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.

Methods
Data Collection and Processing

This dataset encompasses multivariate genomic selection (GS) data targeting water absorption capacity (SRC-W) in wheat, with covariates including grain diameter, hardness, total flour yield, and break-flour yield. Data were collected at the Colorado State University (CSU) Wheat Quality Laboratory from 2014 to 2021.

Phenotypic Data Collection

Water Absorption Capacity (SRC-W):

Measured using the Solvent Retention Capacity (SRC) test according to the American Association of Cereal Chemists (AACC) standard method. This test quantifies the water absorption capacity of wheat flour.

Grain Hardness and Diameter:

Measured using the Single Kernel Characterization System (SKCS), which provides detailed information on individual kernel properties, including hardness and diameter.

Break-Flour Yield and Total Flour Yield:

Assessed using the Quadrumat Senior Milling System, a laboratory milling setup designed to replicate commercial milling processes and accurately determine flour yield.

Data Analysis and Preparation

Phenotypic data were analyzed to calculate Best Linear Unbiased Estimates (BLUEs) across different years, locations, and trial combinations. This approach accounts for environmental variability and provides an unbiased estimate of the trait values.

Genotypic Data Collection

Genotypic data were generated using Genotyping-by-Sequencing (GBS). Variant calling was performed using the TASSEL GBS2 pipeline, resulting in a comprehensive set of single nucleotide polymorphisms (SNPs).

Data Processing

Filtration and Imputation:
SNP markers were filtered to exclude those with low call rates or minor allele frequencies below a certain threshold.
Imputation of missing genotypic data was conducted using the Beagle software, enhancing the dataset’s completeness and accuracy.