Applicability of breast cancer 313 Polygenic Risk Score (PRS313) in the Spanish region of Navarra Edurne Urrutia-Lafuente1, Ane Miren Sagardia Fernández1​, Maria Miranda Pérez1, David Cobos Hermosa1, Óscar Teijido1, David Gómez-Cabrero1#, Ángel Alonso1,2#. 1. Navarrabiomed - IdiSNA - HUN, Genomic Medicine Unit, Pamplona, Spain. 2. Hospital Universitario De Navarra, Genetics Department, Pamplona, Spain. AIM METHODS 2. Individual assessment: · Exploratory analysis · Differential expression · Functional enrichment 1. Systematic review of public repositories and selection of transcriptomic studies: RNA-seq and microarrays. 3. Meta-analysis and functional enrichment 3.1. Synthesis of the functional enrichment The aim of this study was to identify and understand the molecular mechanisms underlying sex-based differences in RA. SCREENING INCLUDED SEARCH #1: GEO 172 records 200 records after removing duplicates 41 full-text articles assessed for eligibility 6 STUDIES INCLUDED in the individual assessment 5 STUDIES INCLUDED in the meta-analysis Figure 1. PRISMA diagram that represents the workflow of the systematic review. Meta-analysis and functional enrichment 10 genes, 90 biological processes and 11 KEGG pathways were identified as significantly sex- dependent. The enrichment results suggest a higher immune system activity and a larger presence of energy precursors in female RA patients, among others. Male RA patients, in contrast, showed an overrepresentation of terms related to protein biosynthesis and negative regulation of osteoblasts (Figures 2, 3). Data analysis workflow Comparison GOslim terms Figure 2. Pyramid of GOslim terms. The horizontal axis shows the number of GO terms associated to each GOslim term. Terms overrepresented in males are displayed in red, and those overrepresented in females, in blue. RA - CONTROL VS RA - CONTROL SEARCH #2: ArrayExpress 78 records Polygenic risk scores (PRS) have demonstrated their potential to predict the probability of developing cancer. This allows risk stratification and thus improved screening programmes. Among them, PRS313 is the best-performing breast cancer PRS for European-ancestry women.1 The NAGENMx project aims to provide the necessary evidence to implement a personalized breast cancer-screening program in Navarra based on this PRS. Therefore, prior validation in our population is essential to ensure an accurate stratification. The goal of this study was to evaluate the applicability of breast cancer PRS313 in the Spanish region of Navarre. METHODS REFERENCES 1. Mavaddat, et al. (2019). Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. The American Journal of Human Genetics, 104(1), 21-34. 2. Collister, J. A., Liu, X., & Clifton, L. (2022). Calculating polygenic risk scores (PRS) in UK Biobank: a practical guide for epidemiologists. Frontiers in Genetics, 13, 105. 3. Choi, S. W., Mak, T. S. H., & O’Reilly, P. F. (2020). Tutorial: a guide to performing polygenic risk score analyses. Nature protocols, 15(9), 2759-2772. CONCLUSIONS Samples Data analysis workflow 1. Sample selection: 2. Exploratory data analysis (EDA): High risk (Hereditary breast cancer criteria) 3. PRS evaluation2,3 INTRODUCTION RESULTS RESULTS • Missing SNPs pose a major limitation when applying existing PRSs to new populations. • The greater separation between the control and moderate-risk groups might suggest a higher polygenic burden in the latter, while the high-risk group with strong family history could be explained by rare mutations not included in the PRS313. However, further exploration is still needed. • Ultimately, applying existing PRSs in different populations is challenging and requires standard guidelines for their evaluation and for dealing with missing data. AIM Moderate risk (Family history) Population risk 3.2. Model selection 1. How many samples were included in the study? 3. 1 How did we address these missing SNPs? 3.1 Missing SNPs Women Navarre Figure 3. Dens