Polygenic Risk

Polygenic risk scores (PRS) aim to predict an individual's risk of developing complex diseases by analyzing the combined effect of many genetic variants. Current research focuses on improving PRS accuracy through advanced machine learning techniques, such as deep neural networks and variational autoencoders, which are showing promise in identifying non-linear relationships between genetic variants and disease risk and in mitigating biases stemming from population stratification. These improvements are crucial for enhancing the precision and generalizability of PRS across diverse populations, ultimately leading to more effective personalized risk assessment and potentially informing preventative strategies.