| name | causal-genomics-fine-mapping |
| display_name | Fine-Mapping: R-native (SuSiE / FINEMAP) |
| description | Resolves GWAS associations to candidate causal variants and credible sets via SuSiE, susie_rss, FINEMAP, CAVIAR, DAP-G, PAINTOR, PolyFun, SuSiEx, MultiSuSiE, and FOCUS. Use when narrowing a GWAS lead SNP to a 95 percent credible set, choosing between in-sample and reference LD, calibrating non-sparse loci with SuSiE-inf or FINEMAP-inf, integrating functional priors via PolyFun, fine-mapping across ancestries with SuSiEx, diagnosing LD mismatch via estimate_s_rss and kriging_rss, handling HLA or long-range LD, or feeding credible sets into coloc.susie for colocalization. |
| tool_type | r |
| primary_tool | susieR |
Version Compatibility
Reference examples tested with: susieR 0.12.27+, coloc 5.2.3+, FINEMAP 1.4.2+, PolyFun (head of omerwe/polyfun 2024), PAINTOR V3.0, SuSiEx (head of getian107/SuSiEx), DAP-G (head of xqwen/dap), pyfocus 0.8+, R 4.3+, PLINK 1.9 / 2.0.
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('susieR') then ?susie_rss to confirm argument names (e.g., prior_weights vs prior_variance semantics)
- CLI:
finemap --help, SuSiEx --help, PAINTOR --help, dap-g --help to confirm flags
- Python:
polyfun.py --help
If a call throws an error about an argument that no longer exists, introspect the installed function and adapt rather than retrying.
Fine-Mapping
"Narrow my GWAS locus to the variants likely to be causal" -> Fit a sparse Bayesian regression that propagates LD into posterior inclusion probabilities (PIPs) and credible sets, then validate that credible sets correspond to physically reasonable haplotypes given the LD reference.
- R (summary statistics + LD):
susieR::susie_rss(z, R, n, L=10) + estimate_s_rss LD diagnostic
- R (individual-level genotypes):
susieR::susie(X, y, L=10)
- CLI (shotgun stochastic search):
finemap --sss --in-files master.z --n-causal-snps 5 --sss-tol 0.001
- CLI (cross-ancestry joint):
SuSiEx --sst_file=eur.sst,eas.sst --n_gwas=N1,N2 --ref_file=eur.bim,eas.bim --ld_file=eur_ld,eas_ld --pop=EUR,EAS --chr_col=1,1 --snp_col=2,2 --bp_col=3,3 --a1_col=4,4 --a2_col=5,5 --eff_col=6,6 --se_col=7,7 --pval_col=8,8 --chr=<chr> --bp=<start,end> --out_dir=<dir> --out_name=<name> (column-number flags and --ld_file are required; see SuSiEx --help)
- Python (functional priors):
polyfun.py --compute-h2-L2 -> per-SNP priors -> susie_rss with prior_weights=
- Python (TWAS fine-mapping):
focus finemap on gene-level Z-scores
Fine-mapping is a Bayesian model selection problem; LD is not noise but structured prior information. Most failure modes trace back to one of three issues: (a) LD reference mismatched to the GWAS sample; (b) the sparse-effects prior being wrong for the locus (polygenic background); or (c) too small an L cap. The estimate_s_rss() lambda and kriging_rss() per-SNP diagnostic catch (a) before downstream credible sets are reported.
Algorithmic Taxonomy
| Tool | Model | Input | Strength | Fails when |
|---|
| SuSiE / susie_rss (Wang 2020 JRSSB 82:1273; Zou 2022 PLoS Genet) | Iterative Bayesian sum-of-single-effects (IBSS), variational | Individual-level (X, y) or (z, R, n) | Fast; native PIP + credible sets; pluggable priors; default in modern pipelines | Reference LD mismatched to GWAS sample; locus dominated by polygenic background; >L true effects |
| SuSiE-inf / FINEMAP-inf (Cui 2024 Nat Genet 56:162) | SuSiE + infinitesimal random-effect component | (z, R, n) | Calibrated credible sets when locus is non-sparse (polygenic shoulder around a sparse causal); recommended for biobank-scale GWAS | Very small loci with truly sparse architecture (over-conservative); slower convergence |
| FINEMAP (Benner 2016 Bioinformatics 32:1493) | Shotgun stochastic search over causal configurations | .z + .ld + .master files | Exact Bayes factors at small k; widely cited | Slow at L > 5; binary install only (christianbenner.com); same LD-mismatch fragility as SuSiE |
| CAVIAR / CAVIARBF (Hormozdiari 2014 Genetics 198:497) | Exhaustive enumeration up to k causals | (z, R) | Exact posterior at small k | Combinatorial explosion beyond k=6; legacy method largely superseded by SuSiE |
| DAP-G (Wen 2016 AJHG 98:1114) | Deterministic posterior approximation with adaptive scan | SBAMS format; TORUS for enrichment priors | Fast at QTL scale (whole-transcriptome); pairs with TORUS hierarchical priors | SBAMS format is awkward; less ubiquitous tooling |
| PAINTOR (Kichaev 2014 PLoS Genet 10:e1004722) | EM with binary functional annotations | (z, R, A) per locus | Locus-level functional priors; multi-trait variant | Single-trait mode often matched by PolyFun + SuSiE; slower than SuSiE |
| PolyFun + SuSiE/FINEMAP (Weissbrod 2020 Nat Genet 52:1355) | Stratified LDSC genome-wide -> per-SNP prior_weights | GWAS sumstats + pre-baked baseline-LF | Most powerful single-trait functional prior; PIPs sharpen 1.5-3x | Requires matched-ancestry baseline-LF; runs in two stages |
| SuSiEx (Yuan 2024 Nat Genet 56:1841) | Joint cross-ancestry SuSiE; shared causal, population-specific LD | Per-pop sumstats + per-pop LD reference | Smaller credible sets than per-ancestry meta or marginal fine-mapping; principled when causal variants are shared | Trans-ethnic heterogeneity violated (population-specific causals); ancestry must be cleanly assigned |
| MultiSuSiE (Tashman 2024 medRxiv / 2025 Nat Genet) | Cross-ancestry SuSiE variant; flexible heterogeneity | Per-pop sumstats + per-pop LD | Similar to SuSiEx; alternative implementation | Same as SuSiEx; newer, less battle-tested |
| FOCUS / MA-FOCUS (Mancuso 2019 Nat Genet 51:675) | Probabilistic TWAS fine-mapping over gene models | TWAS Z-scores + gene LD (predicted expression) | Identifies likely causal gene among co-regulated TWAS hits; cross-ancestry MA-FOCUS variant | Requires pre-computed expression weights (e.g., FUSION/PrediXcan); gene-level rather than variant-level inference |
Methodology evolves; verify the latest susieR vignette and the SuSiE-inf paper before locking on a single method. Wang Lab maintains susieR; the IBSS algorithm is stable but argument semantics (e.g., prior_weights vs prior_variance) have changed across versions.
Decision Tree by Experimental Scenario
| Scenario | Recommended workflow | Why |
|---|
| Individual-level genotypes available (UKB, in-house cohort) | susie(X, y, L=10) | In-sample LD is exact; no mismatch fragility |
| Summary statistics only, ancestry matches reference panel | susie_rss(z, R, n, L=10) + estimate_s_rss diagnostic | Standard external-LD pattern; verify lambda < 0.05 |
| Single-locus EUR GWAS, sparse architecture | susie_rss with L=10, baseline functional priors optional | Most-common setting; SuSiE default works |
| Locus with strong polygenic shoulder (biobank scale) | SuSiE-inf (Cui 2024) | Adds infinitesimal component; calibrates non-sparse PIPs |
| Multi-ancestry GWAS (EUR + EAS + AFR) | SuSiEx with per-pop sumstats and LD | Joint inference shrinks credible sets; per-ancestry meta loses LD information |
| Locus with > 5 expected independent signals (HLA, lipid loci) | susie_rss with L=20-30 | Default L=10 caps signal count; HLA needs extension |
| TWAS hits with co-regulated genes | FOCUS / MA-FOCUS | Variant-level fine-mapping cannot distinguish co-regulated gene candidates |
| Want functional priors (coding, conserved, regulatory) | PolyFun -> susie_rss with prior_weights | Genome-wide SLDSC priors sharpen PIPs more than locus-level annotations |
| QTL fine-mapping (eQTL, sQTL, caQTL) at transcriptome scale | DAP-G + TORUS OR susie_rss per gene | DAP-G is built for QTL throughput; SuSiE works per gene |
| Low-N QTL (GTEx tissue panel, N < 1000) | susie_rss with coverage = 0.9 (or 0.8); document choice | Default 0.95 returns very wide credible sets at low power; report the relaxed coverage explicitly in methods |
| HLA region (chr6:28-34 Mb) or chr8 inversion | Specialized workflow: stratify haplotypes; consider HLA-specific imputation; or exclude | LD structure is too complex; standard methods unreliable |
| Cross-feed into colocalization | susie_rss -> coloc.susie() | Modern coloc operates on credible sets, not single SNPs |
Critical LD Diagnostic Block (susie_rss)
Goal: Detect LD reference mismatch before reporting credible sets.
Approach: estimate_s_rss() quantifies the global Z-score / LD inconsistency as a scalar; kriging_rss() identifies individual SNPs whose Z-scores are inconsistent with the LD reference (typically genotyping errors, strand flips, or wrong reference panel).
library(susieR)
s_hat <- estimate_s_rss(z = z_scores, R = ld_matrix, n = N)
cond_z <- kriging_rss(z = z_scores, R = ld_matrix, n = N)
fit <- susie_rss(z = z_scores, R = ld_matrix, n = N, L = 10, estimate_residual_variance = TRUE)
Skipping this block is the dominant cause of irreproducible fine-mapping. Always run before reporting credible sets.
Per-Tool Failure Modes
LD reference mismatch (most common)
Trigger: External LD matrix from 1000 Genomes / UK Biobank reference used for a GWAS conducted on a different cohort or ancestry mix.
Mechanism: Z-scores reflect the GWAS sample's LD; the reference R does not. The susie_rss likelihood depends on z' R^{-1} z being consistent with the modeled effects, and inconsistency manifests as spurious credible sets containing tag SNPs from the reference but not from the discovery cohort.
Symptom: estimate_s_rss() lambda > 0.05; kriging_rss() flags many SNPs with |z_obs - z_exp| > 3; credible sets contain physically distant SNPs (anti-correlated in LD with the lead) or include all SNPs at the locus.
Fix: Use in-sample LD whenever the cohort genotypes are accessible (compute with plink --r2 square on the GWAS samples themselves). When only summary statistics are available, ancestry-stratify the LD reference exactly (e.g., 1000G EUR FIN+CEU+GBR+IBS+TSI for a Northern European GWAS, not full EUR). For mixed-ancestry GWAS, fine-map per ancestry then meta-analyze, or move to SuSiEx.
Non-sparse architecture (biobank scale)
Trigger: Locus with one strong signal plus hundreds of weakly associated SNPs (polygenic shoulder); typical at biobank scale.
Mechanism: Vanilla SuSiE assumes a sparse sum-of-single-effects prior. With polygenic background, the model misallocates effects, producing inflated credible sets or many small spurious ones. Cui 2024 (Nat Genet 56:162) showed PIPs from SuSiE in this regime are systematically miscalibrated.
Symptom: Many small credible sets (5-15 per locus); replication in independent cohorts fails for non-lead credible sets; PIP distribution has a heavy tail.
Fix: Use SuSiE-inf or FINEMAP-inf (Cui 2024). These augment the sum-of-single-effects with an infinitesimal random-effect component that absorbs polygenic background. Source: github.com/FinucaneLab/fine-mapping-inf.
L too small
Trigger: Locus with > 5 independent signals (HLA region, APOC1/APOE, LPA, IL6R region for some traits).
Mechanism: SuSiE assumes at most L independent effects. When the true number exceeds L, some signals are absorbed into existing components, distorting PIPs and credible sets for the captured signals.
Symptom: length(fit$sets$cs) equals L (all L slots used); credible set purity for higher-indexed sets is low (fit$sets$purity[,'min.abs.corr'] < 0.5); fits with larger L change top-PIP variants.
Fix: Increase L iteratively (L=10 -> 20 -> 30) until length(fit$sets$cs) < L (susieR auto-prunes unsupported effects so the returned CS count is the effective L). For HLA, start at L=30. The cost is mostly computational, not statistical: SuSiE prunes unused slots, so L=30 is safe when L=10 was right.
prior_weights vs prior_variance confusion (PolyFun integration)
Trigger: Passing PolyFun output to susie_rss with prior_variance=polyfun_priors (wrong argument).
Mechanism: prior_variance in susie_rss is a single scalar (or vector of length L) for the per-effect variance, NOT a per-SNP probability. prior_weights is the per-SNP causal probability vector (sums to ~1). Passing PolyFun's per-SNP prior to prior_variance is silently accepted but applies a numerically nonsensical per-effect variance.
Symptom: PIPs nearly identical to the uniform-prior fit; functional annotations appear to have no effect.
Fix: Use prior_weights = polyfun_priors$snpvar. Verify with ?susie_rss in the installed version. Reference: github.com/omerwe/polyfun README, Weissbrod 2020 supplementary methods.
Credible-set misinterpretation
Trigger: Reporting per-variant PIP without distinguishing "in credible set" from "high PIP".
Mechanism: The 95 percent credible-set guarantee is P(causal variant in set) >= 0.95. Per-variant PIPs within a set do not necessarily sum to 1 across all variants, and PIPs across overlapping sets can double-count posterior mass.
Symptom: Reporting "the top PIP variant" when the credible set is wide (size > 50); claiming a single variant is causal when the set contains 30 high-LD SNPs.
Fix: Always report (a) number of credible sets, (b) size of each set, (c) purity (fit$sets$purity[,'min.abs.corr']), (d) the top PIP variant within the set as the candidate lead. The credible set is the unit of inference; the top PIP variant is a candidate, not a conclusion.
Cross-ancestry with single-ancestry LD
Trigger: Multi-ancestry meta-analyzed GWAS, then susie_rss with EUR LD.
Mechanism: Meta-analysis z-scores reflect a weighted mix of population LD structures; no single-population LD matrix matches.
Fix: Move to SuSiEx (joint cross-ancestry SuSiE; Yuan 2024). Per-ancestry fine-mapping followed by manual merging loses the shared-causal-variant information that SuSiEx exploits.
Case-control GWAS passing Ntotal instead of Neff
Trigger: Passing n = N_total to susie_rss() for case-control GWAS derived from logistic regression.
Mechanism: susie_rss expects the effective sample size that determined the standard errors. For case-control logistic regression, Neff = 4 / (1/Ncase + 1/Ncontrol); when cases are rare, total N can exceed Neff by 25x or more. Passing Ntotal rescales z-scores into a regime SuSiE never sees and makes the implied prior variance wrong.
Symptom: PIPs systematically biased; credible sets either too narrow (PIPs collapse to a single SNP that is not robust) or too wide (PIPs flatten); replication poor; sometimes z-score scale warnings from susieR.
Fix: Neff = 4 / (1/Ncase + 1/Ncontrol). Example: Ncase=5000, Ncontrol=495000 -> Neff ~= 19,800 (NOT 500,000). For quantitative traits from linear regression, n = N_total is correct. Reference: Privé 2022 HGG (bigsnpr documents Neff handling); Willer 2010 Bioinformatics (METAL Neff convention).
Allele Harmonization with the LD Reference
Trigger: Effect allele in GWAS sumstats differs from the coding/A1 allele in the LD reference panel; or palindromic SNPs (A/T, C/G) carried without strand resolution.
Mechanism: susie_rss treats z and R as defined on the same allele coding. If the effect allele is swapped relative to the LD-reference A1, the sign of z is wrong and the LD row/column for that SNP is implicitly flipped. SNPs matching by rsID can silently swap alleles between sumstats and reference, breaking the z' R z consistency the model relies on.
Symptom: estimate_s_rss lambda inflated despite ancestry-matched panel; kriging_rss flags many SNPs with |z_obs - z_exp| > 3 clustered at SNPs where reference A1 != GWAS effect allele; credible sets pick up tag-only SNPs anti-correlated with the lead.
Fix: Harmonize before fitting:
harmonize_z_to_ref <- function(z, gwas_a1, gwas_a2, ref_a1, ref_a2) {
palindromic <- (gwas_a1 == 'A' & gwas_a2 == 'T') | (gwas_a1 == 'T' & gwas_a2 == 'A') |
(gwas_a1 == 'C' & gwas_a2 == 'G') | (gwas_a1 == 'G' & gwas_a2 == 'C')
flip <- (gwas_a1 == ref_a2) & (gwas_a2 == ref_a1)
z[flip] <- -z[flip]
drop <- palindromic | !((gwas_a1 == ref_a1 & gwas_a2 == ref_a2) | flip)
list(z = z, keep = !drop)
}
Drop palindromic SNPs at MAF > 0.42 (ambiguous strand); or resolve via external strand info (TopMed, 1000G strand files). TwoSampleMR::harmonise_data() offers an alternative implementation. See causal-genomics/colocalization-analysis for an equivalent harmonize helper used downstream.
Reconciliation: When Methods Disagree
| Pattern | Likely cause | Action |
|---|
| SuSiE finds 3 credible sets, FINEMAP finds 1 | FINEMAP's stochastic search did not converge OR SuSiE absorbed background into spurious sets | Increase FINEMAP --n-iter; check SuSiE purity (sets with purity < 0.5 are spurious) |
| SuSiE PIPs much sharper than FINEMAP | susie_rss assumes single residual variance; FINEMAP marginalizes over noise | Both can be correct; report the intersection of high-PIP variants from both as primary candidates |
| PolyFun + SuSiE collapses 10-variant credible set to 1 | Functional priors are doing real work (coding variant in set) | Verify with prior_weights plot; if priors are coding-specific the result is interpretable |
| SuSiEx credible set excludes the EUR top-PIP variant | EUR signal is tag, true causal shared across ancestries lies elsewhere | Trust SuSiEx if both populations have well-powered GWAS; verify with conditional analysis |
| HLA gives 50-variant credible set in every method | HLA LD structure cannot be fine-mapped by linear methods | Use HLA-specific imputation (HIBAG, SNP2HLA) and haplotype-level analysis |
Operational rule: For high-confidence reporting, require that (a) estimate_s_rss() lambda < 0.05; (b) at least one credible set has purity > 0.5 (min_abs_corr >= 0.5, equivalent to r2 >= 0.25); (c) the lead PIP variant within that set is reproduced by an independent method (FINEMAP, SuSiEx, or in-sample SuSiE if reference-LD was used). Anything failing these three is exploratory.
Quantitative Thresholds
| Quantity | Threshold | Source / Rationale |
|---|
| Credible set coverage (well-powered GWAS) | 0.95 (default) | Wang 2020 JRSSB; standard convention |
| Credible set coverage (low-N eQTL, GTEx tissue) | 0.9 or 0.8 | At N < 1000, default 0.95 returns very wide CS; document choice in methods |
| Credible set purity (rare-variant fine-mapping) | min_abs_corr >= 0.1 | LD genuinely sparse; relax to retain signal |
| Credible set purity (default common-variant) | min_abs_corr >= 0.5 (r2 >= 0.25) | susieR default; below this the set is LD-confounded |
| Credible set purity (publication-strict) | min_abs_corr >= 0.7 | Stringent claim; rare in practice |
| PIP suggestive | > 0.5 | Convention; "more likely than not causal among set" |
| PIP strong | > 0.9 | Convention; high-confidence single candidate |
| PIP very strong | > 0.95 | Convention; near-certain candidate within credible set |
| L (default cap) | 10 | susieR default; sufficient for most non-HLA loci |
| L (HLA / complex loci) | 20-30 | Empirical; HLA hosts > 10 independent signals for many traits |
n for case-control susie_rss | Neff = 4/(1/Ncase + 1/Ncontrol), NOT Ntotal | Privé 2022 HGG; matches the SE scale of logistic-regression sumstats |
estimate_s_rss lambda acceptable | < 0.05 | susieR vignette; > 0.10 indicates serious LD mismatch |
kriging_rss per-SNP flag | | z_obs - z_exp |
| Locus window (default) | +/- 500 kb from sentinel | Conventional; covers most LD blocks |
| Locus window (conditional-p floor) | Extend until conditional -log10(p) < 4 | Avoids truncating a secondary signal whose conditional evidence leaks into the window edge |
| Locus window (long-range LD) | 5+ Mb or stratify | HLA chr6:25-35Mb, chr8 inversion chr8:8.1-11.9Mb hg38, chr17 H1/H2 inversion |
FINEMAP --n-causal-snps | 5 | Default; raise for HLA |
FINEMAP --sss-tol | 0.001 | Convergence tolerance; rarely needs change |
Functional Priors with PolyFun
Goal: Use genome-wide stratified LDSC heritability to weight per-SNP causal priors, sharpening PIPs at coding, conserved, and regulatory variants.
Approach: Run PolyFun once genome-wide to estimate per-SNP h2 from the baseline-LF annotation set; extract per-SNP causal prior; pass to susie_rss as prior_weights.
polyfun.py --compute-h2-L2 \
--output-prefix polyfun_h2 \
--sumstats gwas_munged.sumstats \
--ref-ld-chr UKB_baseline_LF/baselineLF2.2.UKB. \
--w-ld-chr UKB_baseline_LF/weights.UKB.
polyfun.py --compute-h2-bins \
--output-prefix polyfun_priors \
--sumstats gwas_munged.sumstats \
--ref-ld-chr polyfun_h2
library(susieR)
priors <- read.table('polyfun_priors.6.snpvar.gz', header = TRUE)
priors <- priors[match(gwas_df$SNP, priors$SNP), ]
prior_w <- priors$SNPVAR / sum(priors$SNPVAR, na.rm = TRUE)
fit <- susie_rss(z = z_scores, R = ld_matrix, n = N, L = 10,
prior_weights = prior_w)
UKB baseline-LF priors are pre-computed EUR-only at data.broadinstitute.org/alkesgroup/UKBB_LD/ for hg19 and hg38. For EAS, AFR, or SAS GWAS, the EUR weights are NOT valid: use PolyFun-pop (Weissbrod 2022 Nat Genet 54:450) which extends PolyFun via cross-population annotation weighting, OR the cross-ancestry baseline-LF tables from Marquez-Luna 2025. For ancestries lacking matched baseline-LF (admixed, under-represented), accept reduced power and run uniform-prior susie_rss; applying EUR weights to non-EUR sumstats produces miscalibrated PIPs that look sharper than reality.
Manual Coding-Variant Priors Without PolyFun
For postdocs without PolyFun infrastructure or with single-locus inputs, manual annotation-based priors are a reasonable approximation (Hutchinson 2020 Genet Epidemiol 44:313). Coding variants get ~10x uniform weight; broadly conserved variants ~5x (binned by CADD-PHRED quantile).
build_manual_priors <- function(vep_df, cadd) {
w <- rep(1, nrow(vep_df))
w[vep_df$Consequence %in% c('missense_variant', 'stop_gained', 'splice_donor_variant',
'splice_acceptor_variant', 'frameshift_variant')] <- 10
w[cadd >= quantile(cadd, 0.95, na.rm = TRUE)] <- pmax(w[cadd >= quantile(cadd, 0.95, na.rm = TRUE)], 5)
w / sum(w)
}
fit <- susie_rss(z = z_scores, R = ld_matrix, n = Neff, L = 10, prior_weights = build_manual_priors(vep, cadd))
Report the prior construction explicitly; reviewers will ask whether the prior was tuned post hoc.
Cross-Ancestry Fine-Mapping with SuSiEx
Goal: Jointly fine-map a locus across multiple ancestries assuming shared causal variants but population-specific LD.
Approach: Per-ancestry summary statistics + per-ancestry LD reference; SuSiEx runs a joint SuSiE model with population-specific R matrices.
SuSiEx \
--sst_file=eur_sumstats.txt,eas_sumstats.txt,afr_sumstats.txt \
--n_gwas=500000,200000,80000 \
--ref_file=1000G_EUR,1000G_EAS,1000G_AFR \
--ld_file=eur_ld,eas_ld,afr_ld \
--out_dir=susiex_out \
--out_name=locus1 \
--pop=EUR,EAS,AFR \
--chr=6 --bp=30000000,31000000 \
--chr_col=1,1,1 --snp_col=2,2,2 --bp_col=3,3,3 \
--a1_col=4,4,4 --a2_col=5,5,5 --eff_col=6,6,6 \
--se_col=7,7,7 --pval_col=8,8,8 \
--level=0.95
The output includes per-population PIPs and a joint credible set. Credible sets from SuSiEx are typically 2-5x smaller than EUR-only susie_rss when AFR is included, because AFR shorter LD blocks resolve EUR-tagged regions.
FINEMAP CLI Pattern
Goal: Independent confirmation via shotgun stochastic search.
Approach: Build .z, .ld, and master files; run FINEMAP with --sss and parse the .snp and .cred outputs.
cat > locus.master <<'EOF'
z;ld;snp;config;cred;log;n_samples
locus.z;locus.ld;locus.snp;locus.config;locus.cred;locus.log;500000
EOF
finemap --sss \
--in-files locus.master \
--n-causal-snps 5 \
--sss-tol 0.001 \
--n-iter 100000 \
--n-conv-sss 5000
FINEMAP and SuSiE agree when sparsity holds; disagreement often reveals non-sparse loci that need SuSiE-inf.
Coloc.susie Integration
Goal: Test colocalization between two traits using credible sets, not single SNPs.
Approach: Fit susie_rss separately per trait; pass both susie objects to coloc.susie; per-credible-set colocalization probabilities are returned.
library(coloc)
fit_trait1 <- susie_rss(z = z1, R = ld_matrix, n = N1, L = 10)
fit_trait2 <- susie_rss(z = z2, R = ld_matrix, n = N2, L = 10)
coloc_res <- coloc.susie(fit_trait1, fit_trait2)
print(coloc_res$summary)
PP.H4 > 0.8 per credible set is the conventional shared-causal threshold; weaker thresholds suggest distinct or conditional signals. See causal-genomics/colocalization-analysis.
HLA and Long-Range LD: When to Stop
The HLA region (chr6:28-34 Mb), chromosome 8 inversion (chr8:8-12 Mb), and a handful of other extended LD blocks violate the assumptions of every fine-mapping method.
Symptoms of irrecoverable LD structure: Credible sets contain 30+ SNPs at low purity even with L=30; SuSiE-inf credible sets remain wide; kriging_rss flags hundreds of SNPs.
Options:
- Stratify by classical HLA allele (HIBAG, SNP2HLA imputation) and test allelic series
- Conditional analysis on the lead variant before fine-mapping the residual
- Exclude the region from genome-wide fine-mapping summaries and report separately
- For chr8 inversion: stratify by inversion genotype if known
Document the caveat in any methods section; standard PIPs at HLA are not interpretable as causality estimates.
TWAS Fine-Mapping (FOCUS) -- delegated
Variant-level fine-mapping cannot distinguish causal genes among co-regulated TWAS hits. FOCUS / MA-FOCUS extend fine-mapping to the predicted-expression level; see causal-genomics/transcriptome-wide-association for the full FOCUS workflow and reconciliation with variant-level credible sets.
Required Reporting Schema for Fine-Mapping
Every locus reported should carry these columns; missing fields are the most common reviewer complaint.
| Column | Description |
|---|
| locus_id | Locus identifier (chr:start-end or sentinel rsID) |
| method | susie_rss / FINEMAP / PAINTOR / SuSiEx / SuSiE-inf |
| L_used | sum(!fit$sets$pruned) (effective L; not just the cap passed in) |
| n_credible_sets | Number of returned credible sets at the chosen coverage |
| cs_size | Variants per credible set |
| cs_purity_min / cs_purity_mean | min and mean fit$sets$purity[,'min.abs.corr'] per set |
| top_pip_snp | Lead variant in each credible set |
| top_pip | Posterior inclusion probability of top_pip_snp |
| lambda_s | estimate_s_rss diagnostic for the locus |
| kriging_outlier_count | Count of SNPs with ` |
| ld_panel | 1KG-EUR / UKB-EUR / in-sample / TopMed |
| prior_source | uniform / PolyFun-EUR / PolyFun-pop / manual coding-variant |
| coverage | 0.95 default; 0.9 or 0.8 documented for low-N |
| n_effective | Sample size passed to susie_rss (Neff for case-control) |
Anticipated Reviewer Pushback
| Pushback | Standard response |
|---|
| "In-sample vs reference LD?" | In-sample preferred when cohort genotypes available; if reference, report estimate_s_rss lambda < 0.05 plus kriging_rss outlier count |
| "Credible-set purity?" | min_abs_corr >= 0.5 (r2 >= 0.25) default; reported per set; relaxed only with explicit rationale for rare-variant fine-mapping |
| "Is L set high enough?" | If returned CS count < L cap: OK (susieR auto-prunes); otherwise raise L. HLA needs L=20-30 |
| "Why not SuSiE-inf?" | Polygenic-shoulder test: count SNPs with marginal -log10(p) > 4 outside the lead credible set; > 50 indicates a polygenic shoulder and SuSiE-inf (Cui 2024) should be used |
| "Why no functional priors?" | PolyFun applied (or manual coding-variant prior used) and reported; if uniform, justify (low-N, mismatched-ancestry baseline-LF) |
| "Credible set has 50 SNPs -- is that fine-mapping?" | Acknowledged as imprecise; reported alongside diagnostics; cross-trait colocalization or functional fine-mapping (PolyFun, MPRA, allelic series) recommended for resolution |
| "Was Neff used for case-control?" | Yes: Neff = 4/(1/Ncase + 1/Ncontrol); report the value used |
| "Allele harmonization?" | Yes: flipped z when GWAS effect allele differs from reference A1; palindromic SNPs at MAF > 0.42 dropped |
Common Errors
| Error / symptom | Cause | Solution |
|---|
IBSS algorithm did not converge warning | L too small OR LD mismatch | Increase L; run estimate_s_rss; check ancestry match |
| Credible set contains all SNPs at locus | LD reference matches discovery poorly; s_hat > 0.1 | Switch to in-sample LD or stratify reference ancestry |
| PIPs identical to GWAS p-value rank | Effectively no LD information used; check LD matrix orientation | Verify SNP order in z and R match exactly; check for transposed R |
| Negative eigenvalues in LD matrix | Numerical PSD violation from finite-precision storage | Add small ridge: R <- R + diag(1e-4, nrow(R)); or use Matrix::nearPD |
pip all ~ 1/p (uniform) | Convergence failure OR all effects pruned | Check fit$converged; raise L; check Z scale |
FINEMAP Error: SNP names do not match | .z and .ld SNP order differ | Ensure both are sorted identically; pass matched .snp file |
| Coloc.susie returns NULL | One trait has zero credible sets | Verify both fits succeeded; lower coverage to 0.9 if signal is weak |
| SuSiEx output empty | Population label mismatch with reference panel | Verify --pop matches reference filename convention; check --bp window |
| PolyFun priors do not change PIPs | Passed to prior_variance instead of prior_weights | Read susieR docs; use prior_weights= |
References
- Wang G, Sarkar A, Carbonetto P, Stephens M 2020 J R Stat Soc B 82:1273 (SuSiE / IBSS)
- Zou Y, Carbonetto P, Wang G, Stephens M 2022 PLoS Genet 18:e1010299 (susie_rss for summary statistics)
- Cui R, Elzur RA, Kanai M, Ulirsch JC, Weissbrod O et al 2024 Nat Genet 56:162 (SuSiE-inf / FINEMAP-inf for non-sparse loci)
- Benner C, Spencer CC, Havulinna AS, Salomaa V, Ripatti S, Pirinen M 2016 Bioinformatics 32:1493 (FINEMAP)
- Hormozdiari F, Kostem E, Kang EY, Pasaniuc B, Eskin E 2014 Genetics 198:497 (CAVIAR)
- Wen X, Lee Y, Luca F, Pique-Regi R 2016 AJHG 98:1114 (DAP-G)
- Kichaev G, Yang WY, Lindstrom S, Hormozdiari F, Eskin E et al 2014 PLoS Genet 10:e1004722 (PAINTOR)
- Weissbrod O, Hormozdiari F, Benner C, Cui R, Ulirsch J et al 2020 Nat Genet 52:1355 (PolyFun + functional priors)
- Yuan K, Longchamps RJ, Pardinas AF, Yu M, Chen TT et al 2024 Nat Genet 56:1841 (SuSiEx cross-ancestry)
- Tashman KC, Cui R, O'Connor LJ, Pasaniuc B, Price AL 2024 medRxiv / 2025 Nat Genet (MultiSuSiE; published in Nat Genet 2025, doi:10.1038/s41588-025-02450-5)
- Mancuso N, Freund MK, Johnson R, Shi H, Kichaev G et al 2019 Nat Genet 51:675 (FOCUS for TWAS fine-mapping)
- Wallace C 2021 PLoS Genet 17:e1009440 (coloc.susie integration)
- Schaid DJ, Chen W, Larson NB 2018 Nat Rev Genet 19:491 (fine-mapping review)
- Hutchinson A, Asimit J, Wallace C 2020 Genet Epidemiol 44:313 (fine-mapping methods comparison)
Related Skills
- causal-genomics/colocalization-analysis - coloc.susie consumes susie_rss credible sets; equivalent harmonize helper
- causal-genomics/effector-gene-prioritization - Downstream gene-assignment from credible-set variants
- causal-genomics/transcriptome-wide-association - FOCUS / MA-FOCUS for gene-level fine-mapping
- causal-genomics/genomic-sem - Joint multi-trait fine-mapping when credible sets are shared across traits
- causal-genomics/mendelian-randomization - Fine-mapped variants as cis-instruments
- causal-genomics/pleiotropy-detection - Per-credible-set pleiotropy testing
- atac-seq/enhancer-gene-linking - ABC / ENCODE-rE2G linking credible-set variants to target genes
- population-genetics/linkage-disequilibrium - Constructing LD matrices for susie_rss
- population-genetics/association-testing - Upstream GWAS summary statistic generation
- workflows/gwas-pipeline - End-to-end GWAS pipeline producing fine-mapping input
- variant-calling/variant-annotation - Annotating credible-set variants with VEP / coding consequence
- pathway-analysis/go-enrichment - Downstream gene-level interpretation of credible-set targets
