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. Author manuscript; available in PMC: 2022 Apr 9.
Published in final edited form as: Am J Med Genet B Neuropsychiatr Genet. 2021 Mar 1;186(2):113–121. doi: 10.1002/ajmg.b.32840

Genetic versus stress and mood determinants of sleep in the Amish

Heather A Bruce 1, Peter Kochunov 1, Joshua Chiappelli 1, Anya Savransky 1, Kathleen Carino 1, Jessica Sewell 1, Wyatt Marshall 1, Mark Kvarta 1, Francis J McMahon 2, Seth A Ament 3,4, Teodor T Postolache 5,6,7, Jeff O'Connell 8,9, Alan Shuldiner 10, Braxton Mitchell 9,11, L Elliot Hong 1
PMCID: PMC8994156  NIHMSID: NIHMS1792044  PMID: 33650257

Abstract

Sleep is essential to the human brain and is regulated by genetics with many features conserved across species. Sleep is also influenced by health and environmental factors; identifying replicable genetic variants contributing to sleep may require accounting for these factors. We examined how stress and mood disorder contribute to sleep and impact its heritability. Our sample included 326 Amish/Mennonite individuals with a lifestyle with limited technological interferences with sleep. Sleep measures included Pittsburgh Sleep Quality Index (PSQI), bedtime, wake time, and time to sleep onset. Current stress level, cumulative life stressors, and mood disorder were also evaluated. We estimated the heritability of sleep features and examined the impact of current stress, lifetime stress, mood diagnosis on sleep quality. The results showed current stress, lifetime stress, and mood disorder were independently associated with PSQI score (p < .05). Heritability of PSQI was low (0–0.23) before and after accounting for stress and mood. Bedtime, wake time, and minutes to sleep time did show significant heritability at 0.44, 0.42, and 0.29. However, after adjusting for shared environment, only heritability of wake time remained significant. Sleep is affected by environmental stress and mental health factors even in a society with limited technological interference with sleep. Wake time may be a more biological marker of sleep as compared to the evening measures which are more influenced by other household members. Accounting for nongenetic and partially genetic determinants of sleep particularly stress and mood disorder is likely important for improving the precision of genetic studies of sleep.

Keywords: heritability, mood disorder, stress

1 ∣. INTRODUCTION

Sleep and wakefulness are regulated by integrated circadian and homeostatic physiological processes (Borbely, Daan, Wirz-Justice, & Deboer, 2016). These processes as well as many core facets of sleep and arousal circuits and functions are highly conserved across diverse species (Joiner, 2016). Animal studies have identified specific genes affecting circadian rhythms and sleep. For example, the Period (PER1, PER2, and PER3) and the Cryptochrome (CRY1 and CRY2) clock genes affect the circadian rhythm pacemaker in the suprachiasmatic nucleus (Mohawk, Green, & Takahashi, 2012; Partch, Green, & Takahashi, 2014). Genetic studies of sleep disorders also identified multiple variants in these and other genes that affect sleep, including a rare missense mutation in PER2 associated with familial advanced sleep phase syndrome (Toh et al., 2001) and a mutation in the prion protein gene associated with fatal familial insomnia (Llorens, Zarranz, Fischer, Zerr, & Ferrer, 2017). To detect common sleep-related genetic variants with small effect sizes, large genome wide association studies (GWAS) have used a variety of phenotypes, ranging from actigraphy recording, self-reports, insomnia, and other sleep disorder diagnoses, to chronotypes or morningness-eveningness; however, few findings from GWAS or candidate gene studies have been robustly replicated (Dashti et al., 2019; Jansen et al., 2019; Jones et al., 2019; Kalmbach et al., 2017; Stein et al., 2018).

Sleep is a complex behavior that is influenced by many genetic and environmental factors and often their interactions. One of the strongest impacts on sleep quality is one's mood, and sleep disturbances are highly prevalent in people with mood disorders such as depression (Chang, Ford, Mead, Cooper-Patrick, & Klag, 1997; Roberts & Duong, 2014). Sleep is also strongly influenced by stress levels (M. H. Hall et al., 2015). Adequate assessment of mood and stress-related environmental factors and statistically controlling for these influences may assist in clarifying to what extent genetic risk affects common sleep behavioral and biological characteristics. Previous studies attempting to control for some of these influences by studying sleep in the Amish, a rural population with no or limited modern electrical devices (Evans et al., 2011), have found significant but relatively low heritabilities (h2) at around 0.2 for wake time, self-reported morningness-eveningness preference, and daytime sleepiness. This h2 estimate of sleep characteristics is also similar to that of sleep phenotypes in some other populations (Lind, Aggen, Kirkpatrick, Kendler, & Amstadter, 2015; Wing et al., 2012). However, these studies did not explicitly account for the potential contributions from stress and mood factors on sleep quality and their impact on heritability.

Stress in particular has a strong relationship with sleep characteristics in both individuals with psychiatric disorders and healthy controls. Significant early life stress affects the development of insomnia and other sleep disorders later in life (Lo Martire, Caruso, Palagini, Zoccoli, & Bastianini, 2019; Palagini, Drake, Gehrman, Meerlo, & Riemann, 2015). High levels of ongoing stress greatly affect the quality of sleep, partly by preventing the transition into rapid-eye movement sleep and increasing wakefulness (de Zambotti, Sugarbaker, Trinder, Colrain, & Baker, 2016; Nollet et al., 2019; Petersen, Kecklund, D'Onofrio, Nilsson, & Akerstedt, 2013; Sanford, Fang, & Tang, 2003). Conversely, sleep deprivation elevates biological stress markers such as blood pressure and evening cortisol levels, and decreases parasympathetic tone (Leproult, Copinschi, Buxton, & Van Cauter, 1997; Spiegel, Leproult, & Van Cauter, 1999). Subjective stress levels have also been associated with quantitative EEG alterations consistent with hyperarousal such as decreased delta power (M. Hall et al., 2000).

Mood disorders are also associated with significantly reduced sleep quality: 40% of people with insomnia have another psychiatric disorder and having insomnia increases the risk of developing major depression (Ford & Kamerow, 1989; Roberts & Duong, 2014). Loss of sleep is a trigger for mood episodes in patients with Major Depressive Disorder and Bipolar Disorder (Lewis et al., 2017). Conversely, an excess of sleep or hypersomnia can be a sign of the development of psychopathology (Breslau, Roth, Rosenthal, & Andreski, 1996). Disordered sleep can also persist into euthymic periods after a mood episode (Russo et al., 2015). Aside from sleep quality and sleep quantity, variations in the timing of sleep and circadian rhythm increase the risk of mood disorder episodes (Bradley et al., 2017; Kitamura et al., 2010).

We hypothesized that a large part of the variation in sleep phenotypes can be explained by mood disorders and stress. In this study, we build on prior studies of sleep quality and its determinants in the Amish by considering the effects of stress and mood disorders. The Amish live in a rural environment with much reduced modern lifestyle interferences that may otherwise confound the assessment of the role of genetics in sleep. We used a measure of the current experience of stress that ranks feelings of being overwhelmed with current life situations (Cohen, Kamarck, & Mermelstein, 1983) as well as a measure that accounts for stressful life events over a life time (Wolfe, Kimberling, Brown, Chrestman, & Levin, 1997). We used DSM diagnosis to identify the presence of a mood disorder. The goal of this study is to evaluate explicitly the association of stress measures and mood disorders with sleep quality as well as whether they impact the sleep heritability estimate.

2 ∣. METHODS

2.1 ∣. Participants

The study included 326 individuals (216 controls with no psychiatric illnesses, 88 participants with mood disorders including 11 with bipolar disorder, 77 with major depression, 12 with anxiety disorders, 4 with schizophrenia spectrum disorders, 6 with other psychiatric disorders) from Old Order Amish and Old Order Mennonite (OOA/M) families (143 male, 183 female, age [39 ± 18, mean ± SD]) in Pennsylvania and Maryland. As this is a founder population and marriages are typically kept within the community, most families are connected using genealogical records maintained by the OOA (Beiler, 2009) and the OOM communities (Shirk & Shirk, 2007) and digitalized in the NIH Anabaptist Genealogy Database (Agarwala, Biesecker, & Schaffer, 2003; Mitchell et al., 2012). Exclusion criteria included major medical and neurological conditions and substance abuse in the past year. Recruitment was based on the Research Domain Criteria (RDoC) strategy (Insel, 2014) and included all Axis-I psychiatric illnesses, starting with identifying families with at least two cases of any psychiatric illnesses and then followed by recruiting members from the same household regardless of diagnosis. Families without psychiatric illnesses were also recruited. Note that this traditional definition of case and control families is a relative term in a population isolate where most families are interrelated. For this study, individuals without psychiatric illness were labeled as controls irrespective of their family status. The Schedule for Clinical Interview for DSM-4 or 5 (SCID) was used to determine diagnoses by trained clinicians. SCID diagnoses were reviewed in consensus meetings. All participants completed a Beck Depression Inventory. All study participants gave written informed consent as approved by the University of Maryland IRB.

2.2 ∣. Sleep measures

Sleep phenotype was ascertained by self-reported bed time, wake time and time to sleep onset as well as by the self-reported Pittsburgh Sleep Quality Index (PSQI) that assesses sleep quality in the previous month. The PSQI produces seven subscores: duration of sleep, sleep disturbance, sleep latency, daytime sleepiness, sleep efficiency, subjective sleep quality, and use of sleep medications. A total PSQI score is obtained by summing the subscores (Buysse, Reynolds 3rd, Monk, Berman, & Kupfer, 1989). A score greater than 5 is associated with poor sleep. We also collected information on the season during which the participant completed the PSQI. This was used as a covariate in the heritability analysis. Winter was defined as months December through February, spring as March through May, summer as June through August, and fall as September through November.

2.3 ∣. Stress measures

To assess life time stress we adapted items from the Life Stressor Checklist–Revised (Wolfe et al., 1997), which is a self-reported questionnaire that asks about the occurrence of stressful events over the lifetime, for example, experiences of abuse, neglect, or violence. We added questions tailored to the Amish population including questions such as “Has a family member or close friend ever left the Amish community?” or “Have you ever separated from your church community?” This Amish Life Stressor Inventory (LSI) scores the total number of events the participants reported with 14 being the maximum score.

To assess current subjective stress level we used the Perceived Stress Scale (PSS; Cohen et al., 1983). The PSS is a self-report questionnaire that asks the participant to rate the frequency of certain stress-related feelings over the last month, that is, feeling overwhelmed versus feeling confident in the ability to cope with problems.

2.4 ∣. Statistics

We compared sleep quality and stress measures between subjects with and without mood disorders using one-way ANOVA (IBM SPSS Statistics for Windows, Version 23). We examined the effects of cumulative life stressors, current stress levels, mood diagnosis, age, and sex on PSQI using multiple linear regression. We also performed hierarchical regression analysis to test for independent contribution of each of the five variables (life stress, current stress, mood diagnosis, age, and sex). For each regression, we tested the statistical significance of the association of the variable of interest with sleep quality by using two stages. In the first stage, we used four variables and in the second stage, we added the variable of interest. This was then repeated for the other four variables in turn. We examined the relationship between PSQI and Beck Depression Inventory using linear regression.

We estimated the heritability of sleep measures and performed bivariate correlation analyses while accounting for genetic relatedness using variance components analysis implemented in the SOLAR (Sequential Oligogenic Linkage Analysis Routines; http://www.nitrc.org/projects/se_linux) and an empirical pedigree (Almasy & Blangero, 1998; Kochunov et al., 2019). Heritability reflects the proportion of the variance attributed to additive genetic effects. The total variance of a phenotype was partitioned into a genetic component due to additive polygenic effects and a random environmental component. Age and sex were covariates. Relationships among individuals were determined using whole genome genetic kinship.

The degree of genetic closeness among subjects were quantified directly from genetic array data using empirical pedigree approach (Kochunov et al., 2019). All subjects were genotyped using Illumina Infinium Global Screening Array v2.0 SNP-array that provides extended coverage for 613,599 polymorphic markers selected to provide high imputation accuracy for population-scale genetics studies. The 407,171 SNPs that satisfied the quality control criteria (exclusions of SNPs with MAF < 1%, genotype call rate < 95% and Hardy–Weinberg equilibrium <1 × 10−6) were used for quantification of the degree of relatedness and imputation. SOLAR-Eclipse uses coefficients of relationship (ri,j) to code the probability that two alleles from individuals i and j are identical by descent. The coefficient of relationship is a function of identity by descent sharing statistics, ri,j = π 1i,j/2 + π 2i,j, where π 1i,j and π 2i,j are the probabilities that two individuals share one and two alleles through a common ancestry using the Weighted Allelic Correlation approach (Kochunov et al., 2019). It calculates the coefficient of relationship using the correlation coefficient among the allelic scores and weighting it by the minor allele frequency factor. Shared environmental factor was estimated using shared household in SOLAR, which was defined by residing at the same address.

3 ∣. RESULTS

Participants with a mood disorder did not significantly differ in age compared to the non-psychiatric controls (p = .16). Those with mood disorders were more likely to be female than male (60 vs. 53%, p = .015; Table 1). Both current stress (p < .0001) and lifetime stress (p < .0001) were significantly higher, while PSQI was significantly worse (p < .0001), in participants with mood disorders compared to controls (Table 1). Participants with mood disorder showed significantly later wake time and worse scores in the PSQI subcomponents of disturbance, and daytime dysfunction.

TABLE 1.

Stress and sleep in patients and controls

Measure Mood disorder
patients (n = 88)		 Controls
(n = 216)
Age 40.46 ± 14.78 37.11 ± 18.38
Sex (% female) * 60% 55%
PSQI score** 4.34 ± 2.83 3.00 ± 2.15
LSI score** 3.47 ± 1.82 2.43 ± 1.57
PSS score** 16.46 ± 5.40 12.92 ± 4.90
Duration score 0.15 ± 0.42 0.18 ± 0.43
Disturbance* 1.01 ± 0.60 0.87 ± 0.52
Latency 0.70 ± 0.83 0.55 ± 0.73
Daytime dysfunction** 0.88 ± 0.86 0.57 ± 0.57
Efficiency 0.26 ± 0.61 0.15 ± 0.482
Quality 0.66 ± 0.59 0.54 ± 0.53
Medication use** 0.69 ± 1.13 0.01 ± 0.52
Bedtime 21.4 ± 0.77 21.3 ± 0.76
Wake time* 5.57 ± 1.1 5.33 ± 0.95
Time to sleep 17.05 ± 17.34 14.43 ± 14.42
Duration of sleep 7.75 ± 1.10 7.69 ± 1.04
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Note: Mean values and SD of Pittsburgh sleep quality index (PSQI) total, subcomponents, measures of bedtime, wake time, and minutes to sleep onset in subjects with and without mood disorders. Stress measures, age, and sex are also shown. LSI is Amish life stressor inventory. PSS is perceived stress scale. The PSQI has a range of possible values from 0–21. Each subcomponent has a range of 0–3. The PSS score can be a maximum of 40 and the LSI a maximum of 14.

**

indicates p value of <.001

*

indicates p value of .05.

The multiple regression model with PSQI as the outcome was statistically significant (F[5, 294] = 11.81, R2 = .15, p = 2 × 10 −10). PSS, LSI, mood disorder diagnosis, and age significantly contributed to PSQI. We repeated the analysis using hierarchical regression which showed similar results (Table 2). When we repeated the analysis of the association between individual measures and sleep while factoring in the participants' relatedness, current stress (RhoP = 0.24, p < .0001), life time stress (RhoP = 0.21, p = .001), age (RhoP = 0.28, p = 1.2 × 10–6) and mood disorder (RhoP = 0.31, p = 5 × 10−4) remained significantly associated with sleep quality. Also after covarying age and sex, Beck Depression Inventory score was significantly associated with PSQI (t = 6.91, p < .0001). Bedtime, wake time, and minutes to sleep onset were not significantly associated with current or lifetime stress.

TABLE 2.

Relationship between stress, mood, and sleep

Variable R2 change F change p R2 change
Age .047 5.803 <.001
Sex .0001 5.841 .839
Mood diagnosis .016 8.465 .017
LSI .017 16.454 .016
PSS .024 0.41 .004
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Note: Hierarchical regression results where PSQI (Pittsburgh sleep quality index) was the dependent variable and the measures listed were predictors. PSS is perceived stress scale. LSI is Amish life stressor inventory.

In the entire sample, heritability of sleep features ranged from 0 to 0.44. The heritability of PSQI and subscores ranged from 0 to 0.21, and none was statistically significant (Table 3). Heritability of total PSQI was negligible (h2 = 0.02). The subcomponent with the highest heritability was sleep latency (h2 = 0.21), a measure extracted from the participants' report of how long they take to fall asleep as well as their report of frequency of taking >30 min to fall asleep. The participants' usual time to go to bed (bedtime), time to wake up (wake time), and minutes it takes them to fall asleep (time to sleep) had significant h2 of 0.44 (p < .001), 0.42 (p < .001), and 0.29 (p < .05), respectively (Table 3). The analysis was repeated with the season during which PSQI was completed as a covariate. This had no significant effect on heritability measures.

TABLE 3.

Heritability of sleep measures

Entire sample
 Entire sample
(stress as covariate)
 Healthy controls
(stress as covariate)
Sleep quality measures h2 (SE) h2 (SE) h2 (SE)
PSQI 0.02 (0.15) 0 (0.17) 0 (0.25)
Duration 0 (0.10) 0 (0.10) 0 (0.19)
Disturbance 0.20 (0.14) 0.18 (0.15) 0 (0.26)
Latency 0.21(0.17) 0.18 (0.18) 0.23 (0.22)
Daytime dysfunction 0.10 (0.13) 0.13 (0.13) 0.20 (0.18)
Efficiency 0 (0.25) 0 (0.24) 0 (0.24)
Quality 0 (0.14) 0 (0.15) 0.05 (0.18)
Sleeping meds 0.15 (0.15) 0.11 (0.14) N/A
Bedtime 0.44 (0.11)** 0.40 (0.12)** 0.33 (0.20)*
Wake time 0.42 (0.09)** 0.40 (0.09)** 0.64 (0.11)**
Time to sleep 0.29 (0.16)* 0.28 (0.16)* 0.30 (0.26)
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Note: Heritability of sleep characteristics is shown. PSQI is Pittsburg Sleep Quality Index. Bedtime and wake time are self-reported measures, and time to sleep is the estimated minutes the participant took to fall asleep. The heritability was calculated in the entire sample (n = 326) with age and sex as covariates. Then calculated with the additional covariates of current and lifetime stress measures (perceived stress scale and life stressor inventory respectively.) This analysis was repeated in controls (n = 216).

**

indicates p < .001

*

indicates p < .05.

The measures with significant h2 were analyzed for an influence of shared household. After adjusting for shared environment only heritability of wake time remained significant with h2 of 0.41, p < .001 but bedtime (h2 = 0.04, p = .4) and time to sleep (h2 = 0.07, p = .38) were no longer significant. When current and life time stress were added as covariates, PSQI total score was again not heritable (h2 = 0) and the highest subcomponent heritability was sleep latency and sleep disturbance (both h2 = 0.18; Table 3). Bedtime (h2 = 0.4, p < .001), wake time (h2 = 0.4, p < .001) and time to sleep (h2 = 0.28, p < .05) were again significantly heritable (Table 3). After shared household was included as a covariate, again wake time remained significantly heritable (h2 = 0.41, p < .001) but bedtime (h2 = 0.04, p = .41) and time to sleep (h2 = 0.07, p = .38) were no longer significant.

We also repeated the analysis by removing participants with mood disorders. In this psychiatrically healthy group, PSQI total score remained not heritable (h2 = 0), and the highest subcomponent heritability was again sleep latency (h2 = 0.23), though none were significant (Table 3). Bedtime (h2 = 0.33, p < .05) and wake time (h2 = 0.64, p < .001) were significantly heritable in the psychiatrically healthy group. After adding shared household as a covariate, again wake time remained significantly heritable (h2 = 0.52, p = .003) but bedtime (h2 = 0, p = .5) and time to sleep (h2 = 0.21, p = .26) were not significant (Table 3).

4 ∣. DISCUSSION

In this study, we showed that current and lifetime stress, mood disorder, and age impact sleep quality even in a rural environment with minimal modern lifestyle confounds. We found that self-reported wake time is among the most heritable sleep phenotypes in those we have investigated and that this heritability remains significant after accounting for stress, mood disorder, and shared household effects. However, sleep quality as measured by PSQI was not significantly heritable. Finally, bedtime and time to sleep did show significant heritability although their heritability is largely accounted for by shared household effect.

The greater heritability of the wake time measure and the lack of significant effects on this heritability by stress, mood disorder, and shared household suggest it is a more biologically based phenotype. The consistency of wake time heritability over bedtime heritability may be due to the influence of an individual's biological circadian rhythm on wake time being greater than the influence on bedtime in this population.

The low heritability for sleep quality in this study replicates similarly low additive genetic effects in other sleep phenotypes in some urban and suburban populations. Two studies show heritability of insomnia and other sleep characteristics to be between 0 and 0.2 (Lind et al., 2015; Stein et al., 2018). However, there is large variability with two other studies finding higher values, for example, 0.57 for insomnia (Watson, Goldberg, Arguelles, & Buchwald, 2006) and 0.44 for sleep duration and quality (Partinen, Kaprio, Koskenvuo, Putkonen, & Langinvainio, 1983), which is in the same range of the wake time measure in this study. Contrary to our hypothesis, inclusion of stress and mood disorder factors did not substantially change heritability estimates of sleep components for either the low heritability measures such as the PSQI derived sleep phenotypes or the higher heritability measures such as the wake time, bedtime, and time to sleep.

There have been prior studies of the heritability of bedtime, wake time, and time to sleep (de Castro, 2002; Inderkum & Tarokh, 2018; Sletten et al., 2013). The study performed in the population most similar to ours was done in the Hutterite community, a similarly isolated founder population with a uniform lifestyle and minimal environmental confounding factors. That study found significant heritability for wake time (h2 = 0.12, p = .009), and time to sleep (h2 = 0.16, p = .02) with no heritability for bedtime though it used a different method to measure these phenotypes (Klei et al., 2005).

Heritability is a population statistic and so may differ from one population to another. A meta-analysis of 17 studies on the heritability of sleep duration and quality showed significant heritability of 0.31 and 0.38, respectively (Madrid-Valero, Rubio-Aparicio, Gregory, Sanchez-Meca, & Ordonana, 2020). Many of these studies were twin studies which can inflate heritability measures by underestimating shared environmental variance (Mayhew & Meyre, 2017). Another study showed the components of PSQI to have heritabilities ranging from 0 for the sleep duration component to 0.47 for the daytime dysfunction component (Barclay, Eley, Buysse, Rijsdijk, & Gregory, 2010). Studies using the total PSQI score to estimate heritability of sleep quality in diverse populations show findings that range from 0.21 to 0.34 (Genderson et al., 2013; Lai et al., 2014; Madrid-Valero, Sanchez-Romera, Gregory, Martinez-Selva, & Ordonana, 2018; Wing et al., 2012).

Though it may not completely measure the heritable aspects of sleep, PSQI is the most commonly used instrument for sleep quality assessment, and the PSQI itself also shows variation among different populations and different studies (Mollayeva et al., 2016) both in the mean value and the SD. The original study on PSQI showed a mean and SD of 2.67 (±1.70) in controls which is similar to our result of 3.0 (±2.15; Buysse et al., 1989). Likewise their subcomponents were similar, all with means of ≤1 with SD ranging from 0.3 to 0 .7 versus ours 0.4 to 0.7. Studies of different samples have shown mean PSQI values ranging from 3 to 4 (Aloba, Adewuya, Ola, & Mapayi, 2007; Elsenbruch, Harnish, & Orr, 1999; Grandner, Kripke, Yoon, & Youngstedt, 2006). Studies of samples with older subjects show higher PSQI from 5 to 6. (Bush et al., 2012; Buysse et al., 1991; Genderson et al., 2013; Madrid-Valero et al., 2020). There are no known studies of PSQI in an Amish population. There is a study of another rural population which may be similar in some respects, 18,850 Chinese participants with a mean PSQI and SD of 3.70 (±2.68) though again in an older population than ours (mean age 54; H. Zhang, Li, et al., 2019).

Our heritability findings are similar to another study of non-overlapping OOA/M subjects that used actigraphic recording, sleep diaries, the Horne-Östberg Morningness–Eveningness Questionnaire (MEQ), and the Epworth Sleepiness Scale (ESS), to show a similar heritability range (h2 = 0 for sleep duration and bedtime and 0.2 for wake time, MEQ, and ESS) for sleep measures (Evans et al., 2011), though our study had a higher wake time heritability of 0.4. This difference may be due to how wake time was measured, with ours solely by self-report not actigraphy. Furthermore, our sample was smaller which may lead to larger variance in measures. In both studies, wake time heritability was significant, not bedtime heritability, after adjustment for shared household. Another study in the Amish found sleep duration to have no heritability (M. Zhang, Ryan, et al., 2019). The sleep patterns in the Amish are comparable with the patterns in general U.S. population in terms of similar sleep duration; though they have earlier bed times and earlier wake times (M. Zhang, Ryan, et al., 2019). This sleep pattern is related to farming and rural living and may be more consistent with the biological sleep cycle from an evolutionary perspective. In addition, this lifestyle allows for control of environmental factors such as modern technology that affect sleep quality.

The possible environmental influences that account for the non-genetic variance of sleep parameters are many and could substantially confound genetic association studies of sleep, making replications difficult. There is a spectrum from more tangible factors such as bedroom conditions, ambient noise, caffeine intake, and amount of exercise to more complex factors such as stress. Our finding that shared household covariate removed the significant heritability effect of bedtime and time to sleep measures supports that these sleep parameters are influenced by the same house environment. Prospective studies have also highlighted that stressful events can predict insomnia (Drake, Pillai, & Roth, 2014). Longer term stressors occurring in the past can affect sleep directly or by mediating the relationship between current stress and sleep (Hanson & Chen, 2010). Our analyses are consistent with these findings, but further suggest that current stress levels and past stressors can both contribute to reduced sleep quality (Table 2). Furthermore, stress and sleep likely interact bidirectionally, for example current stress has been shown to impact sleep duration while decreased sleep quantity predicts higher next day stress (Yap, Slavish, Taylor, Bei, & Wiley, 2019).

Mood disorder and other psychiatric conditions also have a close relationship with sleep characteristics. There are many investigations of the link between mental illness and sleep using measures such as polysomnography (sleep continuity, depth, architecture, and rapid eye movement sleep) as well as self-report (Baglioni et al., 2016). Some sleep abnormalities are transdiagnostic (Harvey, Murray, Chandler, & Soehner, 2011) with sleep continuity deficits in mood disorders, anxiety disorders, and schizophrenia. However, to our knowledge, no major genetic association study of sleep has attempted to measure and take into account mood disorder and stress, issues that are likely impacting sleep. Although stress and depression are known to be highly correlated themselves, our hierarchical regression analyses show that mood disorder and stress are each still independently significant in association with sleep quality.

There were several limitations to our study. We did not collect more objective sleep measures such as actigraphy or polysomnography. We did not collect information on some factors that may influence sleep such as a participant's physical exercise. Another limitation is our use of the PSQI subscales (Buysse et al., 1989) to measure sleep disturbance rather than using the Insomnia Severity inventory (Bastien, Vallieres, & Morin, 2001) and the clinical diagnostic schedule which can be more accurate. This may have contributed to the lack of sleep disturbance heritability in our study. We did not investigate other specific phenotypes that have shown association with mood such as a measure of morningness–eveningness (Zhang et al., 2015). In addition, while the Amish population as a whole has far less technology use at bedtime, we did not gather this information at the level of specific participants.

This study was conducted in a specific population which may limit its generalizability. It may be more difficult to capture the heritability of sleep characteristics in the Amish due to a lifestyle that encourages regularity and reduces variance. The OOA/M sample has less genetic and less environmental variance than the larger general population. This may include reduced variability of stress measures which can limit the ability to find associations. However, the mean and SD of the PSS in our control sample (12.92 ± 4.90) were similar to some of the largest samples, for example, 13.0 ± 6.4, n = 2,387 (Cohen & Williamson, 1988) or 15.3 ± 6.3, n = 501(Lesage, Berjot, & Deschamps, 2012).

To summarize, GWAS have implicated several genes in different sleep related phenotypes, for example, MEIS1 (Meis Homeobox 1) for insomnia (Hammerschlag et al., 2017; Lane et al., 2017), although most significant findings in one study typically fail to replicate in others (Dashti et al., 2019; Doherty et al., 2018; Gottlieb et al., 2015; Jansen et al., 2019; Jones et al., 2019; Kalmbach et al., 2017; Stein et al., 2018). Our study suggests that self-reported sleep characteristics are often associated with an individual's current experience of stress, their past experience of stressful events, and presence of mood disorder diagnosis; although the wake time measure appears modestly heritable and was not significantly affected by the stress, mood, and shared household environmental effects that we have examined. While GWAS studies have begun to elucidate loci associated with sleep, more specific phenotyping of sleep that accounts for stress and mood disturbances may translate into stronger genetic findings.

ACKNOWLEDGMENTS

Supports were received from National Institute of Health grants U01MH108148, R01NS114628, R01EB015611, and R01MH116948.

Footnotes

CONFLICT OF INTEREST

The authors have no conflicts of interest.

DATA AVAILABILITY STATEMENT

Data available on request from the authors.

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Data Availability Statement

Data available on request from the authors.

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