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The Contribution Plot: Decomposition and Graphical Display of the RV Coefficient, with Application to Genetic and Brain Imaging Biomarkers of Alzheimer's Disease.
Human Heredity ( IF 1.8 ) Pub Date : 2019-08-20 , DOI: 10.1159/000501334
JinCheol Choi 1 , Donghuan Lu 2 , Mirza Faisal Beg 2 , Jinko Graham 1 , Brad McNeney 3 ,
Affiliation  

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.

中文翻译:

贡献图:RV 系数的分解和图形显示,应用于阿尔茨海默病的遗传和脑成像生物标志物。

背景/目的阿尔茨海默病(AD)是一种慢性神经退行性疾病,会导致记忆力减退和认知能力下降。AD 是美国第六大死亡原因,估计有 500 万美国人受到影响。为了评估多种遗传变异与大脑结构变化的多次测量之间的关联,最近的一项 AD 研究使用了线性依赖性的多元测量,即 RV 系数。作者将 RV 系数分解为各个变体的贡献,并以图形方式显示这些贡献。方法 我们根据基本线性模型研究这种“贡献图”的性质,并讨论当相关信号可能稀疏时图的分量的收缩估计。结果 贡献图应用于模拟数据以及来自阿尔茨海默病神经成像倡议 (ADNI) 的基因组和脑成像数据。结论 收缩估计的贡献图可以揭示真正相关的解释变量。
更新日期:2019-11-01
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