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Activity Number:
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154
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #309927 |
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Title:
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An EM Algorithm for Identifying Genotypic Structure Using Genome-Wide Expression Data
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Author(s):
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Ellen Breazel*+ and Paul Schliekelman
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Companies:
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University of Georgia and University of Georgia
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Address:
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3 Pittler Drive, Greenville, SC, 29607,
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Keywords:
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complex traits ; EM algorithm ; genotypic structure
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Abstract:
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Recent studies have found that genomewide expression data may be a useful tool in the difficult task of mapping complex traits. We have developed a method using expression level information to cluster individuals by their genotype on disease causative loci. Standard clustering methods are not well suited to identify genotypic structure because they tend to be overwhelmed by variation that is unrelated to disease genetic variation. We propose an EM algorithm-based method that targets the disease genetic variation and will thus identify disease genotypic variation via the correlation structure in differences in gene expression between disease affected and unaffected individuals. Identifying genotypic structure in a population will allow gene mapping studies to take into account heterogeneity in disease genotype and will improve mapping power.
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