Adventures in Genetics

Drug development needs a new storyline. Although pharmaceutical companies spend billions of dollars to develop new therapeutics, only one out of every ten candidates survives clinical trials, and the majority fail due to a lack of efficacy and/or safety. Genetics may rewrite that story. Preclinical studies model diseases using cell culture or animal models to predict drug efficacy and safety, and, as a result, most drug candidates enter clinical trials without clear evidence of the targeted pathway’s relevance to the disease in humans. Human genetics, on the other hand, uses real-world data to decipher the riddles of human pathology.

“When we’ve looked at using genetics broadly to recapitulate drug activity it’s actually been a pretty good story,” said Joshua Denny, M.D., professor of biomedical informatics and medicine at Vanderbilt University Medical Center. “[The data] really suggests that genetics are helpful in determining what drugs work in a disease.” For example, in 2013 Okada et al., recapitulated 27 genes targeted by drugs currently approved for rheumatoid arthritis using a genome-wide association study (GWAS) meta-analysis.

Down the Rabbit Hole

However, over the past decade, scientists have followed GWAS down the rabbithole only to discover that they need a new paradigm to explain the most prevalent diseases. While GWAS works well for diseases caused by single, rare mutations with high penetrance, the black and white days of “simple” monogenic traits have given way to a more complex framework where multiple genes, and even environmental factors, contribute to disease.

According to Marylyn Ritchie, Ph.D., professor and director of biomedical and translational informatics at Geisinger Health System, the past decade of genetics research “has actually shown us that even traits we would have previously said are simple, like Mendelian traits, turn out to be complex.” The genetics of disease becomes “curiouser and curiouser” as variations in penetrance and expressivity complicate the phenotypes related to monogenic diseases, while pleiotropic genes influence multiple, seemingly unrelated, phenotypes.

In 2010, Denny and Ritchie introduced a new method, published in Bioinformatics, to answer the complex riddles genetics poses. Referred to as a “reverse GWAS,” phenome-wide association studies (PheWAS) created a new paradigm for studying associations between genetics and disease. In contrast to GWAS, which selects a disease phenotype and then compares genetic variants in affected and unaffected individuals, PheWAS selects a genetic variant and then searches for phenotypes common among individuals with that mutation.

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