Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 49 (11), 1584-1592

Genome-wide Association Analysis of Insomnia Complaints Identifies Risk Genes and Genetic Overlap With Psychiatric and Metabolic Traits


Genome-wide Association Analysis of Insomnia Complaints Identifies Risk Genes and Genetic Overlap With Psychiatric and Metabolic Traits

Anke R Hammerschlag et al. Nat Genet.


Persistent insomnia is among the most frequent complaints in general practice. To identify genetic factors for insomnia complaints, we performed a genome-wide association study (GWAS) and a genome-wide gene-based association study (GWGAS) in 113,006 individuals. We identify three loci and seven genes associated with insomnia complaints, with the associations for one locus and five genes supported by joint analysis with an independent sample (n = 7,565). Our top association (MEIS1, P < 5 × 10-8) has previously been implicated in restless legs syndrome (RLS). Additional analyses favor the hypothesis that MEIS1 exhibits pleiotropy for insomnia and RLS and show that the observed association with insomnia complaints cannot be explained only by the presence of an RLS subgroup within the cases. Sex-specific analyses suggest that there are different genetic architectures between the sexes in addition to shared genetic factors. We show substantial positive genetic correlation of insomnia complaints with internalizing personality traits and metabolic traits and negative correlation with subjective well-being and educational attainment. These findings provide new insight into the genetic architecture of insomnia.

Conflict of interest statement

Competing Financial Interests: Authors affiliated with deCODE genetics / Amgen Inc. declare competing financial interests as employees. The other authors declare no competing financial interests.


Figure 1
Figure 1. Manhattan plots for SNP associations with insomnia complaints.
Association results for the frequency of experiencing trouble falling asleep or waking up in the middle of the night in 113,006 individuals of European descent in the UK Biobank study, of whom those experiencing these complaints usually (cases, n = 32,384) were contrasted with those experiencing these complaints never, rarely or sometimes only (controls, n = 80,622). (a) Manhattan plot of the GWAS including all individuals, (b) males only (12,863 cases and 40,776 controls), and (c) females only (19,521 cases and 39,846 controls). Negative log10-transformed P values for each SNP (y axis) are plotted by chromosomal position (x axis). The red and blue lines represent the thresholds for genome-wide statistical significant associations (P = 5 × 10−8) and suggestive associations (P = 1 × 10−5), respectively. Red dots represent top SNPs. (d) Manhattan plot of the gene analysis including all individuals, (e) males only, and (f) females only. Here, each dot represents a gene, and the red and blue lines represent the thresholds for gene-wide statistical significant associations (P = 2.72 × 10−6) and suggestive associations (P = 2.72 × 10−5), respectively.
Figure 2
Figure 2. Comparison of association results for insomnia complaints in males and females.
(a) SNP and (b) gene associations with insomnia complaints in males plotted against females. Contour lines indicate the density of the data in that region. The lines are colored from green to yellow, indicating increasing data density. Dotted lines indicate the P value thresholds used in the Low P value enrichment tests; from yellow to red P = 0.05, P > 1 × 10-3, P = 1 × 10-4, and P = 1 × 10-5 (note that all SNPs present in both GWASes are displayed, while the enrichment tests were performed on pruned data).
Figure 3
Figure 3. Protein-protein-interaction subnetworks identified by the heat diffusion algorithm HotNet2.
The genes most strongly related to insomnia (P < 0.1) in the full and sex-specific GWGASes were used as input to investigate the enrichment of protein-protein interaction networks. Each node (protein) is assigned a score based on the gene P value of the GWGAS. The scores, denoted as “heat” in HotNet2, diffuse across the edges of the network. We report subnetworks that include genes with a wide range of heat scores: red colored nodes send and receive a significant amount of heat, while blue colored nodes do not. (a) Subnetwork identified for females including MEIS1. (b) Subnetwork identified for females including GNAS. Other identified subnetworks for females and males are shown in Supplementary Figure 18.
Figure 4
Figure 4. Genetic and phenotypic overlap between insomnia complaints and other traits and disorders.
Left bar chart: Genetic correlations (rg) between the frequency of experiencing trouble falling asleep or waking up in the middle of the night and various other traits and diseases. LD Score regression tested genome-wide SNP associations for these insomnia complaints against similar data for 29 other anthropometric and cardiovascular traits and neuropsychiatric outcomes (Supplementary Table 33). Error bars represent standard errors on these estimates. Red bars represent the traits that showed a significant genetic correlation after correction for multiple testing (P < 1.72 × 10−3), pink bars the traits that showed nominal association (P < 0.05), and blue bars the traits that did not show a significant genetic association. Right bar chart: The genetic correlations profile was strikingly similar to phenotypic overlap of insomnia with the same subject-characteristics assessed in an independent sample. Of the 29 disorders, traits and characteristics, 18 had been assessed in the NSR as well. Group differences between the 1,073 individuals without insomnia complaints and the 845 likely to have ID were evaluated using t-tests or χ2-tests (Supplementary Table 34). The profile of the magnitudes (d) of phenotypic group differences strongly resembled the genetic correlations profile.

Similar articles

See all similar articles

Cited by 33 PubMed Central articles

See all "Cited by" articles


    1. Wittchen HU, et al. The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur Neuropsychopharmacol. 2011;21:655–679. - PubMed
    1. Zhang B, Wing Y-K. Sex differences in insomnia: a meta-analysis. Sleep. 2006;29:85–93. - PubMed
    1. Morin CM, et al. Insomnia disorder. Nat Rev Dis Prim. 2015;1:15026. - PubMed
    1. Baglioni C, et al. Insomnia as a predictor of depression: A meta-analytic evaluation of longitudinal epidemiological studies. Journal of Affective Disorders. 2011;135:10–19. - PubMed
    1. Palagini L, et al. Sleep Loss and Hypertension: A Systematic Review. Curr Pharm Des. 2013;19:2409–2419. - PubMed

MeSH terms