bioRxivBioinformaticsPreprint
Genomic and transcriptomic profiles influence on brain morphology and their interactions with pain sensitivity individual differences
Yiwen PaniD, Zhiguo ZhangiD, Xiaoke HaoiD, Gan HuangiD, Zhen LiangiD, Li Zhang
Abstract
Pain sensitivity varies widely among individuals and is influenced by a complex interplay of multi-omics factors, including genetic variations, gene expression, and brain morphology. While previous studies have identified associations between pain sensitivity and brain morphology, the exact mechanisms by which genetic profiles interact with brain structure to influence individual pain sensitivity remain unclear. In this study, we used aggregated datasets, including magnetic resonance imaging (MRI) and single nucleotide polymorphism (SNP) genotypes from 432 healthy participants, along with gene expression data from the Allen Human Brain Atlas (AHBA), to explore this multi-omics interplay. We first measured individual pain thresholds using laser stimuli and discovered structural brain differences between high and low pain sensitivity groups. We then identified two key gene sets with polarized expression patterns linked to brain morphology variations, enriched in functions related to ion channels and transmembrane transporter activities. Further statistical and mediation analyses revealed specific SNPs from ECM1, SLC24A2, and SCN9A genes that influence pain sensitivity, mediated through brain morphological changes in multiple basal ganglia regions. Our findings suggested that these SNPs not only affect brain structure but also modulate how individuals pain perception. Finally, we proposed an interpretation model integrating genomic, transcriptomic, and neuroimaging data, providing a detailed framework that illustrates the multi-omics contributions to individual difference in pain sensitivity. This study advances our understanding of how genetic and brain structural factors combine to shape pain perception, offering potential targets for personalized pain management strategies.
- organism: human
- assay: bulk RNA-seq
- assay: multiome
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
- Citations
- 1
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