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PheProb: probabilistic phenotyping using diagnosis codes to improve power for genetic association studies

dc.rights.licenseopenen_US
dc.contributor.authorSINNOTT, J. A.
dc.contributor.authorCAI, F.
dc.contributor.authorYU, S.
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHEJBLUM, Boris
ORCID: 0000-0003-0646-452X
IDREF: 189970316
dc.contributor.authorHONG, C.
dc.contributor.authorKOHANE, I. S.
dc.contributor.authorLIAO, K. P.
dc.date.accessioned2021-01-05T10:49:14Z
dc.date.available2021-01-05T10:49:14Z
dc.date.issued2018-10-01
dc.identifier.issn1527-974X (Electronic) 1067-5027 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/23657
dc.description.abstractEnObjective: Standard approaches for large scale phenotypic screens using electronic health record (EHR) data apply thresholds, such as >/=2 diagnosis codes, to define subjects as having a phenotype. However, the variation in the accuracy of diagnosis codes can impair the power of such screens. Our objective was to develop and evaluate an approach which converts diagnosis codes into a probability of a phenotype (PheProb). We hypothesized that this alternate approach for defining phenotypes would improve power for genetic association studies. Methods: The PheProb approach employs unsupervised clustering to separate patients into 2 groups based on diagnosis codes. Subjects are assigned a probability of having the phenotype based on the number of diagnosis codes. This approach was developed using simulated EHR data and tested in a real world EHR cohort. In the latter, we tested the association between low density lipoprotein cholesterol (LDL-C) genetic risk alleles known for association with hyperlipidemia and hyperlipidemia codes (ICD-9 272.x). PheProb and thresholding approaches were compared. Results: Among n = 1462 subjects in the real world EHR cohort, the threshold-based p-values for association between the genetic risk score (GRS) and hyperlipidemia were 0.126 (>/=1 code), 0.123 (>/=2 codes), and 0.142 (>/=3 codes). The PheProb approach produced the expected significant association between the GRS and hyperlipidemia: p = .001. Conclusions: PheProb improves statistical power for association studies relative to standard thresholding approaches by leveraging information about the phenotype in the billing code counts. The PheProb approach has direct applications where efficient approaches are required, such as in Phenome-Wide Association Studies.
dc.language.isoENen_US
dc.subject.enSISTM
dc.title.enPheProb: probabilistic phenotyping using diagnosis codes to improve power for genetic association studies
dc.title.alternativeJ Am Med Inform Assocen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1093/jamia/ocy056en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed29788308en_US
bordeaux.journalJournal of the American Medical Informatics Associationen_US
bordeaux.page1359-1365en_US
bordeaux.volume25en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.issue10en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamSISTM_BPH
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.exportfalse
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20of%20the%20American%20Medical%20Informatics%20Association&rft.date=2018-10-01&rft.volume=25&rft.issue=10&rft.spage=1359-1365&rft.epage=1359-1365&rft.eissn=1527-974X%20(Electronic)%201067-5027%20(Linking)&rft.issn=1527-974X%20(Electronic)%201067-5027%20(Linking)&rft.au=SINNOTT,%20J.%20A.&CAI,%20F.&YU,%20S.&HEJBLUM,%20Boris&HONG,%20C.&rft.genre=article


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