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Related Experiment Video

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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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Reviewed and updated Algorithm for Genetic Characterization of Syndromic Obesity Phenotypes.

Raquel Rodríguez-López1, Fátima Gimeno-Ferrer1, David Albuquerque do Santos1

  • 1Laboratory of Molecular Genetics, Clinical Analysis Service, General Hospital Consortium of Valencia, Valencia, Spain.

Current Genomics
|February 13, 2023
PubMed
Summary
This summary is machine-generated.

Diagnosing early-onset severe obesity with intellectual disability is complex. This review presents an updated algorithm for molecular characterization, integrating genomics and bioinformatics for improved diagnosis.

Keywords:
Syndromic obesityclassical obesity syndromeexome sequencingnon-canonical obesity syndromenonsyndromic monogenic obesitywhole-genome array

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Area of Science:

  • Genetics
  • Pediatrics
  • Neurology

Background:

  • Early-onset severe obesity with intellectual disability presents complex molecular diagnoses, ranging from monogenic to polygenic traits.
  • Predicting single gene or polygenic influences is challenging, and rare monogenic causes are not the primary etiology for most cases.
  • The diversity of molecular bases necessitates advanced genomics and bioinformatics for accurate diagnosis.

Purpose of the Study:

  • To describe an updated algorithm for molecular characterization and diagnosis of syndromic obesity.
  • To guide clinicians in diagnosing complex cases by integrating genetic and phenotypic data.
  • To improve genotype-correlations through optimized clinical investigation and family evaluation.

Main Methods:

  • Utilizing powerful, routinely applied genomics diagnostic tools.
  • Employing integrated bioinformatics strategies with experienced multidisciplinary clinical teams.
  • Conducting thorough clinical investigations, including detailed phenotype descriptions and family evaluations.

Main Results:

  • The study proposes a systematic approach to differentiate between monogenic obesity syndromes and infrequent molecular cases or polygenic bases.
  • Highlights the importance of considering the coincidence of obesity risk mutations with genetic alterations causing intellectual disability.
  • Emphasizes the need for adequate management of diagnostic phases for cost-efficiency.

Conclusions:

  • An updated algorithm is crucial for the molecular characterization of patients with syndromic obesity.
  • Integrated bioinformatics and clinical expertise are essential for accurate diagnosis.
  • Considering both monogenic and polygenic factors, alongside potential coincidental mutations, improves diagnostic yield.