mbBayesAB: Multi-breed joint prediction model fitting heterogeneous genetic (co)variance

Data and program needed to reproduce the research results of the article "Multi-trait Bayesian models enhance the accuracy of genomic prediction in multi-breed reference populations"

cite: Li, W.; Zhang, M.; Du, H.; Wu, J.; Zhou, L.; Liu, J. Multi-Trait Bayesian Models Enhance the Accuracy of Genomic Prediction in Multi-Breed Reference Populations. Agriculture 2024, 14, 626. https://doi.org/10.3390/agriculture14040626

Running environment

All scripts and program files need to run on the Linux system, and the testing environment is:

CentOS Linux release 7.6.1810 (Core)

configured Slurm Job Management System

run steps

1.Download/Clone GitHub repository

1.1 Download compressed package

Download the compressed package containing data, scripts and program files from the link https://github.com/CAU-TeamLiuJF/mbBayesAB/archive/refs/heads/main.zip, and then decompress it to the specified path. Assuming that the compressed package downloaded from the local computer is copied to the remote server /public/home/liujf/liwn/download path, you can then use the following command to decompress the folder to the /public/home/liujf/liwn/code/GitHub path:

cd /public/home/liujf/liwn/download
unzip -d /public/home/liujf/liwn/code/GitHub mbBayesAB-main.zip
mv /public/home/liujf/liwn/code/GitHub/mbBayesAB-main /public/home/liujf/liwn/code/GitHub/mbBayesAB ## Change folder name

1.2 Clone

Assuming that the Linux system is connected to the Internet, you can clone the Github repository with the following command:

cd /public/home/liujf/liwn/code/GitHub
git clone git@github.com:CAU-TeamLiuJF/mbBayesAB.git

2.Script initialization

2.1 Run the initialization script initialize.sh

In order to modify the working directory in the script and call the R language script directly in the command line terminal, it is necessary to update some paths in the bash script and the path of the R language script interpreter according to the current computer environment. Note that at this time, the command terminal is required to be able to call R language directly through R, that is, the R language installation path has been loaded into the environment variable:

R
# R version 4.3.1 (2023-06-16) -- "Beagle Scouts"
# Copyright (C) 2023 The R Foundation for Statistical Computing
# Platform: x86_64-pc-linux-gnu (64-bit)
# 
# ...
# 
# > 

At the same time, this step will also check whether the required R packages have been installed in the current R language version.

First, switch the working path to the main directory of the project folder, that is, the directory where the script initialize.sh is located, such as:

cd /public/home/liujf/liwn/code/GitHub/debug/mbBayesAB  ## Need to be modified
./initialize.sh

2.2 Modify/comment some commands according to the running environment

The main script of the project is main.sh. The development environment of this project is CentOS 7, and the Slurm workload manager is installed. The number of cores of the computing nodes is more than 50. If you want to run commands in a Linux system without the Slurm job management system, you need to comment out the line where the sbatch command is located in the script main.sh, such as line 72:

...
    ## Parameters need to be ...
    ## Note: If the Slurm workload manager is not installed ...
#    sbatch -c2 --mem=4G \
    $GP_cross \
      --proj ${pro} \
...

The prediction accuracy is calculated by 10 repetitions of the 5-fold cross validation step, so one case needs to run 10x5 subprocesses. If the number of CPUs in the operating environment computer is less than 50, please modify the --thread parameter. For example, if the number of cores is 20, change it to --thread 20

3.Run the program

Run the commands in the main script main.sh line by line in the Linux command line terminal.

Contact

Liweining li.wn@qq.com

---

mbBayesAB：拟合异质遗传（协）方差的多品种联合评估模型

用于复现文章《xxx》的数据和脚本、程序文件等

引用：xxx

运行环境

所有的脚本和程序文件都需要运行在Linux系统中，测试环境为：

CentOS Linux release 7.6.1810 (Core)

同时配置了Slurm作业管理系统

运行步骤

1.下载/克隆GitHub仓库

1.1 下载压缩包

通过链接https://github.com/CAU-TeamLiuJF/mbBayesAB/archive/refs/heads/main.zip下载包含数据、脚本和程序文件的压缩包，然后解压到指定路径中。假设将本地电脑下载的压缩包复制到远程服务器/public/home/liujf/liwn/download路径中，然后可用以下命令将文件夹解压至/public/home/liujf/liwn/code/GitHub路径：

cd /public/home/liujf/liwn/download
unzip -d /public/home/liujf/liwn/code/GitHub mbBayesAB-main.zip
mv /public/home/liujf/liwn/code/GitHub/mbBayesAB-main /public/home/liujf/liwn/code/GitHub/mbBayesAB ## 修改文件夹名称

1.2 克隆

假设Linux系统已连接互联网，可以通过以下命令克隆Github仓库：

cd /public/home/liujf/liwn/code/GitHub
git clone git@github.com:CAU-TeamLiuJF/mbBayesAB.git

2.脚本初始化

2.1 运行初始化脚本initialize.sh

为了修改脚本中的工作目录，以及直接在命令行终端调用R语言脚本，需要根据当前计算机环境更新bash脚本中的部分路径及R语言脚本解释器的路径。注意，此时要求命令终端可以直接通过R调用R语言，即R语言安装路径已加载到环境变量中：

R
# R version 4.3.1 (2023-06-16) -- "Beagle Scouts"
# Copyright (C) 2023 The R Foundation for Statistical Computing
# Platform: x86_64-pc-linux-gnu (64-bit)
# 
# R是自由软件，不带任何担保。
# 在某些条件下你可以将其自由散布。
# 用'license()'或'licence()'来看散布的详细条件。
# 
# R是个合作计划，有许多人为之做出了贡献.
# 用'contributors()'来看合作者的详细情况
# 用'citation()'会告诉你如何在出版物中正确地引用R或R程序包。
# 
# 用'demo()'来看一些示范程序，用'help()'来阅读在线帮助文件，或
# 用'help.start()'通过HTML浏览器来看帮助文件。
# 用'q()'退出R.
# 
# [原来保存的工作空间已还原]
# 
# > 

同时该步骤还会检查当前R语言版本中是否已安装所需的R包。

首先将工作路径切换到项目文件夹的主目录，即脚本initialize.sh所在的目录，如：

cd /public/home/liujf/liwn/code/GitHub/debug/mbBayesAB  ## 需根据实际修改
./initialize.sh

2.2 根据运行环境修改/注释某些命令

项目的主脚本为main.sh，本项目的开发环境为CentOS 7，同时配置了Slurm作业管理系统，计算节点的核心数在50以上。若是想要在没有配置Slurm作业管理系统的Linux系统中运行命令，则需要注释脚本main.sh中sbatch命令所在的行，如72行：

...
    ## Parameters need to be ...
    ## Note: If the Slurm workload manager is not installed ...
#    sbatch -c2 --mem=4G \
    $GP_cross \
      --proj ${pro} \
...

预测准确性由10次重复的五折交叉验证步骤计算得到，因此一种情形需要运行10x5个子进程，若是运行环境计算机中CPU数目小于50，请修改--thread参数，如核心数为20，则修改为--thread 20

3.运行程序

在Linux命令行终端中逐行运行主脚本main.sh中的命令即可。

联系方式

李伟宁 li.wn@qq.com