Data from: Wheat genotypic and phenotypic data for multivariate genomic prediction

The water absorption capacity (WAC) of hard wheat flour affects end-use
quality characteristics, including loaf volume, bread yield, and shelf
life. Despite its importance, improving WAC through phenotypic selection
is challenging. Phenotyping for WAC is time-consuming and, as such, is
often limited to evaluation in the latter stages of the breeding process,
resulting in the retention of suboptimal lines longer than desired. This
study investigates the potential of univariate and multivariate genomic
predictions as an alternative to phenotypic selection for improving WAC. A
total of 497 hard winter wheat genotypes were evaluated in
multi-environment advanced yield and elite trials over eight years
(2014-2021). Phenotyping for WAC was done via the solvent retention
capacity (SRC) using water as a solvent (SRC-W). Traits that exhibited a
significant correlation (r ≥ 0.3) with SRC-W and were evaluated earlier
than SRC-W were included in the multivariate genomic prediction models.
Kernel hardness and diameter were obtained using the single kernel
characterization system (SKCS), and break flour yield (B-Flour) and total
flour yield (T-Flour) were included. Cross-validation showed the mean
univariate genomic prediction accuracy of SRC to be r = 0.69 ± 0.005,
while bivariate and multivariate models showed an improved prediction
accuracy of r = 0.82 ± 0.003. Forward validation showed a prediction
accuracy up to r = 0.81 for a multivariate model that included SRC-W + All
traits (SRC-W, Diameter, SKCS hardness and Diameter, F-Flour, and
T-Flour). These results suggest that incorporating correlated traits into
genomic prediction models can improve early-generation prediction
accuracy.