It is common to fit eigenvectors (PCs) from principal component analysis (PCA) in single SNP association studies to correct for possible population structure23,24. We show that fitting the first 10 PCs and one chromosome at a time or fitting all chromosomes simultaneously without fitting PCs, led to similar estimates of the variance explained by each chromosome (Supplementary Fig. 7), which suggests that the majority of variance attributed to population structure is well captured by the first 10 PCs in these data.
