Show simple item record

dc.contributor.authorJia, Peilin
dc.contributor.authorWang, Lily
dc.contributor.authorFanous, Ayman H
dc.contributor.authorChen, Xiangning
dc.contributor.authorKendler, Kenneth S
dc.contributor.authorZhao, Zhongming
dc.date.accessioned2012-08-15T14:06:58Z
dc.date.available2012-08-15T14:06:58Z
dc.date.issued2012-02
dc.identifier.citationA bias-reducing pathway enrichment analysis of genome-wide association data confirmed association of the MHC region with schizophrenia. 2012, 49 (2):96-103 J. Med. Genet.en_GB
dc.identifier.issn1468-6244
dc.identifier.pmid22187495
dc.identifier.doi10.1136/jmedgenet-2011-100397
dc.identifier.urihttp://hdl.handle.net/10147/238786
dc.description.abstractAfter the recent successes of genome-wide association studies (GWAS), one key challenge is to identify genetic variants that might have a significant joint effect on complex diseases but have failed to be identified individually due to weak to moderate marginal effect. One popular and effective approach is gene set based analysis, which investigates the joint effect of multiple functionally related genes (eg, pathways). However, a typical gene set analysis method is biased towards long genes, a problem that is especially severe in psychiatric diseases.
dc.language.isoenen
dc.rightsArchived with thanks to Journal of medical geneticsen_GB
dc.subject.meshCell Adhesion Molecules
dc.subject.meshComputational Biology
dc.subject.meshDatabases, Genetic
dc.subject.meshGenetic Predisposition to Disease
dc.subject.meshGenome-Wide Association Study
dc.subject.meshHumans
dc.subject.meshImmune System
dc.subject.meshMajor Histocompatibility Complex
dc.subject.meshPolymorphism, Single Nucleotide
dc.subject.meshSchizophrenia
dc.subject.meshSignal Transduction
dc.titleA bias-reducing pathway enrichment analysis of genome-wide association data confirmed association of the MHC region with schizophrenia.en_GB
dc.typeArticleen
dc.contributor.departmentDepartment of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA.en_GB
dc.identifier.journalJournal of medical geneticsen_GB
dc.description.provinceLeinsteren
html.description.abstractAfter the recent successes of genome-wide association studies (GWAS), one key challenge is to identify genetic variants that might have a significant joint effect on complex diseases but have failed to be identified individually due to weak to moderate marginal effect. One popular and effective approach is gene set based analysis, which investigates the joint effect of multiple functionally related genes (eg, pathways). However, a typical gene set analysis method is biased towards long genes, a problem that is especially severe in psychiatric diseases.


This item appears in the following Collection(s)

Show simple item record