Stratifying Lung Adenocarcinoma Risk with Multi-ancestry Polygenic Risk Scores in East Asian Never-Smokers
Blechter, B.; Wang, X.; Shi, J.; Shiraishi, K.; Choi, J.; Matsuo, K.; Chen, T.-Y.; Dai, J.; Hung, R. J.; Chen, K.; Shu, X.-O.; Kim, Y. T.; Choudhury, P. P.; Williams, J.; Landi, M. T.; Lin, D.; Zheng, W.; Yin, Z.; Song, B.; Chang, I.-S.; Hong, Y.-C.; Never Smoker Lung Cancer Working Group, ; Chatterjee, N.; Gorlova, O. Y.; Amos, C. I.; Shen, H.; Hsiung, C. A.; Chanock, S. J.; Rothman, N.; Kohno, T.; Lan, Q.; Zhang, H.
Show abstract
BackgroundLung adenocarcinoma (LUAD) in never-smokers is a major public health burden, especially among East Asian women. Polygenic risk scores (PRSs) are promising for risk stratification but are primarily developed in European-ancestry populations. We aimed to develop and validate single- and multi-ancestry PRSs for East Asian never-smokers to improve LUAD risk prediction. MethodsPRSs were developed using genome-wide association study summary statistics from East Asian (8,002 cases; 20,782 controls) and European (2,058 cases; 5,575 controls) populations. Single-ancestry models included PRS-25, PRS-CT, and LDpred2; multi-ancestry models included LDpred2+PRS-EUR128, PRS-CSx, and CT-SLEB. Performance was evaluated in independent East Asian data from the Female Lung Cancer Consortium (FLCCA) and externally validated in the Nanjing Lung Cancer Cohort (NJLCC). We assessed predictive accuracy via AUC, with 10-year and (age 30-80) absolute risks estimates. ResultsThe best multi-ancestry PRS, using East Asian and European data via CT-SLEB (clumping and thresholding, super learning, empirical Bayes), outperformed the best East Asian-only PRS (LDpred2; AUC=0.629, 95% CI:0.618,0.641), achieving an AUC of 0.640 (95% CI:0.629,0.653) and odds ratio of 1.71 (95% CI:1.61,1.82) per SD increase. NJLCC Validation confirmed robust performance (AUC =0.649, 95% CI: 0.623, 0.676). The top 20% PRS group had a 3.92-fold higher LUAD risk than the bottom 20%. Further, the top 5% PRS group reached a 6.69% lifetime absolute risk. Notably, this group reached the average population 10-year LUAD risk at age 50 (0.42%) by age 41, nine years earlier. ConclusionsMulti-ancestry PRS approaches enhance LUAD risk stratification in East Asian never-smokers, with consistent external validation, suggesting future clinical utility.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A blood- and brain-based EWAS of smoking 95%
- The impact of non-additive genetic associations on age-related complex diseases. 95%
- Expanding the Genetic Architecture of Nicotine Dependence and its Shared Genetics with Multiple Traits: Findings from the Nicotine Dependence GenOmics (iNDiGO) Consortium 95%
Similar papers in this journal
- Polygenic risk score prediction accuracy convergence 95%
- Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations 94%
- Scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications to studying Alzheimer’s disease 94%
Similar papers in this journal
- Large scale genome-wide association study in a Japanese population identified 45 novel susceptibility loci for 22 diseases 95%
- Trans-ethnic genome-wide meta-analysis of 35,732 cases and 34,424 controls identifies novel genomic cross-ancestry loci contributing to lung cancer susceptibility 95%
- Multi-trait and multi-ancestry genetic analysis of comorbid lung diseases and traits improves genetic discovery and polygenic risk prediction 95%
Similar papers in this journal
- Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases 96%
- Genome-wide study on 72,298 Korean individuals in Korean biobank data for 76 traits identifies hundreds of novel loci 95%
- Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.