Population-based studies of myocardial hypertrophy: high resolution cardiovascular magnetic resonance atlases improve statistical power.

BACKGROUND: Cardiac phenotypes, such as left ventricular (LV) mass, demonstrate high heritability although most genes associated with these complex traits remain unidentified. Genome-wide association studies (GWAS) have relied on conventional 2D cardiovascular magnetic resonance (CMR) as the gold-standard for phenotyping. However this technique is insensitive to the regional variations in wall thickness which are often associated with left ventricular hypertrophy and require large cohorts to reach significance. Here we test whether automated cardiac phenotyping using high spatial resolution CMR atlases can achieve improved precision for mapping wall thickness in healthy populations and whether smaller sample sizes are required compared to conventional methods. METHODS: LV short-axis cine images were acquired in 138 healthy volunteers using standard 2D imaging and 3D high spatial resolution CMR. A multi-atlas technique was used to segment and co-register each image. The agreement between methods for end-diastolic volume and mass was made using Bland-Altman analysis in 20 subjects. The 3D and 2D segmentations of the LV were compared to manual labeling by the proportion of concordant voxels (Dice coefficient) and the distances separating corresponding points. Parametric and nonparametric data were analysed with paired t-tests and Wilcoxon signed-rank test respectively. Voxelwise power calculations used the interstudy variances of wall thickness. RESULTS: The 3D volumetric measurements showed no bias compared to 2D imaging. The segmented 3D images were more accurate than 2D images for defining the epicardium (Dice: 0.95 vs 0.93, P<0.001; mean error 1.3 mm vs 2.2 mm, P<0.001) and endocardium (Dice 0.95 vs 0.93, P<0.001; mean error 1.1 mm vs 2.0 mm, P<0.001). The 3D technique resulted in significant differences in wall thickness assessment at the base, septum and apex of the LV compared to 2D (P<0.001). Fewer subjects were required for 3D imaging to detect a 1 mm difference in wall thickness (72 vs 56, P<0.001). CONCLUSIONS: High spatial resolution CMR with automated phenotyping provides greater power for mapping wall thickness than conventional 2D imaging and enables a reduction in the sample size required for studies of environmental and genetic determinants of LV wall thickness.

A distance-based test of association between paired heterogeneous genomic data.

MOTIVATION: Due to rapid technological advances, a wide range of different measurements can be obtained from a given biological sample including single nucleotide polymorphisms, copy number variation, gene expression levels, DNA methylation and proteomic profiles. Each of these distinct measurements provides the means to characterize a certain aspect of biological diversity, and a fundamental problem of broad interest concerns the discovery of shared patterns of variation across different data types. Such data types are heterogeneous in the sense that they represent measurements taken at different scales or represented by different data structures. RESULTS: We propose a distance-based statistical test, the generalized RV (GRV) test, to assess whether there is a common and non-random pattern of variability between paired biological measurements obtained from the same random sample. The measurements enter the test through the use of two distance measures, which can be chosen to capture a particular aspect of the data. An approximate null distribution is proposed to compute P-values in closed-form and without the need to perform costly Monte Carlo permutation procedures. Compared with the classical Mantel test for association between distance matrices, the GRV test has been found to be more powerful in a number of simulation settings. We also demonstrate how the GRV test can be used to detect biological pathways in which genetic variability is associated to variation in gene expression levels in an ovarian cancer sample, and present results obtained from two independent cohorts. AVAILABILITY: R code to compute the GRV test is freely available from http://www2.imperial.ac.uk/∼gmontana

Distance-based differential analysis of gene curves.

MOTIVATION: Time course gene expression experiments are performed to study time-varying changes in mRNA levels of thousands of genes. Statistical methods from functional data analysis (FDA) have recently gained popularity for modelling and exploring such time courses. Each temporal profile is treated as the realization of a smooth function of time, or curve, and the inferred curve becomes the basic unit of statistical analysis. The task of identifying genes with differential temporal profiles then consists of detecting statistically significant differences between curves, where such differences are commonly quantified by computing the area between the curves or the l2 distance. RESULTS: We propose a general test statistic for detecting differences between gene curves, which only depends on a suitably chosen distance measure between them. The test makes use of a distance-based variance decomposition and generalizes traditional MANOVA tests commonly used for vectorial observations. We also introduce the visual l2 distance, which is shown to capture shape-related differences in gene curves and is robust against time shifts, which would otherwise inflate the traditional l2 distance. Other shape-related distances, such as the curvature, may carry biological significance. We have assessed the comparative performance of the test on realistically simulated datasets and applied it to human immune cell responses to bacterial infection over time.