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

By Juan Burgueño, G. de los Campos, Kent A. Weigel, Jose Crossa. 2012

541 citations recorded by the source corpus.

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 confi rmed 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 benefi t greatly from using multienvironment GS models.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/fcfa040d45a47deffea32990bab4c8556292b63a

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Citations in the corpus

Genomic Selection in Wheat Breeding using Genotyping‐by‐Sequencing (2012, background): “Genomic selection models have proven to be advantageous for complex traits such as grain yield where many loci of small ef ects control the trait (Burgueño et al., 2012; Crossa et al., 2010; de los Campos et al., 2009; González-Camacho et al., 2012; Jannink et al., 2010).”
Genomic Prediction of Genetic Values for Resistance to Wheat Rusts (2012, background): “As shown in Burgueño et al. (2012), combining information from correlated environments can improve prediction accuracy.”
Genotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates (2020, background): “Several studies have been focused on the prediction of untested genotypes, such as Burgueño et al. (2012), Dias et al. (2018b) and Dias et al. (2020).”
Perspectives for Genomic Selection Applications and Research in Plants (2015, methodology): “As noted previously, analysis of G*E with multi-trait models becomes more tractable with markers by helping to cope with unbalanced data (Burgueño et al., 2012).”
Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. (2017, methodology): “[23] were the first to use marker- and pedigree-based GBLUP models for assessing G E interactions under genomic prediction, while Heslot et al.”
Impact of Climate Change on Crops Adaptation and Strategies to Tackle Its Outcome: A Review (2019, methodology): “Burgueño et al. (2012) proposed the first statistical design by applying a linear mixed model to G × E model [157].”
Relatedness and Genotype × Environment Interaction Affect Prediction Accuracies in Genomic Selection: A Study in Cassava (2013, unknown): “In that case, G×E interaction would generate a common error component between the predictions and the clone estimates based on the observations (Lorenz et al., 2011, 2012; Burgueño et al., 2012).”