Biological Knowledge Guided Deep Neural Network for Brain Genotype-Phenotype Association Study

  • Yanfu Zhang
  • Liang Zhan
  • Paul M. Thompson
  • Heng HuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11846)


Alzheimer’s Disease (AD) is the main cause for age-related dementia. Many machine learning methods have been proposed to identify important genetic bases which are associated to phenotypes indicating the progress of AD. However, the biological knowledge is seldom considered in spite of the success of previous research. Built upon neuroimaging high-throughput phenotyping techniques, a biological knowledge guided deep network is proposed in this paper, to study the genotype-phenotype associations. We organized the Single Nucleotide Polymorphisms (SNPs) according to linkage disequilibrium (LD) blocks, and designed a group 1-D convolutional layer assembling both local and global convolution operations, to process the structural features. The entire neural network is a cascade of group 1-D convolutional layer, 2-D sliding convolutional layer and a multi-layer perceptron. The experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data show that the proposed method outperforms related methods. A set of biologically meaningful LD groups is also identified for phenotype discovery, which is potentially helpful for disease diagnosis and drug design.


  1. 1.
    Ashford, J.W., Schmitt, F.A.: Modeling the time-course of alzheimer dementia. Curr. Psychiatry Rep. 3(1), 20–28 (2001)CrossRefGoogle Scholar
  2. 2.
    Association, A., et al.: 2017 alzheimer’s disease facts and figures. Alzheimer’s Dement. 13(4), 325–373 (2017)CrossRefGoogle Scholar
  3. 3.
    Bertram, L., McQueen, M.B., Mullin, K., Blacker, D., Tanzi, R.E.: Systematic meta-analyses of alzheimer disease genetic association studies: the alzgene database. Nat. Genet. 39(1), 17 (2007)CrossRefGoogle Scholar
  4. 4.
    Brun, C.C., et al.: Mapping the regional influence of genetics on brain structure variability–a tensor-based morphometry study. Neuroimage 48(1), 37–49 (2009)CrossRefGoogle Scholar
  5. 5.
    Huo, Z., Shen, D., Huang, H.: Genotype-phenotype association study via new multi-task learning model. Pac. Symp. Biocomput. World Sci. 23, 353–364 (2017)Google Scholar
  6. 6.
    Jin, Y., et al.: Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics. Neuroimage 100, 75–90 (2014)CrossRefGoogle Scholar
  7. 7.
    Kabani, N.J., MacDonald, D.J., Holmes, C.J., Evans, A.C.: 3D anatomical atlas of the human brain. Neuroimage 7(4), S717 (1998)CrossRefGoogle Scholar
  8. 8.
    Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint (2014). arXiv:1408.5882
  9. 9.
    Li, Y., Willer, C.J., Ding, J., Scheet, P., Abecasis, G.R.: Mach: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34(8), 816–834 (2010)CrossRefGoogle Scholar
  10. 10.
    Sabatti, C., et al.: Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat. Genet. 41(1), 35 (2009)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Saykin, A.J., et al.: Alzheimer’s disease neuroimaging initiative biomarkers as quantitative phenotypes: genetics core aims, progress, and plans. Alzheimer’s Dement. 6(3), 265–273 (2010)CrossRefGoogle Scholar
  12. 12.
    Shen, D., Davatzikos, C.: Hammer: hierarchical attribute matching mechanism for elastic registration. In: Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001), pp. 29–36. IEEE (2001)Google Scholar
  13. 13.
    Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. imag. 17(1), 87–97 (1998)CrossRefGoogle Scholar
  14. 14.
    Wang, H., et al.: From phenotype to genotype: an association study of longitudinal phenotypic markers to alzheimer’s disease relevant snps. Bioinformatics 28(18), i619–i625 (2012)CrossRefGoogle Scholar
  15. 15.
    Wang, X., Chen, H., Cai, W., Shen, D., Huang, H.: Regularized modal regression with applications in cognitive impairment prediction. In: Advances in Neural Information Processing Systems, pp. 1448–1458 (2017)Google Scholar
  16. 16.
    Wang, X., Shen, D., Huang, H.: Prediction of memory impairment with MRI data: a longitudinal study of alzheimer’s disease. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 273–281. Springer, Cham (2016). Scholar
  17. 17.
    Wang, X., et al.: Longitudinal genotype-phenotype association study via temporal structure auto-learning predictive model. In: Sahinalp, S.C. (ed.) RECOMB 2017. LNCS, vol. 10229, pp. 287–302. Springer, Cham (2017). Scholar
  18. 18.
    Wang, Y., et al.: Knowledge-guided robust mri brain extraction for diverse large-scale neuroimaging studies on humans and non-human primates. PloS One 9(1), e77810 (2014)CrossRefGoogle Scholar
  19. 19.
    Wang, Y., Nie, J., Yap, P.-T., Shi, F., Guo, L., Shen, D.: Robust deformable-surface-based skull-stripping for large-scale studies. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 635–642. Springer, Heidelberg (2011). Scholar
  20. 20.
    Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)Google Scholar
  21. 21.
    Yang, T., et al.: Detecting genetic risk factors for alzheimer’s disease in whole genome sequence data via lasso screening. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 985–989. IEEE (2015)Google Scholar
  22. 22.
    Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)Google Scholar
  23. 23.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain mr images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imag. 20(1), 45–57 (2001)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yanfu Zhang
    • 1
  • Liang Zhan
    • 1
  • Paul M. Thompson
    • 2
  • Heng Huang
    • 1
    Email author
  1. 1.Department of Electrical and Computer EngineeringUniversity of PittsburghPittsburghUSA
  2. 2.Imaging Genetics Center, Institute for Neuroimaging and InformaticsUniversity of Southern CaliforniaLos AngelesUSA

Personalised recommendations