With the exponential growth of genomic data and analysis techniques we are seeing huge breakthroughs in use of polygenic risk scores to predict genetic risk of many common diseases, such as cancer, heart disease and diabetes. The study of genetic risk for common diseases is complex, as many genetic and environmental variants affect the disease risk. But following the success of genome-wide association studies (GWAS) in identifying the causal variants associated with the disease, polygenic risk scores (PRS) provide a way of aggregating all the variants carried by an individual into a single risk score.
Clinical trials increasingly depend on genomic data to identify the right patients, reduce variability in treatment response, and accelerate the path to approval. As sequencing costs continue to fall and the infrastructure for storing, processing, and analyzing large-scale genetic data matures, incorporating genomics into trial design is no longer aspirational. It is becoming a structural requirement. For sponsors and clinical teams working in precision medicine, the question is not whether to use genomics, but how to integrate it effectively across the trial lifecycle.