Schizophrenia is a highly heritable neuropsychiatric disorder with significant public health costs. Understanding the contributions of genetic variability in the disease will help identify better predictors of prognosis and treatment response Current studies are using genome wide scan (GWS) approaches to identify the numerous genes which might play a role in schizophrenia-either in increased risk for the disorder overall, or through modulating the various clinical symptoms. Structural neuroimaging measures implicate gray matter loss in schizophrenia;subjects with schizophrenia tend to have larger ventricles and smaller grey matter volumes than do their healthy counterparts, and regional loss in the medial frontal, temporal and insular gyri have been identified by us and others. Identifying the genetic influences underlying these patterns of gray matter loss is the goal of this project. We propose a multivariate method for analyzing already existing GWS data and voxelwise measures of gray matter density. We will apply parallel ICA, with reference;this technique identifies patterns of spatial variation in the brain structure and patterns of genotypes which are linked. We begin with 3200 structural imaging and GWS samples from healthy controls and schizophrenics, from aggregated legacy data. We constrain the imaging and genetic analyses with reference vectors to incorporate a priori information.
In Aim 1 we will develop initial a prioi spatial patterns, structural networks using source- based morphometry methods;
in Aim 2 we will determine the heritability and quantitative trait loci for these networks in independent famil samples;
in Aim 3 we plan to use the quantitative trait loci as a priori constraints on the genetic data, and the heritable structural networks as constraints on the imaging data on our parallel ICA analysis. Using these methods, we will determine the spatial patterns and genetic profiles that covary within our sample, and which show effects of diagnosis in schizophrenic and control data. We include a split-half analysis for replication and a follow-up high-density genotyping plan. This approach identifies structural networks within the brain allowing for variation in age, medication exposure, and other measures, and links them to genetic combinations. The final results will be the combinations of genotypic profiles which influence the patterns of structural brain loss in the disease. The methods developed will allow complex imaging and GWS data to be analyzed in combination, potentially applicable to many disorders.

Public Health Relevance

The development of both neuroimaging and genome-wide scan technologies has created a proliferation of data about neuropsychiatric disorders. It is possible to collect more information in a study about each subject than there are subjects available to study, creating a challenge for standard statistical techniques. We develop an approach already used separately in imaging and genetics, but apply it here to the combination of imaging genetic data on a massive dataset, to determine genetic effects on brain structure in psychiatric disorders.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH094524-03
Application #
8627647
Study Section
Neural Basis of Psychopathology, Addictions and Sleep Disorders Study Section (NPAS)
Program Officer
Senthil, Geetha
Project Start
2012-03-01
Project End
2017-02-28
Budget Start
2014-03-01
Budget End
2015-02-28
Support Year
3
Fiscal Year
2014
Total Cost
$614,700
Indirect Cost
$168,043
Name
The Mind Research Network
Department
Type
DUNS #
098640696
City
Albuquerque
State
NM
Country
United States
Zip Code
87106
Walton, E; Hibar, D P; van Erp, T G M et al. (2018) Prefrontal cortical thinning links to negative symptoms in schizophrenia via the ENIGMA consortium. Psychol Med 48:82-94
Thoma, Robert J; Haghani Tehrani, Poone; Turner, Jessica A et al. (2018) Neuropsychological analysis of auditory verbal hallucinations. Schizophr Res 192:459-460
Qi, Shile; Calhoun, Vince D; van Erp, Theo G M et al. (2018) Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia. IEEE Trans Med Imaging 37:93-105
Nakahara, Soichiro; Medland, Sarah; Turner, Jessica A et al. (2018) Polygenic risk score, genome-wide association, and gene set analyses of cognitive domain deficits in schizophrenia. Schizophr Res 201:393-399
Hare, Stephanie M; Law, Alicia S; Ford, Judith M et al. (2018) Disrupted network cross talk, hippocampal dysfunction and hallucinations in schizophrenia. Schizophr Res 199:226-234
Kong, Xiang-Zhen; Mathias, Samuel R; Guadalupe, Tulio et al. (2018) Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium. Proc Natl Acad Sci U S A 115:E5154-E5163
Jiang, Rongtao; Abbott, Christopher C; Jiang, Tianzi et al. (2018) SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets. Neuropsychopharmacology 43:1078-1087
van Erp, Theo G M; Walton, Esther; Hibar, Derrek P et al. (2018) Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium. Biol Psychiatry 84:644-654
Blokland, Gabriƫlla A M; Del Re, Elisabetta C; Mesholam-Gately, Raquelle I et al. (2018) The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) consortium: A collaborative cognitive and neuroimaging genetics project. Schizophr Res 195:306-317
Liu, Jingyu; Chen, Jiayu; Perrone-Bizzozero, Nora I et al. (2018) Regional enrichment analyses on genetic profiles for schizophrenia and bipolar disorder. Schizophr Res 192:240-246

Showing the most recent 10 out of 38 publications