  Alzheimer's disease (AD) is a chronic neurodegenerative disease that causes
memory loss and decline in cognitive abilities. AD is the sixth leading cause
of death in the United States, affecting an estimated 5 million Americans. To
assess the association between multiple genetic variants and multiple
measurements of structural changes in the brain a recent study of AD used a
multivariate measure of linear dependence, the RV coefficient. The authors
decomposed the RV coefficient into contributions from individual variants and
displayed these contributions graphically. We investigate the properties of
such a `contribution plot' in terms of an underlying linear model, and discuss
estimation of the components of the plot when the correlation signal may be
sparse. The contribution plot is applied to simulated data and to genomic and
brain imaging data from the Alzheimer's Disease Neuroimaging Initiative.
