Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction
نویسندگان
چکیده
منابع مشابه
Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction.
Genomic tools allow the study of the whole genome, and facilitate the study of genotype-environment combinations and their relationship with phenotype. However, most genomic prediction models developed so far are appropriate for Gaussian phenotypes. For this reason, appropriate genomic prediction models are needed for count data, since the conventional regression models used on count data with ...
متن کاملGenomic Bayesian Prediction Model for Count Data with Genotype x Environment Interaction
Genomic tools allow the study of the whole genome, and facilitate the study of genotypeenvironment combinations and their relationship with phenotype. However, most genomic prediction models developed so far are appropriate for Gaussian phenotypes. For this reason, appropriate genomic prediction models are needed for count data, since the conventional regression models used on count data with a...
متن کاملBayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-...
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When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait at a time taking into account genotype × environment interaction (G × E), because there is a lack of comprehensive models that simultaneously take into account the correlated ...
متن کاملGenomic Prediction of Genotype × Environment Interaction Kernel Regression Models.
In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propose using two nonlinear Gaussian kernels: the reproducing kernel Hilbert space with kernel averaging (RKHS KA) and the Gaussian kernel with the bandwidth estima...
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ژورنال
عنوان ژورنال: G3 Genes|Genomes|Genetics
سال: 2016
ISSN: 2160-1836
DOI: 10.1534/g3.116.028118