Integrative approaches to m6A and m5C RNA modifications in autism spectrum disorder revealing potential causal variants.

PMID 39738578 | DOI 10.1007/s00335-024-10095-8 · Mammalian genome : official journal of the International Mammalian Genome Society · 2025

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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that currently affects approximately 1-2% of the global population. Genome-wide studies have identified several loci associated with ASD; however, pinpointing causal variants remains elusive. Therefore, functional studies are essential to discover potential therapeutics for ASD. RNA modification plays a crucial role in the post-transcriptional regulation of mRNA, with m6A and m5C being the most prevalent internal modifications. Recent research indicates their involvement in the regulation of key genes associated with ASD. In this study, we conducted an integrative genomic analysis of ASD, incorporating m6A and m5C variants, followed by cis-eQTL, gene differential expression, and gene enrichment analyses to identify causal variants from a genome-wide study of ASD. We identified 20,708 common m6A-SNPs and 2,407 common m5C-SNPs. Among these, 647 m6A-SNPs exhibited cis-eQTL signals with a p-value < 0.05, while only 81 m5C-SNPs with a p-value < 0.05 showed cis-eQTL signals. Most of these were functional loss variants, with 38 variants representing the most significant common m6A/m5C-SNPs associated with key ASD-related genes. In the gene differential expression analysis, seven proximal genes corresponding to significant m6A/m5C-SNPs were differentially expressed in at least one of the three microarray gene expression profiles of ASD. Key differentially expressed genes corresponding to m6A/m5C cis-variants included KIAA1671 (rs5752063, rs12627825), INTS1 (rs67049052, rs10237910), VSIG10 (rs7965350), TJP2 (rs3812536), FAM167A (rs9693108), TMEM8A (rs1802752), and NUP43 (rs3924871, rs7818, rs9383844, rs9767113). Cell-specific cis-eQTL analysis for proximal gene identification, combined with gene expression datasets from single-cell RNA-seq analysis, would validate the causal relationship of gene regulation in brain-specific regions, and experimental validation in cell lines would achieve the goal of precision medicine.