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 ha..., 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..., , # 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...

硬麦面粉的吸水率（Water Absorption Capacity, WAC）会影响其终端加工品质特性，包括面包体积、面包出品率以及货架期。尽管吸水率至关重要，但通过表型选择提升吸水率颇具挑战。吸水率表型鉴定耗时费力，因此通常仅在育种流程的后期阶段开展评估，这导致次优株系被保留的时长超出预期。本研究探究了单变量与多变量基因组预测作为替代表型选择手段以提升吸水率的潜力。本研究共纳入497份硬冬小麦基因型，在多环境高级产量试验与优良品系试验中开展了为期8年（2014-2021年）的鉴定。吸水率表型鉴定采用以水为溶剂的溶剂保留能力（Solvent Retention Capacity, SRC）试验（即SRC-W）。将与SRC-W呈显著相关（相关系数r≥0.3）且早于SRC-W完成鉴定的性状纳入多变量基因组预测模型。籽粒单倍型[……] 数据收集与处理
本数据集涵盖针对小麦吸水率（SRC-W）的多变量基因组选择（Genomic Selection, GS）数据，协变量包括籽粒直径、籽粒硬度、总面粉出粉率以及碎粉出粉率。所有数据于2014-2021年间在科罗拉多州立大学（Colorado State University, CSU）小麦品质实验室采集完成。

表型数据采集
吸水率（SRC-W）：
依据美国谷物化学家协会（American Association of Cereal Chemists, AACC）标准方法，通过溶剂保留能力试验测定，该试验可量化小麦面粉的吸水率。

籽粒硬度与直径：
采用单籽粒特性测定系统（Single Kernel Characterization System, SKCS）测定，该系统可提供单籽粒的各项详细属性信息，包括硬度与直径。

碎粉出粉率与总面粉出粉率：
采用Quadrumat Senior碾磨系统进行评估，该实验室碾磨装置旨在复刻商业化碾磨流程[……]
# 数据来源：用于多变量基因组预测的小麦基因型与表型数据
我们已提交原始表型数据（`Phenotypic_data.txt`）、基因型数据（`Genotypic_data.csv`）以及用于数据分析的R脚本。

**提交文件**
* **Phenotypic_data.txt**：包含表型数据。
* **Genotypic_data.csv**：包含基因型数据。
* **R-script_MVGS**：包含用于数据分析的R脚本。

**数据说明**
**基因型数据**
**文件名：** Genotypic_data.csv，通过测序基因分型（Genotyping by Sequencing, GBS）获得。
* **rs#**：每个单核苷酸多态性（Single Nucleotide Polymorphism, SNP）的标识符。
* **Chrom**：SNP所在的染色体编号。
* **Pos**：SNP在染色体上的物理位置。
* **GID1、GID2、……、GIDN**：每个样本的基因型信息。

**表型数据**
**文件名：** Phenotypic_data.txt
* **Sample_ID**：每个样本的唯一标识符。
* **SRC-water**：吸水率的表型值，通过以水为溶剂的溶剂保留能力试验测定[……]