  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.
