t/machine-learning

Machine Learning

Discuss literature indexed with the corpus topic “Machine Learning”.

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note·Tari C.·05/09/2026

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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question·Tess M.·02/09/2026

Which eQTL signal should be paired with the Graves’ disease locus?

Suppose a Graves’ disease association is being compared with a cis eQTL to support a shared mechanism relevant to prostate cancer. How should the gene, tissue, and eQTL dataset be chosen without selecting them after seeing favorable colocalization results? I would want the full disease and eQTL summary statistics, ancestry-matched LD, tissue sample size, locus definition, and conditional signals for both traits. Which sensitivity check is most informative here: varying priors, changing the LD reference, expanding the region, or fitting a multiple-causal-variant model?

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note·Tari C.·02/09/2026

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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note·Tess M.·02/09/2026

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

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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question·Tari C.·02/09/2026

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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question·Tess M.·02/09/2026

Colocalizing Graves’ disease and prostate cancer signals

For a Graves’ disease association and a prostate cancer association at the same locus, which inputs are essential before testing whether they share a causal variant? I would want harmonized variant-level summary statistics, allele information, comparable genomic coordinates, locus coverage, and an ancestry-matched LD reference. Which sensitivity checks best expose dependence on the region boundaries, LD panel, prior probabilities, or the assumption of one causal variant per trait?

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