pop genomics

5.7_Population_Genomics_commands.sh

+
+### STEP 1. SNP calling using ANGSD (output in plink format) and basic filtering:
+angsd -bam list_bam_leon.txt -minMapQ 30 -minQ 20 -minInd 13 -baq 1 -doCounts 1 -setMinDepth 30 -setMaxDepth 450 -GL 1 -out data_raw -ref UMD_5_genome_nospaces_60.fa -doMajorMinor 4 -doMaf 2 -SNP_pval 1e-6 -doGeno 4 -doPost 1 -doPlink 2 -P 10
+
+# Edit tfam file (population should be in the first column):
+cat data_raw.tfam | awk 'print {$2,$1,$3,$4,$5,$6}' > data_raw.tfam2
+mv data_raw.tfam2 data_raw.tfam
+
+
+
+### STEP 2. Population stratification analysis: PCA, genomic distance matrix and cluster as implemented in Plink
+
+# Filter data using plink:
+plink  --tfile data_raw --geno 0.3 --maf 0.05 --hwe 1e-5 --make-bed --out data_clean
+
+# PCA:
+plink --bfile data_clean --pca 3 header --out data.struc1
+
+#### Plot PCA using R (library ggplot2): ##########
+PCA <- read.table("data.struc1.eigenvec", header=T, quote="\"")
+head (PCA)
+library (ggplot2)
+c <- ggplot(PCA, aes(y=PC1, x=PC2, colour=factor(FID)))
+c + geom_point(size = 3)
+####################################################
+
+# Other structure analysis:
+plink --bfile data_clean --cluster -K 3 --out data.struc2
+plink --bfile data_clean --distance triangle 1-ibs --out data.struc3
+
+
+
+
+### STEP 3. LD:
+plink --bfile data_clean --r2 --ld-snp Chr1_10002364 --ld-window-kb 1000 --ld-window 500 --ld-window-r2 0.1 --out data.LD
+ 
+# Plot pattern of LD as the genomic distance increases in R (library ggplot2):
+LD <- read.table("data.LD.ld", header=T, quote="\"")
+head (LD)
+LD$dist <-  LD$BP_B - LD$BP_A
+p <- ggplot(LD, aes(x=dist, y=R2))
+p + stat_smooth(span = 1, se=TRUE)
+
+ 
+ 
+