, Volume 14, Issue 4, pp 439–452 | Cite as

Identifying Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease Status in Alzheimer’s Disease

  • Xiaoke Hao
  • Xiaohui Yao
  • Jingwen Yan
  • Shannon L. Risacher
  • Andrew J. Saykin
  • Daoqiang ZhangEmail author
  • Li ShenEmail author
  • for the Alzheimer’s Disease Neuroimaging Initiative
Original Article


Neuroimaging genetics has attracted growing attention and interest, which is thought to be a powerful strategy to examine the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on structures or functions of human brain. In recent studies, univariate or multivariate regression analysis methods are typically used to capture the effective associations between genetic variants and quantitative traits (QTs) such as brain imaging phenotypes. The identified imaging QTs, although associated with certain genetic markers, may not be all disease specific. A useful, but underexplored, scenario could be to discover only those QTs associated with both genetic markers and disease status for revealing the chain from genotype to phenotype to symptom. In addition, multimodal brain imaging phenotypes are extracted from different perspectives and imaging markers consistently showing up in multimodalities may provide more insights for mechanistic understanding of diseases (i.e., Alzheimer’s disease (AD)). In this work, we propose a general framework to exploit multi-modal brain imaging phenotypes as intermediate traits that bridge genetic risk factors and multi-class disease status. We applied our proposed method to explore the relation between the well-known AD risk SNP APOE rs429358 and three baseline brain imaging modalities (i.e., structural magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET) and F-18 florbetapir PET scans amyloid imaging (AV45)) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The empirical results demonstrate that our proposed method not only helps improve the performances of imaging genetic associations, but also discovers robust and consistent regions of interests (ROIs) across multi-modalities to guide the disease-induced interpretation.


Multimodal intermediate phenotypes Diagnosis-guided Single nucleotide polymorphisms (SNPs) Alzheimer’s disease 



Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Bio-gen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

This research is supported by the National Natural Science Foundation of China (Nos. 61422204, 61473149), the Jiangsu Natural Science Foundation for Distinguished Young Scholar (No. BK20130034), the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20123218110009), the NUAA Fundamental Research Funds (No. NE2013105), the Jiangsu Qinglan Project of China and Nanjing University of Aeronautics and Astronautics Ph.D student short-term visiting scholar project.

At Indiana University, this work was supported by NIH R01 LM011360, U01 AG024904, RC2 AG036535, R01 AG19771, P30 AG10133, UL1 TR001108, R01 AG 042437, and R01 AG046171; NSF IIS-1117335; DOD W81XWH-14-2-0151, W81XWH-13-1-0259, and W81XWH-12-2-0012; NCAA 14132004; and CTSI SPARC Program.


  1. Ashburner, J., & Friston, K. (2007). Voxel-based morphometry. statistical parametric mapping: The analysis of functional brain images, 92–98.Google Scholar
  2. Baranzini, S. E., Wang, J., Gibson, R. A., Galwey, N., Naegelin, Y., Barkhof, F., Radue, E. W., Lindberg, R. L., Uitdehaag, B. M., Johnson, M. R., Angelakopoulou, A., Hall, L., Richardson, J. C., Prinjha, R. K., Gass, A., Geurts, J. J., Kragt, J., Sombekke, M., Vrenken, H., Qualley, P., Lincoln, R. R., Gomez, R., Caillier, S. J., George, M. F., Mousavi, H., Guerrero, R., Okuda, D. T., Cree, B. A., Green, A. J., Waubant, E., Goodin, D. S., Pelletier, D., Matthews, P. M., Hauser, S. L., Kappos, L., Polman, C. H., & Oksenberg, J. R. (2009). Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis. Human Molecular Genetics, 18, 767–778.CrossRefPubMedGoogle Scholar
  3. Batmanghelich, N. K., Dalca, A. V., Sabuncu, M. R., & Polina, G. (2013). Joint modeling of imaging and genetics. Information Processing Medical Imaging, 23, 766–777.CrossRefGoogle Scholar
  4. Beck, A., & Teboulle, M. (2009). A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2, 183–202.CrossRefGoogle Scholar
  5. Belkin, M., & Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15, 1373–1396.CrossRefGoogle Scholar
  6. Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434.Google Scholar
  7. Brookmeyer, R., Johnson, E., Ziegler-Graham, K., & Arrighi, H. M. (2007). Forecasting the global burden of Alzheimer’s disease. Alzheimer’s & Dementia, 3, 186–191.CrossRefGoogle Scholar
  8. Brun, C. C., Lepore, N., Pennec, X., Lee, A. D., Barysheva, M., Madsen, S. K., Avedissian, C., Chou, Y. Y., de Zubicaray, G. I., McMahon, K. L., Wright, M. J., Toga, A. W., & Thompson, P. M. (2009). Mapping the regional influence of genetics on brain structure variability—a tensor-based morphometry study. NeuroImage, 48, 37–49.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Camus, V., Payoux, P., Barre, L., Desgranges, B., Voisin, T., Tauber, C., La Joie, R., Tafani, M., Hommet, C., Chetelat, G., Mondon, K., de La Sayette, V., Cottier, J. P., Beaufils, E., Ribeiro, M. J., Gissot, V., Vierron, E., Vercouillie, J., Vellas, B., Eustache, F., & Guilloteau, D. (2012). Using PET with 18F-AV-45 (florbetapir) to quantify brain amyloid load in a clinical environment. European Journal of Nuclear Medicine and Molecular Imaging, 39, 621–631.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Chen, X., Pan, W. K., Kwok, J. T., Carbonell, J. G. (2009). Accelerated gradient method for multi-task sparse learning problem. 2009 9th Ieee International Conference on Data Mining, 746–751.Google Scholar
  11. Draper, N. R. (2002). Applied regression analysis. Bibliography update 2000–2001. Communications in Statistics Theory and Methods, 31, 2051–2075.CrossRefGoogle Scholar
  12. Dudbridge, F. (2013). Power and predictive accuracy of polygenic risk scores. PLoS Genetics, 9, e1003348.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Filippini, N., Rao, A., Wetten, S., Gibson, R. A., Borrie, M., Guzman, D., Kertesz, A., Loy-English, I., Williams, J., Nichols, T., Whitcher, B., & Matthews, P. M. (2009). Anatomically-distinct genetic associations of APOE epsilon4 allele load with regional cortical atrophy in Alzheimer’s disease. NeuroImage, 44, 724–728.CrossRefPubMedGoogle Scholar
  14. Ge, T., Feng, J., Hibar, D. P., Thompson, P. M., & Nichols, T. E. (2012). Increasing power for voxel-wise genome-wide association studies: the random field theory, least square kernel machines and fast permutation procedures. NeuroImage, 63, 858–873.CrossRefPubMedPubMedCentralGoogle Scholar
  15. Glahn, D. C., Thompson, P. M., & Blangero, J. (2007). Neuroimaging endophenotypes: strategies for finding genes influencing brain structure and function. Human Brain Mapping, 28, 488–501.CrossRefGoogle Scholar
  16. Gottesman, I. I., & Gould, T. D. (2003). The endophenotype concept in psychiatry: etymology and strategic intentions. American Journal of Psychiatry, 160, 636–645.CrossRefPubMedGoogle Scholar
  17. Gray, K. R., Aljabar, P., Heckemann, R. A., Hammers, A., & Rueckert, D. (2013). Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease. NeuroImage, 65, 167–175.CrossRefPubMedGoogle Scholar
  18. Hao, X., Yan, J., Yao, X., Risacher, S. L., Saykin, A. J., Zhang, D., & Shen, L. I. (2016). Diagnosis-guided method for identifying multi-modality neuroimaging biomarkers associated with genetic risk factors in alzheimer’s disease. Pacific Symposium on Biocomputing, 21, 108–119.PubMedPubMedCentralGoogle Scholar
  19. Hibar, D. P., Kohannim, O., Stein, J. L., Chiang, M. C., & Thompson, P. M. (2011). Multilocus genetic analysis of brain images. Frontiers in Genetics, 2, 73.CrossRefPubMedPubMedCentralGoogle Scholar
  20. Jie, B., Zhang, D., Cheng, B., & Shen, D. (2015). Manifold regularized multitask feature learning for multimodality disease classification. Human Brain Mapping, 36, 489–507.CrossRefPubMedGoogle Scholar
  21. Kohannim, O., Hibar, D. P., Stein, J. L., Jahanshad, Hua, N., Rajagopalan, X., Toga, P., Jack, A. W., Weiner, C. R., de Zubicaray, M. W., McMahon, G. I., Hansell, K. L., Martin, N. K., Wright, N. G., Thompson, M. J., Initia, P. M., A.D.N. (2012). Discovery and replication of gene influences on brain structure using LASSO regression. Frontiers in Neuroscience 6.Google Scholar
  22. Kohannim, O., Hibar, D. P., Stein, J. L., Jahanshad, N., Jack, C. R., Weiner, M. W., Toga, A. W., Thompson, P. M., Initi, A.S.D.N. (2011). boosting power to detect genetic associations in imaging using multi-locus, genome-wide scans and ridge regression. 2011 8th Ieee International Symposium on Biomedical Imaging: From Nano to Macro, 1855–1859.Google Scholar
  23. Lambert, J. C., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., DeStafano, A. L., Bis, J. C., Beecham, G. W., Grenier-Boley, B., Russo, G., Thorton-Wells, T. A., Jones, N., Smith, A. V., Chouraki, V., Thomas, C., Ikram, M. A., Zelenika, D., Vardarajan, B. N., Kamatani, Y., Lin, C. F., Gerrish, A., Schmidt, H., Kunkle, B., Dunstan, M. L., Ruiz, A., Bihoreau, M. T., Choi, S. H., Reitz, C., Pasquier, F., Cruchaga, C., Craig, D., Amin, N., Berr, C., Lopez, O. L., De Jager, P. L., Deramecourt, V., Johnston, J. A., Evans, D., Lovestone, S., Letenneur, L., Moron, F. J., Rubinsztein, D. C., Eiriksdottir, G., Sleegers, K., Goate, A. M., Fievet, N., Huentelman, M. W., Gill, M., Brown, K., Kamboh, M. I., Keller, L., Barberger-Gateau, P., McGuiness, B., Larson, E. B., Green, R., Myers, A. J., Dufouil, C., Todd, S., Wallon, D., Love, S., Rogaeva, E., Gallacher, J., St George-Hyslop, P., Clarimon, J., Lleo, A., Bayer, A., Tsuang, D. W., Yu, L., Tsolaki, M., Bossu, P., Spalletta, G., Proitsi, P., Collinge, J., Sorbi, S., Sanchez-Garcia, F., Fox, N. C., Hardy, J., Deniz Naranjo, M. C., Bosco, P., Clarke, R., Brayne, C., Galimberti, D., Mancuso, M., Matthews, F., European Alzheimer’s Disease, I., Genetic, Environmental Risk in Alzheimer’s, D., Alzheimer’s Disease Genetic, C., Cohorts for, H., Aging Research in Genomic, E., Moebus, S., Mecocci, P., Del Zompo, M., Maier, W., Hampel, H., Pilotto, A., Bullido, M., Panza, F., Caffarra, P., Nacmias, B., Gilbert, J. R., Mayhaus, M., Lannefelt, L., Hakonarson, H., Pichler, S., Carrasquillo, M. M., Ingelsson, M., Beekly, D., Alvarez, V., Zou, F., Valladares, O., Younkin, S. G., Coto, E., Hamilton-Nelson, K. L., Gu, W., Razquin, C., Pastor, P., Mateo, I., Owen, M. J., Faber, K. M., Jonsson, P. V., Combarros, O., O’Donovan, M. C., Cantwell, L. B., Soininen, H., Blacker, D., Mead, S., Mosley, T. H., Jr., Bennett, D. A., Harris, T. B., Fratiglioni, L., Holmes, C., de Bruijn, R. F., Passmore, P., Montine, T. J., Bettens, K., Rotter, J. I., Brice, A., Morgan, K., Foroud, T. M., Kukull, W. A., Hannequin, D., Powell, J. F., Nalls, M. A., Ritchie, K., Lunetta, K. L., Kauwe, J. S., Boerwinkle, E., Riemenschneider, M., Boada, M., Hiltuenen, M., Martin, E. R., Schmidt, R., Rujescu, D., Wang, L. S., Dartigues, J. F., Mayeux, R., Tzourio, C., Hofman, A., Nothen, M. M., Graff, C., Psaty, B. M., Jones, L., Haines, J. L., Holmans, P. A., Lathrop, M., Pericak-Vance, M. A., Launer, L. J., Farrer, L. A., van Duijn, C. M., Van Broeckhoven, C., Moskvina, V., Seshadri, S., Williams, J., Schellenberg, G. D., & Amouyel, P. (2013). Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nature Genetics, 45, 1452–1458.CrossRefPubMedPubMedCentralGoogle Scholar
  24. Liu, M., Zhang, D., & Shen, D. (2012). Ensemble sparse classification of Alzheimer’s disease. NeuroImage, 60, 1106–1116.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Liu, Y., Yu, J. T., Wang, H. F., Han, P. R., Tan, C. C., Wang, C., Meng, X. F., Risacher, S. L., Saykin, A. J., & Tan, L. (2015). APOE genotype and neuroimaging markers of Alzheimer’s disease: systematic review and meta-analysis. Journal of Neurology, Neurosurgery, and Psychiatry, 86, 127–134.CrossRefPubMedGoogle Scholar
  26. Mahley, R. W., & Rall, S. C., Jr. (2000). Apolipoprotein E: far more than a lipid transport protein. Annual Review of Genomics and Human Genetics, 1, 507–537.CrossRefGoogle Scholar
  27. Pasinetti, G. M., & Hiller-Sturmhofel, S. (2008). Systems biology in the study of neurological disorders: focus on Alzheimer’s disease. Alcohol Research and Health, 31, 60–65.PubMedPubMedCentralGoogle Scholar
  28. Potkin, S. G., Turner, J. A., Guffanti, G., Lakatos, A., Torri, F., Keator, D. B., & Macciardi, F. (2009). Genome-wide strategies for discovering genetic influences on cognition and cognitive disorders: methodological considerations. Cognitive Neuropsychiatry, 14, 391–418.CrossRefPubMedCentralGoogle Scholar
  29. Putcha, V., & Raton, B. (2008). Handbook of univariate and multivariate data analysis and interpretation with SPSS. Journal of the Royal Statistical Society Series a-Statistics in Society, 171, 317–317.CrossRefGoogle Scholar
  30. Reiman, E. M., Caselli, R. J., Yun, L. S., Chen, K., Bandy, D., Minoshima, S., Thibodeau, S. N., & Osborne, D. (1996). Preclinical evidence of Alzheimer’s disease in persons homozygous for the epsilon 4 allele for apolipoprotein E. The New England Journal of Medicine, 334, 752–758.CrossRefPubMedGoogle Scholar
  31. Risacher, S. L., Kim, S., Nho, K., Foroud, T., Shen, L., Petersen, R. C., Jack, C. R., Jr., Beckett, L. A., Aisen, P. S., Koeppe, R. A., Jagust, W. J., Shaw, L. M., Trojanowski, J. Q., Weiner, M. W., & Saykin, A. J. (2015). APOE effect on Alzheimer’s disease biomarkers in older adults with significant memory concern. Alzheimers Dement, 11, 1417–1429.CrossRefPubMedGoogle Scholar
  32. Sabuncu, M. R., Buckner, R. L., Smoller, J. W., Lee, P. H., Fischl, B., Sperling, R. A., & Neuroimaging, A.s.D. (2012). The association between a polygenic alzheimer score and cortical thickness in clinically normal subjects. Cerebral Cortex, 22, 2653–2661.CrossRefPubMedGoogle Scholar
  33. Shen, L., Thompson, P. M., Potkin, S. G., Bertram, L., Farrer, L. A., Foroud, T. M., Green, R. C., Hu, X., Huentelman, M. J., Kim, S., Kauwe, J. S., Li, Q., Liu, E., Macciardi, F., Moore, J. H., Munsie, L., Nho, K., Ramanan, V. K., Risacher, S. L., Stone, D. J., Swaminathan, S., Toga, A. W., Weiner, M. W., & Saykin, A. J. (2014). Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers. Brain Imaging and Behavior, 8, 183–207.CrossRefPubMedGoogle Scholar
  34. Tibshirani, R. (2011). Regression shrinkage and selection via the lasso: a retrospective. Journal of the Royal Statistical Society Series B-Statistical Methodology, 73, 273–282.CrossRefGoogle Scholar
  35. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15, 273–289.CrossRefPubMedGoogle Scholar
  36. Vounou, M., Janousova, E., Wolz, R., Stein, J. L., Thompson, P. M., Rueckert, D., Montana, G., & Initia, A. D. N. (2012). Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer’s disease. NeuroImage, 60, 700–716.CrossRefPubMedGoogle Scholar
  37. Vounou, M., Nichols, T. E., & Montana, G. (2010). Discovering genetic associations with high-dimensional neuroimaging phenotypes: a sparse reduced-rank regression approach. NeuroImage, 53, 1147–1159.CrossRefGoogle Scholar
  38. Wang, H., Nie, F., Huang, H., Yan, J., Kim, S., Nho, K., Risacher, S. L., Saykin, A. J., & Shen, L. (2012a). From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer’s disease relevant SNPs. Bioinformatics, 28, i619–i625.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Wang, H., Nie, F. P., Huang, H., Kim, S., Nho, K., Risacher, S. L., Saykin, A. J., Shen, L., & Initi, A.s.D.N. (2012b). Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort. Bioinformatics, 28, 229–237.CrossRefPubMedGoogle Scholar
  40. Wishart, H. A., Saykin, A. J., McAllister, T. W., Rabin, L. A., McDonald, B. C., Flashman, L. A., Roth, R. M., Mamourian, A. C., Tsongalis, G. J., & Rhodes, C. H. (2006). Regional brain atrophy in cognitively intact adults with a single APOE epsilon4 allele. Neurology, 67, 1221–1224.CrossRefPubMedGoogle Scholar
  41. Yu, G., Liu, Y., Thung, K. H., Shen, D. (2014). Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals. PLoS One 9.Google Scholar
  42. Yuan, M., & Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society Series B-Statistical Methodology, 68, 49–67.CrossRefGoogle Scholar
  43. Zhang, D., & Shen, D. (2012). Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. NeuroImage, 59, 895–907.CrossRefPubMedGoogle Scholar
  44. Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. (2011). Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage, 55, 856–867.CrossRefPubMedPubMedCentralGoogle Scholar
  45. Zhu, X., Suk, H. I., & Shen, D. (2014a). A novel multi-relation regularization method for regression and classification in AD diagnosis. Medical Image Computing and Comput-Assisted Intervention, 17, 401–408.Google Scholar
  46. Zhu, X., Suk, H. I., Wang, L., Lee, S. W., Shen, D. (2015). A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Medical Image Analysis.Google Scholar
  47. Zhu, X. F., Huang, Z., Yang, Y., Shen, H. T., Xu, C. S., & Luo, J. B. (2013). Self-taught dimensionality reduction on the high-dimensional small-sized data. Pattern Recognition, 46, 215–229.CrossRefGoogle Scholar
  48. Zhu, X. F., Suk, H. I., & Shen, D. (2014b). A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. NeuroImage, 100, 91–105.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Xiaoke Hao
    • 1
    • 2
  • Xiaohui Yao
    • 2
  • Jingwen Yan
    • 2
  • Shannon L. Risacher
    • 2
  • Andrew J. Saykin
    • 2
  • Daoqiang Zhang
    • 1
    Email author
  • Li Shen
    • 2
    Email author
  • for the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Department of Radiology and Imaging Sciences, School of MedicineIndiana UniversityIndianapolisUSA

Personalised recommendations