Decomposing the RV coefficient to identify genetic markers associated with changes in brain structure

Alzheimer’s disease (AD) is a chronic neurodegenerative disease that causes memory loss and decline in cognitive abilities; it is the sixth leading cause of death in the United States, affecting an estimated 5 million Americans and 747,000 Canadians. A recent study of AD pathogenesis (Szefer et al., 2017) used the RV coefficient to measure linear association between multiple genetic variants and multiple measurements of structural changes in the brain, using data from Alzheimer’s Disease Neuroimaging Initiative (ANDI). The authors decomposed the RV coefficient into contributions from individual variants and displayed these contributions graphically. In this project, 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 genomic and brain imaging data from the ADNI-1 study, and to data simulated under various scenarios.