Intermediate phenotypes in psychiatric disorders. Curr Opin Genet Dev

Clinical Brain Disorders Branch, Genes, Cognition, and Psychosis Program, NIMH, NIH, Bethesda, MD, USA.
Current opinion in genetics & development (Impact Factor: 7.57). 03/2011; 21(3):340-8. DOI: 10.1016/j.gde.2011.02.003
Source: PubMed


The small effect size of most individual risk factors for psychiatric disorders likely reflects biological heterogeneity and diagnostic imprecision, which has encouraged genetic studies of intermediate biological phenotypes that are closer to the molecular effects of risk genes than are the clinical symptoms. Neuroimaging-based intermediate phenotypes have emerged as particularly promising because they map risk associated gene effects onto physiological processes in brain that are altered in patients and in their healthy relatives. Recent evidence using this approach has elucidated discrete, dissociable biological mechanisms of risk genes at the level of neural circuitries, and their related cognitive functions. This approach may greatly contribute to our understanding of the genetics and pathophysiology of psychiatric disorders.

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Available from: Roberta Rasetti, May 19, 2014
    • "). The CACNA1C (Paulus et al., 2014) and ZNF804A (Rasetti et al., 2011) GWAS schizophrenia risk SNPs were found to be correlated with reduced prefrontal-hippocampal coupling during WM tasks in healthy controls and schizophrenia patients. Tan et al. (2012) using dynamic causal modelling (Friston et al., 2003) on fMRI WM data in healthy participants differentiated the effects of the COMT, DRD2 and AKT1 genes on the prefrontalparietal WM maintenance and prefrontal-striatal WM manipulation network. "
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    ABSTRACT: Genetic factors account for up to 80% of the liability for schizophrenia and bipolar disorder. Genome-wide association studies (GWAS) have successfully identified several single nucleotide polymorphisms (SNPs) and genes associated with increased risk for both disorders. Single SNP analyses alone do not address the overall genomic or polygenic architecture of psychiatric disorders as the amount of phenotypic variation explained by each GWAS-supported SNP is small whereas the number of SNPs/regions underlying risk for illness is thought to be very large. The polygenic risk score models the aggregate effect of alleles associated with disease status present in each individual and allows us to utilise the power of large GWAS to be applied robustly in small samples. Here we make the case that risk prediction, intervention and personalised medicine can only benefit with the inclusion of polygenic risk scores in imaging genetics research. © The Author(s) 2015.
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    • "Genes, while fundamental to vulnerability, are nevertheless difficult to link to the pathoetiological processes that ultimately determine onset of schizophrenia. Hence there is increasing effort to identify proximate imaging endophenotypes of schizophrenia for clinical utility (Meyer-Lindenberg and Weinberger, 2006; Rasetti and Weinberger, 2011) which can be combined with other objective markers and traits to give an extended endophenotype for schizophrenia (Prasad and Keshavan, 2008; Keshavan et al., 2011). "
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