Caroline F. Wright

The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data

The Human Phenotype Ontology (HPO) project, available at http://www.human-phenotype-ontology.org, provides a structured, comprehensive and well-defined set of 10,088 classes (terms) describing human phenotypic abnormalities and 13,326 subclass relations between the HPO classes. In addition we have developed logical definitions for 46% of all HPO classes using terms from ontologies for anatomy, cell types, function, embryology, pathology and other domains. This allows interoperability with several resources, especially those containing phenotype information on model organisms such as mouse and zebrafish. Here we describe the updated HPO database, which provides annotations of 7,278 human hereditary syndromes listed in OMIM, Orphanet and DECIPHER to classes of the HPO. Various meta-attributes such as frequency, references and negations are associated with each annotation. Several large-scale projects worldwide utilize the HPO for describing phenotype information in their datasets. We have therefore generated equivalence mappings to other phenotype vocabularies such as LDDB, Orphanet, MedDRA, UMLS and phenoDB, allowing integration of existing datasets and interoperability with multiple biomedical resources. We have created various ways to access the HPO database content using flat files, a MySQL database, and Web-based tools. All data and documentation on the HPO project can be found online.

Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data

Summary 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.

The use of cell-free fetal nucleic acids in maternal blood for non-invasive prenatal diagnosis

BACKGROUND Cell-free fetal nucleic acids (cffNA) can be detected in the maternal circulation during pregnancy, potentially offering an excellent method for early non-invasive prenatal diagnosis (NIPD) of the genetic status of a fetus. Using molecular techniques, fetal DNA and RNA can be detected from 5 weeks gestation and are rapidly cleared from the circulation following birth. METHODS We searched PubMed systematically using keywords free fetal DNA and NIPD. Reference lists from relevant papers were also searched to ensure comprehensive coverage of the area. RESULTS Cell-free fetal DNA comprises only 3-6% of the total circulating cell-free DNA, therefore diagnoses are primarily limited to those caused by paternally inherited sequences as well as conditions that can be inferred by the unique gene expression patterns in the fetus and placenta. Broadly, the potential applications of this technology fall into two categories: first, high genetic risk families with inheritable monogenic diseases, including sex determination in cases at risk of X-linked diseases and detection of specific paternally inherited single gene disorders; and second, routine antenatal care offered to all pregnant women, including prenatal screening/diagnosis for aneuploidy, particularly Down syndrome (DS), and diagnosis of Rhesus factor status in RhD negative women. Already sex determination and Rhesus factor diagnosis are nearing translation into clinical practice for high-risk individuals. CONCLUSIONS The analysis of cffNA may allow NIPD for a variety of genetic conditions and may in future form part of national antenatal screening programmes for DS and other common genetic disorders.

The importance of sequence diversity in the aggregation and evolution of proteins

Incorrect folding of proteins, leading to aggregation and amyloid formation, is associated with a group of highly debilitating medical conditions including Alzheimers disease and late-onset diabetes. The issue of how unwanted protein association is normally avoided in a living system is particularly significant in the context of the evolution of multidomain proteins, which account for over 70% of all eukaryotic proteins, where the effective local protein concentration in the vicinity of each domain is very high. Here we describe the aggregation kinetics of multidomain protein constructs of immunoglobulin domains and the ability of different homologous domains to aggregate together. We show that aggregation of these proteins is a specific process and that the efficiency of coaggregation between different domains decreases markedly with decreasing sequence identity. Thus, whereas immunoglobulin domains with more than about 70% identity are highly prone to coaggregation, those with less than 30–40% sequence identity do not detectably interact. A bioinformatics analysis of consecutive homologous domains in large multidomain proteins shows that such domains almost exclusively have sequence identities of less than 40%, in other words below the level at which coaggregation is likely to be efficient. We propose that such low sequence identities could have a crucial and general role in safeguarding proteins against misfolding and aggregation.

Parallel protein-unfolding pathways revealed and mapped

Theoretical studies of protein folding suggest that multiple folding pathways should exist, but there is little experimental evidence to support this. Here we demonstrate changes in the flux between different transition states on parallel folding pathways, resulting in unprecedented upward curvature in the denaturant-dependent unfolding kinetics of a β-sandwich protein. As denaturant concentration increases, the highly compact transition state of one pathway becomes destabilized and the dominant flux of protein molecules shifts toward another pathway with a less structured transition state. Furthermore, point mutations alter the relative accessibility of the pathways, allowing the structure of two transition states on separate, direct folding pathways to be mapped by systematic Φ-value analysis. It has been suggested that pathways with diffuse rather than localized transition states are evolutionarily selected to prevent misfolding, and indeed we find that the transition state favored at high concentrations of denaturant is more polarized than the physiologically relevant one.

DECIPHER: database for the interpretation of phenotype-linked plausibly pathogenic sequence and copy-number variation

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].

Extending the reach of public health genomics: what should be the agenda for public health in an era of genome-based and "personalized" medicine?

The decade following the completion of the Human Genome Project has been marked by divergent claims about the utility of genomics for improving population health. On the one hand, genomics is viewed as the harbinger of a brave new world in which novel treatments rectify known causes of disease. On the other hand, genomics may have little practical relevance to the principal causes or remedies of diseases which are predominantly social or environmental in origin, particularly in low- and middle-income countries. Those supportive of a role for public health genomics argue that increasing knowledge of genomics and molecular pathology could unlock effective diagnostic techniques and treatments, and better target public health interventions. To resolve some of these tensions, an international multidisciplinary meeting was held in May 2010 in Ickworth, United Kingdom, with the aim of setting an agenda for the development of public health in an era of genome-based and “personalized” medicine. A number of key themes emerged, suggesting a need to reconfigure both the focus for existing genomic research and the stage at which funding is targeted, so that priority is given to areas of greatest potential health impact and that translation from basic science to implementation is given greater emphasis. To support these developments, there should be an immediate, sustained and systematic effort to provide an evidence base. These deliberations formed the basis for six key recommendations, which could guide the practice of public health in an era of genomics and personalized medicine.

The Deciphering Developmental Disorders (DDD) study.

BACKGROUND TO THE STUDY One of the main frustrations facing paediatric neurologists, community paediatricians, and clinical geneticists in everyday practice is the poor success rate in making accurate diagnoses for children with severe or profound neurological disability. Accurate diagnosis is the cornerstone of good medical care. However, the chance of achieving a genetic diagnosis for a child with severe developmental delay is low where no diagnosis is apparent after routine investigation – yet the majority of children with developmental disorders fall into this category. Although individually rare, collectively these conditions represent a significant burden for individuals, families, and health services. There has been a growing appreciation in recent years of the importance of genetic disorders in childhood neurodisability, although in many cases the location and nature of the causal mutation is unknown. In clinical practice, a family history of other similarly affected individuals is a strong pointer to a genetic aetiology, but the converse does not necessarily hold and the concept that de novo dominant mutations may cause apparently sporadic disorders is gaining acceptance. If a de novo mutation severely impairs development such that the affected individual does not reproduce, the genetic basis of their condition may remain unrecognized. Speculatively, such events may explain some cases of severe cerebral palsy or severe epileptic encephalopathy as well as severe intellectual disability. In addition, there are many undiagnosed recessively inherited neurodevelopmental disorders collectively responsible for a substantial proportion of the excess morbidity and mortality amongst infants born to consanguineous parents. Genomic microarray analysis has proven to be valuable for improving rates of diagnosis amongst children with developmental delay ⁄ intellectual disability caused by large structural variants (such as deletions and duplications of >50kb). For smaller mutations, the revolution in ‘next generation’ DNA sequencing technologies is now enabling genomic analysis on an unprecedented scale. Massively parallel sequencing enables the rapid, systematic identification of variants on a large scale, either across the whole genome or targeted at functional coding regions (the exome). This has accelerated the pace of gene discovery and disease diagnosis on a molecular level and may substantially improve the diagnosis of children with developmental disorders.

Non-invasive prenatal diagnosis using cell-free fetal DNA technology: applications and implications.

Cell-free fetal DNA and RNA circulating in maternal blood can be used for the early non-invasive prenatal diagnosis (NIPD) of an increasing number of genetic conditions, both for pregnancy management and to aid reproductive decision-making. Here we present a brief review of the scientific and clinical status of the technology, and an overview of key ethical, legal and social issues raised by the analysis of cell-free fetal DNA for NIPD. We suggest that the less invasive nature of the technology brings some distinctive issues into focus, such as the possibility of broader uptake of prenatal diagnosis and access to the technology directly by the consumer via the internet, which have not been emphasised in previous work in this area. We also revisit significant issues that are familiar from previous debates about prenatal testing. Since the technology seems to transect existing distinctions between screening and diagnostic tests, there are important implications for the form and process involved in obtaining informed consent or choice. This analysis forms part of the work undertaken by a multidisciplinary group of experts which made recommendations about the implementation of this technology within the UK National Health Service.

Discovery of four recessive developmental disorders using probabilistic genotype and phenotype matching among 4,125 families.

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.