I understand what GWAS is and I'm able to perform certain tests with the p-values, etc. But what I am having a hard time wrapping my head around is what PCA on GWAS means.
So let's say I have 100,000 individuals and genotype data for 10 million SNPs. And I'm analyzing this for 10 different diseases. I also have p-value information for each of the 10 diseases for the 10 million SNPs.
At my work, they were throwing around the following phrases. I'm not quite sure what exactly it means.
- "Perform PCA on GWAS." Does this mean, I'm performing PCA on the genotype data or p-value data? I've done PCA on classic machine learning problems like "iris." But I'm not able to quite wrap my head around what PCA information tells me in terms of this genetic data.
- "PCA on individual diseases vs. PCA on entire GWAS" What exactly is the difference. I realize this is a broken statement, but they said we have two sets of PCs. One on the individual diseases which are unique, and then PCs on the entire GWAS for all 10 diseases.
Sorry if my questions is not properly asked, I realize it's probably heavily abusing notation.
So, my matrix has 100k rows and 10 million columns. Then, PC1, for example, is a vector with 100k values, each value corresponding to each individual. What exactly does PC1 value that's associated with the first individual tell me with regards to the PC1 value that's associated with 2nd individual, and so on. My current understanding if you think of a number line that represents PC1, then each of the PC values (corresponding to each individual) represents where on the number line that individual falls.
How exactly does this eliminate/account for variation in population stratification?