title: Using network clustering to predict copy number variations associated with health disparities.
abstract: 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. Here, we aimed to investigate whether CNVs with differential frequencies can contribute to health disparities from the perspective of gene networks. We inferred network clusters from human gene/protein networks based on two different data sources. 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 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 two candidate causal genes located in four population-specific CNVs that play potential roles in health disparities.
authors.affiliation: Department of Computer Science and Engineering, University of Tennessee at Chattanooga , TN , USA.
authors.forename: Yi
authors.initials: Y
authors.lastname: Jiang
authors.affiliation: Departement of Biology, Spelman College , Atlanta, GA , United States.
authors.forename: Hong
authors.initials: H
authors.lastname: Qin
authors.affiliation: Department of Computer Science and Engineering, University of Tennessee at Chattanooga , TN , USA.
authors.forename: Li
authors.initials: L
authors.lastname: Yang
keywords: Clustering; Copy Number Variations (CNVs); Gene Ontology; Gene networks; Gene-disease association; Health disparities