Generates complete two-sample Mendelian randomization (MR) research designs from a user-provided research direction. Use when users want to design, plan, or build a study using two-sample MR to test causal relationships. Triggers:"design a two-sample MR study", "build a publishable MR paper", "test whether this biomarker causally affects this disease", "generate Lite/Standard/Advanced MR plans", "screen multiple exposures with MR", "bidirectional MR design", "causal inference using GWAS summary statistics", or "I want to study X and Y using MR". Always outputs four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.
Generates complete two-sample Mendelian randomization (MR) research designs from a user-provided research direction. Use when users want to design, plan, or build a study using two-sample MR to test causal relationships. Triggers:"design a two-sample MR study", "build a publishable MR paper", "test whether this biomarker causally affects this disease", "generate Lite/Standard/Advanced MR plans", "screen multiple exposures with MR", "bidirectional MR design", "causal inference using GWAS summary statistics", or "I want to study X and Y using MR". Always outputs four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.
Two-Sample Mendelian Randomization Research Planner
Generates a complete two-sample MR study design from a user-provided research direction. Always outputs four workload configurations and a recommended primary plan.
Supported Study Styles
Style
Description
Example
A. Single Exposure → Single Outcome
One biomarker or trait to one disease
Serum uric acid → gout; vitamin D → osteoporosis
B. Multi-Exposure Screening
Panel of exposures to one outcome
Dietary factors → endometriosis; cytokine panel → RA
If limited detail is provided, infer a reasonable default design and state all assumptions explicitly
Step-by-Step Execution
Step 1: Infer Study Type
Identify:
Exposure(s) and outcome
Exposure class (dietary, biomarker, metabolite, behavioral, disease trait, molecular)
User goal: screening, bidirectional, causal verification, or publication strength
Whether MVMR or colocalization is justified
Time or resource constraints stated by the user
Step 2: Output Four Configurations
Always generate all four. For each configuration describe: goal, required data, major modules, expected workload, figure set, strengths, and weaknesses.
Config
Goal
Timeframe
Best For
Lite
Fast minimal causal test
2–4 weeks
Quick launch, 1 exposure × 1 outcome
Standard
Publication-ready core MR
4–8 weeks
Single or small panel + sensitivity suite
Advanced
Robust multi-extension design
8–14 weeks
Bidirectional, MVMR, replication GWAS
Publication+
High-impact comprehensive paper
12–20 weeks
Full sensitivity, MVMR, colocalization, power
Step 3: Recommend One Primary Plan
Select the best-fit configuration and explain why, given the exposure type, outcome, and any stated user constraints (time, data access, publication goal).
Step 4: Full Step-by-Step Workflow
For each step include: step name, purpose, input, method, key parameters/thresholds, expected output, failure points, and alternative approaches.
If the target GWAS is unavailable: state this explicitly, suggest the closest publicly available alternative, and recommend the Lite configuration until data access is confirmed
If IV count falls below 3: warn the user that MR is not feasible with current instruments; suggest waiting for larger GWAS or pivoting to a proxy exposure
If F-statistic < 10 for all IVs: do not proceed with IVW as primary; escalate to weak-instrument-robust methods (LIML, sisVIVE) and note this as a study limitation
Step 5: Figure and Deliverable Plan
Always list:
Scatter plots (exposure–outcome per estimator)
Forest plots (leave-one-out)
Funnel plots (pleiotropy visual)
Summary results table (all estimators)
Sensitivity analysis table
Step 6: Validation and Robustness Plan
State what each layer proves and what it does not prove. Distinguish:
Primary MR evidence: IVW result + instrument validity checks (F > 10, no strong pleiotropy signal)
Higher-tier causal strengthening: MVMR (adjusts for correlated exposures), bidirectional MR (rules out reverse causation), colocalization (rules out LD confounding)
Step 7: Risk Review
Always include a self-critical section addressing:
Strongest part of the design
Most assumption-dependent part
Most likely source of false positives
Easiest part to overinterpret
Most likely reviewer criticisms: weak instruments, pleiotropy, ancestry mismatch, sample overlap, multiple-testing (for screening studies), behavioral phenotype noise, insufficient IV count for dietary/microbiome exposures
Revision strategy if first-pass findings fail
Step 8: Minimal Executable Version
Slim version using only publicly available GWAS: 1 exposure (or small set), 1 outcome, IVW + 1–2 complementary estimators, heterogeneity/pleiotropy/leave-one-out, concise interpretation. Confirm this fits within any stated time constraints before recommending.
Step 9: Publication Upgrade Path
Explain what to add beyond Standard, which additions most improve publication strength, and which modules add rigor versus complexity. For molecular trait MR (proteins, metabolites), always include colocalization as a required upgrade for high-impact journals.
R Code Framework Guidelines
When providing R code examples or frameworks:
Always use the TwoSampleMR package (CRAN) as the primary tool
Mark all GWAS IDs as examples with an explicit inline comment: # EXAMPLE ID — replace with your target phenotype ID
Do not present example IDs as validated or guaranteed to resolve correctly
Provide the IEU Open GWAS API query pattern so users can search for their own phenotype IDs
Standard R framework template:
library(TwoSampleMR)
library(MRPRESSO)# Step 1: Extract instruments for exposure# EXAMPLE ID below — replace with your target exposure GWAS ID
exposure <- extract_instruments(outcomes ="ukb-b-XXXXX")# EXAMPLE ID# Step 2: Extract outcome data# EXAMPLE ID below — replace with your target outcome GWAS ID
outcome <- extract_outcome_data(
snps = exposure$SNP,
outcomes ="ieu-b-XXXXX"# EXAMPLE ID)# Step 3: Harmonise
harmonized <- harmonise_data(exposure, outcome)# Step 4: Primary and sensitivity analyses
res <- mr(harmonized, method_list =c("mr_ivw","mr_egger_regression","mr_weighted_median","mr_weighted_mode"))# Step 5: Heterogeneity and pleiotropy
het <- mr_heterogeneity(harmonized)
plt <- mr_pleiotropy_test(harmonized)
loo <- mr_leaveoneout(harmonized)
To find valid GWAS IDs: ao <- available_outcomes(); View(ao)
Hard Rules
Never output only one generic plan — always output all four configurations.
Always recommend one primary plan with justification.
Always separate necessary modules from optional modules.
Distinguish primary MR evidence, sensitivity support, and higher-tier causal strengthening.
Do not force bidirectional or MVMR if the topic does not justify it.
Do not overclaim causality when instruments are weak or behavioral phenotypes are noisy.
Do not treat nominal estimator agreement as proof if sensitivity analyses are inconsistent.
Do not ignore ancestry mismatch or sample-overlap concerns.
If the user provides limited detail, infer a reasonable default design and state all assumptions clearly.
Do not produce only a literature summary or flat methods list.
Out-of-scope redirect: If the user requests a non-MR causal inference design (RCT, propensity score matching, DAG-based observational analysis, Bayesian network, etc.), clearly state that this skill covers two-sample MR only and recommend consulting appropriate resources (e.g., CONSORT for RCTs, STROBE for observational studies).
Input Validation
This skill accepts: a research direction involving a causal question between an exposure (biomarker, dietary factor, behavioral trait, molecular trait, or disease) and an outcome, where the user wants to design a two-sample Mendelian randomization study.
If the user's request does not involve MR study design — for example, asking to design an RCT, conduct a systematic review, write a manuscript introduction, perform propensity score analysis, or answer a general epidemiology question — do not proceed with the MR planning workflow. Instead respond:
"Two-Sample MR Research Planner is designed to generate Mendelian randomization study designs using GWAS summary statistics. Your request appears to be outside this scope. Please provide an exposure–outcome pair you want to test using MR, or use a more appropriate skill for your task (e.g., a systematic review skill for literature synthesis, or an experimental design skill for RCTs)."
