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The rise of antimicrobial resistance (AMR) is one of the greatest public health challenges, already causing up to 1.2 million deaths annually and rising. Current gold-standard antimicrobial susceptibility tests (ASTs) are low-throughput and can take up to 48 hours, with implications for patient care. We present advances towards a novel, rapid AST, based on the deep-learning of single-cell specific phenotypes directly associated with antimicrobial susceptibility inEscherichia coli. Our models can reliably (80% single-cell accuracy) classify untreated and treated susceptible cells, across a range of antibiotics and phenotypes - including phenotypes not visually distinct to a trained, human observer. Applying models trained on lab-reference susceptible strains to clinical isolates ofE. colitreated with ciprofloxacin, we demonstrate our models reveal significant (p

Accurate placenta pathology assessment is essential for managing maternal and newborn health, but the placenta’s heterogeneity and temporal variability pose challenges for histology analysis. To address this issue, we developed the ‘Histology Analysis Pipeline.PY’ (HAPPY), a deep learning hierarchical method for quantifying the variability of cells and micro-anatomical tissue structures across placenta histology whole slide images. HAPPY differs from patch-based features or segmentation approaches by following an interpretable biological hierarchy, representing cells and cellular communities within tissues at a single-cell resolution across whole slide images. We present a set of quantitative metrics from healthy term placentas as a baseline for future assessments of placenta health and we show how these metrics deviate in placentas with clinically significant placental infarction. HAPPY’s cell and tissue predictions closely replicate those from independent clinical experts and placental biology literature.

The 11th International Conference on Rare Diseases and Orphan Drugs (ICORD), South Africa, included the Africa-Rare initiative launch and facilitated multi-stakeholder engagement in the challenges facing, and opportunities for, Africans living with rare diseases. The following ICORD Global Call to Action, developed in collaboration with the International Rare Diseases Research Consortium, synthesizes the outcomes of the deliberations and emphasizes the international collaborative efforts required to address the global effects of rare diseases on public health.

Adipose tissue is an organ with great plasticity and its hypertrophic expansion is associated with adipocyte dysfunction. How changes in adipocyte morphology are linked to gene expression and which cellular functions are affected remains elusive. We show that adipocyte hypertrophy is associated with transcriptomic changes using RNA-Seq data obtained from adipose tissue and size-separated adipocytes. Genes involved in oxidative phosphorylation, protein biosynthesis and fatty acid metabolism were down-regulated in large adipocytes while genes involved in inflammation were upregulated. For mitochondrial function, a reduction in the expression of thermogenesis related genes and estimated brown/beige adipocyte content was observed in individuals with large adipocytes. As a novel finding the total adipose tissue fatty acid composition was dependent on cell size and depot. MR spectroscopy methods for clinical scanning were developed to characterize adipocyte size and fatty acid composition in a fast and non-invasive manner. Together, the present data provides mechanistic insights on how adipocyte hypertrophy contributes to the manifestation of metabolic disease at the molecular and cellular level. MR spectroscopy was identified as a promising technique for an in-parallel assessment of adipose morphology and fatty acid composition allowing to translate our findings into an improved, non-invasive phenotyping of adipose tissue.

We describe the clinical features of nine unrelated individuals with rare de novo missense or in-frame deletions/duplications within the « HX motif » of exon 7 of ATN1. We previously proposed that individuals with such variants should be considered as being affected by the syndromic condition of congenital hypotonia, epilepsy, developmental delay, and digital anomalies (CHEDDA), distinct from dentatorubral-pallidoluysian atrophy (DRPLA) secondary to expansion variants in exon 5 of ATN1. We confirm that the universal phenotypic features of CHEDDA are distinctive facial features and global developmental delay. Infantile hypotonia and minor hand and feet differences are common and can present as arthrogryposis. Common comorbidities include severe feeding difficulties, often requiring gastrostomy support, as well as visual and hearing impairments. Epilepsy and congenital malformations of the brain, heart, and genitourinary systems are frequent but not universal. Our study confirms the clinical entity of CHEDDA secondary to a mutational signature restricted to exon 7 of ATN1. We propose a clinical schedule for assessment upon diagnosis, surveillance, and early intervention including the potential of neuroimaging for prognostication.

Contains fulltext : 202928.pdf (Publisher’s version ) (Open Access) Acetylation of the lysine residues in histones and other DNA-binding proteins plays a major role in regulation of eukaryotic gene expression. This process is controlled by histone acetyltransferases (HATs/KATs) found in multiprotein complexes that are recruited to chromatin by the scaffolding subunit transformation/transcription domain-associated protein (TRRAP). TRRAP is evolutionarily conserved and is among the top five genes intolerant to missense variation. Through an international collaboration, 17 distinct de novo or apparently de novo variants were identified in TRRAP in 24 individuals. A strong genotype-phenotype correlation was observed with two distinct clinical spectra. The first is a complex, multi-systemic syndrome associated with various malformations of the brain, heart, kidneys, and genitourinary system and characterized by a wide range of intellectual functioning; a number of affected individuals have intellectual disability (ID) and markedly impaired basic life functions. Individuals with this phenotype had missense variants clustering around the c.3127G>A p.(Ala1043Thr) variant identified in five individuals. The second spectrum manifested with autism spectrum disorder (ASD) and/or ID and epilepsy. Facial dysmorphism was seen in both groups and included upslanted palpebral fissures, epicanthus, telecanthus, a wide nasal bridge and ridge, a broad and smooth philtrum, and a thin upper lip. RNA sequencing analysis of skin fibroblasts derived from affected individuals skin fibroblasts showed significant changes in the expression of several genes implicated in neuronal function and ion transport. Thus, we describe here the clinical spectrum associated with TRRAP pathogenic missense variants, and we suggest a genotype-phenotype correlation useful for clinical evaluation of the pathogenicity of the variants.

The DNA damage-binding protein 1 (DDB1) is part of the CUL4-DDB1 ubiquitin E3 ligase complex (CRL4), which is essential for DNA repair, chromatin remodeling, DNA replication, and signal transduction. Loss-of-function variants in genes encoding the complex components CUL4 and PHIP have been reported to cause syndromic intellectual disability with hypotonia and obesity, but no phenotype has been reported in association with DDB1 variants. Here, we report eight unrelated individuals, identified through Matchmaker Exchange, with de novo monoallelic variants in DDB1, including one recurrent variant in four individuals. The affected individuals have a consistent phenotype of hypotonia, mild to moderate intellectual disability, and similar facies, including horizontal or slightly bowed eyebrows, deep-set eyes, full cheeks, a short nose, and large, fleshy and forward-facing earlobes, demonstrated in the composite face generated from the cohort. Digital anomalies, including brachydactyly and syndactyly, were common. Three older individuals have obesity. We show that cells derived from affected individuals have altered DDB1 function resulting in abnormal DNA damage signatures and histone methylation following UV-induced DNA damage. Overall, our study adds to the growing family of neurodevelopmental phenotypes mediated by disruption of the CRL4 ubiquitin ligase pathway and begins to delineate the phenotypic and molecular effects of DDB1 misregulation.

The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into RDs has been limited by insufficient patient data and resources, a paucity of trained disease experts, and lack of therapeutics, leading to long delays in diagnosis and treatment. These issues can be ameliorated by following the principles and practices of sharing that are intrinsic to Open Science. Here, we describe how the RD community has adopted the core pillars of Open Science, adding new initiatives to promote care and research for RD patients and, ultimately, for all of medicine. We also present recommendations that can advance Open Science more globally. This work was supported by the U.S. Department of Health and Human Services National Institutes of Health (5r24od011883), National Institutes of Health (NIH) Office of the Director (OD); the Monarch Initiative (1R24OD011883) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (U54 HD079123). Sí

Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the relationship between mean adipocyte area and obesity-related traits, and identify genetic factors associated with adipocyte cell size. To perform the first large-scale study of automatic adipocyte phenotyping using both histological and genetic data, we developed a deep learning-based method, the Adipocyte U-Net, to rapidly derive mean adipocyte area estimates from histology images. We validate our method using three state-of-the-art approaches; CellProfiler, Adiposoft and floating adipocytes fractions, all run blindly on two external cohorts. We observe high concordance between our method and the state-of-the-art approaches (Adipocyte U-net vs. CellProfiler: R2visceral = 0.94, P < 2.2 × 10−16, R2subcutaneous = 0.91, P < 2.2 × 10−16), and faster run times (10,000 images: 6mins vs 3.5hrs). We applied the Adipocyte U-Net to 4 cohorts with histology, genetic, and phenotypic data (total N = 820). After meta-analysis, we found that mean adipocyte area positively correlated with body mass index (BMI) (Psubq = 8.13 × 10−69, βsubq = 0.45; Pvisc = 2.5 × 10−55, βvisc = 0.49; average R2 across cohorts = 0.49) and that adipocytes in subcutaneous depots are larger than their visceral counterparts (Pmeta = 9.8 × 10−7). Lastly, we performed the largest GWAS and subsequent meta-analysis of mean adipocyte area and intra-individual adipocyte variation (N = 820). Despite having twice the number of samples than any similar study, we found no genome-wide significant associations, suggesting that larger sample sizes and a homogenous collection of adipose tissue are likely needed to identify robust genetic associations., Author summary Fundamental aspects of biology such as how the size or number of adipocytes relates to obesity and cardiometabolic health are still unanswered. To answer such questions, fast, accurate and automated measurements need to be acquired, free from human biases. Glastonbury et al., 2020 describe a novel machine learning method to perform rapid acquisition of adipocyte area estimates from histological imaging data. Using these imaging derived phenotypes, Glastonbury et al., 2020 assess the relationship between adipocyte size and a range of cardio-metabolic comorbidities, demonstrating that adipocyte size can vary depending on where adipose is stored throughout the body. By tying genetics with imaging data, Glastonbury et al., 2020 were able to demonstrate that previous findings associating adipocyte size with Type 2 Diabetes variants, are likely to be false positives. This study provides a means of being able to scale up GWAS type analyses to imaging derived phenotypes.

BackgroundDe novo mutations in PURA have recently been described to cause PURA syndrome, a neurodevelopmental disorder characterised by severe intellectual disability (ID), epilepsy, feeding difficulties and neonatal hypotonia.ObjectivesTo delineate the clinical spectrum of PURA syndrome and study genotype-phenotype correlations.MethodsDiagnostic or research-based exome or Sanger sequencing was performed in individuals with ID. We systematically collected clinical and mutation data on newly ascertained PURA syndrome individuals, evaluated data of previously reported individuals and performed a computational analysis of photographs. We classified mutations based on predicted effect using 3D in silico models of crystal structures of Drosophila-derived Pur-alpha homologues. Finally, we explored genotype-phenotype correlations by analysis of both recurrent mutations as well as mutation classes.ResultsWe report mutations in PURA (purine-rich element binding protein A) in 32 individuals, the largest cohort described so far. Evaluation of clinical data, including 22 previously published cases, revealed that all have moderate to severe ID and neonatal-onset symptoms, including hypotonia (96%), respiratory problems (57%), feeding difficulties (77%), exaggerated startle response (44%), hypersomnolence (66%) and hypothermia (35%). Epilepsy (54%) and gastrointestinal (69%), ophthalmological (51%) and endocrine problems (42%) were observed frequently. Computational analysis of facial photographs showed subtle facial dysmorphism. No strong genotype-phenotype correlation was identified by subgrouping mutations into functional classes.ConclusionWe delineate the clinical spectrum of PURA syndrome with the identification of 32 additional individuals. The identification of one individual through targeted Sanger sequencing points towards the clinical recognisability of the syndrome. Genotype-phenotype analysis showed no significant correlation between mutation classes and disease severity.

Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the relationship between adipocyte area and obesity-related traits, and identify genetic factors associated with adipocyte cell size. To perform the first large-scale study of automatic adipocyte phenotyping using both histological and genetic data, we developed a deep learning-based method, the Adipocyte U-Net, to rapidly derive area estimates from histology images. We validate our method using three state-of-the-art approaches; CellProfiler, Adiposoft and floating adipocytes fractions, all run blindly on two external cohorts. We observe high concordance between our method and the state-of-the-art approaches (Adipocyte U-net vs. CellProfiler: R2visceral= 0.94, P < 2.2 × 10−16, R2subcutaneous= 0.91, P < 2.2 × 10−16), and faster run times (10,000 images: 6mins vs 3.5hrs). We applied the Adipocyte U-Net to 4 cohorts with histology, genetic, and phenotypic data (total N = 820). After meta-analysis, we found that adipocyte area positively correlated with body mass index (BMI) (Psubq = 8.13 × 10−69, βsubq = 0.45; Pvisc= 2.5 × 10−55, βvisc= 0.49; average R2 across cohorts = 0.49) and that adipocytes in subcutaneous depots are larger than their visceral counterparts (Pmeta= 9.8 × 10−7). Lastly, we performed the largest GWAS and subsequent meta-analysis of adipocyte area and intra-individual adipocyte variation (N = 820). Despite having twice the number of samples than any similar study, we found no genome-wide significant associations, suggesting that larger sample sizes and a homogenous collection of adipose tissue are likely needed to identify robust genetic associations.

With the propensity for deep learning models to learn unintended signals from data sets there is always the possibility that the network can `cheat' in order to solve a task. In the instance of data sets for visual kinship verification, one such unintended signal could be that the faces are cropped from the same photograph, since faces from the same photograph are more likely to be from the same family. In this paper we investigate the influence of this artefactual data inference in published data sets for kinship verification. To this end, we obtain a large dataset, and train a CNN classifier to determine if two faces are from the same photograph or not. Using this classifier alone as a naive classifier of kinship, we demonstrate near state of the art results on five public benchmark data sets for kinship verification - achieving over 90% accuracy on one of them. Thus, we conclude that faces derived from the same photograph are a strong inadvertent signal in all the data sets we examined, and it is likely that the fraction of kinship explained by existing kinship models is small., Comment: Accepted to ACCV 2018

Purpose The interpretation of genetic variants after genome-wide analysis is complex in heterogeneous disorders such as intellectual disability (ID). We investigate whether algorithms can be used to detect if a facial gestalt is present for three novel ID syndromes and if these techniques can help interpret variants of uncertain significance. Methods Facial features were extracted from photos of ID patients harboring a pathogenic variant in three novel ID genes (PACS1, PPM1D, and PHIP) using algorithms that model human facial dysmorphism, and facial recognition. The resulting features were combined into a hybrid model to compare the three cohorts against a background ID population. Results We validated our model using images from 71 individuals with Koolen–de Vries syndrome, and then show that facial gestalts are present for individuals with a pathogenic variant in PACS1 (p = 8 × 10−4), PPM1D (p = 4.65 × 10−2), and PHIP (p = 6.3 × 10−3). Moreover, two individuals with a de novo missense variant of uncertain significance in PHIP have significant similarity to the expected facial phenotype of PHIP patients (p Conclusion Our results show that analysis of facial photos can be used to detect previously unknown facial gestalts for novel ID syndromes, which will facilitate both clinical and molecular diagnosis of rare and novel syndromes.

Neural networks achieve the state-of-the-art in image classification tasks. However, they can encode spurious variations or biases that may be present in the training data. For example, training an age predictor on a dataset that is not balanced for gender can lead to gender biased predicitons (e.g. wrongly predicting that males are older if only elderly males are in the training set). We present two distinct contributions: 1) An algorithm that can remove multiple sources of variation from the feature representation of a network. We demonstrate that this algorithm can be used to remove biases from the feature representation, and thereby improve classification accuracies, when training networks on extremely biased datasets. 2) An ancestral origin database of 14,000 images of individuals from East Asia, the Indian subcontinent, sub-Saharan Africa, and Western Europe. We demonstrate on this dataset, for a number of facial attribute classification tasks, that we are able to remove racial biases from the network feature representation., Will appear in Workshop on Bias Estimation in Face Analytics, ECCV 2018

Background: INTERBIO-21st is Phase II of the INTERGROWTH-21st Project, the population-based, research initiative involving nearly 70,000 mothers and babies worldwide coordinated by Oxford University and performed by a multidisciplinary network of more than 400 healthcare professionals and scientists from 35 institutions in 21 countries worldwide. Phase I, conducted 2008-2015, consisted of nine complementary studies designed to describe optimal human growth and neurodevelopment, based conceptually on the WHO prescriptive approach. The studies generated a set of international standards for monitoring growth and neurodevelopment, which complement the existing WHO Child Growth Standards. Phase II aims to improve the functional classification of the highly heterogenous preterm birth and fetal growth restriction syndromes through a better understanding of how environmental exposures, clinical conditions and nutrition influence patterns of human growth from conception to childhood, as well as specific neurodevelopmental domains and associated behaviors at 2 years of age. Methods: In the INTERBIO-21st Newborn Case-Control Study, a major component of Phase II, our objective is to investigate the mechanisms potentially responsible for preterm birth and small for gestational age and their interactions, using deep phenotyping of clinical, growth and epidemiological data and associated nutritional, biochemical, omic and histological profiles. Here we describe the study sites, population characteristics, study design, methodology and standardization procedures for the collection of longitudinal clinical data and biological samples (maternal blood, umbilical cord blood, placental tissue, maternal feces and infant buccal swabs) for the study that was conducted between 2012 and 2018 in Brazil, Kenya, Pakistan, South Africa, Thailand and the UK. Discussion: Our study provides a unique resource for the planned analyses given the range of potentially disadvantageous exposures (including poor nutrition, pregnancy complications and infections) in geographically diverse populations worldwide. The study should enhance current medical knowledge and provide new insights into environmental influences on human growth and neurodevelopment.

Gaining access to large, labelled sets of relevant images is crucial for the development and testing of biomedical imaging algorithms. Using images found in biomedical research articles would contribute some way towards a solution to this problem. However, this approach critically depends on being able to identify the most relevant images from very large sets of potentially useful figures. In this paper a deep convolutional neural network (CNN) classifier is trained using only synthetic data, to rapidly and accurately label raw images taken from biomedical articles. We apply this method in the context of detecting faces in biomedical images; and show that the classifier is able to retrieve figures containing faces with an average precision of 94.8%, from a dataset of over 31,000 images taken from articles held in the PubMed database. The utility of the classifier is then demonstrated through a case study, by aiding the mining of photographs of patients with rare genetic disorders from targeted articles. This approach is readily adaptable to facilitate the retrieval of other categories of biomedical images.

Summary The ADAR RNA-editing enzymes deaminate adenosine bases to inosines in cellular RNAs. Aberrant interferon expression occurs in patients in whom ADAR1 mutations cause Aicardi-Goutières syndrome (AGS) or dystonia arising from striatal neurodegeneration. Adar1 mutant mouse embryos show aberrant interferon induction and die by embryonic day E12.5. We demonstrate that Adar1 embryonic lethality is rescued to live birth in Adar1; Mavs double mutants in which the antiviral interferon induction response to cytoplasmic double-stranded RNA (dsRNA) is prevented. Aberrant immune responses in Adar1 mutant mouse embryo fibroblasts are dramatically reduced by restoring the expression of editing-active cytoplasmic ADARs. We propose that inosine in cellular RNA inhibits antiviral inflammatory and interferon responses by altering RLR interactions. Transfecting dsRNA oligonucleotides containing inosine-uracil base pairs into Adar1 mutant mouse embryo fibroblasts reduces the aberrant innate immune response. ADAR1 mutations causing AGS affect the activity of the interferon-inducible cytoplasmic isoform more severely than the nuclear isoform., Graphical Abstract, Highlights • Adar1 mutant mouse embryonic lethality is rescued in Adar1; Mavs double mutants • Aberrant antiviral responses in the Adar1 mutant are due to loss of RNA editing • Human ADAR1 mutations causing AGS affect primarily the interferon-inducible isoform • We propose that inosine helps innate immunity to distinguish cellular from viral RNA, Mice lacking Adar1 have a heightened immune response and stress-related apoptosis. Mannion et al. demonstrate that this mutation can be rescued to birth by generating a double mutant with Mavs, an innate immune gene, indicating the central role ADAR1 plays in innate immunity.

Background: Melting temperature analysis of products amplified with SYBR I Green chemistry is a cheap and effective method for identification of sequence differences. When used in conventional quantitative real-time PCR instruments (qPCR), this method is limited by temperature variations over the heating block and low numbers of fluorescence measurements during the dissociation step, which hamper the ability of most instruments to report accurate and precise melting temperatures.Methods: We designed a molecular beacon–based temperature indicator probe (Tm-probe) to control for variations in temperatures over the heating block of the instrument. In addition, we wrote an automated curve-fit analysis algorithm of dissociation data to use multiple data points with a gaussian curve fit to extrapolate precise melting temperatures.Results: Use of the Tm-probe in conjunction with the analysis algorithm and multiple dissociations improved SDs of melting temperatures over a 96-well plate from 0.19 to 0.06 °CConclusions: Melting temperature analyses with SYBR I Green chemistry on conventional qPCR instruments can be improved by the use of a Tm-probe in conjunction with curve-fit analysis of data. Resolution improvement up to 3-fold is possible and allows additional melting temperatures to be identified.

Recently, two candidate analogs for human syncytin, denoted syncytins A and B, were identified in the murine genome. These were found to have expression patterns and functions similar to human syncytin. In addition, the identification of glial cells missing (GCM)-binding motifs in putative promoter regions of the mouse syncytins imply analogous regulation. Transcriptional modulation of syncytin by exogenous agents was recently suggested by studies reporting transactivation of syncytin in human cell lines following virus infections. The authors report that influenza A virus infection increased the levels of transcripts encoding Gcm1 and syncytin B, but not syncytin A, in NIH-3T3 cells as well as in mouse primary neurons or glia. Overexpression of human GCM1 in NIH-3T3 cells resulted in increased levels of transcripts encoding syncytin B but not syncytin A. Systemic administration of neurotropic influenza A virus resulted in a neuronal infection and increased levels of Gcm1-encoding transcripts in brains of young mice. The mouse may therefore be useful for studies on the expression and function of endogenous retroviral envelope genes and transcription factors regulating their expression in the placenta and brain during physiological or pathological conditions.

Purpose: To characterize features associated with de novo mutations affecting SATB2 function in individuals ascertained on the basis of intellectual disability. Methods: Twenty previously unreported individuals with 19 different SATB2 mutations (11 loss-of-function and 8 missense variants) were studied. Fibroblasts were used to measure mutant protein production. Subcellular localization and mobility of wild-type and mutant SATB2 were assessed using fluorescently tagged protein. Results: Recurrent clinical features included neurodevelopmental impairment (19/19), absent/near absent speech (16/19), normal somatic growth (17/19), cleft palate (9/19), drooling (12/19), and dental anomalies (8/19). Six of eight missense variants clustered in the first CUT domain. Sibling recurrence due to gonadal mosaicism was seen in one family. A nonsense mutation in the last exon resulted in production of a truncated protein retaining all three DNA-binding domains. SATB2 nuclear mobility was mutation-dependent; p.Arg389Cys in CUT1 increased mobility and both p.Gly515Ser in CUT2 and p.Gln566Lys between CUT2 and HOX reduced mobility. The clinical features in individuals with missense variants were indistinguishable from those with loss of function. Conclusion: SATB2 haploinsufficiency is a common cause of syndromic intellectual disability. When mutant SATB2 protein is produced, the protein appears functionally inactive with a disrupted pattern of chromatin or matrix association. Genet Med advance online publication 02 February 2017

The amount of a genome's sequence that is functional has been surprisingly difficult to estimate accurately. This has severely hindered analyses asking whether the amount of functional genomic sequence correlates with organismal complexity. Most studies estimate these amounts by considering nucleotide substitution rates within aligned sequences. These approaches show reduced power to identify sequence that is aligned, functional, and constrained only within narrowly defined phyla. The neutral indel model exploits insertions or deletions (indels) rather than substitutions in predicting functional sequence. Surprisingly, this method indicates that half of all functional sequence is specific to individual eutherian lineages. This review considers the rates at which coding or noncoding and functional or nonfunctional sequence changes among mammalian genomes. In contrast to the slow rate at which protein-coding sequence changes, functional noncoding sequence appears to change or be turned over at rapid rates in mammals.

International audience; We report genome sequences of 17 inbred strains of laboratory mice and identify almost ten times more variants than previously known. We use these genomes to explore the phylogenetic history of the laboratory mouse and to examine the functional consequences of allele-specific variation on transcript abundance, revealing that at least 12% of transcripts show a significant tissue-specific expression bias. By identifying candidate functional variants at 718 quantitative trait loci we show that the molecular nature of functional variants and their position relative to genes vary according to the effect size of the locus. These sequences provide a starting point for a new era in the functional analysis of a key model organism.

Craniofacial characteristics are highly informative for clinical geneticists when diagnosing genetic diseases. As a first step towards the high-throughput diagnosis of ultra-rare developmental diseases we introduce an automatic approach that implements recent developments in computer vision. This algorithm extracts phenotypic information from ordinary non-clinical photographs and, using machine learning, models human facial dysmorphisms in a multidimensional 'Clinical Face Phenotype Space'. The space locates patients in the context of known syndromes and thereby facilitates the generation of diagnostic hypotheses. Consequently, the approach will aid clinicians by greatly narrowing (by 27.6-fold) the search space of potential diagnoses for patients with suspected developmental disorders. Furthermore, this Clinical Face Phenotype Space allows the clustering of patients by phenotype even when no known syndrome diagnosis exists, thereby aiding disease identification. We demonstrate that this approach provides a novel method for inferring causative genetic variants from clinical sequencing data through functional genetic pathway comparisons. DOI: http://dx.doi.org/10.7554/eLife.02020.001, eLife digest Rare genetic disorders affect around 8% of people, many of whom live with symptoms that greatly reduce their quality of life. Genetic diagnoses can provide doctors with information that cannot be obtained by assessing clinical symptoms, and this allows them to select more suitable treatments for patients. However, only a minority of patients currently receive a genetic diagnosis. Alterations in the face and skull are present in 30–40% of genetic disorders, and these alterations can help doctors to identify certain disorders, such as Down’s syndrome or Fragile X. Extending this approach, Ferry et al. trained a computer-based model to identify the patterns of facial abnormalities associated with different genetic disorders. The model compares data extracted from a photograph of the patient’s face with data on the facial characteristics of 91 disorders, and then provides a list of the most likely diagnoses for that individual. The model used 36 points to describe the space, including 7 for the jaw, 6 for the mouth, 7 for the nose, 8 for the eyes and 8 for the brow. This approach of Ferry et al. has three advantages. First, it provides clinicians with information that can aid their diagnosis of a rare genetic disorder. Second, it can narrow down the range of possible disorders for patients who have the same ultra-rare disorder, even if that disorder is currently unknown. Third, it can identify groups of patients who can have their genomes sequenced in order to identify the genetic variants that are associated with specific disorders. The work by Ferry et al. lays out the basic principles for automated approaches to analyze the shape of the face and skull. The next challenge is to integrate photos with genetic data for use in clinical settings. DOI: http://dx.doi.org/10.7554/eLife.02020.002

International audience; Background: Transposable element (TE)-derived sequence dominates the landscape of mammalian genomes and can modulate gene function by dysregulating transcription and translation. Our current knowledge of TEs in laboratory mouse strains is limited primarily to those present in the C57BL/6J reference genome, with most mouse TEs being drawn from three distinct classes, namely short interspersed nuclear elements (SINEs), long interspersed nuclear elements (LINEs) and the endogenous retrovirus (ERV) superfamily. Despite their high prevalence, the different genomic and gene properties controlling whether TEs are preferentially purged from, or are retained by, genetic drift or positive selection in mammalian genomes remain poorly defined. Results: Using whole genome sequencing data from 13 classical laboratory and 4 wild-derived mouse inbred strains, we developed a comprehensive catalogue of 103,798 polymorphic TE variants. We employ this extensive data set to characterize TE variants across the Mus lineage, and to infer neutral and selective processes that have acted over 2 million years. Our results indicate that the majority of TE variants are introduced though the male germline and that only a minority of TE variants exert detectable changes in gene expression. However, among genes with differential expression across the strains there are twice as many TE variants identified as being putative causal variants as expected. Conclusions: Most TE variants that cause gene expression changes appear to be purged rapidly by purifying selection. Our findings demonstrate that past TE insertions have often been highly deleterious, and help to prioritize TE variants according to their likely contribution to gene expression or phenotype variation.

International audience; Structural variation is widespread in mammalian genomes and is an important cause of disease, but just how abundant and important structural variants (SVs) are in shaping phenotypic variation remains unclear. Without knowing how many SVs there are, and how they arise, it is difficult to discover what they do. Combining experimental with automated analyses, we identified 711,920 SVs at 281,243 sites in the genomes of thirteen classical and four wild-derived inbred mouse strains. The majority of SVs are less than 1 kilobase in size and 98% are deletions or insertions. The breakpoints of 160,000 SVs were mapped to base pair resolution, allowing us to infer that insertion of retrotransposons causes more than half of SVs. Yet, despite their prevalence, SVs are less likely than other sequence variants to cause gene expression or quantitative phenotypic variation. We identified 24 SVs that disrupt coding exons, acting as rare variants of large effect on gene function. One-third of the genes so affected have immunological functions.

Background One member of the W family of human endogenous retroviruses (HERV) appears to have been functionally adopted by the human host. Nevertheless, a highly diversified and regulated transcription from a range of HERV-W elements has been observed in human tissues and cells. Aberrant expression of members of this family has also been associated with human disease such as multiple sclerosis (MS) and schizophrenia. It is not known whether this broad expression of HERV-W elements represents transcriptional leakage or specific transcription initiated from the retroviral promoter in the long terminal repeat (LTR) region. Therefore, potential influences of genomic context, structure and orientation on the expression levels of individual HERV-W elements in normal human tissues were systematically investigated. Results Whereas intronic HERV-W elements with a pseudogene structure exhibited a strong anti-sense orientation bias, intronic elements with a proviral structure and solo LTRs did not. Although a highly variable expression across tissues and elements was observed, systematic effects of context, structure and orientation were also observed. Elements located in intronic regions appeared to be expressed at higher levels than elements located in intergenic regions. Intronic elements with proviral structures were expressed at higher levels than those elements bearing hallmarks of processed pseudogenes or solo LTRs. Relative to their corresponding genes, intronic elements integrated on the sense strand appeared to be transcribed at higher levels than those integrated on the anti-sense strand. Moreover, the expression of proviral elements appeared to be independent from that of their corresponding genes. Conclusions Intronic HERV-W provirus integrations on the sense strand appear to have elicited a weaker negative selection than pseudogene integrations of transcripts from such elements. Our current findings suggest that the previously observed diversified and tissue-specific expression of elements in the HERV-W family is the result of both directed transcription (involving both the LTR and internal sequence) and leaky transcription of HERV-W elements in normal human tissues.

Background Human endogenous retroviruses (HERV) constitute approximately 8% of the human genome and have long been considered "junk". The sheer number and repetitive nature of these elements make studies of their expression methodologically challenging. Hence, little is known of transcription of genomic regions harboring such elements. Results Applying a recently developed technique for obtaining high resolution melting temperature data, we examined the frequency distributions of HERV-W gag element into 13 Tm categories in human tissues. Transcripts containing HERV-W gag sequences were expressed in non-random patterns with extensive variations in the expression between both tissues, including different brain regions, and individuals. Furthermore, the patterns of such transcripts varied more between individuals in brain regions than other tissues. Conclusion Thus, regulated expression of non-coding regions of the human genome appears to include the HERV-W family of repetitive elements. Although it remains to be established whether such expression patterns represent leakage from transcription of functional regions or specific transcription, the current approach proves itself useful for studying detailed expression patterns of repetitive regions.

Background: In addition to their use in detecting undesired real-time PCR products, melting temperatures are useful for detecting variations in the desired target sequences. Methodological improvements in recent years allow the generation of high-resolution melting-temperature (Tm) data. However, there is currently no convention on how to statistically analyze such high-resolution Tm data. Results: Mixture model analysis was applied to Tm data. Models were selected based on Akaike's information criterion. Mixture model analysis correctly identified categories in Tm data obtained for known plasmid targets. Using simulated data, we investigated the number of observations required for model construction. The precision of the reported mixing proportions from data fitted to a preconstructed model was also evaluated. Conclusion: Mixture model analysis of Tm data allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined. This approach allows Tm data to be analyzed, classified, and compared in an unbiased manner.