pleiotropy-detection

Name: pleiotropy-detection
Author: GPTomics

1.1kstars185forks

How to install pleiotropy-detection?

gptomics/bioskills/pleiotropy-detection

$npx -y skills add GPTomics/bioSkills --skill pleiotropy-detection

Installs into the current project.

Summary

Detect and adjust for horizontal pleiotropy in two-sample Mendelian randomization by distinguishing uncorrelated (UHP) from correlated (CHP) pleiotropy and choosing among Egger, MR-PRESSO, MR-RAPS, CAUSE, LHC-MR, LCV, MR-Clust, MR-Mix, and contamination-mixture methods. Use when

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SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: TwoSampleMR 0.5.11+, MendelianRandomization 0.9.0+, MR-PRESSO 1.0+, CAUSE 1.2.0+, MR-Clust 0.1.0+, MRMix 0.1+, mr.raps 0.4.1+ (GitHub), LHC-MR 0.0.0.9000+ (GitHub), LCV (script-based, no version tag), simex 1.8+.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
7	- For GitHub-only packages, check the repo HEAD vs the local install date
8
9	If code throws errors, introspect the installed package and adapt the example rather than retrying.
10
11	# Pleiotropy Detection in Mendelian Randomization
12
13	"Validate my MR result against pleiotropic bias" -> Decompose violations of the exclusion-restriction assumption into uncorrelated horizontal pleiotropy (UHP, addressable by Egger / median / mode / MR-PRESSO) and correlated horizontal pleiotropy (CHP, addressable only by CAUSE / LHC-MR / LCV), then run a method battery whose assumptions span both regimes.
14
15	- R: `TwoSampleMR::mr()` (IVW + Egger + median + mode), `mr_pleiotropy_test()`, `mr_heterogeneity()`, `mr_leaveoneout()`, `directionality_test()`
16	- R: `MRPRESSO::mr_presso()` for UHP outlier removal + distortion test
17	- R: `cause::cause()` for CHP-aware estimation; `mrclust::mr_clust_em()` for mechanism-heterogeneous instruments
18	- R: `MendelianRandomization::mr_conmix()` for contamination mixture; `MRMix::MRMix()` for mixture-of-distributions
19
20	## UHP vs CHP: The Central Postdoc-Grade Distinction
21
22	Horizontal pleiotropy comes in two regimes, and most "standard" MR sensitivity methods address only one of them.
23
24	\| Regime \| Definition \| InSIDE assumption \| Methods that handle it \|
25	\|--------\|------------\|--------------------\|------------------------\|
26	\| UHP (uncorrelated horizontal pleiotropy) \| Pleiotropic effect alpha_j independent of instrument-exposure effect gamma_j \| Holds \| IVW (balanced UHP only), MR-Egger, weighted median, weighted mode, MR-PRESSO, MR-RAPS, MR-Mix, contamination mixture \|
27	\| CHP (correlated horizontal pleiotropy) \| alpha_j correlates with gamma_j through a shared upstream factor (heritable confounder, network mediator) \| Violated \| CAUSE, LHC-MR, LCV, MR-Clust (partial), Steiger-filtered MR (partial) \|
28
29	InSIDE = INstrument Strength Independent of Direct Effect (Bowden 2015 IJE 44:512). Plain English: across SNPs, the per-SNP pleiotropic effect alpha and per-SNP instrument-exposure effect gamma are treated as independent random variables. CHP is the case where they covary because both flow from a shared upstream genetic factor.
30
31	The trap (Morrison 2020 Nat Genet 52:740): IVW, MR-Egger, MR-PRESSO, and GSMR are all blind to CHP. Under a shared heritable confounder they each return a plausible-looking corrected causal estimate that is systematically biased in the direction of the confounder. The MR-PRESSO global test does not flag CHP because correlated pleiotropy is not an outlier pattern, it is a population mean shift in the alpha distribution conditional on gamma.
32
33	Operational rule: If genetic correlation rg(exposure, outcome) is high (LDSC `>= 0.3`) or biology strongly suggests a shared upstream factor, the IVW / Egger / PRESSO triple is insufficient. Add CAUSE (preferred when sig SNPs `>= 100`) or LHC-MR (preferred for polygenic genome-wide IVs).
34
35	## Operational Decision Flow (4 Steps)
36
37	1. Compute genetic correlation (LDSC). Run `ldsc.py --rg <exposure.sumstats.gz>,<outcome.sumstats.gz>` (see causal-genomics/genetic-correlation). If `\|rg\| > 0.3`, CHP is plausible -> flag for Step 3 escalation. If the LDSC rg standard error spans zero broadly, treat low-rg evidence as weak rather than confirming absence of CHP.
38	2. Standard battery. IVW (random-effects when Cochran Q p < 0.05) + MR-Egger (with NOME I^2_GX check) + weighted median + weighted mode + MR-PRESSO (NbDistribution `>= 10000` for stringent reporting). Report all five with point estimate, SE, p, 95% CI, and n_SNPs_used. Compute Egger I^2_GX; apply SIMEX if I^2_GX < 0.9 (see examples/simex_egger_correction.R).
39	3. CHP escalation. Trigger when rg > 0.3 OR PRESSO global p < 0.05 with > 50% nominal outliers OR Egger / median / mode disagree by > 2 SE. Run CAUSE (if `>= 100` significant SNPs after pruning) or LHC-MR (any N; uses genome-wide sumstats). Report ELPD delta + z + q (CHP fraction) + gamma (CHP-adj

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pleiotropy-detection

1.1kstars185forks

View on GitHub

How to install pleiotropy-detection?

gptomics/bioskills/pleiotropy-detection

$npx -y skills add GPTomics/bioSkills --skill pleiotropy-detection

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: TwoSampleMR 0.5.11+, MendelianRandomization 0.9.0+, MR-PRESSO 1.0+, CAUSE 1.2.0+, MR-Clust 0.1.0+, MRMix 0.1+, mr.raps 0.4.1+ (GitHub), LHC-MR 0.0.0.9000+ (GitHub), LCV (script-based, no version tag), simex 1.8+.
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
7	- For GitHub-only packages, check the repo HEAD vs the local install date
8
9	If code throws errors, introspect the installed package and adapt the example rather than retrying.
10
11	# Pleiotropy Detection in Mendelian Randomization
12
13	"Validate my MR result against pleiotropic bias" -> Decompose violations of the exclusion-restriction assumption into uncorrelated horizontal pleiotropy (UHP, addressable by Egger / median / mode / MR-PRESSO) and correlated horizontal pleiotropy (CHP, addressable only by CAUSE / LHC-MR / LCV), then run a method battery whose assumptions span both regimes.
14
15	- R: `TwoSampleMR::mr()` (IVW + Egger + median + mode), `mr_pleiotropy_test()`, `mr_heterogeneity()`, `mr_leaveoneout()`, `directionality_test()`
16	- R: `MRPRESSO::mr_presso()` for UHP outlier removal + distortion test
17	- R: `cause::cause()` for CHP-aware estimation; `mrclust::mr_clust_em()` for mechanism-heterogeneous instruments
18	- R: `MendelianRandomization::mr_conmix()` for contamination mixture; `MRMix::MRMix()` for mixture-of-distributions
19
20	## UHP vs CHP: The Central Postdoc-Grade Distinction
21
22	Horizontal pleiotropy comes in two regimes, and most "standard" MR sensitivity methods address only one of them.
23
24	\| Regime \| Definition \| InSIDE assumption \| Methods that handle it \|
25	\|--------\|------------\|--------------------\|------------------------\|
26	\| UHP (uncorrelated horizontal pleiotropy) \| Pleiotropic effect alpha_j independent of instrument-exposure effect gamma_j \| Holds \| IVW (balanced UHP only), MR-Egger, weighted median, weighted mode, MR-PRESSO, MR-RAPS, MR-Mix, contamination mixture \|
27	\| CHP (correlated horizontal pleiotropy) \| alpha_j correlates with gamma_j through a shared upstream factor (heritable confounder, network mediator) \| Violated \| CAUSE, LHC-MR, LCV, MR-Clust (partial), Steiger-filtered MR (partial) \|
28
29	InSIDE = INstrument Strength Independent of Direct Effect (Bowden 2015 IJE 44:512). Plain English: across SNPs, the per-SNP pleiotropic effect alpha and per-SNP instrument-exposure effect gamma are treated as independent random variables. CHP is the case where they covary because both flow from a shared upstream genetic factor.
30
31	The trap (Morrison 2020 Nat Genet 52:740): IVW, MR-Egger, MR-PRESSO, and GSMR are all blind to CHP. Under a shared heritable confounder they each return a plausible-looking corrected causal estimate that is systematically biased in the direction of the confounder. The MR-PRESSO global test does not flag CHP because correlated pleiotropy is not an outlier pattern, it is a population mean shift in the alpha distribution conditional on gamma.
32
33	Operational rule: If genetic correlation rg(exposure, outcome) is high (LDSC `>= 0.3`) or biology strongly suggests a shared upstream factor, the IVW / Egger / PRESSO triple is insufficient. Add CAUSE (preferred when sig SNPs `>= 100`) or LHC-MR (preferred for polygenic genome-wide IVs).
34
35	## Operational Decision Flow (4 Steps)
36
37	1. Compute genetic correlation (LDSC). Run `ldsc.py --rg <exposure.sumstats.gz>,<outcome.sumstats.gz>` (see causal-genomics/genetic-correlation). If `\|rg\| > 0.3`, CHP is plausible -> flag for Step 3 escalation. If the LDSC rg standard error spans zero broadly, treat low-rg evidence as weak rather than confirming absence of CHP.
38	2. Standard battery. IVW (random-effects when Cochran Q p < 0.05) + MR-Egger (with NOME I^2_GX check) + weighted median + weighted mode + MR-PRESSO (NbDistribution `>= 10000` for stringent reporting). Report all five with point estimate, SE, p, 95% CI, and n_SNPs_used. Compute Egger I^2_GX; apply SIMEX if I^2_GX < 0.9 (see examples/simex_egger_correction.R).
39	3. CHP escalation. Trigger when rg > 0.3 OR PRESSO global p < 0.05 with > 50% nominal outliers OR Egger / median / mode disagree by > 2 SE. Run CAUSE (if `>= 100` significant SNPs after pruning) or LHC-MR (any N; uses genome-wide sumstats). Report ELPD delta + z + q (CHP fraction) + gamma (CHP-adj

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