The neuroimaging genetic study usually needs to deal with high dimensionality of both brain imaging data and genetic data, so that often resulting in the issue of curse of dimensionality. In this paper, we propose a group sparse reduced rank regression model to take the relations of both the phenotypes and the genotypes for the neuroimaging genetic study. Specifically, we propose designing a graph sparsity constraint as well as a reduced rank constraint to simultaneously conduct subspace learning and feature selection. The group sparsity constraint conducts feature selection to identify genotypes highly related to neuroimaging data, while the reduced rank constraint considers the relations among neuroimaging data to conduct subspace learning in the feature selection model. Furthermore, an alternative optimization algorithm is proposed to solve the resulting objective function and is proved to achieve fast convergence. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset showed that the proposed method has superiority on predicting the phenotype data by the genotype data, than the alternative methods under comparison.

中文翻译：

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组稀疏减少秩回归用于神经影像遗传学研究。

神经影像遗传学研究通常需要处理大脑影像数据和遗传数据的高维数，因此常常导致维数诅咒的问题。在本文中，我们提出了一种群体稀疏的降阶回归模型，以将表型和基因型之间的关系用于神经影像遗传学研究。具体来说，我们建议设计一个图稀疏约束以及一个降低的秩约束，以同时进行子空间学习和特征选择。群体稀疏性约束进行特征选择以识别与神经影像数据高度相关的基因型，而降秩约束则考虑神经影像数据之间的关系以在特征选择模型中进行子空间学习。此外，提出了一种替代的优化算法来求解目标函数，并证明可以实现快速收敛。在阿尔茨海默氏病神经影像学倡议（ADNI）数据集上的实验结果表明，与通过比较的替代方法相比，该方法在通过基因型数据预测表型数据方面具有优势。