| 1 | Schizophr. Res. 2014 Jun 156: 71-5 |
|---|---|
| PMID | 24768440 |
| Title | 遗传对认知内在表型的影响schizophrenia. |
| Abstract | Cognitive deficits are prominent inschizophreniaand represent promising endophenotypes for genetic research. The current study investigated the importance of two conceptually distinct genetic aggregates, one based on copy number variations (uncommon deletion burden), and one based on single nucleotide polymorphisms identified in recent risk studies (genetic risk score). The impact of these genetic factors, and their interaction, was examined on cognitive endophenotypes defined by principal component analysis (PCA) in a multi-center sample of 50 patients withschizophreniaand 86 controls. PCA was used to identify three different types of executive function (EF: planning, fluency, and inhibition), and in separate analyses, a measure general cognitive ability (GCA). Cognitive deficits were prominent among individuals withschizophrenia, but no group differences were evident for either genetic factor. Among patients the deletion burden measures predicted cognitive deficits across the three EF components andGCA. Further, an interaction was noted between the two genetic factors for both EF andGCAand the observed patterns of interaction suggested antagonistic epistasis. In general, the set of genetic interactions examined predicted a substantial portion of variance in these cognitive endophenotypes. Though adequately powered, our sample size is small for a genetic study. These results draw attention to genetic interactions and the possibility that genetic influences on cognition differ in patients and controls. |
| SCZ Keywords | schizophrenia |
| 2 | J Int Neuropsychol Soc 2016 Feb 22: 240-9 |
| PMID | 26888620 |
| Title | Graph Metrics of Structural Brain Networks in Individuals with Schizophrenia and Healthy Controls: Group Differences, Relationships with Intelligence, and Genetics. |
| Abstract | One of the most prominent features ofschizophreniais relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation inGCArelies on network properties of the brain. In this multi-center study, we determined global characteristics of brain networks using graph theory and related these toGCAin healthy controls and individuals withschizophrenia. Participants (N=116 controls, 80 patients withschizophrenia) were recruited from four sites.GCAwas represented by the first principal component of a large battery of neurocognitive tests. Graph metrics were derived from diffusion-weighted imaging. The global metrics of longer characteristic path length and reduced overall connectivity predicted lowerGCAacross groups, and group differences were noted for both variables. Measures of clustering, efficiency, and modularity did not differ across groups or predictGCA. Follow-up analyses investigated three topological types of connectivity-connections among high degree "rich club" nodes, "feeder" connections to these rich club nodes, and "local" connections not involving the rich club. Rich club and local connectivity predicted performance across groups. In a subsample (N=101 controls, 56 patients), a genetic measure reflecting mutation load, based on rare copy number deletions, was associated with longer characteristic path length. Results highlight the importance of characteristic path lengths and rich club connectivity forGCAand provide no evidence for group differences in the relationships between graph metrics andGCA. (JINS, 2016, 22, 240-249). |
| SCZ Keywords | schizophrenia |