction; Pedigree; Genomics; Prediction; Molecular marker; Breeding value; Modeling
• ISSN: 1435-0653; 0011-183X; 1435-0653
• DOI: 10.2135/cropsci2011.06.0299

Genomic selection (GS) has become an important aid in plant and animal breeding. Multienvironment (multitrait) models allow borrowing of information across environments (traits), which could enhance prediction accuracy. This study presents multienvironment (multitrait) models for GS and compares the predictive accuracy of these models with: (i) multienvironment analysis without pedigree and marker information, and (ii) multienvironment pedigree or/and marker-based models. A statistical framework for incorporating pedigree and molecular marker information in models for multienvironment data is described and applied to data that originate from wheat (Triticum aestivum L.) multienvironment trials. Two prediction problems relevant to plant breeders are considered: (CV1) predicting the performance of untested genotypes (“newly” developed lines), and (CV2) predicting the performance of genotypes that have been evaluated in some environments but not in others. Results confirmed the superiority of models using both marker and pedigree information over those based on pedigree information only. Models with pedigree and/or markers had better predictive accuracy than simple linear mixed models that do not include either of these two sources of information. We concluded that the evaluation of such trials can benefit greatly from using multienvironment GS models.