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

Wondifraw, Meseret1

Published Jul 29, 2025 on Dryad. https://doi.org/10.5061/dryad.6wwpzgn71

Data files

This dataset is embargoed and will be released when the associated article is published. Contact gro.dayrdatad@pleh to notify us of article publication.

Lists of files and downloads will become available to the public when released.

Abstract

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.