A population genetic approach to mapping neurological disorder genes using deep resequencing

Deep resequencing of functional regions in human genomes is key to identifying potentially causal rare variants for complex disorders. Here, we present the results from a large-sample resequencing (n  =  285 patients) study of candidate genes coupled with population genetics and statistical methods to identify rare variants associated with Autism Spectrum Disorder and Schizophrenia. Three genes, MAP1A, GRIN2B, and CACNA1F, were consistently identified by different methods as having significant excess of rare missense mutations in either one or both disease cohorts. In a broader context, we also found that the overall site frequency spectrum of variation in these cases is best explained by population models of both selection and complex demography rather than neutral models or models accounting for complex demography alone. Mutations in the three disease-associated genes explained much of the difference in the overall site frequency spectrum among the cases versus controls. This study demonstrates that genes associated with complex disorders can be mapped using resequencing and analytical methods with sample sizes far smaller than those required by genome-wide association studies. Additionally, our findings support the hypothesis that rare mutations account for a proportion of the phenotypic variance of these complex disorders

Characterization of Genome-Wide Association-Identified Variants for Atrial Fibrillation in African Americans

Despite a greater burden of risk factors, atrial fibrillation (AF) is less common among African Americans than European-descent populations. Genome-wide association studies (GWAS) for AF in European-descent populations have identified three predominant genomic regions associated with increased risk (1q21, 4q25, and 16q22). The contribution of these loci to AF risk in African American is unknown.We studied 73 African Americans with AF from the Vanderbilt-Meharry AF registry and 71 African American controls, with no history of AF including after cardiac surgery. Tests of association were performed for 148 SNPs across the three regions associated with AF, and 22 SNPs were significantly associated with AF (P<0.05). The SNPs with the strongest associations in African Americans were both different from the index SNPs identified in European-descent populations and independent from the index European-descent population SNPs (r(2)<0.40 in HapMap CEU): 1q21 rs4845396 (odds ratio [OR] 0.30, 95% confidence interval [CI] 0.13-0.67, P = 0.003), 4q25 rs4631108 (OR 3.43, 95% CI 1.59-7.42, P = 0.002), and 16q22 rs16971547 (OR 8.1, 95% CI 1.46-45.4, P = 0.016). Estimates of European ancestry were similar among cases (23.6%) and controls (23.8%). Accordingly, the probability of having two copies of the European derived chromosomes at each region did not differ between cases and controls.Variable European admixture at known AF loci does not explain decreased AF susceptibility in African Americans. These data support the role of 1q21, 4q25, and 16q22 variants in AF risk for African Americans, although the index SNPs differ from those identified in European-descent populations

A Population Genetic Approach to Mapping Neurological Disorder Genes Using Deep Resequencing

Deep resequencing of functional regions in human genomes is key to identifying potentially causal rare variants for complex disorders. Here, we present the results from a large-sample resequencing (n = 285 patients) study of candidate genes coupled with population genetics and statistical methods to identify rare variants associated with Autism Spectrum Disorder and Schizophrenia. Three genes, MAP1A, GRIN2B, and CACNA1F, were consistently identified by different methods as having significant excess of rare missense mutations in either one or both disease cohorts. In a broader context, we also found that the overall site frequency spectrum of variation in these cases is best explained by population models of both selection and complex demography rather than neutral models or models accounting for complex demography alone. Mutations in the three disease-associated genes explained much of the difference in the overall site frequency spectrum among the cases versus controls. This study demonstrates that genes associated with complex disorders can be mapped using resequencing and analytical methods with sample sizes far smaller than those required by genome-wide association studies. Additionally, our findings support the hypothesis that rare mutations account for a proportion of the phenotypic variance of these complex disorders

Prognostic model to identify and quantify risk factors for mortality among hospitalised patients with COVID-19 in the USA

Objectives To develop a prognostic model to identify and quantify risk factors for mortality among patients admitted to the hospital with COVID-19.Design Retrospective cohort study. Patients were randomly assigned to either training (80%) or test (20%) sets. The training set was used to fit a multivariable logistic regression. Predictors were ranked using variable importance metrics. Models were assessed by C-indices, Brier scores and calibration plots in the test set.Setting Optum de-identified COVID-19 Electronic Health Record dataset including over 700 hospitals and 7000 clinics in the USA.Participants 17 086 patients hospitalised with COVID-19 between 20 February 2020 and 5 June 2020.Main outcome measure All-cause mortality while hospitalised.Results The full model that included information on demographics, comorbidities, laboratory results, and vital signs had good discrimination (C-index=0.87) and was well calibrated, with some overpredictions for the most at-risk patients. Results were similar on the training and test sets, suggesting that there was little overfitting. Age was the most important risk factor. The performance of models that included all demographics and comorbidities (C-index=0.79) was only slightly better than a model that only included age (C-index=0.76). Across the study period, predicted mortality was 1.3% for patients aged 18 years old, 8.9% for 55 years old and 28.7% for 85 years old. Predicted mortality across all ages declined over the study period from 22.4% by March to 14.0% by May.Conclusion Age was the most important predictor of all-cause mortality, although vital signs and laboratory results added considerable prognostic information, with oxygen saturation, temperature, respiratory rate, lactate dehydrogenase and white cell count being among the most important predictors. Demographic and comorbidity factors did not improve model performance appreciably. The full model had good discrimination and was reasonably well calibrated, suggesting that it may be useful for assessment of prognosis

Handoff Effectiveness Research in Perioperative Environments (Hero) Design Studio: A Conference Report

Ineffective perioperative handoffs can introduce vulnerabilities in patient safety for multiple reasons, including the potential for incomplete or inaccurate transfer of information, conflicting mental models, and misunderstandings of responsibility and accountability for patient care.1 Handoffs are complex sociotechnical procedures that require coordination between clinicians and may be challenged by distractions, cognitive overload, and poor team dynamics.2 Perioperative handoffs are unique in that they represent a series of care transfers over a short period of time and may occur in a number of different patient care locations, including pre-operative holding areas, post-anesthesia care units (PACUs), ICUs, and inpatient wards

Stereoscopy does not improve metric distance estimations in virtual environments

Stereoscopy is widely used to render depth and perceptual spatial cues information in Virtual Environments (VEs). In literature, the use of stereoscopy in VEs reported advantages but also disadvantages on perceptual skills such as metric evaluation of distances. The present study tests the influence of stereoscopy on a verbal metric and nonmetric evaluation of egocentric and allocentric distances task, in closed and open scenarios, correlating performance with Mental Rotation ability. Results show that stereoscopy could be helpful only for nonmetric estimates. More errors occurred in metric evaluation, modulated by gender

ARTICLE Direct Measure of the De Novo Mutation Rate in Autism and Schizophrenia Cohorts

The role of de novo mutations (DNMs) in common diseases remains largely unknown. Nonetheless, the rate of de novo deleterious mutations and the strength of selection against de novo mutations are critical to understanding the genetic architecture of a disease. Discovery of high-impact DNMs requires substantial high-resolution interrogation of partial or complete genomes of families via resequencing. We hypothesized that deleterious DNMs may play a role in cases of autism spectrum disorders (ASD) and schizophrenia (SCZ), two etiologically heterogeneous disorders with significantly reduced reproductive fitness. We present a direct measure of the de novo mutation rate (m) and selective constraints from DNMs estimated from a deep resequencing data set generated from a large cohort of ASD and SCZ cases (n ¼ 285) and population control individuals (n ¼ 285) with available parental DNA. A survey of~430 Mb of DNA from 401 synapse-expressed genes across all cases and 25 Mb of DNA in controls found 28 candidate DNMs, 13 of which were cell line artifacts. Our calculated direct neutral mutation rate (1.36 3 10 À8 ) is similar to previous indirect estimates, but we observed a significant excess of potentially deleterious DNMs in ASD and SCZ individuals. Our results emphasize the importance of DNMs as genetic mechanisms in ASD and SCZ and the limitations of using DNA from archived cell lines to identify functional variants

Differences in distance estimations in real and virtual 3d environments

Computerized 3D modelled spaces are thought to be reliable imitations of Real Environments (REs). Depth perception in displayed Virtual 3D Environments (VEs) is a controversial issue. The present work compared both egocentric and allocentric distances in a RE and a VE. Results showed more errors in the VE (underestimations) than in the RE (overestimations), and a gender effect in the different environments mediated by Mental Rotation ability. Findings suggested that spatial and perceptual processing underlying artificial 3D modelled space may not be similar to cognitive spatial processes in REs

Direct Measure of the De Novo Mutation Rate in Autism and Schizophrenia Cohorts

The role of de novo mutations (DNMs) in common diseases remains largely unknown. Nonetheless, the rate of de novo deleterious mutations and the strength of selection against de novo mutations are critical to understanding the genetic architecture of a disease. Discovery of high-impact DNMs requires substantial high-resolution interrogation of partial or complete genomes of families via resequencing. We hypothesized that deleterious DNMs may play a role in cases of autism spectrum disorders (ASD) and schizophrenia (SCZ), two etiologically heterogeneous disorders with significantly reduced reproductive fitness. We present a direct measure of the de novo mutation rate (mu) and selective constraints from DNMs estimated from a deep resequencing data set generated from a large cohort of ASD and SCZ cases (n = 285) and population control individuals (n = 285) with available parental DNA. A survey of -430 Mb of DNA from 401 synapse-expressed genes across all cases and 25 Mb of DNA in controls found 28 candidate DNMs, 13 of which were cell line artifacts. Our calculated direct neutral mutation rate (1.36 x 10(-8)) is similar to previous indirect estimates, but we observed a significant excess of potentially deleterious DNMs in ASD and SCZ individuals. Our results emphasize the importance of DNMs as genetic mechanisms in ASD and SCZ and the limitations of using DNA from archived cell lines to identify functional variants