Integrative computational analyses implicate regulatory genomic elements contributing to spina bifida

Area of Science:

• Genomics
• Developmental Biology
• Bioinformatics

Background:

• Spina bifida (SB) results from complex genetic interactions disrupting neural tube closure.
• Understanding genomic networks is crucial for identifying SB risk factors.

Purpose of the Study:

• To identify genome-wide regulatory signatures associated with spina bifida (SB) pathophysiology.
• To delineate the role of rare regulatory variants in SB development.

Main Methods:

• Utilized an untargeted, genome-wide approach to analyze regulatory regions for rare single-nucleotide and copy-number variants (rSNVs and rCNVs).
• Employed a deep learning framework to prioritize functionally relevant variants and identify target genes.
• Compared variant data from SB patients and healthy controls.

Main Results:

• Identified specific rSNVs in transcription factor binding sites (TFBSs), notably CCCTC-binding factor binding sites, distinguishing SB cases.
• Prioritized variants implicated genes involved in protein transport, cilia assembly, and central nervous system development.
• Detected rare copy-number variants (rCNVs) disrupting gene regulatory networks and 3D genomic architecture, including brain-specific enhancers.

Conclusions:

• This research offers a valuable resource for understanding the contribution of genomic regulatory DNA variants to SB genetic predisposition.
• Provides insights into the molecular mechanisms underlying SB development.