AR
Data & Repro
arxiv-database
This skill provides Python tools for searching and retrieving preprints from arXiv.org via its public Atom API. It supports keyword search,…
K-Dense-AI/claude-scientific-skillsView
Data & Repro
BI
bio-causal-genomics-fine-mapping
Maintainer FreedomIntelligence · Last updated April 1, 2026
Fine-mapping narrows GWAS association signals to identify likely causal variants. Key outputs: - **PIP** (Posterior Inclusion Probability) - Probability each variant is causal (0-1) - **Credible set** - Minimal set of variants containing the causal variant at a given confidence level (e.g., 95%) - **L** - Number of independent causal signals at the locus.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-causal-genomics-fine-mapping
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- R: susieR::susie_rss() for SuSiE fine-mapping from summary statistics.
- CLI: finemap --sss for shotgun stochastic search.
- Narrow my GWAS locus to the likely causal variant" → Compute posterior inclusion probabilities (PIPs) for each variant and construct credible sets containing the causal variant at a specified confidence level, accounting for LD and multiple causal signals. R: susieR::susie_rss() for SuSiE fine-mapping from summary statistics CLI: finemap --sss for shotgun stochastic search.
- PIP (Posterior Inclusion Probability) - Probability each variant is causal (0-1).
- Credible set - Minimal set of variants containing the causal variant at a given confidence level (e.g., 95%).
Source Doc
Excerpt From SKILL.md
SuSiE (Sum of Single Effects)
Goal: Fine-map a GWAS locus to identify likely causal variants and credible sets from individual-level data.
Approach: Fit SuSiE's sum-of-single-effects model on the genotype matrix, then extract 95% credible sets (each containing the causal variant) and per-variant posterior inclusion probabilities.
library(susieR)
## L: max number of causal variants (10 is a reasonable default)
fit <- susie(X, Y, L = 10)
## min_abs_corr: minimum purity (correlation among variants in set; > 0.5 is good)
cs <- fit$sets$cs
cat('Number of credible sets:', length(cs), '\n')
Use cases
- Use when narrowing GWAS association signals to candidate causal variants or building credible sets for functional validation.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- colocalization-analysis - SuSiE-coloc uses fine-mapping credible sets
- mendelian-randomization - Fine-map instrument loci for causal variants
- population-genetics/linkage-disequilibrium - LD matrices for fine-mapping
- variant-calling/variant-annotation - Annotate fine-mapped variants
Related skills
Related skills
BA
Data & Repro
bayesian-optimizer
Bayesian optimization for experimental design and hyperparameter tuning in biomedical research.
FreedomIntelligence/OpenClaw-Medical-SkillsView
BI
Data & Repro
bio-alignment-files-bam-statistics
Compute alignment statistics: flagstat, idxstats, coverage depth.
FreedomIntelligence/OpenClaw-Medical-SkillsView
BI
Data & Repro
bio-alignment-msa-statistics
Calculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when com…
FreedomIntelligence/OpenClaw-Medical-SkillsView