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The genomes of individuals with severe, undiagnosed developmental disorders are enriched in damaging de novo mutations (DNMs) in developmentally important genes. Here we have sequenced the exomes of 4,293 families containing individuals with developmental disorders, and meta-analysed these data with data from another 3,287 individuals with similar disorders. We show that the most important factors influencing the diagnostic yield of DNMs are the sex of the affected individual, the relatedness of their parents, whether close relatives are affected and the parental ages. We identified 94 genes enriched in damaging DNMs, including 14 that previously lacked compelling evidence of involvement in developmental disorders. We have also characterized the phenotypic diversity among these disorders. We estimate that 42% of our cohort carry pathogenic DNMs in coding sequences; approximately half of these DNMs disrupt gene function and the remainder result in altered protein function. We estimate that developmental disorders caused by DNMs have an average prevalence of 1 in 213 to 1 in 448 births, depending on parental age. Given current global demographics, this equates to almost 400,000 children born per year. Whole-exome analysis of individuals with developmental disorders shows that de novo mutations can equally cause loss or altered protein function, but that most mutations causing altered protein function have not yet been described. De novo mutations in developmental disorders Matthew Hurles, Jeremy McRae and colleagues from the Deciphering Developmental Disorders Study report exome sequencing of 4,293 families containing individuals with severe, undiagnosed developmental disorders. They find enrichment of damaging de novo mutations in 94 genes, implicating them in developmental disorders. They estimate that 42% of the cohort carry pathogenic de novo mutations in coding sequences resulting in disrupted or altered protein function.

Chromatin remodeling is of crucial importance during brain development. Pathogenic alterations of several chromatin remodeling ATPases have been implicated in neurodevelopmental disorders. We describe an index case with a de novo missense mutation in CHD3 , identified during whole genome sequencing of a cohort of children with rare speech disorders. To gain a comprehensive view of features associated with disruption of this gene, we use a genotype-driven approach, collecting and characterizing 35 individuals with de novo CHD3 mutations and overlapping phenotypes. Most mutations cluster within the ATPase/helicase domain of the encoded protein. Modeling their impact on the three-dimensional structure demonstrates disturbance of critical binding and interaction motifs. Experimental assays with six of the identified mutations show that a subset directly affects ATPase activity, and all but one yield alterations in chromatin remodeling. We implicate de novo CHD3 mutations in a syndrome characterized by intellectual disability, macrocephaly, and impaired speech and language. Chromodomain Helicase DNA-binding (CHD) proteins have been implicated in neurodevelopmental processes. Here, the authors identify missense variants in CHD3 that disturb its chromatin remodeling activities and cause a neurodevelopmental disorder with macrocephaly and speech and language impairment.

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.

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

An estimated 400,000 children are born every year with rare genetic disorders that significantly affect their quality of life. Early detection and intervention can significantly improve the quality of life of these children. Craniofacial characteristics contain highly useful information for clinical geneticists for diagnosis. This thesis investigates the use of computer vision to aid in the automatic detection of genetic disorders from ordinary facial photographs. This is a non-trivial task, in part due to patient privacy concerns and the scarcity of training data. In the following, we present several approaches to overcome these challenges. First, we present a method for creating realistic-looking average faces for individuals sharing a syndrome. These averages remove identifiable features, but retain clinically relevant phenotype information and preserve facial asymmetry. This procedure is completely automated, removing the need to expose patient identities at any point during the process, and could be used to help facilitate facial diagnosis in clinical settings. We also investigate creating transitions between averages and exaggerated caricature faces to highlight phenotype differences between patient groups. Second, we investigate the classification of eight genetic disorders with shallow and deep representations. We compare shape and appearance descriptors based on local and dense descriptors and report significant improvements upon previous work. Furthermore, we made use of transfer learning and part-based models to train convolutional networks for syndrome classification. Our results show that deep learning can be used in the context of classifying genetic disorders, and is superior to shallow descriptors, despite small training datasets. Neural networks are prone to learning biases present in training datasets and basing their decisions on them. This is particularly relevant for training on small datasets, as is the case in the domain of genetic disorders. We introduce a bias removal algorithm that aims to overcome this challenge. We report three distinct contributions. First, to ensure that a network is blind to a known bias in the dataset, second, to improve classification performance when faced with an extreme bias, and third, to remove multiple spurious variations from the feature representation of a primary classification task. Lastly, we introduce a novel image augmentation method for learning a deep face embedding, the “Interpolated Clinical Face Phenotype Space”, that aims to describe clinically relevant face variation. Our contributions are two-fold: 1) Interpolations between faces that share a class improve deep representation training from small datasets. 2) Between-class interpolations that model the space between classes improve the generalisation performance of the deep representation to unseen syndromes.

The HTML and PDF versions of this Article were updated after publication to remove images of one individual from Figure 1.The HTML and PDF versions of this Article were updated after publication to remove images of one individual from Figure 1.

The original version of this Article contained an error in the spelling of the author Laurence Faivre, which was incorrectly given as Laurence Faive. This has now been corrected in both the PDF and HTML versions of the Article.

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.

Individuals with severe, undiagnosed developmental disorders (DDs) are enriched for damaging de novo mutations (DNMs) in developmentally important genes. We exome sequenced 4,293 families with individuals with DDs, and meta-analysed these data with published data on 3,287 individuals with similar disorders. We show that the most significant factors influencing the diagnostic yield of de novo mutations are the sex of the affected individual, the relatedness of their parents and the age of both father and mother. We identified 94 genes enriched for damaging de novo mutation at genome-wide significance (P < 7 x 10-7), including 14 genes for which compelling data for causation was previously lacking. We have characterised the phenotypic diversity among these genetic disorders. We demonstrate that, at current cost differentials, exome sequencing has much greater power than genome sequencing for novel gene discovery in genetically heterogeneous disorders. We estimate that 42% of our cohort carry pathogenic DNMs (single nucleotide variants and indels) in coding sequences, with approximately half operating by a loss-of-function mechanism, and the remainder resulting in altered-function (e.g. activating, dominant negative). We established that most haplo insufficient developmental disorders have already been identified, but that many altered-function disorders remain to be discovered. Extrapolating from the DDD cohort to the general population, we estimate that developmental disorders caused by DNMs have an average birth prevalence of 1 in 213 to 1 in 448 (0.22-0.47% of live births), depending on parental age.