IEEE Trans Biomed Eng. 2019 Jan;66(1):165-175. doi: 10.1109/TBME.2018.2824725. Epub 2018 Apr 9.
Brain-wide and genome-wide association (BW-GWA) study is presented in this paper to identify the associations between the brain imaging phenotypes (i.e., regional volumetric measures) and the genetic variants [i.e., single nucleotide polymorphism (SNP)] in Alzheimer's disease (AD). The main challenges of this study include the data heterogeneity, complex phenotype-genotype associations, high-dimensional data (e.g., thousands of SNPs), and the existence of phenotype outliers. Previous BW-GWA studies, while addressing some of these challenges, did not consider the diagnostic label information in their formulations, thus limiting their clinical applicability. To address these issues, we present a novel joint projection and sparse regression model to discover the associations between the phenotypes and genotypes. Specifically, to alleviate the negative influence of data heterogeneity, we first map the genotypes into an intermediate imaging-phenotype-like space. Then, to better reveal the complex phenotype-genotype associations, we project both the mapped genotypes and the original imaging phenotypes into a diagnostic-label-guided joint feature space, where the intraclass projected points are constrained to be close to each other. In addition, we use l-norm minimization on both the regression loss function and the transformation coefficient matrices, to reduce the effect of phenotype outliers and also to encourage sparse feature selections of both the genotypes and phenotypes. We evaluate our method using AD neuroimaging initiative dataset, and the results show that our proposed method outperforms several state-of-the-art methods in term of the average root-mean-square error of genome-to-phenotype predictions. Besides, the associated SNPs and brain regions identified in this study have also been shown in the previous AD-related studies, thus verifying the effectiveness and potential of our proposed method in AD pathogenesis study.
本文提出了一种全脑和全基因组关联（BW-GWA）研究方法，旨在识别阿尔茨海默病（AD）中脑影像表型（即区域体积测量）与遗传变异（即单核苷酸多态性（SNP））之间的关联。该研究的主要挑战包括数据异质性、复杂的表型-基因型关联、高维数据（例如数千个 SNP）和表型异常值的存在。以前的 BW-GWA 研究虽然解决了其中的一些挑战，但在其公式中没有考虑诊断标签信息，因此限制了其临床适用性。为了解决这些问题，我们提出了一种新的联合投影和稀疏回归模型来发现表型和基因型之间的关联。具体来说，为了减轻数据异质性的负面影响，我们首先将基因型映射到中间的影像表型样空间中。然后，为了更好地揭示复杂的表型-基因型关联，我们将映射的基因型和原始影像表型都投影到一个以诊断标签为导向的联合特征空间中，其中类内投影点被约束为彼此靠近。此外，我们在回归损失函数和变换系数矩阵上使用 l-范数最小化，以减少表型异常值的影响，并鼓励基因型和表型的稀疏特征选择。我们使用 AD 神经影像学倡议数据集来评估我们的方法，结果表明，与几种最先进的方法相比，我们提出的方法在基因组到表型预测的平均均方根误差方面表现更好。此外，本研究中鉴定的相关 SNP 和脑区也在前瞻性 AD 相关研究中得到了证实，从而验证了我们提出的方法在 AD 发病机制研究中的有效性和潜力。
