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2017年3月13日星期一

Wright-Fisher model and Kingman Coalescence

In the last course of ecology and model of probability at Polytechnique, we talk about Wright-Fisher model and Kingman coalescence, two models used for the simulation of the genes of human beings. The former is easy to understand and the latter, relates the theory of combinatoire, provides some very interesting formulas.

Wright-Fisher model

Suppose that in the population there exists two types of genes A and a, then we denote Xn the number of A in the population, whose size is always N. Then the evolution is a Markov chain and the transition matrix is a binomial type
P(XNn+1=k|XNn=i)=CkN(iN)k(1iN)Ni
I
It is easy to check that XNn is a martingale and its L2 norm is bounded and the Markov chain is positive recurrent, so
XNnna.sXN{0,N}
Using the theorem of stopping time we get that P(XN=N)=iN where i is the initial state.

Some other version can also be developped like the model with mutation and selection. On another hand,  if we change the scale of time like Zt=1NXN[Nt], the convergence of trajectoire implies that
Zt=Z0+t0Zs(1Zs)dBs
which relates a discrete model with a continuous random process.


Kingman coalescence model


The state is defined on the partition of [1,N] and the initial state is X0={1}{2}{N}. We note Ti the i-th jump time which follows the law Exp((Ni)(Ni1)2) and choose uniformly two block to make one block. Some obvious properties are observed.

1. Each time, the number of block minus one.

2. The expectation of fusion time is N1i=12(Ni)(Ni1)=2(11N1) and it converges.

3. We define the genetic relation ππ if the later is the generated by fusing two blocks of the former. The transition matrix is 
P(π,π)=2π(π1)Iππ

However, the most beautiful formule is 
PTNk(π)=k!N!(Nk)!(k1)!(N1)!li=1Bi
for π=ki=1{Bi}

The proof is just  a recurrence but the structure of formula is really beautiful, isn't?

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