Premium
Genomic Prediction of Breeding Values when Modeling Genotype × Environment Interaction using Pedigree and Dense Molecular Markers
Author(s) -
Burgueño Juan,
Campos Gustavo,
Weigel Kent,
Crossa José
Publication year - 2012
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2011.06.0299
Subject(s) - biology , selection (genetic algorithm) , predictive modelling , genotype , genomic selection , plant breeding , molecular marker , genetic marker , marker assisted selection , best linear unbiased prediction , genetics , computational biology , microbiology and biotechnology , statistics , machine learning , computer science , gene , agronomy , mathematics , single nucleotide polymorphism
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.