We generated datasets where causal SNPs had highly correlated proxies since this is a setting where an in-exhaustive search could theoretically have problems. Five hundred datasets were generated under each combination of m and K in scenarios A and B using the following linear model: y=∑c∈Cβcgc+N(ϵ|0,σ2I), where C is the set of causal SNPs, gc the vector of genotypes at the cth causal SNP, βc and fc respectively the effect size and minor allele frequency of the cth causal SNP and σ2=1−∑c∈C2fc(1−fc)βc2. The number of causal SNPs was five in scenario A and B. In each dataset, the causal SNPs were randomly chosen among those variants that had highly correlated proxies (absolute correlation greater than 0.5) among the other variants. The effect sizes of the causal SNPs were specified so that the statistical power at a significance level of 5×10−8 was approximately 0.5. Single-SNP testing using a linear model was performed to compute z-scores. Each set of z-scores was then analyzed with CAVIARBF (default parameters) and FINEMAP (100 iterations saving the top 50 000 evaluated causal configurations). For both methods,
