We computed the maximum absolute differences between the single-SNP inclusion probabilities in each dataset under scenario B to assess the fine-mapping accuracy of FINEMAP and CAVIARBF (Table 1). The small differences (max < 0.11, median <6×10−4) show that for practical purposes FINEMAP achieves similar accuracy as CAVIARBF despite concentrating only on a small but relevant subset of all possible causal configurations (see Discussion). Figure 4 shows details of those SNPs in Scenario B for which the difference between the methods is larger than 0.01. We see that by ignoring the large majority of very improbable configurations, FINEMAP slightly overestimates the largest probabilities, that typically belong to the truly causal SNPs, and underestimates smaller probabilities, that most often belong to the non-causal SNPs. Table 1Percentiles of absolute maximum differences between FINEMAP’s and CAVIARBF’s single-SNP inclusion probabilities in Scenario Bm=150 |K12345aMax5e−78e−32e−21e−1–99th percentile4e−72e−38e−34e−2–95th percentile3e−75e−43e−31e−2–Median4e−84e−72e−56e−4–aCAVIARBF could not compute single-SNP inclusion probabilities due to a memory allocation failure (std::bad_alloc).
