SEARCH

SEARCH BY CITATION

Keywords:

  • genetic;
  • human leucocyte antigen DQB1*0602 allele;
  • obstructive sleep apnoea syndrome;
  • polymorphism;
  • sleep;
  • spectral analysis

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Subjects
  6. Results
  7. Discussion
  8. Acknowledgements
  9. Disclosure Statement
  10. References

Human leucocyte antigen (HLA) DQB1*0602 allele, a well-known genetic risk factor for narcolepsy, has been associated with sleep parameters in healthy subjects. We aimed to assess the association of this allele with daytime sleepiness and altered sleep electroencephalogram characteristics in the general population and in patients with obstructive sleep apnoea syndrome (OSAS). Eight hundred and ninety-four individuals from the Epidemiologic Study of Sleep were genotyped for the HLA DQB1*0602 allele. Full-night polysomnography was performed, and daytime sleepiness was analysed according to the Epworth Sleepiness Scale. HLA-DQB1*0602 allele-positive and -negative subjects in the general population, as well as in patients with OSAS, exhibited similar sleep parameters and levels of daytime sleepiness. However, spectral analysis showed that allele-positive individuals with OSAS exhibited higher theta power during sleep Stage 1 (< 0.05) in occipital derivations, and lower delta power during sleep Stages 1 and 2 (< 0.01) compared with individuals negative for the allele, even after correction for potential confounders as age, sex, body mass index and European ancestry. No significant differences in the electroencephalogram variables were found in individuals without OSAS. The data highlight the HLA-DQB1*0602 as a potential genetic factor influencing sleep physiology in individuals diagnosed with OSAS.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Subjects
  6. Results
  7. Discussion
  8. Acknowledgements
  9. Disclosure Statement
  10. References

Evidence suggests that sleep disorders, such as narcolepsy and obstructive sleep apnoea syndrome (OSAS), are a result of complex interactions between genetic and environmental factors (Pack and Pien, 2011; Caylak, 2009). Narcolepsy, a condition present in approximately 0.018–0.040% of the population, is characterized by excessive daytime sleepiness. It may be accompanied by rapid eye movement (REM) sleep intrusions into wakefulness, such as cataplexy, sleep paralysis and hypnagogic hallucinations (Coelho et al., 2007). Several studies have demonstrated that variants in the major histocompatibility complex class II DQ beta 1 gene (HLA-DQB1) are associated strongly with increased risk of developing narcolepsy. Up to 95% of Caucasians with narcolepsy and cataplexy and approximately 40–50% of patients without cataplexy carry the HLA-DQB1*0602 allele (Coelho et al., 2007, 2009). Nevertheless, the HLA-DQB1*0602 allele is also present in asymptomatic, healthy individuals with a prevalence of 25% in Caucasian, 12% in Japanese populations and 38% in African American populations (Coelho et al., 2009; Guilleminault and Fromherz, 2005). Studies have suggested that asymptomatic HLA-DQB1*0602-positive individuals present different sleep patterns when compared with HLA-DQB1*0602-negative individuals. These differences include shorter REM sleep latency, increased sleep efficiency and increased REM sleep (Mignot et al., 1999). Furthermore, older insomniacs with the HLA-DQB1*0602 allele consider themselves less well rested than allele-negative subjects with the same sleep efficiency (Zeitzer et al., 2011). In addition, a recent study demonstrated that HLA-DQB1*0602-positive subjects show increased sleepiness, fatigue and more fragmented sleep when compared to allele-negative subjects at baseline and partial sleep deprivation (Goel et al., 2010).

Obstructive sleep apnoea syndrome is characterized by repeated episodes of airflow reduction or cessation due to partial or total upper airway obstruction during sleep (Park et al., 2011). The consequent sleep fragmentation is associated normally with daytime sleepiness, and is thought to cause automobile and work accidents as well as cognitive changes (Leger et al., 2012). A recent epidemiological study conducted by our group showed that OSAS is present in one-third of the population of the city of São Paulo. Of this population, 38.9% subjects experienced fatigue and 55% experienced sleepiness (Tufik et al., 2010), as measured by validated questionnaires, suggesting individual variability in relation to OSAS consequences.

As an extension to these previous studies, we aimed to assess: (i) if the HLA DQB1*0602 allele is associated with different levels of daytime somnolence in patients with and without OSAS; and (ii) if the presence of the HLA DQB1*0602 allele influences sleep and sleep EEG parameters which could, in turn, help to explain the variability observed in neurocognitive performance and excessive daytime sleepiness reported by individuals with OSAS.

Subjects

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Subjects
  6. Results
  7. Discussion
  8. Acknowledgements
  9. Disclosure Statement
  10. References

Participants were selected from the São Paulo Epidemiologic Sleep Study (EPISONO), a study that involved 1101 individuals in the city of São Paulo. This sample was representative of the gender, age (20–80 years) and socioeconomic distribution of the city. To select this sample, a three-stage cluster sampling technique was applied. In the first stage, 96 districts from distinct socioeconomic regions of São Paulo were selected to sample from different strata of wealth. In the second stage, 11 households were selected from each economic region. The inhabitants of the houses selected were arranged according to age and in the third stage, and a subject was selected (Santos-Silva et al., 2009). Informed consent was obtained from all subjects. Subjects underwent full-night polysomnography (PSG). Blood samples were collected the day following PSG for hormone and biochemical measurements, as well as for DNA extraction and polymorphism genotyping. Detailed methods have been published previously (Santos-Silva et al., 2009). The protocol for this study was approved by the Ethics Committee for Research of the Universidade Federal de São Paulo (CEP0593/06) and registered with ClinicalTrials.gov (number NCT00596713).

Polysomnography

A full-night PSG was performed using a digital system (EMBLA_S7000; Embla Systems, Inc., Broomfield, CO, USA) at the sleep laboratory during the subject's regular sleep time. Subjects were monitored with electroencephalography (EEG) (C3-A2, C4-A1, O1-A2, O2-A1), electro-oculography (EOG-Left-A2, EOG-Right-A1), electromyography (EMG) (submentonian region, anterior tibialis muscle, masseter region and seventh intercostal space) and electrocardiography (ECG). Additionally, airflow, respiratory effort, snoring, body position and oxyhaemoglobin saturation (SpO2) measurements were taken. A total of 1042 individuals underwent PSG, and 5.4% of the sample refused the test (Santos-Silva et al., 2009).

Clinical assessment

Obstructive sleep apnoea syndrome was diagnosed according to the International Classification of Sleep Disorders (ICSD-2) criteria (Iber et al., 2007). Subjects were diagnosed with OSAS if they had an Apnoea–Hyponoea index (AHI) between 5 and 14.9 and presented with at least one of the following symptoms: loud snoring, daytime sleepiness, fatigue or interrupted breathing during sleep. Subjects with an AHI equal to or higher than 15 were diagnosed with OSAS regardless of whether they had any additional complaints. Subjects with Epworth Sleepiness Scale scores greater than nine were diagnosed with daytime sleepiness (Johns, 1991). Subjects taking medications that could interfere with sleep (e.g. antidepressants, narcoleptics) were excluded from the analysis.

Genotyping

Genomic DNA was extracted from leucocytes using the salting-out technique. The presence or absence of the HLA-DQB1*0602 allele was determined using polymerase chain reaction (PCR). DQBF (5′-CCGCAGAGGATTTCGTGTT-3′) and DQBR (5′-AACTCCGCCCGGGTCCC-3′)-specific primers were used. EX3F (5′-TGCCAAGTGGAGCACCCAA-3′) and EX3R (5′-GCATCTTGCTCTGTGCAGAT-3′) primers were also included as internal positive controls. Thirty-five cycles at 95 °C for 30 s, 59.6 °C for 30 s and 72 °C for 60 s were used as the PCR cycling conditions.

Considering the high level of heterogeneity of the Brazilian population selected for this study, we performed a population stratification analysis using 31 unlinked polymorphic markers to identify population subdivisions that could confound the case–control association studies. These 31 markers occurred at different frequencies among the Brazilian founder populations. ArrayTape allele-specific PCR (Douglas Scientific, Alexandria, MN, USA) was used for genotyping. structure version 2.1 (Software made by Pritchard et al., 2000; Department of Statistics, University of Oxford, UK), a program based on a Markov chain Monte Carlo algorithm, was used to verify the ancestral population and admixture of the individuals in the sample (Guindalini et al., 2010; Pritchard et al., 2000).

Spectral analysis of sleep EEG

Spectral analysis of sleep EEGs was conducted using a specific syntax using r (version 2.10.1; R Development Core Team, 2012). EEG waves from C3-A2, C4-A1, O1-A2 and O2-A1 derivations were decomposed into delta (<4 Hz), theta (4–7.9 Hz), alpha 1 (8–9.9 Hz), alpha 2 (10–12.9 Hz), beta 1 (13–17.9 Hz), beta 2 (18–29.9 Hz) and gamma (≥30 Hz) bandwidths using fast Fourier transformation. To identify artefacts, descriptive data (mean, standard deviation, median and interquartile range) from each 20-s window were calculated and the 5% time-windows containing the highest amplitude (maximum–minimum) at each sleep stage were identified as outliers and excluded from the analysis. To validate this procedure, an experienced polysomnographist evaluated 30 polysomnographies visually and blindly excluded artefacts. The visual analysis and the r-syntax results were compared using a kappa test (κ = 0.79; = 0.002). The results showed good concordance between the reader and the automatic method and validated the use of these criteria for excluding artefacts. The results are shown as the mean spectral power (μV2 Hz−1) ± standard deviation.

Statistical analysis

Chi-square and Fisher's exact tests were used to assess possible associations between the presence or absence of the HLA-DQB1*0602 allele and clinical and polysomnography variables. Student's t-test was used to analyse quantitative variables. Odds ratios (OR) and 95% confidence intervals (CI) were derived from logistic regression models. A general linear model adjusted for potential confounders was applied to the association between EEG variables and the HLA-DQB1*0602 genotype groups. The Statistical Package for Social Sciences version 18.0 (SPSS, Inc., Chicago, IL, USA) was used, and P-values less than 0.05 were considered significant.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Subjects
  6. Results
  7. Discussion
  8. Acknowledgements
  9. Disclosure Statement
  10. References

Valid genotyping for the HLA-DQB1*0602 allele was available for 894 of the 1042 individuals who underwent PSG. In total, 742 (83.0%) were HLA-DQB1*0602-negative and 152 (17.0%) were HLA-DQB1*0602-positive. A total of 94 subjects (9%) were excluded due to the use of medications that have the potential to influence sleep.

No association was found between the presence of the HLA-DQB1*0602 allele and excessive daytime sleepiness in the general population (= 0.78). The frequency of individuals positive for the allele was 17.4% in individuals without daytime sleepiness and 16.5% in subjects who reported excessive daytime sleepiness. Rates of self-reported sleepiness were also similar in both HLA-DQB1*0602-positive and HLA-DQB1*0602-negative subjects with OSAS. In total, 16.4% of patients diagnosed with OSAS who reported daytime sleepiness were allele-positive, while 13.7% of patients who did not report high levels of daytime sleepiness were allele-positive (= 0.62). Including potential confounding variables [sex, age, body mass index (BMI) and genetic ancestry] in the logistic regression analyses did not change the risk of daytime somnolence in the general population (OR: 0.93; 95% CI: 0.64–1.35; = 0.70) or OSAS patients (OR: 1.05; 95% CI: 0.53–2.10; = 0.89). However, a larger, but not significant, number of individuals with severe OSAS and daytime sleepiness were HLA-DQB1*0602-positive compared with HLA-DQB1*0602-negative individuals (24.1 versus 9.7%; = 0.18; Table 1).

Table 1. Frequency of subjects HLA-DQB1*0602-positive and -negative with and without daytime sleepiness according to the Epworth Sleepiness Scale
 Daytime sleepinessNo daytime sleepiness P
DQB1*062+DQB1*0602–DQB1*0602+DQB1*0602–
  1. OSAS, obstructive sleep apnoea syndrome.

General population (%)16.5 (58)83.5 (293)17.4 (91)82.6 (431)0.78
Mild OSAS (%)12.9 (9)87.1 (61)20.5 (23)79.5 (89)0.23
Moderate OSAS (%)16.7 (4)83.3 (20)11.1 (5)88.9 (40)0.70
Severe OSAS (%)24.1 (7)75.9 (22)9.7 (3)90.3 (28)0.18

The presence of the allele did not seem to influence PSG sleep parameters in individuals with or without OSAS. Sleep latency, REM sleep latency, total sleep time and sleep efficiency, among other variables, were also similar between HLA-DQB1*0602-positive and HLA-DQB1*0602-negative subjects (> 0.05, Table 2). However, spectral analysis showed that individuals with OSAS who were HLA-DQB1*0602-positive presented higher theta power and lower delta power than individuals with OSAS who were negative for the allele. During sleep Stage 1, HLA-DQB1*0602-positive subjects with OSAS exhibited a theta power in the O1-A2 derivation of 4.9 ± 1.41 μV2 Hz−1, while HLA-DQB1*0602-negative subjects showed a theta power of 4.2 ± 1.14 μV2 Hz−1 (= 0.024). During sleep Stage 2, theta power was 4.7 ± 1.27 μV2 Hz−1 in allele-positive subjects and 4.2 ± 1.13 μV2 Hz−1 in allele-negative subjects (= 0.038). Furthermore, delta power measured in the O1-A2 EEG derivation during sleep Stage 1 was 13.6 ± 2.97 μV2 Hz−1 in allele-positive individuals with OSAS and 15.0 ± 2.55 μV2 Hz−1 in allele-negative individuals with OSAS (= 0.004). During sleep Stage 2, delta power was 16.4 ± 3.66 μV2 Hz−1 in allele-positive subjects with OSAS and 18.0 ± 2.18 μV2 Hz−1 in allele-negative subjects with OSAS (= 0.027) (Fig. 1).

image

Figure 1. Spectral analysis of O1-A2 and O2-A1 electroencep-halograph (EEG) derivations of theta and delta waves during sleep Stages 1 and 2 in human leucocyte antigen (HLA)-DQB1*0602-positive and HLA-DQB1*0602-negative subjects with and without obstructive sleep apnoea syndrome (OSAS). O2a1tetaN1: theta power measured in the O2-A1 derivation during Stage 1; o2a1thetaN2: theta power measured in the O2-A1 derivation during Stage 2; o1a2thetaN1: theta power measured in the O1-A2 derivation during Stage 1; o1a2deltaN1: delta power measured in the O1-A2 derivation during Stage 1; o1a2deltaN: delta power measured in the O1-A2 derivation during Stage 2.

Download figure to PowerPoint

Table 2. Sleep variables measured by polysomnography in DQB1*0602-positive and -negative subjects with and without Obstructive Sleep apnoea Syndrome (OSAS) diagnosis; data are described as mean ± standard deviation
 OSA (= 288)No OSA (= 606)
DQB10602+DQB10602− P DQB10602+DQB10602− P
  1. REM, rapid eye movement.

Sleep latency (min)19.27 ± 22.3814.44 ± 15.750.1817.32 ± 22.1115.61 ± 20.840.44
REM sleep latency (min)108.20 ± 56.04101.01 ± 54.830.4395.79 ± 43.8693.70 ± 45.150.66
Total sleep time (min)334.32 ± 77.35330.65 ± 70.010.76359.89 ± 75.24347.65 ± 78.520.14
Sleep efficiency (%)78.18 ± 14.7580.06 ± 12.370.3783.70 ± 11.7383.40 ± 12.200.81
Stage 1 (%)5.01 ± 3.335.15 ± 3.420.814.45 ± 3.814.10 ± 2.870.27
Stage 2 (%)55.75 ± 9.3655.30 ± 9.900.7853.65 ± 8.0653.76 ± 8.520.90
Stages 3 and 4 (%)21.17 ± 8.4220.69 ± 8.380.7322.09 ± 7.3222.88 ± 7.580.32
REM sleep (%)18.06 ± 6.6418.87 ± 6.220.4319.81 ± 5.7019.27 ± 6.440.41
Minutes awake75.80 ± 56.8968.33 ± 45.110.4252.01 ± 37.4653.86 ± 43.510.68
Arousals per hour index23.07 ± 14.6721.65 ± 14.170.5510.67 ± 6.4311.45 ± 6.860.27

A general linear model was applied to assess the effect of possible factors that might confound the associations of the HLA-DQB1*0602 allele among individuals with and without OSAS. After adjustment for age, sex, BMI and European ancestry, the association between the allele and higher theta power during sleep Stage 1 measured in the O1A2 (= 0.025) and O2A1 derivations (= 0.012), and lower delta power during sleep Stages 1 (= 0.003) and 2 (< 0.001), remained significant. The association with higher theta power during sleep Stage 2 approached significance (= 0.062). In control individuals, no significant differences in sleep Stages 1, 2, 3 + 4 or REM were observed for any spectral analysis parameter among individuals positive or negative for the HLA-DQB1*0602 allele.

In addition, to assess the effect of AIH as a continuous variable, instead of the presence of OSAS diagnosis as a dichotomous variable, the same general linear model was performed in all individuals regardless of OSAS status, using AIH as a covariate in the model. Interestingly, the only significant predictor of the lower delta power during sleep Stage 2 measured in O1A2 derivations was the presence of HLA DQB1*0602 allele (= 0.002), suggesting that the association between the allele and EEG spectral power might occur in all individuals corrected for AIH, but are more pronounced when both AIH and clinical complaints are taken into consideration in individuals positive for OSAS.

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Subjects
  6. Results
  7. Discussion
  8. Acknowledgements
  9. Disclosure Statement
  10. References

To the best of our knowledge, this study shows for the first time that patients with OSAS carrying the HLA-DQB1*0602 allele present similar levels of reported daytime sleepiness, as measured by the Epworth Sleepiness Scale, and no differences in macrostructural sleep parameters compared to patients without this allele. However, the presence of the HLA-DQB1*0602 allele is associated with higher theta power during sleep Stage 1 and lower delta power during sleep Stages 1 and 2. Interestingly, in patients without OSAS diagnosis, no significant differences were found in relation to EEG parameters between HLA-DQB1*0602-positive and -negative subjects.

In agreement with our study, Mignot et al. (1999) found no differences in daytime sleepiness between individuals with and without the HLA-DQB1*0602 allele. However, this and other groups demonstrated an association of the HLA-DQB1*0602 allele with interindividual sleep pattern variation (Goel et al., 2010; Mignot et al., 1999; Zeitzer et al., 2011). Goel et al. (2010) showed, among other differences, that individuals who carry the allele presented a higher number of awakenings. Additionally, these individuals exhibited decreased sleep Stage 3, increased Stage 2 and greater disrupted sleep during partial sleep deprivation. Zeitzer et al. (2011) demonstrated that older subjects with insomnia who were HLA-DQB1*0602-positive presented faster EEG frequencies during REM sleep and decrease of restedness when compared with those without the allele. However, there were no significant differences in daytime sleepiness or other sleep characteristics measured by PSG, sleep log or actigraphy between these groups. Mignot et al. (1999) found that HLA-DQB1*0602-positive individuals had shorter REM latencies and differences that indicate greater sleep continuity (increased sleep efficiency, decreased percentage of Stage 1 and percentage of wake after sleep onset, increased percentage of REM sleep and decreased nocturnal sleep latency).

In contrast, Goel et al. (2010) demonstrated an association between healthy, HLA-DQB1*0602-positive subjects and daytime sleepiness and fatigue during baseline and partial sleep deprivation. Differences in study protocols may explain these divergent results. Goel et al. (2010) applied a modified Maintenance of Wakefulness Test, the Karolinska Sleepiness Scale to measure fatigue and the Profile of Mood States to determine sleepiness and fatigue. In contrast, we applied the Epworth Sleepiness Scale. Nevertheless, it should be noted that a relatively large number of HLA-DQB1*0602-positive individuals with severe OSAS report daytime sleepiness compared to allele-negative individuals. Furthermore, we found that allele frequency was lower in individuals with mild and moderate OSAS. This result suggests that the effect of the HLA-DQB1*0602 allele in patients with severe OSAS may be biologically relevant and should be investigated further, despite the lack of statistical significance.

In addition, consistent with Goel et al. (2010) and Zeitzer et al. (2011), we found that, in patients with OSAS, subjects positive for the HLA-DQB1*0602 allele present differences in EEG physiology. Zeitzer et al. (2011) found that older, HLA-DQB1*0602-positive individuals with insomnia had lower theta and alpha bands and higher sigma, beta and gamma bands during REM sleep. However, there were no differences in the delta band of this sample, which indicates faster EEG frequencies. Moreover, Goel et al. (2010) found that HLA-DQB1*0602-positive individuals exhibited decreased slow wave energy and activity during baseline. In this study, we showed that HLA-DQB1*0602-positive individuals with OSAS presented higher theta power in sleep Stage 1 and lower delta power in sleep Stages 1 and 2 than allele-negative individuals with OSAS. These results suggest that allele-positive individuals may experience more superficial sleep which may, in turn, cause the variation in daytime sleepiness reported by these patients. It is important to note that these results should be interpreted cautiously, as delta power is known to be usually high at frontal leads and the present study evaluated only central and occipital derivations, as stated by the Rechtschaffen and Kales (1968) guidelines used to set the polysomnography parameter at the time of the study.

Although we did not find higher levels of sleepiness in individuals who carry the HLA-DQB1*0602 allele, it is possible that the Epworth Sleepiness Scale may not have been sufficiently sensitive to detect intermediate variations in sleep propensity. This lack of sensitivity may be a result of the test's reliance on subjective reports rather than objective measurements. Indeed, correlations between the Epworth Sleepiness Scale and two other most commonly used tests (the Multiple Sleep Latency Test and the Maintenance of Wakefulness Test) have been shown to be statistically significant but correlated weakly in some studies (Chervin et al., 1999; Chervin et al. 1997), and not correlated significantly in evaluations of patients with sleep disorders related to breathing (Benbadis et al., 1999; Chervin and Aldrich, 1999). Therefore, the spectral analysis finding and the negative association between daytime sleepiness and the HLA-DQB1*0602 allele identified by the present study should be confirmed in a different experimental setting using other evaluation tools of sleepiness propensity.

The frequency of the HLA-DQB1*0602 allele in our sample was 16.3%, which is lower than the ~25% reported by previous studies (Mignot et al., 1999; Zeitzer et al., 2011). This difference may be due to the epidemiology of our sample and the well-documented heterogeneous genetic composition of the Brazilian population. This population is considered to be one of the most diverse in the world, consisting of descendents from three major ancestral populations (Europeans, Africans and Native Americans) (Guindalini et al., 2010; Parra et al., 2003). Therefore, in an attempt to eliminate the potential confounding effect of admixture, using genetic informative markers we estimated the individual ancestry proportions and included them as covariates in the tested models.

In conclusion, our findings indicate that the HLA-DQB1*0602 allele influences sleep physiology in individuals diagnosed with OSAS, a prevalent sleep disorder caused by environmental and genetics factors (Kent et al., 2010). However, further studies are required for understanding more clearly the association between the allele and interindividual variability in daytime sleepiness and wakefulness.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Subjects
  6. Results
  7. Discussion
  8. Acknowledgements
  9. Disclosure Statement
  10. References

This work was supported by grants from the Associação Fundo de Incentivo à Psicofarmacologia (AFIP) and FAPESP (no. 07/50 525-1 to RS-S, and CEPID no. 98/14 303-3 to ST). LRAB, ST and CG are recipients of the CNPq fellowship. All the efforts of AFIP's staff, in particular Roberta Siuffi, Diva Maria Lima, Sueli Sugama and Laura Sousa Costa, are deeply appreciated.

Disclosure Statement

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Subjects
  6. Results
  7. Discussion
  8. Acknowledgements
  9. Disclosure Statement
  10. References

All authors declare that there are no conflicts of interest.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Subjects
  6. Results
  7. Discussion
  8. Acknowledgements
  9. Disclosure Statement
  10. References
  • Benbadis, S., Mascha, E., Perry, M., Wolgamuth, B., Smolley, L. and Dinner, D. Association between the Epworth Sleepiness Scale and the Multiple Sleep Latency Test in a clinical population. Ann. Intern. Med., 1999, 16: 289292.
  • Caylak, E. The genetics of sleep disorders in humans: narcolepsy, restless legs syndrome, and obstructive sleep apnea syndrome. Am. J. Med. Genet. A., 2009, 149A: 26122626.
  • Chervin, R. and Aldrich, M. The Epworth Sleepiness Scale may not reflect objective measures of sleepiness or sleep apnea. Neurology, 1999, 52: 125131.
  • Chervin, R. D., Aldrich, M. S., Pickett, R. and Guilleminault, C. Comparison of the results of the Epworth Sleepiness Scale and the Multiple Sleep Latency Test. J. Psychosom. Res., 1997, 42: 145155.
  • Coelho, F., Elias, R., Pradella-Hallinan, M., Bittencourt, L. and Tufik, S. Narcolepsia. Rev. Psiquiatr. Clín., 2007, 34: 133138.
  • Coelho, F. M. S., Pradella-Hallinan, M., Predazolli Neto, M., Bittencourt, L. R. A. and Tufic, S. Prevalence of the HLA-DQB1*0602 allele in narcolepsy and idiopathic hypersomnia patients seen at a sleep disorders outpatient unit in São Paulo. Rev. Bras. Psiquiatr., 2009, 10: 3943.
  • Goel, N., Banks, S., Mignot, E. and Dinges, D. F. DQB1*0602 predicts interindividual differences in physiologic sleep, sleepiness, and fatigue. Neurology, 2010, 75: 15091519.
  • Guilleminault, C. and Fromherz, S. Narcolepsy: diagnosis and management. In: M. H. Kryger, T. Roth and W. C. Dement (Eds) Principles and Practice of Sleep Medicine, 4th edn. W.B. Saunders Company, Philadelphia, 2005: 761779.
  • Guindalini, C., Colugnati, F. A., Pellegrino, R., Santos-Silva, R., Bittencourt, L. R. and Tufik, S. Influence of genetic ancestry on the risk of obstructive sleep apnoea syndrome. Eur. Respir. J., 2010, 36: 834841.
  • Iber, C., Ancoli-Israel, S., Chesson, J. A. and Quan, S. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. American Academy of Sleep Medicine, Westchester, IL, 2007.
  • Johns, M. W. A new method for measuring daytime sleepiness: the Epworth Sleepiness Scale. Sleep, 1991, 14: 540545.
  • Kent, B. D., Ryan, S. and McNicholas, W. T. The genetics of obstructive sleep apnoea. Curr. Opin. Pulm. Med., 2010, 16: 536542.
  • Leger, D., Bayon, V., Laaban, J. P. and Philip, P. Impact of sleep apnea on economics. Sleep Med. Rev., 2012, 16: 455462.
  • Mignot, E., Young, T., Lin, L. and Finn, L. Nocturnal sleep and daytime sleepiness in normal subjects with HLA-DQB1*0602. Sleep, 1999, 22: 347352.
  • Pack, A. I. and Pien, G. W. Update on sleep and its disorders. Ann. Rev. Med., 2011, 62: 477460.
  • Park, J. G., Ramar, K. and Olson, E. J. Updates on definition, consequences, and management of obstructive sleep apnea. Mayo Clin. Proc., 2011, 86: 549554.
  • Parra, F. C., Amado, R. C., Lambertucci, J. R., Rocha, J., Antunes, C. M. and Pena, S. D. Color and genomic ancestry in Brazilians. Proc. Natl Acad. Sci. USA, 2003, 100: 177182.
  • Pritchard, J., Stephens, M. and Donnelly, P. Inference of population structure using multilocus genotype data. Genetics, 2000, 155: 945959.
  • R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, 2012. ISBN 3-900051-07-0, URL http://www.R-project.org/.
  • Rechtschaffen, A. and Kales, A. A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. US Department of Health, Education, and Welfare, Public Health Services—National Institutes of Health, National Institute of Neurological Diseases and Blindness, Neurological Information Network, Bethesda, MD, 1968.
  • Santos-Silva, R., Tufik, S., Conway, S. G., Taddei, J. A. and Bittencourt, L. R. Sao Paulo Epidemiologic Sleep Study: rationale, design, sampling, and procedures. Sleep Med., 2009, 10: 679685.
  • Tufik, S., Santos-Silva, R., Taddei, J. A. and Bittencourt, L. R. Obstructive sleep apnea syndrome in the Sao Paulo Epidemiologic Sleep Study. Sleep Med., 2010, 11: 441446.
  • Zeitzer, J. M., Fisicaro, R. A., Grove, M. E., Mignot, E., Yesavage, J. A. and Friedman, L. Faster REM sleep EEG and worse restedness in older insomniacs with HLA DQB1*0602. Psychiatry Res., 2011, 187: 397400.