Polygenic Risk Prediction for Precision Prevention
Jin Jin, Nilanjan Chatterjee · 2024
The rapidly developing modern genome-wide association studies (GWAS) have led to the discovery of tens of thousands of genetic susceptibility variants which together explain a substantial amount of variability in a large number of human traits and diseases ( Visscher et al., 2017 ; Buniello et al., 2019 ). How to appropriately aggregate signals of a huge amount of genetic variants, specifically single nucleotide polymorphisms (SNPs), across the whole genome into a polygenic risk score (PRS) that can achieve considerable predictive power in the general population has become a key problem in genetic research ( Chatterjee et al., 2016 ). With increasing predictive utility, PRSs been highlighted for their potential importance for predicting future risks of complex diseases and hence aiding development of risk-stratified strategies for prevention ( Torkamani et al., 2018 ; Lambert et al., 2019 ; Sun et al., 2021 ). Real-world implementation of polygenic risk, prediction will require knowledge of how to choose the optimal method for constructing the PRS, and combine it with other critical socio-economic, lifestyle, and environmental risk factors to predict the absolute risk of an individual ( Pal Choudhury et al., 2020 ). In addition, many important aspects that are often overlooked should be considered, such as population-specific genetic architectures potentially caused by differences in human demographic history ( Eyre-Walker, 2010 ; Sanjak et al., 2017 ; O&s;Connor et al., 2019 ; Uricchio, 2020 ), and approaches to dealing with low ancestral diversity of the current genetic studies which could exacerbate potential health disparity issues ( Kim et al., 2018 ; Duncan et al., 2019 ; Martin et al., 2019 ; Rosenberg et al., 2019 ; Lewis and Vassos, 2020 ; Lewis and Green, 2021 ).