JSM 2005 - Toronto

Abstract #304730

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 226
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #304730
Title: Genome Sequence Analysis Using Mixture Trees
Author(s): Shu-Chuan Chen*+ and Bruce G. Lindsay
Companies: Arizona State University and The Pennsylvania State University
Address: Department of Mathematics and Statistics, Tempe, AZ, 85287, United States
Keywords: Ancestral Mixture Models ; Single nucleotide polymorphisms
Abstract:

Clustering methods have been investigated broadly in the last decade. Since the rapid progress of human genome sequencing, more efficient clustering methods are demanded. In this paper, a new method using ancestral mixture model for clustering binary sequences is proposed. I first show how an ancestral mixture model can be used to build up a hierarchical tree from binary sequence data using an example of genetic single nucleotide polymorphisms (SNP) data. Properties of the ancestral mixture model, such as its nested structure and the relationship to the coalescent process of population genetics, are presented. A model selection method based on an easy-to-calculate quadratic-distance is then proposed. This distance arises by first applying kernel smoothing to both the data and the fitted model to get densities e* and a* on the sequence space. Then, one uses the L2 distance between these to assess the fit of the data to the model. An example of SNP data will be presented to demonstrate how our method works.

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Revised March 2005