feat(data): build a case from a real published patient

`make published-case` reads a GA4GH phenopacket from Monarch's Phenopacket
Store and takes two things from it verbatim: the HPO terms the authors
reported and the variant they called causal. The default is the TGFBR2
proband from Loeys et al., Nat Genet 2005 (doi:10.1038/ng1511), the paper
that first defined Loeys-Dietz syndrome -- 30 reported terms and
NM_003242.6:c.1069G>T p.(Gly357Trp).

The rest of that patient's genome is not public, so background variants come
from GIAB HG002 around the locus. They are drawn from coding exons where
possible, via Ensembl's REST API: of ~4,000 HG002 variants in the window only
9 are coding, so a random sample is entirely intronic, the consequence filter
discards all of it, and the causal variant is left as the only candidate --
a funnel that proves nothing.

The real run ranks TGFBR2 first at 0.897 against an OSBPL10 missense at
0.547. Both are rare missense variants the model scores identically (0.887);
only the phenotype separates them, which is the argument for phenotype-driven
triage in one table.

Documented with three caveats rather than left implicit: the phenotype match
is partly circular because HPO's gene annotations are themselves curated from
published cases; rarity contributes nothing without the VEP cache
(--af_gnomade is rejected with --database, and plain --af returns nothing
even for rs429358 at ~15% global frequency); and one healthy genome is not a
diagnostic exome.

.gitignore

@@ -17,4 +17,5 @@ mlruns/
 .terraform/
 data/*.vcf*
 data/*.tsv
+data/*.case.json
 !data/README.md