Browsing by Author "Wang, Huifu"

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    Identifying Candidate Genetic Associations with MRI-Derived AD-Related ROI via Tree-Guided Sparse Learning
    (IEEE, 2019-11) Hao, Xiaoke; Yao, Xiaohui; Risacher, Shannon L.; Saykin, Andrew J.; Yu, Jintai; Wang, Huifu; Tan, Lan; Shen, Li; Zhang, Daoqiang; Radiology and Imaging Sciences, School of Medicine
    Imaging genetics has attracted significant interests in recent studies. Traditional work has focused on mass-univariate statistical approaches that identify important single nucleotide polymorphisms (SNPs) associated with quantitative traits (QTs) of brain structure or function. More recently, to address the problem of multiple comparison and weak detection, multivariate analysis methods such as the least absolute shrinkage and selection operator (Lasso) are often used to select the most relevant SNPs associated with QTs. However, one problem of Lasso, as well as many other feature selection methods for imaging genetics, is that some useful prior information, e.g., the hierarchical structure among SNPs, are rarely used for designing a more powerful model. In this paper, we propose to identify the associations between candidate genetic features (i.e., SNPs) and magnetic resonance imaging (MRI)-derived measures using a tree-guided sparse learning (TGSL) method. The advantage of our method is that it explicitly models the complex hierarchical structure among the SNPs in the objective function for feature selection. Specifically, motivated by the biological knowledge, the hierarchical structures involving gene groups and linkage disequilibrium (LD) blocks as well as individual SNPs are imposed as a tree-guided regularization term in our TGSL model. Experimental studies on simulation data and the Alzheimer's Disease Neuroimaging Initiative (ADNI) data show that our method not only achieves better predictions than competing methods on the MRI-derived measures of AD-related region of interests (ROIs) (i.e., hippocampus, parahippocampal gyrus, and precuneus), but also identifies sparse SNP patterns at the block level to better guide the biological interpretation.