Bio Causal Genomics Fine Mapping

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 --prob-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 --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; populations are assigned by the ORDER of the comma-separated --sst_file/--n_gwas/--ref_file/--ld_file lists, not a --pop flag; 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

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

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)
# s_hat is the inferred scale of LD inconsistency.
# Source: susieR vignette "Diagnostic for summary statistic"; Zou 2022 PLoS Genet.
# Rule of thumb: s_hat < 0.05 acceptable; 0.05-0.10 marginal; > 0.10 refit or change LD reference.

cond_z <- kriging_rss(z = z_scores, R = ld_matrix, n = N)
# cond_z$conditional_dist returns per-SNP expected vs observed z; flag |z_obs - z_exp| > 3
# Common cause: strand flip, allele coding mismatch, or single-SNP imputation error.

# If diagnostic fails: refit with explicit scale parameter to absorb LD mismatch
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 (the PolyFun output column is uppercase SNPVAR; R is case-sensitive). 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 F et al 2022 HGG Adv 3:100136 (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

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.