Reviewed and updated Algorithm for Genetic Characterization of Syndromic Obesity Phenotypes

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.