medRxivGenetic & Genomic MedicinePreprint
Genomic analysis identifies polygenic and region-specific contributions to ADHD-migraine comorbidity
Yaxin LuoiD, Christina DardaniiD, Robyn E. WoottoniD, Evie StergiakouliiD
Abstract
Attention-deficit/hyperactivity disorder (ADHD) and migraine frequently co-occur, yet their shared genetic architecture remains unclear. Using genome-wide association summary data of ADHD and migraine, we applied a multi-layered genetic analysis framework. Conjunctional false discovery rate was used to identify shared pleiotropic variants. Local genetic correlation was performed within semi-independent regions. Mendelian randomization (MR) used eQTLs as instruments to assess overlap of genetically predicted gene expression on ADHD and migraine in relevant brain and blood tissues. Colocalization analyses were conducted to assess whether shared association signals were driven by the same underlying variants. We identified 27 pleiotropic variants shared between ADHD and migraine, 20 with concordant effect directions. Local genetic correlation analysis identified a single shared region on chromosome 11 with evidence of local heritability for both ADHD and migraine and a positive local genetic correlation. Cis-eQTL MR of druggable genes identified multiple genes with evidence for causal effects of their expression on both ADHD and migraine across brain cortex and blood. Genetically predicted MANBA expression showed consistent associations with both traits in brain cortex and blood. Colocalization for MANBA supported a shared causal variant in cortex but not in blood, suggesting tissue-specific mechanisms. Current findings provide evidence for shared genetic architecture between ADHD and migraine across variant, regional, and gene-expression levels. Among our findings, genetically predicted expression of MANBA in the brain cortex appeared to be a potential shared biological contributor to ADHD and migraine.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- approach: statistical model
- approach: Bayesian inference
- Citations
- 0
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