Optimizing whole-genomic prediction for autotetraploid blueberry breeding.

ABSTRACT: Blueberry (Vaccinium spp.) is an important autopolyploid crop with significant benefits for human health. Apart from its genetic complexity, the feasibility of genomic prediction has been proven for blueberry, enabling a reduction in the breeding cycle time and increasing genetic gain. However, as for other polyploid crops, sequencing costs still hinder the implementation of genome-based breeding methods for blueberry. This motivated us to evaluate the effect of training population sizes and composition, as well as the impact of marker density and sequencing depth on phenotype prediction for the species. For this, data from a large real breeding population of 1804 individuals were used. Genotypic data from 86,930 markers and three traits with different genetic architecture (fruit firmness,

SUBMITTER: de Bem Oliveira I 

PROVIDER: S-EPMC7784927 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature