User:Hussein Alasadi/Notebook/stephens/2013/10/03: Difference between revisions
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<math> \sum = (1-\theta)^2 S + \frac{\theta}{2}(1 - \frac{\theta}{2})I </math> | <math> \sum = (1-\theta)^2 S + \frac{\theta}{2}(1 - \frac{\theta}{2})I </math> | ||
where <math> S_{i,j} = \sum_{i,j}^{panel} i = j | where <math> S_{i,j} = \sum_{i,j}^{panel}</math> if i = j | ||
<math> exp(-\frac{\rho_{i,j}}{2m} \sum_{i,j}^{panel})</math> if i not equal to j | |||
Revision as of 10:02, 16 October 2013
analyzing pooled sequenced data with selection | <html><img src="/images/9/94/Report.png" border="0" /></html> Main project page Next entry<html><img src="/images/5/5c/Resultset_next.png" border="0" /></html> |
Notes from MeetingConsider a single lineage for now. [math]\displaystyle{ X_j }[/math] = frequency of "1" allele at SNP j in the pool (i.e. the true frequency of the 1 allele in the pool)
[math]\displaystyle{ (n_j^0, n_j^1) }[/math] = number of "0", "1" alleles at SNP j ([math]\displaystyle{ n_j = n_j^0 + n_j^1 }[/math])
[math]\displaystyle{ n_j^1 }[/math] ~ [math]\displaystyle{ Bin(n_j, X_j) \approx N(n_jX_j, n_jX_j(1-X_j)) }[/math] Normal approximation to binomial [math]\displaystyle{ \frac{n_j^1}{n_j} \approx N(X_j, \frac{X_j(1-X_j)}{n_j}) }[/math] The variance of this distribution results from error due to binomial sampling. To simplify, we just plug in [math]\displaystyle{ \hat{X_j} = \frac{n_j^1}{n_j} }[/math] for [math]\displaystyle{ X_j }[/math] [math]\displaystyle{ \implies \frac{n_j^1}{n_j} | X_j \approx N(X_j, \frac{\hat{X_j}(1-\hat{X_j})}{n_j}) }[/math]
[math]\displaystyle{ f_{i,k,j} = }[/math] frequency of reference allele in group i, replicate and SNP j. [math]\displaystyle{ \vec{f_{i,k}} = }[/math] vector of frequencies Without loss of generality, we assume that the putative selected site is site [math]\displaystyle{ j = 1 }[/math]
We assume a prior on our vector of frequencies [math]\displaystyle{ \vec{f_{i,k}} }[/math] ~ [math]\displaystyle{ MVN(\mu, \sum) }[/math] [math]\displaystyle{ \mu = (1-\theta)f^{panel} + \frac{\theta}{2} 1 }[/math] [math]\displaystyle{ \sum = (1-\theta)^2 S + \frac{\theta}{2}(1 - \frac{\theta}{2})I }[/math] where [math]\displaystyle{ S_{i,j} = \sum_{i,j}^{panel} }[/math] if i = j [math]\displaystyle{ exp(-\frac{\rho_{i,j}}{2m} \sum_{i,j}^{panel}) }[/math] if i not equal to j
[math]\displaystyle{ log \frac{f_{i,k,1}}{1-f_{i,k,1}} = \mu + \beta g_i + \epsilon_{i,k} }[/math]
[math]\displaystyle{ (f_{i,k,2}, .... , f_{i,k,p}) | f_{i,k,1} }[/math] ~ [math]\displaystyle{ MVN(\bar{\mu}, \bar{\sum}) }[/math]
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