Please use this identifier to cite or link to this item: https://doi.org/10.1093/bioinformatics/btx552
Title: Three-dimensional cardiovascular imaging-genetics: A mass univariate framework
Authors: Biffi C.
De Marvao A.
Attard M.I.
Dawes T.J.W.
Whiffin N.
Bai W.
Shi W.
Francis C.
Meyer H.
Buchan R.
Cook S.A. 
Rueckert D.
O'Regan D.P.
Issue Date: 2018
Publisher: Oxford University Press
Citation: Biffi C., De Marvao A., Attard M.I., Dawes T.J.W., Whiffin N., Bai W., Shi W., Francis C., Meyer H., Buchan R., Cook S.A., Rueckert D., O'Regan D.P. (2018). Three-dimensional cardiovascular imaging-genetics: A mass univariate framework. Bioinformatics 34 (1) : 97-103. ScholarBank@NUS Repository. https://doi.org/10.1093/bioinformatics/btx552
Abstract: Motivation: 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. © 2017 The Author.
Source Title: Bioinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/150627
ISSN: 13674803
DOI: 10.1093/bioinformatics/btx552
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