(r3) ProceduresCalculatingMutationRatesFromGenomicData < Lab

---+ Mutation Rates from Genome Resequencing

*Motivation:* You have re-sequenced several genomes after a mutation accumulation or adaptive evolution experiment. How do you infer the rates of different types of mutations from these data? What are the 95% confidence intervals on these values?

---++ Case 1: Mutations with many identical sites

*Assumptions:*
   1 The number of mutations is small compared to the number of sites.
   1 There are no back mutations (reversions).
   1 Mutations rates are constant over time and across sites.

*Example:* Single-base substitutions

*Calculation:*
   1 If you restrict your data to one genome per experimental population, then you can calculate the maximum likelihood value and 95% confidence limits from a Poisson distribution. Count the total number mutation (_m_) and the total elapsed generations or time of independent evolution (_T_). Example: 22 point mutations found in 6 genomes that each evolved for 10,000 generations. %BR%<verbatim>>m = 22
>T = 10000 * 6
>rate = poisson.test(m)
>rate$estimate/T
  event rate 
0.0003666667 
>rate$conf.int/T
[1] 0.0002297880 0.0005551377
attr(,"conf.level")
[1] 0.95
</verbatim> 
   1 If you know the number of sites at risk for the mutation (_s_), then you can calculate a per-site mutation rate. Example: Assume these 22 point mutations are A to G substitutions and there are 1,342,726 A bases in the original genome. %BR%<verbatim>>s = 1342726
>rate$estimate/(T*s)
  event rate 
2.730763e-10 
>rate$conf.int/(T*s)
[1] 1.711355e-10 4.134408e-10
attr(,"conf.level")
[1] 0.95
</verbatim> 

---++ Case 2: One-time mutations

*Assumptions:* 
   1 The mutation can only happen once per genome.
   1 The mutation rate is constant per unit time or generation

*Example:* Deletion of an unstable chromosomal region. Once deleted, it can never be deleted again.

*Calculation:*
   1 Count the number of independent genomes that have the mutation (_m_) and total number of genomes analyzed (_n_) at a given time (_T_). Example: 5 of 12 independently evolved genomes have the mutation after 10,000 generations. %BR% <verbatim>> m = 5
> n = 12
> T = 10000
</verbatim>
   1 Calculate a maximum likelihood value and 95% exact (Clopper-Pearson) confidence limit for the fraction of independently evolved lineages that __do not have__ the mutation from your observations. %BR% <verbatim>p = binom.test(n - m, n)
>p

	Exact binomial test

data:  n - m and n 
number of successes = 7, number of trials = 12, p-value = 0.7744
alternative hypothesis: true probability of success is not equal to 0.5 
95 percent confidence interval:
 0.2766697 0.8483478 
sample estimates:
probability of success 
             0.5833333
</verbatim>
   1 If the mutations happen at a constant rate per unit time, then you can calculate the rate that gives this fraction of independent lineages without a mutation up to the given time point using the zero event term from a [[http://en.wikipedia.org/wiki/Poisson_process][Poisson process]]: %BR% <verbatim>> -log(p$estimate) / T
probability of success 
          5.389965e-05
> -log(p$conf.int) / T
[1] 1.284931e-04 1.644646e-05
attr(,"conf.level")
[1] 0.95
</verbatim>

This is a particularly simple type of [[http://en.wikipedia.org/wiki/Survival_analysis][survival analysis]]. 

---++ Issues: Pseudo-replication

---++ Issues: Different mutation rates in different lineages
Barrick Lab > ProtocolList > ProceduresCalculatingMutationRatesFromGenomicData
Topic revision: r3 - 2012-07-25 - 15:09:39 - Main.JeffreyBarrick