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Results for “Phenotype-driven diagnosis”

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PubMed2026

Respiratory manifestations as clues to inherited metabolic disorders in children: a phenotype-driven diagnostic approach.

UNLABELLED: Inherited metabolic disorders (IMDs) are uncommon but clinically important causes of respiratory morbidity in children. Respiratory involvement may be the first or dominant manifestation, although it may precede, accompany, or follow systemic involvement. Because cough, dyspnea, hypoxemia, recurrent infection, abnormal chest imaging, and ventilatory failure are non-specific, affected children may initially be managed for common respiratory conditions, such as infection, asthma, aspiration, immunodeficiency, or non-metabolic diffuse lung disease, before the underlying IMD is recognized. This narrative mini-review presents a phenotype-driven approach to recognizing IMDs in pediatric respiratory practice. Rather than cataloguing rare disorders by metabolic pathway, it organizes respiratory involvement into practical clinical entry points: diffuse lung disease, pulmonary alveolar proteinosis-like disease, pulmonary vascular disease, recurrent infection or bronchiectasis, upper-airway or thoracic restriction, and neuromuscular respiratory failure and aspiration. For each pattern, we highlight extrapulmonary red flags and first-line biochemical, enzymatic, and genetic tests that may guide early etiological diagnosis. CONCLUSION: Careful recognition of respiratory phenotypes, combined with targeted metabolic and genomic evaluation, may shorten diagnostic delay and allow disease-specific treatment before irreversible pulmonary or neurological injury occurs. WHAT IS KNOWN: • IMDs can involve the respiratory system and may mimic common pediatric respiratory disorders or non-metabolic forms of childhood diffuse lung disease. • Respiratory manifestations may precede, accompany, or follow classical systemic features, and their temporal pattern varies among individual IMDs. WHAT IS NEW: • This mini-review organizes IMD-related respiratory involvement by presenting respiratory phenotype rather than by metabolic pathway. • It links respiratory entry points with extrapulmonary red flags and targeted biochemical, enzymatic, and genetic testing to support earlier diagnosis.

Humans↗

PubMed2024

Leveraging clinical intuition to improve accuracy of phenotype-driven prioritization.

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

Humans↗

PubMed2026

Whole exome sequencing of paediatric patients with Cogan's syndrome to identify monogenic mimics.

OBJECTIVES: Cogan's syndrome (CS) is a rare variable vessel vasculitis, describing sensorineural hearing loss (SNHL), inflammatory ocular disease and vestibular dysfunction. We hypothesized that within paediatric-onset (p)CS, a proportion would have monogenic disease, either autoinflammatory and/or associated with SNHL. METHODS: Whole exome sequencing (WES) was performed and analysed using an in-house pipeline incorporating virtual gene panels for inflammation and SNHL; copy number variant analysis (ExomeDepth); and phenotype-driven variant prioritization (Exomiser). Genetic variants were interpreted by a multi-disciplinary team according to American College of Medical Genetics and Genomics guidelines. RESULTS: Ten patients with a clinical diagnosis of pCS were enrolled. Three/10 (30%) had a monogenic contribution to the phenotype based on Class 4/5 variants: de novo NLRP3 p.T915R (n = 1) associated with Cryopyrin-associated periodic syndrome; MYO7A p.K542Qfs*5 (n = 1) causing SNHL; and HBB homozygous p.E7V causing sickle cell disease (associated with hearing loss and uveitis). A further two cases had possible monogenic contribution with the following rare variants of uncertain significance (class 3): ADGRV1 compound heterozygous variants (n = 1) associated with Usher syndrome; and a novel ALPK1 p.H735P (n = 1), associated with Retinal dystrophy Optic nerve oedema Splenomegaly Anhidrosis Headache (ROSAH) syndrome. CONCLUSIONS: In children presenting with features suggesting CS, genetic screening should be considered before conferring this rare diagnostic label since at least 30% had an alternative monogenic contribution to the phenotype rather than true pCS, with implications for treatment and prognosis. We thus advocate for genetic testing using next-generation sequencing for patients presenting with pCS.

Humans↗

PubMed2026

Guidelines for Genetic Testing of Peripheral Nerve Disorders.

Inherited peripheral neuropathies (IPNs) comprise a clinically and genetically heterogeneous group of disorders affecting approximately 1 in 2500 individuals and represent one of the most common inherited neurologic diseases. The rapidly expanding identification of disease-causing genes and the widespread implementation of next-generation sequencing (NGS) have fundamentally transformed the diagnostic evaluation of these disorders. Contemporary molecular testing has substantially increased diagnostic yield, shortened the diagnostic delay, refined disease classification, and strengthened genotype-phenotype correlations. In the United States, NGS-based multigene panels have become the most cost-effective first-line molecular diagnostic approach for most patients with suspected inherited neuropathies, whereas phenotype-directed single-gene testing remains appropriate in selected clinical circumstances and in healthcare systems in which access to comprehensive sequencing is limited. Despite these advances, challenges continue to affect diagnostic accuracy, including interpretation of variants of uncertain significance, detection of copy number variants and repeat expansions, technical limitations associated with highly homologous genomic regions such as SORD, and variability in gene content and analytic performance among commercially available testing platforms. Accurate diagnosis therefore requires integration of clinical phenotype, electrodiagnostic findings, family history, and molecular data. Establishing a precise genetic diagnosis has become increasingly important because it improves prognostic accuracy, guides genetic counseling and cascade testing, identifies patients with treatable hereditary neuropathies such as transthyretin amyloidosis, and facilitates enrollment in gene-specific clinical trials and emerging precision therapies. An evidence-based, phenotype-driven approach that incorporates contemporary molecular technologies is essential to maximize diagnostic efficiency while recognizing the strengths and limitations of currently available genetic testing strategies.

Charcot–Marie–tooth disease↗

PubMed2026

Artificial intelligence-assisted clinical exome sequencing: Insights and outcomes from 822 pediatric diagnoses.

PURPOSE: This retrospective study examined the clinical and genetic characteristics of pediatric patients undergoing clinical exome sequencing (ES) and evaluated the performance of a commercially available artificial intelligence (AI) platform that was integrated into our analysis pipeline. METHODS: ES was performed in 822 consecutive patients at a single clinical laboratory. AI-based tools were used to jointly assess genetic information and the proband's Human Phenotype Ontology terms to support variant prioritization during the initial case review. RESULTS: A definitive molecular diagnosis was established in 22% (181 of 822) of index cases, while 40% (325 of 822) had variants of uncertain significance. Among those with a definitive diagnosis, 93% (168 of 181) had a single finding and 7% (13 of 181) had multiple findings. Of the 152 reported pathogenic/likely pathogenic variants in the fully resolved cases, 98.7% were successfully flagged by AI, and 75.0% ranked among the top 10 "most likely" variants. CONCLUSION: Clinical ES provides a substantial diagnostic yield in complex pediatric disorders. Integration of AI-powered platforms can accelerate phenotype-driven variant prioritization and facilitate rare disease diagnostics, but underscores the need for careful validation and optimization in clinical workflows.

Artificial intelligence↗

PubMed2001

Phenotype-driven genetic approaches in mice: high-throughput phenotyping for discovering new models of cardiovascular disease.

Cardiovascular diseases such as hypertension, atherosclerosis, cardiac hypertrophy homocysteinemia and arrhythmias impose great health, social and financial costs. Some of these diseases are single gene traits that segregate in a simple Mendelian manner. Most are genetically complex, however, and result from combinations of large numbers of genes (polygenic and epistatic traits) or from interactions between genetic and environmental factors (multifactorial traits). Insights into the genetic control of these diseases could lead to improved diagnosis and treatment as well as a deeper understanding of basic physiological processes.

Animals↗