fine-mapping

Name: fine-mapping
Author: GPTomics

1.1kstars185forks

How to install fine-mapping?

gptomics/bioskills/fine-mapping

$npx -y skills add GPTomics/bioSkills --skill fine-mapping

Installs into the current project.

Summary

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, c

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SKILL.md

1	## Version Compatibility
2
3	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.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- R: `packageVersion('susieR')` then `?susie_rss` to confirm argument names (e.g., `prior_weights` vs `prior_variance` semantics)
7	- CLI: `finemap --help`, `SuSiEx --help`, `PAINTOR --help`, `dap-g --help` to confirm flags
8	- Python: `polyfun.py --help`
9
10	If a call throws an error about an argument that no longer exists, introspect the installed function and adapt rather than retrying.
11
12	# Fine-Mapping
13
14	"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.
15
16	- R (summary statistics + LD): `susieR::susie_rss(z, R, n, L=10)` + `estimate_s_rss` LD diagnostic
17	- R (individual-level genotypes): `susieR::susie(X, y, L=10)`
18	- CLI (shotgun stochastic search): `finemap --sss --in-files master.z --n-causal-snps 5 --prob-tol 0.001`
19	- 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`)
20	- Python (functional priors): `polyfun.py --compute-h2-L2` -> per-SNP priors -> susie_rss with `prior_weights=`
21	- Python (TWAS fine-mapping): `focus finemap` on gene-level Z-scores
22
23	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.
24
25	## Algorithmic Taxonomy
26
27	\| Tool \| Model \| Input \| Strength \| Fails when \|
28	\|------\|-------\|-------\|----------\|------------\|
29	\| 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 \|
30	\| 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 \|
31	\| 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 \|
32	\| CAVIAR (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 \|
33	\| 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 \|
34	\| 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 \|
35	\| PolyFun + SuSiE/FINEMAP (Weissbrod 2020 Nat Genet 52:1355) \| Stratified LDSC genome-wide -> p

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fine-mapping

1.1kstars185forks

View on GitHub

How to install fine-mapping?

gptomics/bioskills/fine-mapping

$npx -y skills add GPTomics/bioSkills --skill fine-mapping

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	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.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- R: `packageVersion('susieR')` then `?susie_rss` to confirm argument names (e.g., `prior_weights` vs `prior_variance` semantics)
7	- CLI: `finemap --help`, `SuSiEx --help`, `PAINTOR --help`, `dap-g --help` to confirm flags
8	- Python: `polyfun.py --help`
9
10	If a call throws an error about an argument that no longer exists, introspect the installed function and adapt rather than retrying.
11
12	# Fine-Mapping
13
14	"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.
15
16	- R (summary statistics + LD): `susieR::susie_rss(z, R, n, L=10)` + `estimate_s_rss` LD diagnostic
17	- R (individual-level genotypes): `susieR::susie(X, y, L=10)`
18	- CLI (shotgun stochastic search): `finemap --sss --in-files master.z --n-causal-snps 5 --prob-tol 0.001`
19	- 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`)
20	- Python (functional priors): `polyfun.py --compute-h2-L2` -> per-SNP priors -> susie_rss with `prior_weights=`
21	- Python (TWAS fine-mapping): `focus finemap` on gene-level Z-scores
22
23	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.
24
25	## Algorithmic Taxonomy
26
27	\| Tool \| Model \| Input \| Strength \| Fails when \|
28	\|------\|-------\|-------\|----------\|------------\|
29	\| 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 \|
30	\| 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 \|
31	\| 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 \|
32	\| CAVIAR (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 \|
33	\| 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 \|
34	\| 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 \|
35	\| PolyFun + SuSiE/FINEMAP (Weissbrod 2020 Nat Genet 52:1355) \| Stratified LDSC genome-wide -> p

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