Multiancestry conditional and joint analysis applied to GWAS summary statistics

Abstract

Abstract Conditional and joint (COJO) analysis of genome-wide association study (GWAS) data is a standard post-GWAS analysis for identifying statistically independent associations. Although GWAS meta-analyses are increasingly conducted across multiple ancestry groups, the optimal strategy for multiancestry COJO analysis remains unclear. Here we introduce Manc-COJO, a method for multiancestry COJO analysis. Simulations and real-data analyses show that Manc-COJO improves detection of independent association signals while reducing false positives compared with single-ancestry COJO implemented in genome-wide complex trait analysis (GCTA) and its ad-hoc multiancestry adaptations. We also introduce Manc-COJO:FIX, a follow-up algorithm to identify ancestry-specific independent associations. The C++ implementation of Manc-COJO is over 120-fold faster than GCTA-COJO and supports linkage disequilibrium references derived from either individual-level genotype data or pre-computed matrices, facilitating analyses when data sharing is limited.

The authors' abstract, as published at the source. Nature Genetics, 2026 · DOI ↗