Before implementation of genomic selection, evaluation of the potential accuracy of prediction can be obtained by cross-validation. In this procedure, a population with both phenotypes and genotypes is split into training and validation sets. The prediction model is fitted using the training set, and its accuracy is calculated on the validation set. The degree of genetic relatedness between the training and validation sets may influence the expected accuracy as may the genotype × environment (G×E) interaction in those sets. The authors developed a method to assess these effects and tested it in cassava.
This work is part of the “Next Generation Cassava Breeding Project” which is supported by the UK Department for International Development, in partnership with the Bill & Melinda Gates Foundation.
Ly, D., M. Hamblin, I. Rabbi, G. Melaku, M. Bakare, H. G. Gauch, R. Okechukwu, A. G.O. Dixon, P. Kulakow, and J. Jannink. 2013. Relatedness and Genotype × Environment Interaction Affect Prediction Accuracies in Genomic Selection: A Study in Cassava. Crop Sci. 53:1312-1325. doi:10.2135/cropsci2012.11.0653
Relatedness and Genotype × Environment Interaction Affect Prediction Accuracies in Genomic Selection: A Study in Cassava