# Central-position SNP distance (discussion)

Not implemented. Design discussion for a substitution-rate estimator that
observes SNPs directly from paired-genome k-mer comparison, as an alternative
to Mash's Poisson-Jaccard inference (see [obicompactvec](../implementation/obicompactvec.md)
for the implemented Jaccard/Mash distances).

## Motivation

**Primary intent: restrict the comparison to what is actually comparable.**
Mash's Jaccard is computed over the **union** of both genomes' k-mer content:
anything not identically shared is folded into a single undifferentiated
mass, whether the cause is a point substitution, a genuinely absent
homologous region (lineage-specific content, gene-family expansion, HGT,
genome-size asymmetry), or a diverged paralogous copy. The model then
back-infers a single mutation rate from that mass, silently attributing
non-homology to mutation. The central-SNP approach instead conditions every
comparison on local, positive evidence of homology: a locus only enters the
statistic if its `2m` flanking bases (`m = (k-1)/2`) are found intact in
*both* genomes — genuinely absent or non-homologous content is excluded from
the comparison entirely (neither numerator nor denominator), rather than
silently counted as divergence. This is a conditioning on comparability, not
just a richer summary statistic — see "Statistic and correspondence with
`shared`" below for how it plays out against genome-size asymmetry and
diverged gene families, and "Heterozygosity, ploidy, and consensus-assembly
inputs" for the corresponding paralogy/heterozygosity filter.

**Secondary benefit: access to the substitution's nature.** Because the
central base of an odd-k window is directly observable once the flanks are
confirmed conserved, this also yields more than a rate — the
transition/transversion split — enabling classical corrected distances
(Jukes-Cantor, Kimura 2-parameter, LogDet) that a single Jaccard scalar
cannot support.

## Statistic and correspondence with `shared`

A genomic position `p` is covered by `k` overlapping k-mer windows. Requiring
the substitution to sit at the window's **center** makes exactly one window
per SNP eligible — a 1:1 correspondence between SNP and center-neighbor k-mer
pair, avoiding the ~k-fold overcount of an any-position neighbor search.

A locus with a fully conserved `k`-window (flanks **and** center) is an
exact-shared k-mer at that locus; a locus with conserved flanks but a
substituted center is a "central SNP". Both count each locus exactly once, in
matching units:

```
p_hat[i,j] = SNP[i,j] / (SNP[i,j] + shared[i,j])
```

`p_hat` is `P(center substituted | 2m flanks conserved)`. `shared[i,j]` here
is **not** the general-purpose `shared_kmers` matrix used by Jaccard/Mash
(`--shared-kmers`, `BitPartials::partial_jaccard` /
`CountPartials::partial_threshold_jaccard`) — that matrix counts raw k-mer
identity with no per-genome copy-number constraint, whereas `p_hat`'s
denominator applies the eligibility rule defined below (raw or
paralogy-filtered). Both `SNP` and `shared` are accumulated by the same
sweep, from the same per-locus candidate set (source k-mer + 3 variants),
under the same eligibility rule — see "Locus eligibility" below, and
"Heterozygosity, ploidy, and consensus-assembly inputs" for why the
copy-number constraint matters and what it costs.

**Canonical invariance**: for odd k, the central position maps to itself under
reverse-complement (`m -> k-1-m = m`, base complemented). A transition maps to
a transition, a transversion to a transversion — the transition/transversion
split is well-defined in canonical space.

### Definitions: family, and the canonical form of a family

**Family.** The family of a k-mer `x` is the set of (up to) 4 k-mers sharing
`x`'s `2m` flanking bases, differing only at the central base `m`. Membership
is a property of the flank pattern, not of `x` itself: any of the 4 possible
central substitutions belongs to the same family.

**`central_canonical_neighbors()`** (`obikseq`, `CanonicalKmerOf::central_canonical_neighbors`)
generates all 4 members from any one of them (observed or not), each
independently canonicalised (`.canonical()`, i.e. `min(kmer, revcomp(kmer))`).
This independent canonicalisation is necessary because a central substitution
can flip which orientation is lexicographically smaller — two members of the
same family can end up canonicalised in *different* orientations. Despite
that, the **set** of 4 resulting canonical k-mers is invariant: calling
`central_canonical_neighbors()` on any member of a family — present in the
index or not — yields the same 4 values. This is relied upon throughout the
rest of this document.

**Canonical form of a family.** Because orientation can differ member to
member, "which of the 4 is the reference" cannot be defined relative to
*whichever member happened to be visited first*, nor relative to the
minorant (see below) — both are data-dependent (they depend on what is
actually observed), so using either as the reference would make the
reference itself vary depending on what happens to be present in a given
index. Instead: **the canonical form of a family is, by definition, the
member whose own central base — read in its own already-canonical
orientation — is `A`.** This is well-defined for every family, computed
purely from the flank pattern, whether or not that specific member (or any
member at all) is actually observed anywhere in the index. Concretely: call
`central_canonical_neighbors()` on any member (observed or not) to get the
family's 4 canonical forms; the one among them whose own centre nucleotide is
`A` is the family's canonical form. The other 3 (`C`, `G`, `T`) are labelled
relative to *that* fixed reference, not relative to the calling member's own
orientation.

**Consequence for the minorant.** With this fixed A-referenced labelling,
`minorant` (the smallest raw encoding among the family's *observed* members,
introduced further below) becomes directly computable rather than needing to
be tracked as extra state: regenerate the family's 4 canonical forms from
any member's own k-mer (cheap, no lookup), compare the raw encodings of
whichever are marked present, and take the smallest. No separate stored bit
is required — see Step 2b below, where this replaces the earlier
minorant-bit design.

## Locus eligibility: raw definition vs. paralogy filter

For each k-mer `x` observed in genome A (source, one MPHF slot; the 3
central-position variants generated as in the sweep below): check whether
A's locus (flanks fixed) is resolvable in genome B under one of the 4
central forms.

**Raw / no model.** The locus counts in the denominator iff at least one of
the 4 forms is present in B; it counts in the numerator iff the form found in
B differs from A's own. No constraint on A's or B's own copy number at this
locus. Open question, not resolved: what if **more than one** of the 4 forms
is present in B simultaneously (ambiguous target — count once arbitrarily,
count all, or drop)? The stringent filter below sidesteps the question by
construction rather than answering it.

**Stringent / paralogy-aware.** The locus counts only if exactly one of the
4 forms is present in A **and** exactly one is present in B (`count == 1` at
that slot too, when a count index is available, to also exclude same-allele
duplicates that presence alone cannot see). This drops the raw definition's
ambiguous-B case automatically, at the cost of also dropping heterozygous
sites indiscriminately alongside true duplications (see "Heterozygosity,
ploidy, and consensus-assembly inputs" below).

**Rejected: parsimony-based multiset pairing for multiplicity > 1.** Rather
than dropping ambiguous loci, pair identical alleles between A and B first
(0-mutation explanation preferred), then take `min(unmatched_A, unmatched_B)`
as inferred SNP pairs. Rejected on two grounds: (1) circularity — selecting
pairs by minimal apparent divergence, then measuring divergence on those same
pairs, deflates the estimate by construction, not a neutral heuristic; (2)
the discriminating signal is a single base among 4 possible values, and the
flanks are *already* guaranteed identical for every candidate by
construction (that is how the locus was selected) — no information remains
in a k-mer window to tell which copy in B truly corresponds to which copy in
A once multiplicity > 1 on either side. Any pairing rule invents a
correspondence the data cannot support. Multiplicity > 1 is treated as
non-identifiable, not as a puzzle to solve with a heuristic.

## Multi-genome framing: family as pseudo-alignment column

**Idea.** Instead of resolving locus eligibility and correspondence one
genome pair at a time, treat a family as a column of a pseudo multiple
alignment across *all* genomes simultaneously: for each family, each genome
has either a net single-copy state (`A`/`C`/`G`/`T`, when the genome carries
exactly one of the 4 forms) or "missing" (`?`, multi-copy or absent). Flank
conservation (the `2m` bases fixed by construction) supplies positional
homology for free — the same role a real MSA would play, without alignment
software, gap penalties, or progressive-alignment approximations. Stacking
one such column per family, genomes as rows, produces a genuine SNP
pseudo-alignment matrix, not just a bag of pairwise distances.

**Precedent.** This is the same principle behind reference-free
k-mer-based phylogenomics tools — SKA (Split K-mer Analysis, Harris 2018) and
kSNP: split the k-mer around a variable center, use flank identity to call
homologous columns across arbitrarily many genomes with no reference and no
MSA step, then feed the resulting pseudo-alignment to standard phylogenetic
tools. Landing on the same design independently is a good sign, not a
coincidence.

**Resolves the pairwise-correspondence problem, properly.** The "Rejected:
parsimony-based multiset pairing" case above failed because, with only two
genomes' cardinalities to look at, there is no external constraint to justify
picking one correspondence between leftover alleles over another — `min(a,b)`
is a lower bound dressed up as a point estimate (see the follow-up discussion
on Felsenstein-style parsimony inconsistency: minimum-event explanations are
systematically biased low whenever homoplasy/multiplicity is real, not
noise-cancelling). With `N` genomes and many families jointly, the same
question can be answered the way real phylogenetics answers it: ancestral
state reconstruction / ML mapping over a tree estimated from the whole
column set. The tree supplies the missing constraint that two isolated
columns cannot — this is the principled way out, not a heuristic replacement
for one.

**Relation to what's already implemented.** `KmerIndex::raw_snp_distance`
already computes, internally, per family, exactly this row — `single_form:
Vec<Option<u8>>`, one entry per genome, `None` where ambiguous/absent —
before immediately collapsing it into pairwise `snp[i,j]`/`shared[i,j]`
tallies. The pivot this section proposes is small at the implementation
level: stop collapsing early, and surface the per-family row as a first-class
artifact (a `families x genomes` matrix). Pairwise raw p-distance becomes one
projection of that matrix (what's computed today), not the primary object;
downstream, the matrix itself could feed real phylogenetic tools (parsimony/
ML, e.g. RAxML/IQ-TREE-style) instead of only NJ/UPGMA on a homemade
pairwise-distance matrix.

**Caveat: column completeness shrinks with `N`.** The probability that a
family's flanks stay intact simultaneously across all `N` genomes decays with
`N` (same ascertainment-bias mechanism as Bias 1 above, compounded over more
genomes) — fully-resolved columns (no `?` anywhere) become rare as more
genomes are added. Same missing-data situation any real multi-species
alignment faces, and phylogenetic tools already handle it well; the practical
implication is that columns should be allowed partial coverage (>=2 resolved
genomes, not unanimous) rather than requiring every genome to be net
single-copy at that locus.

## Context, detectability, and a 3-way ordinal distance per pair

Empirical follow-up to the pseudo-alignment idea above: `obikmer distance
--snp` was run on a real 20-genome benchmark index and the resulting FASTA
fed to `raxml-ng`. Two problems surfaced, both traced back to conflating
distinct notions under one symbol.

**"Context", precisely.** Sharing a central base between two genomes is not
just sharing a nucleotide — it is sharing a **context**: the `2m` flanking
bases, identical, which is a homology claim about that flanked window
(guaranteed non-coincidental by k-specificity, Bias 4 above), *not* a claim
about orthology or paralogy of the copy each genome carries. `A` opposite `C`
= same context, divergent centre. `A` opposite nothing = **this context is
not observed in one of the two genomes** — informative, not neutral.

**Why the IUPAC/DNA encoding used for the first `--snp` test was wrong.**
Feeding IUPAC-coded ambiguity into a standard DNA model (`raxml-ng --model
GTR+G`) is a semantic mismatch: Felsenstein-pruning ML treats an ambiguous
tip as "exactly one true state, unknown which" (a uniform partial-likelihood
vector over compatible bases), not "these states are simultaneously
present". The two encodings look identical (same IUPAC letters) but the
software reads them backwards from what was intended — this invalidates the
literal branch lengths from that first experiment (topology-level groupings
by genus were still informative, see the worked example further down).

**Why `-` (absence) must not be scored as similarity, but also must not be
scored as a shared character between two absences.** Two genomes both
lacking a context are not observed to resemble each other at that locus —
neither is observed to differ from the other either. It is a symmetric
non-observation, uninformative for that pair, and should contribute nothing
(not a small positive nor a small negative signal) to their distance. A
genome carrying a state (`A`) against one carrying none is a different case
entirely: informative, and should not be scored as neutral "missing data"
the way a generic DNA/ML pipeline would.

**Detectability vs existence — a deliberate simplification, accepted.**
"Context not observed" conflates two different biological events: (1) true
loss of the locus, (2) the locus still exists but a mutation/indel *outside*
the centre, anywhere in the `2m` flanks, broke k-mer recognition. The design
adopts a rigorist stance on purpose: any flank-breaking mutation counts as
"this context no longer exists", full stop — because both causes (1) and (2)
independently require *at least* one more mutational event than a lone
central substitution would. This licenses treating "context absent in one of
the two genomes" as a **lower bound** on distance strictly greater than a
plain central SNP, without needing to know which of the two causes applies.
Coarser than a true event count, and accepted as such (fine substitution-type
modelling, e.g. transition/transversion weighting, is a secondary
refinement, not required for this to be useful).

**Resulting ordinal distance between two genomes at one family/context:**

| Comparison | Distance | Meaning |
|---|---|---|
| same centre (`A`/`A`) | `0` | identical |
| different centre, both single-copy (`A`/`C`) | `1` | plain central SNP |
| one genome has a state, the other has none | `>1` (lower bound) | context undetectable in one genome — at least one extra mutational event, of unknown type |
| neither genome has any state (`∅`/`∅`) | excluded | symmetric non-observation, not comparable, contributes nothing |

This is a direct extension of `KmerIndex::raw_snp_distance` (`obikindex/src/siblings.rs`),
which today only implements the `0`/`1` rows and silently drops everything
else (including the informative `>1` row) rather than scoring it.

**Open, not yet resolved:**
- Calibrating `>1` to a real number for tools expecting continuous distances
  (NJ/UPGMA, ML branch lengths), rather than an arbitrary placeholder.
  Natural route: estimate `p_hat` from the resolved (`0`/`1`) sites first,
  then use the already-derived ascertainment formula (`P(usable window
  showing a central SNP) = p * (1-p)^(2m)`, Bias 1 above) to derive a
  model-consistent value for the `>1` bucket instead of guessing a constant.
- Where multi-copy/ambiguous states (the IUPAC case: a genome carrying more
  than one form) fit into this ordinal scheme — plausibly also `>1` by the
  same "at least one extra event" argument (a second form appearing is a
  gain, itself an event), but not yet worked out.

**Practical alternative validated for the pseudo-alignment output itself**
(orthogonal to the ordinal-distance question above, useful regardless of how
`>1` ends up calibrated): re-encode each family as 4 independent binary
presence/absence characters (`A`,`C`,`G`,`T` columns) instead of one IUPAC
column, feed to a `BIN`-type model instead of `DNA`. `∅` becomes an explicit
`0000` state (identity with another `0000`, not missing data) rather than a
gap — removes the semantic mismatch above by construction. Known cost,
accepted for now: a plain central substitution (`A` -> `C`) becomes 2 binary
flips (`1000` -> `0100`), overweighting substitutions relative to true
gain/loss events, and the 4 sub-characters of one family are not
statistically independent the way a generic `BIN` model assumes. A proper
fix (single 16-state alphabet, i.e. the powerset of `{A,C,G,T}`, with a
substitution-rate structure that respects the subset lattice rather than a
fully general 16x16 GTR-analogue) is very likely not expressible in
`raxml-ng`'s `MULTI` datatype as-is (Mk or fully-general rates only) and a
fully general 16-state rate matrix is almost certainly unidentifiable here
(states of cardinality >=3 are ~2% of sites in the benchmark run). Treated as
a longer-term research question, not a near-term implementation target.

## Sankoff parsimony as the resolution of the 16-state model problem

The "longer-term research question" just above (a 16-state alphabet — the
powerset of `{A,C,G,T}` — with a substitution structure that respects the
subset lattice) turns out to have a near-term answer, once the *unification*
question below is worked through.

**Distance methods (NJ/UPGMA/ME) vs. character methods (parsimony/ML): not a
deep philosophical divide, but a real practical distinction for this
project.** Historically "phenetic" (characters -> distances -> tree) and
"cladistic" (characters -> tree directly) approaches were presented as
opposed schools; the modern view is a mathematical continuity, not a
dichotomy — Minimum Evolution (ME: find the tree minimizing total branch
length from a distance matrix) and Maximum Parsimony (MP: find the tree
minimizing total character-state changes) are both instances of "minimise a
global explanatory cost", and coincide under simple encodings (see Farris
1983, "The Logical Basis of Phylogenetic Analysis"; the MP/ME connection is
developed in the Minimum Evolution / Balanced Minimum Evolution literature,
e.g. Nei and colleagues — citations not independently re-verified here, flag
before quoting further). NJ's own agglomeration step already uses the whole
distance matrix jointly (the Q-matrix), not just the pair being merged — an
earlier claim in this discussion that distance methods are "blind" to
cross-taxon structure at every stage was too strong.

What *does* remain a real, structural distinction for this project: in a
character method, a given character's cost is **re-evaluated per candidate
topology** during tree search (the same family can cost 1 change under one
topology, 2 under another). In a pairwise-distance pipeline
(`raw_snp_distance` as it exists today), each family's contribution to
`d(i,j)` is computed **once**, independent of any candidate topology, before
NJ/UPGMA ever runs — so a question like "does this shared `∅` look like a
synapomorphy under topology T" can never be posed in that pipeline, for any
T. That question is only answerable by a method that tests candidate
topologies and re-scores characters under each — i.e. a character method.

**Sankoff parsimony directly resolves the `∅`/gain-loss/substitution
question, without the identifiability problem of a fitted 16-state model.**
Sankoff's algorithm generalises Fitch parsimony to an arbitrary
user-supplied cost matrix between states (`obikseq`/`obikindex` would treat
each family as a `2^{4}`-state character, state = subset of `{A,C,G,T}`
observed in that genome, `∅` included as a real state, not a gap). The
previous 16-state idea failed specifically because *fitting* a full 16x16
rate matrix by ML is unidentifiable at this data volume; Sankoff sidesteps
that because the cost matrix is **fixed a priori from domain knowledge**, not
estimated — e.g. `c({A},{C}) = 1` (a substitution), `c({A},{A,C}) = 1` (a
gain), `c({A,C},{A}) = 1` (a loss), `c({A,C},{G,T}) = 2` (two changes) — no
estimation, no overparameterisation. This reframes gain/loss and central
substitution as two cost categories with independently chosen weights,
exactly the "two families of parameters" (`mu_substitution`, `mu_gain/loss`)
floated earlier in this discussion, now with an actual algorithmic home.

**Caveat, not blocking for this project's scope.** Sankoff is still
parsimony: in principle exposed to Felsenstein's statistical-inconsistency
result under long-branch attraction (already invoked earlier against
`D_F = min(a,b)`) — parsimony and ML only provably coincide in the
short-branch regime. This is not a practical concern here because it is
exactly this estimator's declared target (closely related genomes, short
branches) — the regime where parsimony's known failure mode does not apply —
but worth stating explicitly as a scope guard rather than leaving it
implicit.

**Cheapest next experiment: don't write a Sankoff tree-search engine, use
one that exists.** The hard part of a from-scratch implementation is not the
Sankoff DP itself (a straightforward dynamic program over a *fixed* tree) but
the topology search (SPR/NNI with incremental re-scoring) that comes for
free with `raxml-ng` on the ML side. **TNT** (Tree analysis using New
Technology, free, standard in morphological cladistics) already implements
Sankoff parsimony with a custom cost matrix plus topology search — the
family-state matrix (already close to what `--snp` produces, minus the
IUPAC/DNA-model mismatch) could be fed there directly, no new code required,
before considering a bespoke engine.

**The concrete comparison this unlocks:** run both pipelines on the same
family data —
`k-mer families -> D_ij -> NJ/ME/UPGMA` (phenetic, what exists today) vs.
`k-mer families -> characters -> argmin_T Sankoff-cost(T)` (cladistic, via
TNT) — and compare the resulting topologies. Agreement would validate that
the pairwise-distance projection preserves the phylogenetic signal;
disagreement would pinpoint exactly what the projection to a single number
per pair loses. Not yet run.

### A concrete Sankoff cost matrix for the 16-state alphabet

**Set-edit-distance formula.** For two states `X, Y ⊆ {A,C,G,T}`, split into
`seulement_X = X \ Y` (size `a`) and `seulement_Y = Y \ X` (size `b`).
Elements present in both cost nothing. Pair up to `min(a,b)` of the
remaining elements as **substitutions** (cheaper than treating them as an
unrelated loss + gain whenever `c_sub < 2*c_gl`, which any sane parameter
choice satisfies); whatever is left over after pairing is a pure
**gain/loss**:

```
cost(X,Y) = min(a,b)*c_sub + |a-b|*c_gl
```

Worked examples (`c_sub = c_gl = 1`): `{A}->{C}` = 1 (one substitution);
`{A}->{A,C}` = 1 (one gain, no substitution pair available since nothing is
only-in-Y that matches an only-in-X element after the shared `A` is
excluded); `{A,C}->{G,T}` = 2 (two substitution pairs, `A/C` vs `G/T`, both
same-size sets share nothing); `{A,C,G}->{A,C,T}` = 1 (`G`/`T` is the only
mismatched pair, `A,C` shared).

**`∅` is a flat-cost special case, not a instance of the general formula.**
Applying the formula naively to e.g. `{A,C,G} -> ∅` would charge `3*c_gl`
(three independent losses). Reject that: total context disappearance
(flank-breaking, or true structural loss) is plausibly **one** event, not
`|X|` of them, so:

```
cost(X, ∅) = cost(∅, X) = c_ctx   (constant, independent of |X|)
cost(∅, ∅) = 0
```

`c_ctx` should not be guessed — it is exactly the value already derived for
the `>1` bucket in "Context, detectability, and a 3-way ordinal distance per
pair" above (`(2m*p_hat)/(1-(1-p_hat)^(2m)) + p_hat`), reused rather than
invented.

**Better construction method: shortest path in a small state graph, not the
closed-form formula directly.** Build a graph on the 16 states with two edge
types — substitution edges between same-cardinality sets differing by one
element (weight `c_sub`, or `c_ts`/`c_tv` if split further below), and
gain/loss edges between sets whose cardinality differs by one (weight
`c_gl`) — then define `cost(X,Y)` as shortest-path distance in that graph,
precomputed once (16 nodes, trivial) into a dense 16x16 matrix before
feeding it to Sankoff/TNT. Verified equivalent to the closed-form formula
above in the uniform-cost case (checked by hand on `{A}->{C,G,T}`: both give
`c_sub + 2*c_gl`). The graph construction is not just a reformulation for
its own sake: it is the version that generalises correctly once substitution
costs stop being uniform (next point) — the closed-form's `min(a,b)`
counting silently assumes *any* pairing costs the same, which breaks the
moment `c_sub` depends on which two bases are involved.

**Transition/transversion refinement.** Split `c_sub` into `c_ts` (A<->G or
C<->T) and `c_tv` (the other four pairs) — already well-defined in canonical
space (see "Canonical invariance" above: a transition maps to a transition,
a transversion to a transversion, regardless of orientation). This turns
"pick `min(a,b)` substitution pairs" into a genuine (tiny, <=4 elements per
side, trivially enumerable) minimum-cost bipartite matching problem instead
of a plain count — the state-graph shortest-path construction handles this
automatically, no separate logic needed. Calibration is not a guess either:
the "Sufficient statistic: 4x4 base-pair tally" section below already plans
to collect the joint `(centre_i, centre_j)` distribution over resolved (`0`
or `1`) sites — that tally directly gives the empirical Ts/Tv ratio, from
which `c_ts`/`c_tv` follow (e.g. `cost ∝ -log(observed rate)`, the standard
generalised-parsimony step-weighting heuristic), reusing a statistic already
planned rather than adding a new one.

**`gamma`/`mu` (i.e. `c_gl`/`c_sub`) sensitivity sweep before calibration.**
No strong prior on whether gain/loss events should cost more or less than a
point substitution — duplication/deletion rates are not a priori equal to
point-mutation rates, but the direction isn't obvious, and setting it too
high risks the parsimony search *eliminating* exactly the
heterozygosity/paralogy signal the design is meant to tolerate (see
"Heterozygosity, ploidy..." below). Cheap first step: run the topology at a
handful of ratios (`0.5, 1, 2, 5`) and check whether it's stable — a robust
topology across that range is far more trustworthy than one built on a
single, unvalidated guess. Empirical calibration of `c_gl` from the
family-size distribution already available (`sibling_annex_stats`) is a
natural follow-up once the sensitivity sweep shows the topology is worth
refining further.

**Caveat carried over from the `D_F = min(a,b)` rejection earlier:** this
cost matrix is only valid as **Sankoff step-cost input**, re-evaluated for
every branch of every candidate topology during tree search. Reusing
`cost(leaf_A, leaf_B)` directly as a standalone pairwise distance (bypassing
the tree) would reintroduce the exact circularity already rejected — the
`min(a,b)` pairing here is a locally-defined edit distance between two
states, not a claim about the true evolutionary history between two
specific genomes.

**Feasibility confirmed:** TNT's `costs` command accepts custom step
matrices for multistate characters, so this whole construction (16x16
matrix derived from the state graph, `c_ts`/`c_tv`/`c_gl`/`c_ctx` as
tunable parameters) is directly usable there — no new tooling required
before testing it.

## Heterozygosity, ploidy, and consensus-assembly inputs

A within-genome multiplicity signal (more than one of the 4 central forms
present at a locus) is produced identically by two distinct causes:
paralogous duplication and diploid/polyploid heterozygosity. K-mer data alone
cannot distinguish them. The one real discriminator is sequencing depth
(heterozygous site: total depth of the present forms ~= the genome's
single-copy average; duplication: ~2x or more) — but that signal only exists
if genome "counts" are raw-read depth (FASTQ input), not occurrence counts in
an assembled FASTA, where per-locus depth is not preserved.

**Magnitude is taxon- and mating-system-dependent, not universal.**
Heterozygosity density: mammals ~1 site / 1-1.5 kb (~0.1%); highly
outcrossing plants (maize, poplar) reported an order of magnitude higher
(~1%); self-fertilising plants (*Arabidopsis thaliana*) near zero — but with
a documented failure mode where segmental duplication masquerades as
"pseudo-heterozygosity"; fungi split between haploid vegetative stages
(non-issue) and dikaryotic Basidiomycetes, where two long-diverged haploid
nuclei coexist without fusing. The estimator's target use case (closely
related genomes, k=31) is exactly where the stringent filter above costs the
least for low-heterozygosity taxa and the most for outcrossing/dikaryotic
ones — no universal threshold; this is a scope caveat to document, not a
problem to solve generically.

**Why assembled-consensus inputs don't make measured distances wrong.**
Phylogenetic inputs are near-universally assemblies, not raw reads, and
assemblers collapse heterozygous sites to one consensus allele per
position — effectively an arbitrary, largely uncorrelated-between-assemblies
choice at each het site. This does not inject unbounded noise: standard
population genetics gives `d_xy = d_a + (pi_A + pi_B)/2` — the expected
pairwise difference between a random allele of population A and a random
allele of population B equals the net (fixed) divergence `d_a` plus the
average of the two populations' own within-population diversity `pi`.
Consensus flattening realises exactly this random-allele draw, so the
measured genome-to-genome distance is a `d_xy`-like quantity, not `d_a` —
inflated by heterozygosity by a well-characterised additive term, not
distorted unpredictably. The term is negligible when `pi << d_xy` (the common
case for cross-species comparisons), and becomes material precisely in the
two cases already flagged above: very closely related genomes (this
estimator's explicit target) and highly heterozygous outcrossing organisms,
where `pi` and `d_xy` are the same order of magnitude.

Caveat: this assumes the flattening is uncorrelated with the phylogenetic
signal — plausible for de novo assembly, not guaranteed for reference-guided
assembly biased toward one allele (e.g. the reference's) at each het site,
which would turn the noise term into a systematic bias toward the reference
lineage. Not evaluated here.

**Forward-looking implication, not part of the current design.** The
multiplicity > 1 signal discarded by the stringent filter is a crude
per-genome proxy for `pi` (under low background paralogy). If a `pi_hat` per
genome were tallied alongside `SnpTally`, a `d_a` correction
(`p_hat - mean(pi_hat_i, pi_hat_j)/2`, roughly) could recover an estimate
closer to net divergence instead of `d_xy` — a possible extension, not
scoped here.

## Sufficient statistic: 4x4 base-pair tally

Tabulating the joint distribution of `(center_i, center_j)` over conserved-flank
loci, per genome pair, is sufficient for every downstream correction:

| Estimator | Input | Formula |
|---|---|---|
| Raw p-distance | total off-diagonal / total | `p = SNP / (SNP + shared)` |
| Jukes-Cantor | p | `d = -3/4 * ln(1 - 4p/3)` |
| Kimura 2-parameter | transition rate P, transversion rate Q | `d = 1/2 ln(1/(1-2P-Q)) + 1/4 ln(1/(1-2Q))` |
| LogDet/paralinear | full 4x4 + base-composition margins | `d ~= -1/4 ln det(F)`, robust to non-stationary base composition |

JC/K2P need only the total and the transition/transversion split (the
diagonal collapses to a single "shared" total). LogDet needs the full 4x4,
already populated at no extra cost (see Step 1/2 below).

Memory for the 4x4 tally: `n^2 * 16` counters. Trivial for the project's
genome-scale use case (tens to hundreds of genomes); ~13 GB at n=10^4 — outside
scope but worth flagging if n grows.

## Biases (properties of the estimator, not defects)

1. **Conserved-flank ascertainment bias.** Only SNPs with intact `2m`-base
   flanks are visible; window-intact probability decays as `(1-p)^{2m}`. For
   k=31 (2m=30): 0.74 at p=1%, 0.21 at p=5%, 0.04 at p=10%. This estimator
   targets **closely related genomes**. Under rate heterogeneity across sites
   (universal in practice), conserved flanks correlate with slow centers, so
   `p_hat` underestimates the genome-wide average rate — it specifically
   estimates the substitution rate of **conserved regions**.
   Two distinct factors are at play here, not one: `P(centre of a given
   window is a SNP) = p` exactly, **independent of k** — a direct restatement
   of the raw per-site rate via the bijective window<->centre-position
   correspondence (Statistic section above), not a k-dependent quantity.
   `(1-p)^{2m}` is the *separate*, genuinely k-dependent ascertainment factor
   (are the flanks also intact). The two multiply:
   `P(usable window showing a central SNP) = p * (1-p)^{2m}` — e.g. at
   p=1/31 (~3.2%), k=31: `p * (1-p)^30 ~= 0.0323 * 0.374 ~= 1.2%`, i.e. about
   1 window in 83, not 1 in 31 (which is only the centre-mutated fraction,
   before requiring intact flanks).
2. **Bias toward isolated SNPs.** Two SNPs within k of each other disqualify
   each other's flanks. Hypervariable regions are invisible by construction.
3. **Indels are invisible.** A frameshift destroys k-mer matches in a block;
   this channel captures substitutions only. Indel divergence shows up as lost
   shared k-mers (lower Jaccard/Mash), not as SNP signal.
4. **k-dependent specificity.** "A k-mer match implies common ancestry" is
   quantitative. For a 3 Gbp genome, expected random flank-30 collisions
   (k=31): `(3e9)^2 / 4^30 ~= 8` — negligible. At k=21: `(3e9)^2 / 4^20 ~= 2e6`
   — no longer negligible. k=31 is safe; k<=21 is marginal to unreliable for
   large genomes. The large k that guarantees homology is the same k that
   shrinks the detectable-divergence window — an inherent tension.

## Implementation: avoid materializing a de Bruijn graph

A central-SNP pair is topologically a simple bubble in the colored de Bruijn
graph (source/sink k-mer shared, two length-k branches differing only at the
midpoint). Classical bubble-calling (Cortex/discoSNP-style) finds these, but
requires the graph — nodes plus adjacency for ~10^9 colored k-mers — resident
in memory. **Rejected**: prohibitive RAM for this project's scale.

A naive per-pair generalisation of variant lookup across n genomes (query each
non-shared k-mer's 3 central variants against every counterpart genome's
index) costs `O(n^2 . N . 3)` random lookups, with the same k-mer's 3 variants
regenerated and requeried once per counterpart genome — pure redundant work.
**Rejected** as the basis for an n-genome design.

## Implementation: sequential per-partition sweep (no scratch, no graph)

`KmerIndex::distance()` already opens every partition's `presence_store`/
`count_store` simultaneously, memory-mapped, into one `LayeredStore`
(`distance.rs:73-77`). "Querying another partition" is therefore not a new
I/O pattern to design — it is the same O(1) MPHF+evidence lookup the `query`
command already performs at scale. This lets the SNP tally be computed with
**no scratch files and no auxiliary graph**, by sweeping partitions once each
as a source:

1. For source partition `p`, enumerate its **distinct** k-mers (one per MPHF
   slot; each already carries its full multi-genome presence/count vector —
   no need to explode per (k-mer, genome) occurrence).
2. For each, generate the 3 central-substitution variants and **canonicalise
   each independently** (`min(kmer, revcomp)`, exactly as any normal query) —
   this avoids the orientation edge case a masked-flank grouping would have
   (a substitution that flips canonical orientation is handled correctly
   because each variant is canonicalised on its own, not inferred from a
   fixed-orientation flank key).
3. Compute each variant's target partition `q` via its minimizer; batch/sort
   the partition's outgoing variant queries by `q` for locality.
4. Look up each variant in `q`'s already-mmap'd MPHF+evidence; on a hit,
   combine the source's presence vector (base `a`) with the variant's
   presence vector (base `b`): for every `i` carrying `a` and `j` carrying
   `b`, `tally[i,j][a,b] += 1`.

**Deduplication needs no persisted state.** Sweeping partitions in a fixed
order `p = 0, 1, ..., P-1` and only acting on a variant when its target
partition `q >= p` guarantees each unordered SNP pair is counted exactly
once: a pair with `q < p` was already resolved earlier, when `q` was itself
the source partition and `p` (being `>= q`) was a valid forward target. No
cross-partition flag array is needed — the sweep order *is* the
deduplication rule. Within the same partition (`q == p`), a lightweight
transient tie-break suffices: either a `#slots(p)`-bit scratch flag reset per
partition, or simply comparing the two k-mers' raw `u64` encodings and only
counting when `kmer_source < kmer_variant` — no storage at all.

This is a distinct computation stage, not a `partial_*` in the existing
additive-by-partition sense: step 3-4 read across partition boundaries by
construction, unlike the row-local `partial_jaccard`/`partial_threshold_jaccard`
primitives. But it requires no new index files, permanent or scratch:
`unitigs.bin`, `mphf.bin`, `evidence.bin`, and the presence/count columns are
read as-is, and the only extra memory is the current partition's small
outgoing-query batch (`#kmers(p) * 3`, released once `p` is done) plus the
persistent `tally` accumulator (`n^2 * 16` counters, see above).

**Outer loop (over source partitions `p`) must stay sequential.** Two
independent reasons, not just one: (a) memory — the bounded-footprint claim
above only holds with one partition's outgoing-query batch in flight; running
`T` source partitions concurrently multiplies that batch by `T`, exactly the
blowup the design avoids; (b) correctness — the `q >= p` deduplication rule
requires partitions to be claimed as sources in a fixed order; running `p1 <
p2` concurrently gives no guarantee `p1` has finished claiming its `q >= p1`
targets before `p2` starts claiming its own, breaking the "counted exactly
once" property.

**Inner loop (target-partition lookups for a fixed `p`) parallelises safely.**
Each lookup is an O(1) read against an already-mmap'd structure, independent
of the others, with no growing allocation — no memory blowup, no ordering
dependency between different `q`. The only shared mutable state is `tally`;
give each worker thread a **thread-local partial tally** (fixed `n^2 * 16`
size, independent of partition size) and merge into the global `tally` once
`p`'s inner loop completes — the same reduce-then-merge pattern Rayon already
uses elsewhere in this codebase to open partitions in parallel. Extra memory:
`#threads * n^2 * 16`, negligible (~512 MB at n~1000, 32 threads) and
unrelated to partition size.

**Cost**: `3 * N_distinct` MPHF lookups total across the whole index (each
partition swept once as source) — the same order of magnitude and the same
operation as running `query` over the index's entire k-mer content against
itself, three times. This is the tool's already-optimized regime, not a new
I/O profile to validate.

## Cheaper: subsampling

Since the target is a ratio, restricting the source-partition sweep to a
bottom-`s` hash sketch (only enumerate k-mers with `hash < threshold` as
sources) divides the lookup count by the sampling factor without biasing
`p_hat`. Mash-like tradeoff: rate estimated from a sample, not the full
k-mer set.

## Recommendation

Sequential per-partition sweep (Route D): reuse the already-mmap'd
per-partition MPHF/evidence/presence structures for O(1) variant lookups,
dedup via the fixed sweep-order rule (`q >= p`, plus an in-partition
tie-break), no scratch files, no graph materialisation. Both the SNP
(off-diagonal) and shared (diagonal) counts are accumulated by this same
sweep, under the locus-eligibility rule chosen (raw or paralogy-filtered) —
not reused from the general-purpose `shared_kmers` matrix, whose raw-identity
definition does not apply the same copy-number constraint. Distances (p, JC,
K2P, LogDet) as finalisations of the resulting 4x4 tally, mirroring the
`partial_* -> *_dist_matrix` pattern used for Jaccard/Mash/Bray-Curtis/etc.

## Detailed implementation plan

Grounded in the current codebase. File/type references are anchors, not
prescriptions; adjust to reality when implementing.

### Step 0 — new low-level primitives (`obikseq`)

Two helpers do not yet exist and are prerequisites:

1. **Central neighbours.** `CanonicalKmerOf<L>` already exposes
   `left_canonical_neighbors()` / `right_canonical_neighbors()`
   (`obikseq/src/kmer.rs`), each returning the 4 canonicalised neighbours at
   an end position. Add `central_canonical_neighbors()` returning the 4
   variants at position `m = (k-1)/2` (each independently canonicalised via
   `.canonical()`). The 3 that differ from the source are the query variants;
   skip the identity. Building on `nucleotide(i)` / the raw 2-bit layout keeps
   it O(1).
2. **Lone-k-mer minimiser.** Routing a *synthetic* variant to its partition
   needs its minimiser, but `RollingStat` (`obiskbuilder/src/rolling_stat.rs`)
   only computes minimisers incrementally along a sequence. Add a standalone
   `minimizer(kmer) -> Minimizer` that scans the `k-m+1` m-mer windows
   (`PackedSeq::mmer`, `obikseq/src/packed_seq.rs`), canonicalises each, and
   takes the min by `seq_hash()` — the same selection `RollingStat` performs,
   evaluated once. Partition index is then
   `(minimizer.seq_hash() & (n_partitions - 1)) as usize`, exactly as
   `QueryBatch::from_records` (`obikmer/src/cmd/query.rs:142`); `n_partitions`
   is a power of two so the mask is valid.

### Step 1 — the tally accumulator (`obikindex`)

A `SnpTally` holding, per genome pair, the 4x4 joint count of central bases:
`n * n * 4 * 4` `u64` (or a packed lower-triangular form since it is
symmetric). Provide `merge(&mut self, other: &SnpTally)` for the thread-local
reduce, and accessors yielding, per pair `(i,j)`: total off-diagonal (SNP),
diagonal (shared, i.e. `p_hat`'s denominator minus SNP), transition count
`P`, transversion count `Q`. The diagonal is always populated — it is not an
optional LogDet-only extra, since `p_hat`'s denominator is no longer sourced
from the external `shared_kmers` matrix (see "Locus eligibility" and
"Statistic and correspondence with `shared`" above): the source k-mer's own
presence/count vector, already in hand when it is enumerated, supplies the
diagonal entry directly, at no extra lookup cost.

### Step 2 — the sweep (`obikindex`, new `snp.rs`)

Mirror `distance.rs`: open the presence or count store per partition. But
instead of a per-partition `partial_*`, run the sequential source sweep:

```text
for p in 0..n_partitions:                       # OUTER — sequential
    open source partition p's layers (QueryLayer-style, obikpartitionner)
    enumerate distinct canonical k-mers of p (one per MPHF slot) with their
        presence/count vectors                   # column-major, as query stage 2
    par_iter over these source k-mers:           # INNER — rayon, thread-local tally
        apply eligibility rule to the source's own vector (raw: none;         # diagonal
            stringent: exactly one of the 4 forms present in each genome)     # gate
        for i in genomes eligible with source base a:
            for j in genomes eligible with source base a:
                thread_tally[i,j][a,a] += 1        # diagonal — no extra lookup
        for each of the 3 central variants:
            q = partition_of(variant)
            if q < p: continue                    # dedup: forward targets only
            if q == p and variant <= source.raw(): continue   # in-partition tie-break
            slot = layers[q].find_slot(variant)   # MphfLayer::find, mmap'd
            if hit:
                vb = variant presence/count vector
                apply eligibility rule to vb (as above)
                for i in eligible genomes with source base a:
                    for j in eligible genomes with variant base b:
                        thread_tally[i,j][a,b] += 1
    merge thread-local tallies into global SnpTally
```

The inner lookup is precisely `QueryLayer::find_slot` +
`col_value(g, slot)` (`obikpartitionner/src/query_layer.rs`) — reuse or factor
out that path rather than reimplementing MPHF access. Enumerating "all distinct
k-mers of a partition with their vectors" is the `dump`/`query` stage-2
column-major scan already implemented in `dump_layer.rs` /
`query_partition_with`; factor a reusable iterator if none fits.

`presence_threshold` applies exactly as elsewhere: a genome "carries base b"
iff its count at that slot is `>= presence_threshold` (trivially `>= 1` for
presence indexes).

### Open problem (unresolved, session end — not yet fully convinced)

The `q >= p` / tie-break dedup rule in Step 2's pseudocode above is **flawed**
for the stringent (paralogy-filtered) eligibility rule: it only ever brings
two family members into view at once (the source and one looked-up variant),
never all four simultaneously, and which subset gets compared depends on
partition sweep order. "Exactly one of the 4 forms present in genome A" is a
whole-family property and cannot be decided correctly from a sequence of
pairwise, order-dependent glimpses — the pseudocode above needs revision, not
just the eligibility gate bolted onto it as written.

Direction discussed, **not yet settled**:

1. **Every distinct source k-mer looks up all 3 variants unconditionally**
   (drop the `q < p` skip entirely) so that every observed family member
   independently gathers all 4 vectors (its own + whichever of the 3
   variants exist) at once — a whole-family, order-independent view, computed
   redundantly once per observed member. Same total lookup order of
   magnitude as already budgeted (`3 * N_distinct`), just organised
   differently (no lookup actually skipped, versus the original rule which
   skipped roughly half).
2. **Tie-break after gathering, not before**: only the member whose own
   canonical encoding is the smallest *among the members actually observed*
   (now known, since all were just looked up) writes to `SnpTally`; the
   others silently discard their redundant computation. Deterministic,
   order-independent — as a side effect this also removes the "outer loop
   must stay sequential" constraint from the cost/parallelism discussion
   above, since no step depends on partition processing order any more.
3. **Proposed optimisation**: precompute, once at index build time, a
   compact global (not per-genome) annex per MPHF slot — the count of
   *other* family members observed anywhere in the dataset (0-3). Slots with
   count 0 (majority under low divergence and few genomes, but see the
   scaling caveat below) need no cross-lookup at all: eligibility reduces to
   a local `count == 1` check at that single slot, and only slots with count
   >= 1 enter the 3-lookup sweep machinery above. Revised (see Step 2b
   below): minorant status *is* stored alongside the count after all, on 3
   bits rather than 2 — it comes for free from the same lookups needed to
   count siblings, and storing it lets the sweep discard non-minorant slots
   without re-fetching anything.

**Minorant/sibling-count relationship, worked out precisely.** "Minorant" is
a one-way implication from sibling count, not an equivalence: `0 siblings
=> minorant` (trivially — with no other observed member, the k-mer is by
definition the smallest of the observed set, itself alone), and its
contrapositive `not minorant => >= 1 sibling`. The converse does not hold:
being the minorant says nothing about sibling count — a minorant can have 0,
1, 2 or 3 siblings, all with larger encodings than itself. Consequence: this
confirms, as a logical necessity rather than a heuristic, that a 0-sibling
slot can always write its diagonal contribution with zero ambiguity and no
lookup (it is unconditionally its own minorant) — but it gives no shortcut
for the >= 1-sibling case, where minorant status still requires the actual
comparison of gathered encodings; sibling count alone never determines it.

**When to compute the annex, and cache invalidation.** Sibling count is a
property of the whole set of columns (genomes/groups) currently in the
index, not of any single genome — it cannot be computed correctly at
mono-genome build time (a family may gain siblings, or its minorant may
change, once more genomes are merged in later). Computing it eagerly at
every `merge` would also waste work on intermediate merged states nobody
ever queries. Instead: compute it lazily, on first `distance` call against a
given index, and persist the result alongside that index for subsequent
calls — the same lazy-derived-cache pattern `PersistentBitMatrix` already
uses for `Columnar` -> `Packed`. This requires no explicit invalidation for
`merge` or `filter` (`obikindex/src/merge.rs`, `obikmer/src/cmd/filter.rs`):
both only ever write to a fresh `--output` directory, never mutate an input
index in place, so a re-merged/re-filtered index is simply a new state with
no annex yet. `select --in-place` (`select_layer.rs:139-235`) is the
exception: it aggregates genome columns into groups (Any/All/None/Sum/Min/
Max) by mutating the existing index's files without changing its location.
It does not remove k-mer rows, but it can still change eligibility and
sibling counts derived from those rows (e.g. a `Sum` over several
single-copy genomes can read as multi-copy at the group level). Because it
mutates in place, **`select --in-place` must explicitly invalidate (delete
or mark stale) any cached sibling-count annex for that index** — the one
operation in the current pipeline where this doesn't happen for free.

Not yet convinced this is the right shape, and Step 2's pseudocode above has
not been rewritten to match — flagged for the next pass rather than resolved
here.

### Step 2b — sibling-count / minorant annex (consolidated plan)

Scope: only the precursor annex — not the SNP tally itself, whose Step 2
sweep remains unresolved above. This piece is simpler than the sweep,
because it writes to an independent per-slot value, not a shared
cross-k-mer accumulator, so it needs no dedup/ownership logic at all at this
stage.

**Revised annex encoding — 4-bit presence mask, not 3-bit (minorant +
count).** Superseded after settling the "canonical form of a family"
definition above. The 3-bit design (1 minorant bit + 2-bit sibling count,
§ below, kept for the historical record) had two problems: it discards
*which* variants are present (only how many), so any future consumer
(the SNP sweep, or a stats pass — see below) that needs to know which bases
exist still has to regenerate and blindly re-query all 3 candidates; and
the minorant bit's meaning was tied to whichever member was visited, not to
a fixed reference. Storing instead a **4-bit mask** — one bit per base
(A/C/G/T), set iff that member of the family (labelled relative to the
family's fixed canonical form, i.e. the member with `A` at the centre — see
above) is observed anywhere in the index — fixes both:
- **Sibling count is derived, not stored**: `siblings = popcount(mask) - 1`.
- **Minorant is derived, not stored**: regenerate the family's 4 canonical
  forms from the slot's own k-mer (cheap, no lookup — see above), compare
  the raw encodings of whichever bits are set in the mask, take the
  smallest.
- **A future consumer knows exactly which variants to (re-)query** —
  `popcount(mask) - 1` lookups instead of always 3, and it knows *which*
  3 (or fewer) to issue, not just how many hits to expect.
- The all-zero value (no base present at all) is still logically
  unreachable as a real result — the slot's *own* base is always present in
  its own family — so it remains available as a free "not yet computed"
  sentinel, exactly as before.

1. **Primitive.** Reuse `central_canonical_neighbors()` from Step 0
   unchanged — the 3 canonicalised central-substitution variants of a k-mer
   (plus the identity, i.e. all 4 members of the family — see "Definitions"
   above).
2. **New annex type** (`obicompactvec`, alongside `bitmatrix.rs`): a 4-bit-
   per-slot packed array (the presence mask above), one per partition — same
   on-disk shape family as `PersistentBitMatrix`'s `Packed` variant, but
   simpler (no per-genome columns, a single derived read-only value per
   slot).
   <details><summary>Superseded 3-bit design (historical)</summary>
   3 bits, storing minorant status alongside sibling count directly, since
   it came for free from the same lookups (point 3 below) — 5 real states
   (not-minorant; minorant with 0/1/2/3 siblings) fit in 3 bits (8 states,
   3 unused). This let the future SNP sweep discard a non-minorant slot
   instantly, with no lookup at all. The otherwise-unreachable combination
   "not-minorant + 0 siblings" (0 siblings always implies minorant) doubled
   as the "not yet computed" sentinel. Replaced by the 4-bit mask above,
   which subsumes this benefit (minorant still derivable, now for free at
   read time rather than stored) while also fixing the "which variant"
   blindness.
   </details>
3. **Computation pass** (`obikindex`, new `siblings.rs`): **one
   `obipipeline` run per layer, iterated sequentially over the index's
   layers** — settled after two false starts, worth recording both.
   - *False start 1*: "fully parallel over every partition/slot at once,
     no ordering at all". Correctness is fine with this (sibling count and
     minorant are order-independent, unlike the old `q >= p` dedup they
     replace), but it reintroduces, at a larger scale, exactly the
     memory-blowup the original Step 2 sweep's sequential-outer-loop
     constraint existed to prevent: scattering every source partition at
     once multiplies the in-flight outgoing-query volume by the number of
     partitions.
   - *False start 2*: push the layer loop itself into the pipeline (source
     = the index's layers, a first `Flat` stage expands each layer into
     its k-mers). `obipipeline`'s scheduler already bounds memory on its
     own — it dispatches every item through a **shared** worker pool at
     each stage boundary (`scheduler.rs:217-372`, `dispatch()` into a
     common `worker_tx` queue, any free worker picks up any pending item;
     not "one worker owns a chunk end to end"), with a biased `Select`
     that prioritises draining items already advanced in the chain over
     admitting new source items (`scheduler.rs:271-282`: stage results
     outrank the source, "vider le pipeline en priorité" / "dernier
     recours" for new data) — so bounded channel `capacity` plus this
     drain-first bias already caps in-flight work without any external
     sequential discipline. Correct, but it means k-mers from several
     layers can be completing concurrently, so the sink would need to
     track several open per-layer annex-file writers at once — real,
     avoidable complexity.
   - **Settled design**: keep the layer loop external and sequential —
     not for memory (the pipeline's own `capacity`/priority mechanism
     already provides that, for free, regardless), but so each pipeline
     run's sink targets exactly one layer's annex file, no concurrent
     multi-writer bookkeeping. Per layer: source = that layer's distinct
     k-mers; a `Flat` (1->N) stage generates the 3 central variants of a
     k-mer, each tagged with its origin (local slot); a transform stage
     routes each variant to its target partition (unchanged per-k-mer
     minimiser); a transform stage performs the lookup (existence-only —
     `find_slot` hit/miss, cheaper than the SNP sweep's full column
     fetch); a final stage/sink folds each answer into its origin's
     running state (below) and, once a layer's k-mers are all resolved,
     flushes the completed array to that layer's annex file. Many small,
     single-purpose stages on purpose, to let the scheduler interleave
     them finely across many in-flight items — this deliberately does
     **not** mirror how `obipipeline` is used elsewhere today: `query.rs`'s
     `process_chunk` lumps parse+route+query+serialise into one closure
     (`query.rs:325,743-758`), and `scatter.rs` only pipelines file-
     reading/superkmer construction, routing partitions afterwards in a
     plain sequential loop (`KmerPartition::write_batch`,
     `partition.rs:140`) — both under-use the fine-grained scheduling the
     mechanism offers, so they are not precedents to copy, only existing
     (and arguably improvable, out of scope here) usages. Cross-partition
     lookups (querying another layer's MPHF for a variant) remain
     necessary as before — only the *output* side is kept single-layer.
   - **Reconciliation**: processed at the granularity of one *answer batch
     per destination partition*, not one source k-mer at a time — this is a
     proper shuffle, not a per-k-mer wait. Each source partition `p` holds a
     small array of running states `(minorant = true, siblings = 0)`, one
     per local slot, initialised at scatter time and **persisting across
     however many destination-partition batches answer it** (up to 3, one
     per variant, not necessarily all from the same `q`). Every scattered
     query carries an origin tag (source partition + local slot) so its
     answer can be routed back. When target partition `q` returns its batch
     (all answers for every query that named `q`, regardless of which source
     k-mer or which source partition they came from), that batch is walked
     once, locally, and each answer updates — via its origin tag — the
     matching entry in *its* source partition's array: a miss changes
     nothing; a hit does `siblings += 1`, and if the found sibling's own
     encoding is smaller than the source's, `minorant = false`. A given
     source k-mer's state is final only once every destination batch
     concerning it has been folded in; its partition's array is flushed to
     the persistent annex once complete. Commutative per entry, so the order
     in which destination batches arrive and get folded in doesn't matter.

   **Open optimisation, not adopted yet — real tradeoff, not a free win.**
   Since looking up sibling `y` from `x`'s visit already yields everything
   needed to fill `y`'s own annex entry too, one visit per *family* could in
   principle replace one visit per *observed family member* — cutting this
   pass's cost roughly by the average family size instead of paying
   `3 * N_distinct` regardless. But it means threads processing different
   source k-mers can end up writing the *same* sibling's slot concurrently —
   the fully independent, ownership-free parallelism of the plan above is
   deliberately traded away for this gain. It stays safe only because the
   computed value for a given slot is deterministic regardless of who
   computes it, so redundant concurrent writes converge to the same
   value — correct as long as each write is atomic, no locking needed — but
   it is a real design complexity increase over "every member redoes its
   own 3 lookups independently," not a strict improvement to adopt by
   default.
4. **Trigger and caching** (`obikindex::KmerIndex`/`distance.rs`): compute
   lazily on first `distance` call for an SNP-family metric against a given
   index; check for an existing annex file first (mirrors
   `PersistentBitMatrix::open()`'s auto-detect-and-fall-back,
   `bitmatrix.rs:264-287`); if absent, run step 3 and persist; if present,
   mmap and reuse.
5. **Invalidation.** `merge` and `filter` always write to a fresh `--output`
   directory (`obikindex/src/merge.rs`, `obikmer/src/cmd/filter.rs`) so a
   re-merged/re-filtered index simply has no annex yet — nothing to
   invalidate. `select --in-place` (`select_layer.rs:139-235`) mutates
   columns of an existing index without changing its location, which can
   change sibling counts without removing rows — it must explicitly delete
   any cached annex for that index as part of its in-place rewrite.
6. **Testing**: hand-built tiny indexes with known sibling counts (0-3);
   order-independence (recompute twice on a static index, identical
   result, given the fully-parallel no-ownership design); invalidation
   (annex absent/correctly recomputed after `select --in-place`); once
   Step 2's sweep is fixed, a regression check that sibling_count == 0
   slots are never looked up cross-partition during the sweep.

Cost: `3 * N_distinct` existence-only lookups, computed once per index
state and amortised over every subsequent `distance` call that reuses the
cached annex — cheaper per-lookup than the sweep itself (hit/miss only, no
column fetch).

### Step 3 — finalisation (`obikindex`)

From the global `SnpTally` alone (diagonal and off-diagonal both populated by
the sweep, see Step 1/2 — no dependency on the external `shared_kmers`
matrix), derive n x n distance matrices, each a pure function of the
accumulated counts (same shape as `jaccard_to_mash`):

- `p_hat[i,j] = SNP / (SNP + shared)`
- Jukes-Cantor, Kimura-2P (from `P`, `Q`), optionally LogDet (needs the
  diagonal + base-composition margins).

Guard the singularities (`p >= 3/4` for JC, `1-2P-Q <= 0` or `1-2Q <= 0` for
K2P) by clamping to a max distance, as `jaccard_to_mash` clamps `J <= 0`.

### Step 4 — surfacing (`obikindex` + `obikmer` CLI)

These metrics do not fit `DistanceMetric`'s current `LayeredStore`-partial
dispatch (they need the cross-partition sweep and produce a different
intermediate). Two options, to decide:

- **(a)** New `DistanceMetric` variants (`Pdistance`, `JukesCantor`,
  `Kimura2P`, `LogDet`) whose `KmerIndex::distance` arm calls the sweep
  (`snp.rs`) instead of the partial path, still returning `DistanceOutput`.
  Keeps one CLI surface (`--metric jukes-cantor`), at the cost of a branch in
  `distance()` that ignores the `LayeredStore` it built.
- **(b)** A dedicated pathway (`KmerIndex::snp_distance`) and a distinct CLI
  entry, if mixing a cross-partition sweep into the partition-local `distance`
  command is judged architecturally muddy.

Recommendation: (a) for user ergonomics (all pairwise distances under
`distance`, all feeding NJ/UPGMA/`--shared-kmers` unchanged), but compute the
sweep lazily only when an SNP-family metric is requested, so the existing
metrics keep their partition-local fast path untouched.

### Step 5 — subsampling flag

Add `--snp-sample <fraction>` (or a bottom-`s` hash threshold): restrict the
source-k-mer enumeration in Step 2 to `seq_hash(kmer) < threshold`. Divides
lookups proportionally; `p_hat` is unbiased. Off by default (exact).

### Testing

- **Primitive unit tests**: `central_canonical_neighbors` on hand-checked
  k-mers incl. palindrome-boundary cases; lone-k-mer `minimizer` against
  `RollingStat`'s incremental result on the same k-mer.
- **End-to-end tiny index**: two 1-genome indexes differing by a handful of
  known isolated SNPs (transitions and transversions placed by hand), assert
  exact `SNP`, `P`, `Q` counts and the resulting JC/K2P values.
- **Dedup invariant**: assert the tally is identical regardless of genome/
  partition order and that no pair is double-counted (compare against a
  brute-force all-pairs reference on a small index).
- **Subsampling**: `p_hat` within sampling error of the exact run.

### Suggested phasing

1. Step 0 primitives + their unit tests (self-contained, no distance wiring).
   This also unblocks the long-declared-but-unimplemented `query --mismatch`
   (`obikmer/src/cmd/query.rs:676`, currently a warning), which needs the same
   neighbour + routing machinery.
2. `SnpTally` + finalisation math with a brute-force (non-swept) reference
   backend, validated on a tiny index.
3. The real per-partition sweep (Step 2) behind the same finalisation; assert
   it matches the brute-force backend.
4. CLI surfacing (Step 4a) and NJ/UPGMA integration (already generic over the
   matrix).
5. Subsampling (Step 5).

## References

The Mash mutation-rate model this discussion contrasts with:
[@Mash-distances-doc; @Fan2015-mash-formula].
