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A hybrid correlation analysis with application to imaging genetics

机译：混合相关分析及其在遗传学成像中的应用

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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：研究大脑区域和基因之间的关联仍然是成像遗传学中一个具有挑战性的话题。当前的大脑感兴趣区域（ROI）基因关联研究通常通过平均每个ROI中的体素值来减少数据量。由于存在功能子区域，这种平均可能导致信息丢失。皮尔逊相关被广泛用于关联分析。但是，它仅检测线性相关，而ROI之间可能存在非线性相关。在这项工作中，我们将距离相关性引入到ROI-基因关联分析中，它可以检测线性和非线性相关性，并通过利用每个体素上的信息克服了平均运算的局限性。但是，距离相关通常比Pearson相关具有更低的值。为了解决这个问题，我们提出了一种混合相关分析方法，通过将规范相关分析（CCA）应用于距离协方差矩阵，而不是直接计算距离相关。将CCA纳入距离相关方法可能更适合于复杂疾病研究，因为它可以检测高度相关的ROI和基因组对，并可以改善距离相关水平和统计能力。此外，我们开发了一种新颖的非线性CCA，称为距离核CCA，它寻求具有最大相关性的特征的最佳组合。该方法已用于对来自费城神经发育队列（PNC）的遗传数据进行成像。实验表明，与传统的CCA相比，我们的混合方法在重采样中产生的结果更加一致，并且与距离相关性分析相比，相关性和统计显着性均得到了提高。进一步的基因富集分析和目标区域（ROI）分析证实了已鉴定基因与大脑ROI的关联。因此，我们的方法为寻找大脑成像与基因组数据之间的相关性提供了强大的工具。

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