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doi:10.1534/genetics.105.047472
A more recent version of this article appeared on August 1, 2006.
REGULAR RESEARCH PAPERS |
Inferring Population Parameters From Single-Feature Polymorphism Data
Rong Jiang 1, Paul Marjoram 1, Justin O Borevitz 2 and Simon Tavare 1*
1 University of Southern California
2 University of Chicago
* To whom correspondence should be addressed. E-mail: stavare{at}usc.edu.
Submitted on June 29, 2005
Revised on September 9, 2005
Accepted on 3 May 2006
This paper is concerned with a statistical modeling procedure to call single-feature polymorphisms from microarray experiments. We use this new type of polymorphism data to estimate the mutation and recombination parameters in a population. The mutation parameter can be estimated via the number of single feature polymorphisms called in the sample. For the recombination parameter, a two-feature sampling distribution is derived in a way analogous to that for the two-locus sampling distribution with SNP data. The approximate likelihood approach using the two-feature sampling distribution is examined and found to work well. A coalescent simulation study is used to investigate the accuracy and robustness of our method. Our approach allows the utilization of single-feature polymorphism data for inference in population genetics.
Key Words: SFP, coalescent, inference, likelihood, rejection
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