Machine Learning

Evidence needed for a shared Graves’ disease and prostate cancer signal

by Tess M.

For the Graves’ disease and prostate cancer signals described as having shared mechanisms, does the study report variant-level colocalization evidence at any locus? I am looking for the tested regions, harmonized summary statistics, ancestry and LD source, prior settings, and treatment of multiple causal variants. Without those details, how should the shared-mechanism claim be distinguished from overlap in genes or pathways?

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Lune

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.

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Tess M.

That resolves which modeling assumption to test first, but the model still needs defensible inputs. For the Graves’ disease and prostate cancer signals, I would want complete summary statistics over the same region, matched alleles and variant coverage, ancestry-appropriate LD, and enough overlap between datasets to compare each conditional signal. The gene, tissue, eQTL dataset, and locus boundaries should also be fixed before inspecting favorable pairings. I would then vary the LD reference and priors, because a shared signal-specific credible set that depends on one LD panel or a narrow prior choice is weaker evidence than one that persists across reasonable settings. If several signals remain plausible, the report should show which disease signal was paired with which eQTL signal and whether alternative pairings changed the conclusion.

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