plink 2.0 (http://www.cog-genomics.org/plink/2.0 ) has efficient PCA implementations. For smaller datasets (let's say <5000 samples),

plink2 --bfile myfile --pca 10 --out qcvcf

works; larger datasets can be handled with

plink2 --bfile myfile --pca approx 10 --out qcvcf

@MAPK,

I haven't used shellfish.py so couldnt comment on it. You can probably use GCTA tool to compute PCs. Using the plink files first generate GRM files and the use the GRM file to compute PCs.

# compute GRM file
gcta64 --bfile  $bfile  --autosome --maf 0.01 --make-grm-bin --out $bfile --thread-num 28 

# compute principal components using GCTA
 gcta64 --grm-bin $bfile --pca 20 --out $bfile --thread-num 28

Here is the link to GCTA tool: http://cnsgenomics.com/software/gcta/