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Alzheimer's disease (AD) is a chronic neurodegenerative disease that causesmemory loss and decline in cognitive abilities. AD is the sixth leading causeof death in the United States, affecting an estimated 5 million Americans. Toassess the association between multiple genetic variants and multiplemeasurements of structural changes in the brain a recent study of AD used amultivariate measure of linear dependence, the RV coefficient. The authorsdecomposed the RV coefficient into contributions from individual variants anddisplayed these contributions graphically. We investigate the properties ofsuch a `contribution plot' in terms of an underlying linear model, and discussestimation of the components of the plot when the correlation signal may besparse. The contribution plot is applied to simulated data and to genomic andbrain imaging data from the Alzheimer's Disease Neuroimaging Initiative.

The Contribution Plot: Decomposition and Graphical Display of the RV  Coefficient, with Application to Genetic and Brain Imaging Biomarkers of  Alzheimer's Disease