We found that a correlated factors model provided the best fit (Figure 1, Tables S6–7) to both the genetic and the phenotypic covariance matrices [phenotypic model: (χ2(26)=4252.963, Comparative Fit Index=.994, standardized root mean square residual=.041), genetic model: (χ2(26)=142.689, Comparative Fit Index=.982, standardized root mean square residual=.067)]. That is, the patterns of genetic and phenotypic correlations among the AUDIT items could both be represented by a factor model with two correlated factors: one that captured the covariance among alcohol consumption items (items 1–3, henceforth “Consumption”) and one that captured the covariance among alcohol-related problems (items 4–10, henceforth “Problems”). These two latent factors were highly correlated with each other, phenotypically (rp=.825, SE=.002) and genetically (rg=.801, SE=.037). Nearly all items had large factor loadings across both levels of analyses except item 1, which consistently had markedly smaller factor loadings and larger residual variances.
