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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)


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.


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.


  1. 1.
    Shen, L., et al.: Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. Neuroimage 53, 1051–1063 (2010)CrossRefGoogle Scholar
  2. 2.
    Tibshirani, R.: Regression shrinkage and selection via the lasso: a retrospective. Journal of the Royal Statistical Society Series B-Statistical Methodology 73, 273–282 (2011)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Kohannim, O., et al.: Discovery and Replication of Gene Influences on Brain Structure Using LASSO Regression. Front. Neurosci. 6, 115 (2012)CrossRefGoogle Scholar
  4. 4.
    Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society Series B-Statistical Methodology 68, 49–67 (2006)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Wang, H., et al.: Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort. Bioinformatics 28, 229–237 (2012)CrossRefGoogle Scholar
  6. 6.
    Liu, J., Ye, J.: Moreau-Yosida regularization for grouped tree structure learning. In: Lafferty, J., Williams, C., Shawe-Taylorand, J., Zemel, R., Culotta, A. (eds.) NIPS 2010, vol. 23, pp. 1459–1467. Curran Associates, Inc. (2010)Google Scholar
  7. 7.
    Kim, S., Xing, E.P.: Tree-Guided Group Lasso for Multi-Response Regression with Structured Sparsity, with an Application to Eqtl Mapping. Annals of Applied Statistics 6, 1095–1117 (2012)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Jenatton, R., et al.: Multiscale mining of fMRI data with hierarchical structured sparsity. Siam J. Imaging Sci. 5, 835–856 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Liu, M., Zhang, D., Yap, P.-T., Shen, D.: Tree-guided sparse coding for brain disease classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 239–247. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Barrett, J., et al.: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265 (2005)CrossRefGoogle Scholar
  11. 11.
    Zhang, D., et al.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55, 856–867 (2011)CrossRefzbMATHGoogle Scholar
  12. 12.
    Saykin, A., et al.: Alzheimer’s Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: Genetics core aims, progress, and plans. Alzheimers & Dementia 6, 265–273 (2010)CrossRefGoogle Scholar
  13. 13.
    Shi, H., et al.: Genetic variants influencing human aging from late-onset Alzheimer’s disease (LOAD) genome-wide association studies (GWAS). Neurobiology of Aging 33(8), 1849.e5–1849.e18 (2012)Google Scholar
  14. 14.
    Bertram, L., et al.: Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database. Nature Genetics 39, 17–23 (2007)CrossRefzbMATHGoogle Scholar
  15. 15.
    Li, Y., et al.: MaCH: Using Sequence and Genotype Data to Estimate Haplotypes and Unobserved Genotypes. Genetic Epidemiology 34, 816–834 (2010)CrossRefGoogle Scholar
  16. 16.
    Rosenthal, S., et al.: Beta-amyloid toxicity modifier genes and the risk of Alzheimer’s disease. Am J. Neurodegener. Dis. 1, 191–198 (2012)Google Scholar
  17. 17.
    Cummings, A., et al.: Genome-wide association and linkage study in the Amish detects a novel candidate late-onset Alzheimer disease gene. Ann. Hum. Genet. 76, 342–351 (2012)CrossRefGoogle Scholar
  18. 18.
    Xia, K., et al.: Common genetic variants on 1p13.2 associate with risk of autism. Mol. Psychiatry (November 5, 2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Department of Neurology, Qingdao Municipal HospitalNanjing Medical UniversityNanjingChina

Personalised recommendations