{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/46326"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/46326","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Towards constructing disease relationship networks using genome-wide association studies","abstract":"Background: Genome-wide association studies (GWAS) prove to be a powerful approach to identify the genetic basis of various human[1] diseases. Here we take advantage of existing GWAS data and attempt to build a framework to understand the complex relationships among diseases. Specifically, we examined 49 diseases from all available GWAS with a cascade approach by exploiting network analysis to study the single nucleotide polymorphisms (SNP) effect on the similarity between different diseases. Proteins within perturbation subnetwork are considered to be connection points between the disease similarity networks. Results: shared disease subnetwork proteins are consistent, accurate and sensitive to measure genetic similarity between diseases. Clustering result shows the evidence of phenome similarity. Conclusion: our results prove the usefulness of genetic profiles for evaluating disease similarity and constructing disease relationship networks.","abstract_html":"Background: Genome-wide association studies (GWAS) prove to be a powerful approach to identify the genetic basis of various human[1] diseases. Here we take advantage of existing GWAS data and attempt to build a framework to understand the complex relationships among diseases. Specifically, we examined 49 diseases from all available GWAS with a cascade approach by exploiting network analysis to study the single nucleotide polymorphisms (SNP) effect on the similarity between different diseases. Proteins within perturbation subnetwork are considered to be connection points between the disease similarity networks. Results: shared disease subnetwork proteins are consistent, accurate and sensitive to measure genetic similarity between diseases. Clustering result shows the evidence of phenome similarity. Conclusion: our results prove the usefulness of genetic profiles for evaluating disease similarity and constructing disease relationship networks.","abstract_has_math":false,"creators":["Huang, Wenhui"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Science","degree_department":"Computer Science","school":null,"contributors":[],"advisors":[],"committee_chairs":["Zhang, Liqing"],"committee_members":["Li, Liwu","Fan, Weiguo Patrick"],"year":2009,"date_issued":"2009-12-07","date_published":"2009-12-07","updated_at":"2026-07-22T22:18:52Z","subjects":["DiseaseRelationship","Network","GWAS","SNP","Protein-Protein Interaction"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-12212009-174030"],"render_values":[{"text":"etd-12212009-174030","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/46326","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Zhang, Liqing"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Li, Liwu","Fan, Weiguo Patrick"]},{"key":"dc:contributor.department","label":"Department","values":["Computer Science"]},{"key":"dc:creator","label":"Author","values":["Huang, Wenhui"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T21:52:07Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T21:52:07Z","2010-01-19"]},{"key":"dc:date.issued","label":"Date","values":["2009-12-07"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["DiseaseRelationship","Network","GWAS","SNP","Protein-Protein Interaction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-12212009-174030"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/46326"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Background: Genome-wide association studies (GWAS) prove to be a powerful approach to identify the genetic basis of various human[1] diseases. Here we take advantage of existing GWAS data and attempt to build a framework to understand the complex relationships among diseases. Specifically, we examined 49 diseases from all available GWAS with a cascade approach by exploiting network analysis to study the single nucleotide polymorphisms (SNP) effect on the similarity between different diseases. Proteins within perturbation subnetwork are considered to be connection points between the disease similarity networks. Results: shared disease subnetwork proteins are consistent, accurate and sensitive to measure genetic similarity between diseases. Clustering result shows the evidence of phenome similarity. Conclusion: our results prove the usefulness of genetic profiles for evaluating disease similarity and constructing disease relationship networks."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Towards constructing disease relationship networks using genome-wide association studies"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Zhang, Liqing"],"dc:contributor.committeemember":["Li, Liwu","Fan, Weiguo Patrick"],"dc:contributor.department":["Computer Science"],"dc:creator":["Huang, Wenhui"],"dc:date.accessioned":["2014-03-14T21:52:07Z"],"dc:date.available":["2014-03-14T21:52:07Z","2010-01-19"],"dc:date.issued":["2009-12-07"],"dc:description.abstract":["Background: Genome-wide association studies (GWAS) prove to be a powerful approach to identify the genetic basis of various human[1] diseases. Here we take advantage of existing GWAS data and attempt to build a framework to understand the complex relationships among diseases. Specifically, we examined 49 diseases from all available GWAS with a cascade approach by exploiting network analysis to study the single nucleotide polymorphisms (SNP) effect on the similarity between different diseases. Proteins within perturbation subnetwork are considered to be connection points between the disease similarity networks. Results: shared disease subnetwork proteins are consistent, accurate and sensitive to measure genetic similarity between diseases. Clustering result shows the evidence of phenome similarity. Conclusion: our results prove the usefulness of genetic profiles for evaluating disease similarity and constructing disease relationship networks."],"dc:description.degree":["Master of Science"],"dc:identifier.other":["etd-12212009-174030"],"dc:identifier.uri":["http://hdl.handle.net/10919/46326"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["DiseaseRelationship","Network","GWAS","SNP","Protein-Protein Interaction"],"dc:title":["Towards constructing disease relationship networks using genome-wide association studies"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:18:52Z"}