docker run --rm -it -v $PWD:$PWD -w $PWD \ registry.cn-hangzhou.aliyuncs.com/wybioinfo/tidyverse:4.3 bash docker run --user 0:0 --rm -it -v $PWD:$PWD -w $PWD \ biocontainers/plink1.9:v1.90b6.6-181012-1-deb_cv1 bash
gemma=/workspace/Foxtail-millet-data-analysis/gemma-0.98.5-linux-static-AMD64 plink=/workspace/Foxtail-millet-data-analysis/plink name=`head -n 310 OTU.rank.phe.id | awk '{print $1}' | tail -n 1` # OTU_48 mkdir -p host_OTU/OTU_48 $gemma --bfile ./millet.snp.gwas -p ./OTU.rank.phe -miss 0.1 -maf 0.01 -gk -outdir host_OTU/OTU_48 -o snp.kinship $plink --bfile ./millet.snp.gwas --pca 10 --out host_OTU/OTU_48/PC10 cut -d ' ' -f 3- host_OTU/OTU_48/PC10.eigenvec | sed 's/^/1 /' > host_OTU/OTU_48/snp_PC10 $gemma --bfile ./millet.snp.gwas -p ./OTU.rank.phe -miss 0.1 -n 310 -k host_OTU/OTU_48/snp.kinship.cXX.txt -c host_OTU/OTU_48/snp_PC10 -lmm -outdir host_OTU/OTU_48 -o OTU_48 echo "SNP CHROM BP P" > host_OTU/OTU_48/OTU_48.assoc.for_plot.txt tail -n +2 host_OTU/OTU_48/OTU_48.assoc.txt | perl -ne '@tmp=split; print "si$tmp[0]:$tmp[2] $tmp[0] $tmp[2] $tmp[11]\n"' >> host_OTU/OTU_48/OTU_48.assoc.for_plot.txt
文章 、github
> data(pig60K) #calculated p-values by MLM > data(cattle50K) #calculated SNP effects by rrblup > head(pig60K) SNP Chromosome Position trait1 trait2 trait3 1 ALGA0000009 1 52297 0.7738187 0.51194318 0.51194318 2 ALGA0000014 1 79763 0.7738187 0.51194318 0.51194318 3 ALGA0000021 1 209568 0.7583016 0.98405289 0.98405289 4 ALGA0000022 1 292758 0.7200305 0.48887140 0.48887140 5 ALGA0000046 1 747831 0.9736840 0.22096836 0.22096836 6 ALGA0000047 1 761957 0.9174565 0.05753712 0.05753712 > head(cattle50K) SNP chr pos Somatic cell score Milk yield Fat percentage 1 SNP1 1 59082 0.000244361 0.000484255 0.001379210 2 SNP2 1 118164 0.000532272 0.000039800 0.000598951 3 SNP3 1 177246 0.001633058 0.000311645 0.000279427 4 SNP4 1 236328 0.001412865 0.000909370 0.001040161 5 SNP5 1 295410 0.000090700 0.002202973 0.000351394 6 SNP6 1 354493 0.000110681 0.000342628 0.000105792
作为样本数据集，前三列分别是 SNPs 的名称、染色体、位置，其余列为 GWAS 的 pvalue 或 GS/GP 对性状的影响，性状的数量是无限的。注意: 如果绘制 SNP _ Missouri，只需要前三列。
library(CMplot) df <- read.table("~/Foxtail-millet-data-analysis/Figure1_and_Figure5/data/OTU_106.assoc.txt",header = T) CMplot(head(df,n = 2000), plot.type = "m", LOG10=TRUE, cex=0.4, threshold=2.2E-5,, threshold.lty=2, threshold.col="black", amplify=TRUE, signal.col="red", signal.cex=1,width=14, bin.size=1e6, chr.den.col=c("darkgreen", "yellow", "red"), verbose=TRUE, height=5, file="pdf") data(pig60K) head(pig60K) head(df) CMplot(pig60K,type="p",plot.type="m",LOG10=TRUE,threshold=NULL,file="jpg",file.name="",dpi=300, file.output=TRUE,verbose=TRUE,width=14,height=6,chr.labels.angle=45)
https://github.com/genetics-statistics/GEMMA