Kemal Yaylali
07a01715fd
A table with filters made the user do the work. Rare disease triage is a different task:
which few variants could explain *this* patient's phenotype, and why. The app now answers
that, and lets a reviewer act on the answer.
Domain
- a case is a proband: a VCF plus the HPO terms observed in the patient (samples -> cases)
- HPO's gene-to-phenotype annotations are loaded as reference data (scripts/load-hpo.py)
- each candidate can be shortlisted or dismissed with a reason and a note
Ranking (app/services/triage.py, 21 tests)
- weighted sum of phenotype match, rarity, consequence severity and the model's score,
with every component shown next to the candidate
- rarity and consequence filter; phenotype only ranks, because a real diagnosis can sit in
a gene nobody has annotated yet and filtering on it would hide exactly that case
- ClinVar is deliberately not an input: it appears beside the result as independent
confirmation, so nothing ranks highly merely because ClinVar already said pathogenic
UI
- the funnel is the headline: variants called -> rare -> coding candidates -> phenotype-matched
- ranked candidates with evidence chips, not a grid of everything; filters are demoted
- a variant panel showing the score breakdown, the matched HPO terms, the raw VEP record and
links out to Ensembl/gnomAD/ClinVar, with the decision controls
- a printable case report: phenotype, funnel, shortlisted variants with reasons, provenance
API: /cases with phenotypes, /cases/{id}/candidates (funnel + ranked + weights),
/variants/{id}, /variants/{id}/decision, /cases/{id}/report, /phenotypes for the picker.
Scoring moved under the case and now answers 503 with the reason when no model registry is
reachable, instead of a 500.
Verified end to end on a simulated proband (scripts/make-demo-case.sh: real GIAB HG002
background + one real ClinVar 2-star pathogenic NF2 variant). 13 variants called -> 1 coding
candidate, and the planted variant ranks first at 0.80 on phenotype 1.00, rarity 1.00 and
consequence 1.00, with ClinVar agreeing afterwards.
Tests: api 75, ml 18, loader 16, web 27; ruff, mypy, svelte-check, terraform validate, both
kustomize overlays and the Nextflow stub run all clean.
94 lines
7.2 KiB
Markdown
94 lines
7.2 KiB
Markdown
# Data: what rarelens actually runs on
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Everything below is public, peer-reviewed and consented for open redistribution. No patient data,
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no data access agreement, nothing that needs an application. These are the references to quote
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when showing the platform to someone.
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Citations were verified against [PubMed](https://pubmed.ncbi.nlm.nih.gov/); each row links its DOI.
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## The demo slice
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`make data` fetches two real files, chromosome 22 only (roughly 100 MB, minutes rather than hours):
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| File | What it is | Role |
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| `data/example.vcf.gz` | GIAB HG002 (NA24385) v4.2.1 benchmark calls, GRCh38, chr22 | the sample a scientist annotates |
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| `data/clinvar.chr22.vcf.gz` | ClinVar, GRCh38, chr22 | training labels, and the ClinVar column in the UI |
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HG002 is the NIST Genome in a Bottle Ashkenazi son, recruited through the Personal Genome Project,
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which consents participants to unrestricted public release. It is the reference genome the field
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benchmarks variant callers against, so it is both realistic and unambiguously shareable.
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## Datasets
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| Dataset | Used for | Access | Terms | Citation |
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| **ClinVar** (GRCh38) | pathogenic/benign labels, ClinVar column | `ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/` | NCBI public domain | Landrum et al., *Nucleic Acids Res* 48(D1):D835–D844, 2020. [10.1093/nar/gkz972](https://doi.org/10.1093/nar/gkz972) |
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| **Genome in a Bottle** HG002 v4.2.1 | the demo sample | `ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/`, `s3://giab` | open, no use restriction | Zook et al., *Nat Biotechnol* 37:561–566, 2019. [10.1038/s41587-019-0074-6](https://doi.org/10.1038/s41587-019-0074-6) |
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| **gnomAD** v4 | allele frequency feature and filter | `gs://gcp-public-data--gnomad`, `s3://gnomad-public-us-east-1` | free use, no restriction | Chen et al., *Nature* 625:92–100, 2024. [10.1038/s41586-023-06045-0](https://doi.org/10.1038/s41586-023-06045-0); Karczewski et al., *Nature* 581:434–443, 2020. [10.1038/s41586-020-2308-7](https://doi.org/10.1038/s41586-020-2308-7) |
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| **1000 Genomes** 30x | optional cohort/trio data | EBI FTP, `s3://1000genomes` | fully open, no access restriction | Byrska-Bishop et al., *Cell* 185(18):3426–3440.e19, 2022. [10.1016/j.cell.2022.08.004](https://doi.org/10.1016/j.cell.2022.08.004) |
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| **MANE Select** | one transcript per gene, if transcript choice ever matters | Ensembl/RefSeq | open | Morales et al., *Nature* 604:310–315, 2022. [10.1038/s41586-022-04558-8](https://doi.org/10.1038/s41586-022-04558-8) |
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| **Human Phenotype Ontology** gene-to-phenotype | what the phenotype half of the ranking matches against (`make hpo`) | `purl.obolibrary.org/obo/hp/hpoa/genes_to_phenotype.txt` | free to use with attribution | Gargano et al., *Nucleic Acids Res* 52(D1):D1333–D1346, 2024. [10.1093/nar/gkad1005](https://doi.org/10.1093/nar/gkad1005) |
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## Tools and scores
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| Tool | Role | Terms | Citation |
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| **Ensembl VEP** 113 | annotation (`pipeline/modules/vep.nf`) | Apache 2.0 | McLaren et al., *Genome Biol* 17:122, 2016. [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s13059-016-0974-4) |
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| **CADD** | `cadd_phred` feature | free for non-commercial use; commercial licence required | Rentzsch et al., *Nucleic Acids Res* 47(D1):D886–D894, 2019. [10.1093/nar/gky1016](https://doi.org/10.1093/nar/gky1016); Schubach et al., *Nucleic Acids Res* 52(D1), 2024. [10.1093/nar/gkad989](https://doi.org/10.1093/nar/gkad989) |
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| **AlphaMissense** | `am_pathogenicity` feature | predictions moved to CC BY 4.0 in March 2024 (originally CC BY-NC-SA) | Cheng et al., *Science* 381:eadg7492, 2023. [10.1126/science.adg7492](https://doi.org/10.1126/science.adg7492) |
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Neither score is required: `rarelens_ml.features` treats a missing CADD or AlphaMissense value as
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NaN and LightGBM handles it, so the pipeline runs without the plugin data.
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## The simulated proband
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`make demo-case` builds `data/proband-simulated.vcf.gz`: real GIAB HG002 variants as background
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plus one real ClinVar 2-star pathogenic variant in a disease gene (NF2 by default, giving
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neurofibromatosis type 2). Spiking a known variant into a public genome is how phenotype-driven
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triage tools are benchmarked, every input is public, and the file's header says SIMULATED. It is
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not a patient, and no part of it is invented: both the background and the planted variant are real
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published records.
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The point of it is that the case has a right answer, so the ranking can be checked rather than
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admired. Give the case the phenotype of the planted disease and the planted variant should rank
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first — on phenotype, rarity and consequence, with ClinVar agreeing only afterwards.
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**Caveat when running without a VEP cache.** `VEP_DATABASE=true` queries Ensembl's public database
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instead of the 25 GB cache. It returns no gnomAD frequencies, so every variant looks absent from
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gnomAD and the rarity term stops discriminating. Fine for showing the mechanics; use the cache for
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anything you would quote.
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## Evaluating the model honestly
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The model trains on ClinVar labels and is scored on ClinVar-labelled variants, which is exactly
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where published benchmarks go wrong. What to do about it:
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1. **Never let the label into the features.** `CLIN_SIG` is excluded by construction; `clinvar_sig`
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is stored for display only (`rarelens_ml/features.py` lists the five feature columns).
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2. **Split by gene, not by variant.** Random splits put variants from the same gene on both sides,
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and a model can then score a gene rather than a variant. Grimm et al. showed this inflates
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reported accuracy for exactly this class of tool: *Hum Mutat* 36:513–523, 2015.
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[10.1002/humu.22768](https://doi.org/10.1002/humu.22768)
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3. **Prefer a time-based holdout.** Train on an older ClinVar release (monthly archives live under
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`vcf_GRCh38/archive_2.0/`) and test only on variants classified after that date. This is the
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closest thing to a prospective evaluation available without new patients.
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4. **Filter labels by review status.** ClinVar's `CLNREVSTAT` marks how much evidence backs a
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classification; two-star and above ("multiple submitters, no conflicts") is the usual bar.
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*Known gap*: VEP's `CLIN_SIG` does not carry review status, so this needs ClinVar annotated as a
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custom field before it can be enforced.
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5. **Report against published baselines on the same rows.** CADD PHRED and AlphaMissense are
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already columns in the variant table, so AUROC and AUPRC for the model next to those two, with
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the variant count, is a fair comparison rather than a number with nothing to beat.
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6. **Report AUPRC, not just AUROC.** Pathogenic variants are the minority class; AUROC flatters.
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## What must not be claimed
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ACMG/AMP treats computational predictions as *supporting* evidence only, never sufficient on their
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own for classifying a variant (Richards et al., *Genet Med* 17:405–424, 2015.
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[10.1038/gim.2015.30](https://doi.org/10.1038/gim.2015.30)). rarelens is a learning platform on
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public data: it makes no diagnostic claim, and the UI shows a score next to the evidence rather
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than a verdict. For what a real diagnostic pipeline looks like end to end, see the 100,000 Genomes
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Project rare-disease pilot: Smedley et al., *N Engl J Med* 385:1868–1880, 2021.
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[10.1056/NEJMoa2035790](https://doi.org/10.1056/NEJMoa2035790)
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