Identifying disease associations via genome-wide association studies
| dc.contributor.author | Huang, Wenhui | en |
| dc.contributor.author | Wang, Pengyuan | en |
| dc.contributor.author | Liu, Zhen | en |
| dc.contributor.author | Zhang, Liqing | en |
| dc.contributor.department | Computer Science | en |
| dc.date.accessioned | 2012-08-24T11:50:16Z | en |
| dc.date.available | 2012-08-24T11:50:16Z | en |
| dc.date.issued | 2009-01-30 | en |
| dc.date.updated | 2012-08-24T11:50:17Z | en |
| dc.description.abstract | Background Genome-wide association studies prove to be a powerful approach to identify the genetic basis of different human diseases. We studied the relationship between seven diseases characterized in a previous genome-wide association study by the Wellcome Trust Case Control Consortium. Instead of doing a horizontal association of SNPs to diseases, we did a vertical analysis of disease associations by comparing the genetic similarities of diseases. Our analysis was carried out at four levels - the nucleotide level (SNPs), the gene level, the protein level (through protein-protein interaction network), and the phenotype level. Results Our results show that Crohn's disease, rheumatoid arthritis, and type 1 diabetes share evidence of genetic associations at all levels of analysis, offering strong molecular support for the current grouping of the diseases. On the other hand, coronary artery disease, hypertension, and type 2 diabetes, despite being considered as a natural group with potential aetiological overlap, do not show any evidence of shared genetic basis at all levels. Conclusion Our study is a first attempt on mining of GWA data to examine genetic associations between different diseases. The positive result is apparently not a coincidence and hence demonstrates the promising use of our approach. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | BMC Bioinformatics. 2009 Jan 30;10(Suppl 1):S68 | en |
| dc.identifier.doi | https://doi.org/10.1186/1471-2105-10-S1-S68 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/18876 | en |
| dc.language.iso | en | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.holder | Wenhui Huang et al.; licensee BioMed Central Ltd. | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.title | Identifying disease associations via genome-wide association studies | en |
| dc.title.serial | BMC Bioinformatics | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |