TY - JOUR

T1 - Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification

AU - Wang, Xinan

AU - Zhang, Ziwei

AU - Ding, Yi

AU - Chen, Tony

AU - Mucci, Lorelei

AU - Albanes, Demetrios

AU - Landi, Maria Teresa

AU - Caporaso, Neil E.

AU - Lam, Stephen

AU - Tardon, Adonina

AU - Chen, Chu

AU - Bojesen, Stig E.

AU - Johansson, Mattias

AU - Risch, Angela

AU - Bickeböller, Heike

AU - Wichmann, H. Erich

AU - Rennert, Gadi

AU - Arnold, Susanne

AU - Brennan, Paul

AU - McKay, James D.

AU - Field, John K.

AU - Shete, Sanjay S.

AU - Le Marchand, Loic

AU - Liu, Geoffrey

AU - Andrew, Angeline S.

AU - Kiemeney, Lambertus A.

AU - Zienolddiny-Narui, Shan

AU - Behndig, Annelie

AU - Johansson, Mikael

AU - Cox, Angie

AU - Lazarus, Philip

AU - Schabath, Matthew B.

AU - Aldrich, Melinda C.

AU - Hung, Rayjean J.

AU - Amos, Christopher I.

AU - Lin, Xihong

AU - Christiani, David C.

N1 - Publisher Copyright: © The Author(s) 2024.

PY - 2024

Y1 - 2024

N2 - Background: Although 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. Methods: Lung 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. Results: Considerable 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 × 10−15) compared to using PRS-16-CV mean (OR = 2.23, 95% CI: 1.99–2.49, P-value = 5.70 × 10−46). Improved risk prediction performance with higher AUC was consistently observed in individuals identified by PRS-16-CV CI, and the best performance was achieved by incorporating age, gender, and detailed smoking pack-years (AUC: 0.73, 95% CI = 0.72–0.74). Conclusions: Lung 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.

AB - Background: Although 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. Methods: Lung 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. Results: Considerable 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 × 10−15) compared to using PRS-16-CV mean (OR = 2.23, 95% CI: 1.99–2.49, P-value = 5.70 × 10−46). Improved risk prediction performance with higher AUC was consistently observed in individuals identified by PRS-16-CV CI, and the best performance was achieved by incorporating age, gender, and detailed smoking pack-years (AUC: 0.73, 95% CI = 0.72–0.74). Conclusions: Lung 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.

KW - Cancer control

KW - Genetic epidemiology

KW - Non-small cell lung cancer (NSCLC)

KW - Polygenic risk score (PRSs)

KW - Population science

U2 - 10.1186/s13073-024-01298-4

DO - 10.1186/s13073-024-01298-4

M3 - Journal article

C2 - 38317189

AN - SCOPUS:85184421171

VL - 16

JO - Genome Medicine

JF - Genome Medicine

SN - 1756-994X

IS - 1

M1 - 22

ER -