Quantitative Analysis of Polygenic Risk Score Prediction in the Genes for Good Cohort

Presented by:
Anna Ballou (Smith College)
Abstract:

A promising tool in genetic prognostics is the use of polygenic risk scores (PRS). PRS are the sum of an individuals’ disease-associated alleles weighted by estimated effect sizes from a genome wide association study (GWAS) for a disease or trait of interest. For some diseases, phenotypes can be predicted to a great degree of accuracy based on the PRS and additional risk factors (e.g. age, sex). In future precision medicine approaches, clinicians may use extreme PRS for certain diseases as an indication for medical intervention for patients. After selecting GWAS summary statistics for several phenotypes, we estimated PRS using the software PRSice for 20,228 individuals in the Genes for Good (GfG) dataset. When conducting ROC analysis on hypertension PRS, we obtained an AUC of 0.697. In our research, we uncovered several limitations of PRS. For example, the predictive ability of a PRS for left-handedness was limited due to the low heritability of the phenotype. We also discovered that a small number of cases in the cohort of interest limits the statistical and predictive power of PRS. Lastly, our assessment of PRS in rheumatoid arthritis demonstrated that age of onset is an important factor to consider when choosing a reference dataset. The results of this study emphasize the predictive potential, along with the limitations, of PRS.