Data from: Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials

Breeding for drought tolerance is a challenging task that requires costly,
extensive and precise phenotyping. Genomic selection (GS) can be used to
maximize selection efficiency and the genetic gains in maize (Zea mays L.)
breeding programs for drought tolerance. Here we evaluated the accuracy of
genomic selection of additive (A) against additive+dominance (AD) models
to predict the performance of untested maize single-cross hybrids for
drought tolerance in multi-environment trials. Phenotypic data of five
drought-tolerance traits were measured in 308 hybrids in eight trials
under water-stressed (WS) and well-watered (WW) conditions over two years
and two locations in Brazil. Hybrids’ genotypes were inferred based on
their parents’ genotypes (inbred lines) using single nucleotide
polymorphism data obtained via genotyping-by-sequencing. GS analyses were
performed using genomic best linear unbiased prediction by fitting a
factor analytic (FA) multiplicative mixed model. Results showed
differences in the predictive accuracy between A and AD models for the
five traits under consideration in both water conditions. For grain yield
(GY), the AD model doubled the predictive accuracy in comparison to the A
model. FA framework allowed for investigating the stability of additive
and dominance effects across environments, as well as the additive- and
dominance-by-environment interactions, with interesting applications for
parental and hybrid selection. Prediction performance of untested hybrids
using GS that benefit from borrowing information from correlated trials
increased 40% and 9% for A and AD models, respectively. These results
highlighted the importance of multi-environment trial analysis with GS
that incorporate dominance effects into genomic predictions of GY in maize
single-cross hybrids.