Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants with Treatment Resistance in Schizophrenia

Importance: About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts. Objective: To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples. Design, Setting, and Participants: Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10501) and individuals with non-TRS (n = 20325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]). Main Outcomes and Measures: GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition. Results: The study included a total of 85490 participants (48635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r2 = 2.03%; P =.001), which was replicated in the STRATA-G incidence sample (r2 = 1.09%; P =.04). Conclusions and Relevance: In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance

Pharmacological modulation of pain-related brain activity during normal and central sensitization states in humans.

Abnormal processing of somatosensory inputs in the central nervous system (central sensitization) is the mechanism accounting for the enhanced pain sensitivity in the skin surrounding tissue injury (secondary hyperalgesia). Secondary hyperalgesia shares clinical characteristics with neurogenic hyperalgesia in patients with neuropathic pain. Abnormal brain responses to somatosensory stimuli have been found in patients with hyperalgesia as well as in normal subjects during experimental central sensitization. The aim of this study was to assess the effects of gabapentin, a drug effective in neuropathic pain patients, on brain processing of nociceptive information in normal and central sensitization states. Using functional magnetic resonance imaging (fMRI) in normal volunteers, we studied the gabapentin-induced modulation of brain activity in response to nociceptive mechanical stimulation of normal skin and capsaicin-induced secondary hyperalgesia. The dose of gabapentin was 1,800 mg per os, in a single administration. We found that (i) gabapentin reduced the activations in the bilateral operculoinsular cortex, independently of the presence of central sensitization; (ii) gabapentin reduced the activation in the brainstem, only during central sensitization; (iii) gabapentin suppressed stimulus-induced deactivations, only during central sensitization; this effect was more robust than the effect on brain activation. The observed drug-induced effects were not due to changes in the baseline fMRI signal. These findings indicate that gabapentin has a measurable antinociceptive effect and a stronger antihyperalgesic effect most evident in the brain areas undergoing deactivation, thus supporting the concept that gabapentin is more effective in modulating nociceptive transmission when central sensitization is present

Timing and locations of reef fish spawning off the southeastern United States

<div><p>Managed reef fish in the Atlantic Ocean of the southeastern United States (SEUS) support a multi-billion dollar industry. There is a broad interest in locating and protecting spawning fish from harvest, to enhance productivity and reduce the potential for overfishing. We assessed spatiotemporal cues for spawning for six species from four reef fish families, using data on individual spawning condition collected by over three decades of regional fishery-independent reef fish surveys, combined with a series of predictors derived from bathymetric features. We quantified the size of spawning areas used by reef fish across many years and identified several multispecies spawning locations. We quantitatively identified cues for peak spawning and generated predictive maps for Gray Triggerfish (<i>Balistes capriscus</i>), White Grunt (<i>Haemulon plumierii</i>), Red Snapper (<i>Lutjanus campechanus</i>), Vermilion Snapper (<i>Rhomboplites aurorubens</i>), Black Sea Bass (<i>Centropristis striata</i>), and Scamp (<i>Mycteroperca phenax</i>). For example, Red Snapper peak spawning was predicted in 24.7–29.0°C water prior to the new moon at locations with high curvature in the 24–30 m depth range off northeast Florida during June and July. External validation using scientific and fishery-dependent data collections strongly supported the predictive utility of our models. We identified locations where reconfiguration or expansion of existing marine protected areas would protect spawning reef fish. We recommend increased sampling off southern Florida (south of 27° N), during winter months, and in high-relief, high current habitats to improve our understanding of timing and location of reef fish spawning off the southeastern United States.</p></div

Logistic regression model fit statistics for probability of encountering a fish within 48 hours of spawning, with percent deviance explained (i.e. percent variability explained by inclusion of additional variable); cross-validation results for Area Under the Curve (AUC), False Positive Rate (FPR) and False Negative Rate (FNR); and percentage of 500 runs where random variable inclusion in model outperformed model-selected bathymetric variable (‘Random test’).

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172968#sec002" target="_blank">Methods</a> for additional details.</p

Probability of encountering a spawning condition female Vermilion Snapper.

<p>Predicted mean (left) and standard error (right) probabilities of observing spawning condition female Vermilion Snapper at time and conditions of peak spawning, relative to external validation observations (+). Raster color-coding based on 2.5 standard deviations from the mean. Green boxes denote no-take marine protected areas and SMZs. Basemap courtesy ESRI Ocean Basemap and partners.</p

Number of gear deployments with multispecies observations of spawning females from SERFS.

<p>Number of gear deployments with multispecies observations of spawning females from SERFS.</p

Spawning condition females and valid sets by month.

<p>Percentage of SERFS samples of females within 48 hours of spawning (<i>left</i>) and number of sets (<i>right</i>) where a histological sample was taken, by species and sampling month.</p

Summary statistics (mean ± standard deviation) for apparent use of multi-year spawning locations.

<p>Multi-year spawning location size computed as minimum convex polygon containing all collections within a site.</p