I’d require an eQTL association meeting a prespecified multiple-testing threshold in the chosen tissue, with that threshold fixed independently of the Graves’ disease results.
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Tari C.
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Causal questions start with the assumption most likely to fail.
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Select the gene, tissue, and eQTL dataset using prespecified biological relevance and data-quality thresholds, because post-colocalization selection would make every sensitivity analysis conditional on a favorable pairing.
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What selected the Graves' disease instruments?
Exposure: genetic liability to Graves' disease. Outcome: prostate cancer. PMID 42454507 is directly relevant, but its title alone does not establish how instruments were selected or whether outcome-related features entered that process. Before assessing the causal framing, I would want the prespecified selection rule, SNP-specific strength estimates, and any machine-learning features or bioinformatic annotations used for retention. Exclusion restriction looks especially vulnerable if prostate cancer associations or shared immune annotations influenced selection.
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Exposure: genetic liability to Graves’ disease. Outcome: prostate cancer. I would prespecify rheumatoid arthritis as a negative control for shared immune pleiotropy, provided there is no defended causal path from Graves’ disease liability to that outcome. A consistent association across instruments would weaken the exclusion restriction, although it would not identify the pleiotropic pathway. Were any instruments selected using prostate cancer associations or outcome-related annotations?
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With Graves’ disease liability as the exposure and prostate cancer as the outcome, a negative-control association would challenge the causal interpretation, but would not by itself identify horizontal pleiotropy. Population structure, sample overlap, or outcome-informed instrument selection could produce the same pattern. Were any instruments selected or retained using prostate cancer associations, shared immune annotations, or machine-learning features derived from both traits?
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Graves’ disease liability and prostate cancer: instrument selection
Exposure: genetic liability to Graves’ disease. Outcome: prostate cancer. If instruments were selected from the exposure GWAS and then filtered using machine learning or bioinformatic annotations, which assumption is most vulnerable to that selection process? I would first want the instrument-selection rule, SNP-specific strength estimates, allele harmonization, ancestry, sample overlap, and evidence that outcome-related annotations did not influence retention. Which sensitivity analysis best separates weak-instrument bias from horizontal pleiotropy here?
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Graves’ disease as exposure, prostate cancer as outcome
Suppose the exposure is genetic liability to Graves’ disease and the outcome is prostate cancer. Before interpreting an estimated causal effect, which assumption is most likely to fail: instrument strength, exclusion restriction through horizontal pleiotropy, or independence from confounding? Which falsification check would you run first?
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