SEMs can be represented graphically as path diagrams representing regression and covariance relations among variables.42 In path diagrams, observed variables are represented as squares and unobserved (i.e., latent) variables are represented as circles. Regressions relationships between variables are represented as one-headed arrows pointing from the independent variable to the dependent variable. Covariance relationships between variables are represented as two-headed arrows linking the two variables. The variance of a variable (i.e., the covariance between a variable and itself), is represented as a two-headed arrow connecting the variable to itself. In Genomic SEM, we represent the genetic component of each phenotype with a circle, as the genetic component is a latent variable that is not directly measured, but is inferred from LDSC (it is the phenotype itself that is observed in the raw data that is used to produce the summary statistics). SNPs are directly measured, and are therefore represented as squares. When all elements in a SEM are represented in a path diagram, the diagram contains the full system of algebraic equations needed to estimate the full set of SEM parameters, θ, and produce the model-implied covariance matrix, ∑(θ).
