Mining autism causative SNPs based on CARMA algorithm

Abstract. Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social impairment and repetitive stereotyped behaviors. This widespread neurological disorder not only results in symptoms that affect patients' social lives, such as behavioral and social disorders, but also imposes significant mental and economic burdens on their families. Therefore, research on the causes of ASD is of great practical value and significance. Previous studies have demonstrated a high genetic association with ASD and have identified specific ASD-related genes and single nucleotide polymorphisms (SNPs). However, these studies have primarily focused on establishing correlations rather than establishing causality between specific genes, SNPs, and ASD. Therefore, this experiment utilized public data from Genome-Wide Association Studies (GWAS) and employed a novel Bayesian model called CARMA (causal robust mapping method in meta-analysis) to identify SNPs strongly correlated with ASD. Notably, this study identified 14 novel pathogenic SNPs, four of which were deemed significant. Furthermore, functional annotations were performed on the pathogenic SNPs to assess their impact on gene expression in different brain regions. This analysis revealed the cerebellum, thalamus, and substantia nigra of the midbrain as the three brain regions with the highest correlation to ASD. Additionally, protein interaction analysis was conducted, revealing GATA-4 and NKX2-2 as the genes with the strongest correlation to ASD. The findings of this study provide new insights and data for future research into the genetics and biology of ASD, particularly in the field of basic research.