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Abstract Background A combination of multiple factors, including a strong genetic predisposition and environmental factors, are considered to contribute to the developmental pathways to borderline personality disorder (BPD). However, these factors have mostly been investigated retrospectively, and hardly in adolescents. The current study focuses on maternal factors in BPD features in adolescence. Methods Actual parenting was investigated in a group of referred adolescents with BPD features (N = 101) and a healthy control group (N = 44). Self-reports of perceived concurrent parenting were completed by the adolescents. Questionnaires on parental psychopathology (both Axis I and Axis II disorders) were completed by their mothers. Results Adolescents reported significantly less emotional warmth, more rejection and more overprotection from their mothers in the BPD-group than in the control group. Mothers in the BPD group reported significantly more parenting stress compared to mothers in the control group. Also, these mothers showed significantly more general psychopathology and clusters C personality traits than mothers in the control group. Contrary to expectations, mothers of adolescents with BPD features reported the same level of cluster B personality traits, compared to mothers in the control group. Hierarchical logistic regression revealed that parental rearing styles (less emotional warmth, and more overprotection) and general psychopathology of the mother were the strongest factors differentiating between controls and adolescents with BPD symptoms. Conclusions Adolescents with BPD features experience less emotional warmth and more overprotection from their mothers, while the mothers themselves report more symptoms of anxiety and depression. Addition of family interventions to treatment programs for adolescents might increase the effectiveness of such early interventions, and prevent the adverse outcome that is often seen in adult BPD patients.

Abstract Background Simulation of DNA-microarray data serves at least three purposes: (i) optimizing the design of an intended DNA microarray experiment, (ii) comparing existing pre-processing and processing methods for best analysis of a given DNA microarray experiment, (iii) educating students, lab-workers and other researchers by making them aware of the many factors influencing DNA microarray experiments. Results Our model has multiple layers of factors influencing the experiment. The relative influence of such factors can differ significantly between labs, experiments within labs, etc. Therefore, we have added a module to roughly estimate their parameters from a given data set. This guarantees that our simulated data mimics real data as closely as possible. Conclusion We introduce a model for the simulation of dual-dye cDNA-microarray data closely resembling real data and coin the model and its software implementation "SIMAGE" which stands for simulation of microarray gene expression data. The software is freely accessible at: http://bioinformatics.biol.rug.nl/websoftware/simage.

Abstract Background In research laboratories using DNA-microarrays, usually a number of researchers perform experiments, each generating possible sources of error. There is a need for a quick and robust method to assess data quality and sources of errors in DNA-microarray experiments. To this end, a novel and cost-effective validation scheme was devised, implemented, and employed. Results A number of validation experiments were performed on Lactococcus lactis IL1403 amplicon-based DNA-microarrays. Using the validation scheme and ANOVA, the factors contributing to the variance in normalized DNA-microarray data were estimated. Day-to-day as well as experimenter-dependent variances were shown to contribute strongly to the variance, while dye and culturing had a relatively modest contribution to the variance. Conclusion Even in cases where 90 % of the data were kept for analysis and the experiments were performed under challenging conditions (e.g. on different days), the CV was at an acceptable 25 %. Clustering experiments showed that trends can be reliably detected also from genes with very low expression levels. The validation scheme thus allows determining conditions that could be improved to yield even higher DNA-microarray data quality.

As a consequence of the increased demand for proteins for both feed and food, alternative protein sources from green plants such as alfalfa ( Medicago sativa ) have come into focus, together with methods to recover these proteins. In this study, we have investigated the use of screw presses for protein recovery from alfalfa at laboratory and pilot scale. We found that using a pilot scale screw press, with a working pressure of 6 bar, 16% of the total protein was recovered in one pressing, and that after rehydrating and repressing the alfalfa up to ten times, 48% of the total protein could be recovered. The green alfalfa protein concentrate was analyzed for total protein, amino acid profile, protein digestibility, color, ash, fiber and fat content. It was found that repetitive pressings lowered the digestibility of the protein pool and reduced the total protein concentration due to dilution. To achieve the best quality protein at the highest concentrations, it is recommended to press the alfalfa no more than twice, which results in an alfalfa protein concentrate with more than 32% soluble protein and greater than 82% digestibility.

Using kindergarten up to fourth-grade data from the Early Childhood Longitudinal Study (2010-2011 cohort), we investigated systematic variability in English language learners' (ELLs; n = 303) mathematics growth as well as relations of kindergarten language growth and working memory (WM) to ELLs' mathematics growth. Using growth mixture modeling, only one class of growth emerged from ELLs' English mathematics growth from first through fourth grades. WM related to ELLs' English mathematics growth from Grades 1 to 4, as did kindergarten growth in English early literacy. We also investigated kindergarten to Grade 4 mathematics growth between ELLs and English proficient students (EPSs; n = 4,711) using latent change score models and whether WM differentially predicted growth patterns. ELLs and EPSs did not exhibit markedly different growth patterns, and WM similarly predicted these patterns. Implications for future research as well as practical implications and limitations are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

In vitro neuronal models are essential for studying neurological physiology, disease mechanisms and potential treatments. Most in vitro models lack controlled vasculature, despite its necessity in brain physiology and disease. Organ-on-chip models offer microfluidic culture systems with dedicated micro-compartments for neurons and vascular cells. Such multi-cell type organs-on-chips can emulate neurovascular unit (NVU) physiology, however there is a lack of systematic data on how individual cell types are affected by culturing on microfluidic systems versus conventional culture plates. This information can provide perspective on initial findings of studies using organs-on-chip models, and further optimizes these models in terms of cellular maturity and neurovascular physiology. Here, we analysed the transcriptomic profiles of co-cultures of human induced pluripotent stem cell (hiPSC)-derived neurons and rat astrocytes, as well as one-day monocultures of human endothelial cells, cultured on microfluidic chips. For each cell type, large gene expression changes were observed when cultured on microfluidic chips compared to conventional culture plates. Endothelial cells showed decreased cell division, neurons and astrocytes exhibited increased cell adhesion, and neurons showed increased maturity when cultured on a microfluidic chip. Our results demonstrate that culturing NVU cell types on microfluidic chips changes their gene expression profiles, presumably due to distinct surface-to-volume ratios and substrate materials. These findings inform further NVU organ-on-chip model optimization and support their future application in disease studies and drug testing.

Background: Estimating the genetic component of a complex phenotype is a complicated problem, mainly because there are many allele effects to estimate from a limited number of phenotypes. In spite of this difficulty, linear methods with variable selection have been able to give good predictions of additive effects of individuals. However, prediction of non-additive genetic effects is challenging with the usual prediction methods. In machine learning, non-additive relations between inputs can be modeled with neural networks. We developed a novel method (NetSparse) that uses Bayesian neural networks with variable selection for the prediction of genotypic values of individuals, including non-additive genetic effects., Results: We simulated several populations with different phenotypic models and compared NetSparse to genomic best linear unbiased prediction (GBLUP), BayesB, their dominance variants, and an additive by additive method. We found that when the number of QTL was relatively small (10 or 100), NetSparse had 2 to 28 percentage points higher accuracy than the reference methods. For scenarios that included dominance or epistatic effects, NetSparse had 0.0 to 3.9 percentage points higher accuracy for predicting phenotypes than the reference methods, except in scenarios with extreme overdominance, for which reference methods that explicitly model dominance had 6 percentage points higher accuracy than NetSparse., Conclusions: Bayesian neural networks with variable selection are promising for prediction of the genetic component of complex traits in animal breeding, and their performance is robust across different genetic models. However, their large computational costs can hinder their use in practice.

Motivation: Cellular identity and behavior is controlled by complex gene regulatory networks. Transcription factors (TFs) bind to specific DNA sequences to regulate the transcription of their target genes. On the basis of these TF motifs in cis-regulatory elements we can model the influence of TFs on gene expression. In such models of TF motif activity the data is usually modeled assuming a linear relationship between the motif activity and the gene expression level. A commonly used method to model motif influence is based on Ridge Regression. One important assumption of linear regression is the independence between samples. However, if samples are generated from the same cell line, tissue, or other biological source, this assumption may be invalid. This same assumption of independence is also applied to different yet similar experimental conditions, which may also be inappropriate. In theory, the independence assumption between samples could lead to loss in signal detection. Here we investigate whether a Bayesian model that allows for correlations results in more accurate inference of motif activities., Results: We extend the Ridge Regression to a Bayesian Linear Mixed Model, which allows us to model dependence between different samples. In a simulation study, we investigate the differences between the two model assumptions. We show that our Bayesian Linear Mixed Model implementation outperforms Ridge Regression in a simulation scenario where the noise, which is the signal that can not be explained by TF motifs, is uncorrelated. However, we demonstrate that there is no such gain in performance if the noise has a similar covariance structure over samples as the signal that can be explained by motifs. We give a mathematical explanation to why this is the case. Using four representative real datasets we show that at most ∼​40% of the signal is explained by motifs using the linear model. With these data there is no advantage to using the Bayesian Linear Mixed Model, due to the similarity of the covariance structure., Availability & Implementation: The project implementation is available at https://github.com/Sim19/SimGEXPwMotifs., Competing Interests: The authors declare no conflict of interest.

Remodeling of chromatin accessibility is necessary for successful reprogramming of fibroblasts to neurons. However, it is still not fully known which transcription factors can induce a neuronal chromatin accessibility profile when overexpressed in fibroblasts. To identify such transcription factors, we used ATAC-sequencing to generate differential chromatin accessibility profiles between human fibroblasts and iNeurons, an in vitro neuronal model system obtained by overexpression of Neurog2 in induced pluripotent stem cells (iPSCs). We found that the ONECUT transcription factor sequence motif was strongly associated with differential chromatin accessibility between iNeurons and fibroblasts. All three ONECUT transcription factors associated with this motif (ONECUT1, ONECUT2 and ONECUT3) induced a neuron-like morphology and expression of neuronal genes within two days of overexpression in fibroblasts. We observed widespread remodeling of chromatin accessibility; in particular, we found that chromatin regions that contain the ONECUT motif were in- or lowly accessible in fibroblasts and became accessible after the overexpression of ONECUT1, ONECUT2 or ONECUT3. There was substantial overlap with iNeurons, still, many regions that gained accessibility following ONECUT overexpression were not accessible in iNeurons. Our study highlights both the potential and challenges of ONECUT-based direct neuronal reprogramming., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Cell-based small molecule screening is an effective strategy leading to new medicines. Scientists in the pharmaceutical industry as well as in academia have made tremendous progress in developing both large-scale and smaller-scale screening assays. However, an accessible and universal technology for measuring large numbers of molecular and cellular phenotypes in many samples in parallel is not available. Here we present the immuno-detection by sequencing (ID-seq) technology that combines antibody-based protein detection and DNA-sequencing via DNA-tagged antibodies. We use ID-seq to simultaneously measure 70 (phospho-)proteins in primary human epidermal stem cells to screen the effects of ~300 kinase inhibitor probes to characterise the role of 225 kinases. The results show an association between decreased mTOR signalling and increased differentiation and uncover 13 kinases potentially regulating epidermal renewal through distinct mechanisms. Taken together, our work establishes ID-seq as a flexible solution for large-scale high-dimensional phenotyping in fixed cell populations.

Whole-transcriptome or RNA sequencing (RNA-Seq) is a powerful and versatile tool for functional analysis of different types of RNA molecules, but sample reagent and sequencing cost can be prohibitive for hypothesis-driven studies where the aim is to quantify differential expression of a limited number of genes. Here we present an approach for quantification of differential mRNA expression by targeted resequencing of complementary DNA using single-molecule molecular inversion probes (cDNA-smMIPs) that enable highly multiplexed resequencing of cDNA target regions of ∼100 nucleotides and counting of individual molecules. We show that accurate estimates of differential expression can be obtained from molecule counts for hundreds of smMIPs per reaction and that smMIPs are also suitable for quantification of relative gene expression and allele-specific expression. Compared with low-coverage RNA-Seq and a hybridization-based targeted RNA-Seq method, cDNA-smMIPs are a cost-effective high-throughput tool for hypothesis-driven expression analysis in large numbers of genes (10 to 500) and samples (hundreds to thousands).

Neurons derived from human induced Pluripotent Stem Cells (hiPSCs) provide a promising new tool for studying neurological disorders. In the past decade, many protocols for differentiating hiPSCs into neurons have been developed. However, these protocols are often slow with high variability, low reproducibility, and low efficiency. In addition, the neurons obtained with these protocols are often immature and lack adequate functional activity both at the single-cell and network levels unless the neurons are cultured for several months. Partially due to these limitations, the functional properties of hiPSC-derived neuronal networks are still not well characterized. Here, we adapt a recently published protocol that describes production of human neurons from hiPSCs by forced expression of the transcription factor neurogenin-2 12 . This protocol is rapid (yielding mature neurons within 3 weeks) and efficient, with nearly 100% conversion efficiency of transduced cells (>95% of DAPI-positive cells are MAP2 positive). Furthermore, the protocol yields a homogeneous population of excitatory neurons that would allow the investigation of cell-type specific contributions to neurological disorders. We modified the original protocol by generating stably transduced hiPSC cells, giving us explicit control over the total number of neurons. These cells are then used to generate hiPSC-derived neuronal networks on micro-electrode arrays. In this way, the spontaneous electrophysiological activity of hiPSC-derived neuronal networks can be measured and characterized, while retaining interexperimental consistency in terms of cell density. The presented protocol is broadly applicable, especially for mechanistic and pharmacological studies on human neuronal networks.

Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14 + monocytes, CD16 + neutrophils, and naive CD4 + T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of cis-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: Pharmacogenetic studies of tumour necrosis factor inhibitors (TNFi) response in patients with rheumatoid arthritis (RA) have largely relied on the changes in complex disease scores, such as disease activity score 28 (DAS28), as a measure of treatment response. It is expected that genetic architecture of such complex score is heterogeneous and not very suitable for pharmacogenetic studies. We aimed to select the most optimal phenotype for TNFi response using heritability estimates., Methods: Using two linear mixed-modelling approaches (Bayz and GCTA), we estimated heritability, together with genomic and environmental correlations for the TNFi drug-response phenotype ΔDAS28 and its separate components: Δ swollen joint count (SJC), Δ tender joint count (TJC), Δ erythrocyte sedimentation rate (ESR) and Δ visual-analogue scale of general health (VAS-GH). For this, we used genome-wide single nucleotide polymorphism (SNP) data from 878 TNFi-treated Dutch patients with RA. Furthermore, a multivariate genome-wide association study (GWAS) approach was implemented, analysing separate DAS28 components simultaneously., Results: The highest heritability estimates were found for ΔSJC (h(2)gbayz=0.76 and h(2)gGCTA=0.87) and ΔTJC (h(2)gbayz=0.62 and h(2)gGCTA=0.82); lower heritability was found for ΔDAS28 (h(2)gbayz=0.59 and h(2)gGCTA=0.71) while estimates for ΔESR and ΔVASGH were near or equal to zero. The highest genomic correlations were observed for ΔSJC and ΔTJC (0.49), and the highest environmental correlation was seen between ΔTJC and ΔVASGH (0.62). The multivariate GWAS did not generate excess of low p values as compared with a univariate analysis of ΔDAS28., Conclusions: Our results indicate that multiple SNPs together explain a substantial portion of the variation in change in joint counts in TNFi-treated patients with RA. In conclusion, of the outcomes studied, the joint counts are most suitable for TNFi pharmacogenetics in RA., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

Background: During early embryonic development, one of the two X chromosomes in mammalian female cells is inactivated to compensate for a potential imbalance in transcript levels with male cells, which contain a single X chromosome. Here, we use mouse female embryonic stem cells (ESCs) with non-random X chromosome inactivation (XCI) and polymorphic X chromosomes to study the dynamics of gene silencing over the inactive X chromosome by high-resolution allele-specific RNA-seq., Results: Induction of XCI by differentiation of female ESCs shows that genes proximal to the X-inactivation center are silenced earlier than distal genes, while lowly expressed genes show faster XCI dynamics than highly expressed genes. The active X chromosome shows a minor but significant increase in gene activity during differentiation, resulting in complete dosage compensation in differentiated cell types. Genes escaping XCI show little or no silencing during early propagation of XCI. Allele-specific RNA-seq of neural progenitor cells generated from the female ESCs identifies three regions distal to the X-inactivation center that escape XCI. These regions, which stably escape during propagation and maintenance of XCI, coincide with topologically associating domains (TADs) as present in the female ESCs. Also, the previously characterized gene clusters escaping XCI in human fibroblasts correlate with TADs., Conclusions: The gene silencing observed during XCI provides further insight in the establishment of the repressive complex formed by the inactive X chromosome. The association of escape regions with TADs, in mouse and human, suggests that TADs are the primary targets during propagation of XCI over the X chromosome.

Aims: Genetic factors explain a proportion of the inter-individual variation in the risk for atherosclerotic events, but the genetic basis of atherosclerosis and atherothrombosis in families with Mendelian forms of premature atherosclerosis is incompletely understood. We set out to unravel the molecular pathology in a large kindred with an autosomal dominant inherited form of premature atherosclerosis., Methods and Results: Parametric linkage analysis was performed in a pedigree comprising 4 generations, of which a total of 11 members suffered from premature vascular events. A parametric LOD-score of 3.31 was observed for a 4.4 Mb interval on chromosome 12. Upon sequencing, a non-synonymous variant in KERA (c.920C>G; p.Ser307Cys) was identified. The variant was absent from nearly 28,000 individuals, including 2,571 patients with premature atherosclerosis. KERA, a proteoglycan protein, was expressed in lipid-rich areas of human atherosclerotic lesions, but not in healthy arterial specimens. Moreover, KERA expression in plaques was significantly associated with plaque size in a carotid-collar Apoe-/- mice (r2 = 0.69; p<0.0001)., Conclusion: A rare variant in KERA was identified in a large kindred with premature atherosclerosis. The identification of KERA in atherosclerotic plaque specimen in humans and mice lends support to its potential role in atherosclerosis.

Two alternative universal screening approaches to identify students with early learning difficulties were examined, along with a combination of these approaches. These approaches, consisting of (a) curriculum-based measurement (CBM) and (b) teacher ratings using Performance Screening Guides (PSGs), served as predictors of achievement tests in reading and mathematics. Participants included 413 students in grades 1, 2, and 3 in Tennessee (n=118) and Wisconsin (n=295) who were divided into six subsamples defined by grade and state. Reading and mathematics achievement tests with established psychometric properties were used as criteria within a concurrent and predictive validity framework. Across both achievement areas, CBM probes shared more variance with criterion measures than did teacher ratings, although teacher ratings added incremental validity among most subsamples. PSGs tended to be more accurate for identifying students in need of assistance at a 1-month interval, whereas CBM probes were more accurate at a 6-month interval. Teachers indicated that (a) false negatives are more problematic than are false positives, (b) both screening methods are useful for identifying early learning difficulties, and (c) both screening methods are useful for identifying students in need of interventions. Collectively, these findings suggest that the two types of measures, when used together, yield valuable information about students who need assistance in reading and mathematics., (Copyright © 2013 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.)

Thrombocytopenia with absent radii (TAR) syndrome is a rare disorder combining specific skeletal abnormalities with a reduced platelet count. Rare proximal microdeletions of 1q21.1 are found in the majority of patients but are also found in unaffected parents. Recently it was shown that TAR syndrome is caused by the compound inheritance of a low-frequency noncoding SNP and a rare null allele in RBM8A, a gene encoding the exon-junction complex subunit member Y14 located in the deleted region. This finding provides new insight into the complex inheritance pattern and new clues to the molecular mechanisms underlying TAR syndrome. We discuss TAR syndrome in the context of abnormal phenotypes associated with proximal and distal 1q21.1 microdeletion and microduplications with incomplete penetrance and variable expressivity., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Short insertions and deletions (indels) are the second most abundant form of human genetic variation, but our understanding of their origins and functional effects lags behind that of other types of variants. Using population-scale sequencing, we have identified a high-quality set of 1.6 million indels from 179 individuals representing three diverse human populations. We show that rates of indel mutagenesis are highly heterogeneous, with 43%-48% of indels occurring in 4.03% of the genome, whereas in the remaining 96% their prevalence is 16 times lower than SNPs. Polymerase slippage can explain upwards of three-fourths of all indels, with the remainder being mostly simple deletions in complex sequence. However, insertions do occur and are significantly associated with pseudo-palindromic sequence features compatible with the fork stalling and template switching (FoSTeS) mechanism more commonly associated with large structural variations. We introduce a quantitative model of polymerase slippage, which enables us to identify indel-hypermutagenic protein-coding genes, some of which are associated with recurrent mutations leading to disease. Accounting for mutational rate heterogeneity due to sequence context, we find that indels across functional sequence are generally subject to stronger purifying selection than SNPs. We find that indel length modulates selection strength, and that indels affecting multiple functionally constrained nucleotides undergo stronger purifying selection. We further find that indels are enriched in associations with gene expression and find evidence for a contribution of nonsense-mediated decay. Finally, we show that indels can be integrated in existing genome-wide association studies (GWAS); although we do not find direct evidence that potentially causal protein-coding indels are enriched with associations to known disease-associated SNPs, our findings suggest that the causal variant underlying some of these associations may be indels.

The blood group Vel was discovered 60 years ago, but the underlying gene is unknown. Individuals negative for the Vel antigen are rare and are required for the safe transfusion of patients with antibodies to Vel. To identify the responsible gene, we sequenced the exomes of five individuals negative for the Vel antigen and found that four were homozygous and one was heterozygous for a low-frequency 17-nucleotide frameshift deletion in the gene encoding the 78-amino-acid transmembrane protein SMIM1. A follow-up study showing that 59 of 64 Vel-negative individuals were homozygous for the same deletion and expression of the Vel antigen on SMIM1-transfected cells confirm SMIM1 as the gene underlying the Vel blood group. An expression quantitative trait locus (eQTL), the common SNP rs1175550 contributes to variable expression of the Vel antigen (P = 0.003) and influences the mean hemoglobin concentration of red blood cells (RBCs; P = 8.6 × 10(-15)). In vivo, zebrafish with smim1 knockdown showed a mild reduction in the number of RBCs, identifying SMIM1 as a new regulator of RBC formation. Our findings are of immediate relevance, as the homozygous presence of the deletion allows the unequivocal identification of Vel-negative blood donors.

The exon-junction complex (EJC) performs essential RNA processing tasks. Here, we describe the first human disorder, thrombocytopenia with absent radii (TAR), caused by deficiency in one of the four EJC subunits. Compound inheritance of a rare null allele and one of two low-frequency SNPs in the regulatory regions of RBM8A, encoding the Y14 subunit of EJC, causes TAR. We found that this inheritance mechanism explained 53 of 55 cases (P < 5 × 10(-228)) of the rare congenital malformation syndrome. Of the 53 cases with this inheritance pattern, 51 carried a submicroscopic deletion of 1q21.1 that has previously been associated with TAR, and two carried a truncation or frameshift null mutation in RBM8A. We show that the two regulatory SNPs result in diminished RBM8A transcription in vitro and that Y14 expression is reduced in platelets from individuals with TAR. Our data implicate Y14 insufficiency and, presumably, an EJC defect as the cause of TAR syndrome.

Genome-sequencing studies indicate that all humans carry many genetic variants predicted to cause loss of function (LoF) of protein-coding genes, suggesting unexpected redundancy in the human genome. Here we apply stringent filters to 2951 putative LoF variants obtained from 185 human genomes to determine their true prevalence and properties. We estimate that human genomes typically contain ~100 genuine LoF variants with ~20 genes completely inactivated. We identify rare and likely deleterious LoF alleles, including 26 known and 21 predicted severe disease-causing variants, as well as common LoF variants in nonessential genes. We describe functional and evolutionary differences between LoF-tolerant and recessive disease genes and a method for using these differences to prioritize candidate genes found in clinical sequencing studies.

Two online surveys were completed by editors, associate editors, editorial board members, and members or fellows of the Division 16 of the American Psychological Association. These surveys targeted (a) the criteria for a manuscript to be published in school psychology journals, and (b) the components of the peer-review process that should be improved. Although prior surveys have targeted these issues in general, none have been conducted in school psychology or examined differences in perspectives between those who serve in a reviewing capacity or those who have served only in an author capacity. Results identified the most important characteristics for a manuscript submitted for publication to be positively reviewed as well as identified differences in the expectations for such characteristics between novice authors (who do not contribute to the journal editorial process) and those authors who serve the journal editorial process more extensively (e.g., editors and associate editors). In addition, key areas to target for improvement (e.g., reducing potential reviewer bias) within the reviewing process were identified., (Copyright © 2011 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.)

Background: Cystic fibrosis (CF) is a chronic genetic disease that leads to the accumulation of thick mucus in multiple organ systems, leading to chronic lung infection and affecting the body's ability to absorb nutrients necessary for growth and development. This cross-sectional, correlational study examined the potential effects of CF on students' psychosocial and academic development., Methods: Forty adolescents with CF completed a battery of neuropsychological and psychosocial measures. Their school records were reviewed to abstract information about standardized achievement testing results and grade point average (GPA). Academic outcomes were hypothesized to be associated with (1) self-efficacy, (2) disease and school-specific coping strategies, (3) attitude to school, and (4) depression., Results: Cognitive and academic scores were within the normal range, and self-efficacy had the strongest association with standardized cognitive and academic measures and high school grades. School absences were associated with GPA, but not standardized test scores., Conclusion: Adolescents with CF require supports in school that foster their sense of self-efficacy and accommodations that address the learning time lost from extended health-related absences., (© 2011, American School Health Association.)

Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API., Availability: http://vcftools.sourceforge.net

Gray platelet syndrome (GPS) is a predominantly recessive platelet disorder that is characterized by mild thrombocytopenia with large platelets and a paucity of α-granules; these abnormalities cause mostly moderate but in rare cases severe bleeding. We sequenced the exomes of four unrelated individuals and identified NBEAL2 as the causative gene; it has no previously known function but is a member of a gene family that is involved in granule development. Silencing of nbeal2 in zebrafish abrogated thrombocyte formation.

Small insertions and deletions (indels) are a common and functionally important type of sequence polymorphism. Most of the focus of studies of sequence variation is on single nucleotide variants (SNVs) and large structural variants. In principle, high-throughput sequencing studies should allow identification of indels just as SNVs. However, inference of indels from next-generation sequence data is challenging, and so far methods for identifying indels lag behind methods for calling SNVs in terms of sensitivity and specificity. We propose a Bayesian method to call indels from short-read sequence data in individuals and populations by realigning reads to candidate haplotypes that represent alternative sequence to the reference. The candidate haplotypes are formed by combining candidate indels and SNVs identified by the read mapper, while allowing for known sequence variants or candidates from other methods to be included. In our probabilistic realignment model we account for base-calling errors, mapping errors, and also, importantly, for increased sequencing error indel rates in long homopolymer runs. We show that our method is sensitive and achieves low false discovery rates on simulated and real data sets, although challenges remain. The algorithm is implemented in the program Dindel, which has been used in the 1000 Genomes Project call sets.

We propose an analytical approximation method for the estimation of multipoint identity by descent (IBD) probabilities in pedigrees containing a moderate number of distantly related individuals. We show that in large pedigrees where cases are related through untyped ancestors only, it is possible to formulate the hidden Markov model of the Lander-Green algorithm in terms of the IBD configurations of the cases. We use a first-order Markov approximation to model the changes in this IBD-configuration variable along the chromosome. In simulated and real data sets, we demonstrate that estimates of parametric and nonparametric linkage statistics based on the first-order Markov approximation are accurate. The computation time is exponential in the number of cases instead of in the number of meioses separating the cases. We have implemented our approach in the computer program ALADIN (accurate linkage analysis of distantly related individuals). ALADIN can be applied to general pedigrees and marker types and has the ability to model marker-marker linkage disequilibrium with a clustered-markers approach. Using ALADIN is straightforward: It requires no parameters to be specified and accepts standard input files.

We present CVMHAPLO, a probabilistic method for haplotyping in general pedigrees with many markers. CVMHAPLO reconstructs the haplotypes by assigning in every iteration a fixed number of the ordered genotypes with the highest marginal probability, conditioned on the marker data and ordered genotypes assigned in previous iterations. CVMHAPLO makes use of the cluster variation method (CVM) to efficiently estimate the marginal probabilities. We focused on single-nucleotide polymorphism (SNP) markers in the evaluation of our approach. In simulated data sets where exact computation was feasible, we found that the accuracy of CVMHAPLO was high and similar to that of maximum-likelihood methods. In simulated data sets where exact computation of the maximum-likelihood haplotype configuration was not feasible, the accuracy of CVMHAPLO was similar to that of state of the art Markov chain Monte Carlo (MCMC) maximum-likelihood approximations when all ordered genotypes were assigned and higher when only a subset of the ordered genotypes was assigned. CVMHAPLO was faster than the MCMC approach and provided more detailed information about the uncertainty in the inferred haplotypes. We conclude that CVMHAPLO is a practical tool for the inference of haplotypes in large complex pedigrees.

Recent studies have shown that linkage disequilibrium (LD) between single-nucleotide polymorphism (SNP) markers is widespread. Assuming linkage equilibrium has been shown to cause false positives in linkage studies where parental genotypes are not available. Therefore, linkage analysis methods that can deal with LD are required to accurately analyze SNP marker data sets. We compared three approaches to deal with LD between markers: 1) The clustered-markers approach implemented in the computer program MERLIN; 2) The standard hidden Markov model (HMM) multipoint model augmented with a first-order Markov model for the allele frequencies of the founders, in which we considered both a Bayesian and a maximum-likelihood implementation of this approach; 3) The 'independent' SNPs approach, i.e., removing SNPs from the data set until the remaining SNPs have low levels of LD.We evaluated these approaches on the Illumina 6K SNP data set of affected sib-pairs of Problem 2. We found that the first-order Markov model was able to account for most of the strong LD in this data set. The difference between the Bayesian and maximum- likelihood implementation was small. An advantage of the first-order Markov model is that it does not require the user to specify parameters.

Background: Computing exact multipoint LOD scores for extended pedigrees rapidly becomes infeasible as the number of markers and untyped individuals increase. When markers are excluded from the computation, significant power may be lost. Therefore accurate approximate methods which take into account all markers are desirable., Methods: We present a novel method for efficient estimation of LOD scores on extended pedigrees. Our approach is based on the Cluster Variation Method, which deterministically estimates likelihoods by performing exact computations on tractable subsets of variables (clusters) of a Bayesian network. First a distribution over inheritances on the marker loci is approximated with the Cluster Variation Method. Then this distribution is used to estimate the LOD score for each location of the trait locus., Results: First we demonstrate that significant power may be lost if markers are ignored in the multi-point analysis. On a set of pedigrees where exact computation is possible we compare the estimates of the LOD scores obtained with our method to the exact LOD scores. Secondly, we compare our method to a state of the art MCMC sampler. When both methods are given equal computation time, our method is more efficient. Finally, we show that CVM scales to large problem instances., Conclusion: We conclude that the Cluster Variation Method is as accurate as MCMC and generally is more efficient. Our method is a promising alternative to approaches based on MCMC sampling.

This article provides an update on evidence-based practices and interventions in school settings, with a specific focus on prevention and intervention programs that can be delivered within a three-tiered intervention framework. First, the sources of evidence-based intervention programs including clinical and school psychology task forces, the National Reading Panel, the What Works Clearinghouse, and several other organizations/groups are reviewed. Second, the three-tiered prevention framework (including universal, selected, and indicated programs) and various challenges surrounding their implementation (ie, screening, progress monitoring, professional development, and sustainability) are reviewed. The article concludes with an overview of some representative intervention programs within the three-tiered framework. Future challenges to the evidence-based practice movement are discussed within the context of developmental phases of this movement.

The authors outlined their management of patients with known or suspected metastatic cancer from an unknown primary site. Patients with disease in regional lymph nodes should receive thorough evaluation of sites that drain primarily to those nodes. If the primary site is not located, the node should be biopsied in a manner that allows node dissection if this proves to be indicated. A significant number of these patients will be cured or receive significant palliation. In patients with disseminated disease, an effort should be made to quickly determine whether the tumor is one for which specific treatment is available. There is nothing to be gained by conducting extensive evaluation to locate the primary site if the treatment will not be affected by this knowledge.

We have reevaluated and clinically tested the current concepts of shock and resuscitation on a logical, physiological, and physical basis. We have considered the currently accepted resuscitation paradigm which is based upon the thesis that early rapid resuscitation of "lost" fluid volume is mandatory and that adequacy of resuscitation can be evaluated by central venous pressure, PAP, PAWP, pulse rate, blood pressure, and/or urine volume. Such methods also accept as natural concomitants that capillary beds are "damaged by injury"; that they "leak" salt, fluid, and albumin; and that these are expected occurrences which are injury-related. We have also examined and clinically evaluated the thesis that MAP is a primary reflector of the relationships between volume and the size of the currently available functional vascular space. (Currently available functional vascular space is mediated through the baroreceptor (stretch receptor)/neuroendocrine mechanisms.) Under this hypothesis, fluid resuscitation comprises infusion of a volume per unit time given so as to replete currently measurable fluid losses and to normalize and/or sustain MAP and the normal osmolar and oncotic relationships at the capillary/tissue interface while holding hydrostatic pressure at normal. Using burn injury as a model, we compared statistically homogeneous, randomly selected groups of burn patients who were resuscitated using a hypotonic fluid (130 mOsm/liter) alone (group R: 7 patients), hypertonic fluid (240 mOsm/liter) alone group H: 5 patients), or the hypertonic fluid containing albumin (12.5 g/liter) (group A: 7 patients). The results indicate that significantly smaller volumes of fluid were needed to resuscitate the patients in group A with a significantly more rapid normalization of physical, physiological, and biochemical parameters. We conclude that the physically and physiologically appropriate method of resuscitation, demonstrated in burn injury, comprises the use of a fluid given at a rate: (1) to maintain mean arterial and hydrostatic pressures within normal range; (2) that delivers a volume per unit time which does not exceed the capacity of the currently available functional vascular space; (3) that replaces concurrent measurable fluid losses; (4) that is hypertonic (to normalize capillary/tissue osmotic gradients); and (5) that contains colloid (to normalize capillary/tissue osmotic gradients); and (5) that contains colloid (to normalize capillary/tissue oncotic gradients). We further conclude that salt, fluid, and colloid loss into the interstitium during resuscitation frequently is due to the rate delivered and/or the physical nature of the fluid used and not to capillary bed damage outside the zone of injury.