| 1 | Schizophr Bull 2009 Jan 35: 96-108 |
|---|---|
| PMID | 19023125 |
| Title | A genome-wide association study of schizophrenia using brain activation as a quantitative phenotype. |
| Abstract | Genome-wide association studies (GWASs) are increasingly used to identify risk genes for complex illnesses including精神分裂症. These studies may require thousands of subjects to obtain sufficient power. We present an alternative strategy with increased statistical power over a case-control study that uses brain imaging as a quantitative trait (QT) in the context of a GWAS in精神分裂症. Sixty-four subjects with chronic精神分裂症and 74 matched controls were recruited from the Functional Biomedical Informatics Research Network (FBIRN) consortium. Subjects were genotyped using the Illumina HumanHap300 BeadArray and were scanned while performing a Sternberg Item Recognition Paradigm in which they learned and then recognized target sets of digits in an functional magnetic resonance imaging protocol. The QT was the mean blood oxygen level-dependent signal in the dorsolateral prefrontal cortex during the probe condition for a memory load of 3 items. Three genes or chromosomal regions were identified by having 2 single-nucleotide polymorphisms (SNPs) each significant at P < 10(-6) for the interaction between the imaging QT and the diagnosis (ROBO1-ROBO2, TNIK, and CTXN3-SLC12A2). Three other genes had a significant SNP at <10(-6) (POU3F2, TRAF, andGPC1). Together, these 6 genes/regions identified pathways involved in neurodevelopment and response to stress. Combining imaging and genetic data from a GWAS identified genes related to forebrain development and stress response, already implicated in精神分裂症dysfunction, as affecting prefrontal efficiency. Although the identified genes require confirmation in an independent sample, our approach is a screening method over the whole genome to identify novel SNPs related to risk for精神分裂症. |
| SCZ Keywords | 精神分裂症, schizophrenic |
| 2 | Schizophr Bull 2009 Jan 35: 96-108 |
| PMID | 19023125 |
| Title | A genome-wide association study of schizophrenia using brain activation as a quantitative phenotype. |
| Abstract | Genome-wide association studies (GWASs) are increasingly used to identify risk genes for complex illnesses including精神分裂症. These studies may require thousands of subjects to obtain sufficient power. We present an alternative strategy with increased statistical power over a case-control study that uses brain imaging as a quantitative trait (QT) in the context of a GWAS in精神分裂症. Sixty-four subjects with chronic精神分裂症and 74 matched controls were recruited from the Functional Biomedical Informatics Research Network (FBIRN) consortium. Subjects were genotyped using the Illumina HumanHap300 BeadArray and were scanned while performing a Sternberg Item Recognition Paradigm in which they learned and then recognized target sets of digits in an functional magnetic resonance imaging protocol. The QT was the mean blood oxygen level-dependent signal in the dorsolateral prefrontal cortex during the probe condition for a memory load of 3 items. Three genes or chromosomal regions were identified by having 2 single-nucleotide polymorphisms (SNPs) each significant at P < 10(-6) for the interaction between the imaging QT and the diagnosis (ROBO1-ROBO2, TNIK, and CTXN3-SLC12A2). Three other genes had a significant SNP at <10(-6) (POU3F2, TRAF, andGPC1). Together, these 6 genes/regions identified pathways involved in neurodevelopment and response to stress. Combining imaging and genetic data from a GWAS identified genes related to forebrain development and stress response, already implicated in精神分裂症dysfunction, as affecting prefrontal efficiency. Although the identified genes require confirmation in an independent sample, our approach is a screening method over the whole genome to identify novel SNPs related to risk for精神分裂症. |
| SCZ Keywords | 精神分裂症, schizophrenic |
| 3 | Neuroimage 2010 Nov 53: 839-47 |
| PMID | 20600988 |
| Title | Identifying gene regulatory networks in schizophrenia. |
| Abstract | The imaging genetics approach to studying the genetic basis of disease leverages the individual strengths of both neuroimaging and genetic studies by visualizing and quantifying the brain activation patterns in the context of genetic background. Brain imaging as an intermediate phenotype can help clarify the functional link among genes, the molecular networks in which they participate, and brain circuitry and function. Integrating genetic data from a genome-wide association study (GWAS) with brain imaging as a quantitative trait (QT) phenotype can increase the statistical power to identify risk genes. A QT analysis using brain imaging (DLPFC activation during a working memory task) as a quantitative trait has identified unanticipated risk genes for精神分裂症. Several of these genes (RSRC1, ARHGAP18, ROBO1-ROBO2,GPC1, TNIK, and CTXN3-SLC12A2) have functions related to progenitor cell proliferation, migration, and differentiation, cytoskeleton reorganization, axonal connectivity, and development of forebrain structures. These genes, however, do not function in isolation but rather through gene regulatory networks. To obtain a deeper understanding how the GWAS-identified genes participate in larger gene regulatory networks, we measured correlations among transcript levels in the mouse and human postmortem tissue and performed a gene set enrichment analysis (GSEA) that identified several microRNA associated with精神分裂症(448, 218, 137). The results of such computational approaches can be further validated in animal experiments in which the networks are experimentally studied and perturbed with specific compounds. Glypican 1 and FGF17 mouse models for example, can be used to study such gene regulatory networks. The model demonstrates epistatic interactions between FGF and glypican on brain development and may be a useful model of negative symptom精神分裂症. |
| SCZ Keywords | 精神分裂症, schizophrenic |