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Originally published as Genetics Published Articles Ahead of Print on May 15, 2006.
Genetics, Vol. 173, 2257-2267, August 2006, Copyright © 2006
doi:10.1534/genetics.105.047472
Inferring Population Parameters From Single-Feature Polymorphism Data
Rong Jiang*,
Paul Marjoram
,
Justin O. Borevitz
and
Simon Tavaré*,
,1
* Molecular and Computational Biology Program, University of Southern California, Los Angeles, California 90089, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089, Department of Ecology and Evolution, University of Chicago, Chicago, Illinois 60637 and Department of Oncology, University of Cambridge, Cambridge CB2 2XZ, England
1 Corresponding author: Molecular and Computational Biology Program, University of Southern California, 835 W. 37th St., SHS172, Los Angeles, CA 90089-1340.
E-mail: stavare{at}usc.edu
This article 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.
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