Phenotype Ontology Terms Research Articles

Enhancing human phenotype ontology term extraction through synthetic case reports and embedding-based retrieval: A novel approach for improved biomedical data annotation

Enhancing human phenotype ontology term extraction through synthetic case reports and embedding-based retrieval: A novel approach for improved biomedical data annotation

Leveraging Clinical Intuition to Improve Accuracy of Phenotype-Driven Prioritization

PurposeClinical intuition is commonly incorporated into the differential diagnosis as an assessment of the likelihood of candidate diagnoses based either on the patient population being seen in a specific clinic or on the signs and symptoms of the initial presentation. Algorithms to support diagnostic sequencing in individuals with a suspected rare genetic disease do not yet incorporate intuition and instead assume that each Mendelian disease has an equal pretest probability. MethodsThe LIRICAL algorithm calculates the likelihood ratio of clinical manifestations represented by Human Phenotype Ontology (HPO) terms to rank candidate diagnoses. The initial version of LIRICAL assumed an equal pretest probability for each disease in its calculation of the posttest probability (where the test is diagnostic exome or genome sequencing). We introduce Clinical Intuition for Likelihood Ratios (ClintLR), an extension of the LIRICAL algorithm that boosts the pretest probability of groups of related diseases deemed to be more likely. ResultsThe average rank of the correct diagnosis in simulations using ClintLR showed a statistically significant improvement over a range of adjustment factors. ConclusionClintLR successfully encodes clinical intuition to improve ranking of rare diseases in diagnostic sequencing. ClintLR is freely available at https://github.com/TheJacksonLaboratory/ClintLR.

Phenotypic spectrum of dual diagnoses in developmental disorders

As more patients receive genome-wide sequencing, the number of individuals diagnosed with multiple monogenic conditions is increasing. We sought to investigate the relative phenotypic contribution of dual diagnoses using both manual curation and computational approaches. First, we computed 1,003,236 semantic similarity scores for all possible pairs of 1,417 genes in the Developmental Disorder Gene2Phenotype (DDG2P) database using Human Phenotype Ontology terms. Next, for 62 probands with two molecular diagnoses in the Deciphering Developmental Disorders study, we computed semantic similarity scores between the probands' phenotypes and DDG2P phenotypes associated with the two disorders and compared the results with manual attribution of proband phenotypes to none, one, or both of the genes. We found a spectrum of phenotypic similarity for dual diagnoses, both across all DDG2P genes and within dual diagnosed probands, from phenotypically distinct through blended to indistinguishable conditions. Pairwise semantic similarity scores between two DDG2P genes were a good predictor of the extent of phenotypic blending observed in probands. Dual diagnoses involving genes linked with synergistic phenotypes can result in more extreme presentations while those involving antagonistic phenotypes have spuriously high pairwise semantic similarity scores despite a potentially milder atypical presentation. We suggest that the phenotypic contribution of two molecular diagnoses may contain discrete, synergistic, or antagonistic elements. Conceptual recognition of this phenotypic spectrum is important for making a final clinico-molecular diagnosis and providing accurate genetic counseling.

Quantitative proteomics of patient fibroblasts reveal biomarkers and diagnostic signatures of mitochondrial disease.

BACKGROUNDMitochondrial diseases belong to the group of inborn errors of metabolism (IEM), with a prevalence of 1 in 2,000-5,000 individuals. They are the most common form of IEM, but, despite advances in next-generation sequencing technologies, almost half of the patients are left genetically undiagnosed.METHODSWe investigated a cohort of 61 patients with defined mitochondrial disease to improve diagnostics, identify biomarkers, and correlate metabolic pathways to specific disease groups. Clinical presentations were structured using human phenotype ontology terms, and mass spectrometry-based proteomics was performed on primary fibroblasts. Additionally, we integrated 6 patients carrying variants of uncertain significance (VUS) to test proteomics as a diagnostic expansion.RESULTSProteomic profiles from patient samples could be classified according to their biochemical and genetic characteristics, with the expression of 5 proteins (GPX4, MORF4L1, MOXD1, MSRA, and TMED9) correlating with the disease cohort, thus acting as putative biomarkers. Pathway analysis showed a deregulation of inflammatory and mitochondrial stress responses. This included the upregulation of glycosphingolipid metabolism and mitochondrial protein import, as well as the downregulation of arachidonic acid metabolism. Furthermore, we could assign pathogenicity to a VUS in MRPS23 by demonstrating the loss of associated mitochondrial ribosome subunits.CONCLUSIONWe established mass spectrometry-based proteomics on patient fibroblasts as a viable and versatile tool for diagnosing patients with mitochondrial disease.FUNDINGThe NovoNordisk Foundation, Knut and Alice Wallenberg Foundation, Wellcome Centre for Mitochondrial Research, UK Medical Research Council, and the UK NHS Highly Specialised Service for Rare Mitochondrial Disorders of Adults and Children.

NeoGx: Machine-Recommended Rapid Genome Sequencing for Neonates.

Genetic disease is common in the Level IV Neonatal Intensive Care Unit (NICU), but neonatology providers are not always able to identify the need for genetic evaluation. We trained a machine learning (ML) algorithm to predict the need for genetic testing within the first 18 months of life using health record phenotypes. For a decade of NICU patients, we extracted Human Phenotype Ontology (HPO) terms from clinical text with Natural Language Processing tools. Considering multiple feature sets, classifier architectures, and hyperparameters, we selected a classifier and made predictions on a validation cohort of 2,241 Level IV NICU admits born 2020-2021. Our classifier had ROC AUC of 0.87 and PR AUC of 0.73 when making predictions during the first week in the Level IV NICU. We simulated testing policies under which subjects begin testing at the time of first ML prediction, estimating diagnostic odyssey length both with and without the additional benefit of pursuing rGS at this time. Just by using ML to accelerate initial genetic testing (without changing the tests ordered), the median time to first genetic test dropped from 10 days to 1 day, and the number of diagnostic odysseys resolved within 14 days of NICU admission increased by a factor of 1.8. By additionally requiring rGS at the time of positive ML prediction, the number of diagnostic odysseys resolved within 14 days was 3.8 times higher than the baseline. ML predictions of genetic testing need, together with the application of the right rapid testing modality, can help providers accelerate genetics evaluation and bring about earlier and better outcomes for patients.

Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies

BackgroundThere are approximately 8,000 different rare diseases that affect roughly 400 million people worldwide. Many of them suffer from delayed diagnosis. Ciliopathies are rare monogenic disorders characterized by a significant phenotypic and genetic heterogeneity that raises an important challenge for clinical diagnosis. Diagnosis support systems (DSS) applied to electronic health record (EHR) data may help identify undiagnosed patients, which is of paramount importance to improve patients’ care. Our objective was to evaluate three online-accessible rare disease DSSs using phenotypes derived from EHRs for the diagnosis of ciliopathies.MethodsTwo datasets of ciliopathy cases, either proven or suspected, and two datasets of controls were used to evaluate the DSSs. Patient phenotypes were automatically extracted from their EHRs and converted to Human Phenotype Ontology terms. We tested the ability of the DSSs to diagnose cases in contrast to controls based on Orphanet ontology.ResultsA total of 79 cases and 38 controls were selected. Performances of the DSSs on ciliopathy real world data (best DSS with area under the ROC curve = 0.72) were not as good as published performances on the test set used in the DSS development phase. None of these systems obtained results which could be described as “expert-level”. Patients with multisystemic symptoms were generally easier to diagnose than patients with isolated symptoms. Diseases easily confused with ciliopathy generally affected multiple organs and had overlapping phenotypes. Four challenges need to be considered to improve the performances: to make the DSSs interoperable with EHR systems, to validate the performances in real-life settings, to deal with data quality, and to leverage methods and resources for rare and complex diseases.ConclusionOur study provides insights into the complexities of diagnosing highly heterogenous rare diseases and offers lessons derived from evaluation existing DSSs in real-world settings. These insights are not only beneficial for ciliopathy diagnosis but also hold relevance for the enhancement of DSS for various complex rare disorders, by guiding the development of more clinically relevant rare disease DSSs, that could support early diagnosis and finally make more patients eligible for treatment.

#958 Development and Validation of a Score Estimating the Probability of obtaining a genetic diagnosis in adults with CKD of uncertain origin

Abstract Background and Aims We aim to derive and validate a score estimating the probability of identifying a variant explicative of the renal phenotype using Exome Sequencing (ES) in adults with CKD of uncertain origin. Method Participants: prospective cohort study including all consecutive index patients with a CKD from uncertain origin in three French nephrology units (metropolitan: Sorbonne University Hospitals, Paris, and Conception Hospital, Marseille; overseas: La Réunion University Hospital, Réunion island) who underwent ES between October 11th, 2017 and May 31st, 2023. Outcome measure: identification of a causal variant using ES data, according to the American College of Medical Genetics and Genomics diagnostic criteria. Candiate variables and feature engineering: raw data regarding patients’ phenotypes was prospectively entered in a collection form at the time of ES by the prescribing physician. Structured data (yes/no questions and lists) was mapped to Human Phenotype Ontology (HPO) terms. Unstructured data (free text) was manually translated into HPO terms by the same investigator (nephrologist), which was blinded to ES results. First, second and third-order ancestor HPO terms were included in the analysis. Score's derivation: an optimal weighted average of multiple models (“ensemble model”) was specified, following guidelines provided in a recently published article (PMID:36905602). Score's validation: internal validation using 10-fold cross validation, followed by internal-external validation (data from 2/3 centers was used to develop a model which was then tested using data from the remaining center; this process was repeated in each center). Results We included 2,490 patients, 560/2,490 (22.5%) of whom had a causal variant identified by ES. We collected 1,028 distinct HPO terms describing the patients’ phenotypes. The most common term was “Hypertension”, occurring in 2,079/2,490 (83.5%) patients. In internal validation, the score showed accurate calibration and discrimination (area under the receiver operating characteristics curve (AUC): 0.71, 95% CI 0.68 to 0.73; index of precision accuracy (IPA): 0.11, 95% CI 0.09 to 0.13). Performances were moderately diminished in internal-external validation but remained satisfactory (Tenon: AUC 0.64, IPA 0.04; Marseille: AUC 0.67, IPA 0.04; La Réunion: 0.68, IPA 0.09). Conclusion We derived, internally and internally-externally validated a clinical score which accurately predicts the probability of obtaining a genetic diagnosis using Exome Sequencing. Feature engineering could further increase the score's performances. Automatic computation by clinicians using an interactive tool is achievable and could be of clinical utility, especially to guide resource allocation in case of a low pretest probability.

Variants in mitochondrial disease genes are common causes of inherited peripheral neuropathies

BackgroundPeripheral neuropathies in mitochondrial disease are caused by mutations in nuclear genes encoding mitochondrial proteins, or in the mitochondrial genome. Whole exome or genome sequencing enable parallel testing of nuclear and mtDNA genes, and it has significantly advanced the genetic diagnosis of inherited diseases. Despite this, approximately 40% of all Charcot-Marie-Tooth (CMT) cases remain undiagnosed.MethodsThe genome-phenome analysis platform (GPAP) in RD-Connect was utilised to create a cohort of 2087 patients with at least one Human Phenotype Ontology (HPO) term suggestive of a peripheral neuropathy, from a total of 10,935 patients. These patients’ genetic data were then analysed and searched for variants in known mitochondrial disease genes.ResultsA total of 1,379 rare variants were identified, 44 of which were included in this study as either reported pathogenic or likely causative in 42 patients from 36 families. The most common genes found to be likely causative for an autosomal dominant neuropathy were GDAP1 and GARS1. We also detected heterozygous likely pathogenic variants in DNA2, MFN2, DNM2, PDHA1, SDHA, and UCHL1. Biallelic variants in SACS, SPG7, GDAP1, C12orf65, UCHL1, NDUFS6, ETFDH and DARS2 and variants in the mitochondrial DNA (mtDNA)-encoded MT-ATP6 and MT-TK were also causative for mitochondrial CMT. Only 50% of these variants were already reported as solved in GPAP.ConclusionVariants in mitochondrial disease genes are frequent in patients with inherited peripheral neuropathies. Due to the clinical overlap between mitochondrial disease and CMT, agnostic exome or genome sequencing have better diagnostic yields than targeted gene panels.

Diagnostic yield after next-generation sequencing in pediatric cardiovascular disease

Genetic testing with exome sequencing and genome sequencing is increasingly offered to infants and children with cardiovascular diseases. However, the rates of positive diagnoses after genetic testing within the different categories of cardiac disease and phenotypic subtypes of congenital heart disease (CHD) has been little studied. We report the diagnostic yield after next-generation sequencing in 500 patients with CHD from diverse population subgroups that were enrolled at three different sites in the Clinical Sequencing Evidence-Generating Research consortium. Patients were ascertained due to a primary cardiovascular issue comprising arrhythmia, cardiomyopathy and/or CHD and corresponding human phenotype ontology terms were selected to describe the cardiac and extracardiac findings. We examined the diagnostic yield for patients with arrhythmia, cardiomyopathy and/or CHD and phenotypic subtypes of CHD comprising conotruncal defects, heterotaxy, left ventricular outflow tract obstruction, septal defects, and ‘Other’ heart defects. We found a significant increase in the frequency of positive findings for patients who underwent genome sequencing compared to exome sequencing and for syndromic cardiac defects compared to isolated cardiac defects. We also found significantly higher diagnostic rates for patients who presented with isolated cardiomyopathy compared to isolated CHD. For patients with syndromic presentations who underwent genome sequencing, there were significant differences in the numbers of positive diagnoses for phenotypic subcategories of CHD, ranging from 31.7% for septal defects to 60% for Other. Despite variation in the diagnostic yield at each site, our results support genetic testing in pediatric patients with syndromic and isolated cardiovascular issues and in all subtypes of CHD.

An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases.

Identifying disease-causing variants in Rare Disease patients' genome is a challenging problem. To accomplish this task, we describe a machine learning framework, that we called "Suggested Diagnosis", whose aim is to prioritize genetic variants in an exome/genome based on the probability of being disease-causing. To do so, our method leverages standard guidelines for germline variant interpretation as defined by the American College of Human Genomics (ACMG) and the Association for Molecular Pathology (AMP), inheritance information, phenotypic similarity, and variant quality. Starting from (1) the VCF file containing proband's variants, (2) the list of proband's phenotypes encoded in Human Phenotype Ontology terms, and optionally (3) the information about family members (if available), the "Suggested Diagnosis" ranks all the variants according to their machine learning prediction. This method significantly reduces the number of variants that need to be evaluated by geneticists by pinpointing causative variants in the very first positions of the prioritized list. Most importantly, our approach proved to be among the top performers within the CAGI6 Rare Genome Project Challenge, where it was able to rank the true causative variant among the first positions and, uniquely among all the challenge participants, increased the diagnostic yield of 12.5% by solving 2 undiagnosed cases.

Multi-center implementation of rapid whole genome sequencing provides additional evidence of its utility in the pediatric inpatient setting.

Multi-center implementation of rapid whole genome sequencing with assessment of the clinical utility of rapid whole genome sequencing (rWGS), including positive, negative and uncertain results, in admitted infants with a suspected genetic disease. rWGS tests were ordered at eight hospitals between November 2017 and April 2020. Investigators completed a survey of demographic data, Human Phenotype Ontology (HPO) terms, test results and impacts of results on clinical care. A total of 188 patients, on general hospital floors and intensive care unit (ICU) settings, underwent rWGS testing. Racial and ethnic characteristics of the tested infants were broadly representative of births in the country at large. 35% of infants received a diagnostic result in a median of 6 days. The most common HPO terms for tested infants indicated an abnormality of the nervous system, followed by the cardiovascular system, the digestive system, the respiratory system and the head and neck. Providers indicated a major change in clinical management because of rWGS for 32% of infants tested overall and 70% of those with a diagnostic result. Also, 7% of infants with a negative rWGS result and 23% with a variant of unknown significance (VUS) had a major change in management due to testing. Our study demonstrates that the implementation of rWGS is feasible across diverse institutions, and provides additional evidence to support the clinical utility of rWGS in a demographically representative sample of admitted infants and includes assessment of the clinical impact of uncertain rWGS results in addition to both positive and negative results.

A step-by-step, multidisciplinary strategy to maximize the yield of genetic testing in pediatric patients with chronic kidney diseases.

The use of genetic testing in pediatric patients with chronic kidney diseases (CKD) has increased exponentially in the past few years, particularly with the emergence of novel sequencing techniques. However, the genetic yield remains unexpectedly low in nephrology, with an impact on diagnosis, prognosis and treatment. Moreover, the increasing diversity of genetic testing possibilities can be seen as an obstacle by clinicians, in the absence of a strong background in genetics. Here, we propose a step-by-step, multidisciplinary strategy for the diagnostic evaluation of pediatric patients with CKD, and appropriate genetic test selection to maximize the yield of genetic testing. A total of 126 pediatric patients were enrolled in a retrospective file analysis. Genetic testing techniques used included phenotype-associated next-generation panel sequencing (N = 41), Sanger and SNaPshot sequencing (N = 3) and/or whole exome sequencing (N = 2). Overall genetic yield reached 63% and genetic testing significantly impacted patient management in 70%. The distribution of kidney diseases among patients was balanced and matched previously described pediatric cohorts in terms of glomerulopathies, tubulopathies and ciliopathies. Genetic analyses led to significant treatment modifications, kidney biopsy sparing and personalized nephroprotection, as well as tailored genetic counseling. Of note, the evaluation of Human Phenotype Ontology term accuracy in the cohort showed that causal mutations were precisely identified in 85% of the patients at most. Here we suggest a step-by-step, multidisciplinary strategy to maximize the yield of genetic testing in pediatric patients with CKD. This approach optimizes patient care while avoiding unnecessary treatments or procedures.

Aspirin-exacerbated respiratory disease is associated with variants in filaggrin, epithelial integrity, and cellular interactions

BackgroundPrevious studies identified upto 6% of patients with Aspirin-Exacerbated Respiratory Disease (AERD) have family history of AERD indicating a possible link with genetic polymorphisms. However, whole exome sequencing (WES) studies of such associations are currently lacking. ObjectivesWe sought to examine whether WES can identify pathogenic variants associated with AERD. MethodsAERD diagnosis was confirmed in patients with nasal polyps and asthma. WES was performed using an Illumina sequencing platform. Human Phenotype Ontology (HPO) terms were used to define the patients’ phenotypes. Exomiser was used to annotate, filter, and prioritize possible disease-causing genetic variants. ResultsAmong thirty-nine patients with AERD, 41% reported a family history of asthma and 5% a family history of AERD. Pathogenic exome variants in the filaggrin gene (FLG) were found in two patients (5%). Other variants not known to be pathogenic were detected in an additional 16 patients (41%) in genes related to epithelial integrity and cellular interactions, including genes encoding desmoglein 3 (DSG3), dynein axonemal heavy chain 9 (DNAH9), collagen type VII alpha 1 chain (COL7A1), collagen type XVII alpha 1 chain (COL17A1), chromodomain helicase DNA binding protein-7 (CHD7), TSC complex subunit 2/tuberous sclerosis-2 protein (TSC2), P-selectin (SELP), and platelet-derived growth factor receptor-alpha (PDGFRA). ConclusionWES identified a monogenic susceptibility to AERD in 5% of patients with FLG pathogenic variants. Other variants, not previously identified as pathogenic, were found in genes relevant to epithelial integrity and cellular interactions and may further reveal genetic factors that contribute to this condition.

Unbiased phenotype and genotype matching maximizes gene discovery and diagnostic yield

PurposeWidespread application of next-generation sequencing, combined with data exchange platforms, has provided molecular diagnoses for countless families. To maximize diagnostic yield, we implemented an unbiased semi-automated genematching algorithm based on genotype and phenotype matching. MethodsRare homozygous variants identified in 2 or more affected individuals, but not in healthy individuals, were extracted from our local database of ∼12,000 exomes. Phenotype similarity scores (PSS), based on human phenotype ontology terms, were assigned to each pair of individuals matched at the genotype level using HPOsim. Results33,792 genotype-matched pairs were discovered, representing variants in 7567 unique genes. There was an enrichment of PSS ≥0.1 among pathogenic/likely pathogenic variant-level pairs (94.3% in pathogenic/likely pathogenic variant-level matches vs 34.75% in all matches). We highlighted founder or region-specific variants as an internal positive control and proceeded to identify candidate disease genes. Variant-level matches were particularly helpful in cases involving inframe indels and splice region variants beyond the canonical splice sites, which may otherwise have been disregarded, allowing for detection of candidate disease genes, such as KAT2A, RPAIN, and LAMP3. ConclusionSemi-automated genotype matching combined with PSS is a powerful tool to resolve variants of uncertain significance and to identify candidate disease genes.

MSeqDR Quick-Mitome (QM): Combining Phenotype-Guided Variant Interpretation and Machine Learning Classifiers to Aid Primary Mitochondrial Disease Genetic Diagnosis.

The international Mitochondrial Disease Sequence Data Resource Consortium (MSeqDR) Quick-Mitome (QM) is a web-based platform enabling automated variant interpretation of whole-exome sequencing (WES) datasets for the genetic diagnosis of primary mitochondrial diseases (PMD). Designed specifically to address the unique dual genome nature of PMD etiologies, QM includes features for both nuclear and mitochondrial DNA (mtDNA) genome analysis. QM requires VCF variant lists, HPO ID clinical phenotypes, and pedigree files for multiple-sample VCF inputs. QM maps phenotypes to HPO terms before analysis. QM analysis requires 2 to 20 min for 100,000 variants on an 8-vCPU AWS server using Exomiser's "PASS_ONLY" mode for nuclear variants. QM ranks variants based on allele frequency, phenotype-gene association, functional impact, and inheritance mode. Variants are further annotated with multiple data sources such as OMIM, ClinVar, dbNSFP, gnoMAD, MITOMAP, and MSeqDR. In addition to standard Exomiser results, QM generates an Analysis Report and QM Integrated Report with add-on mtDNA-specific analyses, including haplogroup prediction with Phy-Mer, heteroplasmy calculation, and mvTool annotations. We developed the Mitochondrial Disease Variant (MDV) classifier using XGBoost to predict variant pathogenicity for PMD. The MDV classifier was trained on >120 features and performance benchmarking showed that it correctly classified >98% of nuclear gene variants as being pathogenic or benign, and predicted PMD-causing variants with 94% precision. The MSeqDR QM server is an open-access resource for phenotype-driven dual-genome analyses for PMD diagnosis by the global mitochondrial disease community. It is publicly available for non-commercial, non-clinical research use at https://mseqdr.org/quickmitome.php. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Standardizing clinical phenotypes into human phenotype ontology (HPO) terms as the phenotype input for Quick-Mitome (QM) Basic Protocol 2: Prepare the pedigree input for multiple-sample VCF Basic Protocol 3: Quick-Mitome (QM) analysis Basic Protocol 4: Reviewing and understanding the QM Integrated Report and Analysis Report.

Gene editing and cardiac disease modelling for the interpretation of genetic variants of uncertain significance in congenital heart disease

BackgroundGenomic sequencing in congenital heart disease (CHD) patients often discovers novel genetic variants, which are classified as variants of uncertain significance (VUS). Functional analysis of each VUS is required in specialised laboratories, to determine whether the VUS is disease causative or not, leading to lengthy diagnostic delays. We investigated stem cell cardiac disease modelling and transcriptomics for the purpose of genetic variant classification using a GATA4 (p.Arg283Cys) VUS in a patient with CHD.MethodsWe performed high efficiency CRISPR gene editing with homology directed repair in induced pluripotent stem cells (iPSCs), followed by rapid clonal selection with amplicon sequencing. Genetic variant and healthy matched control cells were compared using cardiomyocyte disease modelling and transcriptomics.ResultsGenetic variant and healthy cardiomyocytes similarly expressed Troponin T (cTNNT), and GATA4. Transcriptomics analysis of cardiomyocyte differentiation identified changes consistent with the patient’s clinical human phenotype ontology terms. Further, transcriptomics revealed changes in calcium signalling, and cardiomyocyte adrenergic signalling in the variant cells. Functional testing demonstrated, altered action potentials in GATA4 genetic variant cardiomyocytes were consistent with patient cardiac abnormalities.ConclusionsThis work provides in vivo functional studies supportive of a damaging effect on the gene or gene product. Furthermore, we demonstrate the utility of iPSCs, CRISPR gene editing and cardiac disease modelling for genetic variant interpretation. The method can readily be applied to other genetic variants in GATA4 or other genes in cardiac disease, providing a centralised assessment pathway for patient genetic variant interpretation.

Term-BLAST-like alignment tool for concept recognition in noisy clinical texts.

Methods for concept recognition (CR) in clinical texts have largely been tested on abstracts or articles from the medical literature. However, texts from electronic health records (EHRs) frequently contain spelling errors, abbreviations, and other nonstandard ways of representing clinical concepts. Here, we present a method inspired by the BLAST algorithm for biosequence alignment that screens texts for potential matches on the basis of matching k-mer counts and scores candidates based on conformance to typical patterns of spelling errors derived from 2.9 million clinical notes. Our method, the Term-BLAST-like alignment tool (TBLAT) leverages a gold standard corpus for typographical errors to implement a sequence alignment-inspired method for efficient entity linkage. We present a comprehensive experimental comparison of TBLAT with five widely used tools. Experimental results show an increase of 10% in recall on scientific publications and 20% increase in recall on EHR records (when compared against the next best method), hence supporting a significant enhancement of the entity linking task. The method can be used stand-alone or as a complement to existing approaches. Fenominal is a Java library that implements TBLAT for named CR of Human Phenotype Ontology terms and is available at https://github.com/monarch-initiative/fenominal under the GNU General Public License v3.0.

Natural language processing and expert follow-up establishes tachycardia association with CDKL5 deficiency disorder

PurposeCDKL5 deficiency disorder (CDD) is a developmental and epileptic encephalopathy with multisystemic comorbidities. Cardiovascular involvement in CDD was shown in animal models but is yet poorly described in CDD cohorts. MethodsWe identified 38 individuals with genetically confirmed CDD through the Cleveland Clinic CDD specialty clinic and matched 190 individuals with non-genetic epilepsy to them as a comparison group. Natural language processing was applied to yield Human Phenotype Ontology (HPO) terms from medical records. We conducted HPO association testing and manual chart review to explore cardiovascular comorbidities associated with CDD. ResultsWe extracted 243,541 HPO terms from 30,512 medical encounters. Phenome-wide analysis confirmed well-established CDD phenotypes and identified association of tachycardia with CDD (Odds ratio 4.2, 95% confidence interval (CI) 1.75-9.93, Padj < .001). We found a 99.6-fold enrichment of supraventricular tachycardia (SVT) in CDD encounter notes (Padj < .001), which led to identification of 2 cases of fetal/neonatal onset SVT previously undescribed in CDD. Tachycardia in CDD individuals was associated with the presence of other autonomic symptoms (Odds ratio 5.63, 95% CI 1.08-40.3, P = .038). ConclusionCDD is associated with tachycardia, potentially including early-onset SVT. Alongside prospective validation studies, semiautomated genotype-phenotype analysis with matched controls is a scalable, rapid, and efficient approach for validating known and identifying novel phenotype associations.

Optimal Protocols and Management of Clinical and Genomic Data Collection to Assist in the Early Diagnosis and Treatment of Multiple Congenital Anomalies.

Standardized protocols have been designed and developed specifically for clinical information collection and obtaining trio genomic information from infants affected with congenital anomalies (CA) and their parents, as well as securing human biological resources. The protocols include clinical and genomic information collection on multiple CA that were difficult to diagnose using pre-existing screening methods. We obtained human-derived resources and genomic information from 138 cases, including 45 families of infants with CA and their parent trios. For the clinical information collection protocol, criteria for target patient selection and a consent system for collecting and utilizing research resources are crucial. Whole genome sequencing data were generated for all participants, and standardized protocols were developed for resource collection and manufacturing. We recorded the phenotype information according to the Human Phenotype Ontology term, and epidemiological information was collected through an environmental factor questionnaire. Updating and recording of clinical symptoms and genetic information that have been newly added or changed over time are significant. The protocols enabled long-term tracking by including the growth and development status that reflect the important characteristics of newborns. Using these clinical and genetic information collection protocols for CA, an essential platform for early genetic diagnosis and diagnostic research can be established, and new genetic diagnostic guidelines can be presented in the near future.

ClinPrior: an algorithm for diagnosis and novel gene discovery by network-based prioritization

BackgroundWhole-exome sequencing (WES) and whole-genome sequencing (WGS) have become indispensable tools to solve rare Mendelian genetic conditions. Nevertheless, there is still an urgent need for sensitive, fast algorithms to maximise WES/WGS diagnostic yield in rare disease patients. Most tools devoted to this aim take advantage of patient phenotype information for prioritization of genomic data, although are often limited by incomplete gene-phenotype knowledge stored in biomedical databases and a lack of proper benchmarking on real-world patient cohorts.MethodsWe developed ClinPrior, a novel method for the analysis of WES/WGS data that ranks candidate causal variants based on the patient’s standardized phenotypic features (in Human Phenotype Ontology (HPO) terms). The algorithm propagates the data through an interactome network-based prioritization approach. This algorithm was thoroughly benchmarked using a synthetic patient cohort and was subsequently tested on a heterogeneous prospective, real-world series of 135 families affected by hereditary spastic paraplegia (HSP) and/or cerebellar ataxia (CA).ResultsClinPrior successfully identified causative variants achieving a final positive diagnostic yield of 70% in our real-world cohort. This includes 10 novel candidate genes not previously associated with disease, 7 of which were functionally validated within this project. We used the knowledge generated by ClinPrior to create a specific interactome for HSP/CA disorders thus enabling future diagnoses as well as the discovery of novel disease genes.ConclusionsClinPrior is an algorithm that uses standardized phenotype information and interactome data to improve clinical genomic diagnosis. It helps in identifying atypical cases and efficiently predicts novel disease-causing genes. This leads to increasing diagnostic yield, shortening of the diagnostic Odysseys and advancing our understanding of human illnesses.