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bio-causal-genomics-pleiotropy-detection
维护者 FreedomIntelligence · 最近更新 2026年4月1日
bio-causal-genomics-pleiotropy-detection：Hori。
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-causal-genomics-pleiotropy-detection
- 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：MRPRESSO：：mr_presso() ，用于 global 、 distortion tests。
- R：TwoSampleMR：：mr_egger_回归() ，用于 Egger intercept test。
- Check my MR results ，用于 pleiotropic bias" → Detect 、 correct ，用于 horizontal pleiotropy ，使用 outlier removal (MR-PRESSO)，directional pleiotropy testing (MR-Egger intercept)，、 variant directionality filtering (Steiger) to validate causal inference results. R：MRPRESSO：：mr_presso() ，用于 global 、 distortion tests R：TwoSampleMR：：mr_egger_回归() ，用于 Egger intercept test。
- Vertical (mediated)：Instrument -> exposure -> outcome (valid，not problem)。
- Horizontal (direct)：Instrument -> outcome bypassing exposure (violates MR assumptions)。
原始文档
SKILL.md 摘录
MR-PRESSO
Goal: Detect and remove pleiotropic outlier instruments from an MR analysis.
Approach: Run MR-PRESSO to test for global pleiotropy, identify individual outlier SNPs, test whether their removal changes the causal estimate (distortion test), and obtain a corrected estimate.
## Columns needed: beta.exposure, beta.outcome, se.exposure, se.outcome
presso_input <- data.frame(
bx = dat$beta.exposure,
by = dat$beta.outcome,
bxse = dat$se.exposure,
byse = dat$se.outcome
)
## SignifThreshold: P-value threshold for outlier detection (0.05 standard)
presso_result <- mr_presso(
BetaOutcome = 'by', BetaExposure = 'bx',
SdOutcome = 'byse', SdExposure = 'bxse',
OUTLIERtest = TRUE, DISTORTIONtest = TRUE,
data = presso_input,
NbDistribution = 5000,
SignifThreshold = 0.05
)
适用场景
- 适合在validating MR results，detecting pleiotropic instruments，或 running sensitivity analyses ，用于 causal inference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
- mendelian-randomization - Primary MR analysis that pleiotropy tests validate
- fine-mapping - Identify causal variants at instrument loci
- population-genetics/association-testing - GWAS data for MR instruments
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