{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-1280"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-1280","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"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. 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.","abstract_html":"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.","abstract_has_math":false,"creators":["Jiang, Yi"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Yang, Li","Winters, Katherine; Kandah, Farah","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T05:46:11Z","subjects":["Bioinformatics","Variation (Biology)","Human genetics","Genetic disorders"],"languages":["English","eng"],"rights":[],"rights_urls":["https://rightsstatements.org/page/InC/1.0/?language=en"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/144","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Yang, Li","Winters, Katherine; Kandah, Farah","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Jiang, Yi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-12-01T08:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Masters theses","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Bioinformatics","Variation (Biology)","Human genetics","Genetic disorders"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://rightsstatements.org/page/InC/1.0/?language=en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/144"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Computer Science and Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Using network clustering to predict copy number variations associated with health disparities"]}]}],"canonical_facts":{"dc:contributor":["Yang, Li","Winters, Katherine; Kandah, Farah","College of Engineering and Computer Science"],"dc:creator":["Jiang, Yi"],"dc:date":["2014-12-01T08:00:00Z"],"dc:description":["Dept. of Computer Science and Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."],"dc:description.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. 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."],"dc:identifier":["https://scholar.utc.edu/theses/144"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["https://rightsstatements.org/page/InC/1.0/?language=en"],"dc:subject":["Bioinformatics","Variation (Biology)","Human genetics","Genetic disorders"],"dc:title":["Using network clustering to predict copy number variations associated with health disparities"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:46:11Z"}