We computed principal components using an algorithm (fastPCA38) that performs well on datasets with hundreds of thousands of samples by approximating only the top n principal components that explain the most variation, in which n is specified in advance. We computed the top 40 principal components using a set of 407,219 unrelated, high quality samples and 147,604 high quality markers pruned to minimise linkage disequilibrium39. We then computed the corresponding principal component-loadings and projected all samples onto the principal components, thus forming a set of principal component scores for all samples in the cohort (Supplementary Information).
