Testing the accuracy of an observation-based classifier for rapid detection of autism risk

Current approaches for diagnosing autism have high diagnostic validity but are time consuming and can contribute to delays in arriving at an official diagnosis. In a pilot study, we used machine learning to derive a classifier that represented a 72% reduction in length from the gold-standard Autism Diagnostic Observation Schedule-Generic (ADOS-G), while retaining >97% statistical accuracy. The pilot study focused on a relatively small sample of children with and without autism. The present study sought to further test the accuracy of the classifier (termed the observation-based classifier (OBC)) on an independent sample of 2616 children scored using ADOS from five data repositories and including both spectrum (n=2333) and non-spectrum (n=283) individuals. We tested OBC outcomes against the outcomes provided by the original and current ADOS algorithms, the best estimate clinical diagnosis, and the comparison score severity metric associated with ADOS-2. The OBC was significantly correlated with the ADOS-G (r=−0.814) and ADOS-2 (r=−0.779) and exhibited >97% sensitivity and >77% specificity in comparison to both ADOS algorithm scores. The correspondence to the best estimate clinical diagnosis was also high (accuracy=96.8%), with sensitivity of 97.1% and specificity of 83.3%. The correlation between the OBC score and the comparison score was significant (r=−0.628), suggesting that the OBC provides both a classification as well as a measure of severity of the phenotype. These results further demonstrate the accuracy of the OBC and suggest that reductions in the process of detecting and monitoring autism are possible

Transcript expression-aware annotation improves rare variant interpretation

The acceleration of DNA sequencing in samples from patients and population studies has resulted in extensive catalogues of human genetic variation, but the interpretation of rare genetic variants remains problematic. A notable example of this challenge is the existence of disruptive variants in dosage-sensitive disease genes, even in apparently healthy individuals. Here, by manual curation of putative loss-of-function (pLoF) variants in haploinsufficient disease genes in the Genome Aggregation Database (gnomAD)(1), we show that one explanation for this paradox involves alternative splicing of mRNA, which allows exons of a gene to be expressed at varying levels across different cell types. Currently, no existing annotation tool systematically incorporates information about exon expression into the interpretation of variants. We develop a transcript-level annotation metric known as the 'proportion expressed across transcripts', which quantifies isoform expression for variants. We calculate this metric using 11,706 tissue samples from the Genotype Tissue Expression (GTEx) project(2) and show that it can differentiate between weakly and highly evolutionarily conserved exons, a proxy for functional importance. We demonstrate that expression-based annotation selectively filters 22.8% of falsely annotated pLoF variants found in haploinsufficient disease genes in gnomAD, while removing less than 4% of high-confidence pathogenic variants in the same genes. Finally, we apply our expression filter to the analysis of de novo variants in patients with autism spectrum disorder and intellectual disability or developmental disorders to show that pLoF variants in weakly expressed regions have similar effect sizes to those of synonymous variants, whereas pLoF variants in highly expressed exons are most strongly enriched among cases. Our annotation is fast, flexible and generalizable, making it possible for any variant file to be annotated with any isoform expression dataset, and will be valuable for the genetic diagnosis of rare diseases, the analysis of rare variant burden in complex disorders, and the curation and prioritization of variants in recall-by-genotype studies.Peer reviewe

High intensity neutrino oscillation facilities in Europe

The EUROnu project has studied three possible options for future, high intensity neutrino oscillation facilities in Europe. The first is a Super Beam, in which the neutrinos come from the decay of pions created by bombarding targets with a 4 MW proton beam from the CERN High Power Superconducting Proton Linac. The far detector for this facility is the 500 kt MEMPHYS water Cherenkov, located in the Fréjus tunnel. The second facility is the Neutrino Factory, in which the neutrinos come from the decay of μ+ and μ− beams in a storage ring. The far detector in this case is a 100 kt magnetized iron neutrino detector at a baseline of 2000 km. The third option is a Beta Beam, in which the neutrinos come from the decay of beta emitting isotopes, in particular He6 and Ne18, also stored in a ring. The far detector is also the MEMPHYS detector in the Fréjus tunnel. EUROnu has undertaken conceptual designs of these facilities and studied the performance of the detectors. Based on this, it has determined the physics reach of each facility, in particular for the measurement of CP violation in the lepton sector, and estimated the cost of construction. These have demonstrated that the best facility to build is the Neutrino Factory. However, if a powerful proton driver is constructed for another purpose or if the MEMPHYS detector is built for astroparticle physics, the Super Beam also becomes very attractive

A Large Hadron Electron Collider at CERN

This document provides a brief overview of the recently published report on the design of the Large Hadron Electron Collider (LHeC), which comprises its physics programme, accelerator physics, technology and main detector concepts. The LHeC exploits and develops challenging, though principally existing, accelerator and detector technologies. This summary is complemented by brief illustrations of some of the highlights of the physics programme, which relies on a vastly extended kinematic range, luminosity and unprecedented precision in deep inelastic scattering. Illustrations are provided regarding high precision QCD, new physics (Higgs, SUSY) and electron-ion physics. The LHeC is designed to run synchronously with the LHC in the twenties and to achieve an integrated luminosity of O(100) fb$^{-1}$. It will become the cleanest high resolution microscope of mankind and will substantially extend as well as complement the investigation of the physics of the TeV energy scale, which has been enabled by the LHC

Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes.

Heterogeneity in early language development in autism spectrum disorder (ASD) is clinically important and may reflect neurobiologically distinct subtypes. Here, we identified a large-scale association between multiple coordinated blood leukocyte gene coexpression modules and the multivariate functional neuroimaging (fMRI) response to speech. Gene coexpression modules associated with the multivariate fMRI response to speech were different for all pairwise comparisons between typically developing toddlers and toddlers with ASD and poor versus good early language outcome. Associated coexpression modules were enriched in genes that are broadly expressed in the brain and many other tissues. These coexpression modules were also enriched in ASD-associated, prenatal, human-specific, and language-relevant genes. This work highlights distinctive neurobiology in ASD subtypes with different early language outcomes that is present well before such outcomes are known. Associations between neuroimaging measures and gene expression levels in blood leukocytes may offer a unique in vivo window into identifying brain-relevant molecular mechanisms in ASD

Gene family information facilitates variant interpretation and identification of disease-associated genes in neurodevelopmental disorders

Background Classifying pathogenicity of missense variants represents a major challenge in clinical practice during the diagnoses of rare and genetic heterogeneous neurodevelopmental disorders (NDDs). While orthologous gene conservation is commonly employed in variant annotation, approximately 80% of known disease-associated genes belong to gene families. The use of gene family information for disease gene discovery and variant interpretation has not yet been investigated on a genome-wide scale. We empirically evaluate whether paralog-conserved or non-conserved sites in human gene families are important in NDDs. Methods Gene family information was collected from Ensembl. Paralog-conserved sites were defined based on paralog sequence alignments; 10,068 NDD patients and 2078 controls were statistically evaluated for de novo variant burden in gene families. Results We demonstrate that disease-associated missense variants are enriched at paralog-conserved sites across all disease groups and inheritance models tested. We developed a gene family de novo enrichment framework that identified 43 exome-wide enriched gene families including 98 de novo variant carrying genes in NDD patients of which 28 represent novel candidate genes for NDD which are brain expressed and under evolutionary constraint. Conclusion This study represents the first method to incorporate gene family information into a statistical framework to interpret variant data for NDDs and to discover new NDD-associated genes

Gene family information facilitates variant interpretation and identification of disease-associated genes in neurodevelopmental disorders

Abstract Background Classifying pathogenicity of missense variants represents a major challenge in clinical practice during the diagnoses of rare and genetic heterogeneous neurodevelopmental disorders (NDDs). While orthologous gene conservation is commonly employed in variant annotation, approximately 80% of known disease-associated genes belong to gene families. The use of gene family information for disease gene discovery and variant interpretation has not yet been investigated on a genome-wide scale. We empirically evaluate whether paralog-conserved or non-conserved sites in human gene families are important in NDDs. Methods Gene family information was collected from Ensembl. Paralog-conserved sites were defined based on paralog sequence alignments; 10,068 NDD patients and 2078 controls were statistically evaluated for de novo variant burden in gene families. Results We demonstrate that disease-associated missense variants are enriched at paralog-conserved sites across all disease groups and inheritance models tested. We developed a gene family de novo enrichment framework that identified 43 exome-wide enriched gene families including 98 de novo variant carrying genes in NDD patients of which 28 represent novel candidate genes for NDD which are brain expressed and under evolutionary constraint. Conclusion This study represents the first method to incorporate gene family information into a statistical framework to interpret variant data for NDDs and to discover new NDD-associated genes

Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population

Almost all genetic risk factors for autism spectrum disorders (ASDs) can be found in the general population, but the effects of that risk are unclear in people not ascertained for neuropsychiatric symptoms. Using several large ASD consortia and population based resources, we find genetic links between ASDs and typical variation in social behavior and adaptive functioning. This finding is evidenced through both inherited and de novo variation, indicating that multiple types of genetic risk for ASDs influence a continuum of behavioral and developmental traits, the severe tail of which can result in an ASD or other neuropsychiatric disorder diagnosis. A continuum model should inform the design and interpretation of studies of neuropsychiatric disease biology

Transcriptome of iPSC-derived neuronal cells reveals a module of co-expressed genes consistently associated with autism spectrum disorder

Evaluation of expression profile in autism spectrum disorder (ASD) patients is an important approach to understand possible similar functional consequences that may underlie disease pathophysiology regardless of its genetic heterogeneity. Induced pluripotent stem cell (iPSC)-derived neuronal models have been useful to explore this question, but larger cohorts and different ASD endophenotypes still need to be investigated. Moreover, whether changes seen in this in vitro model reflect previous findings in ASD postmortem brains and how consistent they are across the studies remain underexplored questions. We examined the transcriptome of iPSC-derived neuronal cells from a normocephalic ASD cohort composed mostly of high-functioning individuals and from non-ASD individuals. ASD patients presented expression dysregulation of a module of co-expressed genes involved in protein synthesis in neuronal progenitor cells (NPC), and a module of genes related to synapse/neurotransmission and a module related to translation in neurons. Proteomic analysis in NPC revealed potential molecular links between the modules dysregulated in NPC and in neurons. Remarkably, the comparison of our results to a series of transcriptome studies revealed that the module related to synapse has been consistently found as upregulated in iPSC-derived neurons-which has an expression profile more closely related to fetal brain-while downregulated in postmortem brain tissue, indicating a reliable association of this network to the disease and suggesting that its dysregulation might occur in different directions across development in ASD individuals. Therefore, the expression pattern of this network might be used as biomarker for ASD and should be experimentally explored as a therapeutic target