A Latent Gaussian Copula Model for Mixed Data Analysis in Brain Imaging Genetics.

ABSTRACT: Recent advances in imaging genetics make it possible to combine different types of data including medical images like functional magnetic resonance imaging (fMRI) and genetic data like single nucleotide polymorphisms (SNPs) for comprehensive diagnosis of mental disorders. Understanding complex interactions among these heterogeneous data may give rise to a new perspective, while at the same time demand statistical models for their integration. Various graphical models have been proposed for the study of interaction or association networks with continuous, binary, and count data as well as the mixture of them. However, limited efforts have been made for the multinomial case, for instance, SNP data. Our goal is therefore to fill the void by developing a graphical model for the integration of

SUBMITTER: Zhang A 

PROVIDER: S-EPMC7756188 | biostudies-literature | 2021 Jul-Aug

REPOSITORIES: biostudies-literature