pii: sp-00441-14

http://dx.doi.org/10.5665/sleep.4888

GENETIC ANCESTRY IS ASSOCIATED WITH SLEEP DEPTH IN OLDER AFRICAN AMERICANS

African Genetic Ancestry is Associated with Sleep Depth in Older African Americans Indrani Halder, PhD1; Karen A. Matthews, PhD2; Daniel J. Buysse, MD2; Patrick J. Strollo, MD1; Victoria Causer, MD1; Steven E. Reis, MD1; Martica H. Hall, PhD2 Department of Medicine and 2Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA

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Study Objectives: The mechanisms that underlie differences in sleep characteristics between European Americans (EA) and African Americans (AA) are not fully known. Although social and psychological processes that differ by race are possible mediators, the substantial heritability of sleep characteristics also suggests genetic underpinnings of race differences. We hypothesized that racial differences in sleep phenotypes would show an association with objectively measured individual genetic ancestry in AAs. Design: Cross sectional. Setting: Community-based study. Participants: Seventy AA adults (mean age 59.5 ± 6.7 y; 62% female) and 101 EAs (mean age 60.5 ± 7 y, 39% female). Measurements and Results: Multivariate tests were used to compare the Pittsburgh Sleep Quality Index (PSQI) and in-home polysomnographic measures of sleep duration, sleep efficiency, apnea-hypopnea index (AHI), and indices of sleep depth including percent visually scored slow wave sleep (SWS) and delta EEG power of EAs and AAs. Sleep duration, efficiency, and sleep depth differed significantly by race. Individual % African ancestry (%AF) was measured in AA subjects using a panel of 1698 ancestry informative genetic markers and ranged from 10% to 88% (mean 67%). Hierarchical linear regression showed that higher %AF was associated with lower percent SWS in AAs (β (standard error) = −4.6 (1.5); P = 0.002), and explained 11% of the variation in SWS after covariate adjustment. A similar association was observed for delta power. No association was observed for sleep duration and efficiency. Conclusion: African genetic ancestry is associated with indices of sleep depth in African Americans. Such an association suggests that part of the racial differences in slow-wave sleep may have genetic underpinnings. Keywords: African Americans, genetic admixture, race, slow wave sleep Citation: Halder I, Matthews KA, Buysse DJ, Strollo PJ, Causer V, Reis SE, Hall MH. African genetic ancestry is associated with sleep depth in older African Americans. SLEEP 2015;38(8):1185–1193.

INTRODUCTION Different characteristics of sleep vary between African Americans (AA) and European Americans (EA).1–6 AAs take longer to fall asleep,2,7 have shorter sleep duration,7–10 lower sleep efficiency2,7,8 and less slow wave sleep (SWS)3,7–11 compared to individuals of European ancestry. Sleep disorders also show race-related differences: AAs have higher prevalence and greater severity of sleep disordered breathing,5,9,12 but report fewer sleep complaints including insomnia9,13 compared to EAs.6,14 Possible mediators and moderators underlying racial differences in clinical and subclinical sleep disturbances include psychosocial stressors,9,15–17 socioeconomic factors,4 health care utilization,18,19 health behaviors, psychiatric and medical disorders,20,21 anatomy,22–24 sex, and age.7,12,25 A recent metaanalysis of moderators of sleep characteristics showed that racial differences in sleep duration and continuity, but not sleep architecture, are partially moderated by such factors.7 For instance, sex explains ~83% of the variability in racial difference in sleep efficiency such that, as the proportion of women in

A commentary on this article appears in this issue on page 1151. Submitted for publication July, 2014 Submitted in final revised form December, 2014 Accepted for publication January, 2015 Address correspondence to: Martica Hall, PhD, WPIC E1131, 3811 O’Hara Street, University of Pittsburgh, Pittsburgh, PA 15213; Tel: (412) 246-6431; Email: [email protected] SLEEP, Vol. 38, No. 8, 2015

the study increased, the effect size of race-related difference in sleep efficiency also increased.7 Similarly, body mass index (BMI) explained 83% to 87% of the variance in race differences in total sleep time such that studies with participants with higher average BMIs showed a larger racial difference in sleep duration.7 These data also suggest that, despite the influence of various mediators and moderators, race remains an independent risk factor for poor sleep when comparing EA versus AAs.2–4 We hypothesized that genes inherited differentially from continental ancestors may play a significant and, as yet unexamined role, in observed differences in sleep between EA and AAs. Twin studies show significant heritability for many sleep measures,26,27 particularly for sleep architecture measures such as SWS.28,29 Although heritability of sleep quality ranges between 34% and 46%,30,31 and sleep duration between 27% and 34%,32,33 estimates for SWS range between 50%34 and 95%.28 Sleep disorders are also heritable: estimates range between 28% and 46% for insomnia35,36 and 10% and 52% for sleep disordered breathing.37,38 Together these studies suggest that critical dimensions of normal and disordered sleep are under partial genetic regulation. Although most studies of the heritability of sleep are based primarily on European Americans, these results raise the possibility that observed racial variations in sleep characteristics may also vary as a function of genetic difference between races. Studies that investigate sleep differences between EAs and AAs typically rely on self-identified race to categorize individuals. Race is a complex composite of biogeographical and sociocultural factors, each of which can exert independent and

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interactive effects on the outcome being studied. However, few research studies of race and sleep specify whether they focus on the biological or sociocultural facet of race when evaluating differences.5 Ancestry-phenotype association tests, which quantify associations between measured genetic ancestry and a phenotype in an admixed population, like AAs, can be used to test the extent to which the genetic characteristics underlying race may be responsible for observed population level differences.39,40 In the context of sleep, ancestry-phenotype association tests assume that multiple genetic variants, each with small effects on sleep, may have different allele frequencies in different continental populations that contributed to the admixed population. Because individuals from the admixed population inherit varying proportions of their genome from different ancestral populations, one expects contribution from any one ancestral population to show a wide range of variation (theoretically, spread between 0–100%). Any association between ancestry and a phenotype in an admixed group, then, indicates that multiple variants across the genome that have been inherited from one particular ancestral population are related to variation in the phenotype.39–45 In this manner, objectively measured genetic ancestry enables us to test the uniquely genetic facet of “race” parsed from the cultural, behavioral, and psychosocial aspects that may be responsible for the observed phenotypic differences. However, the analyses are meaningful only in groups where admixture varies widely, which happens over the course of seven to 10 generations as a result of large-scale migrations between continental populations that were previously separated (like the Europeans and Africans), and where admixture can be reasonably inferred using well defined ancestry informative markers such as those observed in individuals of African ancestry.40,50 AAs have varying proportions of genetic admixture and exhibit a wide range of African genetic ancestry.40–42 Variation in individual genetic ancestry and admixture have been associated with different physical and physiological measurements in AAs including skin pigmentation,43 BMI and blood pressure,14,41 insulin,44 and vitamin D.45 A genetic ancestryby-phenotype association in a population with a history of recent admixture indicates an aggregated presence of many population-specific genetic loci (i.e., loci that vary in allele frequency between the ancestral populations) that directly influence the outcome, thereby indicating a possible genetic basis. In aggregate, these studies indicate that observed phenotypic differences between EAs and AAs may be partly attributed to differences related to continental genetic ancestry within the admixed population, such that individuals who carry more of the risk/susceptibility loci from one ancestral group are likely to exhibit higher prevalence of the disease or exhibit higher levels of the phenotype of interest. We extended this line of inquiry to examine whether observed racial differences in different dimensions of sleep may be explained by genetic variation related to continental ancestry in a sample of selfidentified AA adults. We hypothesized that indices of sleep previously shown to be decreased in AAs compared to EAs (quality, duration, efficiency, and SWS) would show an inverse association with %AF ancestry in AAs, whereas sleep disordered breathing, which tends to be higher in AAs than in EAs would show a direct positive association with %AF. Such associations would suggest that observed racial differences in a SLEEP, Vol. 38, No. 8, 2015

particular sleep phenotype/outcome have a partly genetic basis, specifically related to genetic ancestry. METHODS Study Sample Participants were drawn from the HeartSCORE46 and SleepSCORE4 projects. HeartSCORE, a prospective, single center, community-based participatory research cohort study investigating mechanisms for racial disparities in cardiovascular risk and attempting to decrease these disparities via a communitybased intervention, recruited 2,000 adult subjects (45–74 y at study inclusion, 64% female; 53% White, 43% AA, and 4% “other” races) residing in the greater Pittsburgh metropolitan area. Participants had extensive baseline data collection (demographic history, several traditional and emerging cardiovascular disease risk factors, nutrition, and physical activity assessments) and were followed annually for 5 y to characterize racial differences in atherosclerosis. Exclusion criteria included known comorbidities expected to reduce life expectancy to less than 4 y. There were 464 AA and 771 EA participants (total 1,235) who consented to DNA analyses and were successfully genotyped for ~50,000 single nucleotide polymorphisms (SNPs) included on the IBCchip,47 with less than 5% missing data. Two hundred twenty-four participants (42% AA, 58% EA, mean age ~59 y) from HeartSCORE participated in an extended sleep study, SleepSCORE, which was designed to evaluate sleep quality, duration, continuity, and architecture as risk factors for cardiovascular disease, including the possible influence of race and sex on these relationships.4 Exclusionary criteria for SleepSCORE included pregnancy; use of continuous positive airway pressure treatment for sleep disordered breathing; medication for sleep problems on a regular basis; nighttime work schedule; medication for diabetes; and prior diagnosis of stroke, myocardial infarction, or interventional cardiology procedures. Of the 224 sleep study participants, 101 EAs and 70 AAs had consented to genetic data collection. These 171 participants constitute the current study sample. All participants provided written informed consent and the study was approved by the Institutional Review Board of the University of Pittsburgh. Sleep Measures The sleep measures included in the current report were chosen to represent a broad range of sleep indices previously shown to differ among EAs and AAs including two measures of SWS, which has exhibited high genetic heritability in previous studies.7 Details on measurements of sleep in the SleepSCORE study have been presented elsewhere.4 The current study was based on polysomnography (PSG) and self-reported measures of sleep. For PSG-assessed measures, participants used a Compumedics Siesta monitor (Charlotte, NC, USA) for 2 nights in their homes. The PSG data included bilateral central and occipital electroencephalogram (EEG) channels, bilateral electrooculograms (EOG), bipolar submentalis electromyograms (EMG), and one channel of electrocardiogram (ECG) recording. On the first night of PSG, participants were monitored for sleep disordered breathing using nasal pressure, inductance plethysmography, and fingertip oximetry. High-frequency filter

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settings were 100 Hz for EEG and bilateral EOG and 70 Hz for EMG. Low-frequency filter settings were 0.3 Hz for EEG and 10 Hz for EMG. Quality assurance assessments, scoring, and processing of all PSG were performed at the University of Pittsburgh Neuroscience—Clinical and Translational Research Center (N-CTRC). Records were scored by trained PSG technologists with established reliability (intraclass correlation coefficients for wake, rapid eye movement (REM), and nonrapid eye movement (NREM) periods were each above 0.90), who were blind to participant characteristics. Sleep stages were visually scored in 20-second epochs using Rechtschaffen and Kales criteria,48 as data were collected prior to the updated American Academy of Sleep Medicine manual. Visually scored sleep outcomes included indices of sleep duration, continuity, depth, and sleep disordered breathing. Sleep duration or time spent asleep was defined as actual sleep time, excluding epochs of wakefulness during the night. Sleep continuity was measured by sleep efficiency (percentage of time in bed spent asleep). Visually scored SWS was defined as the percentage of total sleep time spent in NREM stages 3+4 sleep. Apnea-hypopnea index (AHI) was defined as number of apneas and hypopneas per hour of sleep. Values from the 2 nights of the study were averaged for each variable, with the exception of AHI, which was only assessed on the first night of the study. Variables with skewed distributions were transformed using either a log (sleep efficiency, AHI) or square root (percentage stages 3+4 sleep) transformation prior to analysis. Power spectral analysis of the EEG was performed to quantify a second indicator of SWS, EEG power in the delta band (0.5–4.0 Hz). Briefly, EEG signals were digitized at a rate of 256 Hz and decimated to 128 Hz.49 An automated artifact rejection program was run to remove epochs containing EMG artifacts from the EEG record.50 Four-sec epochs were used to compute power spectral analysis of the EEG. Average absolute delta EEG power was computed for NREM sleep; epochs corresponding to “wake” or REM sleep were not included in the analyses. A natural log transformation was used to normalize NREM EEG delta power for analyses. Self-reported sleep quality was assessed by the Pittsburgh Sleep Quality Index (PSQI),51 a standardized measure of subjective sleep quality over the previous month. Eighteen individual items on the PSQI are grouped to create seven component scores (e.g., subjective sleep quality, sleep latency, sleep duration), which are then summed to generate a global score with a range of 0 to 21, with higher scores indicating worse subjective sleep quality. PSQI scores were treated as a continuous variable in analyses. Demographic and Clinical Factors Age, sex, and race were ascertained by self-report. Highest education achieved was assessed on a four-level ordinal scale: high school or less, some college (no degree), 4-y college degree, advanced degree. Income was assessed as a four-level ordinal scale: < $20,000, $20,000–$39,999, $40,000– $79,999, ≥ $80,000. A combined value for income and education was obtained by taking an average of the two values for each person following a previously reported method4 and is henceforth as referred to as socioeconomic status (SES). The composite SES value ranged between 1.5 and 5 and was used SLEEP, Vol. 38, No. 8, 2015

in all regression models. Information on use of sleep medication was collected during the in-home studies and information on use of antidepressant use was collected during baseline HeartSCORE visits. Medication use was coded as a binary variable indicating use/non-use of medications in statistical models. BMI was assessed in the HeartSCORE baseline protocol as weight in kg/height in m 2. DNA Isolation, SNP Selection, Genotyping Blood samples for genotyping were collected in 10 mmol/L ethylenediaminetetraacetic acid (EDTA) and DNA was isolated following standard protocols. A panel of 1,698 genomespanning ancestry informative markers (AIMs) (i.e., markers that maximally distinguish between ancestral populations) were genotyped in all samples. All selected AIMs are SNPs and are included in the Illumina CARe iSelect (IBC) cardiovascular microarray. The IBC array is a customized 50 K SNP chip that assays multiple polymorphisms in 2,100 genes and was developed specifically for cardiometabolic phenotypes.47 The AIMs included in the chip are appropriate for differentiating between European and African ancestries.41 Individual Genetic Ancestry Estimation Ancestral allele frequencies were ascertained in European and West African (Yoruban) samples available in the HapMap database (www.hapmap.org). Individual % African ancestry (%AF) estimates were computed using a maximum likelihood method for inferring individual admixture52 as described previously.40 Briefly, an algorithm is used to compute the probability of observing a marker genotype given ancestral allele frequencies at a locus.52 Summing over the logs of individual locus probabilities combines information across multiple loci. The admixture proportion that maximizes the probability of obtaining the observed genotype is the maximum likelihood estimate of ancestry for the individual. Empirical evidence indicates that %AF ranges between 0–100% in AAs.41–45 Statistical Analysis We have previously reported racial differences in sleep characteristics in the larger SleepSCORE cohort.4 The current analyses focus on a subset of these individuals for whom genetic data were available for testing our hypotheses related to genetic ancestry. Thus, initial racial differences in sleep were reexamined to confirm that original observations4 hold true in this subgroup. Sample characteristics were compared between EAs and AAs using t tests for continuous variables and Chi-square tests for categorical variables. Analysis of variance models were used to confirm that sleep variables (PSG-assessed duration, efficiency, SWS, delta power, AHI; PSQI-assessed subjective sleep quality) differed between racial groups in this genetic substudy. Univariate analyses were followed by additional adjustments for age, sex, and BMI due to their potential associations with sleep. Our primary genetic analysis focuses only on the AAs because large-scale admixture between European and African populations formed this population. Although some admixture may have occurred in populations of primarily European ancestry, the scale of admixture is known to have been of a much smaller magnitude and, consequently, African ancestry is not

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Table 1—Sample characteristics. Measure Total, n (%) Females, n (%) Age, mean ± SD (y) Body mass index, mean ± SD Education, n (%) High school or less Some college, no degree 4-year degree Advanced degree Annual income, n (%) < $20,000 $20,000–$39,999 $40,000–$79,999 ≥ $80,000 Taking antidepressants, n (%) Taking sleep medications, n (%) AHI ≥ 15

European Americans 101 (59%) 39 (39%) 60.5 ± 7 28.7 ± 4.5

African Americans 70 (41%) 44 (62%) 59.9 ± 6.7 31.5 ± 5.8

12 (12%) 28 (28%) 19 (19%) 42 (42%)

14 (20%) 32 (45%) 12 (17%) 11 (15%)

7 (7%) 24 (24%) 36 (36%) 29 (29%) 11 (11%) 3 (3%) 20 (28%)

18 (25%) 20 (28%) 26 (37%) 3 (0.04%) 2 (0.03%) 1 (0.01%) 30 (30%)

P 0.003 0.61 < 0.0001 < 0.0001

< 0.0001

0.08 0.64 0.87

AHI, apnea-hypopnea index; BMI, body mass index; n, sample size; P, P value for t tests (age, BMI, AHI), chi square tests (sex, years of education, annual income) or Fisher exact test (use of antidepressants, use of sleep medicines) comparing European Americans to African Americans.

a significant contributor to EA populations.40–45 Because racial differences in phenotype are primary indicators of possible genetic admixture,40 only those sleep outcomes that differed significantly between races (after adjustment for age, sex, and BMI) were investigated for association with genetic ancestry as measured by %AF in AAs. Possible outliers for ancestry were identified as those individuals whose %AF was outside the mean ± 2 standard deviation boundary. Outlier removed data were tested for normality using the Kolmogorov-Smirnov (KS) test and was found to be normal and no additional transformations were applied. For %AF-phenotype associations, regression models included %AF as the predictor with age, sex, and BMI as covariates in the first set of models. When a significant association with %AF was observed, SES, sleep and antidepressant medication use, and AHI (for models where AHI was not the outcome) were included as additional covariates. Bonferroni correction was applied to control for multiple tests. RESULTS Sample Characteristics Characteristics of EA and AA samples are shown in Table 1. Average age was ~60 y for both EAs and AAs. AAs had higher BMI, were more likely to be female, and had lower SES than EAs. AHI and proportion of participants using sleep medications and antidepressants were similar in both races. Race remained associated with SES even after adjustment for sex (P < 0.001). We first confirmed that the racial differences in sleep previously reported in the larger SleepSCORE cohort4 held in this smaller genetic substudy cohort (Table 2). As previously reported, total sleep duration, average sleep efficiency, and percent SWS were higher in EAs than in AAs and these differences persisted in analysis of variance models adjusted for age, sex, and SLEEP, Vol. 38, No. 8, 2015

BMI. Although significant in the larger SleepSCORE cohort,4 NREM EEG delta power did not differ as a function of race in the present subsample. As in the larger SleepSCORE study,4 sleep quality and sleep disordered breathing did not differ significantly between races either before or after covariate adjustment. For our primary genetic analyses, we examined whether sleep duration, efficiency, and SWS (percent and EEG delta power) varied as a function of African genetic ancestry in AAs. Individual African ancestry (%AF) Varies in AAs and is Associated With Visually Scored SWS In AAs in SleepSCORE, %AF ranged between 10% and 88%, with a mean of 67% (standard deviation 17%; Figure 1), similar to admixture distribution in the larger HeartSCORE cohort.41 Using mean ± 2 standard deviation cutoff established the range of allowable %AF between 33% and 101% and resulted in excluding four individuals who had < 33% AF. No individuals were excluded from the upper end of the distribution. The KS test showed that %AF was normally distributed in the sample (KS test statistic = 0.718) and no further adjustments were made. Sample characteristics for the 66 AAs subsequently analyzed for association with %AF were not significantly different from the values reported for all 70 AAs in Tables 1 and 2. After adjustments for age, sex, and BMI, %AF was found to be a significant predictor of percent visually scored SWS (β = −4.7, S.E. = 1.5, P = 0.002, Figure 2A) among AAs (excluding the four with the aforementioned low %AF values). Higher individual African ancestry was associated with lower percent SWS. Combined, the covariates and predictor explained 24% of the variation in percent SWS, of which %AF uniquely accounted for 13% of the total variance. Among the covariates, only sex was a significant predictor of percent SWS (P = 0.002). Visually scored sleep efficiency and sleep duration

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Table 2—Sleep outcomes differ by self-identified race.

Outcomes Quality (PSQI) Visually scored sleep Duration (h) Efficiency (%) a SWS (%) b AHI (events/h of sleep) a Quantitative EEG Delta power (0.5–4.0 Hz) a

Variable Means ± SD European Americans African Americans (n = 101) (n = 70) 6.1 ± 3.2 6.9 ± 3.5 6.2 ± 0.9 78.9 ± 9.6 6.5 ± 6.9 13 ± 13.6 n = 89 3.11 ± 0.55

5.9 ± 1.1 74.3 ± 12.6 c 3.4 ± 4.9 c 13.5 ± 16.4 n = 58 3.06 ± 0.54

ANCOVA F 0.84 4.7 8.9 21.6 0.1 0.005

P 0.36 0.032 0.003 < 0.001 0.75 0.6

Means of untransformed variables examined using unpaired t tests are shown. ANCOVA compared races after adjustment for age, sex, and BMI. Race coded as 0 for AAs and 1 for EAs. Significant differences in ANCOVA shown in bold. a Natural log transformation was performed prior to testing study hypotheses. b Square root transformation was performed prior to testing study hypotheses. c Bonferroni correction lowers the threshold to P = 0.01. ANCOVA, analyses of covariance; EEG, electroencephalogram; PSQI, Pittsburgh Sleep Quality Index; SD, standard deviation; SWS, visually scored slow wave sleep.

were not associated with %AF after adjusting for age, sex, and BMI (see Table 3). The percent SWS-by-ancestry association was further interrogated with additional adjustment for socioeconomic status, AHI, and use of sleep medication and antidepressants. Three individuals had missing SES data, which reduced the sample for the complete data set with all measures to n = 63. Percent AF remained a significant predictor of percent visually scored SWS (β = −4.6, S.E. = 1.4, P = 0.004; Table 3) and explained 11% of the variance in this measure. Results were unchanged in secondary analyses, which included the four individuals with low %AF, and we confirmed the percent SWS-by-ancestry association adjusted for all covariates (β = −2.84, S.E. = 0.94, R 2 = 11%, P = 0.004). Sex was the only significant covariate in these models as well (P = 0.009). Similar analyses of sleep duration and efficiency remained nonsignificant (Table 3). SWS Measured by Quantitative EEG is Associated With African Ancestry in AAs We had data for both NREM EEG delta power and genotype data on 58 subjects; of these, three had < 33% AF and were excluded from the ancestry-phenotype tests. In the sample of 55 AAs, %AF was negatively associated with delta power after adjustment for age, sex, and BMI (β = −1.83, S.E = 0.067, R 2 = 10%, P = 0.008; Figure 3A) as well as after adjustment for all other covariates (β = −1.63, S.E = 0.69, R2 = 10%, P = 0.022; n = 52 because three individuals had missing SES data; Figure 3B). Similar to analyses of visually scored SWS, results were unchanged in secondary analyses, which included three individuals with low %AF and all covariates; delta power showed a suggestive negative association with %AF (β = −0,77, S.E = 0.4, R2 = 5%, P = 0.06). Sex was the only significant covariate in models of NREM EEG delta power and %AF (P < 0.01 across models). DISCUSSION By utilizing the genetic variability attributable to continental admixture, we show for the first time that visually scored percent SWS and NREM EEG delta are associated with SLEEP, Vol. 38, No. 8, 2015

Figure 1—Distribution of individual African ancestry (%AF) within self-identified African Americans in SleepSCORE. %AF distribution in African Americans ranged between 10% and 88% with a mean of 67% (standard deviation 16%).

%AF in AAs. Even after adjusting for several demographic, socioeconomic and clinical covariates, %AF explained between 9% and 11% of the variance in SWS in AAs. These results show that AAs have inherited multiple alleles (either few alleles of large effect sizes or several alleles of moderate to low effect sizes) from their African ancestors that may predispose them to lower percent SWS. This association between measured genetic ancestry and SWS clearly establishes a partial genetic basis underlying the observed racial differences in this dimension of sleep. Although our subsample (58 AAs and 102 EAs) did not show a significant race difference for NREM delta power, we previously reported race differences in delta power in the larger SleepSCORE study (112 Whites versus 80 AAs; P = 0.045). In contrast to the significant associations between %AF and indices of sleep depth in AAs, sleep duration and efficiency, which also differed by race in the present study, were unrelated to %AF in AAs.

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Most studies of racial differences in sleep have used selfidentification of race to group individuals, without measuring their genetic backgrounds. Our intentions were not to improve race classification schemes by adding genetic information, but

rather to examine how genetic variation within AAs may explain part of the observed racial difference in sleep. Indeed, we have previously shown that genetic ancestry offers no additional benefit in classifying individuals over self-identified

Figure 2—In African Americans, visually scored percent slow wave sleep (SWS) varies inversely with individual African ancestry. The Y axis shows percent SWS values adjusted for covariates and the X axis shows % African ancestry. (A) Percent SWS values were adjusted for age, sex, and body mass index (BMI) excluding individuals with %AF < 0.33 (outliers removed; n = 66). (B) Percent SWS values were adjusted for age, sex, BMI, AHI, sleep medication use, antidepressant use, and socioeconomic status (n = 63; three individuals with missing SES data not included in analyses).

Figure 3—In African Americans, nonrapid eye movement electroencephalography (NREM EEG) delta power (QEEG-δ ) varies inversely with individual African ancestry. The Y axis shows QEEG-δ values adjusted for covariates and the X axis shows % African ancestry. (A) QEEG-δ adjusted for age, sex, and BMI in individuals with %AF > 33% (outliers removed; n = 66). (B) QEEG-δ adjusted for age, sex, BMI, AHI, sleep medication use, antidepressant use, and socioeconomic status (n = 63 excluding three individuals with missing SES data). Table 3—Association of African ancestry with sleep outcomes in AAs (%AF). AAs with %AF > 0.33 SWS Sleep duration Sleep efficiency

Model 1 (n = 66) Beta (Standard Error) P −4.7 (1.5) 0.002 −0.03 (1.4) 0.98 0.18 (0.57) 0.76

Percent Variance 13% NA NA

Model 2 (n = 63) Beta (Standard Error) P −4.6 (1.4) 0.004 0.61 (1.4) 0.66 0.21 (0.62) 0.73

Percent Variance 11% NA NA

Model 1: Covariates included age, sex, body mass index. All phenotype and genotype data were available in all 66 AAs for this model. Model 2: Covariates included age, sex, BMI, SES, AHI, and use of sleep medication and antidepressants. Three individuals had missing data for SES. Bonferroni corrected P value for each model is 0.016 and significant result is shown in bold. AAs, African Americans; SWS, percent slow wave sleep. SLEEP, Vol. 38, No. 8, 2015

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race.41 Hence, for any nongenetic study of group differences, self-identified race is still an adequate descriptor. Genetic admixture offers a unique solution to a rather complex problem: decomposing the sources of variation that lead to racial differences in diseases.40,52 However, this approach is only appropriate in groups such as AAs or Hispanics who have a history of recent large-scale continental admixture.42,43 Our results do not indicate that a non-overlapping panel of genetic markers influences SWS and delta power in EAs and AAs considered in aggregate. Instead, these results suggest that some genetic variants occur in different frequencies in European and African populations and that these variants have a significant effect on indices of sleep depth. Because AAs have inherited a varying proportion of their genome from their African ancestors, we observe that sleep depth varies as a function of genetic ancestry. Although we have presented racial differences in sample characteristics by using data from all AAs for whom we had genotype data available, the primary ancestry-phenotype tests were restricted to those who were not identified as outliers. Because racial differences typically do not include genetic background, any comparison, such as data we have presented in Tables 1 and 2, would include all self-identified AAs. One may therefore question whether the excluded individuals would have any effect on the associations. In secondary analyses we tested our models in the total data and found that the associations between %AF and indices of sleep depth remain. However statistically, these models all suffer from nonnormality and outlier effects and therefore we chose to focus only on AAs whose admixture proportions conform to the normal distribution. SWS is critical to restorative physiological processes that occur during sleep, which may be important to cardiometabolic health. Decreased SWS in AAs3,4,8,13,53 may, thus, contribute to disparities in cardiometabolic disease. A recent meta-analysis of 21 studies showed that racial disparities in SWS are not moderated by age, sex, location (in home versus laboratory), adiposity, income, presence of other comorbidities, or medication usage.7 Because stress affects sleep physiology,54 a second line of investigation has shown that psychological stress attributable to racial discrimination (i.e. specific discrimination attributed to perceived racism)5,55 but not unfair treatment or everyday discrimination in general16,56 is a mediator of racial differences in SWS. Our finding of an association between objectively measured genetic ancestry and SWS in AAs initiates a new line of investigation on observed race differences. Our results indicate that complete decomposition of the racial difference in SWS will require an examination of genetic causes in addition to behavioral, environmental, and/or psychosocial factors. Because genetic ancestry has also been shown to predict hypertension41 and diabetes44 in AAs, it is plausible to propose that sleep and ancestry may interact to influence cardiometabolic risk. Alternately, because genetic ancestry is a predictor of both SWS and hypertension, a common molecular mechanism may govern both physiological processes. Despite the very high heritability of SWS in humans, few studies have examined candidate genes for association with SWS (reviewed by Landolt29). These studies have typically included smaller cohorts (n < 100) and mostly populations of European ancestry.57–60 Whether these variants might also be SLEEP, Vol. 38, No. 8, 2015

responsible for lower SWS in AAs, is unknown. Some studies have included both AAs and EAs in the same model and reported the tests of genetic associations.61,62 However, by combining individuals from both races in the same model and not accounting for admixture effects in AAs, these studies may have introduced additional stratification in the sample, hindering any population-specific interpretations of their findings. The best evidence for a candidate gene study of SWS in a primarily non-European cohort comes from a study of 800 mixedancestry urban residents of Sao Paolo, Brazil.63 This study confirmed an association between the A allele of the adenosine deaminase (ADA) gene and higher SWS, which had previously been reported in two cohorts of European descent.59,60 However, despite the known effects of admixture and consequent genetic heterogeneity in Brazil,64 this large study63 neither elaborated on the ethnic composition of their subjects, nor controlled for genetic heterogeneity in their analyses. Consequently, it remains unclear if the reported association between the ADA gene A allele and SWS generalizes across all race/ethnicities or whether the observed effect might be a false-positive result attributable to the lack of adequate control for genetic heterogeneity in the study subjects. Although our study was neither designed nor powered for detecting novel individual loci that influence sleep phenotypes, our results clearly indicate the presence of multiple genetic loci that influence SWS in AAs as a function of genetic ancestry. Future investigations in larger samples are warranted to identify specific loci that may predict lower SWS in AAs and do so in a race-specific manner. Twin studies of sleep duration and efficiency have estimated heritability of sleep duration and efficiency at less than 40%,29,31,32 some of which may be attributable to shared environments, rather than genes.63,65,66 Thus, the lack of association between %AF and sleep duration and efficiency in the AAs in our study may reflect a true absence of genetic variants related to continental ancestry that simultaneously influence these sleep phenotypes. Alternately, the size (n = 66) of our AA sample may have been too small to capture the small effect sizes of relatively few loci that may have been inherited from continental ancestors in a graded distribution. In post hoc analyses using standard power calculation methods for regression analyses, we estimate 5% and 10% power for detecting an association between %AF and either sleep duration or sleep efficiency respectively (assuming n = 66), R 2 of 1% (as observed for sleep duration and efficiency in our study) and significance level of 0.01 (adjusted for multiple comparison). In contrast we had between 58% and 72% power for detecting R 2 values of 9% to 11% for SWS as observed in our sample. Indeed, much lower estimates of genetic heritability and the relatively small effect sizes for single loci that have thus far been associated with sleep duration,67–70 quality,70–73 and efficiency57,71–74 indicate that significantly larger sample sizes may be required to detect an overall ancestry-by-phenotype association for these dimensions of sleep. The absence of an overall ancestry-phenotype association for sleep duration and efficiency thus does not eliminate the possibility that few loci that vary significantly between races and exert strong influence on normative sleep duration or efficiency may still exist. Small sample size and lack of longitudinal data are the main limitations of this study. Although the available data allowed

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examination of overall ancestry-phenotype association, the small sample size prevented a thorough investigation of locus specific ancestry. The cross-sectional nature of the data does not provide any information on whether the ancestry-phenotype association persists across the lifespan. Despite these limitations, use of a large number of validated AIMs allowed for precise estimation of individual ancestry. Thus, the observed association of ancestry with SWS should be considered as reliable and not subject to errors due to uncertainty of ancestry inference which may occur when using a small number of AIMs.41 Although the potential evolutionary advantages of greater or lesser amounts of SWS remain uncertain, further replication of this association in other cohorts and locus specific investigations are now warranted to identify the precise loci underlying the racial difference in SWS. ABBREVIATIONS %AF, individual African ancestry AA, African American AHI, apnea-hypopnea index ANOVA, analysis of variance BMI, body mass index EA, European American ECG, electrocardiogram EDTA, ethylenediaminetetraacetic acid EEG electroencephalogram EMG, electromyogram EOG, electrooculogram IBC, ITMAT broad human CVD gene chip PSG, polysomnography PSQI, Pittsburgh Sleep Quality Index SNP, single nucleotide polymorphism SWS, slow wave sleep DISCLOSURE STATEMENT This was not an industry supported study. This work was supported by: Pennsylvania Department of Health (contract # ME-02-384), NIH Grants: HL077378, HL094767, HL076379, HL076852, HL076858, HL007560, HL104607 and CTSA/NCTRC # TR000005. Dr. Buysse has served as a paid consultant on scientific advisory boards for Merck, Otsuka, Purdue Pharma, Medscape, Emmi Solutions, Inc., CME Outfitters, and General Sleep Corporation. Dr. Strollo has served as a paid consultant or received grant support from the following companies: ResMed Corp, Philips-Respironics, Emmi Solutions, Inspire Medical Systems, PinMed, and the National Football League. The other authors have indicated no financial conflicts of interest. REFERENCES

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