We have introduced a latent causal variable (LCV) model to identify causal relationships among genetically correlated pairs of complex traits. We applied LCV to 52 traits, finding that many trait pairs do exhibit partially or fully genetically causal relationships. Our method represents an advance for two main reasons. First, unlike existing MR methods, LCV reliably distinguishes between genetic correlation and full or partial genetic causation. Positive findings using LCV are more likely to reflect true causal effects. Second, we define and estimate the genetic causality proportion (gcp) to quantify the degree of causality. This parameter, which provides information orthogonal to the genetic correlation or the causal effect size, enables a non-dichotomous description of the causal architecture.
