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bio-causal-genomics-fine-mapping
维护者 FreedomIntelligence · 最近更新 2026年4月1日
bio-causal-genomics-fine-mapping：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 causal variant at given confidence level (e.g.，95%) - **L** - Number of independent causal signals at locus。
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-causal-genomics-fine-mapping
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- R：susieR：：susie_rss() ，用于 SuSiE fine-mapping ，面向 summary statistics。
- CLI：finemap --sss ，用于 shotgun stochastic search。
- Narrow my GWAS locus to likely causal variant" → Compute posterior inclusion probabilities (PIPs) ，用于 each variant 、 construct credible sets containing causal variant at specified confidence level，accounting ，用于 LD 、 multiple causal signals. R：susieR：：susie_rss() ，用于 SuSiE fine-mapping ，面向 summary statistics CLI：finemap --sss ，用于 shotgun stochastic search。
- PIP (Posterior Inclusion Probability) - Probability each variant is causal (0-1)。
- Credible set - Minimal set of variants containing causal variant at given confidence level (e.g.，95%)。
原始文档
SKILL.md 摘录
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')
适用场景
- 适合在narrowing GWAS association signals to candidate causal variants 或 building credible sets ，用于 functional validation时使用。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
上游相关技能
- 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
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