Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework

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Title: Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework
Authors: Biffi, C
Simoes Monteiro de Marvao, A
Attard, M
Dawes, T
Whiffin, N
Bai, W
Shi, W
Francis, C
Meyer, H
Buchan, R
Cook, S
Rueckert, D
O'Regan, DP
Item Type: Journal Article
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 highthroughput mapping of genotype-phenotype associations in three dimensions (3D). Results: High-resolution cardiac magnetic resonance images were automatically segmented in 1,124 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: 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.
Issue Date: 4-Sep-2017
Date of Acceptance: 1-Sep-2017
URI: http://hdl.handle.net/10044/1/51613
DOI: https://dx.doi.org/10.1093/bioinformatics/btx552
ISSN: 1367-4803
Publisher: Oxford University Press (OUP)
Journal / Book Title: Bioinformatics
Replaces: http://hdl.handle.net/10044/1/50589
10044/1/50589
Copyright Statement: © The Author(s) 2017. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Sponsor/Funder: British Heart Foundation
GlaxoSmithKline Services Unlimited
Imperial College Healthcare NHS Trust- BRC Funding
National Institute for Health Research
British Heart Foundation
The Academy of Medical Sciences
British Heart Foundation
Imperial College Healthcare NHS Trust- BRC Funding
Funder's Grant Number: RE/08/002/23906
COL011953
RD410
RDB02 79560
PG/12/27/29489
nil
NH/17/1/32725
RDB02
Keywords: 01 Mathematical Sciences
06 Biological Sciences
08 Information And Computing Sciences
Bioinformatics
Publication Status: Published online
Article Number: btx552
Appears in Collections:Computing
Clinical Sciences
Imaging Sciences
National Heart and Lung Institute
Faculty of Medicine



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