부산대학교 도서관

Simple Summary: Breast cancer is the most common cancer in women and has been associated with genetic and environmental factors. New developments have led to the creation of genetic risk scores which seek to better approximate an individual's risk of developing cancer and to be used clinically to improve cancer screening. Previous studies have shown that these risk scores can be used to determine an individual's cancer risk, but replication in independent groups is limited. In addition, certain genotyping methods utilize a process called imputation, which has the potential to make polygenic risk scores less accurate. This work aims to validate two breast cancer polygenic risk scores and to interrogate the impact imputation has on their values to improve their clinical utility. Breast cancer (BC) is a complex disease affecting one in eight women in the USA. Advances in population genomics have led to the development of polygenic risk scores (PRSs) with the potential to augment current risk models, but replication is often limited. We evaluated 2 robust PRSs with 313 and 3820 SNPs and the effects of multiple genotype imputation replications in BC cases and control populations. Biological samples from BC cases and cancer-free controls were drawn from three European ancestry cohorts. Genotyping on the Illumina Global Screening Array was followed by stringent quality control measures and 20 genotype imputation replications. A total of 468 unrelated cases and 4337 controls were scored, revealing significant differences in mean PRS percentiles between cases and controls (p < 0.001) for both SNP sets (313-SNP PRS: 52.81 and 48.07; 3820-SNP PRS: 55.45 and 49.81), with receiver operating characteristic curve analysis showing area under the curve values of 0.596 and 0.603 for the 313-SNP and 3820-SNP PRS, respectively. PRS fluctuations (from ~2–3% up to 9%) emerged across imputation iterations. Our study robustly reaffirms the predictive capacity of PRSs for BC by replicating their performance in an independent BC population and showcases the need to average imputed scores for reliable outcomes. [ABSTRACT FROM AUTHOR]