Why genetic evidence matters for drug target validation

Most drug targets fail because something that looks promising in a cell line or a small study can still turn out to be wrong, or unsafe, in humans, and that usually only becomes clear deep into clinical development. Fewer than one in eight drugs entering clinical trials reach approval (Wouters et al., JAMA 2020). By the time a wrong target reveals itself in the clinic, the cost is measured in years and hundreds of millions. Failure is the largest single driver of overall drug-development cost (DiMasi et al., J Health Econ 2016). Picking targets more likely to be real before capital is committed is therefore where the biggest cost savings live.

Yes. Targets with human genetic evidence are more than twice as likely to be approved (Nelson et al., Nature Genetics 2015), and the most recent analysis refines that to a 2.6-fold higher success rate from Phase 1 to approval (Minikel et al., Nature 2024). Human genetics works as an early signal because a genetic link between a gene and a disease in real human populations is direct evidence that modulating that gene may affect the disease. That kind of evidence doesn't depend on a model system being faithful to human biology.

Yes, and this is the part that is least discussed. The predictive advantage from genetic evidence is growing as the field matures, because every new genetic finding adds evidence that wasn't there before (Minikel et al., Nature 2024). A practical consequence: a target assessed 18 months ago may look different against today's evidence, and one that looked weak then may be supported now. Evidence checked once and filed away goes stale.

Public databases and published studies tell you what has been found in general. They do not tell you whether your target specifically holds up, whether it replicates across more than one population, or whether the picture has changed since the last paper. A signal that appears in one cohort does not always replicate in another. Promising targets have looked strong in one dataset and vanished in the next. Turning published evidence into a decision about a specific target means running it against real cohorts and re-running it when variants are reclassified. That is slow, specialized work most teams cannot spare infrastructure for.

CohortLayer partners with your team to test your target or genetic hypothesis against large human cohorts as a scientific collaboration. You receive aggregate evidence: effect size, carrier counts, replication status, and phenotype and safety signals. No individual-level data is returned. We test the hypothesis in more than one cohort separately, so what we report has already survived a second test, and we re-run the analysis continuously as new evidence lands, flagging what moved. The underlying individual data never leaves its secure cohort environment.