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Related Experiment Video

Updated: Jan 16, 2026

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

Published on: June 15, 2011

Sequencing Analysis Demonstrates That a Complex Genetic Architecture Contributes to Risk for Spina Bifida.

Madison Strain¹, Melanie E Garrett¹, Max Bucklan¹

¹Duke Molecular Physiology Institute, Duke University Medical Center, Durham, North Carolina, USA.

Birth Defects Research

|September 27, 2025

Summary

This study investigated genetic risk factors for spina bifida (SB), a common neural tube defect. Researchers identified 16 associated genes, revealing new insights into SB

Keywords:

burden test exome‐wide association neural tube defect single nucleotide variant spina bifida transmission disequilibrium test

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A Novel Strategy Combining Array-CGH, Whole-exome Sequencing and In Utero Electroporation in Rodents to Identify Causative Genes for Brain Malformations

Published on: December 1, 2017

Area of Science:

Genetics
Developmental Biology
Public Health

Background:

Spina bifida (SB) is a common neural tube defect (NTD) with a complex and incompletely understood genetic basis.
Previous research identified rare deleterious single nucleotide variants (SNVs) in SB, but broader genetic contributions to risk require further investigation.

Purpose of the Study:

To investigate shared genetic risk in spina bifida (SB) using an unbiased sequencing approach.
To identify single-variant associations and assess cumulative effects of SNVs within genes.

Main Methods:

Performed an exome-wide association study (ExWAS) on 46,887 SNVs in 256 SB probands and 395 controls.
Conducted gene-based burden tests using all variants and rare variants (MAF < 0.05), repeated in 510 parents to assess mutational burden.

Main Results:

Identified 16 genes associated with SB, including SRCIN1, PDE4DIP, and HLA-A.
ExWAS found 11 significant SNVs (9 common); burden tests revealed 7 genes (4 significant with rare variants).
Five burden-associated genes were not detected by ExWAS, and four showed enrichment in parents, suggesting inherited risk.

Conclusions:

The findings offer novel insights into the multifactorial genetic landscape of spina bifida.
Highlights the utility of unbiased sequencing approaches for developing genetic models of neural tube defects.