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Contains fulltext : 231651.pdf (Publisher’s version ) (Open Access) 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.

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.

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.

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)., Transcriptome sequencing is a powerful tool for functional analysis of different types of RNA molecules in a wide range of applications. Here the authors use targeted resequencing of cDNA with single-molecule molecular inversion probes as a cost-effective, high-throughput tool for mRNA quantification.

MotivationCellular 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.ResultsWe extend the Ridge Regression to a Bayesian Linear Mixed Model, which allows us to model dependence between different samples. In a simulation study, we in-vestigate 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, 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 two representative real data sets 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 & ImplementationThe project implementation is available athttps://github.com/Sim19/SimGEXPwMotifs.

Contains fulltext : 206705.pdf (Publisher’s version ) (Open Access) 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.

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.

Contains fulltext : 168902.pdf (Publisher’s version ) (Open Access) 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-212. 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. 10 p.

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 used ID-seq to simultaneously measure 84 (phospho-)proteins in hundreds of samples and screen the effects of ~300 kinase inhibitor probes on primary human epidermal stem cells to characterise the role of 225 kinases. Our work highlighted a previously unrecognized downregulation of mTOR signaling during differentiation and uncovered 13 kinases regulating epidermal renewal through distinct mechanisms.

The blood group Vel was discovered 60 years ago(1), 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(2). 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 x 10(-15))(3). 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.

For many traits and common human diseases, causal loci uncovered by genetic association studies account for little of the known heritable variation. Such ′ missing heritability ′ may be due to the effect of non-additive interactions between multiple loci, but this has been little explored and difficult to test using existing parametric approaches. We propose a Bayesian non-parametric Gaussian Process Regression model, for identifying associated loci in the presence of interactions of arbitrary order. We analysed 46 quantitative yeast phenotypes and found that over 70% of the total known missing heritability could be explained using common genetic variants, many without significant marginal effects. Additional analysis of an immunological rat phenotype identified a three SNP interaction model providing a significantly better fit (p-value 9.0e-11) than the null model incorporating only the single marginally significant SNP. This new approach, called GPMM, represents a significant advance in approaches to understanding the missing heritability problem with potentially important implications for studies of complex, quantitative traits.

After the publication of this work [1], we noticed there was an error in Fig. 5 where −1,0 and 1 are incorrectly displayed in the y-axis in panel b. Please see the corrected Fig. 5 below. We apologize for this error. Fig. 5 llele-specific RNA-seq on three NPC lines identifies three distal regions of genes that escape XCI. a Ratio of Xi/Xa (y-axis; for each of the three NPC lines sorted from highest to lowest) for genes showing a log2 ratio of at least −5. We set ...

Defective Gene Detective Identifying genes that give rise to diseases is one of the major goals of sequencing human genomes. However, putative loss-of-function genes, which are often some of the first identified targets of genome and exome sequencing, have often turned out to be sequencing errors rather than true genetic variants. In order to identify the true scope of loss-of-function genes within the human genome, MacArthur et al. (p. 823 ; see the Perspective by Quintana-Murci ) extensively validated the genomes from the 1000 Genomes Project, as well as an additional European individual, and found that the average person has about 100 true loss-of-function alleles of which approximately 20 have two copies within an individual. Because many known disease-causing genes were identified in “normal” individuals, the process of clinical sequencing needs to reassess how to identify likely causative alleles.

Contains fulltext : 34702.pdf (author's version ) (Open Access) 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.

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. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0698-x) contains supplementary material, which is available to authorized users.

ObjectivesPharmacogenetic 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.MethodsUsing 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.ResultsThe highest heritability estimates were found for ΔSJC (=0.76 and =0.87) and ΔTJC (=0.62 and =0.82); lower heritability was found for ΔDAS28 (=0.59 and =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.ConclusionsOur 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.

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies., Wellcome Trust (London, England) (Core Award 090532/Z/09/Z), Wellcome Trust (London, England) (Senior Investigator Award 095552/Z/11/Z ), Wellcome Trust (London, England) (WT095908), Wellcome Trust (London, England) (WT109497), Wellcome Trust (London, England) (WT098051), Wellcome Trust (London, England) (WT086084/Z/08/Z), Wellcome Trust (London, England) (WT100956/Z/13/Z ), Wellcome Trust (London, England) (WT097307), Wellcome Trust (London, England) (WT0855322/Z/08/Z ), Wellcome Trust (London, England) (WT090770/Z/09/Z ), Wellcome Trust (London, England) (Major Overseas program in Vietnam grant 089276/Z.09/Z), Medical Research Council (Great Britain) (grant G0801823), Biotechnology and Biological Sciences Research Council (Great Britain) (grant BB/I02593X/1), Biotechnology and Biological Sciences Research Council (Great Britain) (grant BB/I021213/1), Zhongguo ke xue ji shu qing bao yan jiu suo. Office of 863 Programme of China (2012AA02A201), National Basic Research Program of China (2011CB809201), National Basic Research Program of China (2011CB809202), National Basic Research Program of China (2011CB809203), National Natural Science Foundation of China (31161130357), Shenzhen Municipal Government of China (grant ZYC201105170397A), Canadian Institutes of Health Research (grant 136855), Quebec Ministry of Economic Development, Innovation, and Exports (PSR-SIIRI-195), Germany. Bundesministerium für Bildung und Forschung (0315428A), Germany. Bundesministerium für Bildung und Forschung (01GS08201), Germany. Bundesministerium für Bildung und Forschung (BMBF-EPITREAT grant 0316190A), Deutsche Forschungsgemeinschaft (Emmy Noether Grant KO4037/1-1), Beatriu de Pinos Program (2006 BP-A 10144), Beatriu de Pinos Program (2009 BP-B 00274), Spanish National Institute for Health (grant PRB2 IPT13/0001-ISCIII-SGEFI/FEDER), Japan Society for the Promotion of Science (fellowship number PE13075), Marie Curie Actions Career Integration (grant 303772), Fonds National Suisse del la Recherche, SNSF, Scientifique (31003A_130342), National Center for Biotechnology Information (U.S.) (U54HG3067), National Center for Biotechnology Information (U.S.) (U54HG3273), National Center for Biotechnology Information (U.S.) (U01HG5211), National Center for Biotechnology Information (U.S.) (U54HG3079), National Center for Biotechnology Information (U.S.) (R01HG2898), National Center for Biotechnology Information (U.S.) (R01HG2385), National Center for Biotechnology Information (U.S.) (RC2HG5552), National Center for Biotechnology Information (U.S.) (U01HG6513), National Center for Biotechnology Information (U.S.) (U01HG5214), National Center for Biotechnology Information (U.S.) (U01HG5715), National Center for Biotechnology Information (U.S.) (U01HG5718), National Center for Biotechnology Information (U.S.) (U01HG5728), National Center for Biotechnology Information (U.S.) (U41HG7635), National Center for Biotechnology Information (U.S.) (U41HG7497), National Center for Biotechnology Information (U.S.) (R01HG4960), National Center for Biotechnology Information (U.S.) (R01HG5701), National Center for Biotechnology Information (U.S.) (R01HG5214), National Center for Biotechnology Information (U.S.) (R01HG6855), National Center for Biotechnology Information (U.S.) (R01HG7068), National Center for Biotechnology Information (U.S.) (R01HG7644), National Center for Biotechnology Information (U.S.) (DP2OD6514), National Center for Biotechnology Information (U.S.) (DP5OD9154), National Center for Biotechnology Information (U.S.) (R01CA166661), National Center for Biotechnology Information (U.S.) (R01CA172652), National Center for Biotechnology Information (U.S.) (P01GM99568), National Center for Biotechnology Information (U.S.) (R01GM59290), National Center for Biotechnology Information (U.S.) (R01GM104390), National Center for Biotechnology Information (U.S.) (T32GM7790), National Center for Biotechnology Information (U.S.) (R01HL87699), National Center for Biotechnology Information (U.S.) (R01HL104608), National Center for Biotechnology Information (U.S.) (T32HL94284), National Center for Biotechnology Information (U.S.) (HHSN268201100040C), National Center for Biotechnology Information (U.S.) (HHSN272201000025C), Lundbeck Foundation (grant R170-2014-1039, Simons Foundation (SFARI award SF51), National Science Foundation (U.S.) (Research Fellowship DGE-1147470)

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.

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.

Item does not contain fulltext 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 x 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.

Item does not contain fulltext Anaemia is a chief determinant of global ill health, contributing to cognitive impairment, growth retardation and impaired physical capacity. To understand further the genetic factors influencing red blood cells, we carried out a genome-wide association study of haemoglobin concentration and related parameters in up to 135,367 individuals. Here we identify 75 independent genetic loci associated with one or more red blood cell phenotypes at P < 10(-8), which together explain 4-9% of the phenotypic variance per trait. Using expression quantitative trait loci and bioinformatic strategies, we identify 121 candidate genes enriched in functions relevant to red blood cell biology. The candidate genes are expressed preferentially in red blood cell precursors, and 43 have haematopoietic phenotypes in Mus musculus or Drosophila melanogaster. Through open-chromatin and coding-variant analyses we identify potential causal genetic variants at 41 loci. Our findings provide extensive new insights into genetic mechanisms and biological pathways controlling red blood cell formation and function.

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.

Summary Genomic structural variants (SVs) are abundant in humans, differing from other variation classes in extent, origin, and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (i.e., copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analyzing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.

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.

Background Pain is the dominant and prevailing symptom of rheumatoid arthritis (RA). Tumor necrosis factor inihibitors (TNFi) have proven very successful in pain reduction. Interestingly, improvement of pain, is notable rapidly after administration of TNFi agents, and well before anti-inflammatory effects of treatment can be observed. The vast majority of pharmacogenetic studies of response to TNFi utilized the clinical outcome measure, disease activity score 28 (DAS28). This measure is very useful for clinical practice, however it might be less heritable as compared to individual, potentially more homogeneous, disease measures. Usage of less complex phenotypes, such as pain (heritability 28-71%), might significantly aid the identification of genetic markers predicting differential response to TNFi. Objectives We aimed to identify and replicate genetic factors predicting pain reduction upon TNFi treatment in patients with RA using genome-wide association approach. Methods We included 508 TNFi treated RA patients. Association analysis of change of visual analogue scale of pain (VAS-pain) after 14 weeks of treatment was performed on imputed genome-wide genotyping data under additive genetic model with adjustment for baseline VAS-pain. We also conducted a meta-analysis including 1287 RA patients. Gene-based analysis was performed using VEGAS. Results No findings reached the threshold for genome-wide significance (P-value≤1x10 –8 ) in the discovery cohort. Meta-analysis revealed 213 SNPs suggestively associated (P –4 ) with change in VAS-pain after fourteen weeks of TNFi treatment. The most significant SNP rs2295739 (p=2.21x10 –6 ), located ∼50kb upstream from the KCNK10 gene, which belongs to a family of genes involved in sensory perception. The top hit in the gene-based analysis was RET, known to regulate ion channels and receptors participating in detection and transduction of sensory stimuli. Besides Ret-deficient mice show elevated pain responses. Conclusions We have identified several suggestive genomic regions, further studies are required to validate if these regions play a role in pain reduction upon TNFi treatment. Disclosure of Interest : None declared DOI 10.1136/annrheumdis-2014-eular.4094

Abstract 2372 Thrombocytopenia-absent radii syndrome (TAR) is a rare congenital disorder characterized by bilateral radius aplasia and thrombocytopenia due to virtual absence of bone marrow megakaryocytes (MKs). As other blood lineages are not affected, TAR is considered to be a bone marrow failure syndrome. At birth, platelet counts are often below 50/nL, but ameliorate in many patients during the first two years of life, however, without reaching the lower norm. Platelet reactivity in reponse to thrombopoietin is abrogated, mostly when patients are young while it is restored to normal in older patients. Interestingly, this patterning is not correlated with platelet counts. In 2007 we described a microdeletion on chromosome 1q21 in all patients analyzed as necessary, but not sufficient to cause TAR, as it was also present in unaffected family members. Using next generation sequencing, we recently identified the presence of one of two single nucleotide polymorphisms (SNPs) in non-coding, regulatory regions of the gene RBM8A whose compound inheritance together with the microdeletion causes TAR syndrome (Nature Genetics, 44, 435). All patients inherited one of the two SNPs from one of one parents and the microdeletion from the other. In 25% of cases the microdeletion was acquired de novo. This gene, which is located within the microdeleted region, encodes for Y14, a major subunit of the exon junction complex. In the German/Swiss/Polish cohort of 28 patients, 20 patients had the more frequent SNP in the 5'UTR, whereas 8 patients had a previously undescribed SNP in intron 1. The 5'UTR G->A introduces a novel binding site for transcriptional repressor Evi1. This factor is expressed in megakaryocytes and still present in platelets. Using electrophoretic mobility shift assays (EMSA) with lysates from platelets we found a complex exclusively present when a labeled oligonucleotide with the 5'UTR-SNP G->C was used and not with the wildtype sequence. This complex could be partially supershifted with an anti-Evi1 antibody, suggesting that an additional factor might bind to this complex. In contrast, the intronic SNP is predicted to abrogate a binding site for Mzf1. We used EMSA with oligonucleotides of both intronic SNP and wildtype sequence and indeed found a complex binding to the wildtype sequence as well as to a consensus sequence for Mzf1, but no or reduced binding to the intronic SNP. This complex could not be supershifted by adding an anti-Mzf1 antibody. However, when nuclear extracts from Meg01 cells were used, we found that one of three subcomplexes showed reduced binding in the presence of the antibody, even when the intronic SNP was used. As both SNPs showed different binding properties we carefully evaluated clinical data and found that platelet counts were more severely reduced in patients with the 5'UTR compared to patients harboring the intronic SNP. Although case numbers for the intronic SNP are low, in a direct comparison of age-matched patients, platelet counts were always higher in TAR patients with the intronic SNP. However, we did not find any obvious differences in skeletal features between the two groups. Further work is ongoing to better understand the causative role of either SNP for impaired megakaryopoiesis and thrombocytopenia in TAR syndrome. Disclosures: No relevant conflicts of interest to declare.

Contains fulltext : 70751.pdf (Publisher’s version ) (Closed access) 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.

Summary 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., Graphical Abstract, Highlights • Genome, transcriptome, and epigenome reference panel in three human immune cell types • Identified 4,418 genes associated with epigenetic changes independent of genetics • Described genome-epigenome coordination defining cell-type-specific regulatory events • Functionally mapped disease mechanisms at 345 unique autoimmune disease loci, As part of the IHEC consortium, this study integrates genetic, epigenetic, and transcriptomic profiling in three immune cell types from nearly 200 people to characterize the distinct and cooperative contributions of diverse genomic inputs to transcriptional variation. Explore the Cell Press IHEC web portal at http://www.cell.com/consortium/IHEC.

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.