Genomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for crop species such as cassava that have long breeding cycles. Practically, to implement GS in cassava breeding, it is necessary to evaluate different GS models and to develop suitable models for an optimized breeding pipeline. In this paper, the authors compared:
prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for a single-environment genetic evaluation
accuracies from a compound symmetric multi-environment model (uE) parameterized as a univariate multi-kernel model to a multivariate (ME) multi-environment mixed model that accounts for genotype-by-environment interaction for multi-environment genetic evaluation
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.
Uche Godfrey Okeke, Deniz Akdemir, Ismail Rabbi, Peter Kulakow and Jean-Luc Jannink Accuracies of univariate and multivariate genomic prediction models in African cassava. Genetics Selection Evolution 2017 49:88
Accuracies of univariate and multivariate genomic prediction models in African cassava
Published 4 December 2017