Characterisation of the genetic relationship between the domains of sleep and circadian-related behaviours with substance use phenotypes

Abstract

Sleep problems and substance use frequently co-occur. While substance use can result in specific sleep deficits, genetic pleiotropy could explain part of the relationship between sleep and substance use and use disorders. Here we use the largest publicly available genome-wide summary statistics of substance use behaviours (N = 79,729-632,802) and sleep/activity phenotypes to date (N = 85,502-449,734) to (1) assess the genetic overlap between substance use behaviours and both sleep and circadian-related activity measures, (2) estimate clusters from genetic correlations and (3) test processes of causality versus genetic pleiotropy. We found 31 genetic correlations between substance use and sleep/activity after Bonferroni correction. These patterns of overlap were represented by two genetic clusters: (1) tobacco use severity (age of first regular tobacco use and smoking cessation) and sleep health (sleep duration, sleep efficiency and chronotype) and (2) substance consumption/problematic use (drinks per day and cigarettes per day, cannabis use disorder, opioid use disorder and problematic alcohol use) and sleep problems (insomnia, self-reported short sleep duration, increased number of sleep episodes, increased sleep duration variability and diurnal inactivity) and measures of circadian-related activity (L5, M10 and sleep midpoint). Latent causal variable analyses determined that horizontal pleiotropy (rather than genetic causality) underlies a majority of the associations between substance use and sleep/circadian related measures, except one plausible genetically causal relationship for opioid use disorder on self-reported long sleep duration. Results show that shared genetics are likely a mechanism that is at least partly responsible for the overlap between sleep and substance use traits.

Keywords: genetic correlations; sleep; substance use.

Figure 1.

The genetic (rG) correlation matrix between all substance use behaviors and sleep/activity measures. Positive rGs shown in blue and negative in red. Correlations are plotted on X and Y axis based on centroid clustering. Age.Smoke = Age of first becoming a regular smoker, CPD = Cigarettes per day, Former.Smoke = current smoker versus former smoker, DPW= Drinks per week, OUD= Opiate Use Disorder, CUD = Cannabis Use Disorder, PAU = Problematic Alcohol Use, Ever.Smoke = lifetime tobacco use, L5 = L5 timing indication of a preference for going to bed earlier or later in the day, M10 = M10 timing indication of if a person is most active earlier or later in the day, Long sleep = self-report long sleep duration, Short Sleep = self-report short sleep duration, Sleep.Dur.Sd = Accelerometer Sleep Duration (Standard Deviation), and Sleep.Dur.report = self-report sleep duration.

Figure 2.

Silhoutte Coefficents from K-Means Clusting Algorithm A silhouette coefficient determined that a 2-cluster solution was optimal to characterize the relationships between sleep/activity and substance use dimensions.

Figure 3.

Optimal cluster solution demonstrating two distinct clusters, both of which were comprised of substance use behaviors and sleep traits. Clusters are plotted by a two-dimensional representation where the normally multi-dimensional component is plotted with X representing dimension 2 and Y representing dimension 1. Cluster 1 is a “tobacco use severity and sleep health” cluster. Cluster 2 is a “substance consumption/problematic use and sleep problems” cluster. The X and Y axes represent the first two components and the proportion of variance they explain in each trait. Clusters are plotted around their centroid. Age.Smoke = Age of first becoming a regular smoker, CPD = Cigarettes per day, Former.Smoke = current smoker versus former smoker, DPW= Drinks per week, OUD= Opiate Use Disorder, CUD = Cannabis Use Disorder, PAU = Problematic Alcohol Use, Ever.Smoke = lifetime tobacco use, Long sleep = self-report long sleep duration, Short Sleep = self-report short sleep duration, Sleep.Dur.Sd = Accelerometer Sleep Duration (Standard Deviation), and Sleep.Dur.report = self-report sleep duration.