Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources

Limitations of exome sequencing in detecting rare and undiagnosed diseases

While exome sequencing (ES) is commonly the final diagnostic step in clinical genetics, it may miss diagnoses. To clarify the limitations of ES, we investigated the diagnostic yield of genetic tests beyond ES in our Undiagnosed Diseases Network (UDN) participants. We reviewed the yield of additional genetic testing including genome sequencing (GS), copy number variant (CNV), noncoding variant (NCV), repeat expansion (RE), or methylation testing in UDN cases with nondiagnostic ES results. Overall, 36/54 (67%) of total diagnoses were based on clinical findings and coding variants found by ES and 3/54 (6%) were based on clinical findings only. The remaining 15/54 (28%) required testing beyond ES. Of these, 7/15 (47%) had NCV, 6/15 (40%) CNV, and 2/15 (13%) had a RE or a DNA methylation disorder. Thus 18/54 (33%) of diagnoses were not solved exclusively by ES. Several methods were needed to detect and/or confirm the functional effects of the variants missed by ES, and in some cases by GS. These results indicate that tests to detect elusive variants should be considered after nondiagnostic preliminary steps. Further studies are needed to determine the cost-effectiveness of tests beyond ES that provide diagnoses and insights to possible treatment.

Genomic Landscape and Mutational Spectrum of ADAMTS Family Genes in Mendelian Disorders Based on Gene Evidence Review for Variant Interpretation

ADAMTS (a disintegrin and metalloproteinase with thrombospondin motifs) are a family of multidomain extracellular protease enzymes with 19 members. A growing number of ADAMTS family gene variants have been identified in patients with various hereditary diseases. To understand the genomic landscape and mutational spectrum of ADAMTS family genes, we evaluated all reported variants in the ClinVar database and Human Gene Mutation Database (HGMD), as well as recent literature on Mendelian hereditary disorders associated with ADAMTS family genes. Among 1089 variants in 14 genes reported in public databases, 307 variants previously suggested for pathogenicity in Mendelian diseases were comprehensively re-evaluated using the American College of Medical Genetics and Genomics (ACMG) 2015 guideline. A total of eight autosomal recessive genes were annotated as being strongly associated with specific Mendelian diseases, including two recently discovered genes (ADAMTS9 and ADAMTS19) for their causality in congenital diseases (nephronophthisis-related ciliopathy and nonsyndromic heart valve disease, respectively). Clinical symptoms and affected organs were extremely heterogeneous among hereditary diseases caused by ADAMTS family genes, indicating phenotypic heterogeneity despite their structural and functional similarity. ADAMTS6 was suggested as presenting undiscovered pathogenic mutations responsible for novel Mendelian disorders. Our study is the first to highlight the genomic landscape of ADAMTS family genes, providing an appropriate genetic approach for clinical use.

Clinical versus automated assessments of morphological variants in twins with and without neurodevelopmental disorders

Physical examinations are recommended as part of a comprehensive evaluation for individuals with neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder. These examinations should include assessment for morphological variants. Previous studies have shown an increase in morphological variants in individuals with NDDs, particularly ASD, and that these variants may be present in greater amounts in individuals with genetic alterations. Unfortunately, assessment for morphological variants can be subjective and time-consuming, and require a high degree of clinical expertise. Therefore, objective, automated methods of morphological assessment are desirable. This study compared the use of Face2Gene, an automated tool to explore facial morphological variants, to clinical consensus assessment, using a cohort of N = 290 twins enriched for NDDs (n = 135 with NDD diagnoses). Agreement between automated and clinical assessments were satisfactory to complete (78.3-100%). In our twin sample, individuals with NDDs did not have greater numbers of facial morphological variants when compared to those with typical development, nor when controlling for shared genetic and environmental factors within twin pairs. Common facial morphological variants in those with and without NDDs were similar and included thick upper lip vermilion, abnormality of the nasal tip, long face, and upslanted palpebral fissure. We conclude that although facial morphological variants can be assessed reliably in NDDs with automated tools like Face2Gene, clinical utility is limited when just exploring the facial region. Therefore, currently, automated assessments may best complement, rather than replace, in-person clinical assessments.

genoDraw: A Web Tool for Developing Pedigree Diagrams Using the Standardized Human Pedigree Nomenclature Integrated with Biomedical Vocabularies

The integration of genetic information in current clinical routine has raised a need for tools to exploit family genetic knowledge. On the clinical side, an application for managing and visualizing pedigree diagrams could provide genetics specialists with an integrated environment with potential positive impact on their current practice. This article presents a web tool (genoDraw) that provides clinical practitioners with the ability to create, maintain and visualize patients' and their families' information in the form of pedigree diagrams. genoDraw implements a graph-based three-step process for generating diagrams according to a de facto standard in the area and clinical terminologies. It also complies with five characteristics identified as indispensable for the next-generation of pedigree drawing software: comprehensiveness, data-drivenness, automation, interactivity and compatibility with biomedical vocabularies. The platform was implemented and tested, confirming its potential interest to clinical routine.

Bioinformatic framework for analysis of transcription factor changes as the molecular link between replicative cellular senescence signaling pathways and carcinogenesis

Cellular senescence is a natural condition of irreversible cell cycle arrest and apoptotic resistance that occurs in cells exposed to various stress factors, such as replicative stress or overexpression of oncogenes. Unraveling the complex regulation of senescence in cells is essential to strengthen senescence-related therapeutic approaches in cancer, as cellular senescence plays a dual role in tumorigenesis, having both anti- and pro-tumorigenic effects. In our study we created a model of replicative cellular senescence, based on transcriptomic data, including an extra intermediate time-point prior to cells entering senescence, to elucidate the interplay of networks governing cellular senescence with networks involved in tumorigenesis. We reveal specific changes that occur in transcription factor activity at different timepoints before and after cells entering senescence and model the signaling networks that govern these changes.

Genetic Modifiers and Rare Mendelian Disease

Despite advances in high-throughput sequencing that have revolutionized the discovery of gene defects in rare Mendelian diseases, there are still gaps in translating individual genome variation to observed phenotypic outcomes. While we continue to improve genomics approaches to identify primary disease-causing variants, it is evident that no genetic variant acts alone. In other words, some other variants in the genome (genetic modifiers) may alleviate (suppress) or exacerbate (enhance) the severity of the disease, resulting in the variability of phenotypic outcomes. Thus, to truly understand the disease, we need to consider how the disease-causing variants interact with the rest of the genome in an individual. Here, we review the current state-of-the-field in the identification of genetic modifiers in rare Mendelian diseases and discuss the potential for future approaches that could bridge the existing gap.

Molecular characterization of Spanish patients with MECP2 duplication syndrome

MECP2 duplication syndrome (MDS) is an X-linked neurodevelopmental disorder characterized by a severe to profound intellectual disability, early onset hypotonia and diverse psycho-motor and behavioural features. To date, fewer than 200 cases have been published. We report the clinical and molecular characterization of a Spanish MDS cohort that included 19 boys and 2 girls. Clinical suspicions were confirmed by array comparative genomic hybridization and multiplex ligation-dependent probe amplification (MLPA). Using, a custom in-house MLPA assay, we performed a thorough study of the minimal duplicated region, from which we concluded a complete duplication of both MECP2 and IRAK1 was necessary for a correct MDS diagnosis, as patients with partial MECP2 duplications lacked some typical clinical traits present in other MDS patients. In addition, the duplication location may be related to phenotypic severity. This observation may provide a new approach for genotype-phenotype correlations, and thus more personalized genetic counselling.

Expanding Clinical Presentations Due to Variations in THOC2 mRNA Nuclear Export Factor

Multiple TREX mRNA export complex subunits (e.g., THOC1, THOC2, THOC5, THOC6, THOC7) have now been implicated in neurodevelopmental disorders (NDDs), neurodegeneration and cancer. We previously implicated missense and splicing-defective THOC2 variants in NDDs and a broad range of other clinical features. Here we report 10 individuals from nine families with rare missense THOC2 variants including the first case of a recurrent variant (p.Arg77Cys), and an additional individual with an intragenic THOC2 microdeletion (Del-Ex37-38). Ex vivo missense variant testing and patient-derived cell line data from current and published studies show 9 of the 14 missense THOC2 variants result in reduced protein stability. The splicing-defective and deletion variants result in a loss of small regions of the C-terminal THOC2 RNA binding domain (RBD). Interestingly, reduced stability of THOC2 variant proteins has a flow-on effect on the stability of the multi-protein TREX complex; specifically on the other NDD-associated THOC subunits. Our current, expanded cohort refines the core phenotype of THOC2 NDDs to language disorder and/or ID, with a variable severity, and disorders of growth. A subset of affected individuals' has severe-profound ID, persistent hypotonia and respiratory abnormalities. Further investigations to elucidate the pathophysiological basis for this severe phenotype are warranted.

Significantly different clinical phenotypes associated with mutations in synthesis and transamidase+remodeling glycosylphosphatidylinositol (GPI)-anchor biosynthesis genes

BACKGROUND

Defects in the glycosylphosphatidylinositol (GPI) biosynthesis pathway can result in a group of congenital disorders of glycosylation known as the inherited GPI deficiencies (IGDs). To date, defects in 22 of the 29 genes in the GPI biosynthesis pathway have been identified in IGDs. The early phase of the biosynthetic pathway assembles the GPI anchor (Synthesis stage) and the late phase transfers the GPI anchor to a nascent peptide in the endoplasmic reticulum (ER) (Transamidase stage), stabilizes the anchor in the ER membrane using fatty acid remodeling and then traffics the GPI-anchored protein to the cell surface (Remodeling stage).

RESULTS

We addressed the hypothesis that disease-associated variants in either the Synthesis stage or Transamidase+Remodeling-stage GPI pathway genes have distinct phenotypic spectra. We reviewed clinical data from 58 publications describing 152 individual patients and encoded the phenotypic information using the Human Phenotype Ontology (HPO). We showed statistically significant differences between the Synthesis and Transamidase+Remodeling Groups in the frequencies of phenotypes in the musculoskeletal system, cleft palate, nose phenotypes, and cognitive disability. Finally, we hypothesized that phenotypic defects in the IGDs are likely to be at least partially related to defective GPI anchoring of their target proteins. Twenty-two of one hundred forty-two proteins that receive a GPI anchor are associated with one or more Mendelian diseases and 12 show some phenotypic overlap with the IGDs, represented by 34 HPO terms. Interestingly, GPC3 and GPC6, members of the glypican family of heparan sulfate proteoglycans bound to the plasma membrane through a covalent GPI linkage, are associated with 25 of these phenotypic abnormalities.

CONCLUSIONS

IGDs associated with Synthesis and Transamidase+Remodeling stages of the GPI biosynthesis pathway have significantly different phenotypic spectra. GPC2 and GPC6 genes may represent a GPI target of general disruption to the GPI biosynthesis pathway that contributes to the phenotypes of some IGDs.

Evaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes

Characteristic or classic phenotype of Cornelia de Lange syndrome (CdLS) is associated with a recognisable facial pattern. However, the heterogeneity in causal genes and the presence of overlapping syndromes have made it increasingly difficult to diagnose only by clinical features. DeepGestalt technology, and its app Face2Gene, is having a growing impact on the diagnosis and management of genetic diseases by analysing the features of affected individuals. Here, we performed a phenotypic study on a cohort of 49 individuals harbouring causative variants in known CdLS genes in order to evaluate Face2Gene utility and sensitivity in the clinical diagnosis of CdLS. Based on the profile images of patients, a diagnosis of CdLS was within the top five predicted syndromes for 97.9% of our cases and even listed as first prediction for 83.7%. The age of patients did not seem to affect the prediction accuracy, whereas our results indicate a correlation between the clinical score and affected genes. Furthermore, each gene presents a different pattern recognition that may be used to develop new neural networks with the goal of separating different genetic subtypes in CdLS. Overall, we conclude that computer-assisted image analysis based on deep learning could support the clinical diagnosis of CdLS.

Human and mouse essentiality screens as a resource for disease gene discovery

The identification of causal variants in sequencing studies remains a considerable challenge that can be partially addressed by new gene-specific knowledge. Here, we integrate measures of how essential a gene is to supporting life, as inferred from viability and phenotyping screens performed on knockout mice by the International Mouse Phenotyping Consortium and essentiality screens carried out on human cell lines. We propose a cross-species gene classification across the Full Spectrum of Intolerance to Loss-of-function (FUSIL) and demonstrate that genes in five mutually exclusive FUSIL categories have differing biological properties. Most notably, Mendelian disease genes, particularly those associated with developmental disorders, are highly overrepresented among genes non-essential for cell survival but required for organism development. After screening developmental disorder cases from three independent disease sequencing consortia, we identify potentially pathogenic variants in genes not previously associated with rare diseases. We therefore propose FUSIL as an efficient approach for disease gene discovery.

Incremental data integration for tracking genotype-disease associations

Functional annotation of genes remains a challenge in fundamental biology and is a limiting factor for translational medicine. Computational approaches have been developed to process heterogeneous data into meaningful metrics, but often do not address how findings might be updated when new evidence comes to light. To address this challenge, we describe requirements for a framework for incremental data integration and propose an implementation based on phenotype ontologies and Bayesian probability updates. We apply the framework to quantify similarities between gene annotations and disease profiles. Within this scope, we categorize human diseases according to how well they can be recapitulated by animal models and quantify similarities between human diseases and mouse models produced by the International Mouse Phenotyping Consortium. The flexibility of the approach allows us to incorporate negative phenotypic data to better prioritize candidate genes, and to stratify disease mapping using sex-dependent phenotypes. All our association scores can be updated and we exploit this feature to showcase integration with curated annotations from high-precision assays. Incremental integration is thus a suitable framework for tracking functional annotations and linking to complex human pathology.

Human diseases are often caused or influenced by genetic factors. The link between a particular gene and a specific disease is well-established in some cases. However, the roles of many genes are still unclear and many diseases do not have an understood genetic mechanism. Dissecting such interactions requires using a range of experimental approaches and assessing the results in a holistic manner. Computational methods already exist for comparing phenotypes observed in models and patients, and they work well when the phenotypes are detailed. In this work we argue that algorithms should also be able to report meaningful assessments based on preliminary data, and to update reports in a coherent manner when new information comes to light. These requirements lead to specific mathematical properties, which define incremental integration. We implement these requirements in a computational framework. We study the extent individual rare human diseases might be recapitulated by animal models. We compute gene-disease associations using data from public resources, including previously unused negative data. Altogether, these examples illustrate the framework can use observations in model systems to track gene-disease associations in the human context.

Large-Scale Whole-Genome Sequencing Reveals the Genetic Architecture of Primary Membranoproliferative GN and C3 Glomerulopathy

BACKGROUND

Primary membranoproliferative GN, including complement 3 (C3) glomerulopathy, is a rare, untreatable kidney disease characterized by glomerular complement deposition. Complement gene mutations can cause familial C3 glomerulopathy, and studies have reported rare variants in complement genes in nonfamilial primary membranoproliferative GN.

METHODS

We analyzed whole-genome sequence data from 165 primary membranoproliferative GN cases and 10,250 individuals without the condition (controls) as part of the National Institutes of Health Research BioResource-Rare Diseases Study. We examined copy number, rare, and common variants.

RESULTS

Our analysis included 146 primary membranoproliferative GN cases and 6442 controls who were unrelated and of European ancestry. We observed no significant enrichment of rare variants in candidate genes (genes encoding components of the complement alternative pathway and other genes associated with the related disease atypical hemolytic uremic syndrome; 6.8% in cases versus 5.9% in controls) or exome-wide. However, a significant common variant locus was identified at 6p21.32 (rs35406322) (P=3.29×10^-8; odds ratio [OR], 1.93; 95% confidence interval [95% CI], 1.53 to 2.44), overlapping the HLA locus. Imputation of HLA types mapped this signal to a haplotype incorporating DQA1*05:01, DQB1*02:01, and DRB1*03:01 (P=1.21×10^-8; OR, 2.19; 95% CI, 1.66 to 2.89). This finding was replicated by analysis of HLA serotypes in 338 individuals with membranoproliferative GN and 15,614 individuals with nonimmune renal failure.

CONCLUSIONS

We found that HLA type, but not rare complement gene variation, is associated with primary membranoproliferative GN. These findings challenge the paradigm of complement gene mutations typically causing primary membranoproliferative GN and implicate an underlying autoimmune mechanism in most cases.

Mechanistic insights into transcription factor cooperativity and its impact on protein-phenotype interactions

Recent high-throughput transcription factor (TF) binding assays revealed that TF cooperativity is a widespread phenomenon. However, a global mechanistic and functional understanding of TF cooperativity is still lacking. To address this, here we introduce a statistical learning framework that provides structural insight into TF cooperativity and its functional consequences based on next generation sequencing data. We identify DNA shape as driver for cooperativity, with a particularly strong effect for Forkhead-Ets pairs. Follow-up experiments reveal a local shape preference at the Ets-DNA-Forkhead interface and decreased cooperativity upon loss of the interaction. Additionally, we discover many functional associations for cooperatively bound TFs. Examination of the link between FOXO1:ETV6 and lymphomas reveals that their joint expression levels improve patient clinical outcome stratification. Altogether, our results demonstrate that inter-family cooperative TF binding is driven by position-specific DNA readout mechanisms, which provides an additional regulatory layer for downstream biological functions.

Although transcription factor (TF) cooperativity is widespread, a global mechanistic understanding of the role of TF cooperativity is still lacking. Here the authors introduce a statistical learning framework that provides structural insight into TF cooperativity and its functional consequences based on next generation sequencing data and provide mechanistic insights into TF cooperativity and its impact on protein-phenotype interactions.

GWAS Central: a comprehensive resource for the discovery and comparison of genotype and phenotype data from genome-wide association studies

The GWAS Central resource provides a toolkit for integrative access and visualization of a uniquely extensive collection of genome-wide association study data, while ensuring safe open access to prevent research participant identification. GWAS Central is the world's most comprehensive openly accessible repository of summary-level GWAS association information, providing over 70 million P-values for over 3800 studies investigating over 1400 unique phenotypes. The database content comprises direct submissions received from GWAS authors and consortia, in addition to actively gathered data sets from various public sources. GWAS data are discoverable from the perspective of genetic markers, genes, genome regions or phenotypes, via graphical visualizations and detailed downloadable data reports. Tested genetic markers and relevant genomic features can be visually interrogated across up to sixteen multiple association data sets in a single view using the integrated genome browser. The semantic standardization of phenotype descriptions with Medical Subject Headings and the Human Phenotype Ontology allows the precise identification of genetic variants associated with diseases, phenotypes and traits of interest. Harmonization of the phenotype descriptions used across several GWAS-related resources has extended the phenotype search capabilities to enable cross-database study discovery using a range of ontologies. GWAS Central is updated regularly and available at https://www.gwascentral.org.

WormBase: a modern Model Organism Information Resource

WormBase (https://wormbase.org/) is a mature Model Organism Information Resource supporting researchers using the nematode Caenorhabditis elegans as a model system for studies across a broad range of basic biological processes. Toward this mission, WormBase efforts are arranged in three primary facets: curation, user interface and architecture. In this update, we describe progress in each of these three areas. In particular, we discuss the status of literature curation and recently added data, detail new features of the web interface and options for users wishing to conduct data mining workflows, and discuss our efforts to build a robust and scalable architecture by leveraging commercial cloud offerings. We conclude with a description of WormBase's role as a founding member of the nascent Alliance of Genome Resources.

A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain

The ability to compare entities within a knowledge graph is a cornerstone technique for several applications, ranging from the integration of heterogeneous data to machine learning. It is of particular importance in the biomedical domain, where semantic similarity can be applied to the prediction of protein-protein interactions, associations between diseases and genes, cellular localization of proteins, among others. In recent years, several knowledge graph-based semantic similarity measures have been developed, but building a gold standard data set to support their evaluation is non-trivial. We present a collection of 21 benchmark data sets that aim at circumventing the difficulties in building benchmarks for large biomedical knowledge graphs by exploiting proxies for biomedical entity similarity. These data sets include data from two successful biomedical ontologies, Gene Ontology and Human Phenotype Ontology, and explore proxy similarities calculated based on protein sequence similarity, protein family similarity, protein-protein interactions and phenotype-based gene similarity. Data sets have varying sizes and cover four different species at different levels of annotation completion. For each data set, we also provide semantic similarity computations with state-of-the-art representative measures. Database URL: https://github.com/liseda-lab/kgsim-benchmark.

Practical guide to genetic screening for inherited eye diseases

Genetic eye diseases affect around one in 1000 people worldwide for which the molecular aetiology remains unknown in the majority. The identification of disease-causing gene variant(s) allows a better understanding of the disorder and its inheritance. There is now an approved retinal gene therapy for autosomal recessive RPE65-retinopathy, and numerous ocular gene/mutation-targeted clinical trials underway, highlighting the importance of establishing a genetic diagnosis so patients can fully access the latest research developments and treatment options. In this review, we will provide a practical guide to managing patients with these conditions including an overview of inheritance patterns, required pre- and post-test genetic counselling, different types of cytogenetic and genetic testing available, with a focus on next generation sequencing using targeted gene panels, whole exome and genome sequencing. We will expand on the pros and cons of each modality, variant interpretation and options for family planning for the patient and their family. With the advent of genomic medicine, genetic screening will soon become mainstream within all ophthalmology subspecialties for prevention of disease and provision of precision therapeutics.

RA-map: building a state-of-the-art interactive knowledge base for rheumatoid arthritis

Rheumatoid arthritis (RA) is a progressive, inflammatory autoimmune disease of unknown aetiology. The complex mechanism of aetiopathogenesis, progress and chronicity of the disease involves genetic, epigenetic and environmental factors. To understand the molecular mechanisms underlying disease phenotypes, one has to place implicated factors in their functional context. However, integration and organization of such data in a systematic manner remains a challenging task. Molecular maps are widely used in biology to provide a useful and intuitive way of depicting a variety of biological processes and disease mechanisms. Recent large-scale collaborative efforts such as the Disease Maps Project demonstrate the utility of such maps as versatile tools to organize and formalize disease-specific knowledge in a comprehensive way, both human and machine-readable. We present a systematic effort to construct a fully annotated, expert validated, state-of-the-art knowledge base for RA in the form of a molecular map. The RA map illustrates molecular and signalling pathways implicated in the disease. Signal transduction is depicted from receptors to the nucleus using the Systems Biology Graphical Notation (SBGN) standard representation. High-quality manual curation, use of only human-specific studies and focus on small-scale experiments aim to limit false positives in the map. The state-of-the-art molecular map for RA, using information from 353 peer-reviewed scientific publications, comprises 506 species, 446 reactions and 8 phenotypes. The species in the map are classified to 303 proteins, 61 complexes, 106 genes, 106 RNA entities, 2 ions and 7 simple molecules. The RA map is available online at ramap.elixir-luxembourg.org as an open-access knowledge base allowing for easy navigation and search of molecular pathways implicated in the disease. Furthermore, the RA map can serve as a template for omics data visualization.

Genome analysis and knowledge-driven variant interpretation with TGex

BACKGROUND

The clinical genetics revolution ushers in great opportunities, accompanied by significant challenges. The fundamental mission in clinical genetics is to analyze genomes, and to identify the most relevant genetic variations underlying a patient's phenotypes and symptoms. The adoption of Whole Genome Sequencing requires novel capacities for interpretation of non-coding variants.

RESULTS

We present TGex, the Translational Genomics expert, a novel genome variation analysis and interpretation platform, with remarkable exome analysis capacities and a pioneering approach of non-coding variants interpretation. TGex's main strength is combining state-of-the-art variant filtering with knowledge-driven analysis made possible by VarElect, our highly effective gene-phenotype interpretation tool. VarElect leverages the widely used GeneCards knowledgebase, which integrates information from > 150 automatically-mined data sources. Access to such a comprehensive data compendium also facilitates TGex's broad variant annotation, supporting evidence exploration, and decision making. TGex has an interactive, user-friendly, and easy adaptive interface, ACMG compliance, and an automated reporting system. Beyond comprehensive whole exome sequence capabilities, TGex encompasses innovative non-coding variants interpretation, towards the goal of maximal exploitation of whole genome sequence analyses in the clinical genetics practice. This is enabled by GeneCards' recently developed GeneHancer, a novel integrative and fully annotated database of human enhancers and promoters. Examining use-cases from a variety of TGex users world-wide, we demonstrate its high diagnostic yields (42% for single exome and 50% for trios in 1500 rare genetic disease cases) and critical actionable genetic findings. The platform's support for integration with EHR and LIMS through dedicated APIs facilitates automated retrieval of patient data for TGex's customizable reporting engine, establishing a rapid and cost-effective workflow for an entire range of clinical genetic testing, including rare disorders, cancer predisposition, tumor biopsies and health screening.

CONCLUSIONS

TGex is an innovative tool for the annotation, analysis and prioritization of coding and non-coding genomic variants. It provides access to an extensive knowledgebase of genomic annotations, with intuitive and flexible configuration options, allows quick adaptation, and addresses various workflow requirements. It thus simplifies and accelerates variant interpretation in clinical genetics workflows, with remarkable diagnostic yield, as exemplified in the described use cases. TGex is available at http://tgex.genecards.org/.

Increased Cancer Prevalence in Peripartum Cardiomyopathy

Objectives

This study was designed to analyze the prevalence and potential genetic basis of cancer and heart failure in peripartum cardiomyopathy (PPCM).

Background

PPCM manifests as heart failure late in pregnancy or postpartum in women without previous heart disease.

Methods

Clinical history and cancer prevalence were evaluated in a cohort of 236 PPCM patients from Germany and Sweden. Exome sequencing assessed variants in 133 genes associated with cancer predisposition syndromes (CPS) and in 115 genes associated with dilated/hypertrophic cardiomyopathy (DCM/HCM) in 14 PPCM patients with a history of cancer, and in 6 PPCM patients without a history of cancer.

Results

The prevalence of cancer was 16-fold higher (8.9%, 21 of 236 patients) in PPCM patients compared to age-matched women (German cancer registry, Robert-Koch-Institute: 0.59%; p < 0.001). Cancer before PPCM occurred in 12 of 21 patients of whom 11 obtained cardiotoxic cancer therapies. Of those, 17% fully recovered cardiac function by 7 ± 2 months of follow-up compared to 55% of PPCM patients without cancer (p = 0.015). Cancer occurred after PPCM in 10 of 21 patients; 80% had left ventricular ejection fraction of ≥50% after cancer therapy. Whole-exome sequencing in 14 PPCM patients with cancer revealed that 43% (6 of 14 patients) carried likely pathogenic (Class IV) or pathogenic (Class V) gene variants associated with DCM/HCM in CPT2, DSP, MYH7, TTN, and/or with CPS in ATM, ERCC5, NBN, RECQL4, and SLX4. All CPS variants affected DNA damage response genes.

Conclusions

Cardiotoxic cancer therapy before PPCM is associated with delayed full recovery. The high cancer prevalence in PPCM is linked to likely pathogenic/pathogenic gene variants associated with DCM/HCM and/or CPS/DNA damage response-related cancer risk. This may warrant genetic testing and screening for heart failure in pregnant women with a cancer history and screening for cancer in PPCM patients.

Prioritization of genes driving congenital phenotypes of patients with de novo genomic structural variants

BACKGROUND

Genomic structural variants (SVs) can affect many genes and regulatory elements. Therefore, the molecular mechanisms driving the phenotypes of patients carrying de novo SVs are frequently unknown.

METHODS

We applied a combination of systematic experimental and bioinformatic methods to improve the molecular diagnosis of 39 patients with multiple congenital abnormalities and/or intellectual disability harboring apparent de novo SVs, most with an inconclusive diagnosis after regular genetic testing.

RESULTS

In 7 of these cases (18%), whole-genome sequencing analysis revealed disease-relevant complexities of the SVs missed in routine microarray-based analyses. We developed a computational tool to predict the effects on genes directly affected by SVs and on genes indirectly affected likely due to the changes in chromatin organization and impact on regulatory mechanisms. By combining these functional predictions with extensive phenotype information, candidate driver genes were identified in 16/39 (41%) patients. In 8 cases, evidence was found for the involvement of multiple candidate drivers contributing to different parts of the phenotypes. Subsequently, we applied this computational method to two cohorts containing a total of 379 patients with previously detected and classified de novo SVs and identified candidate driver genes in 189 cases (50%), including 40 cases whose SVs were previously not classified as pathogenic. Pathogenic position effects were predicted in 28% of all studied cases with balanced SVs and in 11% of the cases with copy number variants.

CONCLUSIONS

These results demonstrate an integrated computational and experimental approach to predict driver genes based on analyses of WGS data with phenotype association and chromatin organization datasets. These analyses nominate new pathogenic loci and have strong potential to improve the molecular diagnosis of patients with de novo SVs.

A post glycosylphosphatidylinositol (GPI) attachment to proteins, type 2 (PGAP2) variant identified in Mabry syndrome index cases: Molecular genetics of the prototypical inherited GPI disorder

We report that recessive inheritance of a post-GPI attachment to proteins 2 (PGAP2) gene variant results in the hyperphosphatasia with neurologic deficit (HPMRS) phenotype described by Mabry et al., in 1970. HPMRS, or Mabry syndrome, is now known to be one of 21 inherited glycosylphosphatidylinositol (GPI) deficiencies (IGDs), or GPI biosynthesis defects (GPIBDs). Bi-allelic mutations in at least six genes result in HPMRS phenotypes. Disruption of four phosphatidylinositol glycan (PIG) biosynthesis genes, PIGV, PIGO, PIGW and PIGY, expressed in the endoplasmic reticulum, result in HPMRS 1, 2, 5 and 6; disruption of the PGAP2 and PGAP3 genes, necessary for stabilizing the association of GPI anchored proteins (AP) with the Golgi membrane, result in HPMRS 3 and 4. We used exome sequencing to identify a novel homozygous missense PGAP2 variant NM_014489.3:c.881C > T, p.Thr294Met in two index patients and targeted sequencing to identify this variant in an unrelated patient. Rescue assays were conducted in two PGAP2 deficient cell lines, PGAP2 KO cells generated by CRISPR/Cas9 and PGAP2 deficient CHO cells, in order to examine the pathogenicity of the PGAP2 variant. First, we used the CHO rescue assay to establish that the wild type PGAP2 isoform 1, translated from transcript 1, is less active than the wild type PGAP2 isoform 8, translated from transcript 12 (alternatively spliced to omit exon 3). As a result, in our variant rescue assays, we used the more active NM_001256240.2:c.698C > T, p.Thr233Met isoform 8 instead of NM_014489.3:c.881C > T, p.Thr294Met isoform 1. Flow cytometric analysis showed that restoration of cell surface CD59 and CD55 with variant PGAP2 isoform 8, driven by the weak (pTA FLAG) promoter, was less efficient than wild type isoform 8. Therefore, we conclude that recessive inheritance of c.881C > T PGAP2, expressed as the hypomorphic PGAP2 c.698C > T, p.Thr233Met isoform 8, results in prototypical Mabry phenotype, HPMRS3 (GPIBD 8 [MIM: 614207]). This study highlights the need for long-term follow up of individuals with rare diseases in order to ensure that they benefit from innovations in diagnosis and treatment.

Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data

Atrial fibrillation (AF) is the most common arrhythmia and significantly increases stroke risk. This risk is effectively managed by oral anticoagulation. Recent studies using national registry data indicate increased use of anticoagulation resulting from changes in guidelines and the availability of newer drugs. The aim of this study is to develop and validate an open source risk scoring pipeline for free-text electronic health record data using natural language processing. AF patients discharged from 1st January 2011 to 1st October 2017 were identified from discharge summaries (N = 10,030, 64.6% male, average age 75.3 ± 12.3 years). A natural language processing pipeline was developed to identify risk factors in clinical text and calculate risk for ischaemic stroke (CHA2DS2-VASc) and bleeding (HAS-BLED). Scores were validated vs two independent experts for 40 patients. Automatic risk scores were in strong agreement with the two independent experts for CHA2DS2-VASc (average kappa 0.78 vs experts, compared to 0.85 between experts). Agreement was lower for HAS-BLED (average kappa 0.54 vs experts, compared to 0.74 between experts). In high-risk patients (CHA2DS2-VASc ≥2) OAC use has increased significantly over the last 7 years, driven by the availability of DOACs and the transitioning of patients from AP medication alone to OAC. Factors independently associated with OAC use included components of the CHA2DS2-VASc and HAS-BLED scores as well as discharging specialty and frailty. OAC use was highest in patients discharged under cardiology (69%). Electronic health record text can be used for automatic calculation of clinical risk scores at scale. Open source tools are available today for this task but require further validation. Analysis of routinely collected EHR data can replicate findings from large-scale curated registries.

The Hearing Impairment Ontology: A Tool for Unifying Hearing Impairment Knowledge to Enhance Collaborative Research

Hearing impairment (HI) is a common sensory disorder that is defined as the partial or complete inability to detect sound in one or both ears. This diverse pathology is associated with a myriad of phenotypic expressions and can be non-syndromic or syndromic. HI can be caused by various genetic, environmental, and/or unknown factors. Some ontologies capture some HI forms, phenotypes, and syndromes, but there is no comprehensive knowledge portal which includes aspects specific to the HI disease state. This hampers inter-study comparability, integration, and interoperability within and across disciplines. This work describes the HI Ontology (HIO) that was developed based on the Sickle Cell Disease Ontology (SCDO) model. This is a collaboratively developed resource built around the ‘Hearing Impairment’ concept by a group of experts in different aspects of HI and ontologies. HIO is the first comprehensive, standardized, hierarchical, and logical representation of existing HI knowledge. HIO allows researchers and clinicians alike to readily access standardized HI-related knowledge in a single location and promotes collaborations and HI information sharing, including epidemiological, socio-environmental, biomedical, genetic, and phenotypic information. Furthermore, this ontology illustrates the adaptability of the SCDO framework for use in developing a disease-specific ontology.

Artificial intelligence in clinical and genomic diagnostics

Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical AI applications. In clinical diagnostics, AI-based computer vision approaches are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. In some areas, such as clinical genomics, a specific type of AI algorithm known as deep learning is used to process large and complex genomic datasets. In this review, we first summarize the main classes of problems that AI systems are well suited to solve and describe the clinical diagnostic tasks that benefit from these solutions. Next, we focus on emerging methods for specific tasks in clinical genomics, including variant calling, genome annotation and variant classification, and phenotype-to-genotype correspondence. Finally, we end with a discussion on the future potential of AI in individualized medicine applications, especially for risk prediction in common complex diseases, and the challenges, limitations, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those utilizing human genetics and genomics data.

Comorbidity of asthma and hypertension may be mediated by shared genetic dysregulation and drug side effects

Asthma and hypertension are complex diseases coinciding more frequently than expected by chance. Unraveling the mechanisms of comorbidity of asthma and hypertension is necessary for choosing the most appropriate treatment plan for patients with this comorbidity. Since both diseases have a strong genetic component in this article we aimed to find and study genes simultaneously associated with asthma and hypertension. We identified 330 shared genes and found that they form six modules on the interaction network. A strong overlap between genes associated with asthma and hypertension was found on the level of eQTL regulated genes and between targets of drugs relevant for asthma and hypertension. This suggests that the phenomenon of comorbidity of asthma and hypertension may be explained by altered genetic regulation or result from drug side effects. In this work we also demonstrate that not only drug indications but also contraindications provide an important source of molecular evidence helpful to uncover disease mechanisms. These findings give a clue to the possible mechanisms of comorbidity and highlight the direction for future research.

DiNGO: standalone application for Gene Ontology and Human Phenotype Ontology term enrichment analysis

SUMMARY

Although various tools for Gene Ontology (GO) term enrichment analysis are available, there is still room for improvement. Hence, we present DiNGO, a standalone application based on an open source code from BiNGO, a widely-used application to assess the overrepresentation of GO categories. Besides facilitating GO term enrichment analyses, DiNGO has been developed to allow for convenient Human Phenotype Ontology (HPO) term overrepresentation investigation. This is an important contribution considering the increasing interest in HPO in scientific research and its potential in clinical settings. DiNGO supports gene/protein identifier conversion and an automatic updating of GO and HPO annotation resources. Finally, DiNGO can rapidly process a large amount of data due to its multithread design.

AVAILABILITY AND IMPLEMENTATION

DiNGO is implemented in the JAVA language, and its source code, example datasets and instructions are available on GitHub: https://github.com/radoslav180/DiNGO. A pre-compiled jar file is available at: https://www.vin.bg.ac.rs/180/tools/DiNGO.php.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

From cytogenetics to cytogenomics: whole-genome sequencing as a first-line test comprehensively captures the diverse spectrum of disease-causing genetic variation underlying intellectual disability

Background

Since different types of genetic variants, from single nucleotide variants (SNVs) to large chromosomal rearrangements, underlie intellectual disability, we evaluated the use of whole-genome sequencing (WGS) rather than chromosomal microarray analysis (CMA) as a first-line genetic diagnostic test.

Methods

We analyzed three cohorts with short-read WGS: (i) a retrospective cohort with validated copy number variants (CNVs) (cohort 1, n = 68), (ii) individuals referred for monogenic multi-gene panels (cohort 2, n = 156), and (iii) 100 prospective, consecutive cases referred to our center for CMA (cohort 3). Bioinformatic tools developed include FindSV, SVDB, Rhocall, Rhoviz, and vcf2cytosure.

Results

First, we validated our structural variant (SV)-calling pipeline on cohort 1, consisting of three trisomies and 79 deletions and duplications with a median size of 850 kb (min 500 bp, max 155 Mb). All variants were detected. Second, we utilized the same pipeline in cohort 2 and analyzed with monogenic WGS panels, increasing the diagnostic yield to 8%. Next, cohort 3 was analyzed by both CMA and WGS. The WGS data was processed for large (> 10 kb) SVs genome-wide and for exonic SVs and SNVs in a panel of 887 genes linked to intellectual disability as well as genes matched to patient-specific Human Phenotype Ontology (HPO) phenotypes. This yielded a total of 25 pathogenic variants (SNVs or SVs), of which 12 were detected by CMA as well. We also applied short tandem repeat (STR) expansion detection and discovered one pathologic expansion in ATXN7. Finally, a case of Prader-Willi syndrome with uniparental disomy (UPD) was validated in the WGS data.

Important positional information was obtained in all cohorts. Remarkably, 7% of the analyzed cases harbored complex structural variants, as exemplified by a ring chromosome and two duplications found to be an insertional translocation and part of a cryptic unbalanced translocation, respectively.

Conclusion

The overall diagnostic rate of 27% was more than doubled compared to clinical microarray (12%). Using WGS, we detected a wide range of SVs with high accuracy. Since the WGS data also allowed for analysis of SNVs, UPD, and STRs, it represents a powerful comprehensive genetic test in a clinical diagnostic laboratory setting.

VCF/Plotein: visualization and prioritization of genomic variants from human exome sequencing projects

Motivation

Identifying disease-causing variants from exome sequencing projects remains a challenging task that often requires bioinformatics expertise. Here we describe a user-friendly graphical application that allows medical professionals and bench biologists to prioritize and visualize genetic variants from human exome sequencing data.

Results

We have implemented VCF/Plotein, a graphical, fully interactive web application able to display exome sequencing data in VCF format. Gene and variant information is extracted from Ensembl. Cross-referencing with external databases and application-based gene and variant filtering have also been implemented. All data processing is done locally by the user’s CPU to ensure the security of patient data.

Availability and implementation

Freely available on the web at https://vcfplotein.liigh.unam.mx. Website implemented in JavaScript using the Vue.js framework, with all major browsers supported. Source code freely available for download at https://github.com/raulossio/VCF-plotein.

Supplementary information

Supplementary data are available at Bioinformatics online.

A multi-omics digital research object for the genetics of sleep regulation

With the aim to uncover the molecular pathways underlying the regulation of sleep, we recently assembled an extensive and comprehensive systems genetics dataset interrogating a genetic reference population of mice at the levels of the genome, the brain and liver transcriptomes, the plasma metabolome, and the sleep-wake phenome. To facilitate a meaningful and efficient re-use of this public resource by others we designed, describe in detail, and made available a Digital Research Object (DRO), embedding data, documentation, and analytics. We present and discuss both the advantages and limitations of our multi-modal resource and analytic pipeline. The reproducibility of the results was tested by a bioinformatician not implicated in the original project and the robustness of results was assessed by re-annotating genetic and transcriptome data from the mm9 to the mm10 mouse genome assembly.

Genes essential for embryonic stem cells are associated with neurodevelopmental disorders

Mouse embryonic stem cells (mESCs) are key components in generating mouse models for human diseases and performing basic research on pluripotency, yet the number of genes essential for mESCs is still unknown. We performed a genome-wide screen for essential genes in mESCs and compared it to screens in human cells. We found that essential genes are enriched for basic cellular functions, are highly expressed in mESCs, and tend to lack paralog genes. We discovered that genes that are essential specifically in mESCs play a role in pathways associated with their pluripotent state. We show that 29.5% of human genes intolerant to loss-of-function mutations are essential in mouse or human ESCs, and that the human phenotypes most significantly associated with genes essential for ESCs are neurodevelopmental. Our results provide insights into essential genes in the mouse, the pathways which govern pluripotency, and suggest that many genes associated with neurodevelopmental disorders are essential at very early embryonic stages.

Ensembles of natural language processing systems for portable phenotyping solutions

BACKGROUND

Manually curating standardized phenotypic concepts such as Human Phenotype Ontology (HPO) terms from narrative text in electronic health records (EHRs) is time consuming and error prone. Natural language processing (NLP) techniques can facilitate automated phenotype extraction and thus improve the efficiency of curating clinical phenotypes from clinical texts. While individual NLP systems can perform well for a single cohort, an ensemble-based method might shed light on increasing the portability of NLP pipelines across different cohorts.

METHODS

We compared four NLP systems, MetaMapLite, MedLEE, ClinPhen and cTAKES, and four ensemble techniques, including intersection, union, majority-voting and machine learning, for extracting generic phenotypic concepts. We addressed two important research questions regarding automated phenotype recognition. First, we evaluated the performance of different approaches in identifying generic phenotypic concepts. Second, we compared the performance of different methods to identify patient-specific phenotypic concepts. To better quantify the effects caused by concept granularity differences on performance, we developed a novel evaluation metric that considered concept hierarchies and frequencies. Each of the approaches was evaluated on a gold standard set of clinical documents annotated by clinical experts. One dataset containing 1,609 concepts derived from 50 clinical notes from two different institutions was used in both evaluations, and an additional dataset of 608 concepts derived from 50 case report abstracts obtained from PubMed was used for evaluation of identifying generic phenotypic concepts only.

RESULTS

For generic phenotypic concept recognition, the top three performers in the NYP/CUIMC dataset are union ensemble (F₁, 0.634), training-based ensemble (F₁, 0.632), and majority vote-based ensemble (F₁, 0.622). In the Mayo dataset, the top three are majority vote-based ensemble (F₁, 0.642), cTAKES (F₁, 0.615), and MedLEE (F₁, 0.559). In the PubMed dataset, the top three are majority vote-based ensemble (F₁, 0.719), training-based (F₁, 0.696) and MetaMapLite (F₁, 0.694). For identifying patient specific phenotypes, the top three performers in the NYP/CUIMC dataset are majority vote-based ensemble (F₁, 0.610), MedLEE (F₁, 0.609), and training-based ensemble (F₁, 0.585). In the Mayo dataset, the top three are majority vote-based ensemble (F₁, 0.604), cTAKES (F₁, 0.531) and MedLEE (F₁, 0.527).

CONCLUSIONS

Our study demonstrates that ensembles of natural language processing can improve both generic phenotypic concept recognition and patient specific phenotypic concept identification over individual systems. Among the individual NLP systems, each individual system performed best when they were applied in the dataset that they were primary designed for. However, combining multiple NLP systems to create an ensemble can generally improve the performance. Specifically, the ensemble can increase the results reproducibility across different cohorts and tasks, and thus provide a more portable phenotyping solution compared to individual NLP systems.

Gene- and Disease-Based Expansion of the Knowledge on Inborn Errors of Immunity

The recent report of the International Union of Immunological Societies (IUIS) has provided the categorized list of 354 inborn errors of immunity. We performed a systematic analysis of genes and diseases from the IUIS report with the use of the OMIM, ORPHANET, and HPO resources. To measure phenotypic similarity we applied the Jaccard/Tanimoto (J/T) coefficient for HPO terms and top-level categories. Low J/T coefficients for HPO terms for OMIM or ORPHANET disease pairs associated with the same genes indicated high pleiotropy of these genes. Gene ORGANizer enrichment analysis demonstrated that gene sets related to HPO top-level categories were most often enriched in immune, lymphatic, and corresponding body systems (for example, genes from the category "Cardiovascular" were enriched in cardiovascular system). We presented available data on frequent and very frequent clinical signs and symptoms in inborn errors of immunity. With the use of DisGeNET, we generated the list of 25 IUIS/OMIM diseases with two or more relatively high score gene-disease associations, found for unrelated genes and/or for clusters of genes coding for interacting proteins. Our study showed the enrichment of gene sets related to several IUIS categories with neoplastic and autoimmune diseases from the GWAS Catalog and reported individual genes with phenotypic overlap between inborn errors of immunity and GWAS diseases/traits. We concluded that genetic background may play a role in phenotypic diversity of inborn errors of immunity.

VarSight: prioritizing clinically reported variants with binary classification algorithms

BACKGROUND

When applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient's phenotypes. Typically, this is done through annotation, filtering, and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance.

METHODS

We tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network.

RESULTS

We treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20.

CONCLUSIONS

We demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets.

A new mutation in the PAX2 gene in a Papillorenal Syndrome patient

Purpose

To present a new mutation in a patient with Papillorenal Syndrome (PAPRS).

Observations

PAPRS is an autosomal dominant disease that involves ocular and renal abnormalities. We present a patient with PAPRS with a genetically diagnosed PAX2 and new pathogenic mutation. A complete ophthalmological, neurological, nephrological and Ears-Nose-Throat (ENT) examination were undertaken. The patient suffered from Focal Segmental Glomerulosclerosis (FSGS) and some typical ophthalmological signs of PAPRS, including optic nerve coloboma and optic disc pit (ODP) maculopathy associated with an abnormal retinal vessel distribution and numerous cilioretinal arteries in the right eye. The left eye showed similar vessel abnormalities although the optic disc had a normal morphology.

Conclusions

A new mutation in the PAX2 gene was identified in a patient with ocular and renal abnormalities.

Hypoplastic left heart syndrome: From bedside to bench and back

Hypoplastic Left Heart Syndrome (HLHS) is a complex Congenital Heart Disease (CHD) that was almost universally fatal until the advent of the Norwood operation in 1981. Children with HLHS who largely succumbed to the disease within the first year of life, are now surviving to adulthood. However, this survival is associated with multiple comorbidities and HLHS infants have a higher mortality rate as compared to other non-HLHS single ventricle patients. In this review we (a) discuss current clinical challenges associated in the care of HLHS patients, (b) explore the use of systems biology in understanding the molecular framework of this disease, (c) evaluate induced pluripotent stem cells as a translational model to understand molecular mechanisms and manipulate them to improve outcomes, and (d) investigate cell therapy, gene therapy, and tissue engineering as a potential tool to regenerate hypoplastic cardiac structures and improve outcomes.

Applications of molecular networks in biomedicine

Due to the large interdependence between the molecular components of living systems, many phenomena, including those related to pathologies, cannot be explained in terms of a single gene or a small number of genes. Molecular networks, representing different types of relationships between molecular entities, embody these large sets of interdependences in a framework that allow their mining from a systemic point of view to obtain information. These networks, often generated from high-throughput omics datasets, are used to study the complex phenomena of human pathologies from a systemic point of view. Complementing the reductionist approach of molecular biology, based on the detailed study of a small number of genes, systemic approaches to human diseases consider that these are better reflected in large and intricate networks of relationships between genes. These networks, and not the single genes, provide both better markers for diagnosing diseases and targets for treating them. Network approaches are being used to gain insight into the molecular basis of complex diseases and interpret the large datasets associated with them, such as genomic variants. Network formalism is also suitable for integrating large, heterogeneous and multilevel datasets associated with diseases from the molecular level to organismal and epidemiological scales. Many of these approaches are available to nonexpert users through standard software packages.

Comparison of Causative Variant Prioritization Tools Using Next-generation Sequencing Data in Japanese Patients with Mendelian Disorders

Background

During the investigation of causative variants of Mendelian disorders using next-generation sequencing, the enormous number of possible candidates makes the detection process complex, and the use of multidimensional methods is required. Although the utility of several variant prioritization tools has been reported, their effectiveness in Japanese patients remains largely unknown.

Methods

We selected 5 free variant prioritization tools (PhenIX, hiPHIVE, Phen-Gen, eXtasy-order statistics, and eXtasy-combined max) and assessed their effectiveness in Japanese patient populations. To compare these tools, we conducted 2 studies: one based on simulated data of 100 diseases and another based on the exome data of 20 in-house patients with Mendelian disorders. To this end we selected 100 pathogenic variants from the "Database of Pathogenic Variants (DPV)" and created 100 variant call format (VCF) files that each had pathogenic variants based on reference human genome data from the 1000 Genomes Project. The later "in-house" study used exome data from 20 Japanese patients with Mendelian disorders. In both studies, we utilized 1-5 terms of "Human Phenotype Ontology" as clinical information.

Results

In our analysis based on simulated disease data, the detection rate of the top 10 causative variants was 91% for hiPHIVE, and 88% for PhenIX, based on 100 sets of simulated disease VCF data. Also, both software packages detected 82% of the top 1 causative variants. When we used data from our in-house patients instead, we found that these two programs (PhenIX and hiPHIVE) produced higher detection rates than the other three systems in our study. The detection rate of the top 1 causative variant was 71.4% for PhenIX, 65.0% for hiPHIVE.

Conclusion

The rates of detecting causative variants in two Exomizer software packages, hiPHIVE and PhenIX, were higher than for the other three software systems we analyzed, with respect to Japanese patients.

Encoding Clinical Data with the Human Phenotype Ontology for Computational Differential Diagnostics

The Human Phenotype Ontology (HPO) is a standardized set of phenotypic terms that are organized in a hierarchical fashion. It is a widely used resource for capturing human disease phenotypes for computational analysis to support differential diagnostics. The HPO is frequently used to create a set of terms that accurately describe the observed clinical abnormalities of an individual being evaluated for suspected rare genetic disease. This profile is compared with computational disease profiles in the HPO database with the aim of identifying genetic diseases with comparable phenotypic profiles. The computational analysis can be coupled with the analysis of whole-exome or whole-genome sequencing data through applications such as Exomiser. This article explains how to choose an optimal set of HPO terms for these cases and enter them with software, such as PhenoTips and PatientArchive, and demonstrates how to use Phenomizer and Exomiser to generate a computational differential diagnosis. © 2019 by John Wiley & Sons, Inc.

Using phenome-wide association studies to examine the effect of environmental exposures on human health

The field of environmental epidemiology has been using "-omics" technologies, including the exposome, metabolome, and methylome, to understand the potential effects and biological pathways of a number of environmental pollutants. However, the majority of studies have focused on a single disease or phenotype, and have not systematically considered patterns of multimorbidity and whether environmental pollutants have pleiotropic effects. These questions could be addressed by examining the relation between environmental exposures and the phenome - the patterns and profiles of human health that individuals experience from birth to death. By conducting Phenome Wide Association Studies (PheWAS), we can generate new hypotheses about new or poorly understood exposures, identify novel associations for established toxicants, and better understand biological pathways affected by environmental pollutants. In this article, we provide a conceptual framework for conducting PheWAS in environmental epidemiology and summarize some of the advantages and challenges to using the PheWAS to study environmental pollutant exposures. Ultimately, by adding the PheWAS to our "-omics" toolbox, we could substantially improve our understanding of the potential health effects of environmental pollutants.

GAA variants and phenotypes among 1,079 patients with Pompe disease: Data from the Pompe Registry

Identification of variants in the acid α‐glucosidase (GAA) gene in Pompe disease provides valuable insights and systematic overviews are needed. We report on the number, nature, frequency, and geographic distribution of GAA sequence variants listed in the Pompe Registry, a long‐term, observational program and the largest global repository of Pompe disease data. Variant information was reviewed and compared with publicly available GAA databases/resources. Among 1,079 eligible patients, 2,075 GAA variants (80 unique novel) were reported. Variants were listed by groups representing Pompe disease phenotypes. Patients were classified as Group A: Symptom onset ≤ 12 months of age with cardiomyopathy; Group B: Symptom onset ≤ 12 years of age (includes patients with symptom onset ≤ 12 months of age without cardiomyopathy); or Group C: Symptom onset > 12 years of age. Likely impact of novel variants was predicted using bioinformatics algorithms. Variants were classified by pathogenicity using ACMG guidelines. Data reported from the Pompe Registry provide new information about the distribution of GAA variants globally and across the clinical spectrum, add to the number and diversity of GAA variants registered in public databases through published data sharing, provide a first indication of the severity of novel variants, and assist in diagnostic practice and outcome prediction.

Phenome-wide Burden of Copy-Number Variation in the UK Biobank

Copy-number variations (CNVs) represent a significant proportion of the genetic differences between individuals and many CNVs associate causally with syndromic disease and clinical outcomes. Here, we characterize the landscape of copy-number variation and their phenome-wide effects in a sample of 472,228 array-genotyped individuals from the UK Biobank. In addition to population-level selection effects against genic loci conferring high mortality, we describe genetic burden from potentially pathogenic and previously uncharacterized CNV loci across more than 3,000 quantitative and dichotomous traits, with separate analyses for common and rare classes of variation. Specifically, we highlight the effects of CNVs at two well-known syndromic loci 16p11.2 and 22q11.2, previously uncharacterized variation at 9p23, and several genic associations in the context of acute coronary artery disease and high body mass index. Our data constitute a deeply contextualized portrait of population-wide burden of copy-number variation, as well as a series of dosage-mediated genic associations across the medical phenome.

Bone and connective tissue disorders caused by defects in glycosaminoglycan biosynthesis: a panoramic view

Glycosaminoglycans (GAGs) are a heterogeneous family of linear polysaccharides that constitute the carbohydrate moiety covalently attached to the protein core of proteoglycans, macromolecules present on the cell surface and in the extracellular matrix. Several genetic disorders of bone and connective tissue are caused by mutations in genes encoding for glycosyltransferases, sulfotransferases and transporters that are responsible for the synthesis of sulfated GAGs. Phenotypically, these disorders all reflect alterations in crucial biological functions of GAGs in the development, growth and homoeostasis of cartilage and bone. To date, up to 27 different skeletal phenotypes have been linked to mutations in 23 genes encoding for proteins involved in GAG biosynthesis. This review focuses on recent genetic, molecular and biochemical studies of bone and connective tissue disorders caused by GAG synthesis defects. These insights and future research in the field will provide a deeper understanding of the molecular pathogenesis of these disorders and will pave the way for developing common therapeutic strategies that might be targeted to a range of individual phenotypes.