Federated GWAS

The core statistical engine computes allelic odds ratios and chi-squared test statistics locally at each biobank site. Instead of sharing raw genotypes, sites transmit only aggregate summary statistics—allele counts and contingency table margins—to a central analyst. This preserves the mathematical equivalence of a pooled mega-analysis while ensuring that no individual-level variant calls ever leave the originating institution's secure enclave.

For quantitative trait analysis, federated GWAS employs iteratively reweighted least squares executed across sites. Each biobank computes local gradient updates on its own genotype-phenotype matrices, and a central server aggregates these updates using Federated Averaging (FedAvg). This approach handles covariates such as age, sex, and principal components without exposing patient-level confounders, enabling adjusted association testing at genome-wide scale.

The central aggregation node combines site-level summary statistics using inverse-variance weighted meta-analysis methods, including fixed-effects and random-effects models. To prevent gradient leakage or allele frequency reconstruction attacks, the system integrates Secure Aggregation protocols and optional Differential Privacy noise injection. The result is a single set of p-values and effect sizes statistically identical to what would be obtained from a pooled dataset.

A critical preprocessing layer resolves strand ambiguity and reference allele mismatches across genotyping platforms and imputation panels. The system performs distributed allele frequency checks and LD-based variant matching to ensure that effect alleles are consistently oriented across all participating biobanks before association testing begins. This prevents spurious heterogeneity that would otherwise inflate genomic control lambda values.

Federated GWAS addresses Non-IID phenotype distributions across sites by implementing distributed cohort discovery and phenotype harmonization ontologies. Each site maps its local electronic health record codes to a common data model using standardized terminologies such as SNOMED CT and ICD-10. The system validates phenotype definitions through federated cohort counts before launching full association scans, ensuring consistent case-control definitions.

To guard against data poisoning attacks and inadvertent protocol violations, the system applies Byzantine Fault Tolerance principles to quality control. Site-level summary statistics are tested for deviation from expected genomic inflation factors and allele frequency distributions. Outlier sites exhibiting anomalous test statistics are flagged and can be excluded from meta-analysis, ensuring that a single misconfigured biobank cannot corrupt the global association results.

A data distribution characteristic in federated settings where local client datasets are statistically heterogeneous and do not represent the overall population distribution, posing a significant convergence challenge.

• Sources in GWAS: Population stratification, differing genotyping arrays, heterogeneous phenotype definitions, and site-specific ascertainment bias.
• Impact: Standard inverse-variance-weighted meta-analysis can produce biased results when effect sizes truly differ across populations.
• Mitigation: Mixed-effects meta-analysis and trans-ancestry fine-mapping methods account for heterogeneity rather than assuming a single fixed effect.

A distributed computational method for harmonizing non-biological systematic technical variation across data from different sites or experiments without requiring the raw data to be centralized for joint normalization.

• Sources of Batch Effects: Different genotyping platforms, sequencing depths, laboratory protocols, and sample processing pipelines.
• Approaches: ComBat and Harmony algorithms adapted to federated settings using secure aggregation of sufficient statistics rather than raw data sharing.
• Importance: Uncorrected batch effects can produce spurious genotype-phenotype associations and mask true biological signals in multi-site GWAS.