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 ({{{PRS}}}{{₌₀}}) which outperforms 32 published PRSs. To enhance predictive power, we construct a multitrait PRS ({{{PRS}}}{{₌ₓ}}) using CatBoost, integrating 32 cross-trait PRSs across three ancestries. Combining {{{PRS}}}{{₌₀}} and {{{PRS}}}{{₌ₓ}}, we generate {{{PRS}}}{{₌₀₌ₓ}} and validate it in independent cohorts (OncoArray, TRICL and All of Us). {{{PRS}}}{{₌₀₌ₓ}} 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.