Difference between revisions of "Drummond:PopGen"

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(Continuous-time approximation)
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==Continuous-time approximation==
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Let <math>r_1</math> and <math>r_2</math> be the instantaneous rates of increase of type 1 and type 2, respectively.  Then
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:<math>{dn_i(t) \over dt} = r_i n_i(t).</math>
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With the total population size
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:<math>n(t) = n_1(t) + n_2(t)</math>
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we have the proportion of type 1
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:<math>p(t) = {n_1(t) \over n(t)}</math>
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Define the fitness advantage
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:<math>s \equiv s_{12} = r_1 - r_2\!</math>
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Given our interest in understanding the change in gene frequencies, our goal is to compute the rate of change of <math>p(t)</math>.
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:{|
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|<math>{\partial p(t) \over \partial t}</math>
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|<math>= {\partial  \over \partial t}\left({n_1(t) \over n(t)}\right)</math>
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|-
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|
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|<math>= {\partial n_1(t) \over \partial t}\left({1 \over n(t)}\right) + n_1(t){-1 \over n(t)^2}{\partial n(t) \over \partial t}</math>
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|-
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|
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|<math>= {\partial n_1(t) \over \partial t}\left({1 \over n(t)}\right) + n_1(t){-1 \over n(t)^2}\left({\partial n_1(t) \over \partial t} + {\partial n_2(t) \over \partial t}\right)</math>
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|-
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|
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|<math>= {\left({r_1 n_1(t) \over n(t)}\right) - {n_1(t) \over n(t)^2}\left({\partial n_1(t) \over \partial t} + {\partial n_2(t) \over \partial t}\right)</math>
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|}
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==Diffusion approximation==
  
 
==Diffusion approximation==
 
==Diffusion approximation==

Revision as of 09:31, 14 July 2008

Per-generation and instantaneous growth rates

Let [math]n_i(t)[/math] be the number of organisms of type [math]i[/math] at time [math]t[/math], and let [math]R[/math] be the per-capita reproductive rate per generation. If [math]t[/math] counts generations, then

[math]n_i(t+1) = n_i(t)R\![/math]
and
[math]n_i(t) = n_i(0)R^t.\![/math]

Now we wish to move to the case where [math]t[/math] is continuous and real-valued. As before,

[math]n_i(t+1) = n_i(t)R\![/math]
but now
[math]n_i(t+\Delta t)\![/math] [math]=n_i(t)R^{\Delta t}\![/math]
[math]n_i(t+\Delta t) - n_i(t)\![/math] [math]= n_i(t)R^{\Delta t} - n_i(t)\![/math]
[math]\frac{n_i(t+\Delta t) - n_i(t)}{\Delta t}[/math] [math]=\frac{n_i(t)R^{\Delta t} - n_i(t)}{\Delta t}[/math]
[math]\frac{n_i(t+\Delta t) - n_i(t)}{\Delta t}[/math] [math]=n_i(t) \frac{R^{\Delta t} - 1}{\Delta t}[/math]
[math]\lim_{\Delta t \to 0} \left[{n_i(t+\Delta t) - n_i(t) \over \Delta t}\right][/math] [math]=\lim_{\Delta t \to 0} \left[ n_i(t) \frac{R^{\Delta t} - 1}{\Delta t}\right][/math]
[math]\frac{d n_i(t)}{dt}[/math] [math]=n_i(t) \lim_{\Delta t \to 0} \left[\frac{R^{\Delta t} - 1}{\Delta t}\right][/math]
[math]\frac{d n_i(t)}{dt}[/math] [math]=n_i(t) \ln R\![/math]

where the last simplification follows from L'Hôpital's rule. Explicitly, let [math]\epsilon=\Delta t[/math]. Then

[math]\lim_{\Delta t \to 0} \left[{R^{\Delta t} - 1 \over \Delta t}\right][/math] [math]= \lim_{\epsilon \to 0} \left[\frac{R^{\epsilon} - 1}{\epsilon}\right][/math]
[math]=\lim_{\epsilon \to 0} \left[\frac{\frac{d}{d\epsilon}\left(R^{\epsilon} - 1\right)}{\frac{d}{d\epsilon}\epsilon}\right][/math]
[math]=\lim_{\epsilon \to 0} \left[\frac{R^{\epsilon}\ln R}{1}\right][/math]
[math]=\ln R \lim_{\epsilon \to 0} \left[R^{\epsilon}\right][/math]
[math]=\ln R\![/math]

The solution to the equation

[math]\frac{d n_i(t)}{dt} = n_i(t) \ln R[/math]
is
[math]n_i(t) = n_i(0) e^{t\ln R} = n_i(0) R^{t}.\![/math]
Note that the continuous case and the original discrete-generation case agree for all values of [math]t[/math]. We can define the instantaneous rate of increase [math]r = \ln R[/math] for convenience.

Let [math]r_1[/math] and [math]r_2[/math] be the instantaneous rates of increase of type 1 and type 2, respectively. Then

[math]{dn_i(t) \over dt} = r_i n_i(t).[/math]

With the total population size

[math]n(t) = n_1(t) + n_2(t)[/math]

we have the proportion of type 1

[math]p(t) = {n_1(t) \over n(t)}[/math]

Define the fitness advantage

[math]s \equiv s_{12} = r_1 - r_2\![/math]

Given our interest in understanding the change in gene frequencies, our goal is to compute the rate of change of [math]p(t)[/math].

[math]{\partial p(t) \over \partial t}[/math] [math]= {\partial \over \partial t}\left({n_1(t) \over n(t)}\right)[/math]
[math]= {\partial n_1(t) \over \partial t}\left({1 \over n(t)}\right) + n_1(t){-1 \over n(t)^2}{\partial n(t) \over \partial t}[/math]
[math]= {\partial n_1(t) \over \partial t}\left({1 \over n(t)}\right) + n_1(t){-1 \over n(t)^2}\left({\partial n_1(t) \over \partial t} + {\partial n_2(t) \over \partial t}\right)[/math]
[math]= {\left({r_1 n_1(t) \over n(t)}\right) - {n_1(t) \over n(t)^2}\left({\partial n_1(t) \over \partial t} + {\partial n_2(t) \over \partial t}\right)[/math]

Diffusion approximation

==Diffusion approximation==