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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Whole genome sequencing in ROHHAD trios proved inconclusive: what's beyond?
A Grossi1, M Rusmini1,2, R Cusano3
1Laboratory of Genetics and Genomics of Rare Diseases, IRCCS Istituto Giannina Gaslini, Genova, Italy.
Frontiers in Genetics
|August 23, 2023
Summary
This study investigated genetic factors in Rapid-onset Obesity with Hypothalamic dysfunction, Hypoventilation and Autonomic Dysregulation (ROHHAD). Researchers found no shared genetic variants in two pediatric ROHHAD cases, suggesting other factors may be involved.
Area of Science:
- Pediatric rare diseases
- Genetics and Genomics
- Neuroendocrinology
Background:
- Rapid-onset Obesity with Hypothalamic dysfunction, Hypoventilation and Autonomic Dysregulation (ROHHAD) is a rare, life-threatening pediatric disorder.
- Diagnosis is challenging due to poorly understood clinical manifestations and lack of confirmatory tests.
- ROHHAD is characterized by rapid weight gain, hypothalamic and autonomic dysfunction, and hypoventilation.
Purpose of the Study:
- To investigate potential genetic risk factors for ROHHAD by analyzing non-coding variants.
- To identify shared genetic changes in pediatric ROHHAD trios.
Main Methods:
- Analysis of non-coding variants (SNVs, indels, splice variants, copy number variations) in two ROHHAD trios (proband with parents).
- Recruitment of patients from Gaslini Children's Hospital, Genoa, Italy.
- Search for shared gene variants or altered genomic regions between the two probands.
Main Results:
- No potentially pathogenic genetic changes, involving shared genes, regions, or pathways, were identified between the two ROHHAD trios.
- The findings are consistent with the clinical phenotype of ROHHAD.
Conclusions:
- The current analysis did not reveal shared genetic etiology for ROHHAD in the studied trios.
- Further investigation using advanced sequencing technologies (long-read sequencing, optical mapping) and multi-omic datasets (immunological, autoimmune) is recommended to explore other potential causes.
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