ArticleGenome medicine2024
Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification.
Article in Genome medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Racial inequalities in eligibility and access to lung cancer screening: systematic review of United States studies.BMC public health · 2025Pooled it
- Combined association of genetic predisposition and healthy lifestyle with lung cancer risk: a population-based cohort study.Translational lung cancer research · 2026Article
- Clinical usefulness of polygenic risk scores in risk prediction models for lung cancer screening and lung nodule management.Translational oncology · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Neglected Occupational Risk Factors-A Contributor to Diagnostic Delays in Lung Cancer.Healthcare (Basel, Switzerland) · 2026Article
- PGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohorts.Genome medicine · 2025Article
- Precision Medicine in Lung Cancer Screening: A Paradigm Shift in Early Detection-Precision Screening for Lung Cancer.Diagnostics (Basel, Switzerland) · 2025Review
- PennPRS: a centralized cloud computing platform for efficient polygenic risk score training in precision medicine.medRxiv : the preprint server for health sciences · 2025Article
- Attitudes regarding polygenic risk testing for lung cancer: a mixed-methods study.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2025Article
- Evaluation of diverse polygenic risk score models for lung cancer in a small-scale Chinese cohort.Frontiers in genetics · 2025Article
- The causal nexus between diverse smoking statuses, potential therapeutic targets, and NSCLC: insights from Mendelian randomization and mediation analysis.Frontiers in oncology · 2024Article
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Authors and funding
37 authors at 20 institutions in 12 countries.
Funding
Abstract
backgroundAlthough polygenic risk score (PRS) has emerged as a promising tool for predicting cancer risk from genome-wide association studies (GWAS), the individual-level accuracy of lung cancer PRS and the extent to which its impact on subsequent clinical applications remains largely unexplored.
methodsLung cancer PRSs and confidence/credible interval (CI) were constructed using two statistical approaches for each individual: (1) the weighted sum of 16 GWAS-derived significant SNP loci and the CI through the bootstrapping method (PRS-16-CV) and (2) LDpred2 and the CI through posteriors sampling (PRS-Bayes), among 17,166 lung cancer cases and 12,894 controls with European ancestry from the International Lung Cancer Consortium. Individuals were classified into different genetic risk subgroups based on the relationship between their own PRS mean/PRS CI and the population level threshold.
resultsConsiderable variances in PRS point estimates at the individual level were observed for both methods, with an average standard deviation (s.d.) of 0.12 for PRS-16-CV and a much larger s.d. of 0.88 for PRS-Bayes. Using PRS-16-CV, only 25.0% of individuals with PRS point estimates in the lowest decile of PRS and 16.8% in the highest decile have their entire 95% CI fully contained in the lowest and highest decile, respectively, while PRS-Bayes was unable to find any eligible individuals. Only 19% of the individuals were concordantly identified as having high genetic risk (> 90th percentile) using the two PRS estimators. An increased relative risk of lung cancer comparing the highest PRS percentile to the lowest was observed when taking the CI into account (OR = 2.73, 95% CI: 2.12-3.50, P-value = 4.13 × 10
conclusionsLung cancer PRS estimates using different methods have modest correlations at the individual level, highlighting the importance of considering individual-level uncertainty when evaluating the practical utility of PRS.
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