面向超高维遗传数据的快速概率白化变换方法
Fast probabilistic whitening transformation for ultra-high dimensional genetic data
作者提出一种用于数据白化的概率模型，并给出参数估计与高效算法，以去除遗传数据中的相关性结构。在精神分裂症全基因组关联研究的应用中，该方法用于插补未观测遗传变异的 z 统计量，均方误差低于其他方法，速度最高快一个数量级。作者还用这一方法识别出可解释疾病相关基因遗传调控信号的串联重复序列。相关分析实现在开源 R 包 decorrelate 和 imputez 中。
来源：PLOS Genetics · journals.plos.org
by Gabriel E. Hoffman, Christian P. Dillard, Kiran Girdhar, Panos Roussos
Statistical methods often make assumptions about independence between the samples or features of a dataset, yet correlation structure is ubiquitous in real data. In statistical genetics, correlation between regression summary statistics due to linkage disequilibrium (LD) between nearby variants complicates downstream analysis. Many methods use LD reference panels to account for this correlation. Whitening transformations comprise a family of methods to remove correlation structure, and are used implicitly by many common models. Here we propose a probabilistic model for data whitening, and propose parameter estimates and efficient algorithms to improve statistical and computational performance. In an application to impute z-statistics from unobserved genetic variants from a genome-wide association study of schizophrenia, the probabilistic whitening transformation, had the lowest mean square error while being up to an order of magnitude faster than other methods. Using this approach, we also identify tandem repeats that explain genetic regulatory signals for disease-relevant genes. Analyses are implemented in our novel open source R packages decorrelate and imputez.