Interpretable Clinical Genomics with a Likelihood Ratio Paradigm

Towards a standard benchmark for variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework

Background

Computational approaches to support rare disease diagnosis are challenging to build, requiring the integration of complex data types such as ontologies, gene-to-phenotype associations, and cross-species data into variant and gene prioritisation algorithms (VGPAs). However, the performance of VGPAs has been difficult to measure and is impacted by many factors, for example, ontology structure, annotation completeness or changes to the underlying algorithm. Assertions of the capabilities of VGPAs are often not reproducible, in part because there is no standardised, empirical framework and openly available patient data to assess the efficacy of VGPAs - ultimately hindering the development of effective prioritisation tools.

Results

In this paper, we present our benchmarking tool, PhEval, which aims to provide a standardised and empirical framework to evaluate phenotype-driven VGPAs. The inclusion of standardised test corpora and test corpus generation tools in the PhEval suite of tools allows open benchmarking and comparison of methods on standardised data sets.

Conclusions

PhEval and the standardised test corpora solve the issues of patient data availability and experimental tooling configuration when benchmarking and comparing rare disease VGPAs. By providing standardised data on patient cohorts from real-world case-reports and controlling the configuration of evaluated VGPAs, PhEval enables transparent, portable, comparable and reproducible benchmarking of VGPAs. As these tools are often a key component of many rare disease diagnostic pipelines, a thorough and standardised method of assessment is essential for improving patient diagnosis and care.

Naturally occurring splice variants dissect the functional domains of BHC80 and emphasize the need for RNA analysis

Pathogenic PHF21A variation causes PHF21A-related neurodevelopmental disorders (NDDs). Although amorphic alleles, including haploinsufficiency, have been established as a disease mechanism, increasing evidence suggests that missense variants as well as frameshift variants extending the BHC80 carboxyl terminus also cause disease. Expanding on these, we report a proposita with intellectual disability and overgrowth and a novel de novo heterozygous PHF21A splice variant (NM_001352027.3:c.[153+1G>C];[=]) causing skipping of exon 6, which encodes an in-frame BHC80 deletion (p.(Asn30_Gln51del)). This deletion disrupts a predicted leucine zipper domain and implicates this domain in BHC80 function and as a target of variation causing PHF21A-related NDDs. This extension of understanding emphasizes the application of RNA analysis in precision genomic medicine practice.

A corpus of GA4GH Phenopackets: case-level phenotyping for genomic diagnostics and discovery

The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present phenopacket-store. Version 0.1.12 of phenopacket-store includes 4916 phenopackets representing 277 Mendelian and chromosomal diseases associated with 236 genes, and 2872 unique pathogenic alleles curated from 605 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.

AI-MARRVEL - A Knowledge-Driven AI System for Diagnosing Mendelian Disorders

BACKGROUND

Diagnosing genetic disorders requires extensive manual curation and interpretation of candidate variants, a labor-intensive task even for trained geneticists. Although artificial intelligence (AI) shows promise in aiding these diagnoses, existing AI tools have only achieved moderate success for primary diagnosis.

METHODS

AI-MARRVEL (AIM) uses a random-forest machine-learning classifier trained on over 3.5 million variants from thousands of diagnosed cases. AIM additionally incorporates expert-engineered features into training to recapitulate the intricate decision-making processes in molecular diagnosis. The online version of AIM is available at https://ai.marrvel.org. To evaluate AIM, we benchmarked it with diagnosed patients from three independent cohorts.

RESULTS

AIM improved the rate of accurate genetic diagnosis, doubling the number of solved cases as compared with benchmarked methods, across three distinct real-world cohorts. To better identify diagnosable cases from the unsolved pools accumulated over time, we designed a confidence metric on which AIM achieved a precision rate of 98% and identified 57% of diagnosable cases out of a collection of 871 cases. Furthermore, AIM's performance improved after being fine-tuned for targeted settings including recessive disorders and trio analysis. Finally, AIM demonstrated potential for novel disease gene discovery by correctly predicting two newly reported disease genes from the Undiagnosed Diseases Network.

CONCLUSIONS

AIM achieved superior accuracy compared with existing methods for genetic diagnosis. We anticipate that this tool may aid in primary diagnosis, reanalysis of unsolved cases, and the discovery of novel disease genes. (Funded by the NIH Common Fund and others.).

Improving prenatal diagnosis through standards and aggregation

Advances in sequencing and imaging technologies enable enhanced assessment in the prenatal space, with a goal to diagnose and predict the natural history of disease, to direct targeted therapies, and to implement clinical management, including transfer of care, election of supportive care, and selection of surgical interventions. The current lack of standardization and aggregation stymies variant interpretation and gene discovery, which hinders the provision of prenatal precision medicine, leaving clinicians and patients without an accurate diagnosis. With large amounts of data generated, it is imperative to establish standards for data collection, processing, and aggregation. Aggregated and homogeneously processed genetic and phenotypic data permits dissection of the genomic architecture of prenatal presentations of disease and provides a dataset on which data analysis algorithms can be tuned to the prenatal space. Here we discuss the importance of generating aggregate data sets and how the prenatal space is driving the development of interoperable standards and phenotype-driven tools.

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders

BACKGROUND

In the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the ACMG/AMP genome interpretation guidelines and features related to the clinical interpretation of the variants. The system can explain the result based on annotated evidence and feature contributions.

RESULTS

We trained various machine learning algorithms using in-house patient data. The performance of variant ranking was assessed using the recall rate of identifying causative variants in the top-ranked variants. The best practice model was a random forest classifier that showed top 1 recall of 85.6% and top 3 recall of 94.4%. The 3ASC annotates the ACMG/AMP criteria for each genetic variant of a patient so that clinical geneticists can interpret the result as in the CAGI6 SickKids challenge. In the challenge, 3ASC identified causal genes for 10 out of 14 patient cases, with evidence of decreased gene expression for 6 cases. Among them, two genes (HDAC8 and CASK) had decreased gene expression profiles confirmed by transcriptome data.

CONCLUSIONS

3ASC can prioritize genetic variants with higher sensitivity compared to previous methods by integrating various features related to clinical interpretation, including features related to false positive risk such as quality control and disease inheritance pattern. The system allows interpretation of each variant based on the ACMG/AMP criteria and feature contribution assessed using explainable AI techniques.

Enhancing Variant Prioritization in VarFish through On-Premise Computational Facial Analysis

Genomic variant prioritization is crucial for identifying disease-associated genetic variations. Integrating facial and clinical feature analyses into this process enhances performance. This study demonstrates the integration of facial analysis (GestaltMatcher) and Human Phenotype Ontology analysis (CADA) within VarFish, an open-source variant analysis framework. Challenges related to non-open-source components were addressed by providing an open-source version of GestaltMatcher, facilitating on-premise facial analysis to address data privacy concerns. Performance evaluation on 163 patients recruited from a German multi-center study of rare diseases showed PEDIA's superior accuracy in variant prioritization compared to individual scores. This study highlights the importance of further benchmarking and future integration of advanced facial analysis approaches aligned with ACMG guidelines to enhance variant classification.

Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases

Phenotype-guided gene prioritizers have proved a highly efficient approach to identifying causal genes for Mendelian diseases. In our previous study, we preliminarily evaluated the performance of ten prioritizers. However, all the selected software was run based on default settings and singleton mode. With a large-scale family dataset from Deciphering Developmental Disorders (DDD) project (N = 305) and an in-house trio cohort (N = 152), the four optimal performers in our prior study including Exomiser, PhenIX, AMELIE, and LIRCIAL were further assessed through parameter optimization and/or the utilization of trio mode. The in-depth assessment revealed high diagnostic yields of the four prioritizers with refined preferences, each alone or together: (1) 83.3-91.8% of the causal genes were presented among the first ten candidates in the final ranking lists of the four tools; (2) Over 97.7% of the causal genes were successfully captured within the top 50 by either of the four software. Exomiser did best in directly hitting the target (ranking the causal gene at the very top) while LIRICAL displayed a predominant overall detection capability. Besides, cases affected by low-penetrance and high-frequency pathogenic variants were found misjudged during the automated prioritization process. The discovery of the limitations shed light on the specific directions of future enhancement for causal-gene ranking tools.

RDmaster: A novel phenotype-oriented dialogue system supporting differential diagnosis of rare disease

BACKGROUND

Clinicians often lack the necessary expertise to differentially diagnose multiple underlying rare diseases (RDs) due to their complex and overlapping clinical features, leading to misdiagnoses and delayed treatments. The aim of this study is to develop a novel electronic differential diagnostic support system for RDs.

METHOD

Through integrating two Bayesian diagnostic methods, a candidate list was generated with enhance clinical interpretability for the further Q&A based differential diagnosis (DDX). To achieve an efficient Q&A dialogue strategy, we introduce a novel metric named the adaptive information gain and Gini index (AIGGI) to evaluate the expected gain of interrogated phenotypes within real-time diagnostic states.

RESULTS

This DDX tool called RDmaster has been implemented as a web-based platform (http://rdmaster.nbscn.org/). A diagnostic trial involving 238 published RD patients revealed that RDmaster outperformed existing RD diagnostic tools, as well as ChatGPT, and was shown to enhance the diagnostic accuracy through its Q&A system.

CONCLUSIONS

The RDmaster offers an effective multi-omics differential diagnostic technique and outperforms existing tools and popular large language models, particularly enhancing differential diagnosis in collecting diagnostically beneficial phenotypes.

Phenotypic similarity-based approach for variant prioritization for unsolved rare disease: a preliminary methodological report

Rare diseases (RD) have a prevalence of not more than 1/2000 persons in the European population, and are characterised by the difficulty experienced in obtaining a correct and timely diagnosis. According to Orphanet, 72.5% of RD have a genetic origin although 35% of them do not yet have an identified causative gene. A significant proportion of patients suspected to have a genetic RD receive an inconclusive exome/genome sequencing. Working towards the International Rare Diseases Research Consortium (IRDiRC)'s goal for 2027 to ensure that all people living with a RD receive a diagnosis within one year of coming to medical attention, the Solve-RD project aims to identify the molecular causes underlying undiagnosed RD. As part of this strategy, we developed a phenotypic similarity-based variant prioritization methodology comparing submitted cases with other submitted cases and with known RD in Orphanet. Three complementary approaches based on phenotypic similarity calculations using the Human Phenotype Ontology (HPO), the Orphanet Rare Diseases Ontology (ORDO) and the HPO-ORDO Ontological Module (HOOM) were developed; genomic data reanalysis was performed by the RD-Connect Genome-Phenome Analysis Platform (GPAP). The methodology was tested in 4 exemplary cases discussed with experts from European Reference Networks. Variants of interest (pathogenic or likely pathogenic) were detected in 8.8% of the 725 cases clustered by similarity calculations. Diagnostic hypotheses were validated in 42.1% of them and needed further exploration in another 10.9%. Based on the promising results, we are devising an automated standardized phenotypic-based re-analysis pipeline to be applied to the entire unsolved cases cohort.

Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT

To enhance phenotype recognition in clinical notes of genetic diseases, we developed two models-PhenoBCBERT and PhenoGPT-for expanding the vocabularies of Human Phenotype Ontology (HPO) terms. While HPO offers a standardized vocabulary for phenotypes, existing tools often fail to capture the full scope of phenotypes due to limitations from traditional heuristic or rule-based approaches. Our models leverage large language models to automate the detection of phenotype terms, including those not in the current HPO. We compare these models with PhenoTagger, another HPO recognition tool, and found that our models identify a wider range of phenotype concepts, including previously uncharacterized ones. Our models also show strong performance in case studies on biomedical literature. We evaluate the strengths and weaknesses of BERT- and GPT-based models in aspects such as architecture and accuracy. Overall, our models enhance automated phenotype detection from clinical texts, improving downstream analyses on human diseases.

The Human Phenotype Ontology in 2024: phenotypes around the world

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT

To enhance phenotype recognition in clinical notes of genetic diseases, we developed two models - PhenoBCBERT and PhenoGPT - for expanding the vocabularies of Human Phenotype Ontology (HPO) terms. While HPO offers a standardized vocabulary for phenotypes, existing tools often fail to capture the full scope of phenotypes, due to limitations from traditional heuristic or rule-based approaches. Our models leverage large language models (LLMs) to automate the detection of phenotype terms, including those not in the current HPO. We compared these models to PhenoTagger, another HPO recognition tool, and found that our models identify a wider range of phenotype concepts, including previously uncharacterized ones. Our models also showed strong performance in case studies on biomedical literature. We evaluated the strengths and weaknesses of BERT-based and GPT-based models in aspects such as architecture and accuracy. Overall, our models enhance automated phenotype detection from clinical texts, improving downstream analyses on human diseases.

Simulation of undiagnosed patients with novel genetic conditions

Rare Mendelian disorders pose a major diagnostic challenge and collectively affect 300-400 million patients worldwide. Many automated tools aim to uncover causal genes in patients with suspected genetic disorders, but evaluation of these tools is limited due to the lack of comprehensive benchmark datasets that include previously unpublished conditions. Here, we present a computational pipeline that simulates realistic clinical datasets to address this deficit. Our framework jointly simulates complex phenotypes and challenging candidate genes and produces patients with novel genetic conditions. We demonstrate the similarity of our simulated patients to real patients from the Undiagnosed Diseases Network and evaluate common gene prioritization methods on the simulated cohort. These prioritization methods recover known gene-disease associations but perform poorly on diagnosing patients with novel genetic disorders. Our publicly-available dataset and codebase can be utilized by medical genetics researchers to evaluate, compare, and improve tools that aid in the diagnostic process.

Curation and expansion of the Human Phenotype Ontology for systemic autoinflammatory diseases improves phenotype-driven disease-matching

Introduction

Accurate and standardized phenotypic descriptions are essential in diagnosing rare diseases and discovering new diseases, and the Human Phenotype Ontology (HPO) system was developed to provide a rich collection of hierarchical phenotypic descriptions. However, although the HPO terms for inborn errors of immunity have been improved and curated, it has not been investigated whether this curation improves the diagnosis of systemic autoinflammatory disease (SAID) patients. Here, we aimed to study if improved HPO annotation for SAIDs enhanced SAID identification and to demonstrate the potential of phenotype-driven genome diagnostics using curated HPO terms for SAIDs.

Methods

We collected HPO terms from 98 genetically confirmed SAID patients across eight different European SAID expertise centers and used the LIRICAL (Likelihood Ratio Interpretation of Clinical Abnormalities) computational algorithm to estimate the effect of HPO curation on the prioritization of the correct SAID for each patient.

Results

Our results show that the percentage of correct diagnoses increased from 66% to 86% and that the number of diagnoses with the highest ranking increased from 38 to 45. In a further pilot study, curation also improved HPO-based whole-exome sequencing (WES) analysis, diagnosing 10/12 patients before and 12/12 after curation. In addition, the average number of candidate diseases that needed to be interpreted decreased from 35 to 2.

Discussion

This study demonstrates that curation of HPO terms can increase identification of the correct diagnosis, emphasizing the high potential of HPO-based genome diagnostics for SAIDs.

diseaseGPS: auxiliary diagnostic system for genetic disorders based on genotype and phenotype

SUMMARY

The next-generation sequencing brought opportunities for the diagnosis of genetic disorders due to its high-throughput capabilities. However, the majority of existing methods were limited to only sequencing candidate variants, and the process of linking these variants to a diagnosis of genetic disorders still required medical professionals to consult databases. Therefore, we introduce diseaseGPS, an integrated platform for the diagnosis of genetic disorders that combines both phenotype and genotype data for analysis. It offers not only a user-friendly GUI web application for those without a programming background but also scripts that can be executed in batch mode for bioinformatics professionals. The genetic and phenotypic data are integrated using the ACMG-Bayes method and a novel phenotypic similarity method, to prioritize the results of genetic disorders. diseaseGPS was evaluated on 6085 cases from Deciphering Developmental Disorders project and 187 cases from Shanghai Children's hospital. The results demonstrated that diseaseGPS performed better than other commonly used methods.

AVAILABILITY AND IMPLEMENTATION

diseaseGPS is available to freely accessed at https://diseasegps.sjtu.edu.cn with source code at https://github.com/BioHuangDY/diseaseGPS.

Integration of EpiSign, facial phenotyping, and likelihood ratio interpretation of clinical abnormalities in the re-classification of an ARID1B missense variant

Heterozygous ARID1B variants result in Coffin-Siris syndrome. Features may include hypoplastic nails, slow growth, characteristic facial features, hypotonia, hypertrichosis, and sparse scalp hair. Most reported cases are due to ARID1B loss of function variants. We report a boy with developmental delay, feeding difficulties, aspiration, recurrent respiratory infections, slow growth, and hypotonia without a clinical diagnosis, where a previously unreported ARID1B missense variant was classified as a variant of uncertain significance. The pathogenicity of this variant was refined through combined methodologies including genome-wide methylation signature analysis (EpiSign), Machine Learning (ML) facial phenotyping, and LIRICAL. Trio exome sequencing and EpiSign were performed. ML facial phenotyping compared facial images using FaceMatch and GestaltMatcher to syndrome-specific libraries to prioritize the trio exome bioinformatic pipeline gene list output. Phenotype-driven variant prioritization was performed with LIRICAL. A de novo heterozygous missense variant, ARID1B p.(Tyr1268His), was reported as a variant of uncertain significance. The ACMG classification was refined to likely pathogenic by a supportive methylation signature, ML facial phenotyping, and prioritization through LIRICAL. The ARID1B genotype-phenotype has been expanded through an extended analysis of missense variation through genome-wide methylation signatures, ML facial phenotyping, and likelihood-ratio gene prioritization.

PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework

Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore's ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 37 of 40 investigated syndromes against clinical features observed in individuals with other neurodevelopmental disorders and show it is an improvement on existing approaches. PhenoScore provides predictions for individuals with variants of unknown significance and enables sophisticated genotype-phenotype studies by testing hypotheses on possible phenotypic (sub)groups. PhenoScore confirmed previously known phenotypic subgroups caused by variants in the same gene for SATB1, SETBP1 and DEAF1 and provides objective clinical evidence for two distinct ADNP-related phenotypes, already established functionally.

Do PACS1 variants impeding adaptor protein binding predispose to syndromic intellectual disability?

To date, PACS1-neurodevelopmental disorder (PACS1-NDD) has been associated with recurrent variation of Arg203 and is considered diagnostic of PACS1-NDD, an autosomal dominant syndromic intellectual disability disorder. Although incompletely defined, the proposed disease mechanism for this variant is altered PACS1 affinity for its client proteins. Given this proposed mechanism, we hypothesized that PACS1 variants that interfere with binding of adaptor proteins might also give rise to syndromic intellectual disability. Herein, we report a proposita and her mother with phenotypic features overlapping PACS1-NDD and a novel PACS1 variant (NM_018026.3:c.[755C > T];[=], p.(Ser252Phe)) that impedes binding of the adaptor protein GGA3 (Golgi-associated, gamma-adaptin ear-containing, ARF-binding protein 3). We hypothesize that attenuating PACS1 binding of GGA3 also gives rise to a disorder with features overlapping those of PACS1-NDD. This observation better delineates the mechanism by which PACS1 variation predisposes to syndromic intellectual disability.

Krawitz PDMDPP. The future role of facial image analysis in ACMG classification guidelines

The use of next-generation sequencing (NGS) has dramatically improved the diagnosis of rare diseases. However, the analysis of genomic data has become complex with the increasing detection of variants by exome and genome sequencing. The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) developed a 5-tier classification scheme in 2015 for variant interpretation, that has since been widely adopted. Despite efforts to minimise discrepancies in the application of these criteria, inconsistencies still occur. Further specifications for individual genes were developed by Variant Curation Expert Panels (VCEPs) of the Clinical Genome Resource (ClinGen) consortium, that also take into consideration gene or disease specific features. For instance, in disorders with a highly characerstic facial gestalt a "phenotypic match" (PP4) has higher pathogenic evidence than e.g. in a non-syndromic form of intellectual disability. With computational approaches for quantifying the similarity of dysmorphic features results of such analysis can now be used in a refined Bayesian framework for the ACMG/AMP criteria.

Phenopacket-tools: Building and validating GA4GH Phenopackets

The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.

Resources and tools for rare disease variant interpretation

Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

A Robust Phenotype-driven Likelihood Ratio Analysis Approach Assisting Interpretable Clinical Diagnosis of Rare Diseases

Phenotype-based prioritization of candidate genes and diseases has become a well-established approach for multi-omics diagnostics of rare diseases. Most current algorithms exploit semantic analysis and probabilistic statistics based on Human Phenotype Ontology and are commonly superior to naive search methods. However, these algorithms are mostly less interpretable and do not perform well in real clinical scenarios due to noise and imprecision of query terms, and the fact that individuals may not display all phenotypes of the disease they belong to. We present a Phenotype-driven Likelihood Ratio analysis approach (PheLR) assisting interpretable clinical diagnosis of rare diseases. With a likelihood ratio paradigm, PheLR estimates the posterior probability of candidate diseases and how much a phenotypic feature contributes to the prioritization result. Benchmarked using simulated and realistic patients, PheLR shows significant advantages over current approaches and is robust to noise and inaccuracy. To facilitate clinical practice and visualized differential diagnosis, PheLR is implemented as an online web tool (http://phelr.nbscn.org).

GA4GH Phenopackets: A Practical Introduction

The Global Alliance for Genomics and Health (GA4GH) is developing a suite of coordinated standards for genomics for healthcare. The Phenopacket is a new GA4GH standard for sharing disease and phenotype information that characterizes an individual person, linking that individual to detailed phenotypic descriptions, genetic information, diagnoses, and treatments. A detailed example is presented that illustrates how to use the schema to represent the clinical course of a patient with retinoblastoma, including demographic information, the clinical diagnosis, phenotypic features and clinical measurements, an examination of the extirpated tumor, therapies, and the results of genomic analysis. The Phenopacket Schema, together with other GA4GH data and technical standards, will enable data exchange and provide a foundation for the computational analysis of disease and phenotype information to improve our ability to diagnose and conduct research on all types of disorders, including cancer and rare diseases.

How to choose a test for prenatal genetic diagnosis: a practical overview

Establishing the diagnosis of a fetal genetic disease in utero expands decision-making opportunities for individuals during pregnancy and enables providers to tailor prenatal care and surveillance to disease-specific risks. The selection of prenatal genetic tests is guided by key details from fetal imaging, family and obstetrical history, suspected diagnoses and mechanisms of disease, an accurate understanding of what abnormalities each test is designed to detect, and, at times, the gestational age at which testing is initiated. Pre- and posttest counseling, by or in conjunction with providers trained in genetics, ensure an accurate understanding of genetic tests, their potential results and limitations, estimated turnaround time for results, and the clinical implications of their findings. As prenatal diagnosis and testing options continue to expand rapidly, it is increasingly important for obstetrical providers to understand how to choose appropriate genetic testing and contextualize the clinical implications of their results.

The practice of genomic medicine: A delineation of the process and its governing principles

Genomic medicine, an emerging medical discipline, applies the principles of evolution, developmental biology, functional genomics, and structural genomics within clinical care. Enabling widespread adoption and integration of genomic medicine into clinical practice is key to achieving precision medicine. We delineate a biological framework defining diagnostic utility of genomic testing and map the process of genomic medicine to inform integration into clinical practice. This process leverages collaboration and collective cognition of patients, principal care providers, clinical genomic specialists, laboratory geneticists, and payers. We detail considerations for referral, triage, patient intake, phenotyping, testing eligibility, variant analysis and interpretation, counseling, and management within the utilitarian limitations of health care systems. To reduce barriers for clinician engagement in genomic medicine, we provide several decision-making frameworks and tools and describe the implementation of the proposed workflow in a prototyped electronic platform that facilitates genomic care. Finally, we discuss a vision for the future of genomic medicine and comment on areas for continued efforts.

Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes

BACKGROUND

Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.

METHODS

We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.

FINDINGS

We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems.

INTERPRETATION

Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.

FUNDING

NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Phenotype-aware prioritisation of rare Mendelian disease variants

A molecular diagnosis from the analysis of sequencing data in rare Mendelian diseases has a huge impact on the management of patients and their families. Numerous patient phenotype-aware variant prioritisation (VP) tools have been developed to help automate this process, and shorten the diagnostic odyssey, but performance statistics on real patient data are limited. Here we identify, assess, and compare the performance of all up-to-date, freely available, and programmatically accessible tools using a whole-exome, retinal disease dataset from 134 individuals with a molecular diagnosis. All tools were able to identify around two-thirds of the genetic diagnoses as the top-ranked candidate, with LIRICAL performing best overall. Finally, we discuss the challenges to overcome most cases remaining undiagnosed after current, state-of-the-art practices.

IMPROVE-DD: Integrating multiple phenotype resources optimizes variant evaluation in genetically determined developmental disorders

Diagnosing rare developmental disorders using genome-wide sequencing data commonly necessitates review of multiple plausible candidate variants, often using ontologies of categorical clinical terms. We show that Integrating Multiple Phenotype Resources Optimizes Variant Evaluation in Developmental Disorders (IMPROVE-DD) by incorporating additional classes of data commonly available to clinicians and recorded in health records. In doing so, we quantify the distinct contributions of sex, growth, and development in addition to Human Phenotype Ontology (HPO) terms and demonstrate added value from these readily available information sources. We use likelihood ratios for nominal and quantitative data and propose a classifier for HPO terms in this framework. This Bayesian framework results in more robust diagnoses. Using data systematically collected in the Deciphering Developmental Disorders study, we considered 77 genes with pathogenic/likely pathogenic variants in ≥10 individuals. All genes showed at least a satisfactory prediction by receiver operating characteristic when testing on training data (AUC ≥ 0.6), and HPO terms were the best predictor for the majority of genes, though a minority (13/77) of genes were better predicted by other phenotypic data types. Overall, classifiers based upon multiple integrated phenotypic data sources performed better than those based upon any individual source, and importantly, integrated models produced notably fewer false positives. Finally, we show that IMPROVE-DD models with good predictive performance on cross-validation can be constructed from relatively few individuals. This suggests new strategies for candidate gene prioritization and highlights the value of systematic clinical data collection to support diagnostic programs.

Computational analysis of neurodevelopmental phenotypes: Harmonization empowers clinical discovery

Making a specific diagnosis in neurodevelopmental disorders is traditionally based on recognizing clinical features of a distinct syndrome, which guides testing of its possible genetic etiologies. Scalable frameworks for genomic diagnostics, however, have struggled to integrate meaningful measurements of clinical phenotypic features. While standardization has enabled generation and interpretation of genomic data for clinical diagnostics at unprecedented scale, making the equivalent breakthrough for clinical data has proven challenging. However, increasingly clinical features are being recorded using controlled dictionaries with machine readable formats such as the Human Phenotype Ontology (HPO), which greatly facilitates their use in the diagnostic space. Improving the tractability of large-scale clinical information will present new opportunities to inform genomic research and diagnostics from a clinical perspective. Here, we describe novel approaches for computational phenotyping to harmonize clinical features, improve data translation through revising domain-specific dictionaries, quantify phenotypic features, and determine clinical relatedness. We demonstrate how these concepts can be applied to longitudinal phenotypic information, which represents a critical element of developmental disorders and pediatric conditions. Finally, we expand our discussion to clinical data derived from electronic medical records, a largely untapped resource of deep clinical information with distinct strengths and weaknesses.

Computational approaches for predicting variant impact: An overview from resources, principles to applications

One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.

The utility of pathologic examination and comprehensive phenotyping for accurate diagnosis with perinatal exome sequencing

OBJECTIVE

Exome sequencing (ES) offers the ability to assess for variants in thousands of genes and is particularly useful in the setting of fetal anomalies. However, the ES pipeline relies on a thorough understanding of an individual patient's phenotype, which may be limited in the prenatal setting. Additional pathology evaluations in the pre- and postnatal settings can add phenotypic details important for clearly establishing and characterizing a diagnosis.

METHODS

This is a case series of prenatal ES performed at our institution in which pathology evaluations, including autopsy, dysmorphology examination, histology, and peripheral blood smear, augmented the understanding of the fetal phenotype. ES was performed at our institution and a multidisciplinary panel reviewed and classified the variants for each case.

RESULTS

We present four cases wherein pathology evaluations were beneficial for supporting a perinatal diagnosis identified with ES. In each of these cases, pathology findings provided additional data to support a more complete understanding of the relationship between the perinatal phenotype and variants identified with ES.

CONCLUSION

These cases highlight challenges of perinatal ES related to incomplete prenatal phenotyping, demonstrate the utility of pathology evaluations to support diagnoses identified with ES, and further characterize the disease manifestations of specific genetic variants.

Phenotype-driven approaches to enhance variant prioritization and diagnosis of rare disease

Rare disease diagnostics and disease gene discovery have been revolutionized by whole-exome and genome sequencing but identifying the causative variant(s) from the millions in each individual remains challenging. The use of deep phenotyping of patients and reference genotype-phenotype knowledge, alongside variant data such as allele frequency, segregation, and predicted pathogenicity, has proved an effective strategy to tackle this issue. Here we review the numerous tools that have been developed to automate this approach and demonstrate the power of such an approach on several thousand diagnosed cases from the 100,000 Genomes Project. Finally, we discuss the challenges that need to be overcome if we are going to improve detection rates and help the majority of patients that still remain without a molecular diagnosis after state-of-the-art genomic interpretation.

Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs

Accurate stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, the natural history of long COVID is incompletely understood and characterized by an extremely wide range of manifestations that are difficult to analyze computationally. In addition, the generalizability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modeling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning procedures. Using k-means clustering of this similarity matrix, we found six distinct clusters of PASC patients, each with distinct profiles of phenotypic abnormalities. There was a significant association of cluster membership with a range of pre-existing conditions and with measures of severity during acute COVID-19. Two of the clusters were associated with severe manifestations and displayed increased mortality. We assigned new patients from other healthcare centers to one of the six clusters on the basis of maximum semantic similarity to the original patients. We show that the identified clusters were generalizable across different hospital systems and that the increased mortality rate was consistently observed in two of the clusters. Semantic phenotypic clustering can provide a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.

The Clinical Variant Analysis Tool: Analyzing the evidence supporting reported genomic variation in clinical practice

PURPOSE

Genomic test results, regardless of laboratory variant classification, require clinical practitioners to judge the applicability of a variant for medical decisions. Teaching and standardizing clinical interpretation of genomic variation calls for a methodology or tool.

METHODS

To generate such a tool, we distilled the Clinical Genome Resource framework of causality and the American College of Medical Genetics/Association of Molecular Pathology and Quest Diagnostic Laboratory scoring of variant deleteriousness into the Clinical Variant Analysis Tool (CVAT). Applying this to 289 clinical exome reports, we compared the performance of junior practitioners with that of experienced medical geneticists and assessed the utility of reported variants.

RESULTS

CVAT enabled performance comparable to that of experienced medical geneticists. In total, 124 of 289 (42.9%) exome reports and 146 of 382 (38.2%) reported variants supported a diagnosis. Overall, 10.5% (1 pathogenic [P] or likely pathogenic [LP] variant and 39 variants of uncertain significance [VUS]) of variants were reported in genes without established disease association; 20.2% (23 P/LP and 54 VUS) were in genes without sufficient phenotypic concordance; 7.3% (15 P/LP and 13 VUS) conflicted with the known molecular disease mechanism; and 24% (91 VUS) had insufficient evidence for deleteriousness.

CONCLUSION

Implementation of CVAT standardized clinical interpretation of genomic variation and emphasized the need for collaborative and transparent reporting of genomic variation.

Creation and evaluation of full-text literature-derived, feature-weighted disease models of genetically determined developmental disorders

There are >2500 different genetically determined developmental disorders (DD), which, as a group, show very high levels of both locus and allelic heterogeneity. This has led to the wide-spread use of evidence-based filtering of genome-wide sequence data as a diagnostic tool in DD. Determining whether the association of a filtered variant at a specific locus is a plausible explanation of the phenotype in the proband is crucial and commonly requires extensive manual literature review by both clinical scientists and clinicians. Access to a database of weighted clinical features extracted from rigorously curated literature would increase the efficiency of this process and facilitate the development of robust phenotypic similarity metrics. However, given the large and rapidly increasing volume of published information, conventional biocuration approaches are becoming impractical. Here, we present a scalable, automated method for the extraction of categorical phenotypic descriptors from the full-text literature. Papers identified through literature review were downloaded and parsed using the Cadmus custom retrieval package. Human Phenotype Ontology terms were extracted using MetaMap, with 76-84% precision and 65-73% recall. Mean terms per paper increased from 9 in title + abstract, to 68 using full text. We demonstrate that these literature-derived disease models plausibly reflect true disease expressivity more accurately than widely used manually curated models, through comparison with prospectively gathered data from the Deciphering Developmental Disorders study. The area under the curve for receiver operating characteristic (ROC) curves increased by 5-10% through the use of literature-derived models. This work shows that scalable automated literature curation increases performance and adds weight to the need for this strategy to be integrated into informatic variant analysis pipelines. Database URL: https://doi.org/10.1093/database/baac038.

Genomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes

PURPOSE

This study aimed to provide comprehensive diagnostic and candidate analyses in a pediatric rare disease cohort through the Genomic Answers for Kids program.

METHODS

Extensive analyses of 960 families with suspected genetic disorders included short-read exome sequencing and short-read genome sequencing (srGS); PacBio HiFi long-read genome sequencing (HiFi-GS); variant calling for single nucleotide variants (SNV), structural variant (SV), and repeat variants; and machine-learning variant prioritization. Structured phenotypes, prioritized variants, and pedigrees were stored in PhenoTips database, with data sharing through controlled access the database of Genotypes and Phenotypes.

RESULTS

Diagnostic rates ranged from 11% in patients with prior negative genetic testing to 34.5% in naive patients. Incorporating SVs from genome sequencing added up to 13% of new diagnoses in previously unsolved cases. HiFi-GS yielded increased discovery rate with >4-fold more rare coding SVs compared with srGS. Variants and genes of unknown significance remain the most common finding (58% of nondiagnostic cases).

CONCLUSION

Computational prioritization is efficient for diagnostic SNVs. Thorough identification of non-SNVs remains challenging and is partly mitigated using HiFi-GS sequencing. Importantly, community research is supported by sharing real-time data to accelerate gene validation and by providing HiFi variant (SNV/SV) resources from >1000 human alleles to facilitate implementation of new sequencing platforms for rare disease diagnoses.

Prenatal phenotyping: A community effort to enhance the Human Phenotype Ontology

Technological advances in both genome sequencing and prenatal imaging are increasing our ability to accurately recognize and diagnose Mendelian conditions prenatally. Phenotype-driven early genetic diagnosis of fetal genetic disease can help to strategize treatment options and clinical preventive measures during the perinatal period, to plan in utero therapies, and to inform parental decision-making. Fetal phenotypes of genetic diseases are often unique and at present are not well understood; more comprehensive knowledge about prenatal phenotypes and computational resources have an enormous potential to improve diagnostics and translational research. The Human Phenotype Ontology (HPO) has been widely used to support diagnostics and translational research in human genetics. To better support prenatal usage, the HPO consortium conducted a series of workshops with a group of domain experts in a variety of medical specialties, diagnostic techniques, as well as diseases and phenotypes related to prenatal medicine, including perinatal pathology, musculoskeletal anomalies, neurology, medical genetics, hydrops fetalis, craniofacial malformations, cardiology, neonatal-perinatal medicine, fetal medicine, placental pathology, prenatal imaging, and bioinformatics. We expanded the representation of prenatal phenotypes in HPO by adding 95 new phenotype terms under the Abnormality of prenatal development or birth (HP:0001197) grouping term, and revised definitions, synonyms, and disease annotations for most of the 152 terms that existed before the beginning of this effort. The expansion of prenatal phenotypes in HPO will support phenotype-driven prenatal exome and genome sequencing for precision genetic diagnostics of rare diseases to support prenatal care.

Predicting genes from phenotypes using human phenotype ontology (HPO) terms

The interpretation of genomic variants following whole exome sequencing (WES) can be aided using human phenotype ontology (HPO) terms to standardize clinical features and predict causative genes. We performed WES on 453 patients diagnosed prior to 18 years of age and identified 114 pathogenic (P) or likely pathogenic (LP) variants in 112 patients. We utilized PhenoDB to extract HPO terms from provider notes and then used Phen2Gene to generate a gene score and gene ranking from each list of HPO terms. We assigned Phen2Gene gene rankings to 6 rank classes, with class 1 covering raw gene rankings of 1 to 10 and class 2 covering rankings from 11 to 50 out of a total of 17,126 possible gene rankings. Phen2Gene ranked causative genes into rank class 1 or 2 in 27.7% of cases and the genes in rank class 1 were all associated with well-characterized phenotypes. We found significant associations between the gene score and the number of years, since the gene was first published, the number of HPO terms with an hierarchical depth greater or equal to 11, and the number of Online Mendelian Inheritance in Man terms associated with the phenotype and gene. We conclude that genes associated with recognizable phenotypes and terms deep in the HPO hierarchy have the best chance of producing a high gene score and ranking in class 1 to 2 using Phen2Gene software with HPO terms. Clinicians and laboratory staff should consider these results when HPO terms are employed to prioritize candidate genes.

Advances in the development of PubCaseFinder, including the new application programming interface and matching algorithm

Over 10,000 rare genetic diseases have been identified, and millions of newborns are affected by severe rare genetic diseases each year. A variety of Human Phenotype Ontology (HPO)-based clinical decision support systems (CDSS) and patient repositories have been developed to support clinicians in diagnosing patients with suspected rare genetic diseases. In September 2017, we released PubCaseFinder (https://pubcasefinder.dbcls.jp), a web-based CDSS that provides ranked lists of genetic and rare diseases using HPO-based phenotypic similarities, where top-listed diseases represent the most likely differential diagnosis. We also developed a Matchmaker Exchange (MME) application programming interface (API) to query PubCaseFinder, which has been adopted by several patient repositories. In this paper, we describe notable updates regarding PubCaseFinder, the GeneYenta matching algorithm implemented in PubCaseFinder, and the PubCaseFinder API. The updated GeneYenta matching algorithm improves the performance of the CDSS automated differential diagnosis function. Moreover, the updated PubCaseFinder and new API empower patient repositories participating in MME and medical professionals to actively use HPO-based resources. This article is protected by copyright. All rights reserved.

Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases

It’s challenging work to identify disease-causing genes from the next-generation sequencing (NGS) data of patients with Mendelian disorders. To improve this situation, researchers have developed many phenotype-driven gene prioritization methods using a patient’s genotype and phenotype information, or phenotype information only as input to rank the candidate’s pathogenic genes. Evaluations of these ranking methods provide practitioners with convenience for choosing an appropriate tool for their workflows, but retrospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate. In this research, the performance of ten recognized causal-gene prioritization methods was benchmarked using 305 cases from the Deciphering Developmental Disorders (DDD) project and 209 in-house cases via a relatively unbiased methodology. The evaluation results show that methods using Human Phenotype Ontology (HPO) terms and Variant Call Format (VCF) files as input achieved better overall performance than those using phenotypic data alone. Besides, LIRICAL and AMELIE, two of the best methods in our benchmark experiments, complement each other in cases with the causal genes ranked highly, suggesting a possible integrative approach to further enhance the diagnostic efficiency. Our benchmarking provides valuable reference information to the computer-assisted rapid diagnosis in Mendelian diseases and sheds some light on the potential direction of future improvement on disease-causing gene prioritization methods.

VPMBench: a test bench for variant prioritization methods

Background

Clinical diagnostics of whole-exome and whole-genome sequencing data requires geneticists to consider thousands of genetic variants for each patient. Various variant prioritization methods have been developed over the last years to aid clinicians in identifying variants that are likely disease-causing. Each time a new method is developed, its effectiveness must be evaluated and compared to other approaches based on the most recently available evaluation data. Doing so in an unbiased, systematic, and replicable manner requires significant effort.

Results

The open-source test bench “VPMBench” automates the evaluation of variant prioritization methods. VPMBench introduces a standardized interface for prioritization methods and provides a plugin system that makes it easy to evaluate new methods. It supports different input data formats and custom output data preparation. VPMBench exploits declaratively specified information about the methods, e.g., the variants supported by the methods. Plugins may also be provided in a technology-agnostic manner via containerization.

Conclusions

VPMBench significantly simplifies the evaluation of both custom and published variant prioritization methods. As we expect variant prioritization methods to become ever more critical with the advent of whole-genome sequencing in clinical diagnostics, such tool support is crucial to facilitate methodological research.

Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases

BACKGROUND

Clinical interpretation of genetic variants in the context of the patient's phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation.

METHODS

We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed.

RESULTS

GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases.

CONCLUSIONS

GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review.

CADA: phenotype-driven gene prioritization based on a case-enriched knowledge graph

Many rare syndromes can be well described and delineated from other disorders by a combination of characteristic symptoms. These phenotypic features are best documented with terms of the Human Phenotype Ontology (HPO), which are increasingly used in electronic health records (EHRs), too. Many algorithms that perform HPO-based gene prioritization have also been developed; however, the performance of many such tools suffers from an over-representation of atypical cases in the medical literature. This is certainly the case if the algorithm cannot handle features that occur with reduced frequency in a disorder. With Cada, we built a knowledge graph based on both case annotations and disorder annotations. Using network representation learning, we achieve gene prioritization by link prediction. Our results suggest that Cada exhibits superior performance particularly for patients that present with the pathognomonic findings of a disease. Additionally, information about the frequency of occurrence of a feature can readily be incorporated, when available. Crucial in the design of our approach is the use of the growing amount of phenotype–genotype information that diagnostic labs deposit in databases such as ClinVar. By this means, Cada is an ideal reference tool for differential diagnostics in rare disorders that can also be updated regularly.

Knowledge Representation and Management: Interest in New Solutions for Ontology Curation

Objective: To select, present and summarize some of the best papers in the field of Knowledge Representation and Management (KRM) published in 2020.

Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2020, based on PubMed queries. This review was conducted according to the IMIA Yearbook guidelines.

Results: Four best papers were selected among 1,175 publications. In contrast with the papers selected last year, the four best papers of 2020 demonstrated a significant focus on methods and tools for ontology curation and design. The usual KRM application domains (bioinformatics, machine learning, and electronic health records) were also represented.

Conclusion: In 2020, ontology curation emerges as a significant topic of research interest. Bioinformatics, machine learning, and electronics health records remain significant research areas in the KRM community with various applications. Knowledge representations are key to advance machine learning by providing context and to develop novel bioinformatics metrics. As in 2019, representations serve a great variety of applications across many medical domains, with actionable results and now with growing adhesion to the open science initiative.

Early-Onset Dementia Associated with a Heterozygous, Nonsense, and de novo Variant in the MBD5 Gene

The haploinsufficiency of the methyl-binding domain protein 5 (MBD5) gene has been identified as the determinant cause of the neuropsychiatric disorders grouped under the name MBD5-neurodevelopment disorders (MAND). MAND includes patients with intellectual disability, behavioral problems, and seizures with a static clinical course. However, a few reports have suggested regression. We describe a non-intellectually disabled female, with previous epilepsy and personality disorder, who developed early-onset dementia. The extensive etiologic study revealed a heterozygous nonsense de novo pathogenic variant in the MBD5 gene. This finding could support including the MBD5 gene in the study of patients with atypical early-onset dementia.

GenomeDiver: a platform for phenotype-guided medical genomic diagnosis

Purpose:

Making a diagnosis from clinical genomic sequencing requires well-structured phenotypic data to guide genotype interpretation. A patient’s phenotypic features can be documented using the Human Phenotype Ontology (HPO), generating terms used to prioritize genes potentially causing the patient’s disease. We have developed GenomeDiver to provide a user interface for clinicians that allows more effective collaboration with the clinical diagnostic laboratory, with the goal of improving the success of the diagnostic process.

Methods:

GenomeDiver uses genomic data to prompt reverse phenotyping of patients undergoing genetic testing, enriching the amount and quality of structured phenotype data for the diagnostic laboratory, and helping clinicians to explore and flag diseases potentially causing their patient’s presentation.

Results:

We show how GenomeDiver communicates the clinician’s informed insights to the diagnostic lab in the form of HPO terms for interpretation of genomic sequencing data. We describe our user-driven design process, the engineering of the software for efficiency, security and portability, and examples of the performance of GenomeDiver using genomic testing data.

Conclusions:

GenomeDiver is a first step in a new approach to genomic diagnostics that enhances laboratory-clinician interactions, with the goal of directly engaging clinicians to improve the outcome of genomic diagnostic testing.

The Human Phenotype Ontology in 2021

The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.