Secure GWAS

Individual-level genetic data remains encrypted or secret-shared across multiple independent servers throughout the entire computation. No single party ever reconstructs a complete genome. This architecture eliminates the central honeypot risk inherent in traditional pooled GWAS, ensuring compliance with GDPR and HIPAA mandates.

• Data is split using additive secret sharing before analysis
• Each computing node holds only a random share of each variant
• The raw genotype never exists in plaintext at any compute node

Standard statistical tests like the chi-squared test and logistic regression are re-expressed as arithmetic circuits and evaluated using secure multi-party computation (MPC). The protocol computes p-values and odds ratios for each single nucleotide polymorphism (SNP) without revealing the underlying contingency tables.

• Cochran-Armitage trend test implemented via garbled circuits
• Secure matrix multiplication for covariate adjustment
• Outputs only aggregate association statistics, never individual counts

Confounding due to ancestry differences is addressed by securely incorporating principal component analysis (PCA) on the combined, private datasets. The protocol computes eigenvectors of the genetic relationship matrix without revealing pairwise kinship coefficients between individuals held by different parties.

• Secure eigendecomposition via iterative MPC protocols
• Covariates are secret-shared and integrated into the regression model
• Prevents spurious associations from population structure

Standard GWAS quality control metrics—Hardy-Weinberg equilibrium, minor allele frequency, and call rate—are computed obliviously across the distributed cohort. The protocol flags problematic variants for removal without any party learning the allele frequencies at non-significant loci.

• Secure frequency counting using oblivious aggregation
• Threshold-based filtering applied inside the MPC engine
• Only QC-passing variants proceed to association testing

Secure GWAS outputs are formatted to be directly compatible with standard meta-analysis frameworks like METAL and PLINK. The resulting summary statistics can be combined with publicly available GWAS results to increase statistical power, while the private individual-level data remains permanently isolated.

• Outputs standard beta coefficients and standard errors
• No proprietary format lock-in
• Enables federated replication across independent cohorts

The protocol interfaces with secure genotype imputation services, allowing the study to infer unobserved variants against a private haplotype reference panel. This increases the density of tested variants without exposing either the study genotypes or the proprietary reference panel.

• Hidden Markov Model inference executed via MPC
• Reference panel remains encrypted at the server
• Imputed dosages fed directly into secure association testing