Multi-trait Improvement by Predicting Genetic Correlations in Breeding Crosses.

ABSTRACT: The many quantitative traits of interest to plant breeders are often genetically correlated, which can complicate progress from selection. Improving multiple traits may be enhanced by identifying parent combinations - an important breeding step - that will deliver more favorable genetic correlations (r_G ). Modeling the segregation of genomewide markers with estimated effects may be one method of predicting r_G in a cross, but this approach remains untested. Our objectives were to: (i) use simulations to assess the accuracy of genomewide predictions of r_G and the long-term response to selection when selecting crosses on the basis of such predictions; and (ii) empirically measure the ability to predict genetic correlations using data from

SUBMITTER: Neyhart JL 

PROVIDER: S-EPMC6778794 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature