Improved Polygenic Risk Score Calculation and Sub-classification of Disease by the Incorporation of Functional Data.

Polygenic risk scores (PRSs) represent a method for calculating a person’s predisposition for the development of a particular disease. Utilizing data generated from genome-wide association studies (GWAS) where genetic differences are identified as having an association with a particular disease we can calculate a polygenic risk score for an individual based on just their genetic code. Based on a person’s risk defined by their PRS, physicians can deliver more personalized care. If a person is found to be at high risk for a particular disease they can be monitored more regularly and any prophylactic measures available can be used to prevent or mitigate progression of the disease. Alternatively, when a person is found to be low risk for a disease, especially one that requires invasive testing, the monitoring regime can be relaxed to appropriately match the person’s actual risk. In this project that will last approximately 36 months, we hope to improve the current generation of PRSs, creating scores that are more accurate and ones that clinicians feel more comfortable using to guide their care for patients. Instead of focusing solely on the genetic code of a patient we plan to do this by incorporating other relevant biological data that is not currently considered. Another aim of this study is to improve the classification of patients within a particular disease’s patient population. Better sub-classification of patients allows for more personalized treatment as well, as a clinician can treat a patient’s specific case of the disease rather than just the generic disease. Sub-classification has enabled more personalized care in the field of cancer for decades, allowing clinicians to target mutations specific to a particular tumor rather than just targeting a standard definition tumor. Again by including more biological data, in fact the same biological data we would like to include in polygenic risk scores, we think we will be able to create a better sub-classification system, and for diseases beyond just cancer. This will allow patients that suffer from many different diseases besides cancer to get that same type of personalized therapy.