title: Inferring population parameters from single-feature polymorphism data.
abstract: 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.
authors.affiliation: Molecular and Computational Biology Program, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles 90089, USA.
authors.forename: Rong
authors.initials: R
authors.lastname: Jiang
authors.forename: Paul
authors.initials: P
authors.lastname: Marjoram
authors.forename: Justin O
authors.initials: JO
authors.lastname: Borevitz
authors.forename: Simon
authors.initials: S
authors.lastname: Tavaré
mesh_terms: D017360:Arabidopsis / Q000235:genetics*; D003198:Computer Simulation; D018744:DNA, Plant / Q000235:genetics*; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci / Q000235:genetics*; D011995:Recombination, Genetic
chemical_list: D018744:DNA, Plant