Federated Mendelian Randomization on OMOP Common Data Model • Medusa

Mendelian Estimation in Distributed Standardized Analytics

Federated two-sample Mendelian Randomization on the OMOP Common Data Model.

PACKAGE UNDER CONSTRUCTION: DO NOT USE

Overview

Medusa implements two-sample Mendelian Randomization (MR) natively within the OHDSI ecosystem. It enables causal inference across distributed health networks without requiring individual-level data to leave any site.

The core innovation is one-shot federated pooling via profile likelihood aggregation: each site computes a log-likelihood profile and shares only site-level summary files centered on that numeric vector. The coordinator sums profiles across sites to obtain a pooled estimate with no iterative communication protocol.

Architecture

    ┌──────────────────────────────────────────────────────────┐
    │                    COORDINATOR NODE                      │
    │                                                          │
    │  getMRInstruments()  ──────►  Instrument Table           │
    │       │                       (from OpenGWAS)            │
    │       │ distribute                                       │
    │       ▼                                                  │
    │  ┌─────────┐  ┌─────────┐  ┌─────────┐                   │
    │  │ Site A  │  │ Site B  │  │ Site C  │   OMOP CDM        │
    │  │─────────│  │─────────│  │─────────│   Sites           │
    │  │buildMR  │  │buildMR  │  │buildMR  │                   │
    │  │Cohort() │  │Cohort() │  │Cohort() │                   │
    │  │    ▼    │  │    ▼    │  │    ▼    │                   │
    │  │fitOut   │  │fitOut   │  │fitOut   │                   │
    │  │come     │  │come     │  │come     │                   │
    │  │Model()  │  │Model()  │  │Model()  │                   │
    │  │    │    │  │    │    │  │    │    │                   │
    │  └────┼────┘  └────┼────┘  └────┼────┘                   │
    │       │            │            │                        │
    │       ▼            ▼            ▼                        │
    │    Profile       Profile      Profile   ◄── Only these   │
    │    vectors       vectors      vectors       leave sites  │
    │       │            │            │                        │
    │       └────────────┼────────────┘                        │
    │                    ▼                                     │
    │         poolLikelihoodProfiles()                         │
    │                    │                                     │
    │                    ▼                                     │
    │           computeMREstimate()                            │
    │           runSensitivityAnalyses()                       │
    │           generateMRReport()                             │
    └──────────────────────────────────────────────────────────┘

Installation

# Install from GitHub
remotes::install_github("OHDSI/Medusa")

# Or install with all dependencies
remotes::install_github("OHDSI/Medusa", dependencies = TRUE)

Quick Start

library(Medusa)

# 1. Assemble instruments from OpenGWAS (coordinator)
instruments <- getMRInstruments(
  exposureTraitId = "ieu-a-1119",  # IL-6 receptor
  pThreshold = 5e-8,
  r2Threshold = 0.001
)

# 2. At each site: build cohort and fit outcome model
cohort <- buildMRCohort(
  connectionDetails = connectionDetails,
  cdmDatabaseSchema = "cdm",
  cohortDatabaseSchema = "results",
  cohortTable = "cohort",
  outcomeCohortId = 1234,
  instrumentTable = instruments,
  genomicDatabaseSchema = "genomics"  # Schema with VARIANT_OCCURRENCE
)

profile <- fitOutcomeModel(
  cohortData = cohort,
  instrumentTable = instruments,
  siteId = "my_site"
)

# 3. At coordinator: pool profiles and estimate
combined <- poolLikelihoodProfiles(list(siteA = profileA, siteB = profileB))
estimate <- computeMREstimate(combined, instruments)

# 4. Generate report
generateMRReport(
  mrEstimate = estimate,
  combinedProfile = combined,
  exposureLabel = "IL-6 signaling",
  outcomeLabel = "Colorectal cancer"
)

Main Functions

Function	Module	Description
`getMRInstruments()`	Instrument Assembly	Query OpenGWAS for instruments, LD clump
`createInstrumentTable()`	Instrument Assembly	Build instruments from local data
`buildMRCohort()`	Cohort Extraction	Extract cohort + genotypes from OMOP CDM
`buildMRCovariates()`	Covariate Assembly	Assemble covariates via FeatureExtraction
`runInstrumentDiagnostics()`	Diagnostics	F-stats, PheWAS, allele-frequency and missingness checks
`fitOutcomeModel()`	Outcome Model	Fit outcome model, evaluate profile likelihood
`poolLikelihoodProfiles()`	Pooling	Sum log-likelihood profiles across sites
`computeMREstimate()`	Estimation	Wald ratio with delta method SE
`runSensitivityAnalyses()`	Sensitivity	IVW, MR-Egger, weighted median, etc.
`generateMRReport()`	Reporting	Self-contained HTML report

Vignettes

Getting Started — Installation, concepts, quick example with simulated data
IL-6 and Colorectal Cancer — Complete scientific walkthrough
Federated Analysis Guide — Network coordinator instructions

Requirements

R >= 4.1.0
OHDSI packages: DatabaseConnector, SqlRender, Cyclops, FeatureExtraction
OMOP CDM database with OMOP Genomic CDM (VARIANT_OCCURRENCE table)
For instrument retrieval: internet access to OpenGWAS API

License

Apache License 2.0

References

Davey Smith & Hemani (2014). Mendelian randomization: genetic anchors for causal inference. Human Molecular Genetics.
Bowden et al. (2015). Mendelian randomization with invalid instruments: MR-Egger. IJE.
Bowden et al. (2016). Weighted median estimator. Genetic Epidemiology.
Suchard et al. (2013). Cyclops: massive parallelization of serial inference. ACM TOMACS.