mrmrmr

LLM-powered automated Mendelian Randomization for causal discovery in biomedical research

MRAgent - Automated Mendelian Randomization Analysis Agent

Description

MRAgent is an intelligent agent that automates the entire process of Mendelian randomization analysis for causal discovery in biomedical research. It:

1. Knowledge Discovery Mode (KD): Given a disease (outcome), automatically scans PubMed literature to discover potential modifiable exposure (risk factor) that have correlation but no established causal inference, then performs comprehensive Mendelian randomization analysis using OpenGWAS data to identify novel causal relationships.

2. Causal Validation Mode (CV): Directly test whether a user-specified exposure (e.g., "body mass index") has a causal effect on a specific outcome (e.g., "type 2 diabetes") using two-sample Mendelian randomization.

MRAgent handles all steps automatically:

• PubMed literature crawling
• LLM-based extraction of candidate exposure-outcome pairs
• Check for existing MR studies via PubMed search
• Optional STROBE-MR quality assessment of existing studies
• UMLS medical synonym expansion
• OpenGWAS database query for GWAS summary statistics
• LLM selection of most appropriate GWAS datasets
• Multiple MR methods: Inverse variance weighted, MR-Egger, weighted median, etc.
• Heterogeneity analysis and pleiotropy sensitivity testing
• MRlap correction for sample overlap
• Automatic generation of publication-ready PDF reports with LLM-written interpretation

Requirements

• Python packages (install via pip install -r {baseDir}/requirements.txt)
• R language (>= 4.3.4) with the following packages installed:
  • TwoSampleMR - core Mendelian randomization
  • ieugwasr - OpenGWAS interface
  • vcfR - required for MRlap (optional)
  • MRlap - sample overlap correction (optional)
  • jsonlite - JSON processing (required for MRlap)
• OPENAI_API_KEY environment variable must be set (OpenAI API key)
• OPENGWAS_JWT environment variable (optional, for OpenGWAS access token)

Usage

Knowledge Discovery Mode (recommended for novel discoveries)

Discover novel causal relationships starting from a disease:

bash

python {baseDir}/run_mragent.py --mode KD --outcome "back pain" --num-pubmed 100 --bidirectional

Discover novel causal relationships starting from an exposure:

(Currently the CLI implements KD starting from outcome; for exposure-based discovery use the Python API directly)

Causal Validation Mode

Validate a specific hypothesis: does exposure causally affect outcome?

bash

python {baseDir}/run_mragent.py --mode CV --exposure "body mass index" --outcome "type 2 diabetes"

Common Options

Option	Description	Example
--mode KD|CV	Required. KD=knowledge discovery, CV=causal validation	--mode CV
--outcome NAME	Required. Outcome (disease) to study	--outcome "back pain"
--exposure NAME	Required in CV mode. Exposure factor to test	--exposure "osteoporosis"
--num-pubmed N	Number of papers to fetch from PubMed (default: 100)	--num-pubmed 50
--model MR|MR_MOE	MR model type. MR=standard, MR_MOE=mixture of experts	--model MR_MOE
--bidirectional	Perform bidirectional analysis (also test outcome → exposure)	--bidirectional
--no-synonyms	Disable synonym expansion (faster)	--no-synonyms
--strobe-mr	Enable STROBE-MR quality assessment of existing studies	--strobe-mr
--mrlap	Enable MRlap sample overlap correction	--mrlap
--output-dir DIR	Output directory (default: ./output)	--output-dir /tmp/mragent-out
--steps 1,2,3	Only run specific steps (for debugging/intervention)	--steps 1,2

Environment Variables

Variable	Required	Description
OPENAI_API_KEY	Yes	OpenAI API key
OPENGWAS_JWT	Optional	OpenGWAS JWT access token
LLM_MODEL	No	LLM model name (default: gpt-4o)
LLM_PROVIDER	No	openai or ollama (default: openai)
OPENAI_BASE_URL	No	Custom base URL for OpenAI-compatible API
MRAGENT_SOURCE_PATH	No	Path to original MRAgent source if not installed globally

Output

MRAgent outputs a JSON summary to stdout with:

• success: boolean indicating success
• output_directory: directory containing all results
• discovered_pairs: number of candidate exposure-outcome pairs found
• selected_for_mr: number of pairs selected for MR analysis
• reports: list of paths to generated PDF files (Report.pdf, Introduction.pdf, Conclusion.pdf, etc.)

Example output:

json

{
  "success": true,
  "mode": "CV",
  "outcome": "back pain",
  "exposure": "osteoarthritis",
  "output_directory": "./output/osteoarthritis_back_pain_gpt-4o",
  "discovered_pairs": 1,
  "selected_for_mr": 1,
  "reports": [
    "./output/osteoarthritis_back_pain_gpt-4o/osteoarthritis_back_pain/Introduction.pdf",
    "./output/osteoarthritis_back_pain_gpt-4o/osteoarthritis_back_pain/osteoarthritis_back_pain/MR_ieu-a-2_ieu-a-1008/Report.pdf",
    "./output/osteoarthritis_back_pain_gpt-4o/osteoarthritis_back_pain/Conclusion.pdf"
  ]
}

Workflow Steps

When running all steps (default):

Step	Description
1	Crawl PubMed, extract candidate exposure-outcome pairs
2	Check if each pair already has MR studies in literature
3	Extract unique terms, expand with medical synonyms
4	Check which terms have available GWAS data in OpenGWAS
5	LLM selects most appropriate GWAS IDs for each term
6	Generate all combinations of exposure-outcome pairs
7	Check new combinations for existing MR studies
8	Select final set of novel pairs for analysis
9	Run MR analysis, generate plots and LLM-interpreted PDF reports

Notes

• This is a computationally intensive process. The full analysis can take tens of minutes to hours depending on the number of pairs.
• All intermediate results are saved as CSV files in the output directory, allowing manual editing and intervention between steps.
• MRAgent requires R to be installed with the necessary packages, because the actual MR analysis is performed by R's TwoSampleMR package.