Plot PCA for ethnicity from any given VCF file combined with 1000 genomes data

gistfile1.txt

## Plot PCA for ethnicity of a given VCF file

## Import publication theme
## A nice R ggplot theme that I found online. Original can be found here:
## https://rpubs.com/Koundy/71792

theme_Publication <- function(base_size=14, base_family="helvetica") {
  library(grid)
  library(ggthemes)
  (theme_foundation(base_size=base_size, base_family=base_family)
  + theme(plot.title = element_text(face = "bold",
                                    size = rel(1.2), hjust = 0.5),
          text = element_text(),
          panel.background = element_rect(colour = NA),
          plot.background = element_rect(colour = NA),
          panel.border = element_rect(colour = NA),
          axis.title = element_text(face = "bold",size = rel(1)),
          axis.title.y = element_text(angle=90,vjust =2),
          axis.title.x = element_text(vjust = -0.2),
          axis.text = element_text(), 
          axis.line = element_line(colour="black"),
          axis.ticks = element_line(),
          panel.grid.major = element_line(colour="#f0f0f0"),
          panel.grid.minor = element_blank(),
          legend.key = element_rect(colour = NA),
          legend.position = "bottom",
          legend.direction = "horizontal",
          legend.key.size= unit(0.2, "cm"),
          legend.margin = unit(0, "cm"),
          legend.title = element_text(face="italic"),
          plot.margin=unit(c(10,5,5,5),"mm"),
          strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
          strip.text = element_text(face="bold")
  ))
  
}

scale_fill_Publication <- function(...){
  library(scales)
  discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
  
}

scale_colour_Publication <- function(...){
  library(scales)
  discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
  
}

## Import libraries
library(gdsfmt)
library(SNPRelate)
library(ggplot2)
library(ggdendro)
library(RColorBrewer)
library(plyr)
library(dplyr)

## Set working directory
## Set color palette for populations
color_palette <- c("#912CEE","#f40000","#4DBD33","#4885ed","#ffc300","#2b2b28")

## Specify your VCF file
vcf.fn <- "my.vcf"

# Transform the VCF file into gds format
snpgdsVCF2GDS(vcf.fn, "Variants_1000G_exomes.gds", method="biallelic.only")

## Data Analysis
## open the newly created gds file
genofile <- snpgdsOpen('Variants_1000G_exomes.gds')

## LD based SNP pruning
set.seed(1000)
snpset <- snpgdsLDpruning(genofile, ld.threshold=0.2)
snpset.id <- unlist(snpset)

## Perform PCA on the SNP data
pca <- snpgdsPCA(genofile, snp.id=snpset.id, num.thread=4)
pc.percent <- pca$varprop*100
head(round(pc.percent, 2))

### Plotting
### Plot results from PCA analysis of variant data
tab12 <- data.frame(sample.id = pca$sample.id,
                  EV1 = pca$eigenvect[,1], # the first eigenvector
                  EV2 = pca$eigenvect[,2], # the second eigenvector
                  stringsAsFactors = FALSE)

tab34 <- data.frame(sample.id = pca$sample.id,
                 EV3 = pca$eigenvect[,3], # the first eigenvector
                 EV4 = pca$eigenvect[,4], # the second eigenvector
                 stringsAsFactors = FALSE)

plot(tab34$EV2, tab34$EV1, xlab="eigenvector 3", ylab="eigenvector 4")

## Assign ethnicity description from 1000G file
sample.id <- read.gdsn(index.gdsn(genofile, "sample.id"))
populations <- read.table("All_samples_populations.tab",header=T)

tab <- data.frame(sample.id = pca$sample.id,
                  EV1 = pca$eigenvect[,1],    # the first eigenvector
                  EV2 = pca$eigenvect[,2],    # the second eigenvector
                  stringsAsFactors = FALSE)
colnames(tab) <- c("sample.id","EV1","EV2")

PCA_allVariants2 <- ggplot(tab,aes(x=EV1,y=EV2)) +
  geom_point(size=2) +
  scale_x_continuous("PC1") +
  scale_y_continuous("PC2") +
  scale_colour_manual(values=color_palette[1:length(unique(tab$pop))]) +
  theme_Publication()
PCA_allVariants2
ggsave(file="Variants_PCA_pointsize2.svg",plot=PCA_allVariants2)

ggsave(file="Variants_PCA_pointsize1.svg",plot=PCA_allVariants1)

Hello Florian
I removed the chrM from my data file then change the chr1 chr2 chr3 into 1 2 3 as per the fasta file to make the vcf file and the fasta file have the same.I am using the GRCh37 / hg19 reference genome.

first i was gettinh chrM not found then i removed the chrM and i get the chr1 not found error which explains the difference between my fasta and my vcf file so i used emeditor an editor to change my vcf file (as other methods were not preserving the header).
now chrM is gone the annotation of fasta and vcf is same but still i get the error
code :
bcftools norm -m-any --check-ref w -f human_g1k_v37.fasta \
data.vcf | \
bcftools annotate -x ID -I +'%CHROM:%POS:%REF:%ALT' | \
bcftools norm -Ob --rm-dup both \
> data.bcf ;
`

I am giving to the orginal data(it is single sample) which will make it is easier for check the annotation error
Link to my data
I think the plot should look like this(like the plot in the biostar tutorial)
Thanks alot for helping and keef safe !
i wish for your best health