medRxivGenetic & Genomic MedicinePreprint
Global and local genetic correlation between congenital heart disease and neurodevelopmental disorders shared genetic architecture
Abstract
Individuals with congenital heart disease (CHD) have an increased risk of neurodevelopmental disorders (NDDs), a relationship likely driven by multiple interacting factors, including shared genetic influences, the underlying cardiac malformation, and risks associated with medical and surgical care. To investigate the genetic component of this association, we analyzed genome-wide association study (GWAS) summary statistics for CHD from the UK Biobank and FinnGen, focusing on two phenotypes present in both cohorts: congenital malformations of cardiac septa (Q21) and congenital malformations of great arteries (Q25). A fixed-effect meta-analysis was performed using METAL to increase statistical power. Neurodevelopmental phenotypes were represented by Austism Spectrum Disorder (ASD) and Attention-deficit/hyperactivity disorder (ADHD) GWAS results from the Psychiatric Genomics Consortium. Genome-wide genetic correlations were estimated using the High-Definition Likelihood. Local genetic correlations were assessed using LAVA. Multiple testing was controlled using Bonferroni correction. No significant genome-wide genetic correlations were observed between CHD phenotypes and either ASD or ADHD. However, LAVA identified a single significant local genetic correlation between cardiac septal defects and ASD on chromosome 6. Within this block, two loci near GMDS showed significant association with cardiac septal malformations. Bayesian colocalization did not reveal a variant with strong evidence for a shared causal signal, though three GMDS variants demonstrated moderate support for colocalization. These findings suggest that while global genetic overlap between CHD and NDDs is limited, specific loci, such as the GMDS region on chromosome 6, may contribute to shared developmental pathways underlying both phenotypes.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 0
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