Replication Data for: Bayesian multi-trait kernel methods improve multi-environment genome based prediction
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'When multi-trait data are available and the degree of correlation between phenotypic traits is moderate or large, genomic prediction accuracy can increase when models are used that account for correlations between the phenotypic traits. The data files associated with this dataset were used to explore Bayesian multi-trait kernel methods for genomic prediction. Linear, Gaussian, polynomial and sigmoid kernels were studied and compared with the GBLUP multi-trait models. The results of the analysis are reported in the accompanying article' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Agricultural research","Grain yield","Heading time","Maturity time","Plant Breeding","Plant height","Triticum aestivum","Wheat"]} <|eot_id|>
 Here is the Description of a dataset. Description:'When multi-trait data are available and the degree of correlation between phenotypic traits is moderate or large, genomic prediction accuracy can increase when models are used that account for correlations between the phenotypic traits. The data files associated with this dataset were used to explore Bayesian multi-trait kernel methods for genomic prediction. Linear, Gaussian, polynomial and sigmoid kernels were studied and compared with the GBLUP multi-trait models. The results of the analysis are reported in the accompanying article' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
When multi-trait data are available and the degree of correlation between phenotypic traits is moderate or large, genomic prediction accuracy can increase when models are used that account for correlations between the phenotypic traits. The data files associated with this dataset were used to explore Bayesian multi-trait kernel methods for genomic prediction. Linear, Gaussian, polynomial and sigmoid kernels were studied and compared with the GBLUP multi-trait models. The results of the analysis are reported in the accompanying article
Agricultural research; Grain yield; Heading time; Maturity time; Plant Breeding; Plant height; Triticum aestivum; Wheat