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Local Patterns of Genetic Sharing Challenge the Boundaries Between Neuropsychiatric and Insulin Resistance-related Conditions

Abstract

The co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a complex public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N=9,725-933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|r|=0.21-1, p<0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer's disease, bipolar disorder, and Tourette's syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such as and , that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings underscore the complex genetic landscape of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.

References
1.
Wallace C . A more accurate method for colocalisation analysis allowing for multiple causal variants. PLoS Genet. 2021; 17(9):e1009440. PMC: 8504726. DOI: 10.1371/journal.pgen.1009440. View

2.
Fanelli G, Franke B, De Witte W, Ruisch I, Haavik J, van Gils V . Insulinopathies of the brain? Genetic overlap between somatic insulin-related and neuropsychiatric disorders. Transl Psychiatry. 2022; 12(1):59. PMC: 8844407. DOI: 10.1038/s41398-022-01817-0. View

3.
van Rheenen W, Peyrot W, Schork A, Lee S, Wray N . Genetic correlations of polygenic disease traits: from theory to practice. Nat Rev Genet. 2019; 20(10):567-581. DOI: 10.1038/s41576-019-0137-z. View

4.
Ogilvie R, Patel S . The Epidemiology of Sleep and Diabetes. Curr Diab Rep. 2018; 18(10):82. PMC: 6437687. DOI: 10.1007/s11892-018-1055-8. View

5.
Karki R, Tom Kodamullil A, Hofmann-Apitius M . Comorbidity Analysis between Alzheimer's Disease and Type 2 Diabetes Mellitus (T2DM) Based on Shared Pathways and the Role of T2DM Drugs. J Alzheimers Dis. 2017; 60(2):721-731. PMC: 5611890. DOI: 10.3233/JAD-170440. View