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PubMed · 16702439

Inferring population parameters from single-feature polymorphism data.

Rong Jiang·Paul Marjoram·Justin O Borevitz·Simon Tavaré
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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.

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ArabidopsisComputer SimulationDNA, PlantModels, GeneticOligonucleotide Array Sequence AnalysisPolymorphism, Single NucleotideQuantitative Trait LociRecombination, GeneticJournal ArticleResearch Support, N.I.H., Extramural

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Rong Jiang, Paul Marjoram, Justin O Borevitz, Simon Tavaré. 2006-05-15. Inferring population parameters from single-feature polymorphism data.. https://doi.org/10.1534/genetics.105.047472

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