SummaryBackgroundHuman 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.MethodsThe 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.FindingsAround 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.InterpretationImplementation 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.FundingHealth Innovation Challenge Fund, a parallel funding partnership between the Wellcome Trust and the UK Department of Health.

Prevailing opinion suggests that only substances up to 64 nm in diameter can move at appreciable rates through the brain extracellular space (ECS). This size range is large enough to allow diffusion of signaling molecules, nutrients, and metabolic waste products, but too small to allow efficient penetration of most particulate drug delivery systems and viruses carrying therapeutic genes, thereby limiting effectiveness of many potential therapies. We analyzed the movements of nanoparticles of various diameters and surface coatings within fresh human and rat brain tissue ex vivo and mouse brain in vivo. Nanoparticles as large as 114-nm in diameter diffused within the human and rat brain, but only if they were densely coated with poly(ethylene glycol) (PEG). Using these minimally adhesive PEG-coated particles, we estimated that human brain tissue ECS has some pores larger than 200 nm, and that more than one-quarter of all pores are ≥100 nm. These findings were confirmed in vivo in mice, where 40- and 100-nm, but not 200-nm, nanoparticles, spread rapidly within brain tissue, only if densely coated with PEG. Similar results were observed in rat brain tissue with paclitaxel-loaded biodegradable nanoparticles of similar size (85 nm) and surface properties. The ability to achieve brain penetration with larger nanoparticles is expected to allow more uniform, longer-lasting, and effective delivery of drugs within the brain, and may find use in the treatment of brain tumors, stroke, neuroinflammation, and other brain diseases where the blood-brain barrier is compromised or where local delivery strategies are feasible.

Congenital Heart Defects (CHD) have a neonatal incidence of 0.8-1%1,2. Despite abundant examples of monogenic CHD in humans and mice, CHD has a low absolute sibling recurrence risk (~2.7%)3, suggesting a considerable role for de novo mutations (DNM), and/or incomplete penetrance4,5. De novo protein-truncating variants (PTVs) have been shown to be enriched among the 10% of ‘syndromic’ patients with extra-cardiac manifestations6,7. We exome sequenced 1,891 probands, including both syndromic (S-CHD, n=610) and non-syndromic cases (NS-CHD, n=1,281). In S-CHD, we confirmed a significant enrichment of de novo PTVs, but not inherited PTVs, in known CHD-associated genes, consistent with recent findings8. Conversely, in NS-CHD we observed significant enrichment of PTVs inherited from unaffected parents in CHD-associated genes. We identified three novel genome-wide significant S-CHD disorders caused by DNMs in CHD4, CDK13 and PRKD1. Our study reveals distinct genetic architectures underlying the low sibling recurrence risk in S-CHD and NS-CHD.

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for “the needle in a haystack” to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can “match” these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.

The DECIPHER database (https://decipher.sanger.ac.uk/) is an accessible online repository of genetic variation with associated phenotypes that facilitates the identification and interpretation of pathogenic genetic variation in patients with rare disorders. Contributing to DECIPHER is an international consortium of >200 academic clinical centres of genetic medicine and ≥1600 clinical geneticists and diagnostic laboratory scientists. Information integrated from a variety of bioinformatics resources, coupled with visualization tools, provides a comprehensive set of tools to identify other patients with similar genotype–phenotype characteristics and highlights potentially pathogenic genes. In a significant development, we have extended DECIPHER from a database of just copy-number variants to allow upload, annotation and analysis of sequence variants such as single nucleotide variants (SNVs) and InDels. Other notable developments in DECIPHER include a purpose-built, customizable and interactive genome browser to aid combined visualization and interpretation of sequence and copy-number variation against informative datasets of pathogenic and population variation. We have also introduced several new features to our deposition and analysis interface. This article provides an update to the DECIPHER database, an earlier instance of which has been described elsewhere [Swaminathan et al. (2012) DECIPHER: web-based, community resource for clinical interpretation of rare variants in developmental disorders. Hum. Mol. Genet., 21, R37–R44].

The blood-brain barrier (BBB) presents a significant obstacle for the treatment of many central nervous system (CNS) disorders, including invasive brain tumors, Alzheimer’s, Parkinson’s and stroke. Therapeutics must be capable of bypassing the BBB and also penetrate the brain parenchyma to achieve a desired effect within the brain. In this study, we test the unique combination of a noninvasive approach to BBB permeabilization with a therapeutically relevant polymeric nanoparticle platform capable of rapidly penetrating within the brain microenvironment. MR-guided focused ultrasound (FUS) with intravascular microbubbles (MBs) is able to locally and reversibly disrupt the BBB with submillimeter spatial accuracy. Densely poly(ethylene-co-glycol) (PEG) coated, brain-penetrating nanoparticles (BPNs) are long-circulating and diffuse 10-fold slower in normal rat brain tissue compared to diffusion in water. Following intravenous administration of model and biodegradable BPN in normal healthy rats, we demonstrate safe, pressure-dependent delivery of 60 nm BPNs to the brain parenchyma in regions where the BBB is disrupted by FUS and MBs. Delivery of BPNs with MR-guided FUS has the potential to improve efficacy of treatments for many CNS diseases, while reducing systemic side effects by providing sustained, well-dispersed drug delivery into select regions of the brain.

Discovery of most autosomal recessive disease genes has involved analysis of large, often consanguineous, multiplex families or small cohorts of unrelated individuals with a well-defined clinical condition. Discovery of novel dominant causes of rare, genetically heterogenous developmental disorders has been revolutionized by exome analysis of large cohorts of phenotypically diverse parent-offspring trios 1,2. Here we analysed 4,125 families with diverse, rare, genetically heterogeneous developmental disorders and identified four novel autosomal recessive disorders. These four disorders were identified by integrating Mendelian filtering (identifying probands with rare biallelic 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 the same recessive candidate gene. This new paradigm promises to catalyse discovery of novel recessive disorders, especially those with less consistent or nonspecific clinical presentations, and those caused predominantly by compound heterozygous genotypes.

Histone lysine methyltransferases (KMTs) and demethylases (KDMs) underpin gene regulation. Here we demonstrate that variants causing haploinsufficiency of KMTs and KDMs are frequently encountered in individuals with developmental disorders. Using a combination of human variation databases and existing animal models, we determine 22 KMTs and KDMs as additional candidates for dominantly inherited developmental disorders. We show that KMTs and KDMs that are associated with, or are candidates for, dominant developmental disorders tend to have a higher level of transcription, longer canonical transcripts, more interactors, and a higher number and more types of post-translational modifications than other KMT and KDMs. We provide evidence to firmly associate KMT2C, ASH1L, and KMT5B haploinsufficiency with dominant developmental disorders. Whereas KMT2C or ASH1L haploinsufficiency results in a predominantly neurodevelopmental phenotype with occasional physical anomalies, KMT5B mutations cause an overgrowth syndrome with intellectual disability. We further expand the phenotypic spectrum of KMT2B-related disorders and show that some individuals can have severe developmental delay without dystonia at least until mid-childhood. Additionally, we describe a recessive histone lysine-methylation defect caused by homozygous or compound heterozygous KDM5B variants and resulting in a recognizable syndrome with developmental delay, facial dysmorphism, and camptodactyly. Collectively, these results emphasize the significance of histone lysine methylation in normal human development and the importance of this process in human developmental disorders. Our results demonstrate that systematic clinically oriented pathway-based analysis of genomic data can accelerate the discovery of rare genetic disorders.