Phenotype Ontology Terms Research Articles

A Phenotypic Description of Congenital Myotonic Dystrophy using PhenoStacks.

Congenital Myotonic Dystrophy (CDM1) is a rare neuromuscular condition caused by a triplet repeat expansion in the DMPK gene. Despite there being a well-recognized clinical syndrome, there has not been an effort to use a standardized ontology system to describe the disease characteristics in existing literature. Thus, comparing or contrasting different cohorts from the literature can be challenging, and coding disease features for clinical research or for registry data items is not uniform. PhenoStacks is a visualization analytics tool which helps graphically illustrate phenotypes of patients with genetic disorders using Human Phenotype Ontology (HPO) terms and can sort phenotypes by different disease characteristics. To demonstrate the efficacy of PhenoStacks and the HPO system as clinical research tools when describing CDM1 cohorts. Health Endpoints and Longitudinal progression in congenital myotonic dystrophy (HELP-CDM) is an ongoing study which longitudinally follows patients with CDM1. Items from the HELP-CDM data sheet were matched to corresponding HPO terms and analyzed using PhenoStacks. In total 40 subjects' phenotypes were visualized through PhenoStacks and 73 HPO terms were used for the analysis. Frequent phenotypic features included "high narrow palate", "facial palsy", "ptosis", "hyporeflexia", and "weak voice". Contractures were associated with higher repeat sizes. Hypoplastic muscles and infantile axial hypotonia were more frequently observed in infants. PhenoStacks is a valuable clinical and scientific tool as it identifies variability within cohorts and highlights significant phenotypic features.

ClinPhen extracts and prioritizes patient phenotypes directly from medical records to expedite genetic disease diagnosis

PurposeDiagnosing monogenic diseases facilitates optimal care, but can involve the manual evaluation of hundreds of genetic variants per case. Computational tools like Phrank expedite this process by ranking all candidate genes by their ability to explain the patient’s phenotypes. To use these tools, busy clinicians must manually encode patient phenotypes from lengthy clinical notes. With 100 million human genomes estimated to be sequenced by 2025, a fast alternative to manual phenotype extraction from clinical notes will become necessary. MethodsWe introduce ClinPhen, a fast, high-accuracy tool that automatically converts clinical notes into a prioritized list of patient phenotypes using Human Phenotype Ontology (HPO) terms. ResultsClinPhen shows superior accuracy and 20× speedup over existing phenotype extractors, and its novel phenotype prioritization scheme improves the performance of gene-ranking tools. ConclusionWhile a dedicated clinician can process 200 patient records in a 40-hour workweek, ClinPhen does the same in 10minutes. Compared with manual phenotype extraction, ClinPhen saves an additional 3–5hours per Mendelian disease diagnosis. Providers can now add ClinPhen’s output to each summary note attached to a filled testing laboratory request form. ClinPhen makes a substantial contribution to improvements in efficiency critically needed to meet the surging demand for clinical diagnostic sequencing.

Novel pathogenic variants and multiple molecular diagnoses in neurodevelopmental disorders

BackgroundRare denovo variants represent a significant cause of neurodevelopmental delay and intellectual disability (ID).MethodsExome sequencing was performed on 4351 patients with global developmental delay, seizures, microcephaly, macrocephaly, motor delay, delayed speech and language development, or ID according to Human Phenotype Ontology (HPO) terms. All patients had previously undergone whole exome sequencing as part of diagnostic genetic testing with a focus on variants in genes implicated in neurodevelopmental disorders up to January 2017. This resulted in a genetic diagnosis in 1336 of the patients. In this study, we specifically searched for variants in 14 recently implicated novel neurodevelopmental disorder (NDD) genes.ResultsWe identified 65 rare, protein-changing variants in 11 of these 14 novel candidate genes. Fourteen variants in CDK13, CHD4, KCNQ3, KMT5B, TCF20, and ZBTB18 were scored pathogenic or likely pathogenic. Of note, two of these patients had a previously identified cause of their disease, and thus, multiple molecular diagnoses were made including pathogenic/likely pathogenic variants in FOXG1 and CDK13 or in TMEM237 and KMT5B.ConclusionsLooking for pathogenic variants in newly identified NDD genes enabled us to provide a molecular diagnosis to 14 patients and their close relatives and caregivers. This underlines the relevance of re-evaluation of existing exome data on a regular basis to improve the diagnostic yield and serve the needs of our patients.

Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.

PhenoPro: a novel toolkit for assisting in the diagnosis of Mendelian disease.

Whole-exome sequencing (WES) is now being used in clinical practice for the diagnosis of the causal genes of Mendelian diseases. In order to make the diagnosis, however, the clinical phenotypes [e.g. Human Phenotype Ontology (HPO) terms] of a patient are needed for prioritizing the variants called from the WES data of the patient. Computational tools are therefore needed to standardize and accelerate this process. Here, we introduce a tool named PhenoPro for prioritizing the causal gene of Mendelian disease given both the HPO terms assigned to and the variants called from the WES data of a patient. PhenoPro has been benchmarked using both simulated patients and 287 real diagnosed patients of Chinese ancestry, and shows significant improvements over five previous tools. Moreover, the addition of an internal variant data of Chinese ancestry and the variant data from the patients' parents can further improve PhenoPro's performance. To make PhenoPro a fully automated tool, we also include a natural language processing component for automated HPO term assignment from clinical reports, and demonstrate that the natural language processing is as effective as manual HPO assignment using real clinical reports. In conclusion, PhenoPro can be used as a pre-screening tool to assist in the diagnosis of Mendelian disease genes. The web server of PhenoPro is freely available at http://app.tianlab.cn. Supplementary data are available at Bioinformatics online.

The Rare Diseases Pilot for the 100,000 Genomes Project: Findings in Known and New Genes by Analysis of 3,549 Whole Genome Sequenced Samples from Patients and Relatives with Haematological, Haemostasis and Immune Disorders

The Rare Diseases Pilot study of the 100,000 Genomes Project had two objectives. Firstly, to identify the DNA variants underlying unresolved Mendelian disorders. Secondly, to develop an accredited framework for delivering whole genome sequencing (WGS) results across a national healthcare system. From February 2014 to June 2017, 13,037 individuals with a rare disease and their relatives were recruited at 57 National Health Service (NHS) hospitals in the UK and 26 non-UK hospitals using standardized eligibility criteria for 12 rare disease domains. This cohort includes cases with haematology (n=1021), immunology (n=1359) and haemostasis disorders (n=1169).With informed consent, clinical and laboratory data were collected and coded into a single research database using Human Phenotype Ontology (HPO) terms and 13,037 samples of DNA were Illumina WGS analysed to clinical standard at a mean depths >30X in all samples and 90% of the reference genome was covered at 19X minimum in all samples. The pilot resource contains over 165 million unique variants with 91.5%, 8,5% and 5.6% single nucleotide variants (SNVs), short insertions / deletions and large deletions of the 10,258 genetically independent samples with 47% of variants previously unobserved in large scale genome datasets (e.g. TopMED, gnomAD, UK10K). Across all domains 2,067 unique diagnostic-grade genes (DGGs) were curated to clinical standards to support pertinent finding reporting by 12 multi-disciplinary teams (MDTs) with domain-relevant clinical and genetic expertise. Over 1,300 MDT reports assigning pathogenic or likely pathogenic causal variants have been returned to referring clinicians, with the diagnostic yield ranging from 1.6 to 53.8%, depending on the extent of genetic screening pre-enrolment and the importance of the non-genetic component of the disorder (e.g. in immune disorders). About 30% of the causal variants identified have never been reported (absent from the Human Gene Mutation Database v.2018.1); interestingly, 51 variants have been reported in 11 DGGs linked to phenotypes belonging to different domains. A comparison with standard whole exome sequencing results revealed WGS to have at least 12.5% superiority in sensitivity for detecting known pathogenic variants.For the haematology, immunology and haemostasis domains 330 causal variants were reported in 83 DGGs, revealing novel modes of inheritance (Sivapalaratnam et al Blood 2016), and entire new clinical phenotypes linked to mutations in ABCC4, GNE, KDSR and STIM1 amongst other DGGs. The genotype and HPO-coded phenotypes of all pilot cases were analysed with BeviMed, a rapid and scalable Bayesian association test (Greene et al AJHG 2017) to identify causal variants in hitherto unknown genes. This identified more than 30 genes with posterior probabilities indicating a high likelihood of being implicated in underlying as yet unresolved Mendelian disorders. Results from co-segregation and cell biology studies have already corroborated this statistical inference and 15 novel genes acquired DGG status. Including a new method for the analysis of the ‘gene-regulatory’ elements we also identified an example of a causal variant in such an element controlling the function of both GATA1 and HDAC6 resulting in a severe syndromic pathology characterized by abnormal erythropoiesis and megakaryopoiesis.In conclusion, the pilot of the 100,000 Genomes Project has shown the feasibility of using WGS across a national health system, such as the NHS, to deliver a molecular diagnosis for patients with rare inherited diseases and how a national genotype/HPO-coded phenotype resource provides a powerful platform for the identification of novel diagnostic-grade genes. DisclosuresNo relevant conflicts of interest to declare.

HPO2GO: prediction of human phenotype ontology term associations for proteins using cross ontology annotation co-occurrences.

Analysing the relationships between biomolecules and the genetic diseases is a highly active area of research, where the aim is to identify the genes and their products that cause a particular disease due to functional changes originated from mutations. Biological ontologies are frequently employed in these studies, which provides researchers with extensive opportunities for knowledge discovery through computational data analysis. In this study, a novel approach is proposed for the identification of relationships between biomedical entities by automatically mapping phenotypic abnormality defining HPO terms with biomolecular function defining GO terms, where each association indicates the occurrence of the abnormality due to the loss of the biomolecular function expressed by the corresponding GO term. The proposed HPO2GO mappings were extracted by calculating the frequency of the co-annotations of the terms on the same genes/proteins, using already existing curated HPO and GO annotation sets. This was followed by the filtering of the unreliable mappings that could be observed due to chance, by statistical resampling of the co-occurrence similarity distributions. Furthermore, the biological relevance of the finalized mappings were discussed over selected cases, using the literature. The resulting HPO2GO mappings can be employed in different settings to predict and to analyse novel gene/protein—ontology term—disease relations. As an application of the proposed approach, HPO term—protein associations (i.e., HPO2protein) were predicted. In order to test the predictive performance of the method on a quantitative basis, and to compare it with the state-of-the-art, CAFA2 challenge HPO prediction target protein set was employed. The results of the benchmark indicated the potential of the proposed approach, as HPO2GO performance was among the best (Fmax = 0.35). The automated cross ontology mapping approach developed in this work may be extended to other ontologies as well, to identify unexplored relation patterns at the systemic level. The datasets, results and the source code of HPO2GO are available for download at: https://github.com/cansyl/HPO2GO.

Whole-exome sequencing identifies novel pathogenic mutations and putative phenotype-influencing variants in Polish limb-girdle muscular dystrophy patients

BackgroundLimb girdle muscular dystrophies (LGMD) are a group of heterogeneous hereditary myopathies with similar clinical symptoms. Disease onset and progression are highly variable, with an elusive genetic background, and around 50% cases lacking molecular diagnosis.MethodsWhole exome sequencing (WES) was performed in 73 patients with clinically diagnosed LGMD. A filtering strategy aimed at identification of variants related to the disease included integrative analysis of WES data and human phenotype ontology (HPO) terms, analysis of genes expressed in muscle, analysis of the disease-associated interactome and copy number variants analysis.ResultsGenetic diagnosis was possible in 68.5% of cases. On average, 36.3 rare variants in genes associated with various muscle diseases per patient were found that could relate to the clinical phenotype. The putative causative mutations were mostly in LGMD-associated genes, but also in genes not included in the current LGMD classification (DMD, COL6A2, and COL6A3). In three patients, mutations in two genes were suggested as the joint cause of the disease (CAPN3+MYH7, COL6A3+CACNA1S, DYSF+MYH7). Moreover, a variety of phenotype-influencing variants were postulated, including in patients with an identified already known primary pathogenic mutation.ConclusionsWe hypothesize that LGMD could be better described as oligogenic disorders in which dominant clinical presentation can result from the combined effect of mutations in a set of genes. In this view, the inter- and intrafamilial variability could reflect a specific genetic background and the presence of sets of phenotype-influencing or co-causative mutations in genes that either interact with the known LGMD-associated genes or are a part of the same pathways or structures.

Gene prioritization using Bayesian matrix factorization with genomic and phenotypic side information.

MotivationMost gene prioritization methods model each disease or phenotype individually, but this fails to capture patterns common to several diseases or phenotypes. To overcome this limitation, we formulate the gene prioritization task as the factorization of a sparsely filled gene-phenotype matrix, where the objective is to predict the unknown matrix entries. To deliver more accurate gene-phenotype matrix completion, we extend classical Bayesian matrix factorization to work with multiple side information sources. The availability of side information allows us to make non-trivial predictions for genes for which no previous disease association is known.ResultsOur gene prioritization method can innovatively not only integrate data sources describing genes, but also data sources describing Human Phenotype Ontology terms. Experimental results on our benchmarks show that our proposed model can effectively improve accuracy over the well-established gene prioritization method, Endeavour. In particular, our proposed method offers promising results on diseases of the nervous system; diseases of the eye and adnexa; endocrine, nutritional and metabolic diseases; and congenital malformations, deformations and chromosomal abnormalities, when compared to Endeavour.Availability and implementationThe Bayesian data fusion method is implemented as a Python/C++ package: https://github.com/jaak-s/macau. It is also available as a Julia package: https://github.com/jaak-s/BayesianDataFusion.jl. All data and benchmarks generated or analyzed during this study can be downloaded at https://owncloud.esat.kuleuven.be/index.php/s/UGb89WfkZwMYoTn.Supplementary information Supplementary data are available at Bioinformatics online.

Clinical efficacy of a next-generation sequencing gene panel for primary immunodeficiency diagnostics.

Primary immunodeficiencies (PIDs) are rare monogenic inborn errors of immunity that result in impairment of functions of the human immune system. PIDs have a broad phenotype with increased morbidity and mortality, and treatment choices are often complex. With increased accessibility of next-generation sequencing (NGS), the rate of discovery of genetic causes for PID has increased exponentially. Identification of an underlying monogenic diagnosis provides important clinical benefits for patients with the potential to alter treatments, facilitate genetic counselling, and pre-implantation diagnostics. We investigated a NGS PID panel of 242 genes within clinical care across a range of PID phenotypes. We also evaluated Phenomizer to predict causal genes from human phenotype ontology (HPO) terms. Twenty-seven participants were recruited, and a total of 15 reportable variants were identified in 48% (13/27) of the participants. The panel results had implications for treatment in 37% (10/27) of participants. Phenomizer identified the genes harbouring variants from HPO terms in 33% (9/27) of participants. This study shows the clinical efficacy that genetic testing has in the care of PID. However, it also highlights some of the disadvantages of gene panels in the rapidly moving field of PID genomics and current challenges in HPO term assignment for PID.

Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods

BackgroundThe prediction of human gene–abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the abnormalities associated with human diseases. While the problem of the prediction of gene–disease associations has been widely investigated, the related problem of gene–phenotypic feature (i.e., HPO term) associations has been largely overlooked, even if for most human genes no HPO term associations are known and despite the increasing application of the HPO to relevant medical problems. Moreover most of the methods proposed in literature are not able to capture the hierarchical relationships between HPO terms, thus resulting in inconsistent and relatively inaccurate predictions.ResultsWe present two hierarchical ensemble methods that we formally prove to provide biologically consistent predictions according to the hierarchical structure of the HPO. The modular structure of the proposed methods, that consists in a “flat” learning first step and a hierarchical combination of the predictions in the second step, allows the predictions of virtually any flat learning method to be enhanced. The experimental results show that hierarchical ensemble methods are able to predict novel associations between genes and abnormal phenotypes with results that are competitive with state-of-the-art algorithms and with a significant reduction of the computational complexity.ConclusionsHierarchical ensembles are efficient computational methods that guarantee biologically meaningful predictions that obey the true path rule, and can be used as a tool to improve and make consistent the HPO terms predictions starting from virtually any flat learning method. The implementation of the proposed methods is available as an R package from the CRAN repository.

CentoICU™: A time sensitive targeted panel approach for genetic testing in neonatal and pediatric intensive care units

Objective: About 3 to 4% of newborns will be born with a genetic disease or major birth defect. Genetic disorders including congenital anomalies are a leading cause of death. One of 20 newborn babies is admitted to an Intensive Care Unit (ICU). Common presentations in the ICU are due to genetic diseases. Methods: While it is not uncommon to use Karyotyping, FISH, aCGH and single gene sequencing in ICU, the emergence of Next Generation Sequencing (NGS) promises to detect both sequence variants with copy number variants within a single test, making the whole procedure significantly more effective in a clinical setting. CentoICU™ offers a targeted panel based solution for early and fast diagnosis of critically ill newborns and children less than 24 months. In the current format, this panel screens for several hundreds of diseases in less than 4–7 days. Selection criteria of targeted genes include early onset, severe disease, ICU related symptomatology and diseases/syndromes of differential diagnostic value. Results: Using Illumina technology, CentoICU™ includes 806 genes from different categories including metabolic diseases, neurological disorders, muscular disorders, seizures disorders, kidney diseases, anemias, complex malformations, immunological disorders and mitochondrial disorders. Over 99% of those genes are covered by 100%. High quality variant annotation, variant prioritization by frequency and function, phenotype filtering by Human Phenotype Ontology terms and variant classification using elaborated data bases like CentoMD™ lead to a high quality report including phenotype associated pathogenic and likely pathogenic variants and a diagnostic interpretation considering all available data. Conclusion: CentoICU™ is an excellent answer for a specific, urgent, clinical question. It is recommended for newborns and children less than 24 months admitted to the ICU and presenting with unclear symptomatology. Small amount of material needed and short turnaround time complete the advantages of the method.

Panel-Based Clinical Genetic Testing in 85 Children with Inherited Retinal Disease

To assess the clinical usefulness of genetic testing in a pediatric population with inherited retinal disease (IRD). Single-center retrospective case series. Eighty-five unrelated children with a diagnosis of isolated or syndromic IRD who were referred for clinical genetic testing between January 2014 and July2016. Participants underwent a detailed ophthalmic examination, accompanied by electrodiagnostic testing (EDT) and dysmorphologic assessment where appropriate. Ocular and extraocular features were recorded using Human Phenotype Ontology terms. Subsequently, multigene panel testing (105 or 177 IRD-associated genes) was performed in an accredited diagnostic laboratory, followed by clinical variant interpretation. Diagnostic yield and clinical usefulness of genetic testing. Overall, 78.8% of patients (n= 67) received a probable molecular diagnosis; 7.5% (n= 5) of these had autosomal dominant disease, 25.4% (n= 17) had X-linked disease, and 67.2% (n= 45) had autosomal recessive disease. In a further 5.9% of patients (n= 5), a single heterozygous ABCA4 variant was identified; all these participants had a spectrum of clinical features consistent with ABCA4 retinopathy. Most participants (84.7%; n= 72) had undergone EDT and 81.9% (n= 59) of these patients received a probable molecular diagnosis. The genes most frequently mutated in the present cohort were CACNA1F and ABCA4, accounting for 14.9% (n= 10) and 11.9% (n= 8) of diagnoses respectively. Notably, in many cases, genetic testing helped to distinguish stationary from progressive IRD subtypes and to establish a precise diagnosis in a timely fashion. Multigene panel testing pointed to a molecular diagnosis in 84.7% of children with IRD. Thediagnostic yield in the study population was significantly higher compared with that in previously reported unselected IRD cohorts. Approaches similar to the one described herein are expected to become a standard component of care in pediatric ophthalmology. We propose the introduction of genetic testing early in the diagnostic pathway in children with clinical and/or electrophysiologic findings, suggestive ofIRD.

Pathogenic Variants in Thrombocytopenia Genes in 119 Cases with Undiagnosed Heritable Thrombocytopenia

The incidence of inherited rare bleeding, thrombotic and platelet disorders (BPD) is estimated to be 200-250 per million individuals. A considerable portion have heritable thrombocytopenia (HT), a genetically heterogeneous group of disorders with which 27 of the 76 ISTH-approved Tier 1 BPD genes have been causally associated. It is important to distinguish cases because variants in some HT genes confer risk of malignancy and pathologies outside hemopoiesis. This can be seldom achieved by considering clinical and laboratory characteristics alone. In order to improve the precision of diagnosis for HT subtypes and to identify novel genes underlying HT, we have developed two complementary high throughput sequencing (HTS) approaches. The first is for BPD cases with an assumed molecular diagnosis; 500 DNA samples have been sequenced with the ThromboGenomics HTS gene panel test for the 76 Tier 1 BPD genes. The second is for patients with rare disorders of unclear molecular aetiology enrolled in the NIHR BioResource. To identify the genetic basis of these rare disorders 10,000 DNA samples have been whole genome sequenced, including samples from 1,378 BPD probands, 123 affected and 41 unaffected relatives. Upon consenting, clinical parameters, laboratory results and pedigree history were deposited in a shared single study database and human phenotype ontology (HPO) terms appended to each patient. Novel statistical methods have been deployed to identify causal variants. Here we report on the pathogenic and likely pathogenic variants (PV and LPV) in 27 Tier 1 genes identified in cases with HT who had completed evaluation by either approach by July 2016.We defined PV as either high impact variants or variants present in 4 or more independent HT cases reported in the literature or our study database, and LPV as other credible variants with fewer than 4 independent cases previously observed. All cases were reviewed at a multi-disciplinary team meeting before being reported as "explained", "unexplained" or "partially explained" if components of the broader phenotype could not be explained by the variants observed. Between January 2011 and July 2016, 119 cases from 104 pedigrees with HT completed analysis with the identification of a PV or LPV in a Tier 1 HT gene believed to explain or partially explain the described phenotype. Variants were identified in 18 of the 27 HT genes (Table 1), including genes encoding transcription regulators (11/104 pedigrees) and platelet membrane proteins (8/104 pedigrees). However, variants in 5 genes encoding cytoskeletal components or regulators comprised the largest single functional group (57/104 pedigrees).We identified 18 different PV or LPV (8 novel) in ACTN1 in 29 pedigrees with non-syndromic macrothrombocytopenia (MTP) and 8 different PV or LPV (6 novel) in TUBB1 in 8 further pedigrees with non-syndromic MTP. PV or LPV in Tier 1 cytoskeletal HT genes associated with thrombocytopenia and variable non-haematological syndromic features were identified in 20 pedigrees, including 3 pedigrees with 2 different gain-of-function variants in DIAPH1 causing MTP, intermittent neutropenia and sensorineural hearing impairment. One variant has recently been reported by our Consortium, the other is novel. The remainder comprised 2 PV in WAS in 2 pedigrees and 14 different PV or LPV (4 novel) in MYH9 in 15 pedigrees, some of whom had isolated MTP without neutrophil inclusion bodies. Of note, common variants in four of these five genes have been found to be associated with the count and volume of platelets in genome wide association studies, confirming the notion that platelet-GWAS loci are enriched for genes underlying Mendelian platelet disorders.In conclusion, HT are genetically diverse disorders for which there is an unmet need for precision diagnosis. For HT cases with clinical and laboratory characteristics similar to previously reported ones for HT, the ThromboGenomics test is a valuable and affordable approach to obtain an accredited genetic diagnosis. Whole genome sequencing is a powerful approach to identify novel genes implicated in HT for the rapid enhancement of the ThromboGenomics HTS panel test. The high prevalence of variants causal of HT in Tier 1 BPD genes that encode cytoskeletal elements highlights the pivotal role of the cytoskeleton in platelet formation by mediating pro-platelet protrusion and budding from megakaryocytes. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Rare Variants in GP1BB underlie Autosomal Dominant Macrothrombocytopenia; Findings of Large Unique Bleeding and Platelet Disorders Cohort

The incidence of inherited rare bleeding, thrombotic and platelet disorders (BPD) is estimated to be 200-250 per million individuals. For at least 15% of these cases the molecular basis is unresolved (Lentaigne et al, Blood, 2016). We aim to discover the genetic basis of these unresolved BPDs, to improve diagnosis and treatment. In addition this will increase our knowledge of the molecular pathways of megakaryopoiesis, haemostasis and thrombus formation. For this purpose we have established a prospective BPD cohort, which at time of writing consist of 1,378 probands, 123 affected relatives and 41 unaffected relatives. After consenting, all these individuals have been precisely phenotyped using human phenotype ontology (HPO) terms (Westbury et al, Genome Medicine, 2015). This includes clinical parameters, laboratory results and pedigree history. Ten thousand DNA samples from the BPD cases, patients with other rare diseases and their close relatives, who were all enrolled in the NIHR BioResource were analysed by whole genome sequencing (Turro et al, Science Trans. Med, 2016). We applied phenotype similarity regression to identify statistical associations between presence of a coding variants with consequences in a gene and similarity to a latent HPO-coded phenotype (Greene et al, AJHG, 2016). We identified a strong statistical association between presence of 8 unique rare coding DNA variants with consequences in GP1BB and 8 probands with macrothrombocytopenia (SimReg posterior probability = 0.93 with inferred characteristic phenotype preferentially included the term "Increased mean platelet volume", Fisher's p = 2.10 x 10-6). We sought to validate these discovery findings through identification of further cases in the cohorts of 75 and 301 macrothrombocytopenia cases from the ThromboGenomics consortium (Simeoni et al, Blood, 2016) and the Nagoya Medical Center in Japan, respectively. Three additional variants in GP1BB were identified in 9 individuals from 8 pedigrees. Systematic review of the sequencing results of 27 BPD genes (including GP1BA, GP9) implicated in thrombocytopenia in 10 probands did not reveal any alternative variants that could plausibly explaining the phenotype.In aggregate 59 affected macrothrombocytopenia cases were observed in 16 pedigrees with 9 unique GP1BB variants, with the Y113C variant, which was observed in 6 pedigrees thought to be a Japanese founder variant. The means of the count and volume of platelets of the probands was 104.6 x109/l (range 47-172 x109/l) and 12.6 fL, respectively. Inspection of blood smears revealed anisocytosis with a small number of giant platelets and electron micrograph images were reminiscent of those from platelets of a patient with Bernard Soulier syndrome (BSS). In 11 pedigrees measurement by cytometry showed reduced levels of the GpIb/IX/V complex on the platelets of 8 genetically independent individuals and bleeding diathesis was reported in 7 of 16 pedigrees.Altogether, we identified 9 unique variants in the GP1BB gene, which encodes the 202 amino acid long Type 1 transmembrane protein GpIb□, which together with GpIba, GpV and GpIX form the receptor complex for von Willebrand Factor on megakaryocytes and platelets. They result in a disruption of the canonical methionine start codon, another resulting in a premature stop at residue 46, 5 missense variants at L16P, G43W, T68M, Y113C and L132Q, a deletion removing PAL at residues 79-81, and finally a frameshift in the codon for residue A150 leading to an alternative open reading frame predicted to result in a protein of 193 instead of 202 amino acids long. All 9 variants but the G43W one, which was observed in one of the 61,000 ExAC subjects were unobserved in the ExAC database.In summary before our study there was only one isolated report of a Gp1ba-R42C variant assumed to be causal of macrothrombocytopenia, but no segregation study was performed to corroborate this observation. Our findings in 16 pedigrees with 59 subjects with macrothrombocytopenia provide robust statistical and convincing co-segregation evidence that some variants in GP1BB if present as a single alleleexert a dominant effect on the count and volume of platelets, resulting in some pedigrees in a bleeding diathesis rejecting the dogma that BSS is mainly an autosomal recessive disorder. DisclosuresNo relevant conflicts of interest to declare.

Clinical Next Generation Sequencing to Identify Novel Platelet Disorders

Inherited platelet disorders (IPDs) comprise a heterogeneous group of disorders with a complex genetic etiology, characterized by impairments in platelet formation, morphology and function. Since the implementation of next generation sequencing (NGS) in 2009, the gene list for diagnosis of IPDs rapidly expanded from 39 to 53 genes. A diagnostic high-throughput targeted NGS platform (referred to as ThromboGenomics; www.thrombogenomics.org.uk) was very recently described as an affordable DNA-based test of 76 genes to diagnose patients ’suspected of having a known inherited platelet, thrombotic or bleeding disorder’ (BPD). When the phenotype is strongly indicative of the presence of a particular disease etiology but the variants are unknown, sensitivity remains high (>90% based on 61 samples) while patients included with an uncertain disease such as delta storage pool disease, mostly receive no genetic diagnosis (only 10% a genetic diagnosis was obtained). Such IPDs should be included in gene discovery NGS programs such as the BRIDGE-BPD2 study. For this study, whole genome sequencing results of the DNA samples of nearly 1000 probands with uncharacterized IPDs, analyzed using assigned Human Phenotype Ontology (HPO) terms have helped to identify pathogenic variants in almost 20% of cases. New clustering algorithms to group cases with similar phenotypes have been used to identify two novel IPD genes (DIAPH1 and SRC2) and several putative ones. Still many IPD patients don’t receive a genetic diagnosis. A majority of cases either harbor pathogenic variants in unknown genes or in regulatory regions or are the result of a digenic mode of inheritance. NGS combined with data from RNA-seq, ChIP-seq, gene regulatory network analysis, epigenome, proteomics and mouse knock-out studies amongst others will also help explore the non-coding regulatory space and gene-gene interactions. Given the existence of many non-pathogenic variants in any individual’s genome, the main challenge faced by researchers when interpreting NGS data of an IPD case is determining which variants are causing the disorder.3Interpreting the functional consequences of novel rare variants is not easy and it is extremely important to apply rigorous standards when assigning pathogenicity. Clinical genomic data are the same as other complex medical data and should be interpreted by a multidisciplinary team comprising typically a statistical geneticist, clinical geneticist, and genetic counselors, who have the skills to interpret these results in the context of the test methodology, the theoretical background of genetics, Bayesian reasoning, and a myriad of other factors.1. Simeoni I, Stephens JC, Hu F, et al. A comprehensive high-throughput sequencing test for the diagnosis of inherited bleeding, thrombotic and platelet disorders. Blood. 2016; 127: 279.2. Turro E, Greene D, Wijgaerts A, et al. A dominant gain-of-function mutation in universal tyrosine kinase SRC causes thrombocytopenia, myelofibrosis, bleeding, and bone pathologies. Sci Transl Med. 2016;8:328.3. Lentaigne C, Freson K, Laffan MA, et al. Inherited platelet disorders: towards DNA-based diagnosis. Blood. 2016; 127: 2814. DisclosuresNo relevant conflicts of interest to declare.

A Dominant Gain-of-Function Mutation in Universal Tyrosine Kinase SRC Causes Enhanced Podosome Formation in a Syndrome with Thrombocytopenia, Myelofibrosis, Bleeding and Bone Pathologies

For the BRIDGE-Bleeding and Platelet Disorder Consortium.We here present a pedigree with nine cases that present with a new syndrome characterized by early onset myelofibrosis, thrombocytopenia, bleeding and bone pathologies. The syndrome is caused by a dominant gain-of-function mutation in universal tyrosine kinase SRC and it is the first example of a pathogenic germline mutation in this extensively studied kinase. The genetic discovery has been made possible by making use of our collection of 1,115 patients with inherited bleeding and platelet disorders of unknown molecular etiology enrolled by us at 25 tertiary referral centers as part of the NIHR BioResource - Rare Diseases pilot phase of the 100,000 Genomes Project. The analysis of genome sequencing data for 2 affected individuals was empowered by comparison of Human Phenotype Ontology (HPO; Westbury et al., Genome Medicine, 2015) terms of the patients with OMIM and the Mouse Phenotype Ontology terms from 7,541 strains. The selection of the SRC gene as the most likely candidate gene was also informed by the use of our Blueprint blood cell progenitor RNA-seq data (Chen et al., Science 2014).Modeling of the E527K substitution predicts loss of SRC's self-inhibitory capacity, which we confirmed with in vitro studies showing increased SRC kinase activity and enhanced Tyr419 phosphorylation in COS7 cells overexpressing E527K SRC. The active form of SRC predominates in patients' platelets, resulting in enhanced overall tyrosine phosphorylation and reduced platelet activity. Patients with myelofibrosis have a hypercellular bone marrow with trilineage dysplasia and their stem cells grown in vitro form more myeloid and megakaryocyte (MK) colonies than control cells. Patient MKs generate platelets which are dysmorphic, low in number, highly variable in size and with a paucity of α-granules. Over-active SRC in patient-derived MKs causes a reduction in proplatelet formation, which can be rescued by SRC kinase inhibition. Stem cells transduced with lentiviral E527K SRC form MKs with enhanced tyrosine phosphorylation levels and with reduced ploidy compared to cells transduced with wild-type SRC. Patient-derived and E527K-transduced MKs show Y419 SRC positive stained podosomes that induce a strongly altered organization of filamentous actin. Expression of mutated src in zebrafish recapitulates patients' blood and bone phenotypes. Our findings reveal an unexpected and wide-ranging pathobiology for this universal kinase Src with podosome formation.In hematopoiesis constitutive active Src primarily affects megakaryopoiesis with ineffective formation of platelets. Its inadequate function is associated with bleeding. The kinase Src is activated frequently in human cancers hence new SFK inhibitors are undergoing clinical trials and side effects with severe bleeding are observed frequently. It remains to be determined whether this new class of SFK inhibitors inadvertently causes inadequate formation of platelets, which do not function properly. Disclosures:No relevant conflicts of interest to declare.

GWASdb v2: an update database for human genetic variants identified by genome-wide association studies

Genome-wide association studies (GWASs), now as a routine approach to study single-nucleotide polymorphism (SNP)-trait association, have uncovered over ten thousand significant trait/disease associated SNPs (TASs). Here, we updated GWASdb (GWASdb v2, http://jjwanglab.org/gwasdb) which provides comprehensive data curation and knowledge integration for GWAS TASs. These updates include: (i) Up to August 2015, we collected 2479 unique publications from PubMed and other resources; (ii) We further curated moderate SNP-trait associations (P-value < 1.0×10−3) from each original publication, and generated a total of 252 530 unique TASs in all GWASdb v2 collected studies; (iii) We manually mapped 1610 GWAS traits to 501 Human Phenotype Ontology (HPO) terms, 435 Disease Ontology (DO) terms and 228 Disease Ontology Lite (DOLite) terms. For each ontology term, we also predicted the putative causal genes; (iv) We curated the detailed sub-populations and related sample size for each study; (v) Importantly, we performed extensive function annotation for each TAS by incorporating gene-based information, ENCODE ChIP-seq assays, eQTL, population haplotype, functional prediction across multiple biological domains, evolutionary signals and disease-related annotation; (vi) Additionally, we compiled a SNP-drug response association dataset for 650 pharmacogenetic studies involving 257 drugs in this update; (vii) Last, we improved the user interface of website.

Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency.

Purpose:Medical diagnosis and molecular or biochemical confirmation typically rely on the knowledge of the clinician. Although this is very difficult in extremely rare diseases, we hypothesized that the recording of patient phenotypes in Human Phenotype Ontology (HPO) terms and computationally ranking putative disease-associated sequence variants improves diagnosis, particularly for patients with atypical clinical profiles.Genet Med 18 6, 608–617.Methods:Using simulated exomes and the National Institutes of Health Undiagnosed Diseases Program (UDP) patient cohort and associated exome sequence, we tested our hypothesis using Exomiser. Exomiser ranks candidate variants based on patient phenotype similarity to (i) known disease–gene phenotypes, (ii) model organism phenotypes of candidate orthologs, and (iii) phenotypes of protein–protein association neighbors.Genet Med 18 6, 608–617.Results:Benchmarking showed Exomiser ranked the causal variant as the top hit in 97% of known disease–gene associations and ranked the correct seeded variant in up to 87% when detectable disease–gene associations were unavailable. Using UDP data, Exomiser ranked the causative variant(s) within the top 10 variants for 11 previously diagnosed variants and achieved a diagnosis for 4 of 23 cases undiagnosed by clinical evaluation.Genet Med 18 6, 608–617.Conclusion:Structured phenotyping of patients and computational analysis are effective adjuncts for diagnosing patients with genetic disorders.Genet Med 18 6, 608–617.

Capturing phenotypes for precision medicine

Deep phenotyping followed by integrated computational analysis of genotype and phenotype is becoming ever more important for many areas of genomic diagnostics and translational research. The overwhelming majority of clinical descriptions in the medical literature are available only as natural language text, meaning that searching, analysis, and integration of medically relevant information in databases such as PubMed is challenging. The new journal Cold Spring Harbor Molecular Case Studies will require authors to select Human Phenotype Ontology terms for research papers that will be displayed alongside the manuscript, thereby providing a foundation for ontology-based indexing and searching of articles that contain descriptions of phenotypic abnormalities—an important step toward improving the ability of researchers and clinicians to get biomedical information that is critical for clinical care or translational research.