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A Comprehensive Workflow for Diagnosis and Management of Rare Bone Diseases by Integrating Deep Phenotyping and Genetic Analysis

Clinical Genetics and Therapeutics
  • Primary Categories:
    • Clinical Genetics
  • Secondary Categories:
    • Clinical Genetics
Introduction:
In medical genetics, phenotypes are crucial, serving as the external expressions of underlying genotypes. This relationship underscores that understanding phenotypes is essential for effective genetic research, as encapsulated in the saying, "Phenotype is king, genotype is queen." Advances in next-generation sequencing, such as exome and genome sequencing, have significantly enhanced our ability to detect genomic mutations. The American College of Medical Genetics and Genomics (ACMG) provides guidelines for interpreting these variants, reinforcing a phenotype-driven approach to genomic data analysis. However, the field lacks standardized protocols for phenotype evaluation, leading to vague clinical records that impede understanding and comparison of research outcomes. For example, descriptions of conditions like scoliosis often omit critical details, such as age of onset and severity. This study discusses the integration of deep phenotyping with genetic analysis as a workflow for diagnosing and managing rare bone diseases at the Genetics Clinic of Skeletal Deformity at Peking Union Medical College Hospital (PUMCH), aiming to improve clarity and outcomes in genetic research.

Methods:
We developed a workflow of deep phenotyping for rare bone diseases from the Genetics Clinic of Skeletal Deformity at Peking Union Medical College Hospital. This study utilized a methodology of deep phenotyping and genetic analysis at PUMCH. Patients were referred from various clinics, and informed consent was obtained prior to data collection. A structured patient profile was established, incorporating unique identifiers and detailed family histories. Deep phenotyping involved a checklist aligned with the Human Phenotype Ontology (HPO), capturing phenotypic abnormalities and patient growth parameters. The evaluation included systematic reviews of multiple organ systems, with referrals to specialists as needed. Genetic testing was indicated for patients with hereditary conditions, multi-system involvement, or unexplained symptoms, using trio-based genome sequencing combined with RNA sequencing. Machine learning tools like PhenoApt were integrated for phenotype-driven gene prioritization. Multidisciplinary team (MDT) meetings facilitated comprehensive management, while regular follow-ups ensured ongoing assessment and adaptation of treatment strategies.

 




Results:
The workflow confirmed molecular diagnoses for over 500 patients with rare bone diseases through a structured workflow that integrated deep phenotyping and genetic analysis. A pediatric case of 16p11.2 microdeletion syndrome illustrated the effectiveness of this approach. A 4-year-old girl, initially diagnosed with congenital scoliosis, underwent thorough phenotypic assessment leading to the identification of renal hypoplasia and developmental delays. Chromosomal microarray analysis confirmed the diagnosis, prompting an MDT intervention that included orthopedic and pediatric care, resulting in a tailored treatment plan. The patient showed good postoperative recovery, with ongoing developmental support and monitoring of renal function. This case exemplifies how deep phenotyping and a coordinated MDT approach enhance diagnosis and management, highlighting the clinic's role in addressing the complexities of rare bone diseases and improving patient outcomes through comprehensive care.

Conclusion:
Deep phenotyping for rare bone diseases significantly enhances precision medicine by providing a standardized framework for comprehensive phenotype evaluation, genetic analysis, and personalized interventions. This approach allows for patient stratification into subgroups, such as those with the 16p11.2 deletion, categorized by neurodevelopmental, urinary, reproductive, and musculoskeletal traits. It facilitates the identification of phenotypic patterns and the development of predictive scoring systems, advancing our understanding of congenital deformities and genotype-phenotype interactions, as seen with TBX6 mutations linked to conditions like congenital scoliosis. However, challenges exist, including evaluator subjectivity and the time-consuming nature of manual HPO term entry. Utilizing AI-driven software to automate HPO extraction can enhance efficiency. Informed consent is crucial as deep phenotyping requires detailed patient engagement. Overall, by systematically cataloging phenotypic data and employing multidisciplinary strategies, deep phenotyping improves diagnosis, treatment strategies, and patient outcomes while advancing medical genetics research globally.

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