The clinical value of knowing the genomic basis of a given trait is heightened if that information can be used to identify individuals who would benefit either from close monitoring for the possible onset of a specific disease, or from targeted treatment. Polygenic risk scores draw on the results of GWAS, combined with growing access to sequencing data from individuals, to predict disease risk based on the genetic changes present in a given individual. Using the set of genetic changes previously discovered to be associated with a trait of interest, polygenic risk scoring uses phenotypes from a “test” cohort to infer the weights for each factor, inferring the set of coefficients for each associated genetic variant that best predict a phenotype from genome data. This approach has been applied, with varying degrees of success, for calculation of risk with respect to phenotypes such as cancer, as discussed by Amit Sud, Clare Turnbull, and Richard Houlston in “Will Polygenic Risk Scores for Cancer Ever Be Clinically Useful?” and for cardiovascular disease, as reported by Jack O’Sullivan and colleagues in “Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement from the American Heart Association.” Polygenic risk scores have also been proposed as a tool to develop recommended frequencies of disease screening for individuals of disparate risk profiles. The challenges of determining optimal screening protocols, even when genomic data are available, are considerable, as reviewed by Andrew Vickers and colleagues in “Polygenic Risk Scores to Stratify Cancer Screening Should Predict Mortality Not Incidence.” The National Human Genome Research Institute provides a web-based tutorial, Polygenic Risk Scores, designed for the general public, while for more advanced readers Cathryn Lewis and Evangelos Vassos offer a prospectus on the challenging process of using polygenic risk scores to guide patient screening and treatment in “Polygenic Risk Scores: From Research Tools to Clinical Instruments."