Integrative Multi-Omics and Mendelian Randomization Analysis Identify Core Genes and Potent Drug Targets for Insomnia

Area of Science:

• Genetics and Genomics
• Computational Biology
• Pharmacology

Background:

• Insomnia is a prevalent sleep disorder with limited specific therapeutic targets.
• Current insomnia treatments offer symptomatic relief but lack targeted mechanisms.
• There is a need for novel, safe, and effective therapeutic targets for insomnia.

Purpose of the Study:

• To integrate multi-omics data and machine learning to identify novel therapeutic targets for insomnia.
• To explore the diagnostic potential of identified genes in insomnia.
• To provide insights for targeted drug development and precision medicine in insomnia.

Main Methods:

• Intersected druggable gene sets with blood, brain, and plasma Quantitative Trait Loci (QTL) data.
• Utilized two-sample Mendelian randomization (MR) for causality assessment.
• Performed differential gene expression, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning (LASSO, random forests) for target identification and diagnostic efficacy evaluation.
• Conducted Summary-data-based Mendelian Randomization (SMR), colocalization, immune infiltration, and molecular docking simulations.

Main Results:

• Identified five key hub genes (HLA-G, S1PR1, LGALS3, VIM, PIK3CG) through multi-omics and machine learning analyses.
• These genes were upregulated in insomnia patients and demonstrated high diagnostic accuracy (AUC).
• Showed genetic associations with comorbidities like depression, anxiety, hypertension, and diabetes; HLA-G had suggestive causal evidence.

Conclusions:

• Identified novel druggable targets for insomnia, potentially acting via immune-inflammatory pathways.
• These targets link insomnia to comorbidity risks and possess diagnostic and translational value.
• Preliminary findings suggest potential for novel biomarkers and targeted therapies, pending independent validation.