PM is the premise that an individual’s unique physiologic characteristics play a significant role in both disease vulnerability and response to therapies. The major goals of PM are to predict susceptibility to developing illnesses, achieve accurate diagnoses, and to predict the response to treatment. A major component of PM is ancestry, which can help us understand the genetic variation among individuals. Mendelian Randomization (MR) is a tool which uses common genetic variants as surrogates for an exposure of interest to statistically estimate the causal effect of an exposure on a disease. MR is a powerful, rapid, and cheap alternative to randomized control trials, which may be unfeasible to execute. However, MR is highly susceptible to ancestry, which limits the accuracy of its findings and hinders PM solutions. To improve MR and PM, two complementary approaches are required: first, improve the statistical foundations of MR and clinical trials and identify drugs with ancestry-specific effects. Second, characterize an individual’s response to therapy when given his physiological and ancestral information.
Currently, clinical trials designed to test new drugs match patient cases with controls based solely on simplistic criteria (age, sex, etc.), making them highly vulnerable to “stratification bias.” This bias is a result of differences in genetic ancestry between cases and controls, which is not considered when trial participants are separated based on demographics. This undetected bias can contribute to incorrect interpretations of trial results, leading to false negative or positive results for a drug, with subsequent financial and patient health impacts. There is an immediate need to develop a methodology to overcome these problems.
Classifiers of therapeutic response are also limited to crude racial classifications that are poor predictors for mixed individuals. These limitations hinder progress in the fields of personalized medicine and pharmacogenomics where treatment and medications are becoming ancestry-specific. There is an urgent need to develop an alternative framework to define ancestry and handle the complexity of such individualized approaches to therapy.
- Elhaik E, Ryan DM: Pair Matcher (PaM): fast model-based optimisation of treatment/case-control matches. Bioinformatics 2018:bty946-bty946.
- Elhaik E, Tatarinova T, Chebotarev D, Piras IS, Maria Calò C, De Montis A, Atzori M, Marini M, Tofanelli S, Francalacci P et al: Geographic population structure analysis of worldwide human populations infers their biogeographical origins. Nat Commun 2014, 5:1-12.