Multiancestry conditional and joint analysis applied to GWAS summary statistics.

Manc-COJO enhances detection of independent associations in multiancestry GWAS, outperforming GCTA-COJO with over 120-fold faster processing and improved accuracy.

• Why it matters: Identifying independent genetic associations across diverse ancestry groups is crucial for understanding complex traits, but current methods lack optimal strategies for multiancestry data. Improving these analyses can lead to more accurate genetic insights and better understanding of population-specific effects.
• What they did: The authors developed Manc-COJO, a new method for multiancestry COJO analysis, validated through simulations and real-data, and introduced Manc-COJO:FIX to identify ancestry-specific associations, with a C++ implementation supporting various linkage disequilibrium references.
• The result: Manc-COJO improves detection of independent signals and reduces false positives compared to existing methods, while its faster processing and flexible reference support facilitate analyses with limited data sharing.