Prediction of Disease and Phenotype Associations from Genome-Wide Association Studies

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dc.contributor.authorLewis, Stephanie N.en
dc.contributor.authorNsoesie, Elaine O.en
dc.contributor.authorWeeks, Charlesen
dc.contributor.authorQiao, Danen
dc.contributor.authorZhang, Liqingen
dc.contributor.departmentBiochemistryen
dc.contributor.departmentComputer Scienceen
dc.contributor.departmentFralin Life Sciences Instituteen
dc.date.accessioned2018-11-05T15:13:16Zen
dc.date.available2018-11-05T15:13:16Zen
dc.date.issued2011-11-04en
dc.description.abstractBackground Genome wide association studies (GWAS) have proven useful as a method for identifying genetic variations associated with diseases. In this study, we analyzed GWAS data for 61 diseases and phenotypes to elucidate common associations based on single nucleotide polymorphisms (SNP). The study was an expansion on a previous study on identifying disease associations via data from a single GWAS on seven diseases. Methodology/Principal Findings Adjustments to the originally reported study included expansion of the SNP dataset using Linkage Disequilibrium (LD) and refinement of the four levels of analysis to encompass SNP, SNP block, gene, and pathway level comparisons. A pair-wise comparison between diseases and phenotypes was performed at each level and the Jaccard similarity index was used to measure the degree of association between two diseases/phenotypes. Disease relatedness networks (DRNs) were used to visualize our results. We saw predominant relatedness between Multiple Sclerosis, type 1 diabetes, and rheumatoid arthritis for the first three levels of analysis. Expected relatedness was also seen between lipid- and blood-related traits. Conclusions/Significance The predominant associations between Multiple Sclerosis, type 1 diabetes, and rheumatoid arthritis can be validated by clinical studies. The diseases have been proposed to share a systemic inflammation phenotype that can result in progression of additional diseases in patients with one of these three diseases. We also noticed unexpected relationships between metabolic and neurological diseases at the pathway comparison level. The less significant relationships found between diseases require a more detailed literature review to determine validity of the predictions. The results from this study serve as a first step towards a better understanding of seemingly unrelated diseases and phenotypes with similar symptoms or modes of treatment.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0027175en
dc.identifier.eissn1932-6203en
dc.identifier.issue11en
dc.identifier.othere27175en
dc.identifier.pmid22076134en
dc.identifier.urihttp://hdl.handle.net/10919/85645en
dc.identifier.volume6en
dc.language.isoenen
dc.publisherPLOSen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titlePrediction of Disease and Phenotype Associations from Genome-Wide Association Studiesen
dc.title.serialPLOS ONEen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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