DOI: 10.5061/dryad.6wwpzgn71
title: Data from: Wheat genotypic and phenotypic data for multivariate genomic prediction

description: 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.
description: <strong>Data Collection and
 Processing</strong>  
 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.  
 <strong>Phenotypic Data
 Collection</strong>  
 <strong>Water Absorption Capacity
 (SRC-W):</strong>  
 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.   <strong>Grain
 Hardness and Diameter:</strong>  
 Measured using the Single Kernel Characterization System
 (SKCS), which provides detailed information on individual kernel
 properties, including hardness and diameter.
   <strong>Break-Flour Yield and
 Total Flour Yield:</strong>  
 Assessed using the Quadrumat Senior Milling System, a
 laboratory milling setup designed to replicate commercial milling
 processes and accurately determine flour yield.
   <strong>Data Analysis and
 Preparation</strong>  
 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.
   <strong>Genotypic Data
 Collection</strong>  
 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).   <strong>Data
 Processing</strong>  
 <strong>Filtration and
 Imputation:</strong> 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.
description: # Data from: Wheat genotypic and phenotypic data for multivariate genomic
 prediction We have submitted our raw phenotypic data
 (`Phenotypic_data.txt), genotypic data (Genotypic_data.csv`) and R-script
 used to analyze the data. **Files Submitted** * **Phenotypic_data.txt**:
 Contains the phenotypic data. * **Genotypic_data.csv**: Contains the
 genotypic data. * \**R-script_MVGS: **Contains the R-script used to
 analyze the data. **Descriptions** **Genotypic Data** **Filename:**
 Genotypic_data.csv, obtained via genotyping by sequencing (GBS). *
 **rs#:** Identifier for each SNP. * **Chrom:** Chromosome number where the
 SNP is located. * **Pos:** Physical position of the SNP on the chromosome.
 * **GID1, GID2, ..., GIDN:** Genotype information for each sample.
 **Phenotypic Data** **Filename:** Phenotypic_data.txt * **Sample_ID:**
 Unique identifier for each sample. * **SRC-water:** Phenotypic value for
 water absorption capacity, measured by solvent retention capacity test
 using water as a solvent. * **Diameter:** Phenotypic value for grain
 diameter and hardness, measured by single kernel characterization system.
 * **B-Flour:** Phenotypic value for break flour yield, the amount of flour
 obtained from the initial breaking of the grain in the milling process. *
 **T-Flour:** Phenotypic value for total flour yield, the total amount of
 flour produced from the entire milling process. **Note:** The phenotypic
 data is a Best Linear Unbiased Estimate (BLUE) calculated across
 trial-location-year interaction. **Relationships Between Data Files** The
 Genotypic_data.csv and Phenotypic_data.txt files are linked through the
 sample identifiers. Each sample in the phenotypic data corresponds to a
 specific sample in the genotypic data based on the Sample_ID. No missing
 data is found in the dataset. **Dataset Information** This dataset
 comprises genotypic and phenotypic data of 337 hard winter wheat (Triticum
 aestivum L.) genotypes collected for a multivariate genomic selection (GS)
 study focusing on wheat end-use quality traits. The study involved the
 genotyping of wheat samples using Genotyping-by-Sequencing (GBS) and the
 measurement of various phenotypic traits related to wheat quality,
 including water absorption capacity, grain diameter and hardness, break
 flour yield, and total flour yield. The experimental procedures were
 designed to identify genetic markers associated with these key traits to
 enhance wheat breeding programs. **Key Information Sources** *
 **Genotyping Data:** Generated via Genotyping-by-Sequencing (GBS) and
 processed using the TASSEL software. * **Phenotypic Measurements:**
 Conducted using standard protocols for solvent retention capacity, single
 kernel characterization, and milling processes. **Code/Software** R is
 required to run R-script_MVGS; the script was created using version 4.2.3.
 Annotations are provided throughout the script for: * Library loading *
 Dataset loading and cleaning * Analyses * Figure creation **Usage Notes**
 Phenotypic_data.txt and Genotypic_data.csv can be viewed using any text
 editor or spreadsheet software like Microsoft Excel. R is required to run
 the analysis script provided in the repository. **Additional Information**
 The data and script used in this study can be found in the GitHub
 repository:
 [https://github.com/MeseretAW/MVGS](https://github.com/MeseretAW/MVGS).