Decoding the Missing Heritability: Insights into the Complex Genetic Architecture of Spina Bifida

Abstract

Spina bifida (SB), the most prevalent subtype of neural tube defect, is a complex congenital condition with both genetic and environmental contributions. While recent advances in genomic sequencing have enabled progress in identifying potential risk loci, the genetic architecture underlying SB remains largely unresolved. To address this, I employed a comprehensive analytic strategy leveraging whole-genome and whole-exome sequencing data from case-parent trios and ancestry-matched public controls. My analyses included both single-variant association testing and gene-based mutational burden tests, enabling the detection of genetic risk signals driven by individual variants as well as the cumulative effect of multiple variants within genes.Using this approach, I identified 16 novel candidate genes associated with SB. These genes span diverse biological processes relevant to neural tube closure, including cytoskeletal remodeling, transcriptional regulation, immunity, and metabolism, and the majority have data supporting their expression in the developing human neural tube. Some have also been implicated in other neurodevelopmental and craniofacial disorders, further supporting their potential relevance to SB. Associations were driven by both common and rare variants, including two genes (GLB1L2 and PLA1A) where rare single nucleotide variants (SNVs) were implicated by both ExWAS and gene burden testing. Additionally, three genes (SPIRE2, TVP23B, CHD5) were identified solely through gene-level burden, indicating a cumulative mutational effect in the absence of individually significant variants. To assess whether genetic risk is also detectable in unaffected parents, I performed a parallel burden analysis in the parental cohort compared to controls. Ten genes were significantly associated in parents, four of which overlapped with those found in probands, suggesting the presence of latent genetic risk in the parental genome. The remaining six were uniquely associated with parental samples, though the biological interpretation of these signals remains uncertain and may reflect differences in power or unmeasured genetic modifiers. Together, these findings expand the list of candidate genes for SB, highlight the value of complementary association strategies, and suggest a more complex, polygenic architecture than previously appreciated. This work also underscores the importance of including both common and rare variation in efforts to resolve the missing heritability of neural tube defects.