GWAS分析

Plink
FaST-LMM
TASSEL 有Windows版本
FaST-LMM-Select
GAPIT基于R

GAPIT和Tassle等的对比。

TASSEL5命令行模式运行方法

1. 典型的MLM（混合线性模型）分析管道命令如下：

       perl run_pipeline.pl

       -fork1 -h genotype.hmp -filterAlign -filterAlignMinFreq 0.05        注：导入基因型数据并过滤

       -fork2 -r trait.txt                                         注：导入表型数据

       -fork3 -r pop_structure.txt -excludeLastTrait               注：导入群体结构数据

       -fork4 -k kinship.txt -combine5 -input1 -input2 -input3 -intersect -combine6 -input5 -input4 -mlm -mlmVarCompEst P3D -mlmCompressionLevel None -export result       注：导入kinship矩阵，合并表型、基因型和群体结构，设定MLM参数


2. 例子：在此基础上结合批处理实现对基因型数据的MLM分析：


       perl run_pipeline.pl

       -fork1 -h ./hmp/mouse.hmp -filterAlign -filterAlignMinFreq 0.05

       -fork2 -r trait.txt

       -fork3 -r pop_structure -excludeLastTrait

       -fork4 -k kinship -combine5 -input1 -input2 -input3 -intersect -combine6 -input5 -input4 -mlm -mlmVarCompEst P3D -mlmCompressionLevel None -export result

将以上脚本保存为bat格式，放在TASSEL5的安装目录里，其它数据也放在安装目录中。

3.tassel5路径
nohup /home/guo/tool/tasseladmin-tassel-5-standalone-5100767ae9e7/run_pipeline.pl -fork1 -h All_Merged_1.25M_MAF0.05.hmp -filterAlign -filterAlignMinFreq 0.05 \
-fork2 -r 2011—.txt\
-fork3 -r 513lines_27229snps_pop_structure_110608.txt -excludeLastTrait \
-fork4 -k 513lines_27229snps_kinship_110608.txt -combine5 -input1 -input2 -input3 -intersect -combine6 -input5 -input4 -mlm -mlmVarCompEst P3D -mlmCompressionLevel None -export result &



4.工作路径：
/home/fzy/
/disks/workin/fzy/
存储路径：
/disks/backup/fzy/


5.提交任务：
nohup 完整命令行 &


/home/guo/tool/tasseladmin-tassel-5-standalone-5100767ae9e7/run_pipeline.pl -fork1 -h All_Merged_1.25M_MAF0.05.hmp -filterAlign -filterAlignMinFreq 0.05 -fork2 -r 2011.txt -fork3 -r 513lines_27229sn
ps_pop_structure_110608.txt -excludeLastTrait -fork4 -k 513lines_27229snps_kinship_110608.txt -combine5 -input1 -input2 -input3 -intersect -combine6
-input5 -input4 -mlm -mlmVarCompEst P3D -mlmCompressionLevel None -export result


/home/guo/tool/tasseladmin-tassel-5-standalone-5100767ae9e7/run_pipeline.pl -Xms10G -Xmx10G -fork1 -h All_Merged_1.25M_MAF0.05.hmp -filterAlign -filterAlignMinFreq 0.05 -fork2 -r 2011.txt -fork3 -r 513lines_27229snps_pop_structure_110608.txt -excludeLastTrait -fork4 -k 513lines_27229snps_kinship_110608.txt -combine5 -input1 -input2 -input3 -intersect -combine6 -input5 -input4 -mlm -mlmVarCompEst P3D -mlmCompressionLevel None -export result

/home/fzy/tassel3.0_standalone_110430/tassel3.0_standalone/run_pipeline.pl  -Xms10G -Xmx10G -fork1 -h All_Merged_1.25M_MAF0.05.hmp -filterAlign -filterAlignMinFreq 0.05 -fork2 -r 2011.txt -fork3 -r 513lines_27229snps_pop_structure_110608.txt -excludeLastTrait -fork4 -k 513lines_27229snps_kinship_110608.txt -combine5 -input1 -input2 -input3 -intersect -combine6 -input5 -input4 -mlm -mlmVarCompEst P3D -mlmCompressionLevel None -export result

GAPTI使用方法 以下内容为R语言，直接保存为test.R 在终端运行，Rscript test.R即可。

#!/path/to/Rscript
#Author：Frank Chai
#Email:chaimol@163.com
#this is a code for GWAS analysis.use packages is GAPIT
#参考网址http://blog.sina.com.cn/s/blog_83f77c940102wg16.html
#安装包
source("http://www.bioconductor.org/biocLite.R")
#options(BioC_mirror="http://mirrors.ustc.edu.cn/bioc/")
biocLite("multtest",destdir = "~/disk/GWAS/R",lib="~/disk/GWAS/GAPIT/")
#biocLite("ggbio")

install.packages("gplots",destdir = "~/disk/GWAS/R",lib="~/disk/GWAS/GAPIT/")
install.packages("LDheatmap",destdir = "~/disk/GWAS/R",lib="~/disk/GWAS/GAPIT/")
install.packages("genetics",destdir = "~/disk/GWAS/R",lib="~/disk/GWAS/GAPIT/")
install.packages("ape",destdir = "~/disk/GWAS/R",lib="~/disk/GWAS/GAPIT/")
install.packages("EMMREML",destdir = "~/disk/GWAS/R",lib="~/disk/GWAS/GAPIT/")
install.packages("scatterplot3d",destdir = "~/disk/GWAS/R",lib="~/disk/GWAS/GAPIT/")


#加载包
library(multtest)
library(gplots)
library(LDheatmap)
library(genetics)
library(ape)
library(EMMREML)
library(compiler)

source("http://zzlab.net/GAPIT/gapit_functions.txt")
source("http://zzlab.net/GAPIT/emma.txt")
setwd("~/disk/GWAS/test/")
myG <- read.table("mdp_traits_validation.txt",head=TRUE)
myY <- read.table("mdp_genotype_test.hmp.txt",head=TRUE) 
myGAPIT <- GAPIT(Y=myY,G=myG,kinship.cluster=c("average","complete","ward"),kinship.group=c("Mean","Max"),SNP.MAF=0,SNP.FDR=1,PCA.total=3,Model.selection=TRUE)