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Tytuł pozycji:

Cox regression increases power to detect genotype-phenotype associations in genomic studies using the electronic health record.

Tytuł :
Cox regression increases power to detect genotype-phenotype associations in genomic studies using the electronic health record.
Autorzy :
Hughey JJ; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA. .; Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA. .
Rhoades SD; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Fu DY; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Bastarache L; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Denny JC; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Chen Q; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
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Źródło :
BMC genomics [BMC Genomics] 2019 Nov 04; Vol. 20 (1), pp. 805. Date of Electronic Publication: 2019 Nov 04.
Typ publikacji :
Journal Article
Język :
English
Imprint Name(s) :
Original Publication: London : BioMed Central, [2000-
MeSH Terms :
Electronic Health Records*
Genomics*
Genotype*
Phenotype*
Proportional Hazards Models*
Genome-Wide Association Study ; Humans ; Neoplasms/genetics
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Grant Information :
R35 GM124685 United States GM NIGMS NIH HHS; R01LM016085 U.S. National Library of Medicine; U24 CA194215 United States CA NCI NIH HHS; R01 LM010685 United States LM NLM NIH HHS; R35GM124685 United States GM NIGMS NIH HHS; U24CA194215 United States CA NCI NIH HHS; T32HG008341 United States HG NHGRI NIH HHS
Contributed Indexing :
Keywords: Cox regression; Electronic health record; GWAS; Time-to-event modeling
Entry Date(s) :
Date Created: 20191106 Date Completed: 20200316 Latest Revision: 20200717
Update Code :
20201023
PubMed Central ID :
PMC6829851
DOI :
10.1186/s12864-019-6192-1
PMID :
31684865
Czasopismo naukowe
Background: The growth of DNA biobanks linked to data from electronic health records (EHRs) has enabled the discovery of numerous associations between genomic variants and clinical phenotypes. Nonetheless, although clinical data are generally longitudinal, standard approaches for detecting genotype-phenotype associations in such linked data, notably logistic regression, do not naturally account for variation in the period of follow-up or the time at which an event occurs. Here we explored the advantages of quantifying associations using Cox proportional hazards regression, which can account for the age at which a patient first visited the healthcare system (left truncation) and the age at which a patient either last visited the healthcare system or acquired a particular phenotype (right censoring).
Results: In comprehensive simulations, we found that, compared to logistic regression, Cox regression had greater power at equivalent Type I error. We then scanned for genotype-phenotype associations using logistic regression and Cox regression on 50 phenotypes derived from the EHRs of 49,792 genotyped individuals. Consistent with the findings from our simulations, Cox regression had approximately 10% greater relative sensitivity for detecting known associations from the NHGRI-EBI GWAS Catalog. In terms of effect sizes, the hazard ratios estimated by Cox regression were strongly correlated with the odds ratios estimated by logistic regression.
Conclusions: As longitudinal health-related data continue to grow, Cox regression may improve our ability to identify the genetic basis for a wide range of human phenotypes.
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