Using network clustering to predict copy number variations associated with health disparities

Substantial health disparities exist between African Americans and Caucasians in the United States. Copy number variations (CNVs) are one form of human genetic variations that have been linked with complex diseases and often occur at different frequencies among African Americans and Caucasian populations. In this study, we aimed to investigate whether CNVs with differential population frequencies can contribute to health disparities from the perspective of gene networks. We inferred network clusters from two different human gene/protein networks. We then evaluated each network cluster for the occurrences of known pathogenic genes and genes located in CNVs with different population frequencies, and used false discovery rates (FDRs) to rank network clusters. This approach let us identify five clusters enriched with known pathogenic genes and with genes located in CNVs with different frequencies between African Americans and Caucasians. These clustering patterns predict four candidate causal population-specific CNVs that play potential roles in health disparities.

Grantor dc:publisher

University of Tennessee at Chattanooga

Author dc:creator

• Jiang, Yi

Contributors dc:contributor

• Yang, Li
• Winters, Katherine; Kandah, Farah
• College of Engineering and Computer Science

• Bioinformatics
• Variation (Biology)
• Human genetics
• Genetic disorders

Licence

• https://rightsstatements.org/page/InC/1.0/?language=en

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/144

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-1280