Choi JinCheol, Lu Donghuan, Beg Mirza Faisal, Graham Jinko, McNeney Brad
Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada.
School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada.
Hum Hered. 2019;84(2):59-72. doi: 10.1159/000501334. Epub 2019 Aug 20.
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.
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.
The contribution plot is applied to simulated data and to genomic and brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
The contribution plot with shrinkage estimation can reveal truly associated explanatory variables.
背景/目的：阿尔茨海默病（AD）是一种慢性神经退行性疾病，会导致记忆丧失和认知能力下降。AD是美国第六大死因，估计影响500万美国人。为了评估多个基因变异与大脑结构变化的多种测量之间的关联，最近一项关于AD的研究使用了线性依赖性的多变量测量指标——RV系数。作者将RV系数分解为各个变异的贡献，并以图形方式展示了这些贡献。
我们根据一个潜在的线性模型研究这种“贡献图”的性质，并讨论当相关信号可能稀疏时该图各成分的收缩估计。
贡献图应用于模拟数据以及来自阿尔茨海默病神经影像倡议（ADNI）的基因组和脑成像数据。
带有收缩估计的贡献图可以揭示真正相关的解释变量。
