Polymorphisms in the PER3 promoter could affect its expression, leading to potential differences in the observed functions of PER3.

Introduction Cigarette smokers are at increased risk of poor sleep behaviors. However, it is largely unknown whether these associations are due to shared (genetic) risk factors and/or causal effects (which may be bidirectional). Methods We obtained summary-level data of genome-wide association studies of smoking (smoking initiation [ n = 74 035], cigarettes per day [ n = 38 181], and smoking cessation [ n = 41 278]) and sleep behaviors (sleep duration and chronotype, or “morningness” [ n = 128 266] and insomnia [ n = 113 006]). Using linkage disequilibrium (LD) score regression, we calculated genetic correlations between smoking and sleep behaviors. To investigate causal effects, we employed Mendelian randomization (MR), both with summary-level data and individual-level data ( n = 333 581 UK Biobank participants). For MR with summary-level data, individual genetic variants were combined with inverse variance–weighted meta-analysis, weighted median regression, MR-Robust Adjusted Profile Score, and MR Egger methods. Results We found negative genetic correlations between smoking initiation and sleep duration ( r g = −.14, 95% CI = −0.26 to −0.01) and smoking cessation and chronotype ( r g = −.18, 95% CI = −0.31 to −0.06), and positive genetic correlations between smoking initiation and insomnia ( r g = .27, 95% CI = 0.06 to 0.49) and cigarettes per day and insomnia ( r g = .15, 95% CI = 0.01 to 0.28). MR provided strong evidence that smoking more cigarettes causally decreases the odds of being a morning person, (RAPS) and weak evidence that insomnia causally increases smoking heaviness and decreases smoking cessation odds. Conclusions Smoking and sleep behaviors show moderate genetic correlation. Heavier smoking seems to causally affect circadian rhythm and there is some indication that insomnia increases smoking heaviness and hampers cessation. Our findings point to sleep as a potentially interesting smoking treatment target. Implications Using LD score regression, we found evidence that smoking and different sleep behaviors (sleep duration, chronotype (morningness), and insomnia) are moderately genetically correlated—genetic variants associated with less or poorer sleep also increased the odds of smoking (more heavily). MR analyses suggested that heavier smoking causally affects circadian rhythm (decreasing the odds of being a morning person) and there was some indication that insomnia increases smoking heaviness and hampers smoking cessation. Our findings indicate a complex, bidirectional relationship between smoking and sleep behaviors and point to sleep as a potentially interesting smoking treatm...

Background The use of Mendelian randomization (MR) in epidemiology has increased considerably in recent years, with a subsequent increase in systematic reviews of MR studies. We conducted a systematic review of tools designed for assessing risk of bias and/or quality of evidence in MR studies and a review of systematic reviews of MR studies. Methods We systematically searched MEDLINE, Embase, the Web of Science, preprints servers and Google Scholar for articles containing tools for assessing, conducting and/or reporting MR studies. We also searched for systematic reviews and protocols of systematic reviews of MR studies. From eligible articles we collected data on tool characteristics and content, as well as details of narrative description of bias assessment. Results Our searches retrieved 2464 records to screen, from which 14 tools, 35 systematic reviews and 38 protocols were included in our review. Seven tools were designed for assessing risk of bias/quality of evidence in MR studies and evaluation of their content revealed that all seven tools addressed the three core assumptions of instrumental variable analysis, violation of which can potentially introduce bias in MR analysis estimates. Conclusion We present an overview of tools and methods to assess risk of bias/quality of evidence in MR analysis. Issues commonly addressed relate to the three standard assumptions of instrumental variables analyses, the choice of genetic instrument(s) and features of the population(s) from which the data are collected (particularly in two-sample MR), in addition to more traditional non-MR-specific epidemiological biases. The identified tools should be tested and validated for general use before recommendations can be made on their widespread use. Our findings should raise awareness about the importance of bias related to MR analysis and provide information that is useful for assessment of MR studies in the context of systematic reviews.

SummaryObservationally, higher caffeine consumption is associated with poorer sleep and insomnia. We investigated whether these associations are a result of shared genetic risk factors and/or (possibly bidirectional) causal effects. Summary‐level data were available from genome‐wide association studies on caffeine intake (n = 91 462), plasma caffeine and caffeine metabolic rate (n = 9876), sleep duration and chronotype (being a “morning” versus an “evening” person) (n = 128 266), and insomnia complaints (n = 113 006). First, genetic correlations were calculated, reflecting the extent to which genetic variants influencing caffeine consumption and those influencing sleep overlap. Next, causal effects were estimated with bidirectional, two‐sample Mendelian randomization. This approach utilizes the genetic variants most robustly associated with an exposure variable as an “instrument” to test causal effects. Estimates from individual variants were combined using inverse‐variance weighted meta‐analysis, weighted median regression and MR‐Egger regression. We found no clear evidence for a genetic correlation between caffeine intake and sleep duration (rg = 0.000, p = .998), chronotype (rg = 0.086, p = .192) or insomnia complaints (rg = −0.034, p = .700). For plasma caffeine and caffeine metabolic rate, genetic correlations could not be calculated because of the small sample size. Mendelian randomization did not support causal effects of caffeine intake on sleep, or vice versa. There was weak evidence that higher plasma caffeine levels causally decrease the odds of being a morning person. Although caffeine may acutely affect sleep when taken shortly before bedtime, our findings suggest that a sustained pattern of high caffeine consumption is more likely to be associated with poorer sleep through shared environmental factors. Future research should identify such environments, which could aid the development of interventions to improve sleep.