Functionally informed fine-mapping and polygenic localization of complex trait heritability.

Calibration, power and computational cost of fine-mapping methods in main simulations.(a-b) FDR at PIP=0.95 (a) and PIP=0.5 (b). Upper dashed horizontal lines denote conservative FDR estimates. Lower dotted horizontal lines denote anti-conservative FDR estimates, which are not recommended (Supplementary Note). (c-d) Power at PIP=0.95 (c) and PIP=0.5 (d). The first bar of each method uses non-functionally informed fine-mapping (denoted −), and the second uses functionally informed fine-mapping (denoted +). (e) The average runtime required to fine-map a 3Mb locus in a genome-wide analysis (log scale). The first bar of each method uses non-functionally informed fine-mapping (denoted −), and the second uses functionally informed fine-mapping (denoted +). (f) The total runtime required to fine-map different numbers of loci, for functionally informed fine-mapping methods only (log scale). The runtimes of PolyFun + SuSiE and PolyFun + FINEMAP are sub-linear because they include the fixed preprocessing cost of computing prior causal probabilities (630 minutes). Error bars denote standard errors. Numerical results, including results for CAVIARBF2− and CAVIARBF2, and including panel (f) results for non-functionally informed methods, are reported in Supplementary Table 4.

Assessing the individual impact of step 5 of PolyFun (specifying prior causal probabilities in proportion of the re-estimated per-SNP heritabilities) via perturbation analysis, by randomly permuting estimated prior causal probabilities.The figure is similar to Extended Data Figure 1 but applies a different perturbation (randomly permuting estimated prior causal probabilities). Numerical reports are provided in Supplementary Table 6.

Visualization of fine-mapping results for UK Biobank traits.We display an ideogram of all 2,225 PIP>0.95 fine-mapped SNPs identified by PolyFun + SuSiE across 49 UK Biobank traits. Traits are color-coded into groups (see legend and Supplementary Table 8). White circles indicate SNPs that are pleiotropic for ≥2 genetically uncorrelated traits, with circles to the right of a white circle denoting the genetically uncorrelated traits (max of 5 colored circles due to space limitations). Numerical results are reported in Supplementary Table 10.

Functional enrichment of PolyFun + SuSiE fine-mapped common SNPs for UK Biobank traits.The figure is analogous to Figure 4 but uses PIPs computed by PolyFun + SuSiE instead of SuSiE. Numerical results are reported in Supplementary Table 26.

Functional enrichment of SuSiE fine-mapped MAF>0.001 SNPs for UK Biobank traits.The figure is analogous to Figure 4 but uses MAF>0.001 SNPs instead of common (MAF>0.05) SNPs. Numerical results are reported in Supplementary Table 27.

Functional enrichment of SuSiE fine-mapped low-frequency and rare SNPs for UK Biobank traits.The figure is analogous to Figure 4 but uses only low-frequency and rare SNPs (0.05>MAF>0.001) instead of common (MAF>0.05) SNPs. Numerical results are reported in Supplementary Table 28.

Summary of fine-mapping results for UK Biobank traits.(a) the number of SNPs with PIP>0.95 identified by SuSiE (black bars) and PolyFun + SuSiE (gray bars) across 16 genetically uncorrelated traits in the UK Biobank. Traits are ordered by PolyFun + SuSiE results. The numbers in the legend refer to the sum of all 49 traits analyzed. (b) The proportion of MAF>0.001 SNP-heritability (hg2) tagged by lead GWAS SNPs (gray bars) and by PolyFun + SuSiE PIP>0.95 SNPs (black bars). Traits are ordered as in panel (a). For hair color, the hg2 tagged by PIP>0.95 SNPs is greater than hg2 tagged by lead GWAS SNPs. MPV: Mean platelet volume; BMD: bone mineral density; MCH: mean corpuscular hemoglobin; MC: monocyte count; HLSRC: high light scatter reticulocyte count; FEV1/FVC: ratio of forced expiratory volume to forced vital capacity; DBP: diastolic blood pressure; FVC: forced vital capacity. Numerical results are reported in Supplementary Tables 9,14.

Examples of the advantages of functionally-informed fine-mapping for UK Biobank traits.We report four examples where PolyFun + SuSiE identified a fine-mapped common SNP (PIP>0.95) but SuSiE did not (PIP<0.5 for all SNPs within 1Mb). Circles denote PolyFun + SuSiE PIPs and squares denote SuSiE PIPs. SNPs are shaded according to their prior causal probabilities as estimated by PolyFun. The top PolyFun + SuSiE SNP is labeled (next to its PolyFun + SuSiE PIP and its SuSiE PIP). The annotation of each top PolyFun + SuSiE SNP that is most enriched among SuSiE PIP>0.95 SNPs (Methods) is reported in parentheses below its label. Asterisks denote lead GWAS SNPs. Numerical results are reported in Supplementary Table 16.

Functional enrichment of SuSiE fine-mapped common SNPs for UK Biobank traits.We report the functional enrichment of fine-mapped common SNPs (defined as the proportion of common SNPs in a PIP range lying in an annotation divided by the proportion of genome-wide common SNPs lying in the annotation) for 5 selected binary annotations, meta-analyzed across 14 genetically uncorrelated UK Biobank traits with ≥10 PIP>0.95 SNPs (log scale). The proportion of common SNPs lying in each binary annotation is reported above its name. The horizontal dashed line denotes no enrichment. Error bars denote standard errors. Numerical results for all 50 main binary annotations and all traits are reported in Supplementary Table 17.

Polygenic localization results for UK Biobank traits.(a) M50% estimates across 16 genetically uncorrelated traits. For each trait, we report the number of top-ranked common SNPs (using PolyFun + SuSiE posterior per-SNP heritability estimates for ranking) causally explaining 50% of common SNP heritability, and its standard error (log scale). The horizontal dashed line denotes the total number of common SNPs in the analysis (7.0 million). (b-d) The proportion of common SNP heritability of (b) hair color, (c) height, and (d) number of children explained by different numbers of top-ranked SNPs, for all 7.0 million common SNPs (left) and the 5,000 top-ranked common (right). Gray shading denotes standard errors. Dashed black lines denote a null model with a constant per-SNP heritability. We also report the number of top-ranked SNPs causally explaining 50% of common SNP heritability, denoted M50%. Discontinuities in the slope indicate transitions between SNP bins. Numerical results for all 49 UK Biobank traits are reported in Supplementary Table 31.

Assessing the individual impact of step 1 of PolyFun (estimating functional enrichment) via perturbation analysis, by randomly shuffling different proportions of annotation coefficient estimates.For each evaluated value of the proportion of shuffled annotation coefficient estimates, we report the number of experiments having each obtained FDR level >0 (left panel) and the number of experiments having each obtained power level >0 (right panel), out of 1000 experiments. FDR and power are reported with respect to identifying PIP≥0.95 SNPs. Experiments with FDR=0 (resp. power=0) are not reported in the left panel (resp. right panel) to improve clarity. Numerical reports are provided in Supplementary Table 6.

Assessing the individual impact of step 2 of PolyFun (estimating per-SNP heritabilities on odd/even chromosomes) via perturbation analysis, by using both odd and even chromosomes to estimate functional enrichment.The figure is similar to Extended Data Figure 1 but applies a different perturbation (using both odd and even chromosomes to estimate functional enrichment). Numerical reports are provided in Supplementary Table 6.

Assessing the individual impact of step 3 of PolyFun (partitioning all SNPs into 20 bins of similar per-SNP heritability) via perturbation analysis, by varying the number of per-SNP heritability bins.The figure is similar to Extended Data Figure 1 but applies a different perturbation (changing the number of per-SNP heritability bins). Numerical reports are provided in Supplementary Table 6.

Assessing the individual impact of step 4 of PolyFun (re-estimating per-SNP heritabilities within each bin excluding the target chromosome) via perturbation analysis, by not excluding the target chromosome from the re-estimation procedure.The figure is similar to Extended Data Figure 1 but applies a different perturbation (disables the exclusion of the target chromosome, either when using the default sample size N=320K or when using a smaller sample size of N=10K). Numerical reports are provided in Supplementary Table 6.