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dc.contributor.authorBiffi, Cen_US
dc.contributor.authorde Marvao, Aen_US
dc.contributor.authorAttard, MIen_US
dc.contributor.authorDawes, TJWen_US
dc.contributor.authorWhiffin, Nen_US
dc.contributor.authorBai, Wen_US
dc.contributor.authorShi, Wen_US
dc.contributor.authorFrancis, Cen_US
dc.contributor.authorMeyer, Hen_US
dc.contributor.authorBuchan, Ren_US
dc.contributor.authorCook, SAen_US
dc.contributor.authorRueckert, Den_US
dc.contributor.authorO'Regan, DPen_US
dc.coverage.spatialEnglanden_US
dc.date.accessioned2019-03-05T16:35:38Z
dc.date.issued2018-01-01en_US
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/28968671en_US
dc.identifier4103396en_US
dc.identifier.citationBioinformatics, 2018, 34 (1), pp. 97 - 103en_US
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3137
dc.identifier.eissn1367-4811en_US
dc.identifier.doi10.1093/bioinformatics/btx552en_US
dc.description.abstractMotivation: Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for high-throughput mapping of genotype-phenotype associations in three dimensions (3D). Results: High-resolution cardiac magnetic resonance images were automatically segmented in 1124 healthy volunteers to create a 3D shape model of the heart. Mass univariate regression was used to plot a 3D effect-size map for the association between wall thickness and a set of predictors at each vertex in the mesh. The vertices where a significant effect exists were determined by applying threshold-free cluster enhancement to boost areas of signal with spatial contiguity. Experiments on simulated phenotypic signals and SNP replication show that this approach offers a substantial gain in statistical power for cardiac genotype-phenotype associations while providing good control of the false discovery rate. This framework models the effects of genetic variation throughout the heart and can be automatically applied to large population cohorts. Availability and implementation: The proposed approach has been coded in an R package freely available at https://doi.org/10.5281/zenodo.834610 together with the clinical data used in this work. Contact: declan.oregan@imperial.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.en_US
dc.format.extent97 - 103en_US
dc.languageengen_US
dc.language.isoengen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subjectFemaleen_US
dc.subjectGenetic Association Studiesen_US
dc.subjectGenetic Predisposition to Diseaseen_US
dc.subjectHearten_US
dc.subjectHumansen_US
dc.subjectHypertrophy, Left Ventricularen_US
dc.subjectImaging, Three-Dimensionalen_US
dc.subjectMaleen_US
dc.subjectPhenotypeen_US
dc.subjectPolymorphism, Single Nucleotideen_US
dc.subjectSoftwareen_US
dc.titleThree-dimensional cardiovascular imaging-genetics: a mass univariate framework.en_US
dc.typeJournal Article
dcterms.dateAccepted2017-09-01en_US
rioxxterms.versionofrecord10.1093/bioinformatics/btx552en_US
rioxxterms.licenseref.startdate2018-01-01en_US
rioxxterms.typeJournal Article/Reviewen_US
dc.relation.isPartOfBioinformaticsen_US
pubs.issue1en_US
pubs.notesNot knownen_US
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology/Molecular & Population Genetics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Molecular & Population Genetics
pubs.publication-statusPublisheden_US
pubs.volume34en_US
pubs.embargo.termsNot knownen_US
icr.researchteamMolecular & Population Geneticsen_US
dc.contributor.icrauthorWhiffin, Nicolaen_US


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Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/