Next generation phenotyping with quantitative narration for DEGCAGS syndrome

Area of Science:

• Genetics and Genomics
• Medical Diagnostics
• Bioinformatics

Background:

• Rare Mendelian disorders pose diagnostic challenges due to reliance on subjective prose and lack of objective phenotypic data.
• Developmental delay with gastrointestinal, cardiovascular, genitourinary, and skeletal abnormalities (DEGCAGS) is one such complex rare disorder.

Purpose of the Study:

• To develop and implement a next-generation phenotyping (NGP) workflow for objective and reproducible characterization of DEGCAGS.
• To quantitatively analyze phenotypic data for patients with DEGCAGS using Human Phenotype Ontology (HPO) terms.

Main Methods:

• Extracted 252 unique HPO terms from patient records and literature for 15 DEGCAGS individuals.
• Utilized co-occurrence matrices to identify co-segregating phenotypes.
• Generated quantitative narratives with violin plots to visualize phenotype progression over hospital admission time.

Main Results:

• Identified common and co-occurring HPO terms, providing a quantitative phenotypic profile.
• Demonstrated phenotype progression over time using violin plots.
• Established NGP as a method for generating objective, reproducible phenotypic data.

Conclusions:

• NGP offers a quantitative, objective, and reproducible approach to phenotyping rare Mendelian disorders.
• This methodology enhances diagnostic capabilities for complex conditions like DEGCAGS.
• The approach is comparable to next-generation sequencing (NGS) in its rigor and potential impact.