Theoretical analysis reveals LAVA struggles with type-I error in genetic correlation, suggesting HDL-L is safer.
The LAVA method has been proposed for detecting local genetic correlations between traits. We show, using theoretical analysis and simulations, that LAVA’s “fixed-effects” model does not estimate genetic correlation in the statistical genetics sense, but instead detects the presence of regional genetic effects. This mis-specification leads to inflated type-I error under the null, particularly when causal variants are sparse, and can produce apparent “correlations” arising solely from unshared, trait-specific signals. In contrast, the random-effects model implemented in HDL-L correctly targets variant-level genetic correlation and remains well-calibrated. Our results indicate that LAVA findings may be widely misinterpreted, and we recommend caution and the use of random-effects–based approaches such as HDL-L for valid inference.
No takes yet. Share an insight, caveat, or question.
Li et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: