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An Integrative Bayesian Modeling Approach to Imaging Genetics

Author

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  • Francesco C. Stingo
  • Michele Guindani
  • Marina Vannucci
  • Vince D. Calhoun

Abstract

In this article we present a Bayesian hierarchical modeling approach for imaging genetics, where the interest lies in linking brain connectivity across multiple individuals to their genetic information. We have available data from a functional magnetic resonance imaging (fMRI) study on schizophrenia. Our goals are to identify brain regions of interest (ROIs) with discriminating activation patterns between schizophrenic patients and healthy controls, and to relate the ROIs' activations with available genetic information from single nucleotide polymorphisms (SNPs) on the subjects. For this task, we develop a hierarchical mixture model that includes several innovative characteristics: it incorporates the selection of ROIs that discriminate the subjects into separate groups; it allows the mixture components to depend on selected covariates; it includes prior models that capture structural dependencies among the ROIs. Applied to the schizophrenia dataset, the model leads to the simultaneous selection of a set of discriminatory ROIs and the relevant SNPs, together with the reconstruction of the correlation structure of the selected regions. To the best of our knowledge, our work represents the first attempt at a rigorous modeling strategy for imaging genetics data that incorporates all such features.

Suggested Citation

  • Francesco C. Stingo & Michele Guindani & Marina Vannucci & Vince D. Calhoun, 2013. "An Integrative Bayesian Modeling Approach to Imaging Genetics," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 876-891, September.
  • Handle: RePEc:taf:jnlasa:v:108:y:2013:i:503:p:876-891
    DOI: 10.1080/01621459.2013.804409
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    Cited by:

    1. Priyam Das & Christine B. Peterson & Yang Ni & Alexandre Reuben & Jiexin Zhang & Jianjun Zhang & Kim‐Anh Do & Veerabhadran Baladandayuthapani, 2023. "Bayesian hierarchical quantile regression with application to characterizing the immune architecture of lung cancer," Biometrics, The International Biometric Society, vol. 79(3), pages 2474-2488, September.
    2. Nie Yunlong & Opoku Eugene & Yasmin Laila & Song Yin & Wang Jie & Wu Sidi & Scarapicchia Vanessa & Gawryluk Jodie & Wang Liangliang & Cao Jiguo & Nathoo Farouk S., 2020. "Spectral dynamic causal modelling of resting-state fMRI: an exploratory study relating effective brain connectivity in the default mode network to genetics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(3), pages 1-18, June.
    3. Yin Song & Shufei Ge & Jiguo Cao & Liangliang Wang & Farouk S. Nathoo, 2022. "A Bayesian spatial model for imaging genetics," Biometrics, The International Biometric Society, vol. 78(2), pages 742-753, June.

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