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Volume 07 No. 05
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Supplementary Articles

Behavioral and Genetic Markers of Sleepiness

Namni Goel, Ph.D.; David F. Dinges, Ph.D.
Division of Sleep and Chronobiology, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA


Neurobehavioral responses to acute total and chronic partial sleep deprivation occur in healthy adults and are particularly evident in vigilant attention performance. There are large inter-individual differences in the degree of cognitive deficits—such differences are manifested in proportionality between the mean and variance as sleep loss progresses. It has recently been demonstrated via laboratory experiments that differential neurobehavioral vulnerability to sleep deprivation is not random—but rather is stable and trait-like—strongly suggesting a phenotypic response with possible genotypic involvement. These experiments also showed that vulnerability was not explained by subjects' baseline functioning or a number of other potential predictors. Differential vulnerability has been shown to extend to chronic partial sleep deprivation. One potential genetic biomarker for such differential vulnerability is the human leukocyte antigen (HLA) DQB1*0602, an allele which we recently demonstrated predicts interindividual differences in sleepiness, physiological sleep, and fatigue to chronic partial sleep deprivation in healthy adults. Determination of biomarkers of individual differences to sleep loss will help identify those individuals in the general population who are most in need of prevention of sleep debt and in need of effective countermeasures for sleep loss; will further understanding and management of vulnerability to excessive sleepiness due to common sleep and medical disorders; and will inform public policies pertaining to the need for adequate sleep.


Goel N; Dinges DF. Behavioral and genetic markers of sleepiness. J Clin Sleep Med 2011;7(5):Supplement S19–S21.

Prevalence and Consequences of Sleep Loss

Studies estimate that 20% to 40% of the adult US population sleep less than 7 hours per night1—the minimum sleep duration necessary to prevent cumulative deterioration in performance on a range of cognitive tasks.2,3 The proportion of people curtailing their sleep due to lifestyle is increasing,1 and is likely higher than surveys indicate, since physiological sleep duration is typically at least one hour less than self-reported sleep duration.4,5 Moreover, sleep loss has become a significant public health concern as population studies have found reduced sleep duration (less than 7 hours) associated with increased risks of obesity, morbidity, and mortality.68

Sleep loss, including chronic partial sleep deprivation (PSD)—a condition experienced by millions of people on a consecutive and daily basis—can result from medical conditions, sleep disorders, work demands, stress/emotional distress, and social/domestic responsibilities.1 In addition, for the majority of people, sleep loss directly causes significant risks via increased fatigue and sleep propensity, and via deficits in mood and neurocognitive functions including vigilant and executive attention, cognitive speed and working memory, and executive functions.1,9,10

Stable Trait-like Individual Differences in Response to Sleep Loss

Our laboratory was the first to experimentally demonstrate that subjects undergoing acute total sleep deprivation (TSD)—in which no sleep is obtained—show differential vulnerability to sleep loss, demonstrating robust inter-individual (trait-like, phenotypic) differences in response to the same laboratory conditions, as measured by various physiological and subjective sleep measures and neurobehavioral tasks sensitive to sleep loss.11,12 The intraclass correlation coefficients (ICCs)—which express the proportion of variance in the data explained by systematic interindividual variability—revealed that stable responses accounted for 58% and 68% of the overall variance in Psychomotor Vigilance Test (PVT) lapses (greater than 500 ms reaction times) between multiple sleep-deprivation exposures in the same subjects.2,1214 Thus, individuals who showed high PVT lapse rates during TSD after one exposure also showed high PVT lapse rates during a second exposure; similarly, those with low PVT lapse rates during one exposure showed low PVT lapse rates during a second exposure. Most importantly, because these high ICCs were found when the subjects were exposed to TSD 2-3 times under markedly different conditions (e.g., high versus low stimulation13; 6 h versus 12 h sleep time per night11), the vast differences in cognitive vulnerability to sleep deprivation are considered trait-like. While some individuals are highly vulnerable to cognitive performance deficits when sleep deprived (Type 3 responses), others show remarkable levels of cognitive resistance to sleep loss (Type 1 responses), and others show intermediate (Type 2) responses.12

Other researchers have confirmed our findings of large, stable differences in cognitive responses to acute TSD.15,16 Notably, such differences have not been accounted for by baseline functioning, by circadian morningness-eveningness, by demographic factors (e.g., age, sex, IQ), or by habitual sleep timing; psychometric scales also have not reliably identified cognitively vulnerable individuals.2,11,14 Our group2,17 and others18 have found similar differential vulnerability to chronic PSD, in which sleep is restricted to 3-7 hours time in bed per night.

Genetic Biomarkers of Individual Differences in Response to Sleep Loss

The stable, trait-like inter-individual differences observed in response to acute TSD, with intraclass correlation coefficients accounting for 58%-92% of the variance in neurobehavioral measures,11,12 point to an underlying genetic component. Despite this link, however, relatively little is known about the genetic basis of differential vulnerability in healthy subjects undergoing deprivation.9,19

Available data suggest that common genetic polymorphisms (variations) involved in sleep-wake, circadian, and cognitive regulation may underlie these large phenotypic differences in neurobehavioral vulnerability to sleep deprivation and may thus represent biomarkers in healthy adults.9,19,20

To this end, we investigated the role of the human leukocyte antigen (HLA) DQB1*0602 allele in response to chronic PSD. The DQB1*0602 allele is closely associated with narcolepsy, a neurological sleep disorder characterized by excessive daytime sleepiness, fragmented sleep, and shortened REM latency,21 and is also found in 12-38% of healthy adult sleepers in the general population.21,22 During baseline, although DQB1*0602 positive subjects were significantly sleepier and more fatigued by self report, they showed greater sleep fragmentation, and decreased sleep homeostatic pressure (measured by slow-wave energy; SWE) and differentially sharper declines during the night.23 During chronic PSD, despite SWE elevation comparable to DQB1*0602 negative subjects, DQB1*0602 positive subjects were sleepier and showed more fragmented sleep. Moreover, they showed differentially greater reductions in REM latency and smaller reductions in stage 2 sleep, along with differentially greater increases in fatigue. Both groups demonstrated comparable cumulative decreases in cognitive performance and increases in physiological sleepiness to chronic PSD.23

DQB1*0602 positivity in a healthy population may represent a continuum of some sleep-wake features of narcolepsy. DQB1*0602 was associated with inter-individual differences in sleep homeostasis, physiological sleep, sleepiness and fatigue, but not in cognitive measures, during baseline and chronic PSD.23 Therefore, DQB1*0602 may represent a genetic biomarker for predicting individual differences in both basal and sleep loss conditions. The influence of the DQB1*0602 allele on sleep homeostatic and neurobehavioral responses has not been examined in healthy subjects undergoing acute TSD nor have our findings been replicated in an independent sample of individuals undergoing chronic PSD.

Future Directions in the Search for Biomarkers to Sleep Loss

Currently, it remains unknown whether the same individuals vulnerable to the adverse effects of acute TSD are also vulnerable to chronic PSD. The handful of published reports comparing responses to both acute TSD and chronic PSD have used small sample sizes and limited assessments,2,24,25 and only one published study24 has systematically studied the same subjects in both types of sleep loss. Considering this, specific candidate genes may play different roles in the degree of vulnerability and/or resilience to the neurobehavioral and homeostatic effects of acute TSD and chronic PSD. Future studies are needed to explore this avenue of research and to determine predictors of those individuals most vulnerable to the neurobehavioral effects of both types of sleep loss.

With the exception of two recent studies,17,23 all candidate gene studies involving sleep physiological and neurobehavioral variables have employed small sample sizes and have only examined homozygotic individuals.2631 Larger sample sizes and assessment of phenotype-genotype relationships in both homozygous and heterozygous individuals are needed to definitively determine whether such candidate genes involved in regulation of sleep-wake, circadian, and cognitive functions are associated with inter-individual neurobehavioral responses to sleep loss across an entire population. This is particularly critical given that individuals are necessarily categorized into different genotypes, reducing sample sizes in each subgroup. Thus, future candidate gene studies must employ sample sizes in the hundreds to detect statistically reliable differences across genotypes. In addition, replication of findings in independent samples is needed to determine whether findings are genuine and are not due to chance. Ideally, studies should also be replicated in different ethnic groups to increase generalizability of the findings. Finally, other genetic approaches, including GWAS and linkage studies, are needed to complement candidate gene methods, for assessing individual differences at baseline as well as in response to sleep deprivation.


The impairing effects of sleep loss on a variety of neurobehavioral functions are well-established consequences of sleep deprivation. However, there are substantial differences in the extent to which individuals experience such deficits when sleep deprived. In recent years—since our group originally identified such stable, phenotypic neurobehavioral vulnerability to sleep loss in healthy adults—a rapidly growing search for biomarkers of this neurobehavioral vulnerability has emerged in an effort to identify this large and critical source of variance in human neurobehavioral responses to sleep deprivation. Indeed, recent seminal findings employing candidate gene techniques highlight the feasibility of such efforts, but future research in this area must employ both initial and replicate sample sizes large enough to ensure that findings are reliable.


Dr. Goel has indicated no financial conflicts of interest. Dr. Dinges serves on the science advisory board of Mars, Inc.; has received research support from Merck – Co.; and is compensated by the Associated Professional Sleep Societies, LLC for serving as Editor in Chief of SLEEP.


This conference summary was supported by NIH NR004281, CTRC UL1RR024134, by the National Space Biomedical Research Institute through NASA NCC 9-58 and by the Department of the Navy, Office of Naval Research (Award No. N00014-11-1-0361). Editing of the conference proceedings supported by HL104874.

This summary is based on a presentation delivered by Namni Goel, Ph.D. at the “Finding a Research Path for the Identification of Biomarkers of Sleepiness” Conference, Harvard University Medical School, Division of Sleep Medicine, Boston, MA, USA, September 21-22, 2010.



Banks S, Dinges DF, authors. Behavioral and physiological consequences of sleep restriction in humans. J Clin Sleep Med. 2007;3:519–28. [PubMed Central][PubMed]


Van Dongen HPA, Maislin G, Mullington JM, et al., authors. The cumulative cost of additional wakefulness: Dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation. Sleep. 2003;26:117–26. [PubMed]


Belenky G, Wesensten NJ, Thorne DR, et al., authors. Patterns of performance degradation and restoration during sleep restriction and subsequent recovery: A sleep-dose response study. J Sleep Res. 2003;12:1–12. [PubMed]


Silva GE, Goodwin JL, Sherrill DL, et al., authors. Relationship between reported and measured sleep times: The Sleep Heart Health Study (SHHS). J Clin Sleep Med. 2007;3:622–30. [PubMed Central][PubMed]


Lauderdale DS, Knutson KL, Yan LL, et al., authors. Self-reported and measured sleep duration: How similar are they? Epidemiology. 2008;19:838–45. [PubMed Central][PubMed]


Krueger PM, Friedman EM, authors. Sleep duration in the United States: A cross-sectional population-based study. Am J Epidemiol. 2009;169:1052–63. [PubMed Central][PubMed]


Ferrie JE, Shipley MJ, Cappuccio FP, et al., authors. A prospective study of change in sleep duration: Associations with mortality in the Whitehall II cohort. Sleep. 2007;30:1659–66. [PubMed Central][PubMed]


Cappuccio FP, Taggart FM, Kandala NB, et al., authors. Meta-analysis of short duration and obesity in children and adults. Sleep. 2008;31:619–26. [PubMed Central][PubMed]


Goel N, Rao H, Durmer JS, Dinges DF, authors. Neurocognitive consequences of sleep deprivation. Semin Neurol. 2009;29:320–39. [PubMed]


Lim J, Dinges DF, authors. A meta-analysis of the impact of short-term sleep deprivation on cognitive variables. Psychol Bull. 2010;136:375–89. [PubMed]


Van Dongen HPA, Baynard MD, Maislin G, et al., authors. Systematic interindividual differences in neurobehavioral impairment from sleep loss: Evidence of trait-like differential vulnerability. Sleep. 2004;27:423–33. [PubMed]


Van Dongen HP, Maislin G, Dinges DF, authors. Dealing with interindividual differences in the temporal dynamics of fatigue and performance: importance and techniques. Aviat Space Environ Med. 2004;75:A147–A154. [PubMed]


Dijkman M, Sachs N, Levine E, et al., authors. Effects of reduced stimulation on neurobehavioral alertness depend on circadian phase during human sleep deprivation. Sleep Res. 1997;26:265


Van Dongen HPA, Dijkman MV, Maislin G, et al., authors. Phenotypic aspect of vigilance decrement during sleep deprivation. Physiologist. 1999;42:A-5


Leproult R, Colecchia EF, Berardi AM, et al., authors. Individual differences in subjective and objective alertness during sleep deprivation are stable and unrelated. Am J Physiol Regulat Integr Comp Physiol. 2003;284:R280–90


Frey DJ, Badia P, Wright KP Jr, authors. Inter- and intra-individual variability in performance near the circadian nadir during sleep deprivation. J Sleep Res. 2004;13:305–315. [PubMed]


Goel N, Banks S, Mignot E, Dinges DF, authors. PER3 polymorphism predicts cumulative sleep homeostatic but not neurobehavioral changes to chronic partial sleep deprivation. PLoS ONE. 2009;4:e5874[PubMed Central][PubMed]


Bliese PD, Wesensten NJ, Balkin TJ, authors. Age and individual variability in performance during sleep restriction. J Sleep Res. 2006;15:376–85. [PubMed]


Goel N, author. Genetics of sleep timing, duration and homeostasis in humans. Sleep Med Clin. 2011;6:171–82


Landolt HP, author. Genotype-dependent differences in sleep, vigilance, and response to stimulants. Curr Pharm Des. 2008;14:3396–407. [PubMed]


Dauvilliers Y, Tafti M, authors. Molecular genetics and treatment of narcolepsy. Ann Med. 2006;38:252–62. [PubMed]


Mignot E, Young T, Lin L, Finn L, authors. Nocturnal sleep and daytime sleepiness in normal subjects with HLA DQB1*0602. Sleep. 1999;22:347–52. [PubMed]


Goel N, Banks S, Mignot E, Dinges DF, authors. DQB1*0602 predicts interindividual differences in physiologic sleep, sleepiness and fatigue. Neurology. 2010;75:1509–19. [PubMed]


Drake CL, Roehrs TA, Burduvali E, et al., authors. Effects of rapid versus slow accumulation of eight hours of sleep loss. Psychophysiology. 2001;38:979–87. [PubMed]


Rowland LM, Thomas ML, Thorne DR, et al., authors. Oculomotor responses during partial and total sleep deprivation. Aviat Space Environ Med. 2005;76:C104–13. [PubMed]


Viola AU, Archer SN, James LM, et al., authors. PER3 polymorphism predicts sleep structure and waking performance. Curr Biol. 2007;17:613–8. [PubMed]


Groeger JA, Viola AU, Lo JC, et al., authors. Early morning executive functioning during sleep deprivation is compromised by a PERIOD3 polymorphism. Sleep. 2008;31:1159–67. [PubMed Central][PubMed]


Bodenmann S, Xu S, Luhmann U, et al., authors. Pharmacogenetics of modafinil after sleep loss: Catechol-O-Methyltransferase genotype modulates waking functions but not recovery sleep. Clin Pharmacol Ther. 2009;85:296–304. [PubMed]


Bodenmann S, Rusterholz T, Dürr R, et al., authors. The functional Val158Met polymorphism of COMT predicts interindividual differences in brain α oscillations in young men. J Neurosci. 2009;29:10855–10862. [PubMed]


Rétey JV, Adam M, Honegger E, et al., authors. A functional genetic variation of adenosine deaminase affects the duration and intensity of deep sleep in humans. Proc Natl Acad Sci USA. 2005;102:15676–15681. [PubMed Central][PubMed]


Rétey JV, Adam M, Khatami R, et al., authors. A genetic variation in the adenosine A(2A) receptor gene (ADORA2A) contributes to individual sensitivity to caffeine effects on sleep. Clin Pharmacol Ther. 2007;81:692–698. [PubMed]