Intragenic recombination between pseudogenes as a source of new disease specificity at a simple resistance locus

BACKGROUND: Pooling of multi-site MRI data is often necessary when a large cohort is desired. However, different scanning platforms can introduce systematic differences which confound true effects of interest. One may reduce multi-site bias by calibrating pivotal scanning parameters, or include them as covariates to improve the data integrity. NEW METHOD: In the present study we use a source-based morphometry (SBM) model to explore scanning effects in multi-site sMRI studies and develop a data-driven correction. Specifically, independent components are extracted from the data and investigated for associations with scanning parameters to assess the influence. The identified scanning-related components can be eliminated from the original data for correction. RESULTS: A small set of SBM components captured most of the variance associated with the scanning differences. In a dataset of 1460 healthy subjects, pronounced and independent scanning effects were observed in brainstem and thalamus, associated with magnetic field strength-inversion time and RF-receiving coil. A second study with 110 schizophrenia patients and 124 healthy controls demonstrated that scanning effects can be effectively corrected with the SBM approach. COMPARISON WITH EXISTING METHOD(S): Both SBM and GLM correction appeared to effectively eliminate the scanning effects. Meanwhile, the SBM-corrected data yielded a more significant patient versus control group difference and less questionable findings. CONCLUSIONS: It is important to calibrate scanning settings and completely examine individual parameters for the control of confounding effects in multi-site sMRI studies. Both GLM and SBM correction can reduce scanning effects, though SBM's data-driven nature provides additional flexibility and is better able to handle collinear effects

Chitayat syndrome: hyperphalangism, characteristic facies, hallux valgus and bronchomalacia results from a recurrent c.266A>G p.(Tyr89Cys) variant in the ERF gene.

BACKGROUND: In 1993, Chitayat et al., reported a newborn with hyperphalangism, facial anomalies, and bronchomalacia. We identified three additional families with similar findings. Features include bilateral accessory phalanx resulting in shortened index fingers; hallux valgus; distinctive face; respiratory compromise. OBJECTIVES: To identify the genetic aetiology of Chitayat syndrome and identify a unifying cause for this specific form of hyperphalangism. METHODS: Through ongoing collaboration, we had collected patients with strikingly-similar phenotype. Trio-based exome sequencing was first performed in Patient 2 through Deciphering Developmental Disorders study. Proband-only exome sequencing had previously been independently performed in Patient 4. Following identification of a candidate gene variant in Patient 2, the same variant was subsequently confirmed from exome data in Patient 4. Sanger sequencing was used to validate this variant in Patients 1, 3; confirm paternal inheritance in Patient 5. RESULTS: A recurrent, novel variant NM_006494.2:c.266A>G p.(Tyr89Cys) in ERF was identified in five affected individuals: de novo (patient 1, 2 and 3) and inherited from an affected father (patient 4 and 5). p.Tyr89Cys is an aromatic polar neutral to polar neutral amino acid substitution, at a highly conserved position and lies within the functionally important ETS-domain of the protein. The recurrent ERF c.266A>C p.(Tyr89Cys) variant causes Chitayat syndrome. DISCUSSION: ERF variants have previously been associated with complex craniosynostosis. In contrast, none of the patients with the c.266A>G p.(Tyr89Cys) variant have craniosynostosis. CONCLUSIONS: We report the molecular aetiology of Chitayat syndrome and discuss potential mechanisms for this distinctive phenotype associated with the p.Tyr89Cys substitution in ERF

4pi Models of CMEs and ICMEs

Coronal mass ejections (CMEs), which dynamically connect the solar surface to the far reaches of interplanetary space, represent a major anifestation of solar activity. They are not only of principal interest but also play a pivotal role in the context of space weather predictions. The steady improvement of both numerical methods and computational resources during recent years has allowed for the creation of increasingly realistic models of interplanetary CMEs (ICMEs), which can now be compared to high-quality observational data from various space-bound missions. This review discusses existing models of CMEs, characterizing them by scientific aim and scope, CME initiation method, and physical effects included, thereby stressing the importance of fully 3-D ('4pi') spatial coverage.Comment: 14 pages plus references. Comments welcome. Accepted for publication in Solar Physics (SUN-360 topical issue

Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia

Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale thalamo-cortical connectivity as being disrupted in patients. However, observed connectivity differences in schizophrenia have been inconsistent between studies, with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on a multi-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4minutes in length), and a dynamic sense, computed using sliding windows (44s in length) and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual), as well as reduced connectivity (hypoconnectivity) between sensory networks from all modalities. Dynamic analysis suggests that (1), on average, schizophrenia patients spend much less time than healthy controls in states typified by strong, large-scale connectivity, and (2), that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical–subcortical antagonism (anti-correlations) and strong positive connectivity between sensory networks are those that show the group differences of thalamic hyperconnectivity and sensory hypoconnectivity. Group differences are weak or absent during other connectivity states. Dynamic analysis also revealed hypoconnectivity between the putamen and sensory networks during the same states of thalamic hyperconnectivity; notably, this finding cannot be observed in the static connectivity analysis. Finally, in post-hoc analyses we observed that the relationships between sub-cortical low frequency power and connectivity with sensory networks is altered in patients, suggesting different functional interactions between sub-cortical nuclei and sensorimotor cortex during specific connectivity states. While important differences between patients with schizophrenia and healthy controls have been identified, one should interpret the results with caution given the history of medication in patients. Taken together, our results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences.Highlights•Studied both static and dynamic connectivity changes in schizophrenia during rest•Small but significant connectivity differences might be obscured in static analysis.•Patients show significant differences in dwell times in multiple states.•Disrupted thalamo-cortical connectivity in schizophrenia in a state-specific manne

Practicing Imperfect Forgiveness

Forgiveness is typically regarded as a good thing - even a virtue - but acts of forgiveness can vary widely in value, depending on their context and motivation. Faced with this variation, philosophers have tended to reinforce everyday concepts of forgiveness with strict sets of conditions, creating ideals or paradigms of forgiveness. These are meant to distinguish good or praiseworthy instances of forgiveness from problematic instances and, in particular, to protect the self-respect of would-be forgivers. But paradigmatic forgiveness is problematic for a number of reasons, including its inattention to forgiveness as a gendered trait. We can account for the values and the risks associated with forgiving far better if we treat it as a moral practice and not an ideal

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting

Reproducibility in the absence of selective reporting : An illustration from large-scale brain asymmetry research

Altres ajuts: Max Planck Society (Germany).The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p-hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left-right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta-analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an "ideal publishing environment," that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically-used sample sizes

The genetic architecture of the human cerebral cortex

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function