Identifying Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease Status in Alzheimer’s Disease
Tóm tắt
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.
Tài liệu tham khảo
Ashburner, J., & Friston, K. (2007). Voxel-based morphometry. statistical parametric mapping: The analysis of functional brain images, 92–98.
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.
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.
Beck, A., & Teboulle, M. (2009). A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2, 183–202.
Belkin, M., & Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15, 1373–1396.
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.
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.
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.
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.
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.
Draper, N. R. (2002). Applied regression analysis. Bibliography update 2000–2001. Communications in Statistics Theory and Methods, 31, 2051–2075.
Dudbridge, F. (2013). Power and predictive accuracy of polygenic risk scores. PLoS Genetics, 9, e1003348.
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.
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.
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.
Gottesman, I. I., & Gould, T. D. (2003). The endophenotype concept in psychiatry: etymology and strategic intentions. American Journal of Psychiatry, 160, 636–645.
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.
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.
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.
Jie, B., Zhang, D., Cheng, B., & Shen, D. (2015). Manifold regularized multitask feature learning for multimodality disease classification. Human Brain Mapping, 36, 489–507.
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.
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.
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.
Liu, M., Zhang, D., & Shen, D. (2012). Ensemble sparse classification of Alzheimer’s disease. NeuroImage, 60, 1106–1116.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.