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 = 10 501) and individuals with non-TRS (n = 20 325). 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 85 490 participants (48 635 [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

Pan-African Genetic Structure in the African Buffalo (Syncerus caffer): Investigating Intraspecific Divergence

The African buffalo (Syncerus caffer) exhibits extreme morphological variability, which has led to controversies about the validity and taxonomic status of the various recognized subspecies. The present study aims to clarify these by inferring the pan-African spatial distribution of genetic diversity, using a comprehensive set of mitochondrial D-loop sequences from across the entire range of the species. All analyses converged on the existence of two distinct lineages, corresponding to a group encompassing West and Central African populations and a group encompassing East and Southern African populations. The former is currently assigned to two to three subspecies (S. c. nanus, S. c. brachyceros, S. c. aequinoctialis) and the latter to a separate subspecies (S. c. caffer). Forty-two per cent of the total amount of genetic diversity is explained by the between-lineage component, with one to seventeen female migrants per generation inferred as consistent with the isolation-with-migration model. The two lineages diverged between 145 000 to 449 000 years ago, with strong indications for a population expansion in both lineages, as revealed by coalescent-based analyses, summary statistics and a star-like topology of the haplotype network for the S. c. caffer lineage. A Bayesian analysis identified the most probable historical migration routes, with the Cape buffalo undertaking successive colonization events from Eastern toward Southern Africa. Furthermore, our analyses indicate that, in the West-Central African lineage, the forest ecophenotype may be a derived form of the savanna ecophenotype and not vice versa, as has previously been proposed. The African buffalo most likely expanded and diverged in the late to middle Pleistocene from an ancestral population located around the current-day Central African Republic, adapting morphologically to colonize new habitats, hence developing the variety of ecophenotypes observed today

Cellular phosphoinositides and the maturation of bluetongue virus, a non-enveloped capsid virus.

BACKGROUND: Bluetongue virus (BTV), a member of Orbivirus genus in the Reoviridae family is a double capsid virus enclosing a genome of 10 double-stranded RNA segments. A non-structural protein of BTV, NS3, which is associated with cellular membranes and interacts with outer capsid proteins, has been shown to be involved in virus morphogenesis in infected cells. In addition, studies have also shown that during the later stages of virus infection NS3 behaves similarly to HIV protein Gag, an enveloped viral protein. Since Gag protein is known to interact with membrane lipid phosphatidylinositol (4,5) bisphosphate [PI(4,5)P2] and one of the known binding partners of NS3, cellular protein p11 also interacts with annexin a PI(4,5)P2 interacting protein, this study was designed to understand the role of this negatively charged membrane lipid in BTV assembly and maturation. METHODS: Over expression of cellular enzymes that either depleted cells of PI(4,5)P2 or altered the distribution of PI(4,5)P2, were used to analyze the effect of the lipid on BTV maturation at different times post-infection. The production of mature virus particles was monitored by plaque assay. Microscopic techniques such as confocal microscopy and electron microscopy (EM) were also undertaken to study localization of virus proteins and virus particles in cells, respectively. RESULTS: Initially, confocal microscopic analysis demonstrated that PI(4,5)P2 not only co-localized with NS3, but it also co-localized with VP5, one of the outer capsid proteins of BTV. Subsequently, experiments involving depletion of cellular PI(4,5)P2 or its relocation demonstrated an inhibitory effect on normal BTV maturation and it also led to a redistribution of BTV proteins within the cell. The data was supported further by EM visualization showing that modulation of PI(4,5)P2 in cells indeed resulted in less particle production. CONCLUSION: This study to our knowledge, is the first report demonstrating involvement of PI(4,5)P2 in a non-enveloped virus assembly and release. As BTV does not have lipid envelope, this finding is unique for this group of viruses and it suggests that the maturation of capsid and enveloped viruses may be more closely related than previously thought

EPIPOI: A user-friendly analytical tool for the extraction and visualization of temporal parameters from epidemiological time series

<p>Abstract</p> <p>Background</p> <p>There is an increasing need for processing and understanding relevant information generated by the systematic collection of public health data over time. However, the analysis of those time series usually requires advanced modeling techniques, which are not necessarily mastered by staff, technicians and researchers working on public health and epidemiology. Here a user-friendly tool, EPIPOI, is presented that facilitates the exploration and extraction of parameters describing trends, seasonality and anomalies that characterize epidemiological processes. It also enables the inspection of those parameters across geographic regions. Although the visual exploration and extraction of relevant parameters from time series data is crucial in epidemiological research, until now it had been largely restricted to specialists.</p> <p>Methods</p> <p>EPIPOI is freely available software developed in Matlab (The Mathworks Inc) that runs both on PC and Mac computers. Its friendly interface guides users intuitively through useful comparative analyses including the comparison of spatial patterns in temporal parameters.</p> <p>Results</p> <p>EPIPOI is able to handle complex analyses in an accessible way. A prototype has already been used to assist researchers in a variety of contexts from didactic use in public health workshops to the main analytical tool in published research.</p> <p>Conclusions</p> <p>EPIPOI can assist public health officials and students to explore time series data using a broad range of sophisticated analytical and visualization tools. It also provides an analytical environment where even advanced users can benefit by enabling a higher degree of control over model assumptions, such as those associated with detecting disease outbreaks and pandemics.</p