We had a helpful discussion on the selection of covariates for GWAS analysis on Day4. While GRMs capture genetic relatedness, including PCs is also recommended to account for overall genotype similarity across populations.
I am working on a problem where I use the logistic SAIGE model and include PCs to account for population stratification. However, the statistical significance of my results changes depending on how many PCs I include.
Several suggestions came up during the discussion:
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Use a GRM plus PC1–PC4 if the data come from a single country, and include more PCs if the data come from multiple countries.
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Check lambda.
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Generate an elbow plot for the PCs and identify the PCs that explain the greatest amount of variance.
I tried looking for publications that support these approaches. I was wondering whether anyone has come across articles that discuss the nuanced use of these parameters as covariates, especially related to point 1.