Psychiatric genetics and the structure of psychopathology

For over a century, psychiatric disorders have been defined by expert opinion and clinical observation. The modern DSM has relied on a consensus of experts to define categorical syndromes based on clusters of symptoms and signs, and, to some extent, external validators, such as longitudinal course and response to treatment. In the absence of an established etiology, psychiatry has struggled to validate these descriptive syndromes, and to define the boundaries between disorders and between normal and pathologic variation. Recent advances in genomic research, coupled with large-scale collaborative efforts like the Psychiatric Genomics Consortium, have identified hundreds of common and rare genetic variations that contribute to a range of neuropsychiatric disorders. At the same time, they have begun to address deeper questions about the structure and classification of mental disorders: To what extent do genetic findings support or challenge our clinical nosology? Are there genetic boundaries between psychiatric and neurologic illness? Do the data support a boundary between disorder and normal variation? Is it possible to envision a nosology based on genetically informed disease mechanisms? This review provides an overview of conceptual issues and genetic findings that bear on the relationships among and boundaries between psychiatric disorders and other conditions. We highlight implications for the evolving classification of psychopathology and the challenges for clinical translation

Functional effects of schizophrenia-linked genetic variants on intrinsic single-neuron excitability: A modeling study

Background: Recent genome-wide association studies (GWAS) have identified a large number of genetic risk factors for schizophrenia (SCZ) featuring ion channels and calcium transporters. For some of these risk factors, independent prior investigations have examined the effects of genetic alterations on the cellular electrical excitability and calcium homeostasis. In the present proof-of-concept study, we harnessed these experimental results for modeling of computational properties on layer V cortical pyramidal cell and identify possible common alterations in behavior across SCZ-related genes. Methods: We applied a biophysically detailed multi-compartmental model to study the excitability of a layer V pyramidal cell. We reviewed the literature on functional genomics for variants of genes associated with SCZ, and used changes in neuron model parameters to represent the effects of these variants. Results: We present and apply a framework for examining the effects of subtle single nucleotide polymorphisms in ion channel and Ca2+ transporter-encoding genes on neuron excitability. Our analysis indicates that most of the considered SCZ- related genetic variants affect the spiking behavior and intracellular calcium dynamics resulting from summation of inputs across the dendritic tree. Conclusions: Our results suggest that alteration in the ability of a single neuron to integrate the inputs and scale its excitability may constitute a fundamental mechanistic contributor to mental disease, alongside with the previously proposed deficits in synaptic communication and network behavior

Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation.

Accumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. Here we introduce a statistical tool, MiXeR, which quantifies polygenic overlap irrespective of genetic correlation, using GWAS summary statistics. MiXeR results are presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that 8.3 K variants causally influence schizophrenia and 6.4 K influence bipolar disorder. Among these variants, 6.2 K are shared between the disorders, which have a high genetic correlation. Further, MiXeR uncovers polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, the phenotypes share 8.3 K causal variants, while 2.5 K additional variants influence only educational attainment. By considering the polygenicity, discoverability and heritability of complex phenotypes, MiXeR analysis may improve our understanding of cross-trait genetic architectures

Is workaholism associated with inflammatory response? The moderating role of work engagement

The aim of this study was to investigate the association between two different forms of heavy work investment, namely workaholism and work engagement, and serum levels of the proinflammatory cytokine interleukin-17 (IL-17), a possible biomarker of stress. Given the different motivational underpinnings and outcomes of workaholism and work engagement and drawing on the allostatic load and the effort-recovery models, we hypothesized that workaholism is positively associated with IL-17, and that work engagement buffers this association. Workers in an Italian healthcare organization (88 females and 31 males) completed a self-report questionnaire, and then underwent blood sample collection. Data were analysed using moderated multiple regression. Results showed that workaholism was positively associated with IL-17, controlling for the effect of gender, age, and body mass index. Work engagement buffered this association, which was nonsignificant when work engagement was high. To reduce the risk of future health complaints, interventions should be aimed at preventing workaholism and promoting work engagement

Maritime Safety in The High North - Risk and Preparedness

Author's accepted version (postprint).This is an Accepted Manuscript of an article published by the International Society of Offshore and Polar Engineers in ISOPE - International Society of Offshore and Polar Engineers. Proceedings on 07/2016, available online: http://www.isope.org/publications/proceedings/ISOPE/ISOPE%202016/index.ht

Observation of the Decay B^-→D_s^((*)+)K^-ℓ^-ν̅ _ℓ

We report the observation of the decay B^- → D_s^((*)+)K^-ℓ^-ν̅ _ℓ based on 342  fb^(-1) of data collected at the Υ(4S) resonance with the BABAR detector at the PEP-II e^+e^- storage rings at SLAC. A simultaneous fit to three D_s^+ decay chains is performed to extract the signal yield from measurements of the squared missing mass in the B meson decay. We observe the decay B^- → D_s^((*)+)K^-ℓ^-ν̅ _ℓ with a significance greater than 5 standard deviations (including systematic uncertainties) and measure its branching fraction to be B(B^- → D_s^((*)+)K^-ℓ^-ν̅ _ℓ)=[6.13_(-1.03)^(+1.04)(stat)±0.43(syst)±0.51(B(D_s))]×10^(-4), where the last error reflects the limited knowledge of the D_s branching fractions