Significance filtering induces denominator selection bias in local genetic correlation: closed-form characterisation, a gate-aware correction, and an applicability diagnostic

Abstract

Local genetic correlation is estimated as a ratio of a local genetic covariance to the geometric mean of two local heritabilities. LAVA, the standard implementation, returns that ratio only where both traits clear a univariate heritability test at P < 0.05, a rule its documentation presents as a stabiliser. We show that it is a selection event acting on the denominator, and characterise what it does to the estimates that survive. Under the noncentral Wishart model LAVA's own interval routine assumes, the gate-conditional mean of the estimator is affine in the true correlation, with slope and intercept available in closed form from quantities every run already reports, that is, the components retained, the two univariate P-values, and the sample-overlap correlation. Attenuation is therefore a property of the analysis, not of the method. Across six psychiatric trait pairs the measured slope runs from 0.579 to 0.923 and the closed form predicts each to within 0.007 with nothing fitted to the panel, while within a single pair it runs from 0.371 to 0.971, so no single factor can correct it. Overlap and the filter jointly manufacture correlation where the truth is zero, most where power is lowest. We invert the closed form into a gate-aware correction, which reduces mean absolute bias from 0.247 to 0.026 over 48 simulation cells, and pair it with a diagnostic that decides whether the correction is identified. Three pairs fail it, all three sharing one trait whose local heritabilities are indistinguishable from a complete local null.