Question: PCA: summarize results from principal components analysis and look for population heterogeneity
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filippo.corponi20 wrote:

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!

modified 15 months ago by Kevin Blighe50k • written 15 months ago by filippo.corponi20
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Kevin Blighe50k wrote:

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
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("African","Hispanic","East Asian","Caucasian","South Asian"), fill=c("yellow","forestgreen","grey","royalblue","black"))

Sample image

[need to manage colours and layout yourself] [from: Produce PCA bi-plot for 1000 Genomes Phase III in VCF format]

Kevin

ADD COMMENTlink modified 15 months ago • written 15 months ago by Kevin Blighe50k