Strategies to apply genomic selection into a wheat breeding program

Doctoral thesis abstract

Abstract

The inclusion of genomic selection (GS) in a breeding program allows us to shorten breeding time, increasing the genetic gain per unit of time. GS can be used to predict the best crosses, accounting for allelic complementation between parents and/or predicting individuals’ performance. Mixed models used in GS are a powerful tool to include individuals´ correlations, environments and traits, allowing the use of the whole available information. The goal of this study is the optimization of GS as a tool in the wheat breeding program, to manage the genotype by environment interaction, multi-trait prediction, and best crosses selection. Historical data from the national wheat breeding program were used; data were obtained from 35 environments, 1495 experimental lines, evaluated for yield; and 695 lines evaluated for baking quality. Multi-trait and multi-environment models were compared modeling correlations to predict new individuals, and individuals partially phenotyped. Best crosses were also predicted considering or not considering the variance between parents. Results showed that multi- environment and multi-trait models were useful to optimize the breeding program resources, by predicting partially phenotyped lines. Finally, crosses selection considering parent’s mean performance was as good as including the variance in selection for grain yield, but not for quality traits.