Target Safety Assessment
Two public datasets answer the question that comes before every programme: what happens to people who naturally have less of this protein? gnomAD says whether such people exist. The GWAS Catalog says what else changes when they do. Targets with human genetic support are roughly twice as likely to survive clinical development, and this is the only evidence class available before a molecule does.
Services: https://gnomad.broadinstitute.org/api (GraphQL, POST) ·
https://www.ebi.ac.uk/gwas/rest/api/v2 (REST). Both unauthenticated.
Checked against: gnomAD v4.1 and the live GWAS Catalog v2 API, August 2026.
Read references/gnomad-constraint.md before quoting a constraint number, references/gwas-catalog.md before writing a query by hand, and references/interpreting-genetic-evidence.md before drawing a conclusion — that one is judgement, not syntax.
The three scripts
| Script | Answers |
|---|---|
gnomad_constraint.py | Do healthy humans exist who have lost this protein? |
gwas_evidence.py | What traits is this gene associated with, and how solidly? |
safety_dossier.py | Both at once, as a verdict with its inputs beside it |
A drug is a chemical phenocopy of a loss-of-function variant
That equivalence is the whole idea. If people carrying LoF variants in a gene are healthy and common, inhibiting the protein is likely survivable. If those variants have been removed from the population by selection, that is a warning you can read years before a tox study.
bashpython skills/target-safety/scripts/gnomad_constraint.py compare LRRK2 PCSK9 SCN2A KRAS HTT
symbol loeuf band pLI obs_lof exp_lof SCN2A 0.154 constrained 1 20 188.7 KRAS 0.2264 constrained 0.9998 1 20.95 HTT 0.3379 constrained 1 104 362.3 LRRK2 0.7537 tolerant 1.317e-43 203 302.6 PCSK9 1.144 unconstrained 2.765e-18 57 62.12
obs_lof against exp_lof is the whole argument. KRAS: one observed loss-of-function variant
where twenty-one were expected. PCSK9: fifty-seven observed against sixty-two expected — no
depletion at all. Human PCSK9 knockouts are healthy and have low LDL cholesterol, which is
exactly why evolocumab and alirocumab exist. The constraint table said so before the drugs did.
Use LOEUF, not pLI. pLI is a posterior forced toward 0 or 1 and cannot rank two intolerant
genes; LOEUF is continuous and carries its uncertainty in the value. Note how pLI calls both
SCN2A and HTT exactly 1 while LOEUF separates them by a factor of two.
An unrecognised GWAS filter returns the entire catalogue
This is the trap to get right on the association side:
GET /associations?mappedGene=LRRK2 -> 93 associations GET /associations?gene=LRRK2 -> 1142122 associations
Both are HTTP 200. gene is not a parameter, so it was silently dropped and the response is the
whole catalogue. Nothing says the filter was ignored. gwas_get() validates parameter names
locally and refuses anything unknown, because there is no server-side signal to react to.
Association is positional, not causal
bashpython skills/target-safety/scripts/gwas_evidence.py traits LRRK2
trait best_p associations studies sole_mapping bone density 1e-300 1 1 0 Parkinson disease 4e-148 23 18 18 cathepsin L1 measurement 4e-39 2 2 0
The strongest p-value in that table is not the answer. Bone density at 1e-300 has zero associations mapping to LRRK2 alone — the causal gene at that locus is almost certainly a neighbour. Parkinson disease has eighteen sole-mapping associations across eighteen studies. Ranking by p-value alone picks the wrong one.
Never rank by association count either: the same locus is rediscovered by every new cohort, so a count measures genotyping effort.
The combined readout
bashpython skills/target-safety/scripts/safety_dossier.py gene PCSK9 LRRK2 HTT
symbol loeuf band genome_wide_traits sole_mapped_hits verdict PCSK9 1.144 unconstrained 44 115 genetically supported LRRK2 0.7537 tolerant 37 39 genetically supported HTT 0.3379 constrained 59 92 associated but constrained
The two axes are independent and mean opposite things. Tolerant plus associated is the pattern that most improves the odds. Constrained plus associated — HTT — means the biology is real but systemic inhibition should expect mechanism-based toxicity, which is precisely the live debate about lowering wild-type huntingtin.
Four ways this evidence misleads
- Absent constraint is missing data, not zero. A gene with too little coverage has no
estimate. Reporting that as LOEUF 0 inverts the conclusion. The scripts return
unknown. - Constraint only speaks to inhibition. An agonist is not phenocopied by loss of function.
- Late-onset effects are invisible. Selection acts on reproductive fitness, so a gene whose loss causes disease at seventy looks unconstrained — in a population that is mostly patients.
- Association gives no direction. Whether to inhibit or activate needs an allelic series or Mendelian randomisation, not the association table.
When to stop using these APIs
Direction of effect, effect size, and causal-gene resolution all need summary statistics rather
than the association table — fine-mapping, colocalisation, and MR with TwoSampleMR. For
variant-level detail, homozygous LoF counts, and per-population frequencies, use the gnomAD
browser directly.
Composing with the rest of the bundle
open-targets→ alongside: aggregated evidence plus L2G scoring, which resolves some of the causal-gene ambiguity this API leaves open.depmap→ alongside: cellular essentiality is a different question from human constraint — a gene can be pan-essential in culture and unconstrained in people.uniprot-rcsb→ before: resolve an alias to a current HGNC symbol; neither API resolves them.openfda→ after: for a target already drugged, what actually happened in people.clinicaltrials→ alongside: whether anyone has taken the genetic hypothesis into a trial.
Reporting results honestly
Give LOEUF with its band and the observed/expected counts behind it. Give the best p-value, the number of independent studies, and how many associations map to the gene alone. Call the verdict a heuristic and show both axes. Say "loss of function is tolerated in humans", not "the target is safe"; say "this locus is associated with", not "this gene causes". Genetic support roughly doubles the odds of approval — from low to less low.
