Document Type


Date of Degree

Summer 2012

Degree Name

MS (Master of Science)

Degree In

Electrical and Computer Engineering

First Advisor

Thomas L. Casavant

Second Advisor

Benjamin W. Darbro

First Committee Member

Val C Sheffield

Second Committee Member

Terry A Braun

Third Committee Member

John P Robinson


Many disorders found in humans are caused by abnormalities in DNA. Genetic testing of DNA provides a way for clinicians to identify disease-causing mutations in patients. Once patients with potentially disease-causing mutations are identified, they can be enrolled in treatment or preventative programs to improve the patients' long term quality of life. Array-based comparative genomic hybridization (aCGH) provides a high- resolution, genome-wide method for detecting chromosomal abnormalities. Using computer software, chromosome abnormalities, or copy number variations (CNVs) can be identified from aCGH data. The development of a software tool to analyze the performance of CGH microarrays is of great benefit to clinical laboratories. Calibration of parameters used in aCGH software tools can maximize the performance of these arrays in a clinical setting. According to the American College of Medical Genetics, the validation of a clinical chromosomal microarray platform should be performed by testing a large number (200-300) of well-characterized cases, each with unique CNVs located throughout the genome. Because of the Clinical Laboratory Improvement Amendment of 1988 and the lack of an FDA approved whole genome chromosomal microarray platform the ultimate responsibility for validating the performance characteristics of this technology falls to the clinical laboratory performing the testing. To facilitate this task, we have established a computational analytical validation procedure for CGH microarrays that is comprehensive, efficient, and low cost. This validation uses a higher resolution microarray to validate a lower resolution microarray with a receiver operating characteristic (ROC)-based analysis. From the results we are able to estimate an optimal log2 threshold range for determining the presence or absence (calling) of CNVs.


calibration, chromosomal microarray, receiver operating characteristic, sensitivity, specificity, validation


xii, 83 pages


Includes bibliographical references (pages 41-44).


Copyright 2012 Corey William Goodman