利用NGS数据识别多效性全局结构的二次正则函数典型相关分析
A Quadratically Regularized Functional Canonical Correlation Analysis for Identifying the Global Structure of Pleiotropy with NGS Data
摘要：
研究遗传变异的多效性可以增加统计功效，为深入了解疾病的复杂遗传结构提供重要信息，并为设计具有更少副作用的有效治疗方法提供强大工具。然而，目前的多表型关联分析范式缺乏广度（同时联合分析的表型和遗传变异的数量）和深度（表型和基因型的层次结构）。高维多效性分析的一个关键问题是有效地从高维基因型和表型数据中提取信息的内部表示和特征。为了探索遗传变异的多层次表征，了解疾病发展中涉及的内部模式，并克服推进遗传多效性分析的新统计方法和计算算法发展的关键障碍，我们提出了一种称为二次正则化功能CCA（QRFCCA）的关联分析新框架，它结合了三种方法：（1）二次正则化矩阵分解，（2）函数数据分析和（3）典型相关分析（CCA）。大规模的模拟表明，QRFCCA有一个更高的权力比9个竞争的统计，同时保留适当的类型1错误。为了进一步评估性能，将QRFCCA和其他九个统计量应用于TwinsUK研究的全基因组测序数据集。我们使用QRFCCA鉴定了与46个性状显著相关的79个具有罕见变异的基因和67个具有常见变异的基因。结果表明，QRFCCA大大优于其他九个统计。
英文摘要：
Investigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful tools for designing effective treatments with fewer side effects. However, the current multiple phenotype association analysis paradigm lacks breadth (number of phenotypes and genetic variants jointly analyzed at the same time) and depth (hierarchical structure of phenotype and genotypes). A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data. To explore multiple levels of representations of genetic variants, learn their internal patterns involved in the disease development, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new framework referred to as a quadratically regularized functional CCA (QRFCCA) for association analysis which combines three approaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) canonical correlation analysis (CCA). Large-scale simulations show that the QRFCCA has a much higher power than that of the nine competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and nine other statistics are applied to the whole genome sequencing dataset from the TwinsUK study. We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA. The results show that the QRFCCA substantially outperforms the nine other statistics.
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