Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
120 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

The historical Moran model (1511.05781v1)

Published 18 Nov 2015 in math.PR and q-bio.PE

Abstract: We consider a multi-type Moran model (in continuous time) with selection and type-dependent mutation. This paper is concerned with the evolution of genealogical information forward in time. For this purpose we define and analytically characterize a path-valued Markov process that contains in its state at time $t$ the extended ancestral lines (adding genealogical distances) of the population alive at time $t$. The main result is a representation for the conditional distribution of the extended ancestral lines of a subpopulation alive at a fixed time $T$ (present time) given the type information of the subpopulation at time $T$ in terms of the distribution of the sample paths (up to time $T$) of a special Markov process (different from the ancestral selection graph) to which we refer as backward process. This representation allows us both to prove that the extended ancestral lines converge in the limit $T \to \infty$ if the type information converges in the limit $T \to \infty$ and to study the resulting limit of the extended ancestral lines by means of the backward process. The limit theorem has two applications: First, we can represent the stationary type distribution of the common ancestor type process in terms of the equilibrium distribution of a functional of the backward process, where in the two type case we recover the common ancestor process of Fearnhead if we let the population size tend to infinity. Second, we obtain that the conditioned genealogical distance of two individuals given the types of the two individuals is distributed as a certain stopping time of a further functional of the backward process which is a new approach towards a proof that genealogical distances are stochastically smaller under selection.

Summary

We haven't generated a summary for this paper yet.