How CohortLayer validates a drug target
CohortLayer validates a drug target in three steps, without the underlying genetic data ever leaving the cohort. You define a target or genetic hypothesis. We test it inside the secure environments of two large human cohorts, separately. You receive aggregate evidence you can take straight into a decision: effect size, replication status, and phenotype and safety signals. The two cohorts are tested independently and only the aggregate results are combined, so a result that comes back has already survived a second, independent test. That evidence is then kept live and bound to the cohort continuously, so it stays current as variants are reclassified and new findings are published. Individual-level data is never downloaded, moved, or re-identified; only aggregate statistics are returned.
Three steps from question to evidence.
- 01
You define the question
A target, a variant, or a gene–disease hypothesis. We help shape it into something testable against population-scale data: the phenotype definition, the comparison, and the exact question the cohort can answer. - 02
We test it where the data lives
The analysis runs inside each cohort's secure environment, across two large human populations, separately. - 03
You receive aggregate evidence
Effect size, replication status, carrier counts, and phenotype and safety signals, returned as one clear aggregate result. Keep the target monitored and we'll flag it when the evidence shifts.
What the evidence looks like
Evidence report
14 Jun 2026
PCSK9 loss-of-function, cardiovascular disease risk
| Cohort | Carriers | Effect size (OR) | p-value | Direction | Safety signal |
|---|---|---|---|---|---|
| UK Biobank | 4,821 | 0.71 | 3.2 × 10⁻¹⁴ | Protective ↓ | None |
| All of Us | 3,104 | 0.68 | 8.1 × 10⁻¹¹ | Protective ↓ | None |
| Cross-cohort | 7,925 | 0.70 | 2.1 × 10⁻²³ | Protective ↓ | None |
Why does CohortLayer test two cohorts separately instead of pooling them?
Separate replication is a stronger test than one larger pooled dataset. A signal that appears in one population does not always appear in another, and pooling everything into a single analysis can hide that. By testing each cohort independently and only then comparing the aggregates, a result that holds in both has already passed a replication check. That is a stronger filter against a false signal than any single-cohort look. Only the aggregate outputs are ever combined; the individual data stays separate and in place.
What does "the data never leaves the cohort" actually mean?
It means the analysis runs where the data lives. Managed-access human cohorts are governed environments, and individual genetic records cannot be exported from them. That is a hard governance requirement of the cohorts themselves. CohortLayer runs the validation inside those environments and returns only aggregate statistics: counts, effect sizes, and signals that describe the population. Nothing is re-identified, downloaded, or moved.
What do you receive at the end?
A clear, aggregate result you can act on: whether the association holds, how strong it is, whether it replicated across both cohorts, and what the phenotype and safety picture looks like. You also get ongoing alerts when the evidence changes. Individual-level data is not part of what you receive, and you do not need to build or run any infrastructure yourself.
How long does the evidence stay valid?
As long as you keep the target monitored, it stays current. Genetic evidence is not static: variants are reclassified and new findings are published constantly, and the predictive value of human genetics keeps growing. CohortLayer re-runs the validation as that evidence moves and flags what changed, so a target you cleared months ago gets re-checked against today's picture.
Frequently asked
Do you download or store our genetic data?
No. The analysis runs inside each cohort's secure environment, and only aggregate results leave.Is my hypothesis kept confidential?
Your commercial strategy and the IP in the results stay yours. Each validation is structured as a scientific collaboration, run to publishable standards.How is this different from querying a public database?
Public databases report what has been published in general. CohortLayer binds that evidence to real cohorts, replicates it across two populations separately, keeps it continuously updated, and validates your specific hypothesis as a scientific collaboration. Then it reports what that means for your target.Is this a medical or diagnostic service?
No. CohortLayer returns aggregate, population-level evidence to organizations. It does not provide individual medical or diagnostic results.