Advertisement

A Novel Structure-Aware Sparse Learning Algorithm for Brain Imaging Genetics

  • Lei Du
  • Jingwen Yan
  • Sungeun Kim
  • Shannon L. Risacher
  • Heng Huang
  • Mark Inlow
  • Jason H. Moore
  • Andrew J. Saykin
  • Li Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.

Keywords

Mild Cognitive Impairment Independence Assumption Sparse Canonical Correlation Analysis Improve Prediction Performance Canonical Loading 
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.

References

  1. 1.
    Ashburner, J., Friston, K.J.: Voxel-based morphometry–the methods. Neuroimage 11(6 Pt. 1), 805–821 (2000)CrossRefGoogle Scholar
  2. 2.
    Chen, J., Bushman, F.D., et al.: Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis. Biostatistics 14(2), 244–258 (2013)CrossRefGoogle Scholar
  3. 3.
    Chen, X., Liu, H., Carbonell, J.G.: Structured sparse canonical correlation analysis. In: International Conference on Artificial Intelligence and Statistics (2012)Google Scholar
  4. 4.
    Chi, E., Allen, G., et al.: Imaging genetics via sparse canonical correlation analysis. In: 2013 IEEE 10th Int. Sym. on Biomedical Imaging (ISBI), pp. 740–743 (2013)Google Scholar
  5. 5.
    Hibar, D.P., Kohannim, O., et al.: Multilocus genetic analysis of brain images. Front. Genet. 2, 73 (2011)CrossRefGoogle Scholar
  6. 6.
    Lin, D., Calhoun, V.D., Wang, Y.P.: Correspondence between fMRI and SNP data by group sparse canonical correlation analysis. Med. Image Anal. (2013)Google Scholar
  7. 7.
    Parkhomenko, E., Tritchler, D., Beyene, J.: Sparse canonical correlation analysis with application to genomic data integration. Statistical Applications in Genetics and Molecular Biology 8, 1–34 (2009)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Risacher, S.L., Saykin, A.J., et al.: Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Curr. Alzheimer Res. 6(4), 347–361 (2009)CrossRefGoogle Scholar
  9. 9.
    Shen, L., Kim, S., 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(3), 1051–1063 (2010)CrossRefGoogle Scholar
  10. 10.
    Sheng, J., Kim, S., et al.: Data synthesis and method evaluation for brain imaging genetics. In: IEEE Int. Sym. on Biomedical Imaging (ISBI), pp. 1202–1205 (2014)Google Scholar
  11. 11.
    Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58(1), 267–288 (1996)zbMATHMathSciNetGoogle Scholar
  12. 12.
    Vounou, M., Nichols, T.E., Montana, G.: Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach. NeuroImage 53(3), 1147–1159 (2010)CrossRefGoogle Scholar
  13. 13.
    Wang, H., Nie, F., et al.: Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort. Bioinformatics 28(2), 229–237 (2012)CrossRefGoogle Scholar
  14. 14.
    Witten, D.M., Tibshirani, R., Hastie, T.: A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10(3), 515–534 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lei Du
    • 1
  • Jingwen Yan
    • 1
    • 2
  • Sungeun Kim
    • 1
  • Shannon L. Risacher
    • 1
  • Heng Huang
    • 3
  • Mark Inlow
    • 4
  • Jason H. Moore
    • 5
  • Andrew J. Saykin
    • 1
  • Li Shen
    • 1
    • 2
  1. 1.Radiology and Imaging SciencesIndiana University School of MedicineUSA
  2. 2.School of Informatics and ComputingIndiana University IndianapolisUSA
  3. 3.Computer Science and EngineeringUniversity of Texas at ArlingtonUSA
  4. 4.MathematicsRose-Hulman Institute of TechnologyUSA
  5. 5.Genetics, Geisel School of MedicineDartmouth CollegeUSA

Personalised recommendations