Data from: Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models

Genomic selection have been proposed as the standard method to predict
breeding values in animal and plant breeding. Although some crops have
benefited from this methodology, studies in Coffea are still emerging. To
date, there have been no studies of how well genomic prediction models
work across populations and environments for different complex traits in
coffee. Considering that predictive models are based on biological and
statistical assumptions, it is expected that their performance vary
depending on how well these assumptions align with the true genetic
architecture of the phenotype. To investigate this, we used data from two
recurrent selection populations of Coffea canephora, evaluated in two
locations, and single nucleotide polymorphisms identified by
Genotyping-by-Sequencing. In particular, we evaluated the performance of
13 statistical approaches to predict three important traits in the coffee
— production of coffee beans, leaf rust incidence and yield of green
beans. Analyses were performed for predictions within-environment, across
locations and across populations to assess the reliability of genomic
selection. Overall, differences in the prediction accuracy of the
competing models were small, although the Bayesian methods showed a modest
improvement over other methods, at the cost of more computation time. As
expected, predictive accuracy for within-environment analysis, on average,
were higher than predictions across locations and across populations. Our
results support the potential of genomic selection to reshape traditional
plant breeding schemes. In practice, we expect to increase the genetic
gain per unit of time by reducing the length cycle of recurrent selection
in coffee.