Clustering Sequences Using Mixture Trees

• Activity Number: 289
• Type: Contributed
• Date/Time: Wednesday, August 14, 2002 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section*
• Author(s): Shu-Chuan Chen*+ and Bruce Lindsay
• Affiliation(s): Pennsylvania State University and Pennsylvania State University
• Keywords: single nucleotide polymorphisms ; ancestral mixture model ; coalescent process

Abstract

Clustering methods have been broadly investigated in the last decade. Since the rapid progress of human genome sequencing, more efficient clustering methods are demanded. For example, a map of 1.42 million single nucleotide polymorphisms (SNPs) at a density of 1.91 kilobases is found by the international SNP map working group (Sachidanandam et al., 2001). Such a SNP map can be a public resource for defining nucleotide variation among human genome, which is believed to be useful in identifying disease genes. In this talk, an ancestral mixture model is proposed for clustering binary sequences. This model has a natural relationship to the coalescent process of population genetics. The sieve parameter in the model plays an important role of time in the evolutionary tree of the sequences. By sliding the sieve parameter, one can create a hierarchical tree. Some properties of the proposed model will be discussed.