Abstract
Background/Aims: Alzheimer’s disease (AD) is a chronic neurodegenerative disease that causes memory loss and a decline in cognitive abilities. AD is the sixth leading cause of death in the USA, 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. Methods: We investigate the properties of such a “contribution plot” in terms of an underlying linear model, and discuss shrinkage estimation of the components of the plot when the correlation signal may be sparse. Results: The contribution plot is applied to simulated data and to genomic and brain imaging data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Conclusions: The contribution plot with shrinkage estimation can reveal truly associated explanatory variables.
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