Integrative genomics analysis identifies five promising genes implicated in insomnia risk based on multiple omics datasets

Abstract

In recent decades, many genome-wide association studies on insomnia have reported numerous genes harboring multiple risk variants. Nevertheless, the molecular functions of these risk variants conveying risk to insomnia are still ill-studied. In this study, we integrated GWAS summary statistics (N = 386,533) with two independent brain expression quantitative trait loci (eQTL) datasets (N = 329) to determine whether expression-associated SNPs convey risk to insomnia. Furthermore, we applied numerous bioinformatics analyses to highlight promising genes associated with insomnia risk. By using Sherlock integrative analysis, we detected 449 significant insomnia-associated genes in the discovery stage. These identified genes were significantly overrepresented in 6 biological pathways including Huntington’s disease (P = 5.58×10-5), Alzheimer’s disease (P = 5.58×10-5), Parkinson’s disease (P = 6.34×10-5), spliceosome (P = 1.17×10-4), oxidative phosphorylation (P = 1.09×10-4), and wnt signaling pathways (P = 2.07×10-4). Further, 5 of these identified genes were replicated in an independent brain eQTL dataset. Through a PPI network analysis, we found that there existed highly functional interactions among these 5 identified genes. 3 genes of LDHA (P = 0.044), DALRD3 (P = 5.0×10-5) and HEBP2 (P = 0.032) showed significantly lower expression in brain tissues of insomnic patients than that in controls. In addition, the expression levels of these 5 genes showed prominently dynamic changes across different time points between behavioral states of sleep and sleep deprivation in mice brain cortex. Together, this study strongly suggested that these 5 identified genes may represent candidate genes and contributed risk to the etiology of insomnia.

Potential genetic and epigenetic mechanisms in insomnia: A systematic review

“…In this framework, GWAS SNPs genetic pre‐disposition to insomnia were found to have a significant causal effect on the risk of some mental conditions such as major depression, bipolar disorder type II, schizophrenia, autism spectrum disorder, alcohol, nicotine and opioid use, attention‐deficit/hyperactivity disorder, anxiety and post‐traumatic stress disorder (PTSD), and suicidal behaviours with reverse causality observed for major depression, nicotine use, and PTSD only (Jansen, Dolinoy, et al, 2019, Jansen, Watanabe, et al, 2019; Gao et al, 2019; Song et al, 2020; Pasman et al, 2020; Lewis et al, 2020; Cai et al, 2021; Huang et al, 2021; Carpena et al, 2021; Watanabe et al, 2022; Sun et al, 2022; Baranova et al, 2022; Zhou et al, 2022; Nassan et al, 2022). Similarly, GWAS SNPs genetic pre‐disposition to insomnia were found to have a significant causal one‐way effect on the risk of some medical conditions including: coronavirus disease 2019 (COVID‐19) susceptibility (Peng et al, 2022), cognitive impairment, neurodegenerative conditions (Sun et al, 2020; Zhang et al, 2022), cardiovascular diseases (Jansen, Dolinoy, et al, 2019; Jansen, Watanabe, et al, 2019; Jia et al, 2022; Liu et al, 2021; Zheng et al, 2020), diabetes, cardio‐metabolic risks (Gao et al, 2020; Jansen, Dolinoy, et al, 2019; Jansen, Watanabe, et al, 2019; Liu et al, 2021; Liu et al, 2022), increasing the odds of reporting pain conditions (An et al, 2022; Broberg et al, 2021; Chu et al, 2021; Shu et al, 2022) and for other medical conditions (Bao et al, 2022; He et al, 2022; Huo et al, 2021; Zha et al, 2021).…”

“…Other potential polymorphisms involve genes regulating neural function, brain regions, immune responses, nervous system development, processes of neurodegeneration, release cycle of neurotransmitters including gamma-aminobutyric acid (GABA) and various monoamines, epigenetic modifications, and sensory perception (Amin et al, 2016;Bragantini et al, 2019aBragantini et al, , 2019bDing et al, 2018;Jansen, Dolinoy, et al, 2019;Jansen, Watanabe, et al, 2019;Lane et al, 2017;Lin et al, 2021;Liu et al, 2020;Stein et al, 2018;Sun et al, 2020;Xiang et al, 2019) However, none of the SNPs remained significant after multiple corrections (Sakurada et al, 2021).…”

Treatment of insomnia based on the mechanism of pathophysiology by acupuncture combined with herbal medicine: A review

“…For example, genes are involved in 5-hydroxytryptamine transport or metabolism. [35] One study found that Apoε4 allele carriers had an increased likelihood of developing insomnia, [36] and overall sleep disturbance measured by applying the Pittsburgh sleep quality index (PSQI) was not significantly associated with dopamine-regulated catecholamine-O-methyltransferase. [37] The GWAS approach is considered more appropriate than the Candidate gene studies approach because complex diseases such as insomnia are highly polygenic, that is, their pathogenesis is determined by any combination of variants among many genes rather than by a specific gene.…”

“…For example, genes are involved in 5-hydroxytryptamine transport or metabolism. [35] One study found that Apoε4 allele carriers had an increased likelihood of developing insomnia, [36] and overall sleep disturbance measured by applying the Pittsburgh sleep quality index (PSQI) was not significantly associated with dopamine-regulated catecholamine-O-methyltransferase. [37]…”

Integrated multi-omics data analysis identifies a novel genetics-risk gene ofIRF4associated with prognosis of oral cavity cancer

“…Mounting genomics analyses [22][23][24][25] have been carried out to examine the regulatory mechanisms of GWAS-derived genetic variants, and determine whether GWAS-nominated genes whose aberrant alterations of expression contributing to disease pathogenesis due to genetic pleiotropy. For example, He et al [22] reported a Sherlock integrative genomics tool based on Bayesian inference algorithm, which could incorporate genetic statistical values from GWAS summary data and expression quantitative trait loci (eQTL) data to systematically uncover the regulatory effects of genetic variants on gene expression for complex diseases [23,[26][27][28]. Recently, Barbeira and coworkers [29] introduced an efficient GWAS summary result-based extension statistical method termed S-MultiXcan, which could utilize the substantial sharing of eQTL across multiple tissues to enhance the capability to pinpoint potential target genes.…”