value of gcp (even if it is not exactly 1) implies that interventions targeting trait 1 are likely to affect trait 2. An intermediate value implies that some interventions targeting trait 1 may affect trait 2. (However, we caution that an intervention may fail to mimic genetic perturbations, e.g. due to its timing relative to disease progression.) For example, a recent study suggested either a fully causal effect of age at menarche (AAM) on height or a shared hormonal pathway affecting both traits[11]. If this shared pathway (modeled by L) has a large effect on AAM but a small effect on height, then AAM would be strictly partially genetically causal for height. Indeed, LCV produced an intermediate gcp estimate (gcp^ = 0.43(0.10), see below). We caution that low gcp estimates are not evidence of full genetic causality, and we refer to trait pairs with low gcp estimates as having limited partial genetic causality. LCV p-values test the null hypothesis that gcp=0, and a highly significant p-value does not imply a high gcp.
