Huo Zhouyuan, Shen Dinggang, Huang Heng
Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, United States,
Pac Symp Biocomput. 2018;23:353-364.
Research on the associations between genetic variations and imaging phenotypes is developing with the advance in high-throughput genotype and brain image techniques. Regression analysis of single nucleotide polymorphisms (SNPs) and imaging measures as quantitative traits (QTs) has been proposed to identify the quantitative trait loci (QTL) via multi-task learning models. Recent studies consider the interlinked structures within SNPs and imaging QTs through group lasso, e.g. ℓ2, 1-norm, leading to better predictive results and insights of SNPs. However, group sparsity is not enough for representing the correlation between multiple tasks and ℓ2, 1-norm regularization is not robust either. In this paper, we propose a new multi-task learning model to analyze the associations between SNPs and QTs. We suppose that low-rank structure is also beneficial to uncover the correlation between genetic variations and imaging phenotypes. Finally, we conduct regression analysis of SNPs and QTs. Experimental results show that our model is more accurate in prediction than compared methods and presents new insights of SNPs.
随着高通量基因分型和脑成像技术的进步，关于基因变异与成像表型之间关联的研究正在不断发展。有人提出将单核苷酸多态性（SNP）和成像测量作为数量性状（QT）进行回归分析，以通过多任务学习模型识别数量性状基因座（QTL）。最近的研究通过组套索（例如ℓ2,1范数）考虑SNP和成像QT中的相互关联结构，从而获得更好的预测结果和对SNP的洞察。然而，组稀疏性不足以表示多个任务之间的相关性，并且ℓ2,1范数正则化也不够稳健。在本文中，我们提出了一种新的多任务学习模型来分析SNP与QT之间的关联。我们假设低秩结构也有助于揭示基因变异与成像表型之间的相关性。最后，我们对SNP和QT进行回归分析。实验结果表明，我们的模型在预测方面比比较方法更准确，并呈现了对SNP的新见解。
