Pathway networks generated from human disease phenome
| dc.contributor.author | Cirincione, Ann G. | |
| dc.contributor.author | Clark, Kaylyn L. | |
| dc.contributor.author | Kann, Maricel G. | |
| dc.date.accessioned | 2018-10-16T13:42:37Z | |
| dc.date.available | 2018-10-16T13:42:37Z | |
| dc.date.issued | 2018-09-14 | |
| dc.description.abstract | Background: Understanding the effect of human genetic variations on disease can provide insight into phenotype-genotype relationships, and has great potential for improving the effectiveness of personalized medicine. While some genetic markers linked to disease susceptibility have been identified, a large number are still unknown. In this paper, we propose a pathway-based approach to extend disease-variant associations and find new molecular connections between genetic mutations and diseases. Methods: We used a compilation of over 80,000 human genetic variants with known disease associations from databases including the Online Mendelian Inheritance in Man (OMIM), Clinical Variance database (ClinVar), Universal Protein Resource (UniProt), and Human Gene Mutation Database (HGMD). Furthermore, we used the Unified Medical Language System (UMLS) to normalize variant phenotype terminologies, mapping 87% of unique genetic variants to phenotypic disorder concepts. Lastly, variants were grouped by UMLS Medical Subject Heading (MeSH) identifiers to determine pathway enrichment in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results: By linking KEGG pathways through underlying variant associations, we elucidated connections between the human genetic variant-based disease phenome and metabolic pathways, finding novel disease connections not otherwise detected through gene-level analysis. When looking at broader disease categories, our network analysis showed that large complex diseases, such as cancers, are highly linked by their common pathways. In addition, we found Cardiovascular Diseases and Skin and Connective Tissue Diseases to have the highest number of common pathways, among 35 significant main disease category (MeSH) pairings. Conclusions: This study constitutes an important contribution to extending disease-variant connections and new molecular links between diseases. Novel disease connections were made by disease-pathway associations not otherwise detected through single-gene analysis. For instance, we found that mutations in different genes associated to Noonan Syndrome and Essential Hypertension share a common pathway. This analysis also provides the foundation to build novel disease-drug networks through their underlying common metabolic pathways, thus enabling new diagnostic and therapeutic interventions. | en_US |
| dc.description.sponsorship | The National Institutes of Health Grants 1K22CA143148 to M.G.K. (PI) and R01LM009722 to M.G.K. (coPI); NSF (award #1446406, PI: M.G.K.), and the American Cancer Society, American Cancer Society Institutional Research Awards grant to M.G.K. (PI); This investigation was sponsored, in part, by NIH/NIGMS MARC U*STAR T34 08663 National Research Service Award to UMBC (A.G.C.), and through an Undergraduate Research Award from the UMBC Office of Undergraduate Education (A.G.C). The cost of publication was funded by NIH/NIGMS MARC U*STAR. | en_US |
| dc.description.uri | https://bmcmedgenomics.biomedcentral.com/articles/10.1186/s12920-018-0386-2 | en_US |
| dc.format.extent | 9 pages | en_US |
| dc.genre | Journal Article | en_US |
| dc.identifier | doi:10.13016/M2V97ZW0D | |
| dc.identifier.citation | Ann G. Cirincione, Kaylyn L. Clark and Maricel G. Kann ,Pathway networks generated from human disease phenome, BMC Medical Genomics201811 (Suppl 3) :75 https://doi.org/10.1186/s12920-018-0386-2 | en_US |
| dc.identifier.uri | https://doi.org/10.1186/s12920-018-0386-2 | |
| dc.identifier.uri | http://hdl.handle.net/11603/11572 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | BMC Medical Genomics | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Biological Sciences Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | Attribution 4.0 International (CC BY 4.0) | * |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | Networks | en_US |
| dc.subject | Phenome | en_US |
| dc.subject | Disease mutations | en_US |
| dc.title | Pathway networks generated from human disease phenome | en_US |
| dc.type | Text | en_US |