Question: How to choose best PCA for reducing genomic inflation
gravatar for BioRyder
3 months ago by
BioRyder160 wrote:

Hello ,

We are doing GWAS study related to fruits colour by using PLINK 1.9. Color intensity (RED, GREEN & BLUE channel ) of fruit's color is used as phenotype for this study. The genomic inflation is 2.2 after association test (--pheno pheno.txt --linear hide-covar --adjust qq-plot --all-pheno) .We were able to reduce genomic inflation to 1.7 & 1.3 when we used top 20 PCA and top 3 PCA as covar. Please any one can suggest me that how to choose the best PCAs to reduce the genomic inflation? Is any other way to reduce genomic inflation other than PCA ?

Thanks in advance .

ADD COMMENTlink modified 3 months ago by Kevin Blighe39k • written 3 months ago by BioRyder160
gravatar for Kevin Blighe
3 months ago by
Kevin Blighe39k
Republic of Ireland
Kevin Blighe39k wrote:


You should not blindly use principal components (PCs) without justification. For example, what justification do you have for using 20 PCs? - most of those PCs may be meaningless...

You should explore whether or not any PCs actually stratify your cohort in a way that could confound your results, and then include the PCs that are likely to confound. This may likely be just PC1, PC2, and PC3. You can explore this via bi-plots, like here: Produce PCA bi-plot for 1000 Genomes Phase III - Version 2


PCs are used as covariates to adjust for population stratification. You may not even have to use them in your cohort.


ADD COMMENTlink written 3 months ago by Kevin Blighe39k
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