To explore a wider range of potential factor solutions, we next conducted exploratory factor analyses (EFA) using the stats R package.43 We tested two to four factor solutions using oblique (i.e., correlated) and orthogonal (i.e., uncorrelated) rotations. We tested solutions with a maximum of four factors to ensure factors would have more than two indicators. We conducted two sets of EFAs and follow-up confirmatory factor analyses (CFAs), which replicated results from the exploratory models, one in which we used the genetic covariance matrix of odd chromosomes to test exploratory models and the genetic covariance matrix of even chromosomes to test follow-up models, and one in which we flipped the use of odd and even chromosomes. We ran our models in this way to account for the possibility that causal variants were enriched on certain chromosomes thereby impacting the results of the exploratory models.
