ArticleBioinformatics (Oxford, England)2018
Three-dimensional cardiovascular imaging-genetics: a mass univariate framework.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed.
- Translating genome-wide association studies at multiple scales: Drug target prioritization, cellular architectures, and organ imaging.Cell genomics · 2026Review
- Changes in abdominal subcutaneous adipose tissue thickness associate with disease and anthropometric factors.International journal of obesity (2005) · 2025Article
- Automated Bi-Ventricular Segmentation and Regional Cardiac Wall Motion Analysis for Rat Models of Pulmonary Hypertension.Pulmonary circulation · 2025Article
- Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer's disease.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2024Article
- Liver shape analysis using statistical parametric maps at population scale.BMC medical imaging · 2024Article
- Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology.Nature machine intelligence · 2024Article
- Kidney shape statistical analysis: associations with disease and anthropometric factors.BMC nephrology · 2023Article
- Genotype-Phenotype Taxonomy of Hypertrophic Cardiomyopathy.Circulation. Genomic and precision medicine · 2023Article
- Environmental and genetic predictors of human cardiovascular ageing.Nature communications · 2023Article
- Identification of an increased lifetime risk of major adverse cardiovascular events in UK Biobank participants with scoliosis.Open heart · 2023Article
- Artificial Intelligence and Cardiovascular Genetics.Life (Basel, Switzerland) · 2022Review
- Phenotypic Expression and Outcomes in Individuals With Rare Genetic Variants of Hypertrophic Cardiomyopathy.Journal of the American College of Cardiology · 2021Article
- A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning.BioMed research international · 2021Article
- Sex and regional differences in myocardial plasticity in aortic stenosis are revealed by 3D model machine learning.European heart journal. Cardiovascular Imaging · 2020Article
- Paradoxical Higher Myocardial Wall Stress and Increased Cardiac Remodeling Despite Lower Mass in Females.Journal of the American Heart Association · 2020Article
- Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization.Nature communications · 2020Article
- Artificial Intelligence in Cardiac Imaging With Statistical Atlases of Cardiac Anatomy.Frontiers in cardiovascular medicine · 2020Review
- Machine learning in cardiovascular magnetic resonance: basic concepts and applications.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2019Review
- Metabolic pathways associated with right ventricular adaptation to pulmonary hypertension: 3D analysis of cardiac magnetic resonance imaging.European heart journal. Cardiovascular Imaging · 2019Article
- Independent Left Ventricular Morphometric Atlases Show Consistent Relationships with Cardiovascular Risk Factors: A UK Biobank Study.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
13 authors.
Funding
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. 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.