Development and validation of a multiancestry and multitrait polygenic risk score for lung cancer

Polygenic risk scores (PRSs) quantify genetic susceptibilities, yet ancestry imbalance in genome-wide association studies (GWASs) limits the accuracy of monoracial PRSs in non-European populations. Here, we perform a multiancestry GWAS meta-analysis for lung cancer (76,953 cases and 1,886,372 controls), identifying 87 conditionally independent genome-wide significant loci, including two unreported cytobands. We use a PRS construction method, PRS-CSx, to develop a multiancestry PRS ($${{{{\rm{PRS}}}}}_{{{{\rm{MA}}}}}$$) which outperforms 32 published PRSs. To enhance predictive power, we construct a multitrait PRS ($${{{{\rm{PRS}}}}}_{{{{\rm{MT}}}}}$$) using CatBoost, integrating 32 cross-trait PRSs across three ancestries. Combining $${{{{\rm{PRS}}}}}_{{{{\rm{MA}}}}}$$ and $${{{{\rm{PRS}}}}}_{{{{\rm{MT}}}}}$$, we generate $${{{{\rm{PRS}}}}}_{{{{\rm{MAMT}}}}}$$ and validate it in independent cohorts (OncoArray, TRICL and All of Us). $${{{{\rm{PRS}}}}}_{{{{\rm{MAMT}}}}}$$ demonstrates superior discriminability in European, Asian, and African populations, improves risk stratification, and identifies approximately 10% additional lung cancer cases in the UK Biobank. Individuals with elevated PLCOm2012 scores and high genetic risk exhibit a 12.64-fold higher cumulative risk than those with low scores and low genetic risk, supporting precision prevention strategies. The ancestry imbalance in genome-wide association studies (GWAS) for lung cancer has led to the poor accuracy in existing monoracial polygenic risk scores in non-European populations. Here, the authors demonstrate that multiancestry and multitrait polygenic risk score (PRSMAMT) has superior predictive performance in multiple cohorts.