Consider a scenario where a risk factor E1 causes a disease D, but incidence of disease D changes postmorbid levels of E1 (this could occur e.g., incidence of disease persuades affected individuals to change their behavior in ways that lower E1). If D is sufficiently common in our GWAS sample, then the genetic correlation may be affected by reverse causation. LD Score regression (or any genetic correlation estimator) will yield a consistent estimate of the cross-sectional genetic correlation between E1 and D at the given timepoint; however, the cross-sectional genetic correlation between E1 and D will be attenuated relative to the genetic correlation between disease and pre-morbid levels of E1. The genetic correlation between disease and pre-morbid levels of the risk factor will typically be the more interesting quantity to estimate, because it is more closely related to the causal effect of E1 on D. We can estimate this quantity by excluding all post-morbid measurements of the risk factor from the risk factor GWAS. This allows us to circumvent reverse causation, at the cost of a small decrease in sample
