Timepiece II: SMC++

Timepiece II: SMC++#

From two sequences to many – demographic inference with the distinguished lineage.

The Mechanism at a Glance#

SMC++ (Terhorst, Kamm & Song, 2017) extends PSMC from a single diploid genome to multiple unphased diploid genomes. Where PSMC reads population size history from two haplotypes – one simple watch with two hands – SMC++ adds more hands to the dial without requiring phased data or full ARG inference. The result is sharper resolution in the recent past, exactly where PSMC’s two-sequence approach runs out of steam.

The key insight is the distinguished lineage. Rather than tracking the full genealogy of all samples (which would require exponentially many states), SMC++ singles out one lineage and tracks how it relates to a demographic background of \(n - 1\) undistinguished lineages. The coalescence time of the distinguished lineage is hidden; the presence or absence of the other lineages provides additional signal about population size. This trick keeps the state space manageable while extracting far more information than PSMC’s two-haplotype approach.

If PSMC is a two-hand watch, SMC++ is a chronograph – a complication that adds sub-dials tracking multiple time measurements simultaneously. Each additional sample genome is another sub-dial, providing independent readings of the same demographic history. The distinguished lineage is the central seconds hand, and the undistinguished lineages sweep around their own sub-dials, all driven by the same escapement (the coalescent process under variable population size).

Primary Reference

[Terhorst et al., 2016]

The four gears of SMC++:

  1. The Distinguished Lineage (the escapement) – The setup: one lineage is singled out, and its coalescence time \(T\) is tracked as a hidden variable. The remaining \(n - 1\) lineages form a demographic background that modifies the coalescence rate. This is where PSMC’s two-lineage framework generalizes to many.

  2. The ODE System (the gear train) – A system of ordinary differential equations that tracks the probability \(p_j(t)\) that \(j\) undistinguished lineages remain at time \(t\). The matrix exponential of the rate matrix gives exact transition probabilities. This replaces PSMC’s simple exponential coalescence with a richer model.

  3. The Continuous HMM (the mainspring) – A modified transition matrix built from the ODE rates, combined via composite likelihood across pairs of sites. Gradient-based optimization (L-BFGS or EM) estimates the piecewise-constant population size function \(\lambda(t)\). This is the inference engine.

  4. Population Splits (a complication) – Cross-population analysis: modified ODEs that track lineage counts before and after a population split, enabling joint estimation of \(N_A(t)\), \(N_B(t)\), and the split time \(T_{\text{split}}\).

These gears mesh together into a complete inference machine:

Multiple unphased diploid genomes
                 |
                 v
       +-------------------------+
       |  CHOOSE DISTINGUISHED   |
       |  LINEAGE                |
       |                         |
       |  Pair it with each of   |
       |  the n-1 undistinguished|
       |  lineages               |
       +-------------------------+
                 |
                 v
+-------> SOLVE ODE SYSTEM
|         p_j(t): probability j
|         undistinguished lineages
|         remain at time t
|                  |
|                  v
|         BUILD HMM
|         States: discretized T
|         Emissions: P(data | T)
|         Transitions: from ODE
|                  |
|                  v
|         COMPOSITE LIKELIHOOD
|         across all pairs
|                  |
|                  v
|         OPTIMIZE (L-BFGS)
|         update lambda_k
|                  |
|         Converged?
|         NO ---+
|
YES
|
v
Output: lambda_0, ..., lambda_n
|
v
Scale to real units: N(t)

Prerequisites for this Timepiece

SMC++ builds directly on PSMC. Before starting, you should have worked through:

  • PSMC – the transition density, discretization, and HMM inference for two sequences. SMC++ generalizes every gear in PSMC.

  • Coalescent Theory – coalescence rates with multiple lineages, the relationship between population size and coalescence time

  • The SMC – the sequential Markov coalescent approximation

If you have built PSMC, you have most of the tools you need. SMC++ adds the multi-lineage generalization, but the underlying mathematical framework is the same.

Chapters#

Each chapter derives the math, explains the intuition, implements the code, and verifies it works. By the end, you’ll have built a complete multi-sample demographic inference engine – and you’ll see how PSMC’s simple watch grows into a chronograph.

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