PhenoVar: a phenotype-driven approach in clinical genomics for the diagnosis of polymalformative syndromes

PdmIRD: missense variants pathogenicity prediction for inherited retinal diseases in a disease-specific manner

Accurate discrimination of pathogenic and nonpathogenic variation remains an enormous challenge in clinical genetic testing of inherited retinal diseases (IRDs) patients. Computational methods for predicting variant pathogenicity are the main solutions for this dilemma. The majority of the state-of-the-art variant pathogenicity prediction tools disregard the differences in characteristics among different genes and treat all types of mutations equally. Since missense variants are the most common type of variation in the coding region of the human genome, we developed a novel missense mutation pathogenicity prediction tool, named Prediction of Deleterious Missense Mutation for IRDs (PdmIRD) in this study. PdmIRD was tailored for IRDs-related genes and constructed with the conditional random forest model. Population frequencies and a newly available prediction tool were incorporated into PdmIRD to improve the performance of the model. The evaluation of PdmIRD demonstrated its superior performance over nonspecific tools (areas under the curves, 0.984 and 0.910) and an existing eye abnormalities-specific tool (areas under the curves, 0.975 and 0.891). We also demonstrated the submodel that used a smaller gene panel further slightly improved performance. Our study provides evidence that a disease-specific model can enhance the prediction of missense mutation pathogenicity, especially when new and important features are considered. Additionally, this study provides guidance for exploring the characteristics and functions of the mutated proteins in a greater number of Mendelian disorders.

High-throughput Second-generation Sequencing Technology Assisted Diagnosis of Familial Partial Lipodystrophy (Type 2 Kobberling-Dunnigan Syndrome): A Case Report

BACKGROUND

Whole exome sequencing (WES) provides support for clinical diagnosis and treatment of genetically related diseases based on specific probe capture and high-throughput second-generation sequencing technology. Familial partial lipodystrophy 2 (FPLD2; OMIM # 151660) or type 2 Köbberling-Dunnigan syndrome with insulin resistance syndrome is uncommon in mainland China and elsewhere.

AIMS

We report the case in order to have a further understanding of FPLD2 or type 2 Kobberling- Dunnigan syndrome) with the assistance of WES and improve the clinical and genetic understanding and diagnosis of this disease.

CASE REPORT

A 30-year-old woman was admitted to the cadre department of our hospital at 14:00 on July 11, 2021, because of hyperglycemia, a rapid heart rate, and excessive sweating during pregnancy. An oral glucose tolerance test (OGTT) showed that insulin and C-peptide increased slowly after glucose stimulation, and the peak value was extended backward (Table 1). It was suggested that the patient had developed insulin antibodies, resulting in insulin resistance. Her clinical features and familial inheritance were consistent with FPLD2 (type 2 Kobberling-Dunnigan syndrome). The results of WES indicated that a heterozygous mutation occurred in exon 8 of the LMNA gene, because the base C at position 1444 was mutated into T during transcription. This mutation changed the amino acid position 482 of the encoded protein from Arg to Trp. Type 2 Kobberling- Dunnigan syndrome is associated with an LMNA gene mutation. According to the patient's clinical manifestations, hypoglycemic and lipid-lowering therapy is recommended.

CONCLUSION

WES can assist in the simultaneous clinical investigation or confirmation of FPLD2 and help identify diseases with similar clinical phenotypes. This case demonstrates that familial partial lipodystrophy is associated with an LMNA gene mutation on chromosome 1q21-22. This is one of the few cases of familial partial lipodystrophy diagnosed by WES.

eyeVarP: A computational framework for the identification of pathogenic variants specific to eye disease

PURPOSE

Disease-specific pathogenic variant prediction tools that differentiate pathogenic variants from benign have been improved through disease specificity recently. However, they have not been evaluated on disease-specific pathogenic variants compared with other diseases, which would help to prioritize disease-specific variants from several genes or novel genes. Thus, we hypothesize that features of pathogenic variants alone would provide a better model.

METHODS

We developed an eye disease-specific variant prioritization tool (eyeVarP), which applied the random forest algorithm to the data set of pathogenic variants of eye diseases and other diseases. We also developed the VarP tool and generalized pipeline to filter missense and insertion-deletion variants and predict their pathogenicity from exome or genome sequencing data, thus we provide a complete computational procedure.

RESULTS

eyeVarP outperformed pan disease-specific tools in identifying eye disease-specific pathogenic variants under the top 10. VarP outperformed 12 pathogenicity prediction tools with an accuracy of 95% in correctly identifying the pathogenicity of missense and insertion-deletion variants. The complete pipeline would help to develop disease-specific tools for other genetic disorders.

CONCLUSION

eyeVarP performs better in identifying eye disease-specific pathogenic variants using pathogenic variant features and gene features. Implementing such complete computational procedure would significantly improve the clinical variant interpretation for specific diseases.

Individualised human phenotype ontology gene panels improve clinical whole exome and genome sequencing analytical efficacy in a cohort of developmental and epileptic encephalopathies

BACKGROUND

The majority of genetic epilepsies remain unsolved in terms of specific genotype. Phenotype-based genomic analyses have shown potential to strengthen genomic analysis in various ways, including improving analytical efficacy.

METHODS

We have tested a standardised phenotyping method termed 'Phenomodels' for integrating deep-phenotyping information with our in-house developed clinical whole exome/genome sequencing analytical pipeline. Phenomodels includes a user-friendly epilepsy phenotyping template and an objective measure for selecting which template terms to include in individualised Human Phenotype Ontology (HPO) gene panels. In a pilot study of 38 previously solved cases of developmental and epileptic encephalopathies, we compared the sensitivity and specificity of the individualised HPO gene panels with the clinical epilepsy gene panel.

RESULTS

The Phenomodels template showed high sensitivity for capturing relevant phenotypic information, where 37/38 individuals' HPO gene panels included the causative gene. The HPO gene panels also had far fewer variants to assess than the epilepsy gene panel.

CONCLUSION

We have demonstrated a viable approach for incorporating standardised phenotype information into clinical genomic analyses, which may enable more efficient analysis.

Portrait of autosomal recessive diseases in the French-Canadian founder population of Saguenay-Lac-Saint-Jean

The population of the Saguenay-Lac-Saint-Jean (SLSJ) region, located in the province of Quebec, Canada, is recognized as a founder population, where some rare autosomal recessive diseases show a high prevalence. Through the clinical and molecular study of 82 affected individuals from 60 families, this study outlines 12 diseases identified as recurrent in SLSJ. Their carrier frequency was estimated with the contribution of 1059 healthy individuals, increasing the number of autosomal recessive diseases with known carrier frequency in this region from 14 to 25. We review the main clinical and molecular features previously reported for these disorders. Five of the studied diseases have a potential lethal effect and three are associated with intellectual deficiency. Therefore, we believe that the provincial program for carrier screening should be extended to include these eight disorders. The high-carrier frequency, together with the absence of consanguinity in most of these unrelated families, suggest a founder effect and genetic drift for the 12 recurrent variants. We recommend further studies to validate this hypothesis, as well as to extend the present study to other regions in the province of Quebec, since some of these disorders could also be present in other French-Canadian families.

Diagnostic yield and clinical utility of whole exome sequencing using an automated variant prioritization system, EVIDENCE

EVIDENCE, an automated variant prioritization system, has been developed to facilitate whole exome sequencing analyses. This study investigated the diagnostic yield of EVIDENCE in patients with suspected genetic disorders. DNA from 330 probands (age range, 0‐68 years) with suspected genetic disorders were subjected to whole exome sequencing. Candidate variants were identified by EVIDENCE and confirmed by testing family members and/or clinical reassessments. EVIDENCE reported a total 228 variants in 200 (60.6%) of the 330 probands. The average number of organs involved per patient was 4.5 ± 5.0. After clinical reassessment and/or family member testing, 167 variants were identified in 141 probands (42.7%), including 105 novel variants. These variants were confirmed as being responsible for 121 genetic disorders. A total of 103 (61.7%) of the 167 variants in 95 patients were classified as pathogenic or probably to be pathogenic before, and 161 (96.4%) variants in 137 patients (41.5%) after, clinical assessment and/or family member testing. Factor associated with a variant being regarded as causative includes similar symptom scores of a gene variant to the phenotype of the patient. This new, automated variant interpretation system facilitated the diagnosis of various genetic diseases with a 42.7% diagnostic yield.

An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data

Next-generation sequencing has revolutionized rare disease diagnostics, but many patients remain without a molecular diagnosis, particularly because many candidate variants usually survive despite strict filtering. Exomiser was launched in 2014 as a Java tool that performs an integrative analysis of patients' sequencing data and their phenotypes encoded with Human Phenotype Ontology (HPO) terms. It prioritizes variants by leveraging information on variant frequency, predicted pathogenicity, and gene-phenotype associations derived from human diseases, model organisms, and protein-protein interactions. Early published releases of Exomiser were able to prioritize disease-causative variants as top candidates in up to 97% of simulated whole-exomes. The size of the tested real patient datasets published so far are very limited. Here, we present the latest Exomiser version 12.0.1 with many new features. We assessed the performance using a set of 134 whole-exomes from patients with a range of rare retinal diseases and known molecular diagnosis. Using default settings, Exomiser ranked the correct diagnosed variants as the top candidate in 74% of the dataset and top 5 in 94%; not using the patients' HPO profiles (i.e., variant-only analysis) decreased the performance to 3% and 27%, respectively. In conclusion, Exomiser is an effective support tool for rare Mendelian phenotype-driven variant prioritization.

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

PURPOSE

Diagnosing monogenic diseases facilitates optimal care, but can involve the manual evaluation of hundreds of genetic variants per case. Computational tools like Phrank expedite this process by ranking all candidate genes by their ability to explain the patient's phenotypes. To use these tools, busy clinicians must manually encode patient phenotypes from lengthy clinical notes. With 100 million human genomes estimated to be sequenced by 2025, a fast alternative to manual phenotype extraction from clinical notes will become necessary.

METHODS

We introduce ClinPhen, a fast, high-accuracy tool that automatically converts clinical notes into a prioritized list of patient phenotypes using Human Phenotype Ontology (HPO) terms.

RESULTS

ClinPhen shows superior accuracy and 20× speedup over existing phenotype extractors, and its novel phenotype prioritization scheme improves the performance of gene-ranking tools.

CONCLUSION

While a dedicated clinician can process 200 patient records in a 40-hour workweek, ClinPhen does the same in 10 minutes. Compared with manual phenotype extraction, ClinPhen saves an additional 3-5 hours per Mendelian disease diagnosis. Providers can now add ClinPhen's output to each summary note attached to a filled testing laboratory request form. ClinPhen makes a substantial contribution to improvements in efficiency critically needed to meet the surging demand for clinical diagnostic sequencing.

A new method to measure the semantic similarity from query phenotypic abnormalities to diseases based on the human phenotype ontology

Background

Although rapid developed sequencing technologies make it possible for genotype data to be used in clinical diagnosis, it is still challenging for clinicians to understand the results of sequencing and make correct judgement based on them. Before this, diagnosis based on clinical features held a leading position. With the establishment of the Human Phenotype Ontology (HPO) and the enrichment of phenotype-disease annotations, there throws much more attention to the improvement of phenotype-based diagnosis.

Results

In this study, we presented a novel method called RelativeBestPair to measure similarity from the query terms to hereditary diseases based on HPO and then rank the candidate diseases. To evaluate the performance, we simulated a set of patients based on 44 complex diseases. Besides, by adding noise or imprecision or both, cases closer to real clinical conditions were generated. Thus, four simulated datasets were used to make comparison among RelativeBestPair and seven existing semantic similarity measures. RelativeBestPair ranked the underlying disease as top 1 on 93.73% of the simulated dataset without noise and imprecision, 93.64% of the simulated dataset with noise and without imprecision, 39.82% of the simulated dataset without noise and with imprecision, and 33.64% of the simulated dataset with both noise and imprecision.

Conclusion

Compared with the seven existing semantic similarity measures, RelativeBestPair showed similar performance in two datasets without imprecision. While RelativeBestPair appeared to be equal to Resnik and better than other six methods in the simulated dataset without noise and with imprecision, it significantly outperformed all other seven methods in the simulated dataset with both noise and imprecision. It can be indicated that RelativeBestPair might be of great help in clinical setting.

The functional genomics laboratory: functional validation of genetic variants

Currently, one of the main challenges in human molecular genetics is the interpretation of rare genetic variants of unknown clinical significance. A conclusive diagnosis is of importance for the patient to obtain certainty about the cause of the disease, for the clinician to be able to provide optimal care to the patient and to predict the disease course, and for the clinical geneticist for genetic counseling of the patient and family members. Conclusive evidence for pathogenicity of genetic variants is therefore crucial. This review gives an introduction to the problem of the interpretation of genetic variants of unknown clinical significance in view of the recent advances in genetic screening, and gives an overview of the possibilities for functional tests that can be performed to answer questions about the function of genes and the functional consequences of genetic variants ("functional genomics") in the field of inborn errors of metabolism (IEM), including several examples of functional genomics studies of mitochondrial disorders and several other IEM.

Clinical validity of phenotype-driven analysis software PhenoVar as a diagnostic aid for clinical geneticists in the interpretation of whole-exome sequencing data

PURPOSE

We sought to determine the diagnostic yield of whole-exome sequencing (WES) combined with phenotype-driven analysis of variants in patients with suspected genetic disorders.

METHODS

WES was performed on a cohort of 51 patients presenting dysmorphisms with or without neurodevelopmental disorders of undetermined etiology. For each patient, a clinical geneticist reviewed the phenotypes and used the phenotype-driven analysis software PhenoVar (http://phenovar.med.usherbrooke.ca/) to analyze WES variants. The prioritized list of potential diagnoses returned was reviewed by the clinical geneticist, who selected candidate variants to be confirmed by segregation analysis. Conventional analysis of the individual variants was performed in parallel. The resulting candidate variants were subsequently reviewed by the same geneticist, to identify any additional potential diagnoses.

RESULTS

A molecular diagnosis was identified in 35% of the patients using the conventional analysis, and 17 of these 18 diagnoses were independently identified using PhenoVar. The only diagnosis initially missed by PhenoVar was rescued when the optional "minimal phenotypic cutoff" filter was omitted. PhenoVar reduced by half the number of potential diagnoses per patient compared with the conventional analysis.

CONCLUSION

Phenotype-driven software prioritizes WES variants, provides an efficient diagnostic aid to clinical geneticists and laboratories, and should be incorporated in clinical practice.

The Human Phenotype Ontology in 2017

Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

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

PURPOSE

Medical diagnosis and molecular or biochemical confirmation typically rely on the knowledge of the clinician. Although this is very difficult in extremely rare diseases, we hypothesized that the recording of patient phenotypes in Human Phenotype Ontology (HPO) terms and computationally ranking putative disease-associated sequence variants improves diagnosis, particularly for patients with atypical clinical profiles.

METHODS

Using simulated exomes and the National Institutes of Health Undiagnosed Diseases Program (UDP) patient cohort and associated exome sequence, we tested our hypothesis using Exomiser. Exomiser ranks candidate variants based on patient phenotype similarity to (i) known disease-gene phenotypes, (ii) model organism phenotypes of candidate orthologs, and (iii) phenotypes of protein-protein association neighbors.

RESULTS

Benchmarking showed Exomiser ranked the causal variant as the top hit in 97% of known disease-gene associations and ranked the correct seeded variant in up to 87% when detectable disease-gene associations were unavailable. Using UDP data, Exomiser ranked the causative variant(s) within the top 10 variants for 11 previously diagnosed variants and achieved a diagnosis for 4 of 23 cases undiagnosed by clinical evaluation.

CONCLUSION

Structured phenotyping of patients and computational analysis are effective adjuncts for diagnosing patients with genetic disorders.Genet Med 18 6, 608-617.