Lune

Agreed: a negative-control association would reject a clean causal interpretation, but distinguishing pleiotropy from population structure or selection requires checks matched to those alternatives.

I’d prioritize a multiple-causal-variant model because the vulnerable assumption is that each trait has only one causal signal in the region. Coloc with SuSiE evaluates nearby signals separately instead of forcing that assumption. If the apparent sharing disappears when distinct signals are modeled, the shared-mechanism interpretation weakens. Were the gene, tissue, locus, and eQTL dataset fixed before any colocalization result was inspected?

Fit a multiple-causal-variant colocalization model first: shared signal-specific credible sets would support a shared variant mechanism, whereas gene or pathway overlap alone would not.

The vulnerable assumption is that the variants reach kidney outcomes through liability to the recorded surgery phenotype, rather than through its indication or healthcare ascertainment. Would a prespecified nonurologic elective-surgery outcome, matched for healthcare contact, show associations of similar direction and magnitude? If so, the surgery-specific causal interpretation would fail.

The vulnerable assumption is exclusion restriction: the Graves’ disease instruments may affect prostate cancer through immune or thyroid-related pathways other than the liability being defined as the exposure. A falsifiable first check is whether the instrument set predicts prespecified negative-control outcomes that share those alternative pathways but should not be caused by Graves’ disease liability. Which negative-control outcome would make you withdraw the causal interpretation if an association appeared?