Dissertation Defense Announcement
To:  The George Mason University Community

Candidate: Jesus Enrique Herrera-Galeano
Program:    PhD Bioinformatics and Computational Biology

Date:   Wednesday May 1, 2013
Time:   12:30 p.m.
Place:  George Mason University
            Prince William Campus<http://www.gmu.edu/resources/welcome/Directions-to-GMU.html>
            Occoquan Building, Room 203

Dissertation Director: Dr. Jeffrey Solka
Committee Chair: Dr. Iosif Vaisman
Committee members: Dr. Patrick Gillevet, Dr. David Hirschberg

Title: "Meta-Analysis of Genetic Associations Using Knowledge Representation"

The dissertation is on reserve in the Johnson Center Library, Fairfax campus.
The doctoral project will not be read at the meeting, but should be read in advance.

All members of the George Mason University community are invited to attend.


ABSTRACT:
Recent advances in genomic technology have resulted in the availability of an unprecedented amount of genetic data. However, despite the impressive resolution in genetic markers currently available, those who study complex diseases still are haunted by the "missing heritability problem," the problem of not being able to explain a large portion of the expected genetic heritability of a disease.  Many efforts are currently being conducted to try to explain a larger portion of the heritability by finding combinations of genes or markers that affect the phenotype of interest. Here, we introduce a methodology to utilize structured knowledge of the phenotypes to find correlations among genes/markers.  As a motivating example, we focused on answering questions such as: Is there a common gene related to groups of related phenotypes and is the meta-analysis of associations related by the ontology significant? This work presents the methodology and tools necessary to answer such questions. Here we present a new application, the ontology of genetic associations (OGA).  OGA is completely standalone and allows the user to (1) navigate the phenotype ontology and observe the corresponding gene associations, (2) find the genes common to two or more phenotypes, and (3) find an empirical p value to indicate the probability of arriving at the same findings by chance.

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