A Modular Pipeline for the Construction and Validation of Polygenic Risk Scores in Oncology (Epidemiology, Biostatistics, and Public Health, 2025)
INTRODUCTIONPolygenic Risk Scores (PRS) are statistical tools designed to estimate individual predisposition to complex diseases by aggregating the effects of numerous genetic variants (Single Nucleotide Polymorphisms SNPs). In oncology, PRS hold promise for enhancing cancer risk stratification and personalizing screening strategies. However, their effectiveness depends on a well-defined computational framework that guarantees high-quality data processing and consistent predictive performance. OBJECTIVEThis study aims to describe a modular and reproducible pipeline for the construction and validation of PRS in cancer research, detailing each analytical step from genotype preprocessing to risk score validation. Given that cancer is characterized by a highly polygenic architecture, involving thousands of loci with small effect sizes, such efforts require analytical workflows capable of handling complex and large-scale genomic data in a reliable and scalable manner. METHODSThe pipeline begins with raw Variant Call Format (VCF) files obtained through genotyping. To ensure statistical power for detecting associations and constructing robust and accurate PRSs, large sample sizes, typically comprising several thousand cases and controls, are essential. Maintaining a balanced case-control ratio of 1:1 is crucial to minimize bias and maximize model stability. When relevant covariates are available, propensity score matching [1] can be applied to further balance cases and controls on clinical or demographic characteristics. Quality control (QC) is implemented using PLINK, a tool for handling SNP data, to remove variants and individuals based on call rate (