The causal relationship between sleep traits and the risk of schizophrenia: a two-sample bidirectional Mendelian randomization study | BMC Psychiatry

Study design

We conducted a two-sample bidirectional Mendelian randomization analysis to evaluate the bidirectional causal relationships between sleep traits and schizophrenia. The sleep traits included morning diurnal preference, sleep duration (short and long sleep duration), daytime sleepiness, daytime napping, and insomnia. Genetic summary statistics for exposures and outcomes were obtained from the largest available GWAS. All contributing GWASs sought informed consent from their study participants. Three assumptions must be satisfied in this MR analysis: 1) the genetic variant used in MR is associated with exposures; 2) associations of the genetic variant with sleep traits and schizophrenia must not be confounded; and 3) the genetic variants must have an association with outcomes only through the effect associated with exposures.

Data sources and instrument selection

As a genetic variant for morning diurnal preference, we selected variants from a meta-analysis of GWAS conducted using data from the UKB and 23andMe cohorts [7]. We only used the summary statistics from the UKB, which included 449,734 European individuals. The meta-analysis of GWASs identified 340 (P <5 × 10–8) genetic loci, which were present in both UKB and 23andMe and were associated with morning diurnal preference. They also conducted a sensitivity analysis that excluded shift workers and those either on medication or with disorders affecting sleep. The summary statistics of continuous sleep duration were obtained from a recent GWAS in the UKB [8]. Through analysis in 446,118 European ancestry participants, the GWAS identified 78 genetic loci for self-reported habitual sleep duration, 27 genetic loci for short sleep duration (<7 h), and long sleep durations (≥ 9 h) (P<5 × 10–8). Sensitivity analyzes indicated that these genetic associations were largely independent of known risk factors such as insomnia, caffeine, chronotype, additional lifestyle and clinical condition. Effect estimates were largely consistent in GWAS excluding shift workers and those with prevalent chronic and psychiatric disorders (excluding n= 119,894 participants). The summary statistics of daytime napping were obtained from a GWAS of self-reported daytime napping in the UKB (n= 452,633), and 123 distinct genetic loci were identified (P<5 × 10–8) [9]. Effect estimates for these SNPs were largely consistent in GWAS restricted to 338,764 participants self-reporting excellent or good overall health. For daytime sleepiness, the summary-level data were extracted from a larger GWAS of self-reported daytime sleepiness [10]. They identified 42 loci for daytime sleepiness in the GWAS of 452,071 individuals from the UKB. Sensitivity analyzes were performed to adjust for potential confounders (including depression, socioeconomic status, alcohol intake frequency, smoking status, caffeine intake, employment status, marital status, neurodegenerative disorders, and psychiatric problems). They also used another GWAS (N= 255,426) that excluded shiftworkers and individuals with chronic health or psychiatric illnesses. The summary statistics of insomnia were extracted from a recent GWAS in the UKB including 453,379 European ancestry participants [11]. A total of 57 loci for self-reported insomnia symptoms were identified. The results from secondary GWAS excluding current shift workers or individuals reporting the use of hypnotic, antianxiolytic, or psychiatric medications and / or having selected chronic diseases or psychiatric illnesses (excluding n= 76,470 participants) were consistent with the primary GWAS. As genetic variants for schizophrenia, we extracted genetic variants from a large meta-analysis of GWAS including 77,096 European ancestry participants (33,640 cases and 43,456 controls) [12]. They identified 108 schizophrenia-associated genetic loci in their analysis (P<5 × 10–8). The studies and datasets included in our analysis are presented in Supplementary Table 1. There was no participant overlap between the exposure dataset and outcomes datasets.

The linkage disequilibrium analysis among exposure-associated SNPs (single nucleotide polymorphism) was investigated using the clump function (r2 <0.01 and clump window was 10,000 kb) in the TwoSampleMR package based on the 1000 Genomes LD (linkage disequilibrium) reference panel of Europeans only [13, 14]. We removed SNPs with effect sizes greater in the outcome than in the exposure (P<5 × 10–8 in outcome). Among these, we selected 316 independent SNPs for instrumental variations for morning diurnal preference, 74 independent SNPs for sleep duration, 26 independent SNPs for short sleep duration, 7 independent SNPs for daytime napping, 40 independent SNPs for daytime sleepiness, and 52 independent SNPs for insomnia (Supplementary Tables 2, 3, 4, 5, 6, 7 and 8). When schizophrenia was considered exposure, we selected 105 independent SNPs as instrumental variables (Supplementary Table 9).

If no matching SNP was available for an outcome, we selected proxies (r2 > 0.60) in LD Link ( Detailed information on the proxies used for genetic variables is presented in Supplementary Table 10. We excluded 6 SNPs (rs12249410, rs147439581, rs113397282, rs12055602, rs186545906, and rs114012503) because no proxy SNP was available. Finally, there were 315 SNPs as instrumental variables for morning diurnal preference, 101 SNPs as instrumental variables for schizophrenia, and 100 SNPs as instrumental variables for schizophrenia when the outcome was daytime napping.

Statistical analysis

The causal relationship between sleep traits and schizophrenia was obtained from the fixed effects of the inverse-variance weighted (IVW) method. The Cochran’s Q statistic and the I2statistic were performed to assess heterogeneity among estimates across individual SNPs. We considered there to be heterogeneity if P<0.05 and used I2to quantify heterogeneity (I2≤ 25%: small heterogeneity; 25% 2≤ 50%: moderate heterogeneity; I2≥ 50%: greater heterogeneity). Leave-one-out analyzes were conducted as a sensitivity analysis to preclude the possibility that the causal inference was driven by a single SNP.

Given that there was a positive correlation between sleep traits [10] and some genetic predictors predicting more than one sleep trait, we performed multivariable MR to explore the direct effects of morning diurnal preference, sleep duration, daytime napping, daytime sleepiness, and insomnia on schizophrenia by using the TwoSampleMR R package. We adjusted the thresholds for significance by Bonferroni correction. For the primary analyzes (association of sleep traits with schizophrenia), we set 2-sided Pvalues ​​of <0.007 (= 0.05 / 7 outcomes or exposures) as the thresholds for significance. All statistical analyzes were performed using the MRPRESSO and TwoSampleMR packages in R version 4.1.0 (R Core Team, Vienna, Austria).

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