High dimensional latent Gaussian copula model for mixed data in imaging genetics

Aiying Zhang, Jian Fang, Vince D. Calhoun, Yu Ping Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent advances in imaging genetics combine different types of data including medical images like functional MRI images and genetic data like single nucleotide polymorphisms (SNPs). Many studies have proved that several mental diseases such as autism, ADHD, schizophrenia are affected by gene mutations. Understanding the complex interactions among these heterogeneous datasets may give rise to a new perspective for diseases diagnosis and prevention. In statistics, various graphical models have been proposed for the study of association networks with continuous data, binary data, and count data as well as the mixture of them. However, limited efforts have been made for the multinomial case, which has a direct application for SNP data. Therefore, in this paper, we propose a latent Gaussian copula model for mixed data containing multinomial components, which fills a vacancy of graphical models in imaging genetics. The performance of the proposed methods is first numerically assessed through simulation studies. Then it is validated with fMRI and SNP data collected by the Mind Clinical Imaging Consortium (MCIC) for a schizophrenia study.

Original languageEnglish (US)
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages105-109
Number of pages5
ISBN (Electronic)9781538636367
DOIs
StatePublished - May 23 2018
Externally publishedYes
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: Apr 4 2018Apr 7 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2018-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
CountryUnited States
CityWashington
Period4/4/184/7/18

Keywords

  • Gaussian copula model
  • Graphical model
  • Imaging genetics
  • Mixed data

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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