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Diagnosing rare developmental disorders using genome-wide sequencing data commonly necessitates review of multiple plausible candidate variants, often using ontologies of categorical clinical terms. We show that Integrating Multiple Phenotype Resources Optimizes Variant Evaluation in Developmental Disorders (IMPROVE-DD) by incorporating additional classes of data commonly available to clinicians and recorded in health records. In doing so, we quantify the distinct contributions of sex, growth, and development in addition to Human Phenotype Ontology (HPO) terms and demonstrate added value from these readily available information sources. We use likelihood ratios for nominal and quantitative data and propose a classifier for HPO terms in this framework. This Bayesian framework results in more robust diagnoses. Using data systematically collected in the Deciphering Developmental Disorders study, we considered 77 genes with pathogenic/likely pathogenic variants in ≥10 individuals. All genes showed at least a satisfactory prediction by receiver operating characteristic when testing on training data (AUC ≥ 0.6), and HPO terms were the best predictor for the majority of genes, though a minority (13/77) of genes were better predicted by other phenotypic data types. Overall, classifiers based upon multiple integrated phenotypic data sources performed better than those based upon any individual source, and importantly, integrated models produced notably fewer false positives. Finally, we show that IMPROVE-DD models with good predictive performance on cross-validation can be constructed from relatively few individuals. This suggests new strategies for candidate gene prioritization and highlights the value of systematic clinical data collection to support diagnostic programs., Competing Interests: M.E.H. is a co-founder, consultant, and non-executive director of Congenica Ltd., (© 2022 The Authors.)

Structural variation (SV) describes a broad class of genetic variation greater than 50 bp in size. SVs can cause a wide range of genetic diseases and are prevalent in rare developmental disorders (DDs). Individuals presenting with DDs are often referred for diagnostic testing with chromosomal microarrays (CMAs) to identify large copy-number variants (CNVs) and/or with single-gene, gene-panel, or exome sequencing (ES) to identify single-nucleotide variants, small insertions/deletions, and CNVs. However, individuals with pathogenic SVs undetectable by conventional analysis often remain undiagnosed. Consequently, we have developed the tool InDelible, which interrogates short-read sequencing data for split-read clusters characteristic of SV breakpoints. We applied InDelible to 13,438 probands with severe DDs recruited as part of the Deciphering Developmental Disorders (DDD) study and discovered 63 rare, damaging variants in genes previously associated with DDs missed by standard SNV, indel, or CNV discovery approaches. Clinical review of these 63 variants determined that about half (30/63) were plausibly pathogenic. InDelible was particularly effective at ascertaining variants between 21 and 500 bp in size and increased the total number of potentially pathogenic variants identified by DDD in this size range by 42.9%. Of particular interest were seven confirmed de novo variants in MECP2, which represent 35.0% of all de novo protein-truncating variants in MECP2 among DDD study participants. InDelible provides a framework for the discovery of pathogenic SVs that are most likely missed by standard analytical workflows and has the potential to improve the diagnostic yield of ES across a broad range of genetic diseases., Competing Interests: Declaration of interests M.E.H. is a founder of, consultant to, director of, and holds shares in Congenica Ltd and is a consultant to the AZ Centre for Genomics Research. H.V.F. is a Section Editor for genetics for UpToDate. All other authors declare no conflict of interest., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Over 130 X-linked genes have been robustly associated with developmental disorders, and X-linked causes have been hypothesised to underlie the higher developmental disorder rates in males. Here, we evaluate the burden of X-linked coding variation in 11,044 developmental disorder patients, and find a similar rate of X-linked causes in males and females (6.0% and 6.9%, respectively), indicating that such variants do not account for the 1.4-fold male bias. We develop an improved strategy to detect X-linked developmental disorders and identify 23 significant genes, all of which were previously known, consistent with our inference that the vast majority of the X-linked burden is in known developmental disorder-associated genes. Importantly, we estimate that, in male probands, only 13% of inherited rare missense variants in known developmental disorder-associated genes are likely to be pathogenic. Our results demonstrate that statistical analysis of large datasets can refine our understanding of modes of inheritance for individual X-linked disorders.

De novo mutations in protein-coding genes are a well-established cause of developmental disorders 1 . However, genes known to be associated with developmental disorders account for only a minority of the observed excess of such de novo mutations 1,2 . Here, to identify previously undescribed genes associated with developmental disorders, we integrate healthcare and research exome-sequence data from 31,058 parent-offspring trios of individuals with developmental disorders, and develop a simulation-based statistical test to identify gene-specific enrichment of de novo mutations. We identified 285 genes that were significantly associated with developmental disorders, including 28 that had not previously been robustly associated with developmental disorders. Although we detected more genes associated with developmental disorders, much of the excess of de novo mutations in protein-coding genes remains unaccounted for. Modelling suggests that more than 1,000 genes associated with developmental disorders have not yet been described, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of genes associated with developmental disorders.

Our ability to make accurate and specific genetic diagnoses in individuals with severe developmental disorders has been transformed by data derived from genomic sequencing technologies. These data reveal both the patterns and rates of different mutational mechanisms and identify regions of the human genome with fewer mutations than would be expected. In outbred populations, the most common identifiable cause of severe developmental disorders is de novo mutation affecting the coding region in one of approximately 500 different genes, almost universally showing constraint. Simply combining the location of a de novo genomic event with its predicted consequence on the gene product gives significant diagnostic power. Our knowledge of the diversity of phenotypic consequences associated with comparable diagnostic genotypes at each locus is improving. Computationally useful phenotype data will improve diagnostic interpretation of ultrarare genetic variants and, in the long run, indicate which specific embryonic processes have been perturbed.

Trio-based whole-exome sequence (WES) data have established confident genetic diagnoses in ∼40% of previously undiagnosed individuals recruited to the Deciphering Developmental Disorders (DDD) study. Here we aim to use the breadth of phenotypic information recorded in DDD to augment diagnosis and disease variant discovery in probands. Median Euclidean distances (mEuD) were employed as a simple measure of similarity of quantitative phenotypic data within sets of ≥10 individuals with plausibly causative de novo mutations (DNM) in 28 different developmental disorder genes. 13/28 (46.4%) showed significant similarity for growth or developmental milestone metrics, 10/28 (35.7%) showed similarity in HPO term usage, and 12/28 (43%) showed no phenotypic similarity. Pairwise comparisons of individuals with high-impact inherited variants to the 32 individuals with causative DNM in ANKRD11 using only growth z-scores highlighted 5 likely causative inherited variants and two unrecognized DNM resulting in an 18% diagnostic uplift for this gene. Using an independent approach, naive Bayes classification of growth and developmental data produced reasonably discriminative models for the 24 DNM genes with sufficiently complete data. An unsupervised naive Bayes classification of 6,993 probands with WES data and sufficient phenotypic information defined 23 in silico syndromes (ISSs) and was used to test a "phenotype first" approach to the discovery of causative genotypes using WES variants strictly filtered on allele frequency, mutation consequence, and evidence of constraint in humans. This highlighted heterozygous de novo nonsynonymous variants in SPTBN2 as causative in three DDD probands., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Mobile genetic Elements (MEs) are segments of DNA which can copy themselves and other transcribed sequences through the process of retrotransposition (RT). In humans several disorders have been attributed to RT, but the role of RT in severe developmental disorders (DD) has not yet been explored. Here we identify RT-derived events in 9738 exome sequenced trios with DD-affected probands. We ascertain 9 de novo MEs, 4 of which are likely causative of the patient's symptoms (0.04%), as well as 2 de novo gene retroduplications. Beyond identifying likely diagnostic RT events, we estimate genome-wide germline ME mutation rate and selective constraint and demonstrate that coding RT events have signatures of purifying selection equivalent to those of truncating mutations. Overall, our analysis represents a comprehensive interrogation of the impact of retrotransposition on protein coding genes and a framework for future evolutionary and disease studies.

Approximately 2% of de novo single-nucleotide variants (SNVs) appear as part of clustered mutations that create multinucleotide variants (MNVs). MNVs are an important source of genomic variability as they are more likely to alter an encoded protein than a SNV, which has important implications in disease as well as evolution. Previous studies of MNVs have focused on their mutational origins and have not systematically evaluated their functional impact and contribution to disease. We identified 69,940 MNVs and 91 de novo MNVs in 6688 exome-sequenced parent-offspring trios from the Deciphering Developmental Disorders Study comprising families with severe developmental disorders. We replicated the previously described MNV mutational signatures associated with DNA polymerase zeta, an error-prone translesion polymerase, and the APOBEC family of DNA deaminases. We estimate the simultaneous MNV germline mutation rate to be 1.78 × 10 -10 mutations per base pair per generation. We found that most MNVs within a single codon create a missense change that could not have been created by a SNV. MNV-induced missense changes were, on average, more physicochemically divergent, were more depleted in highly constrained genes (pLI ≥ 0.9), and were under stronger purifying selection compared with SNV-induced missense changes. We found that de novo MNVs were significantly enriched in genes previously associated with developmental disorders in affected children. This shows that MNVs can be more damaging than SNVs even when both induce missense changes, and are an important variant type to consider in relation to human disease., (© 2019 Kaplanis et al.; Published by Cold Spring Harbor Laboratory Press.)

We aimed to develop an efficient, flexible and scalable approach to diagnostic genome-wide sequence analysis of genetically heterogeneous clinical presentations. Here we present G2P ( www.ebi.ac.uk/gene2phenotype ) as an online system to establish, curate and distribute datasets for diagnostic variant filtering via association of allelic requirement and mutational consequence at a defined locus with phenotypic terms, confidence level and evidence links. An extension to Ensembl Variant Effect Predictor (VEP), VEP-G2P was used to filter both disease-associated and control whole exome sequence (WES) with Developmental Disorders G2P (G2P DD ; 2044 entries). VEP-G2P DD shows a sensitivity/precision of 97.3%/33% for de novo and 81.6%/22.7% for inherited pathogenic genotypes respectively. Many of the missing genotypes are likely false-positive pathogenic assignments. The expected number and discriminative features of background genotypes are defined using control WES. Using only human genetic data VEP-G2P performs well compared to other freely-available diagnostic systems and future phenotypic matching capabilities should further enhance performance.

Mutations that perturb normal pre-mRNA splicing are significant contributors to human disease. We used exome sequencing data from 7833 probands with developmental disorders (DDs) and their unaffected parents, as well as more than 60,000 aggregated exomes from the Exome Aggregation Consortium, to investigate selection around the splice sites and quantify the contribution of splicing mutations to DDs. Patterns of purifying selection, a deficit of variants in highly constrained genes in healthy subjects, and excess de novo mutations in patients highlighted particular positions within and around the consensus splice site of greater functional relevance. By using mutational burden analyses in this large cohort of proband-parent trios, we could estimate in an unbiased manner the relative contributions of mutations at canonical dinucleotides (73%) and flanking noncanonical positions (27%), and calculate the positive predictive value of pathogenicity for different classes of mutations. We identified 18 patients with likely diagnostic de novo mutations in dominant DD-associated genes at noncanonical positions in splice sites. We estimate 35%-40% of pathogenic variants in noncanonical splice site positions are missing from public databases., (© 2019 Lord et al.; Published by Cold Spring Harbor Laboratory Press.)

We estimated the genome-wide contribution of recessive coding variation in 6040 families from the Deciphering Developmental Disorders study. The proportion of cases attributable to recessive coding variants was 3.6% in patients of European ancestry, compared with 50% explained by de novo coding mutations. It was higher (31%) in patients with Pakistani ancestry, owing to elevated autozygosity. Half of this recessive burden is attributable to known genes. We identified two genes not previously associated with recessive developmental disorders, KDM5B and EIF3F , and functionally validated them with mouse and cellular models. Our results suggest that recessive coding variants account for a small fraction of currently undiagnosed nonconsanguineous individuals, and that the role of noncoding variants, incomplete penetrance, and polygenic mechanisms need further exploration., (Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Purpose: Given the rapid pace of discovery in rare disease genomics, it is likely that improvements in diagnostic yield can be made by systematically reanalyzing previously generated genomic sequence data in light of new knowledge., Methods: We tested this hypothesis in the United Kingdom-wide Deciphering Developmental Disorders study, where in 2014 we reported a diagnostic yield of 27% through whole-exome sequencing of 1,133 children with severe developmental disorders and their parents. We reanalyzed existing data using improved variant calling methodologies, novel variant detection algorithms, updated variant annotation, evidence-based filtering strategies, and newly discovered disease-associated genes., Results: We are now able to diagnose an additional 182 individuals, taking our overall diagnostic yield to 454/1,133 (40%), and another 43 (4%) have a finding of uncertain clinical significance. The majority of these new diagnoses are due to novel developmental disorder-associated genes discovered since our original publication., Conclusion: This study highlights the importance of coupling large-scale research with clinical practice, and of discussing the possibility of iterative reanalysis and recontact with patients and health professionals at an early stage. We estimate that implementing parent-offspring whole-exome sequencing as a first-line diagnostic test for developmental disorders would diagnose >50% of patients.

The sodium leak channel nonselective protein (NALCN) is a regulator of the pacemaker neurons that are responsible for rhythmic behavior (including respiration), maintaining the resting membrane potential, and are required for action potential production. NALCN -null mice show early death associated with disrupted respiratory rhythms, characterized by frequent and profound apneas. We report 3 children (2 siblings) with compound heterozygous mutations in NALCN associated with developmental impairment, hypotonia, and central sleep-disordered breathing causing apneas. Supplemental oxygen normalized the respiratory rhythm. NALCN mutations have been previously reported to cause severe hypotonia, speech impairment, and cognitive delay as well as infantile neuroaxonal dystrophy and facial dysmorphism. Nonsynonymous changes in the 2 affected extracellular loops may be responsible for the deleterious effect on the stability of the respiratory rhythm. Although oxygen is known to be a stabilizer of respiratory rhythm in central apnea in children, its role in NALCN dysfunction requires further investigation., Competing Interests: POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose., (Copyright © 2018 by the American Academy of Pediatrics.)

An international collaboration has assembled DNA samples and detailed phenotypic information from >13,000 individuals with a clinical diagnosis of either autism spectrum disorder (ASD) or intellectual disability/developmental delay (ID/DD). The application of molecular inversion probe technologies to sequence 208 candidate genes at scale in this impressive resource has identified a large set of plausibly causative mutations for these disorders.

Loss of function mutations in CTNNB1 have been reported in individuals with intellectual disability [MIM #615075] associated with peripheral spasticity, microcephaly and central hypotonia, suggesting a recognisable phenotype associated with haploinsufficiency for this gene. Trio based whole exome sequencing via the Deciphering Developmental Disorders (DDD) study has identified eleven further individuals with de novo loss of function mutations in CTNNB1. Here we report detailed phenotypic information on ten of these. We confirm the features that have been previously described and further delineate the skin and hair findings, including fair skin and fair and sparse hair with unusual patterning., (Copyright © 2016 Elsevier Masson SAS. All rights reserved.)

Discovery of most autosomal recessive disease-associated genes has involved analysis of large, often consanguineous multiplex families or small cohorts of unrelated individuals with a well-defined clinical condition. Discovery of new dominant causes of rare, genetically heterogeneous developmental disorders has been revolutionized by exome analysis of large cohorts of phenotypically diverse parent-offspring trios. Here we analyzed 4,125 families with diverse, rare and genetically heterogeneous developmental disorders and identified four new autosomal recessive disorders. These four disorders were identified by integrating Mendelian filtering (selecting probands with rare, biallelic and putatively damaging variants in the same gene) with statistical assessments of (i) the likelihood of sampling the observed genotypes from the general population and (ii) the phenotypic similarity of patients with recessive variants in the same candidate gene. This new paradigm promises to catalyze the discovery of novel recessive disorders, especially those with less consistent or nonspecific clinical presentations and those caused predominantly by compound heterozygous genotypes.

Background: Human genome sequencing has transformed our understanding of genomic variation and its relevance to health and disease, and is now starting to enter clinical practice for the diagnosis of rare diseases. The question of whether and how some categories of genomic findings should be shared with individual research participants is currently a topic of international debate, and development of robust analytical workflows to identify and communicate clinically relevant variants is paramount., Methods: The Deciphering Developmental Disorders (DDD) study has developed a UK-wide patient recruitment network involving over 180 clinicians across all 24 regional genetics services, and has performed genome-wide microarray and whole exome sequencing on children with undiagnosed developmental disorders and their parents. After data analysis, pertinent genomic variants were returned to individual research participants via their local clinical genetics team., Findings: Around 80,000 genomic variants were identified from exome sequencing and microarray analysis in each individual, of which on average 400 were rare and predicted to be protein altering. By focusing only on de novo and segregating variants in known developmental disorder genes, we achieved a diagnostic yield of 27% among 1133 previously investigated yet undiagnosed children with developmental disorders, whilst minimising incidental findings. In families with developmentally normal parents, whole exome sequencing of the child and both parents resulted in a 10-fold reduction in the number of potential causal variants that needed clinical evaluation compared to sequencing only the child. Most diagnostic variants identified in known genes were novel and not present in current databases of known disease variation., Interpretation: Implementation of a robust translational genomics workflow is achievable within a large-scale rare disease research study to allow feedback of potentially diagnostic findings to clinicians and research participants. Systematic recording of relevant clinical data, curation of a gene-phenotype knowledge base, and development of clinical decision support software are needed in addition to automated exclusion of almost all variants, which is crucial for scalable prioritisation and review of possible diagnostic variants. However, the resource requirements of development and maintenance of a clinical reporting system within a research setting are substantial., Funding: Health Innovation Challenge Fund, a parallel funding partnership between the Wellcome Trust and the UK Department of Health., (Copyright © 2015 Wright et al. Open Access article distributed under the terms of CC BY. Published by Elsevier Ltd. All rights reserved.)

We previously estimated that 42% of patients with severe developmental disorders carry pathogenic de novo mutations in coding sequences. The role of de novo mutations in regulatory elements affecting genes associated with developmental disorders, or other genes, has been essentially unexplored. We identified de novo mutations in three classes of putative regulatory elements in almost 8,000 patients with developmental disorders. Here we show that de novo mutations in highly evolutionarily conserved fetal brain-active elements are significantly and specifically enriched in neurodevelopmental disorders. We identified a significant twofold enrichment of recurrently mutated elements. We estimate that, genome-wide, 1-3% of patients without a diagnostic coding variant carry pathogenic de novo mutations in fetal brain-active regulatory elements and that only 0.15% of all possible mutations within highly conserved fetal brain-active elements cause neurodevelopmental disorders with a dominant mechanism. Our findings represent a robust estimate of the contribution of de novo mutations in regulatory elements to this genetically heterogeneous set of disorders, and emphasize the importance of combining functional and evolutionary evidence to identify regulatory causes of genetic disorders.

Background Exome sequencing has led to the discovery of mutations in novel causative genes for epilepsy. One such gene is EEF1A2, encoding a neuromuscular specific translation elongation factor, which has been found to be mutated de novo in five cases of severe epilepsy. We now report on a further seven cases, each with a different mutation, of which five are newly described. Methods New cases were identified and sequenced through the Deciphering Developmental Disabilities project, via direct contact with neurologists or geneticists, or recruited via our website. Results All the mutations cause epilepsy and intellectual disability, but with a much wider range of severity than previously identified. All new cases share specific subtle facial dysmorphic features. Each mutation occurs at an evolutionarily highly conserved amino acid position indicating strong structural or functional selective pressure. Conclusions EEF1A2 should be considered as a causative gene not only in cases of epileptic encephalopathy but also in children with less severe epilepsy and intellectual disability. The emergence of a possible discernible phenotype, a broad nasal bridge, tented upper lip, everted lower lip and downturned corners of the mouth may help in identifying patients with mutations in EEF1A2. [ABSTRACT FROM AUTHOR]

In developed countries, malformations of the eye are among the most common causes of serious visual impairment in newborns. The identification of pathogenic mutations in autosomal and X-linked transcription factors has advanced our understanding of the critical stages in human eye development and has begun to explain some unusual inheritance characteristics of these disorders. The functional characterisation of these genes in model organisms has prompted reinvestigation of affected individuals to identify previously unrecognized but consistent extra-ocular malformations. This dialogue between clinical genetics and basic developmental biology provides a paradigm to enhance our understanding of many critical developmental processes in human embryogenesis. [Copyright &y& Elsevier]

We report the identification of six patients with 3q29 microdeletion syndrome. The clinical phenotype is variable despite an almost identical deletion size. The phenotype includes mild-to-moderate mental retardation, with only slightly dysmorphic facial features that are similar in most patients: a long and narrow face, short philtrum, and high nasal bridge. Autism, gait ataxia, chest-wall deformity, and long and tapering fingers were noted in at least two of six patients. Additional features—including microcephaly, cleft lip and palate, horseshoe kidney and hypospadias, ligamentous laxity, recurrent middle ear infections, and abnormal pigmentation—were observed, but each feature was only found once, in a single patient. The microdeletion is ∼1.5 Mb in length, with molecular boundaries mapping within the same or adjacent bacterial artificial chromosome (BAC) clones at either end of the deletion in all patients. The deletion encompasses 22 genes, including PAK2 and DLG1, which are autosomal homologues of two known X-linked mental retardation genes, PAK3 and DLG3. The presence of two nearly identical low-copy repeat sequences in BAC clones on each side of the deletion breakpoint suggests that nonallelic homologous recombination is the likely mechanism of disease causation in this syndrome. [ABSTRACT FROM AUTHOR]