- 进入目标文件夹
cd /public/ejye/other/GraduateData/zzhsnp/single_pop/ROH/ROH2022.2.17/pure20220217
- 分群体的单个个体
ls *.vcf |tr "\t" "\n"|while read id ;do bgzip $id;done #批量压缩
ls *.vcf.gz |tr "\t" "\n"|while read id ;do tabix -p vcf $id;done #批量建立索引
ls *.vcf.gz |tr "\t" "\n"|sed "s/\.purein\.vcf\.gz//g"|while read id ;do zcat $id.purein.vcf.gz |grep -v "##"|grep "#"|cut -f10- |tr "\t" "\n"|while read i;do bcftools view $id.purein.vcf.gz -s $i -Oz -o $id/$i.vcf.gz;done;done #批量提取单个样本
- 计算每个个体的ROH
BN 62
BRA 70
Laos 53
LX 65
RJF 114
TLF 72
WL 152
ls *.vcf.gz |tr "\t" "\n"|sed "s/\.purein\.vcf\.gz//g"|while read id ;do mkdir -p $id/single_sample_ROH ;done #批量新建文件夹
cd BN
ls *.vcf.gz |tr "\t" "\n"|sed "s/\.vcf\.gz//g"|while read id ;do plink --vcf $id.vcf.gz --chr-set 33 --homozyg --homozyg-window-snp 10 --homozyg-window-het 3 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-snp 62 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --out single_sample_ROH/$id ; done
head -n1 BN01_BN01.hom>BN_all.hom.txt
cat *.hom|grep -v "KB" >>BN_all.hom.txt
cd ../BRA
ls *.vcf.gz |tr "\t" "\n"|sed "s/\.vcf\.gz//g"|while read id ;do plink --vcf $id.vcf.gz --chr-set 33 --homozyg --homozyg-window-snp 10 --homozyg-window-het 3 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-snp 70 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --out single_sample_ROH/$id ; done
head -n1 BRA01_BRA01.hom >BRA_all.hom.txt
cat *.hom|grep -v "KB" >>BRA_all.hom.txt
cd ../Laos
ls *.vcf.gz |tr "\t" "\n"|sed "s/\.vcf\.gz//g"|while read id ;do plink --vcf $id.vcf.gz --chr-set 33 --homozyg --homozyg-window-snp 10 --homozyg-window-het 3 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-snp 53 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --out single_sample_ROH/$id ; done
head -n1 Laos01_Laos01.hom >Laos_all.hom.txt
cat *.hom|grep -v "KB" >>Laos_all.hom.txt
cd ../LX
ls *.vcf.gz |tr "\t" "\n"|sed "s/\.vcf\.gz//g"|while read id ;do plink --vcf $id.vcf.gz --chr-set 33 --homozyg --homozyg-window-snp 10 --homozyg-window-het 3 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-snp 65 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --out single_sample_ROH/$id ; done
head -n1 LX01_LX01.hom >LX_all.hom.txt
cat *.hom|grep -v "KB" >>LX_all.hom.txt
cd ../RJF
ls *.vcf.gz |tr "\t" "\n"|sed "s/\.vcf\.gz//g"|while read id ;do plink --vcf $id.vcf.gz --chr-set 33 --homozyg --homozyg-window-snp 10 --homozyg-window-het 3 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-snp 114 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --out single_sample_ROH/$id ; done
head -n1 RJF01_RJF01.hom >RJF_all.hom.txt
cat *.hom|grep -v "KB" >>RJF_all.hom.txt
cd ../TLF
ls *.vcf.gz |tr "\t" "\n"|sed "s/\.vcf\.gz//g"|while read id ;do plink --vcf $id.vcf.gz --chr-set 33 --homozyg --homozyg-window-snp 10 --homozyg-window-het 3 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-snp 72 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --out single_sample_ROH/$id ; done
head -n1 TLF01_TLF01.hom >TLF_all.hom.txt
cat *.hom|grep -v "KB" >>TLF_all.hom.txt
cd ../WL
ls *.vcf.gz |tr "\t" "\n"|sed "s/\.vcf\.gz//g"|while read id ;do plink --vcf $id.vcf.gz --chr-set 33 --homozyg --homozyg-window-snp 10 --homozyg-window-het 3 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-snp 152 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --out single_sample_ROH/$id ; done
head -n1 WL01_WL01.hom >WL_all.hom.txt
cat *.hom|grep -v "KB" >>WL_all.hom.txt
- 统计每个样本检测出来的总ROH数目
cat *.log|grep -wo "found\ [0-9]\{2,3\}"|grep -wo "[0-9]\{2,3\}"
- 分长中短聚类
setwd("C:/Users/TE/Desktop/叶尔江ROH分析/ROH样本分布")
library(mclust)
data <- read.table("BN_all.hom.txt",header=TRUE)
mod2 <- Mclust(data[,9],G=3)
summary(mod2,parameters=TRUE)
a <- as.data.frame(mod2[15])
write.table(a,file="BN.class.txt",row.names=FALSE,col.names=TRUE,quote=FALSE)
- 计算每个样本每个类别的数目
cat BRA.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat BRA.txt|grep -w "$id"|awk '{if($1==1)print $0}'|wc -l;done
cat BRA.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat BRA.txt|grep -w "$id"|awk '{if($1==2)print $0}'|wc -l;done
cat BRA.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat BRA.txt|grep -w "$id"|awk '{if($1==3)print $0}'|wc -l;done
cat BN.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat BN.txt|grep -w "$id"|awk '{if($1==1)print $0}'|wc -l;done
cat BN.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat BN.txt|grep -w "$id"|awk '{if($1==2)print $0}'|wc -l;done
cat BN.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat BN.txt|grep -w "$id"|awk '{if($1==3)print $0}'|wc -l;done
cat Laos.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat Laos.txt|grep -w "$id"|awk '{if($1==1)print $0}'|wc -l;done
cat Laos.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat Laos.txt|grep -w "$id"|awk '{if($1==2)print $0}'|wc -l;done
cat Laos.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat Laos.txt|grep -w "$id"|awk '{if($1==3)print $0}'|wc -l;done
cat LX.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat LX.txt|grep -w "$id"|awk '{if($1==1)print $0}'|wc -l;done
cat LX.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat LX.txt|grep -w "$id"|awk '{if($1==2)print $0}'|wc -l;done
cat LX.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat LX.txt|grep -w "$id"|awk '{if($1==3)print $0}'|wc -l;done
cat RJF.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat RJF.txt|grep -w "$id"|awk '{if($1==1)print $0}'|wc -l;done
cat RJF.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat RJF.txt|grep -w "$id"|awk '{if($1==2)print $0}'|wc -l;done
cat RJF.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat RJF.txt|grep -w "$id"|awk '{if($1==3)print $0}'|wc -l;done
cat TLF.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat TLF.txt|grep -w "$id"|awk '{if($1==1)print $0}'|wc -l;done
cat TLF.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat TLF.txt|grep -w "$id"|awk '{if($1==2)print $0}'|wc -l;done
cat TLF.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat TLF.txt|grep -w "$id"|awk '{if($1==3)print $0}'|wc -l;done
cat WL.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat WL.txt|grep -w "$id"|awk '{if($1==1)print $0}'|wc -l;done
cat WL.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat WL.txt|grep -w "$id"|awk '{if($1==2)print $0}'|wc -l;done
cat WL.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat WL.txt|grep -w "$id"|awk '{if($1==3)print $0}'|wc -l;done
- 计算每个样本每个类别的总长度
cat BRA.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat BRA.txt|grep -w "$id"|awk '{if($1==1){s+=$10}}END{print s}';done
cat BRA.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat BRA.txt|grep -w "$id"|awk '{if($1==2){s+=$10}}END{print s}';done
cat BRA.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat BRA.txt|grep -w "$id"|awk '{if($1==3){s+=$10}}END{print s}';done
cat BN.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat BN.txt|grep -w "$id"|awk '{if($1==1){s+=$10}}END{print s}';done
cat BN.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat BN.txt|grep -w "$id"|awk '{if($1==2){s+=$10}}END{print s}';done
cat BN.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat BN.txt|grep -w "$id"|awk '{if($1==3){s+=$10}}END{print s}';done
cat Laos.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat Laos.txt|grep -w "$id"|awk '{if($1==1){s+=$10}}END{print s}';done
cat Laos.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat Laos.txt|grep -w "$id"|awk '{if($1==2){s+=$10}}END{print s}';done
cat Laos.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat Laos.txt|grep -w "$id"|awk '{if($1==3){s+=$10}}END{print s}';done
cat LX.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat LX.txt|grep -w "$id"|awk '{if($1==1){s+=$10}}END{print s}';done
cat LX.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat LX.txt|grep -w "$id"|awk '{if($1==2){s+=$10}}END{print s}';done
cat LX.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat LX.txt|grep -w "$id"|awk '{if($1==3){s+=$10}}END{print s}';done
cat RJF.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat RJF.txt|grep -w "$id"|awk '{if($1==1){s+=$10}}END{print s}';done
cat RJF.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat RJF.txt|grep -w "$id"|awk '{if($1==2){s+=$10}}END{print s}';done
cat RJF.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat RJF.txt|grep -w "$id"|awk '{if($1==3){s+=$10}}END{print s}';done
cat TLF.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat TLF.txt|grep -w "$id"|awk '{if($1==1){s+=$10}}END{print s}';done
cat TLF.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat TLF.txt|grep -w "$id"|awk '{if($1==2){s+=$10}}END{print s}';done
cat TLF.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat TLF.txt|grep -w "$id"|awk '{if($1==3){s+=$10}}END{print s}';done
cat WL.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat WL.txt|grep -w "$id"|awk '{if($1==1){s+=$10}}END{print s}';done
cat WL.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat WL.txt|grep -w "$id"|awk '{if($1==2){s+=$10}}END{print s}';done
cat WL.txt|sed "1d"|cut -f2|sort|uniq|while read id;do cat WL.txt|grep -w "$id"|awk '{if($1==3){s+=$10}}END{print s}';done
- 计算ROH重叠区域和热点区域
cd /public/ejye/other/GraduateData/zzhsnp/single_pop/ROH/ROH2022.2.17/pure20220217
plink --vcf BN.purein.vcf.gz --chr-set 33 --homozyg group --homozyg-window-snp 10 --homozyg-window-het 3 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-snp 65 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --out BN/BN
cat BN/BN.hom.overlap|grep "CON" #查看重叠区域，定义在所有样本都检测到的区域为热点区域（最多允许10%个体的缺失）
plink --vcf BRA.purein.vcf.gz --chr-set 33 --homozyg group --homozyg-window-snp 10 --homozyg-window-het 3 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-snp 65 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --out BRA/BRA
cat NRA/BRA.hom.overlap|grep "CON"
plink --vcf Laos.purein.vcf.gz --chr-set 33 --homozyg group --homozyg-window-snp 10 --homozyg-window-het 3 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-snp 65 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --out Laos/Laos
cat Laos/Laos.hom.overlap|grep "CON"
plink --vcf LX.purein.vcf.gz --chr-set 33 --homozyg group --homozyg-window-snp 10 --homozyg-window-het 3 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-snp 65 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --out LX/LX
cat LX/LX.hom.overlap|grep "CON"
plink --vcf RJF.purein.vcf.gz --chr-set 33 --homozyg group --homozyg-window-snp 10 --homozyg-window-het 3 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-snp 65 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --out RJF/RJF
cat RJF/RJF.hom.overlap|grep "CON"
plink --vcf TLF.purein.vcf.gz --chr-set 33 --homozyg group --homozyg-window-snp 10 --homozyg-window-het 3 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-snp 65 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --out TLF/TLF
cat TLF/TLF.hom.overlap|grep "CON"
plink --vcf WL.purein.vcf.gz --chr-set 33 --homozyg group --homozyg-window-snp 10 --homozyg-window-het 3 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-snp 65 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --out WL/WL
cat WL/WL.hom.overlap|grep "CON"
- 统计snp在ROH区域出现的频数
cd /public/ejye/other/GraduateData/zzhsnp/single_pop/ROH/ROH2022.2.17/pure20220217
vi TLF.position
#文件包含每个ROH片段的染色体 起始位置和终止位置三列，数据来自计算ROH得到的hom文件
bcftools filter -R TLF.position TLF.purein.vcf.gz >TLF.snp.ROH.vcf
gzip -d TLF.purein.vcf.gz
cat TLF.purein.vcf|grep -v "#"|cut -f1,2>1.tmp
cat TLF.snp.ROH.vcf|grep -v "#"|cut -f1,2>>1.tmp #合并所有的snp和在ROH中的snp
cat 1.tmp|sort -k1,1n -k2,2n |uniq -c >2.tmp #排序，求每个位点出现的次数
cat 2.tmp|awk '{print$2"\t"$3"\t"$1-1}'>TLF.manhattan.txt #整理得到画图文件
rm -f *.tmp
- 画曼哈顿图
R
mydata<-read.table("TLF.manhattan.txt",header = TRUE,sep="\t")
library(doBy)
library(plyr)
library(ggplot2)
library(cowplot)
mydata$SNP<-seq(1,nrow(mydata),1)
mydata$P<-mydata$number*100/21
n<- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,30,31,32,33)
for (i in n){mydata$SNP[which(mydata$CHR==i)]<-mydata$SNP[which(mydata$CHR==i)]+10000*(i-1)}
chr<- summaryBy(SNP~CHR, mydata, FUN = median)
data1=subset(mydata,P>=90)
p2<-ggplot(mydata,aes(SNP,P)) +
geom_point( data = mydata,pch=20,show.legend = F, size=1,aes(color=as.factor(mydata$CHR))) +
geom_point(data = data1,pch=20,show.legend = F, alpha=0.8, size=2,color="#e7b005") +
scale_color_manual(values = rep(c("#D3D3D3", "#666666"), 32 ))+
theme_bw(base_size = 16)+
theme(panel.grid = element_blank(),
axis.line = element_line(colour = 'black'),
panel.background = element_rect(fill = "transparent"),
axis.title.y = element_text(face = "bold"))+
labs(x ='Chromsome', y = expression("Percentage")) +
scale_x_continuous(breaks = chr$SNP.median,labels = chr$CHR,expand=(c(0.02,0)))+
scale_y_continuous(breaks =seq(-5,100,20),limits=c(-1,100),expand=(c(0.02,0.02)))+
geom_hline(yintercept =90,col="#fd348a",lty=1,lwd=0.25,alpha=0.5)+
theme(axis.text.x = element_text(angle = 270,size=9.5,face="bold", hjust = 0.5, vjust = 0.5))+theme(plot.title = element_text(hjust = 0.5,size=11,face="bold"))
ggsave(p2,filename="TLF.snp.png",width = 12,height = 4)
mydata<-read.table("TLF.manhattan.txt",header = TRUE,sep="\t")
library(doBy)
library(plyr)
library(ggplot2)
library(cowplot)
mydata$SNP<-seq(1,nrow(mydata),1)
n<- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,30,31,32,33)
for (i in n){mydata$SNP[which(mydata$CHR==i)]<-mydata$SNP[which(mydata$CHR==i)]+10000*(i-1)}
chr<- summaryBy(SNP~CHR, mydata, FUN = median)
data1=subset(mydata,number>=20)
p2<-ggplot(mydata,aes(SNP,number)) +
geom_point( data = mydata,pch=20,show.legend = F, size=1,aes(color=as.factor(mydata$CHR))) +
geom_point(data = data1,pch=20,show.legend = F, alpha=0.8, size=2,color="#e7b005") +
scale_color_manual(values = rep(c("#D3D3D3", "#666666"), 32 ))+
theme_bw(base_size = 16)+
theme(panel.grid = element_blank(),
axis.line = element_line(colour = 'black'),
panel.background = element_rect(fill = "transparent"),
axis.title.y = element_text(face = "bold"))+
labs(x ='Chromsome', y = expression("Percentage")) +
scale_x_continuous(breaks = chr$SNP.median,labels = chr$CHR,expand=(c(0.02,0)))+
scale_y_continuous(breaks =seq(-1,22,5),limits=c(-1,22),expand=(c(0.02,0.02)))+
geom_hline(yintercept =90,col="#fd348a",lty=1,lwd=0.25,alpha=0.5)+
theme(axis.text.x = element_text(angle = 270,size=9.5,face="bold", hjust = 0.5, vjust = 0.5))+theme(plot.title = element_text(hjust = 0.5,size=11,face="bold"))
ggsave(p2,filename="TLF.snp.number.png",width = 16,height = 4)
- 125个鸡连锁不平衡过滤+计算ROH
cd /public/ejye/other/GraduateData/zzhsnp/single_pop/ROH/chicken125
plink --vcf chicken125.zk.vcf.gz --maf 0.05 --recode vcf --out chicken125.qc --chr-set 33
cat chicken125.qc.vcf|"#" >chicken125.vcf
cat chicken125.qc.vcf|grep -v "#"|awk '$3=$1":"$2{print$0}'|tr " " "\t" >>chicken125.vcf
plink --vcf chicken125.vcf --recode --out chicken125 --chr-set 33
plink -file chicken125 --indep-pairwise 50 5 0.5 --out chicken125 --chr-set 33
plink --threads 10 --file chicken125 --extract chicken125.prune.in --recode vcf --out chicken125.prunein --chr-set 33
###########################
plink --vcf chicken125.prunein.vcf.gz --chr-set 33 --homozyg group --homozyg-window-snp 10 --homozyg-window-het 3 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-snp 50 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --out chicken125
cat chicken125.hom|awk '{print$4"\t"$7"\t"$8}'|sed "1d">chicken125.ROH.region
tabix -p vcf chicken125.prunein.vcf.gz
bcftools filter -R chicken125.ROH.region chicken125.prunein.vcf.gz |grep -v "#"|cut -f1,2>1.tmp
zcat chicken125.prunein.vcf.gz|grep -v "#"|cut -f1,2>>1.tmp
cat 1.tmp|sort -k1,1n -k2,2n |uniq -c >2.tmp
cat 2.tmp|awk '{print$2"\t"$3"\t"$1-1}'>chicken.manhattan.txt
rm -f *.tmp
--------------------------------------------------------------
mydata<-read.table("chicken.manhattan.txt",header = TRUE,sep="\t")
library(doBy)
library(plyr)
library(ggplot2)
library(cowplot)
mydata$SNP<-seq(1,nrow(mydata),1)
mydata$P<-mydata$number*100/125
n<- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,30,31,32,33)
for (i in n){mydata$SNP[which(mydata$CHR==i)]<-mydata$SNP[which(mydata$CHR==i)]+50000*(i-1)}
chr<- summaryBy(SNP~CHR, mydata, FUN = median)
data1=subset(mydata,P>=90)
p2<-ggplot(mydata,aes(SNP,P)) +
geom_point( data = mydata,pch=20,show.legend = F, size=1.4,aes(color=as.factor(mydata$CHR))) +
geom_point(data = data1,pch=20,show.legend = F, alpha=0.8, size=2,color="#e7b005") +
scale_color_manual(values = rep(c("#D3D3D3", "#666666"), 32 ))+
theme_bw(base_size = 16)+
theme(panel.grid = element_blank(),
axis.line = element_line(colour = 'black'),
panel.background = element_rect(fill = "transparent"),
axis.title.y = element_text(face = "bold"))+
labs(x ='Chromsome', y = expression("Percentage")) +
scale_x_continuous(breaks = chr$SNP.median,labels = chr$CHR,expand=(c(0.02,0.02)))+
scale_y_continuous(breaks =seq(0,100,20),limits=c(-1,100),expand=(c(0.02,0.02)))+
geom_hline(yintercept =90,col="#fd348a",lty=2,lwd=0.5,alpha=0.8)+
theme(axis.text.x = element_text(angle = 270,size=9.5,face="bold", hjust = 0.5, vjust = 0.5))+theme(plot.title = element_text(hjust = 0.5,size=11,face="bold"))
ggsave(p2,filename="chicken.snp.png",width = 16,height = 4)
改变ROH的检测阈值条件
cd /public/ejye/other/GraduateData/zzhsnp/single_pop/ROH/chicken125
plink --vcf chicken125.prunein.vcf.gz --chr-set 33 --homozyg group --homozyg-window-snp 10 --homozyg-window-het 2 --homozyg-window-missing 3 --homozyg-window-threshold 0.05 --homozyg-snp 50 --homozyg-kb 500 --homozyg-density 50 --homozyg-gap 1000 --out chicken125
cat chicken125.hom|awk '{print$4"\t"$7"\t"$8}'|sed "1d">chicken125.ROH.region
tabix -p vcf chicken125.prunein.vcf.gz
bcftools filter -R chicken125.ROH.region chicken125.prunein.vcf.gz |grep -v "#"|cut -f1,2>1.tmp
zcat chicken125.prunein.vcf.gz|grep -v "#"|cut -f1,2>>1.tmp
cat 1.tmp|sort -k1,1n -k2,2n |uniq -c >2.tmp
cat 2.tmp|awk '{print$2"\t"$3"\t"$1-1}'>chicken.manhattan.txt
rm -f *.tmp
--------------------------------------------------------------
mydata<-read.table("chicken.manhattan.txt",header = TRUE,sep="\t")
library(doBy)
library(plyr)
library(ggplot2)
library(cowplot)
mydata$SNP<-seq(1,nrow(mydata),1)
mydata$P<-mydata$number*100/125
n<- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,30,31,32,33)
for (i in n){mydata$SNP[which(mydata$CHR==i)]<-mydata$SNP[which(mydata$CHR==i)]+50000*(i-1)}
chr<- summaryBy(SNP~CHR, mydata, FUN = median)
data1=subset(mydata,P>=quantile(mydata$P,0.99))
p2<-ggplot(mydata,aes(SNP,P)) +
geom_point( data = mydata,pch=20,show.legend = F, size=1.4,aes(color=as.factor(mydata$CHR))) +
geom_point(data = data1,pch=20,show.legend = F, alpha=0.6, size=2.4,color="#e7b005") +
scale_color_manual(values = rep(c("#D3D3D3", "#666666"), 32 ))+
theme_bw(base_size = 16)+
theme(panel.grid = element_blank(),
axis.line = element_line(colour = 'black'),
panel.background = element_rect(fill = "transparent"),
axis.title.y = element_text(face = "bold"))+
labs(x ='Chromsome', y = expression("Percentage")) +
scale_x_continuous(breaks = chr$SNP.median,labels = chr$CHR,expand=(c(0.02,0.02)))+
scale_y_continuous(breaks =seq(0,100,20),limits=c(-1,100),expand=(c(0.02,0.02)))+
geom_hline(yintercept =quantile(mydata$P,0.99),col="#fd348a",lty=2,lwd=0.5,alpha=0.8)+
theme(axis.text.x = element_text(angle = 270,size=9.5,face="bold", hjust = 0.5, vjust = 0.5))+theme(plot.title = element_text(hjust = 0.5,size=11,face="bold"))
ggsave(p2,filename="chicken.snp.png",width = 14,height = 4)
------------------------------------------------------------------
data1=subset(mydata,P>=50)
p2<-ggplot(mydata,aes(SNP,P)) +
geom_point( data = mydata,pch=20,show.legend = F, size=1.4,aes(color=as.factor(mydata$CHR))) +
geom_point(data = data1,pch=20,show.legend = F, alpha=0.6, size=2.4,color="#e7b005") +
scale_color_manual(values = rep(c("#D3D3D3", "#666666"), 32 ))+
theme_bw(base_size = 16)+
theme(panel.grid = element_blank(),
axis.line = element_line(colour = 'black'),
panel.background = element_rect(fill = "transparent"),
axis.title.y = element_text(face = "bold"))+
labs(x ='Chromsome', y = expression("Percentage")) +
scale_x_continuous(breaks = chr$SNP.median,labels = chr$CHR,expand=(c(0.02,0.02)))+
scale_y_continuous(breaks =seq(0,100,20),limits=c(-1,100),expand=(c(0.02,0.02)))+
geom_hline(yintercept =50,col="#fd348a",lty=2,lwd=0.5,alpha=0.8)+
theme(axis.text.x = element_text(angle = 270,size=9.5,face="bold", hjust = 0.5, vjust = 0.5))+theme(plot.title = element_text(hjust = 0.5,size=11,face="bold"))
ggsave(p2,filename="chicken.snp50.png",width = 14,height = 4)
画分布热图
- 1号染色体
cd /mnt/c/Users/TE/Desktop/叶尔江ROH分析/125只鸡/严格条件
echo "sample chr start end" |tr " " "\t">chicken125.chr1.roh.hetmap
cat chicken125.hom|awk '{if($4==1)print$0}'|awk '{if(($7>=70000000&&$7<=80000000)||($8>=70000000&&$8<=80000000)){print$1"\t"$4"\t"$7"\t"$8}}'|awk '{if($3<70000000){print $1"\t"$2"\t"70000001"\t"$4}else if($4>80000000){print $1"\t"$2"\t"$3"\t"79999999}else {print$0}}'>>chicken125.chr1.roh.hetmap
然后把缺失的样本起始和终止坐标用NA表示，并在每个群体的最后行加上该群体的代码99，并使坐标充满整个区域以区分不同的群体
library(ggplot2)
data=read.table(file="chicken125.chr1.roh.hetmap",header = T,sep = "\t")
ggplot(data)+scale_x_continuous(breaks =seq(70,80,2),limits=c(70,80),expand=(c(0,0)))+scale_y_continuous(expand=(c(0.01,0.01)))+theme_bw(base_size = 16)+theme(panel.grid = element_blank(),axis.line = element_line(colour = 'black'),panel.background = element_rect(fill = "transparent"),axis.text.y =element_blank(),axis.ticks.y=element_blank(),axis.text.x = element_text(size=9.5,face="bold", hjust = 0.5, vjust = 0.5))+labs(x ='Chromsome 1 Position (Mb)', y = expression("")) +geom_rect(aes(xmin=data$start/1000000, xmax=data$end/1000000, ymin=as.numeric(data$sample)-0.4,ymax=as.numeric(data$sample)+0.18),fill='#72e972')+geom_vline(xintercept =74.247989,col="gray",lty=2,lwd=1.2,alpha=1)
ggsave("chr1rohhet.pdf",width=8,height=12)
- 25号染色体
cd /mnt/c/Users/TE/Desktop/叶尔江ROH分析/125只鸡/严格条件
echo "sample chr start end" |tr " " "\t">chicken125.chr25.roh.hetmap
cat chicken125.hom|awk '{if($4==25)print$0}'|awk '{if(($7>=0&&$7<=4000000)||($8>=0&&$8<=4000000)){print$1"\t"$4"\t"$7"\t"$8}}'|awk '{if($3<=0){print $1"\t"$2"\t"1"\t"$4}else if($4>4000000){print $1"\t"$2"\t"$3"\t"3999999}else {print$0}}'>>chicken125.chr25.roh.hetmap
然后把缺失的样本起始和终止坐标用NA表示，并在每个群体的最后行加上该群体的代码99，并使坐标充满整个区域以区分不同的群体
library(ggplot2)
data=read.table(file="chicken125.chr25.roh.hetmap",header = T,sep = "\t")
ggplot(data)+scale_x_continuous(breaks =seq(0,4,1),limits=c(0,4),expand=(c(0,0)))+scale_y_continuous(expand=(c(0.01,0.01)))+theme_bw(base_size = 16)+theme(panel.grid = element_blank(),axis.line = element_line(colour = 'black'),panel.background = element_rect(fill = "transparent"),axis.text.y =element_blank(),axis.ticks.y=element_blank(),axis.text.x = element_text(size=9.5,face="bold", hjust = 0.5, vjust = 0.5))+labs(x ='Chromsome 25 Position (Mb)', y = expression("")) +geom_rect(aes(xmin=data$start/1000000, xmax=data$end/1000000, ymin=as.numeric(data$sample)-0.4,ymax=as.numeric(data$sample)+0.18),fill='#72e972')+geom_vline(xintercept =0.736312,col="gray",lty=2,lwd=1.2,alpha=1)+geom_vline(xintercept =1.438820,col="gray",lty=2,lwd=1.2,alpha=1)
ggsave("chr25rohhet.pdf",width=8,height=12)
- 22号染色体
cd /mnt/c/Users/TE/Desktop/叶尔江ROH分析/125只鸡/严格条件
echo "sample chr start end" |tr " " "\t">chicken125.chr22.roh.hetmap
cat chicken125.hom|awk '{if($4==22)print$0}'|awk '{if(($7>=0&&$7<=6000000)||($8>=0&&$8<=6000000)){print$1"\t"$4"\t"$7"\t"$8}}'|awk '{if($3<=0){print $1"\t"$2"\t"1"\t"$4}else if($4>6000000){print $1"\t"$2"\t"$3"\t"5999999}else {print$0}}'>>chicken125.chr22.roh.hetmap
然后把缺失的样本起始和终止坐标用NA表示，并在每个群体的最后行加上该群体的代码99，并使坐标充满整个区域以区分不同的群体
library(ggplot2)
data=read.table(file="chicken125.chr22.roh.hetmap",header = T,sep = "\t")
ggplot(data)+scale_x_continuous(breaks =seq(0,6,1),limits=c(0,6),expand=(c(0,0)))+scale_y_continuous(expand=(c(0.01,0.01)))+theme_bw(base_size = 16)+theme(panel.grid = element_blank(),axis.line = element_line(colour = 'black'),panel.background = element_rect(fill = "transparent"),axis.text.y =element_blank(),axis.ticks.y=element_blank(),axis.text.x = element_text(size=9.5,face="bold", hjust = 0.5, vjust = 0.5))+labs(x ='Chromsome 22 Position (Mb)', y = expression("")) +geom_rect(aes(xmin=data$start/1000000, xmax=data$end/1000000, ymin=as.numeric(data$sample)-0.4,ymax=as.numeric(data$sample)+0.18),fill='#72e972')+geom_vline(xintercept =3.062142,col="gray",lty=2,lwd=1.2,alpha=1)
ggsave("chr22rohhet.pdf",width=8,height=12)
- 2号染色体
cd /mnt/c/Users/TE/Desktop/叶尔江ROH分析/125只鸡/严格条件
echo "sample chr start end" |tr " " "\t">chicken125.chr2.roh.hetmap
cat chicken125.hom|awk '{if($4==2)print$0}'|awk '{if(($7>=50000000&&$7<=58000000)||($8>=50000000&&$8<=58000000)){print$1"\t"$4"\t"$7"\t"$8}}'|awk '{if($3<=50000000){print $1"\t"$2"\t"50000001"\t"$4}else if($4>58000000){print $1"\t"$2"\t"$3"\t"57999999}else {print$0}}'>>chicken125.chr2.roh.hetmap
然后把缺失的样本起始和终止坐标用NA表示，并在每个群体的最后行加上该群体的代码99，并使坐标充满整个区域以区分不同的群体
library(ggplot2)
data=read.table(file="chicken125.chr2.roh.hetmap",header = T,sep = "\t")
ggplot(data)+scale_x_continuous(breaks =seq(50,58,2),limits=c(50,58),expand=(c(0,0)))+scale_y_continuous(expand=(c(0.01,0.01)))+theme_bw(base_size = 16)+theme(panel.grid = element_blank(),axis.line = element_line(colour = 'black'),panel.background = element_rect(fill = "transparent"),axis.text.y =element_blank(),axis.ticks.y=element_blank(),axis.text.x = element_text(size=9.5,face="bold", hjust = 0.5, vjust = 0.5))+labs(x ='Chromsome 2 Position (Mb)', y = expression("")) +geom_rect(aes(xmin=data$start/1000000, xmax=data$end/1000000, ymin=as.numeric(data$sample)-0.4,ymax=as.numeric(data$sample)+0.18),fill='#72e972')+geom_vline(xintercept =53.490610,col="gray",lty=2,lwd=1.2,alpha=1)+geom_vline(xintercept =53.805665,col="gray",lty=2,lwd=1.2,alpha=1)
ggsave("chr2rohhet.pdf",width=8,height=12)
