Search papers, labs, and topics across Lattice.
This study introduces a privacy-preserving federated meta-analysis pipeline for genome-wide association studies (GWAS) that leverages the APPFL framework, allowing institutions to compute local GWAS summary statistics without sharing individual-level genotypes. By utilizing the Global Alliance for Genomics and Health (GA4GH) Task Execution Service (TES), the method facilitates the analysis of diverse genomic data while adhering to privacy regulations and data-residency constraints. The results from a five-site simulation involving 100,000 synthetic individuals demonstrate that this federated approach can effectively reproduce association signals for traits like Type 2 Diabetes and Body Mass Index, validating its potential for global collaboration in genomic research.
Federated meta-analysis can yield accurate GWAS results without ever centralizing sensitive genotype data.
Genome-wide association studies (GWAS) gain statistical power from large, ancestrally diverse cohorts, but privacy regulations and data-residency constraints often prevent genomic data from being centrally pooled across institutional or national borders. We present a privacy-preserving federated GWAS meta-analysis pipeline built on the APPFL framework, in which each site computes local GWAS summary statistics and transmits only aggregate results, never individual-level genotypes. Analysis is executed through a global network of Global Alliance for Genomics and Health (GA4GH) Task Execution Service (TES) endpoints, which allows computation to move to the data rather than the reverse. The server performs inverse-variance-weighted fixed-effect meta-analysis and returns aggregated results to all sites, while HiveWatch, our developed geographic observability toolkit, provides real-time monitoring of distributed task execution. In a five-site simulation over 100,000 synthetic individuals and roughly 240,000 variants for Type 2 Diabetes and Body Mass Index, the federated meta-analysis reproduces the association signal expected from a pooled analysis without centralizing any genotype data, showing that standards-based task execution and federated learning enables a practical privacy-preserving infrastructure for international GWAS meta-analysis.
