This is a test version of Biostars. For the public version, visit https://www.biostars.org.
PCA: summarize results from principal components analysis and look for population heterogeneity

Hi! I am conducting a GWAS. I used plink 1.9 (--pca) to obtain first 10 population principal components which I entered in the regression model as covariates to correct for population stratification. I would like to the use the .eigenvec file, which is the output from plink --pca containing population principal components, to summarize the population structure and look for potential heterogeneity. I would be super grateful if anyone could help or refer me to a practical tutorial. Thanks a lot!

pca principal components analysis plink
• 6,310 views • link
updated by karenunr100 • karenunr100 • written by F_cm_C • F_cm_C

hi there, shouldn't the command be "apply(eigen, 2, sd) ...." for the columns of the data table?

• 0 views • link
written by karenunr100 • karenunr100

Oh, pray, tell... Should it be?

• 0 views • link
written by Kevin Blighe Kevin Blighe

1 answer

You know for sure that you need to include all 10 principal components as covariates? One should only include the principal components that are actually segregating your groups of interests and that are therefore likely to affect the statistical inferences that you make from your data. This is 'adjusting' for population stratification.

With a .eigenvec file, one can easily generate a principal components bi-plot for any pairwise combination of PCs:

R

setwd("/YourDir/")
options(scipen=100, digits=3)

#Read in the eigenvectors
eigen <- data.frame(read.table("plink.eigenvec", header=FALSE, skip=0, sep=" "))
rownames(eigen) <- eigen[,2]
eigen <- eigen[,3:ncol(eigen)]

summary(eigen)

#Determine the proportion of variance of each component
proportionvariances <- ((apply(eigen, 1, sd)^2) / (sum(apply(eigen, 1, sd)^2)))*100

plot(eigen[,1], eigen[,2])

legend("topleft", bty="n",
  cex=1.5, title="",
  c("Population 1","Population 2","Population 3","Population 4","Population 5"),
  fill = c("yellow","forestgreen","grey","royalblue","black"))

Sample image

[need to manage colours and layout yourself] biplot-new

[from: Produce PCA bi-plot for 1000 Genomes Phase III in VCF format (old)]

Kevin

• 1 views • link
written by Kevin Blighe Kevin Blighe

Really insightful, Thx Kevin.

• 0 views • link
written by Hamid Ghaedi Hamid Ghaedi

Log in to answer this question.

More posts like this