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multiscaleanalysisofschizophreniariskgenesbrainstructureandclinicalsymptomsrevealsintegrativecluesforsubtypingschizophreniapatients
Alternative TitleMulti-scale analysis of schizophrenia risk genes, brain structure, and clinical symptoms reveals integrative clues for subtyping schizophrenia patients
Ma Liang1; Rolls Edmund T2; Liu Xiuqin3; Liu Yuting4; Jiao Zeyu5; Wang Yue4; Gong Weikang6; Ma Zhiming7; Gong Fuzhou7; Wan Lin7
2019
Source Publicationjournalofmolecularcellbiology
ISSN1674-2788
Volume11Issue:8Pages:678-687
AbstractAnalysis linking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare. Here, we describe a multi-scale analysis using genome-wide SNPs, gene expression, grey matter volume (GMV), and the positive and negative syndrome scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development. Among these 108 candidates, 19 distinct genes were found associated with 16 brain regions referred to as hot clusters (HCs), primarily in the frontal cortex, sensory-motor regions and temporal and parietal regions. The patients were subtyped into three groups with distinguishable PANSS scores by the GMV of the identified HCs. Furthermore, we found that HCs with common GMV among patient groups are related to genes that mostly mapped to pathways relevant to neural signaling, which are associated with the risk for schizophrenia. Our results provide an integrated view of how genetic variants may affect brain structures that lead to distinct disease phenotypes. The method of multi-scale analysis that was described in this research, may help to advance the understanding of the etiology of schizophrenia.
Other AbstractAnalysis linking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare. Here, we describe a multi-scale analysis using genome-wide SNPs, gene expression, grey matter volume (GMV), and the positive and negative syndrome scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development. Among these 108 candidates, 19 distinct genes were found associated with 16 brain regions referred to as hot clusters (HCs), primarily in the frontal cortex, sensory-motor regions and temporal and parietal regions. The patients were subtyped into three groups with distinguishable PANSS scores by the GMV of the identified HCs. Furthermore, we found that HCs with common GMV among patient groups are related to genes that mostly mapped to pathways relevant to neural signaling, which are associated with the risk for schizophrenia. Our results provide an integrated view of how genetic variants may affect brain structures that lead to distinct disease phenotypes. The method of multi-scale analysis that was described in this research, may help to advance the understanding of the etiology of schizophrenia.
Indexed ByCSCD
Language英语
CSCD IDCSCD:6595613
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/51212
Collection中国科学院数学与系统科学研究院
Affiliation1.中国科学院北京基因组研究所
2.华威大学
3.北京科技大学
4.北京交通大学
5.复旦大学
6.中国科学院上海生命科学研究院计算生物学研究所
7.中国科学院数学与系统科学研究院
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GB/T 7714
Ma Liang,Rolls Edmund T,Liu Xiuqin,et al. multiscaleanalysisofschizophreniariskgenesbrainstructureandclinicalsymptomsrevealsintegrativecluesforsubtypingschizophreniapatients[J]. journalofmolecularcellbiology,2019,11(8):678-687.
APA Ma Liang.,Rolls Edmund T.,Liu Xiuqin.,Liu Yuting.,Jiao Zeyu.,...&Wan Lin.(2019).multiscaleanalysisofschizophreniariskgenesbrainstructureandclinicalsymptomsrevealsintegrativecluesforsubtypingschizophreniapatients.journalofmolecularcellbiology,11(8),678-687.
MLA Ma Liang,et al."multiscaleanalysisofschizophreniariskgenesbrainstructureandclinicalsymptomsrevealsintegrativecluesforsubtypingschizophreniapatients".journalofmolecularcellbiology 11.8(2019):678-687.
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