gmap software validation

These recorded simulation results assess selected behavior on ten fixed datasets from the shared design. They do not revalidate the statistical method or cover every setting. The use snippets show the current package interfaces. This report step summarizes retained artifacts and does not rerun analyses. The gmap guide describes current inputs and options.

Use

mapping_stat <- gmap::gmap_stat(stat, Glist,
                          phenotype_variance = phenotype_variance)
fit <- gmap::gmap(mapping_stat[c("A", "B")], Glist,
            regions = regions, method = "multi_effect",
            prior = prior, control = control)
shared <- gmap::gmap_coloc(
  stat$A, stat$B, region = names(regions)[1],
  phenotype_sd1 = sqrt(phenotype_variance[["A"]]),
  phenotype_sd2 = sqrt(phenotype_variance[["B"]]))

Results

Check	Ten-replicate result
Component sets containing the causal marker	40/40
Regional fits converged	40/40
Mean H4 in shared-signal region	1.000
Mean H3 in distinct-signal region	1.000

Held-out prediction, regional mapping and colocalisation across the fixed simulations.

The first two regions were constructed for these hypotheses and contain one causal variant per trait. This is a focused regional check, not broad credible-set coverage or colocalisation calibration across realistic loci.

Component credible sets · Colocalisation probabilities