A method for building a genome-connectome bipartite graph model

Qingbao Yu; Jiayu Chen; Yuhui Du; Jing Sui; Eswar Damaraju; Jessica A Turner; Theo G.M van Erp; Fabio Macciardi; Aysenil Belger; Judith M Ford; Sarah McEwen; Daniel H Mathalon; Bryon A Mueller; Adrian Preda; Jatin Vaidya; Godfrey D Pearlson; Vince D Calhoun

doi:10.1016/j.jneumeth.2019.03.011

Back

A method for building a genome-connectome bipartite graph model

Journal article

Peer reviewed

A method for building a genome-connectome bipartite graph model

Qingbao Yu, Jiayu Chen, Yuhui Du, Jing Sui, Eswar Damaraju, Jessica A Turner, Theo G.M van Erp, Fabio Macciardi, Aysenil Belger, Judith M Ford, …

Journal of neuroscience methods, Vol.320, pp.64-71

05/15/2019

DOI: 10.1016/j.jneumeth.2019.03.011

PMID: 30902651

Files and links (1)

url

https://www.ncbi.nlm.nih.gov/pmc/articles/6504548View

Open Access

Abstract

•A genome-connectome bipartite graph model for imaging genomic analysis.•We used this model to explore associations of schizophrenia-related SNPS with group-discriminative brain connectivity.•Genetic nodes with high degree were identified to indicate their role in modulating brain connectivity.•A bi-clustering analysis detected a cluster where 15 genetic variants interact with 38 functional connectivity features. It has been widely shown that genomic factors influence both risk for schizophrenia and variation in functional brain connectivity. Moreover, schizophrenia is characterized by disrupted brain connectivity. In this work, we proposed a genome-connectome bipartite graph model to perform imaging genomic analysis. Functional network connectivity (FNC) was estimated after decomposing resting state functional magnetic resonance imaging data from both healthy controls (HC) and patients with schizophrenia (SZ) into spatial brain components using group independent component analysis (G-ICA). Then 83 FNC connections showing a group difference (HC vs SZ) were selected as fMRI nodes, and eighty-one schizophrenia-related single nucleotide polymorphisms (SNPs) were selected as genetic nodes respectively in the bipartite graph. Edges connecting pairs of genetic and fMRI nodes were defined based on the SNP-FNC associations across subjects evaluated by a general linear model. Results show that some SNP nodes in the bipartite graph have a high degree implying they are influential in modulating brain connectivity and may be more strongly associated with the risk of schizophrenia than other SNPs. A bi-clustering analysis detected a cluster with 15 SNPs interacting with 38 FNC connections, most of which were within or between somato-motor and visual brain areas. This suggests that the activity of these brain regions may be related to common SNPs and provides insights into the pathology of schizophrenia. The findings suggest that the SNP-FNC bipartite graph approach is a novel model to investigate genetic influences on functional brain connectivity in mental illness.

SNPs

fMRI

FNC

Bipartite graph

Details

Title: Subtitle: A method for building a genome-connectome bipartite graph model
Creators: Qingbao Yu - The Mind Research Network, Albuquerque, NM, 87106, USA
Jiayu Chen - The Mind Research Network, Albuquerque, NM, 87106, USA
Yuhui Du - The Mind Research Network, Albuquerque, NM, 87106, USA
Jing Sui - The Mind Research Network, Albuquerque, NM, 87106, USA
Eswar Damaraju - The Mind Research Network, Albuquerque, NM, 87106, USA
Jessica A Turner - Department of Psychology, Georgia State University, GA, 30303, USA
Theo G.M van Erp - Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, 92697, USA
Fabio Macciardi - Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, 92697, USA
Aysenil Belger - Department of Psychiatry, University of North Carolina, Chapel Hill, NC, 27514, USA
Judith M Ford - Department of Psychiatry, University of California San Francisco, CA, 94143, USA
Sarah McEwen - Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, CA, 90095, USA
Daniel H Mathalon - Department of Psychiatry, University of California San Francisco, CA, 94143, USA
Bryon A Mueller - Department of Psychiatry, University of Minnesota, Minneapolis, MN, 55454, USA
Adrian Preda - Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, 92697, USA
Jatin Vaidya - Department of Psychiatry, University of Iowa, IA, 52242, USA
Godfrey D Pearlson - Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA
Vince D Calhoun - The Mind Research Network, Albuquerque, NM, 87106, USA
Resource Type: Journal article
Publication Details: Journal of neuroscience methods, Vol.320, pp.64-71
DOI: 10.1016/j.jneumeth.2019.03.011
PMID: 30902651
NLM abbreviation: J Neurosci Methods
ISSN: 0165-0270
eISSN: 1872-678X
Publisher: Elsevier B.V
Grant note: DOI: 10.13039/100000002, name: National Institutes of Health, award: P20GM103472/5P20RR021938; name: R01, award: R01EB005846, 1R01EB006841, 1R01DA040487, R01REB020407, R01EB000840, R37MH43775; DOI: 10.13039/100000001, name: National Science Foundation, award: #1539067, #1618551, #1631838; name: National Center for Research Resources at the National Institutes of Health, award: NIH 1 U24 RR021992, NIH 1 U24 RR025736-01
Language: English
Date published: 05/15/2019
Academic Unit: Psychiatry; Iowa Neuroscience Institute; University College Courses
Record Identifier: 9984003455902771

Metrics

25 Record Views

4 Times Cited - Web of Science

See more details