mr-research

name: mr-research description: Complete Two-Sample Mendelian Randomization (MR) research workflow. Use when the user needs to perform causal inference studies using genetic instruments, including: (1) Analyzing causal effects between exposure and outcome, (2) Generating MR visualizations (scatter plots, forest plots, funnel plots, leave-one-out plots), (3) Conducting sensitivity analyses (heterogeneity, pleiotropy, Steiger test), (4) Creating research reports and GitHub Pages for sharing results.

Mendelian Randomization Research Skill

Complete workflow for conducting Two-Sample MR studies using TwoSampleMR R package.

Quick Start

Minimal Example

library(TwoSampleMR)

# 1. Get exposure data
exposure <- extract_instruments("ieu-a-299")  # HDL-C

# 2. Get outcome data
outcome <- extract_outcome_data(snps = exposure$SNP, outcomes = "ieu-a-7")  # CAD

# 3. Harmonise
dat <- harmonise_data(exposure, outcome)

# 4. Run MR
results <- mr(dat)

# 5. Generate plots
mr_scatter_plot(results, dat)

Research Workflow

Step 1: Environment Setup

Create Docker environment with required packages:

FROM rocker/r-ver:4.3.0

# System dependencies
RUN apt-get update && apt-get install -y \
    libcurl4-openssl-dev libssl-dev libxml2-dev \
    libfontconfig1-dev libharfbuzz-dev libfribidi-dev \
    libfreetype6-dev libpng-dev libtiff5-dev libjpeg-dev \
    curl git && rm -rf /var/lib/apt/lists/*

# CRAN packages
RUN R -e "install.packages(c('ggplot2', 'dplyr', 'tidyr', 'knitr', 'rmarkdown', 'remotes', 'patchwork'), repos='https://cloud.r-project.org/')"

# GitHub packages (with proxy if needed)
ARG HTTP_PROXY
ARG HTTPS_PROXY
RUN R -e "remotes::install_github('mrcieu/ieugwasr', upgrade='never')"
RUN R -e "remotes::install_github('MRCIEU/TwoSampleMR', upgrade='never')"

WORKDIR /research

Build with proxy:

docker build --build-arg HTTP_PROXY=http://proxy:port \
             --build-arg HTTPS_PROXY=http://proxy:port \
             --network=host -t mr-research:latest .

Step 2: Configure OpenGWAS API

# Set environment variable
export OPENGWAS_JWT="your_jwt_token_here"

# Or in R
Sys.setenv(OPENGWAS_JWT = "your_jwt_token_here")

Get free token at: https://api.opengwas.io/

Step 3: Data Extraction

library(TwoSampleMR)

# Exposure data (instrument selection)
exposure_dat <- extract_instruments(
  outcomes = "ieu-a-299",  # GWAS ID for exposure
  p1 = 5e-8,               # Genome-wide significance
  clump = TRUE,            # LD clumping
  r2 = 0.001               # LD threshold
)

# Outcome data
outcome_dat <- extract_outcome_data(
  snps = exposure_dat$SNP,
  outcomes = "ieu-a-7"     # GWAS ID for outcome
)

Step 4: Data Harmonisation

dat <- harmonise_data(exposure_dat, outcome_dat)

Step 5: MR Analysis

# Run multiple MR methods
mr_results <- mr(dat, method_list = c(
  "mr_ivw",              # Inverse variance weighted (primary)
  "mr_egger_regression", # MR-Egger (pleiotropy test)
  "mr_weighted_median",  # Weighted median
  "mr_weighted_mode"     # Weighted mode
))

Step 6: Sensitivity Analysis

# Heterogeneity test
heterogeneity <- mr_heterogeneity(dat)

# Pleiotropy test (MR-Egger intercept)
pleiotropy <- mr_pleiotropy_test(dat)

# Steiger directionality test
steiger <- directionality_test(dat)

# Leave-one-out analysis
loo <- mr_leaveoneout(dat)

# Single SNP analysis
single <- mr_singlesnp(dat)

Step 7: Visualization

# Scatter plot
p1 <- mr_scatter_plot(mr_results, dat)
ggsave("scatter_plot.png", p1[[1]], width = 10, height = 8, dpi = 300)

# Forest plot
p2 <- mr_forest_plot(single)
ggsave("forest_plot.png", p2[[1]], width = 10, height = 12, dpi = 300)

# Funnel plot
p3 <- mr_funnel_plot(single)
ggsave("funnel_plot.png", p3[[1]], width = 10, height = 8, dpi = 300)

# Leave-one-out plot
p4 <- mr_leaveoneout_plot(loo)
ggsave("leave_one_out.png", p4[[1]], width = 10, height = 8, dpi = 300)

Project Structure

mr-research/
├── scripts/
│   ├── analysis.R          # Main MR analysis
│   └── sensitivity.R       # Sensitivity analyses
├── data/                   # GWAS data (cached)
├── results/
│   ├── mr_results.csv
│   ├── heterogeneity.csv
│   ├── pleiotropy.csv
│   └── sensitivity/
├── figures/
│   ├── scatter_plot.png
│   ├── forest_plot.png
│   ├── funnel_plot.png
│   └── leave_one_out.png
├── report/
│   └── report.html
├── docs/                   # GitHub Pages
│   └── index.html
├── Dockerfile
└── README.md

Common GWAS Datasets

Lipids

Cardiovascular

Metabolic

Troubleshooting

Result Interpretation

AI Prompt Template

Use this prompt to quickly set up similar MR studies with AI:

请帮我完成一个两样本孟德尔随机化研究，分析 [暴露因素] 对 [结局变量] 的因果效应。

### 数据来源
- 暴露因素：[因素名称]，数据集 ID：[ieu-a-xxx]
- 结局变量：[疾病名称]，数据集 ID：[ieu-a-xxx]

### 分析流程
1. 从 OpenGWAS 获取 GWAS 汇总数据
2. 选择工具变量（P < 5×10⁻⁸）
3. Harmonise 数据
4. 运行 MR 分析（IVW, MR-Egger 等）
5. 敏感性分析
6. 生成图表和报告

Full template: references/ai_prompt_template.md

Reference Documents

References

• TwoSampleMR Documentation
• OpenGWAS Database
• OpenGWAS API Registration
• MR Methods Guide