Several recent successes have corroborated the power of leveraging genetic data to predict the success of a new drug targets [5]. For example, the gain of function mutations in PCSK9 [6–9], which cause familial hypercholesterolemia and coronary artery disease led to to the launch of evolocumab (Amgen) and alirocumab (Regeneron). How widely the pharmaceutical industry can expect genetics and genomics to yield increased success rates beyond these more narrowly defined examples that have unambiguous causal genes and multiple verified Mendelian mutations remains to be determined. If the association between human genetic evidence and approved drugs is genuine and continues to hold for present-day drug development, we expect better variant to gene mapping methods and more sophisticated predictive approaches will further improve our ability to prioritize drug targets. Because of the foundational nature of the Nelson et al. work [3], it is important to determine whether the reported association holds prospectively, and whether it replicates on independent data subsets not used in the original model construction.
