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                                             BIOINFORMATICS COLLOQUIUM
						College of Science
                                              George Mason University
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                         Hidden Markov Models for the Assessment of Chromosomal Alterations 
                                          using High-throughput SNP Arrays
						 
                                                    Ingo Ruczinski

   				                    Johns Hopkins

Abstract:
Many chromosomal alterations such as amplifications and deletions have been associated with disease. 
For the genome-wide assessment of such alterations, high-throughput single nucleotide polymorphism 
(SNP) arrays are often used, estimating DNA copy number and genotypes at up to 1 million loci. 
Hidden Markov Models (HMMs) are particularly useful for inferring chromosomal alterations from SNP 
array data, modeling the spatial dependence between neighboring SNPs. We propose a novel HMM for the 
assessment of the underlying chromosomal states, simultaneously integrating copy number estimates, 
genotype calls, and the corresponding measures of uncertainty when available. We also show how 
parent-of-origin effects can be assessed in family data, and discuss the publicly available software 
packages we have developed.