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Data synthesis and method evaluation for brain imaging genetics

机译:脑成像遗传学的数据合成和方法评估

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Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. We present initial efforts on evaluating a few SCCA methods for brain imaging genetics. This includes a data synthesis method to create realistic imaging genetics data with known SNP-QT associations, application of three SCCA algorithms to the synthetic data, and comparative study of their performances. Our empirical results suggest, approximating covariance structure using an identity or diagonal matrix, an approach used in these SCCA algorithms, could limit the SCCA capability in identifying the underlying imaging genetics associations. An interesting future direction is to develop enhanced SCCA methods that effectively take into account the covariance structures in the imaging genetics data.
机译:脑成像遗传学是一个新兴的研究领域,其中评估了遗传变异(如单核苷酸多态性(SNP)和神经影像定量特征(QT))之间的关联。稀疏规范相关分析(SCCA)是一种双多变量分析方法,具有揭示复杂的多SNP-多QT关联的潜力。我们目前在评估脑成像遗传学的几种SCCA方法方面做出了初步努力。这包括使用已知的SNP-QT关联创建逼真的成像遗传数据的数据合成方法,将三种SCCA算法应用于合成数据,以及对其性能进行比较研究。我们的经验结果表明,使用恒等式或对角矩阵来近似协方差结构(这些SCCA算法中使用的一种方法)可能会限制SCCA识别基础成像遗传学关联的能力。一个有趣的未来方向是开发增强的SCCA方法,该方法可以有效地考虑成像遗传学数据中的协方差结构。

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