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Identifying Genetic Associations with MRI-derived Measures via Tree-Guided Sparse Learning

  • Xiaoke Hao
  • Jintai Yu
  • Daoqiang Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

In recent imaging genetic studies, much work has been focused on regression analysis that treats large-scale single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) as association variables. To deal with the weak detection and high-throughput data problem, feature selection methods such as the least absolute shrinkage and selection operator (Lasso) are often used for selecting 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, i.e., the hierarchical structure among SNPs throughout the whole genome, are rarely used for designing 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 priori hierarchical grouping structure among the SNPs in the objective function for feature selection. Specifically, two kinds of hierarchical structures, i.e., group by gene and group by linkage disequilibrium (LD) clusters, are imposed as a tree-guided regularization term in our sparse learning model. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database show that our method not only achieves better predictions on the two MRI measures (i.e., left and right hippocampal formation), but also identifies the informative SNPs to guide the disease-induced interpretation compared with other reference methods.

Keywords

Root Mean Square Error Feature Selection Method Hippocampal Formation Group Lasso Sparse Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiaoke Hao
    • 1
  • Jintai Yu
    • 2
  • Daoqiang Zhang
    • 1
  1. 1.Department of Computer Science and EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Department of Neurology, Qingdao Municipal HospitalNanjing Medical UniversityNanjingChina

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