Polygenic risk score for breast cancer in the Thai population: Addressing genetic disparities in underrepresented populations.

10572 Background: Effective breast cancer prevention and management require accurate risk prediction tools. Polygenic risk scores (PRS) have shown promise but are often less effective in non-European populations due to differences in genetic architecture. This study evaluates PRS performance and adaptation for breast cancer in the Thai population, addressing disparities in underrepresented groups. Methods: We retrospectively analyzed breast cancer cases from the Genomics Thailand project at Siriraj Hospital and general population controls from the National Health Examination Survey (NHES) in Thailand. Whole-genome sequencing was performed for cases, and genotyping with imputation was done for controls using the TOPMed r2 reference panel. Clinical data were extracted from electronic medical records. PRS were constructed using SBayesRC, incorporating variants from publicly available genome-wide association study (GWAS) summary statistics and variant functional annotations. Logistic regression and area under the receiver operating characteristic curve (AUC) analyses were conducted using R. Results: The discovery cohort included 975 cases and 1,502 controls, with 230 cases and 265 controls in the validation cohort. Of the 330 previously reported GWAS loci, only 231 lead variants were identified in our dataset. We further analyzed variants near these lead variants within the 330 loci, identifying nominal associations with breast cancer for 329 loci (p<0.05). Four PRS models were tested: (1) 231 variants, (2) ~7 million functional variants based on European (EUR) data, (3) East Asian (EAS) models, and (4) combined EUR and EAS models. The EUR-based model (AUC 0.66) outperformed the 231-variant model (AUC 0.59) and the population-specific EAS model (AUC 0.58) at p<0.05. The combined EUR and EAS models showed no significant improvement over the EUR model alone (AUC 0.66 for both, p=0.69). Individuals in the highest PRS risk group (above the 90 th percentile) had an odds ratio (OR) of 3.34 for breast cancer compared to the rest of the population (95% confidence interval: 2.54–4.42, p<0.05). Among 249 patients with pathology data, PRS was not associated with tumor size, estrogen receptor status, or nodal metastasis. Conclusions: In the Thai population, PRS derived from large-scale European GWAS provided the highest prediction accuracy for breast cancer risk. The limited transferability of a top-variant PRS (e.g., 330-variant model) underscores the challenge posed by variant availability in this population. Validation in prospective studies is essential to optimize PRS utility and address disparities in genetic risk prediction.