Table 1. Summary of performance for various fine-mapping methods.

				Variant Ranking Across All Loci		Variant Ranking Independently at Each Locus
Method	Summary Data	Incorporates Annotation	Assumed Num. of Causal Variants	Causals Identified		Causals Identified
				50%	90%	50%	90%
P-value	Yes	No	n/a	5.74	12.60	2.94	19.15
CAT score [12]	No	No	n/a	2.04	13.29	2.56	17.80
LLARRMA [13]	No	No	Mult	1.98	21.93	2.46	23.11
piMass-RB [14]	No	No	Mult	2.83	16.31	2.18	15.15
Maller et al. [5]	Yes	No	Single	2.68	25.44	2.96	19.13
fgwas (no annot)	Yes	No	Single	2.69	25.48	2.95	19.11
PAINTOR (no annot,1CV)	Yes	No	Single	2.69	22.49	2.95	19.09
fgwas [10]	Yes	Yes	Single	1.95	24.77	2.05	17.37
PAINTOR (1CV)	Yes	Yes	Single	1.95	21.51	2.03	17.43
PAINTOR (no annot)	Yes	No	Mult	1.76	12.25	2.24	16.86
PAINTOR	Yes	Yes	Mult	1.26	10.42	1.61	13.68
PAINTOR True	Yes	Yes	Mult	1.23	10.22	1.59	13.48

Methods were benchmarked using the average number of SNPs per locus selected to find (50%,90%) of all causal variants. We simulated a trait with 100 risk loci explaining fine-mapped through sequencing of N = 10,000 samples and assessed accuracy only at loci that harbor at least one casual variant (64 loci on the average). We explored two methods to prioritizing variants: (1) “Variant Ranking Across All Loci” prioritizes SNPs across all loci while (2) “Variant Ranking Independently at Each Locus”, first prioritizes variants at each risk locus followed by merging across all loci. We note that PAINTOR 1CV and/or no annot corresponds to running PAINTOR assuming a single causal variant and/or not providing access to annotations. PAINTOR True did not empirically estimate enrichment but used the true enrichment values for each functional annotation data.