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.

Download full-text


Available from: Roberta Rasetti, May 19, 2014
    • "To this respect, it has been recently indicated that combining disorders with similar genetic risk profiles improves power to detect shared risk loci (Ruderfer et al. 2014 ). Similarly, genotype–phenotypebased approaches and the use of features with strong aetiological significance have been suggested as a useful strategy to reduce heterogeneity and to identify specific genetic factors associated with such traits (Rasetti and Weinberger 2011; Swerdlow et al. 2015). Then, the observed variability on traits such as cognitive impairments and age at onset among patients may reflect differences in the distribution of aetiological factors and possibly also differences underlying vulnerability. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Objectives Neuritin 1 gene (NRN1) is involved in neurodevelopment processes and synaptic plasticity and its expression is regulated by brain-derived neurotrophic factor (BDNF). We aimed to investigate the association of NRN1 with schizophrenia-spectrum disorders (SSD) and bipolar disorders (BPD), to explore its role in age at onset and cognitive functioning, and to test the epistasis between NRN1 and BDNF. Methods The study was developed in a sample of 954 SSD/BPD patients and 668 healthy subjects. Genotyping analyses included 11 SNPs in NRN1 and one functional SNP in BDNF. Results The frequency of the haplotype C-C (rs645649–rs582262) was significantly increased in patients compared to controls (P = 0.0043), while the haplotype T-C-C-T-C-A (rs3763180–rs10484320–rs4960155–rs9379002–rs9405890–rs1475157) was more frequent in controls (P = 3.1 × 10−5). The variability at NRN1 was nominally related to changes in age at onset and to differences in intelligence quotient, in SSD patients. Epistasis between NRN1 and BDNF was significantly associated with the risk for SSD/BPD (P = 0.005). Conclusions Results suggest that: (i) NRN1 variability is a shared risk factor for both SSD and BPD, (ii) NRN1 may have a selective impact on age at onset and intelligence in SSD, and (iii) the role of NRN1 seems to be not independent of BDNF.
    No preview · Article · Dec 2015 · The World Journal of Biological Psychiatry
    • "To date, more emphasis is being put into the investigation of working memory neuronal networks and on how genetic vulnerability might influence the developmental trajectory of working memory deficits in schizophrenia. Indeed, variations in different schizophrenia-candidate genes have been observed to influence working memory functions in schizophrenia (Harrison & Weinberger, 2005;Rasetti & Weinberger, 2011). These include, but not only, functional polymorphisms in COMT (Egan et al., 2001), genetic variations in the Neuregulin 1 gene, its receptor ERBB4 (Nicodemus et al., 2010), and in the dys- bindin-1 gene (Donohoe et al., 2007). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Cognitive impairments, especially in higher order cognitive functions, are core features of schizophrenia. Importantly, despite their early onset, long-lasting presence, and serious impact on the life quality of patients and their families, cognitive deficits are still mostly incurable and their specific causes are still unknown. In this context, mouse/rat models with cautious and well-designed translational valence constitute an invaluable instrument in dissecting the selective nature of schizophrenia-relevant cognitive deficits, including their genetic, environmental, and neuronal/cellular mechanisms. Moreover, these models are also crucial for the implementation of more effective therapeutical strategies. Thus, based on clinical evidence in schizophrenia, here we will specifically address cognitive domains such as executive control, working memory, attention, and social cognition. We first briefly present human tasks commonly used to measure each of these domains; thereafter, we describe relevant equivalent tasks developed and now available for use in rodents.
    No preview · Chapter · Nov 2015
    • "). 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. "
    [Show abstract] [Hide abstract]
    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.
    No preview · Article · May 2015 · Journal of Psychopharmacology
Show more