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

## TL;DR

This paper introduces a 3D imaging-genetics framework that uses high-resolution cardiac MRI and mass univariate regression to map genetic influences on heart structure, enhancing statistical power and automation for large cohorts.

## Contribution

It presents a novel 3D shape modeling and analysis framework for genotype-phenotype mapping in cardiac imaging, with improved power and false discovery control.

## Key findings

- Enhanced statistical power for genotype-phenotype associations
- Automatic analysis pipeline for large cohorts
- Good control of false discovery rate

## 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 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.