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A hybrid correlation analysis with application to imaging genetics

机译：一种杂交相关分析与成像遗传学的应用

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Investigating the association between brain regions and genes continues to be a challenging topic in imaging genetics. Current brain region of interest (ROI)-gene association studies normally reduce data dimension by averaging the value of voxels in each ROI. This averaging may lead to a loss of information due to the existence of functional sub-regions. Pearson correlation is widely used for association analysis. However, it only detects linear correlation whereas nonlinear correlation may exist among ROIs. In this work, we introduced distance correlation to ROI-gene association analysis, which can detect both linear and nonlinear correlations and overcome the limitation of averaging operations by taking advantage of the information at each voxel. Nevertheless, distance correlation usually has a much lower value than Pearson correlation. To address this problem, we proposed a hybrid correlation analysis approach, by applying canonical correlation analysis (CCA) to the distance covariance matrix instead of directly computing distance correlation. Incorporating CCA into distance correlation approach may be more suitable for complex disease study because it can detect highly associated pairs of ROI and gene groups, and may improve the distance correlation level and statistical power. In addition, we developed a novel nonlinear CCA, called distance kernel CCA, which seeks the optimal combination of features with the most significant dependence. This approach was applied to imaging genetic data from the Philadelphia Neurodevelopmental Cohort (PNC). Experiments showed that our hybrid approach produced more consistent results than conventional CCA across resampling and both the correlation and statistical significance were increased compared to distance correlation analysis. Further gene enrichment analysis and region of interest (ROI) analysis confirmed the associations of the identified genes with brain ROIs. Therefore, our approach provides a powerful tool for finding the correlation between brain imaging and genomic data.
机译：调查大脑地区和基因之间的关联在成像遗传学中继续成为一个具有挑战性的话题。感兴趣的当前脑区域（ROI） - 基因协会研究通常通过平均每个ROI中的体素的值来减少数据尺寸。由于功能子区域的存在，该平均可能导致信息丢失。 Pearson相关性广泛用于关联分析。然而，它只检测线性相关性，而ROI之间可能存在非线性相关性。在这项工作中，我们引入了与ROI-Gene关联分析的距离相关性，其可以通过利用每个体素处的信息来检测线性和非线性相关性并克服平均操作的限制。然而，距离相关通常具有比Pearson相关性更低的值。为了解决这个问题，我们提出了一种混合相关性分析方法，通过将规范相关性分析（CCA）应用于距离协方差矩阵而不是直接计算距离相关性。将CCA掺入距离相关方法可能更适合于复杂的疾病研究，因为它可以检测高度相关的ROI和基因组对，并且可以改善距离相关水平和统计功率。此外，我们开发了一种名为距离内核CCA的新型非线性CCA，其旨在具有最重要的依赖性的特征的最佳组合。该方法应用于费城神经发育队（PNC）的成像遗传数据。实验表明，与距离相关分析相比，我们的混合方法产生比传统CCA的结果更加一致的结果，并且与距离相关分析相比增加了相关性和统计显着性。进一步的基因富集分析和感兴趣区域（ROI）分析证实了所鉴定基因与脑罗伊斯的关联。因此，我们的方法提供了一种强大的工具，用于找到脑成像和基因组数据之间的相关性。

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