Abstract
We propose a unified Bayesian framework for detecting genetic variants associated with a disease while exploiting image-based features as an intermediate phenotype. Traditionally, imaging genetics methods comprise two separate steps. First, image features are selected based on their relevance to the disease phenotype. Second, a set of genetic variants are identified to explain the selected features. In contrast, our method performs these tasks simultaneously to ultimately assign probabilistic measures of relevance to both genetic and imaging markers. We derive an efficient approximate inference algorithm that handles high dimensionality of imaging genetic data. We evaluate the algorithm on synthetic data and show that it outperforms traditional models. We also illustrate the application of the method on ADNI data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Batmanghelich, N.K., Taskar, B., Davatzikos, C.: Generative-discriminative basis learning for medical imaging. IEEE Trans. Med. Imaging 31(1), 51–69 (2012)
Bishop, C.M.: Pattern recognition and machine learning. Springer, New York (2006)
Carbonetto, P., Stephens, M.: Scalable Variational Inference for Bayesian Variable Selection in Regression, and its Accuracy in Genetic Association Studies. Bayesian Analysis 7, 73–108 (2012)
Fan, Y., Batmanghelich, N., Clark, C.M., Davatzikos, C., ADNI: Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 39(4), 1731–1743 (2008)
Filippini, N., Rao, A., Wetten, S., Gibson, R.A., et al.: Anatomically-distinct genetic associations of APOE epsilon4 allele load with regional cortical atrophy in Alzheimer’s disease. Neuroimage 44(3), 724–728 (2009)
Harold, D., Abraham, R., Hollingworth, P., Sims, R., et al.: Genome-wide association study identifies variants at clu and picalm associated with Alzheimer’s disease. Nat. Genet. 41(10), 1088–1093 (2009)
Hernandez-Laborto, J.M., Hernandezi-Lobato, D.: Convergent Expectation Propagation in Linear Models with Spike-and-Slab Priors (December 2011)
Jaakkola, T.S., Jordan, M.I.: Bayesian Paramater Estimation via Variational Methods. Statistics and Computing (10), 25–37 (2000)
Le Floch, E., Guillemot, V., Frouin, V., Pinel, P., et al.: Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares. Neuroimage 63(1), 11–24 (2012)
Lee, J.H., Cheng, R., Graff-Radford, N., Foroud, T., et al.: Analyses of the national institute on aging late-onset Alzheimer’s disease family study: implication of additional loci. Archives of Neurology 65(11), 1518 (2008)
Lucchi, A., Smith, K., Achanta, R., Knott, G., Fua, P.: Supervoxel-based segmentation of mitochondria in em image stacks with learned shape features. IEEE Trans. Med. Imaging 31(2), 474–486 (2012)
Lvovs, D., Favorova, O.O., Favorov, A.V.: A polygenic approach to the study of polygenic diseases. Acta Naturae 4(3), 59 (2012)
Mueller, S.G., Weiner, M.W., Thal, L.J., Petersen, R.C., et al.: The Alzheimer’s disease neuroimaging initiative. Neuroimaging Clinics of North America 15(4), 869 (2005)
O’Hara, R.B., Sillanpää, M.J.: A Review of Bayesian Variable Selection Methods: What, How and Which. Bayesian Analisis 4(1), 85–118 (2009)
Potkin, S.G., Turner, J.A., Guffanti, G., Lakatos, A., et al.: A genome-wide association study of schizophrenia using brain activation as a quantitative phenotype. Schizophr. Bull. 35(1), 96–108 (2009)
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., et al.: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81(3), 559–575 (2007)
Sabuncu, M.R., Van Leemput, K.: The Relevance Voxel Machine (RVoxM): A Bayesian Method for Image-Based Prediction. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 99–106. Springer, Heidelberg (2011)
Stein, J.L., Hua, X., Lee, S., Ho, A.J., et al.: Voxelwise genome-wide association study (vGWAS). Neuroimage 53(3), 1160–1174 (2010)
Vounou, M., Janousova, E., Wolz, R., Stein, J.L., et al.: Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer’s disease. Neuroimage 60(1), 700–716 (2012)
Vounou, M., Nichols, T.E., Montana, G., ADNI: Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach. Neuroimage 53(3), 1147–1159 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Batmanghelich, N.K., Dalca, A.V., Sabuncu, M.R., Golland, P. (2013). Joint Modeling of Imaging and Genetics. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds) Information Processing in Medical Imaging. IPMI 2013. Lecture Notes in Computer Science, vol 7917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38868-2_64
Download citation
DOI: https://doi.org/10.1007/978-3-642-38868-2_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38867-5
Online ISBN: 978-3-642-38868-2
eBook Packages: Computer ScienceComputer Science (R0)