Associated Genetics and Connectomic Circuitry in Schizophrenia and Bipolar Disorder

Genetic risks for schizophrenia are theoretically mediated by genetic effects on brain structure but it has been unclear which genes are associated with both schizophrenia and cortical phenotypes. We accessed genome-wide association studies (GWAS) of schizophrenia (N = 69,369 cases; 236,642 controls), and of three magnetic resonance imaging (MRI) metrics (surface area, cortical thickness, neurite density index) measured at 180 cortical areas (N = 36,843, UK Biobank). Using Hi-C-coupled MAGMA, 61 genes were significantly associated with both schizophrenia and one or more MRI metrics. Whole genome analysis with partial least squares demonstrated significant genetic covariation between schizophrenia and area or thickness of most cortical regions. Genetic similarity between cortical areas was strongly coupled to their phenotypic covariance, and genetic covariation between schizophrenia and brain phenotypes was strongest in the hubs of structural covariance networks. Pleiotropically associated genes were enriched for neurodevelopmental processes and positionally concentrated in chromosomes 3p21, 17q21 and 11p11. Mendelian randomization analysis indicated that genetically determined variation in a posterior cingulate cortical area could be causal for schizophrenia. Parallel analyses of GWAS on bipolar disorder, Alzheimer’s disease and height showed that pleiotropic association with MRI metrics was stronger for schizophrenia compared to other disorders.

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Transcriptomic and neuroimaging data integration enhances machine learning classification of schizophrenia

Wang,

Zhao,

et al. 2024

Psychoradiology

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Background Schizophrenia is a polygenetic disorder associated with changes in brain structure and function. Integrating macroscale brain features and microscale genetic data may provide a more complete overview of the disease etiology and may serve as potential diagnostic markers for schizophrenia. Objective We aim to systematically evaluate the impact of multi-scale neuroimaging and transcriptomic data fusion in schizophrenia classification models. Methods we collected brain imaging data and blood RNA-seq data from 43 schizophrenia patients and 60 age-, gender-matched healthy controls, and we extracted multi-omics features of macroscale brain morphology, brain structural connectivity and functional connectivity, and gene transcription of schizophrenia risk genes. Multi-scale data fusion was performed using a machine learning integration framework, together with several conventional machine learning methods and neural networks for patient classification. Results We found that multi-omics data fusion in conventional machine learning models achieved the highest accuracy in contrast to the single-modality models, with AUC improvements of 8.88% to 22.64%. Similar findings were observed for the neural network, showing an increase of 16.57% for the multimodal classification accuracy compared to the single-modal average. In addition, we identified several brain regions in the left posterior cingulate and right frontal pole that contribute to disease classification. Conclusion We provide empirical evidence for the increased accuracy achieved by imaging genetic data integration in schizophrenia classification. Multi-scale data fusion holds promise for enhancing diagnostic precision, facilitating early detection and personalizing treatment regimens in schizophrenia.

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Associated Genetics and Connectomic Circuitry in Schizophrenia and Bipolar Disorder

Cited by 4 publications

References 74 publications

Towards interpretable imaging genomics analysis: Methodological developments and applications

Towards interpretable imaging genomics analysis: Methodological developments and applications

The genetic relationships between brain structure and schizophrenia

Transcriptomic and neuroimaging data integration enhances machine learning classification of schizophrenia

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