Literature DB >> 25469926

Novel loci associated with usual sleep duration: the CHARGE Consortium Genome-Wide Association Study.

D J Gottlieb1,2,3,4, K Hek5,6, T-H Chen1,3, N F Watson7,8, G Eiriksdottir9, E M Byrne10,11, M Cornelis12,13, S C Warby14, S Bandinelli15, L Cherkas16, D S Evans17, H J Grabe18, J Lahti19,20, M Li21, T Lehtimäki22, T Lumley23, K D Marciante24,25, L Pérusse26,27, B M Psaty24,25,28,29, J Robbins30, G J Tranah17, J M Vink31, J B Wilk32, J M Stafford33, C Bellis34, R Biffar35, C Bouchard36, B Cade2, G C Curhan13,37, J G Eriksson20,38,39,40,41, R Ewert42, L Ferrucci43, T Fülöp44, P R Gehrman45, R Goodloe46, T B Harris47, A C Heath48, D Hernandez49, A Hofman6, J-J Hottenga31, D J Hunter37,50, M K Jensen12, A D Johnson51, M Kähönen52, L Kao21, P Kraft37,50, E K Larkin53, D S Lauderdale54, A I Luik6, M Medici55, G W Montgomery11, A Palotie56,57,58, S R Patel2, G Pistis59,60,61,62, E Porcu61,62, L Quaye16, O Raitakari63, S Redline2, E B Rimm12,13,37, J I Rotter64, A V Smith9,65, T D Spector16, A Teumer66,67, A G Uitterlinden6,55,68, M-C Vohl27,69, E Widen56, G Willemsen31, T Young70, X Zhang51, Y Liu33, J Blangero34, D I Boomsma31, V Gudnason9,65, F Hu12,13,37, M Mangino16, N G Martin11, G T O'Connor3,4, K L Stone17, T Tanaka43, J Viikari71, S A Gharib8,24, N M Punjabi21,72, K Räikkönen19, H Völzke67, E Mignot14, H Tiemeier5,6,73.   

Abstract

Usual sleep duration is a heritable trait correlated with psychiatric morbidity, cardiometabolic disease and mortality, although little is known about the genetic variants influencing this trait. A genome-wide association study (GWAS) of usual sleep duration was conducted using 18 population-based cohorts totaling 47 180 individuals of European ancestry. Genome-wide significant association was identified at two loci. The strongest is located on chromosome 2, in an intergenic region 35- to 80-kb upstream from the thyroid-specific transcription factor PAX8 (lowest P=1.1 × 10(-9)). This finding was replicated in an African-American sample of 4771 individuals (lowest P=9.3 × 10(-4)). The strongest combined association was at rs1823125 (P=1.5 × 10(-10), minor allele frequency 0.26 in the discovery sample, 0.12 in the replication sample), with each copy of the minor allele associated with a sleep duration 3.1 min longer per night. The alleles associated with longer sleep duration were associated in previous GWAS with a more favorable metabolic profile and a lower risk of attention deficit hyperactivity disorder. Understanding the mechanisms underlying these associations may help elucidate biological mechanisms influencing sleep duration and its association with psychiatric, metabolic and cardiovascular disease.

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Year:  2014        PMID: 25469926      PMCID: PMC4430294          DOI: 10.1038/mp.2014.133

Source DB:  PubMed          Journal:  Mol Psychiatry        ISSN: 1359-4184            Impact factor:   15.992


INTRODUCTION

Usual sleep duration is an important determinant of daytime sleepiness; moreover, both short and long sleep duration have been consistently associated with psychiatric illness, hypertension, diabetes mellitus, coronary heart disease and mortality, although the mechanisms underlying these associations are poorly understood. Significant heritability of usual sleep duration has been reported from twin studies, with heritability estimates generally in the range of 0.40-0.55 [1-3]. A number of neurotransmitters and neuropeptides are known to regulate sleep-wake behavior, and genetic screens in non-mammalian vertebrates have demonstrated an important role of ion channels, which regulate neural activity (reviewed in [4]). Polymorphisms in the human period 2 (PER2) and casein kinase 1d (CSNK1D) genes, known elements of the circadian molecular clock, are associated with autosomal dominant advanced sleep phase syndrome in isolated pedigrees [5,6]. The genetic basis for heritability of usual sleep duration, however, remains largely unknown. Candidate gene studies have inconsistently implicated genes associated with the mammalian circadian clock, including BHLHE41 (DEC2) and CLOCK [7-9], and the glutamate receptor-encoding GRIA3 [10]. In a small genome-wide association study (GWAS) of usual sleep duration in 749 Framingham Heart Study participants, no genome-wide significant associations were identified [11]. Recently, a GWAS in over 4000 individuals in seven European cohorts identified a polymorphism in ABCC9, encoding an ATP-sensitive potassium channel, that explained approximately 5% of the variance in usual sleep duration [12]. This finding was not replicated in other cohorts; however, knockdown of this gene in Drosophila results in lack of sleep during the first 3 hours of the night. To date, no replicated associations between common genetic variants and sleep duration (or other sleep parameters) have been reported from GWAS studies. In the present study, we utilize self-report data on usual sleep duration, collected by 18 community-based cohort studies that have genotyped their cohorts, in order to identify common genetic variants associated with sleep duration. This study comprises a community-based sample of 47 180 individuals, approximately 10-fold larger than all previously reported GWAS studies of this phenotype [11, 12], and is the first to show replication in an independent sample.

METHODS

Cohorts

Participating cohorts were prospective studies that had collected self-report data on usual sleep duration. The analysis was initiated by the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) [13], but was extended beyond that initial group in order to obtain sufficient power for the analysis. Eighteen cohorts were ultimately included in the discovery sample: the Atherosclerosis Risk in Communities (ARIC) Study [14], Cardiovascular Health Study (CHS) [15], Framingham Heart Study (FHS) [16-18], Health Aging and Body Composition (HABC) Study [19], Helsinki Birth Cohort Study (HBCS) [20], Invecchiare in Chianti (InCHIANTI) [21], Osteoporotic Fractures in Men (MrOS) Study [22, 23], Quebec Family Study (QFS) [24], Queensland Institute of Medical Research Twins Study (QIMR) [2], Rotterdam Study I and II (RSI and RSII) [25], Study of Health in Pomerania (SHIP) [26], Study of Osteoporotic Fractures (SOF) [27], TwinsUK [28], Wisconsin Sleep Cohort (WiSC) [29], Young Finns Study (YFS) [30], and genotyped subsets of the Health Professionals Follow-up Study (HPFS) [31] and the Nurses Health Study (NHS) [32]. As shown in Table 1, the discovery cohorts were located in Europe, Australia and North America. This analysis included only participants of European ancestry, as determined by self-report, with additional exclusion in some cohorts for failure to cluster with European samples in principal components analysis or multidimensional scaling (ARIC, HPFS, MrOS, NHS, RS I&2, SHIP, SOF, TwinsUK, WiSC). Replication of the findings of this meta-analysis was sought in the African-American participants of the Candidate Gene Association Resource (CARe), which included the Cleveland Family Study (CFS), the Coronary Artery Risk Development in Young Adults (CARDIA) study, the Jackson Heart Study (JHS), and the Multi-Ethnic Study of Atherosclerosis (MESA) [33].
Table 1

Characteristics of the discovery cohorts

Cohort nameNLocationAgeSex, %FUsual sleep duration, hrsReference
Atherosclerosis Risk in Communities (ARIC)3578US62.6 (5.6)53.2%7.4 (1.1) 14
Cardiovascular Health Study (CHS)1515US77.9 (4.6)62.1%7.3 (1.3) 15
Framingham Heart Study (FHS)7531US51.3 (13.2)54.1%7.9 (1.3)16-18
Health Aging and Body Composition (HABC)1661US73.8 (2.8)47.0%7.0 (1.2) 19
Helsinki Birth Cohort Study (HBCS)1175Finland69.0 (2.7)60.7%8.2 (1.1) 20
Health Professionals Follow-up Study (HPFS)3542US56.0 (8.7)0.0%7.2 (0.9) 31
Invecchiare in Chianti (InCHIANTI)1205Italy68.3 (15.5)55.4%6.8 (1.5) 21
Osteoporotic Fractures in Men Study (MrOS)2354US76.7 (5.7)0.0%7.0 (1.2)22, 23
Nurses Health Study (NHS)6638US54.4 (6.7)100.0%7.0 (0.9) 32
Quebec Family Study (QFS)865Canada41.1 (15.4)56.3%7.7 (1.1) 24
Queensland Institute of Medical Research Twins Study (QIMR)2286Australia34.5 (14.3)74.2%7.7 (1.0) 2
Rotterdam Study I (RS I)2834Netherlands76.1 (6.3)59.5%6.8 (1.3) 25
Rotterdam Study II (RS II)1425Netherlands68.9 (7.6)57.6%6.9 (1.3) 25
Study of Health in Pomerania (SHIP)2859Germany49.4 (16.5)57.9%7.5 (1.3) 26
Study of Osteoporotic Fractures (SOF)3303US77.0 (5.1)100.0%7.0 (1.2) 27
TwinsUK1531UK53.1 (12.6)86.1%6.8 (0.8) 28
Wisconsin Sleep Cohort Study (WiSC)850US55.7 (7.5)45.6%7.1 (0.9) 29
Young Finns Study (YFS)2028Finland37.7 (5.0)54.9%7.4 (0.8) 30

Phenotype definition

Phenotype data were obtained from standardized personal interviews or self-completion questionnaires (Supplementary Table 1). The most widely available measure of sleep duration in the participating cohorts was self-report of usual hours of sleep at night; response options were typically whole number values. A smaller number of cohorts also collected self-reported usual bed and rise times, from which time in bed could be calculated. The relation between self-reported usual sleep duration and calculated time in bed was assessed in 9400 participants in the ARIC, CHS and FHS cohorts with data for both measures. Although the correlation between measures was fairly high at r=0.70, values differed by at least 1 hour in 24.3% of subjects and by at least two hours in 7.1% of subjects. Based on these differences, and for consistency on phenotype definition, calculated time in bed was not used as a proxy for self-reported usual sleep duration in meta-analyses. Measures of sleep duration were also ascertained separately for weekday nights and weekend nights in some cohorts. In advance of performing genetic association testing, the heritability of weekday versus weekend sleep duration, assessed by the questions, “How many hours of sleep do you usually get at night (or your main sleep period) on weekdays or workdays?” and “How many hours of sleep do you usually get at night (or your main sleep period) on weekends or your non-workdays?” was explored in the Framingham Heart Study Offspring cohort, which includes many sibling pairs, using the program SOLAR [34]. Based on 2388 individuals in 726 sibships, the estimated heritability of age- and sex-adjusted usual sleep duration on weekday nights was 23.6% (SD 7.7%), somewhat lower than the previously reported heritability of sleep duration from twin studies [1-3], while for weekend nights heritability was estimated at only 12.3% (SD 7.2%). Therefore, where weekday and weekend night sleep duration were both available, weekday night sleep duration was analyzed. In order to exclude subjects in whom night shift work might lead to spurious estimates of sleep duration, subjects reporting a usual bedtime between 5 AM and 6 PM were excluded from analysis, where these data were known. Those whose usual sleep duration differed by more than two hours between weekdays and weekends were also excluded from analysis, where this difference was known, as behavioral factors were presumed to have a major influence on this measure.

Genotyping and association analysis

Genotyping arrays and cohort-specific quality control filters are provided in Supplementary Tables 2a and 2b. Allele dosage was imputed using the software indicated. Association testing was performed independently in each of the contributing cohorts, using an additive model and untransformed sleep duration, adjusted for age and sex, which are both strong predictors of sleep duration, plus any covariates used by the individual cohorts to account for likely sources of population stratification or for relatedness among subjects (Supplementary Table 3). A fixed-effects meta-analysis of the cohort-specific results was performed using the inverse variance-weighted method in METAL [35], with a total of 2,033,301 single nucleotide polymorphisms (SNPs) tested. Genomic control correction was applied at the time of meta-analysis; individual cohort inflation factors ranged from 0.98 to 1.05. Only SNPs with minor allele frequency >0.05 and without significant heterogeneity across cohorts at p<0.01 were considered. A threshold p<5 × 10−8 was specified for statistical significance, corresponding to a Bonferroni correction for an estimated 1 million independent tests. All SNPs for the replication in African-Americans were present on the Affymetrix 6.0 SNP array used to genotype the CARe African-American sample, and were thus directly genotyped rather than imputed. Association analyses were adjusted for age, sex and the first 10 principal components to control for population stratification, and results of the four cohorts combined using fixed-effects meta-analysis in METAL. Conditional multi-SNP association testing was performed using GWAS summary statistics, as previously described [36]. Power analyses were performed using Quanto v1.2 [37].

Evaluation of possible SNP function

Evidence that SNPs significantly associated with sleep duration, and those in linkage disequilibrium (LD) with these SNPs at r2 >0.5 in HapMap Utah residents with ancestry from northern and western Europe (CEU), as defined by SNP Annotation and Proxy Search (SNAP) queries [38], had an influence on gene expression was sought in the expression quantitative trait locus (eQTL) database of the Pritchard Lab (eqtl.uchicago.edu, accessed November 18, 2012) and in a separate query of significant results from >50 gene expression datasets covering multiple tissues (Supplementary Table 4). Evidence for an effect of SNPs of interest on thyroid function was sought through a lookup of results of the Meta-Thyroid consortium [39]. Cohorts in the consortium include several participating in the current analysis of sleep duration (CHS, FHS, HBCS, InCHIANTI, Rotterdam Study and UK Twins), as well as multiple additional cohorts. Data on glycemic traits were contributed by MAGIC investigators and downloaded from www.magicinvestigators.org. Data on Type 2 diabetes mellitus were contributed by DIAGRAM investigators and downloaded from diagram-consortium.org. Data on psychiatric illnesses was obtained from published GWAS analyses of the Psychiatric Genomics Consortium (PGC), with data visualized using the Ricopili tool (http://www.broadinstitute.org/mpg/ricopili/) and downloaded from the PGC website (http://www.med.unc.edu/pgc/data-sharing#SharingOpp).

RESULTS

Cohort-specific genome-wide association analyses of self-reported usual sleep duration from 18 population-based cohorts were meta-analyzed (Fig. 1). All included subjects from these discovery cohorts were European or of European descent (Table 1). No evidence of population stratification was noted in the meta-analysis of self-reported usual sleep duration (Supplementary Fig. 1; overall λ=1.06; the range of inflation factors for individual cohorts was 0.98 – 1.05). Two independent loci showed genome-wide significant association with usual sleep duration (Table 2, Fig. 2, and Fig. 3).
Figure 1

Manhattan plot for genome-wide association with usual sleep duration in cohorts of European descent.

Table 2

SNPs with genome-wide significant association with usual sleep duration in discovery cohorts in two independent loci

SNP IDChrPosition, bpEffectalleleAllelefrequencyNCoefficient(β), minutesSEβ,minutesP value
rs11916852113,811,454C0.37445632.870.471.06 × 10−9
rs18231252113,806,882G0.26452813.010.501.71 × 10−9
rs18072822113,826,506T0.26468052.890.493.91 × 10−9
rs19644632113,785,491G0.25452812.840.501.07 × 10−8
rs4587207630,874,924G0.2046807−3.140.562.02 × 10−8
rs4248149630,875,606C0.2046810−3.080.563.95 × 10−8
rs2394403630,875,848T0.2046811−3.070.564.39 × 10−8

Abbreviations: SNP – single nucleotide polymorphism; Chr – chromosome; bp – base pairs; N - number of individuals contributing to analysis for each SNP; SE – standard error of the estimated coefficient

Figure 2

Chromosome 2 regional association plot for usual sleep duration in cohorts of European descent

Figure was constructed using the Broad Institute SNAP tool (http://www.broadinstitute.org/mpg/snap/).

Figure 3

Chromosome 6 regional association plot for usual sleep duration in cohorts of European descent

Figure was constructed using the Broad Institute SNAP tool (http://www.broadinstitute.org/mpg/snap/).

Identification and replication of a novel sleep duration locus on chromosome 2

The most strongly associated locus is located between two genes on chromosome 2: 30-80 kb upstream from paired box gene 8 (PAX8) and, on the opposite strand, 80-130 kb upstream from cobalamin synthase W domain-containing protein 2 gene (CBWD) (Fig. 2). PAX8 is a well-characterized transcription factor essential to the formation of thyroxine-producing follicular cells during thyroid development. PAX8 mutations produce thyroid dysgenesis, but the transcription factor is more widely expressed and may have other functions. In contrast, CBWD2 is a poorly characterized gene highly expressed in the brain. The intergenic region also overlies a poorly characterized, predicted non-coding RNA (LOC101927400). This locus contains four SNPs meeting pre-specified criteria for genome-wide significance: rs1191685 (p=1.1 × 10−9), rs1823125 (p=1.7 × 10−9), rs1807282 (p=3.9 × 10−9), and rs1964463 (p=1.1 × 10−8), with minor allele frequencies of 0.25 to 0.37, that were associated with an increase in self-reported usual sleep duration of 2.8 (SE 0.5) to 3.0 (SE 0.5) minutes per night per copy of the minor allele, explaining an estimated 0.07% of phenotypic heterogeneity. Linkage disequilibrium between the most strongly associated SNP and each of the other three significantly associated SNPs at this locus was modest, with r2 values between 0.51 and 0.64 in the HapMap 2 CEU sample. Conditional association testing was performed using summary-level statistics from the meta-analysis as previously described [36] with LD estimates derived from a representative sample of 4000 unrelated Australians of European descent. Conditioning on rs1191685, the effect sizes for the other SNPs reported above were reduced by approximately 60% and were no longer genome-wide significant (range of p values 0.003 to 0.01). The direction of effect was positive in all but one cohort (Supplementary Fig. 2). Although there was no significant heterogeneity across cohorts, in 9 of the European-descent cohorts, the estimated effect was >5.0 minutes per night per copy of the minor allele, while in 8 of the cohorts the estimated effect was <2.6 minutes per night. The former cohorts were on average substantially older, with a mean age of 70 (SD 8) years, versus a mean age of 50 (SD 12) years in the latter group, and there was a strong correlation between mean age of cohort participants and estimated effect size (r=0.72, p=0.001). Although most of the participating cohorts excluded related individuals, two were twin studies (QIMR, TwinsUK) and two were family studies (FHS, QFS). A sensitivity analysis excluding these cohorts from the meta-analysis found a somewhat stronger effect size for all four SNPs, with effect estimates of 3.3 to 3.7 minutes per night. The strongest association in this sample was at rs1807282 (p=2.4 × 10−10). Three of the significantly associated SNPs in this region were directly genotyped in the Candidate-gene Association Resource (CARe) [33] African-American sample (rs1823125, rs1807282, rs1964463 ); a fourth directly genotyped SNP (rs1191684) was in perfect linkage disequilibrium with rs1191685 in the HapMap 2 Yoruba in Ibadan, Nigeria (YRI) sample. Interestingly, these four SNPs have very little linkage disequilibrium in the HapMap 2 YRI sample, with r2 values of 0.001 to 0.04 (Supplementary Fig. 3). Association testing in this sample of 4771 individuals replicated the finding from the discovery cohorts (Table 3), with effect sizes in the African-American sample that were in the same direction and somewhat larger than those seen in the discovery sample in three of the four SNPs, with 2 out of 4 SNPs reaching significance in the replication sample after Bonferroni correction. The strongest association in African-Americans was at rs1807282, with an effect size 11.2 (SE 3.4) minutes per night per copy of the minor allele (p=9.34 × 10−4), explaining 0.15% of phenotypic variance in this sample. When meta-analyzed together, the strongest association was with SNP rs1823125, the minor allele of which was associated with a sleep duration 3.1 minutes per night longer (p=1.47 × 10−10, Table 3).
Table 3

Replication of chromosome 2 locus association with sleep duration in African-American cohorts

SNP IDChrPosition, bpEffectalleleAllelefrequencyNCoefficient (β),minutesSEβ,minutesP valueCoefficient (β),minutesP value
African-American Sample Combined Samples
rs18231252113,806,882G0.1247477.282.676.35 × 10−33.141.47 × 10−10
rs1191684*2113,811,749C0.2547703.702.026.78 × 10−22.902.32 × 10−10
rs18072822113,826,506T0.08477111.153.379.34 × 10−43.053.35 × 10−10
rs19644632113,785,491G0.0747660.783.428.14 × 10−12.801.25 × 10−8

Abbreviations: See Table 2.

SNP rs1191685 was not genotyped in the CARe sample. SNP rs1191684 is located 295 bp away and is in perfect LD with rs1191685 in the HapMap release 22 YRI sample, and is used as a proxy in this analysis; the effect allele is C at both rs1191685 and rs1191684.

The locus of significant association is in the vicinity of an enhancer that is associated with an in vitro increase in PAX8 gene expression of up to 250-fold [40]. None of the SNPs that were significantly associated with sleep duration are associated with significant differential expression of PAX8 in published gene expression databases; no thyroid tissue gene expression databases are available, however. A lookup of these SNPs in the Meta-Thyroid consortium analysis of over 20,000 individuals of European ancestry [39] found no evidence for association of any of these SNPs with blood levels of either thyroid stimulating hormone or free thyroxine. The SNP rs1191685 is significantly associated with differential expression in skin of a transcript of IL1RN (p=3.30 × 10−5), which encodes the interleukin-1 receptor antagonist [41]. This eQTL signal peaks at rs1191683, which is in high LD with rs1191685 (r2=0.80); a signal was not seen in a smaller skin eQTL dataset [42]. Because of a strong association of short sleep duration with diabetes mellitus and other glycemic traits [43], we performed a look-up of these SNPs in published GWAS analyses of these traits. Three of the SNPs (rs1807282, rs1823125, rs1964463) showed nominally significant association with the homeostatic model assessment of beta cell function (HOMA-β, p=0.04, [44]) and with glycated hemoglobin (HgbA1C, p=0.008 to 0.011, [45]). In each case, the minor allele, which is associated with longer sleep duration, is associated with a more favorable metabolic profile, i.e., higher HOMA-β and lower HgbA1C. No association of these SNPs with Type 2 diabetes mellitus was present in data from the DIAGRAM consortium (DIAGRAMv3.2012DEC17). Because sleep disturbance is a common symptom in a number of psychiatric illnesses, we also performed a lookup of these SNPs in published PGC GWAS analyses. Attention deficit hyperactivity disorder (ADHD) was associated with SNPs rs1823125, rs1807282, and rs1964463 (p=0.03 for each) [46]. In each case, the allele that was that associated with longer usual sleep duration in the present study was associated with a lower ADHD risk in the PGC analysis. No significant association of these SNPs, or any in LD with these SNPs at r2 >0.4, was present for schizophrenia, depression or bipolar disorder. The association of ADHD has two local peaks, at rs1191694 and rs13032628 (each p=0.004), which are in very low LD with one another (r2=0.07). The sleep duration-associated SNPs rs1823125 and rs1807282 are approximately 5600 and 2800 bp, respectively, from these ADHD-associated SNPs (r2=0.12-0.13 for LD between sleep duration-associated SNPs and rs1191694; r2=0.06 for LD between sleep duration-associated SNPs and rs13032628).

Identification of a second novel sleep duration locus on chromosome 6

The second region of genome-wide significant association in the cohorts of European descent is located on chromosome 6 in an intergenic region approximately 50 kb upstream of IER3 and FLOT1 (Fig. 3), which also contains a long intergenic non-coding RNA (LINC00243) of uncertain function. The three SNPs with genome-wide significant association span only 924 bp and are in perfect linkage disequilibrium in both the CEU and YRI samples. In the discovery cohorts, the minor allele frequency was 0.20 and the strongest estimated association with sleep duration was 3.1 (SE 0.6) minutes less sleep per night per copy of the minor allele, explaining 0.07% of phenotypic heterogeneity. These SNPs are also in perfect linkage disequilibrium with two SNPs (rs4713380 and rs4713385) that are significantly associated with the expression in peripheral whole blood of transcripts of the genes IER3 (p=8.40 × 10−23), FLOT1 (p=4.00 × 10−17), VARS2 (p=4.60 × 10−12) and TUBB (p=4.20 × 10−5) [47], and in both skin (p=1.60 × 10−9) and B-lymphoblastoid cell lines (p=6.49 × 10−5) with expression of IER3 [41]. A lookup in the PGC cross-disorder GWAS, which analyzed the association of genotype jointly with five psychiatric disorders (ADHD, autism spectrum disorders, bipolar disorder, major depressive disorder and schizophrenia), indicated that the three SNPs on chromosome 6 that are associated with sleep duration are significantly associated with psychiatric disorders (p=0.0003 to 0.001) [48]. These SNPs are part of a LD block spanning approximately 30,000 bp, and are in complete LD (r2=1.0) with 10 additional SNPs in this block, each of which is associated with the psychiatric disorders at p=4.7-9.9 × 10−5. This association was driven by associations with major depressive disorder (p=0.02 to 0.05) [49] and schizophrenia (p=0.04 to 0.08) [50]. In each case, the allele associated with shorter sleep duration in the present analysis was associated with increased depression and schizophrenia risk in the PGC analyses. Notwithstanding these suggestive correlates, this region did not replicate in the African-American sample. The effect was in the same direction but somewhat smaller (2.2 – 2.6 minutes less sleep per night per copy of the minor allele), and not statistically significant (lowest p=0.39), although given a MAF of 0.09-0.10, the power to detect a significant replication of an effect of the magnitude seen in the discovery sample was low (range 17-18%).

Additional novel sleep duration loci and evaluation of candidate genes

An additional 11 loci were associated with usual sleep duration in the GWAS of the discovery cohorts at a nominal p <10−5 (Table 4); none was significantly associated with sleep duration in the replication cohort, albeit with low power to detect significant effects (power <30% for each locus). A number of genes have been associated with sleep duration or chronotype through smaller candidate gene or GWAS studies that had no overlap with the cohorts included in the present study. The present GWAS included multiple SNPs in these genes and other core mammalian clock genes, including ABCC9 (131 SNPs), PER2 (22 SNPs), PER3 (56 SNPs), CLOCK (110 SNPs), ARNTL (114 SNPs), ARNTL2 (106 SNPs), and CSNK1D (6 SNPs). No SNPs in BHLHE41 were present in the GWAS. There was no evidence for association at any of these SNPs with usual sleep duration, using a liberal threshold of p<0.01. The SNP rs12649507 in CLOCK, previously reported to be associated with usual sleep duration with an effect size of approximately 5 minutes shorter sleep per night in homozygotes for the minor allele than in homozygotes for the major allele [8], had a smaller estimated effect in the present study of 0.9 minutes shorter sleep per night per copy of the minor allele (p=0.03). The SNP rs11046205 in ABCC9, previously reported to be associated with usual sleep duration with an effect size of approximately 10 minutes longer sleep per night per copy of the minor allele [11], had an estimated effect in the present analysis of only 0.9 minutes longer sleep per night per copy of the minor allele (p=0.11).
Table 4

Additional loci associated with usual sleep duration not reaching genome-wide significance

ChrPosition, bp# of SNPsat p<10−5SNP ID of strongest association at locusMAFNCoefficient (β),minutesSEβ,minutesP valueFunctionClosestgene
1215,423,0241rs22212850.29460622.150.498.60 × 10−6Intergenic ESRRG
2158,551,2641rs64371220.1045283−3.250.726.30 × 10−6Intergenic UPP2
5178,328,4562rs117416880.45460841.960.422.91 × 10−6Intergenic ZNF454
670,547,2322rs93463530.4142595−2.030.433.00 × 10−6Intron LMBRD1
11124,764,5101rs7317160.48436161.960.435.18 × 10−6Intron PKNOX2
1231,890,2611rs21286140.38468021.870.428.71 × 10−6Intergenic LOC440093
1379,449,56426rs95310060.31445672.430.484.22 × 10−7Intergenic SPRY2
1397,373,8442rs95171320.3144567−2.620.562.61 × 10−6Intergenic RANBP5
185,568,9821rs116645360.1846075−3.190.705.53 × 10−6Intergenic EPB41L3
1834,934,4972rs121650980.1644568−3.180.705.26 × 10−6Intergenic BRUNOL4
199,820,0147rs22878380.4746079−2.210.411.05 × 10−7Intron PIN1

Abbreviations: MAF – minor allele frequency; other abbreviations see Table 2.

DISCUSSION

This genome-wide association study identified two loci with genome-wide significant age- and sex-adjusted association to self-reported usual sleep duration in a large, multi-national sample of adults from Europe or of European descent. One of these loci was replicated in a sample of African-Americans, strengthening the finding. This first locus is located approximately 30-80 kb upstream from the thyroid-specific transcription factor PAX8 and, at a somewhat greater distance, upstream from CBWD2. It also overlies a predicted non-coding RNA LOC101927400 and is approximately 200 kb from an interleukin-1 gene cluster. While little is known about the function of CBWD2, PAX8 is a transcription factor that is most highly expressed in thyroid tissue, where it is important both in thyroid development and in maintaining adult thyroid function [51]. Thyroid-stimulating hormone levels are reduced by sleep deprivation [52, 53], with both higher [52] and lower [53] free thyroxine levels reported. Hypothyroidism is associated with excessive sleepiness [54] and with reductions in slow-wave sleep that can be corrected with hormone replacement [55], while hyperthyroidism is associated with insomnia [56]. These findings suggest a role for thyroid hormone in sleep-wake regulation and thus a plausible role for PAX8 effects on sleep duration. The locus of interest is in the vicinity of an enhancer that is associated with an in vitro increase in PAX8 gene expression of up to 250-fold [40]. As no thyroid-specific eQTL databases are available, and PAX8 is expressed in adults primarily in the thyroid gland, it was not possible to assess whether these variants are associated with changes in PAX8 expression; however, these SNPs were not associated with measures of thyroid function in a genome-wide association study, albeit in a smaller sample [39]. An alternative possibility is suggested by the association of rs1191685 with expression of IL1RN, as its product, the interleukin-1 receptor antagonist, has been shown to block the somnogenic effect of interleukin-1 [57], which is hypothesized to be involved in the physiologic regulation of sleep. The IL1RN is located 100 kb downstream of PAX8 and a regulatory element for this receptor could be distantly located between PAX8 and CBWD2 (Fig.1). Although the mechanisms discussed above remain speculative, the cross-racial replication of the association suggests a true effect of this locus on usual sleep duration. While the magnitude of the association of the SNPs upstream of PAX8 with sleep duration appears modest, with each copy of the minor allele associated with an estimated increase in usual sleep duration of approximately 3 minutes per night, the 0.07% of variance in sleep duration explained by this variant is typical of GWAS studies. For example, of 32 loci showing genome-wide significant association with body mass index in the GIANT Consortium analysis of almost 250 000 individuals, only four explained greater than 0.07% of the variance in body mass index, with the two most strongly associated loci (FTO and TMEM18) explaining 0.34% and 0.15% of variance, respectively, and the remaining 30 variants explaining an average of 0.03% of variance each [58]. Moreover, self-reported usual sleep duration is likely to be an imprecise correlate of the underlying biological construct of interest, which is the innate sleep period of the individual free of environmental constraints. This reflects both technical factors, including imprecision in self-report estimates of sleep duration compared to objective measures such as actigraphy [59] and the coarse-grained response options typical of sleep duration questionnaires, as well as extensive socio-environmental influences on sleep behaviors, including the widespread consumption of caffeine and alcohol, the impact of medical and psychiatric illness on sleep, and most importantly the impact of work and social schedules that are often unrelated to individual differences in optimal sleep duration. In older individuals, retirement from work and lack of childrearing responsibility often reduce the impact of social demands on sleep schedule, perhaps explaining the stronger effect observed in the older cohorts included in this analysis. Although the present study is more than 10-fold larger than the two previously published GWAS studies of sleep duration, it remains small compared to recent studies of traits such as body mass index and hypertension, which are more widely available in large population-based cohorts. The replicated locus upstream from PAX8 is therefore likely to represent the first of a larger number of associations that will appear as future population-based GWAS studies of sleep duration benefit from more rigorous phenotyping and larger sample size. Self-reported short sleep duration and experimental sleep restriction are strongly associated with impaired glucose metabolism [43]. Sleep disturbance and short sleep duration are also common in psychiatric disorders, including ADHD. It is therefore of interest that the alleles upstream from PAX8 that are associated with longer sleep duration in this study were in prior GWAS studies associated with higher HOMA-β and lower HgbA1C [44,45] and with lower ADHD risk [46]. As sleep duration is associated with other important illnesses, including incident post-traumatic stress disorder [60], obesity, hypertension, and coronary heart disease, as well as with mortality, elucidating the molecular pathways that regulate sleep duration may both identify novel mechanisms affecting sleep regulation and help to explain its association with psychiatric and cardiometabolic disease.
  57 in total

1.  Cohort profile: the cardiovascular risk in Young Finns Study.

Authors:  Olli T Raitakari; Markus Juonala; Tapani Rönnemaa; Liisa Keltikangas-Järvinen; Leena Räsänen; Matti Pietikäinen; Nina Hutri-Kähönen; Leena Taittonen; Eero Jokinen; Jukka Marniemi; Antti Jula; Risto Telama; Mika Kähönen; Terho Lehtimäki; Hans K Akerblom; Jorma S A Viikari
Journal:  Int J Epidemiol       Date:  2008-02-08       Impact factor: 7.196

2.  SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap.

Authors:  Andrew D Johnson; Robert E Handsaker; Sara L Pulit; Marcia M Nizzari; Christopher J O'Donnell; Paul I W de Bakker
Journal:  Bioinformatics       Date:  2008-10-30       Impact factor: 6.937

3.  Cohort profile: the study of health in Pomerania.

Authors:  Henry Völzke; Dietrich Alte; Carsten Oliver Schmidt; Dörte Radke; Roberto Lorbeer; Nele Friedrich; Nicole Aumann; Katharina Lau; Michael Piontek; Gabriele Born; Christoph Havemann; Till Ittermann; Sabine Schipf; Robin Haring; Sebastian E Baumeister; Henri Wallaschofski; Matthias Nauck; Stephanie Frick; Andreas Arnold; Michael Jünger; Julia Mayerle; Matthias Kraft; Markus M Lerch; Marcus Dörr; Thorsten Reffelmann; Klaus Empen; Stephan B Felix; Anne Obst; Beate Koch; Sven Gläser; Ralf Ewert; Ingo Fietze; Thomas Penzel; Martina Dören; Wolfgang Rathmann; Johannes Haerting; Mario Hannemann; Jürgen Röpcke; Ulf Schminke; Clemens Jürgens; Frank Tost; Rainer Rettig; Jan A Kors; Saskia Ungerer; Katrin Hegenscheid; Jens-Peter Kühn; Julia Kühn; Norbert Hosten; Ralf Puls; Jörg Henke; Oliver Gloger; Alexander Teumer; Georg Homuth; Uwe Völker; Christian Schwahn; Birte Holtfreter; Ines Polzer; Thomas Kohlmann; Hans J Grabe; Dieter Rosskopf; Heyo K Kroemer; Thomas Kocher; Reiner Biffar; Ulrich John; Wolfgang Hoffmann
Journal:  Int J Epidemiol       Date:  2010-02-18       Impact factor: 7.196

4.  Design and baseline characteristics of the osteoporotic fractures in men (MrOS) study--a large observational study of the determinants of fracture in older men.

Authors:  Eric Orwoll; Janet Babich Blank; Elizabeth Barrett-Connor; Jane Cauley; Steven Cummings; Kristine Ensrud; Cora Lewis; Peggy M Cawthon; Robert Marcus; Lynn M Marshall; Joan McGowan; Kathy Phipps; Sherry Sherman; Marcia L Stefanick; Katie Stone
Journal:  Contemp Clin Trials       Date:  2005-10       Impact factor: 2.226

5.  The Third Generation Cohort of the National Heart, Lung, and Blood Institute's Framingham Heart Study: design, recruitment, and initial examination.

Authors:  Greta Lee Splansky; Diane Corey; Qiong Yang; Larry D Atwood; L Adrienne Cupples; Emelia J Benjamin; Ralph B D'Agostino; Caroline S Fox; Martin G Larson; Joanne M Murabito; Christopher J O'Donnell; Ramachandran S Vasan; Philip A Wolf; Daniel Levy
Journal:  Am J Epidemiol       Date:  2007-03-19       Impact factor: 4.897

6.  Functional consequences of a CKIdelta mutation causing familial advanced sleep phase syndrome.

Authors:  Ying Xu; Quasar S Padiath; Robert E Shapiro; Christopher R Jones; Susan C Wu; Noriko Saigoh; Kazumasa Saigoh; Louis J Ptácek; Ying-Hui Fu
Journal:  Nature       Date:  2005-03-31       Impact factor: 49.962

7.  Candidate gene association resource (CARe): design, methods, and proof of concept.

Authors:  Kiran Musunuru; Guillaume Lettre; Taylor Young; Deborah N Farlow; James P Pirruccello; Kenechi G Ejebe; Brendan J Keating; Qiong Yang; Ming-Huei Chen; Nina Lapchyk; Andrew Crenshaw; Liuda Ziaugra; Anthony Rachupka; Emelia J Benjamin; L Adrienne Cupples; Myriam Fornage; Ervin R Fox; Susan R Heckbert; Joel N Hirschhorn; Christopher Newton-Cheh; Marcia M Nizzari; Dina N Paltoo; George J Papanicolaou; Sanjay R Patel; Bruce M Psaty; Daniel J Rader; Susan Redline; Stephen S Rich; Jerome I Rotter; Herman A Taylor; Russell P Tracy; Ramachandran S Vasan; James G Wilson; Sekar Kathiresan; Richard R Fabsitz; Eric Boerwinkle; Stacey B Gabriel
Journal:  Circ Cardiovasc Genet       Date:  2010-04-17

8.  Common variants at 10 genomic loci influence hemoglobin A₁(C) levels via glycemic and nonglycemic pathways.

Authors:  Nicole Soranzo; Serena Sanna; Eleanor Wheeler; Christian Gieger; Dörte Radke; Josée Dupuis; Nabila Bouatia-Naji; Claudia Langenberg; Inga Prokopenko; Elliot Stolerman; Manjinder S Sandhu; Matthew M Heeney; Joseph M Devaney; Muredach P Reilly; Sally L Ricketts; Alexandre F R Stewart; Benjamin F Voight; Christina Willenborg; Benjamin Wright; David Altshuler; Dan Arking; Beverley Balkau; Daniel Barnes; Eric Boerwinkle; Bernhard Böhm; Amélie Bonnefond; Lori L Bonnycastle; Dorret I Boomsma; Stefan R Bornstein; Yvonne Böttcher; Suzannah Bumpstead; Mary Susan Burnett-Miller; Harry Campbell; Antonio Cao; John Chambers; Robert Clark; Francis S Collins; Josef Coresh; Eco J C de Geus; Mariano Dei; Panos Deloukas; Angela Döring; Josephine M Egan; Roberto Elosua; Luigi Ferrucci; Nita Forouhi; Caroline S Fox; Christopher Franklin; Maria Grazia Franzosi; Sophie Gallina; Anuj Goel; Jürgen Graessler; Harald Grallert; Andreas Greinacher; David Hadley; Alistair Hall; Anders Hamsten; Caroline Hayward; Simon Heath; Christian Herder; Georg Homuth; Jouke-Jan Hottenga; Rachel Hunter-Merrill; Thomas Illig; Anne U Jackson; Antti Jula; Marcus Kleber; Christopher W Knouff; Augustine Kong; Jaspal Kooner; Anna Köttgen; Peter Kovacs; Knut Krohn; Brigitte Kühnel; Johanna Kuusisto; Markku Laakso; Mark Lathrop; Cécile Lecoeur; Man Li; Mingyao Li; Ruth J F Loos; Jian'an Luan; Valeriya Lyssenko; Reedik Mägi; Patrik K E Magnusson; Anders Mälarstig; Massimo Mangino; María Teresa Martínez-Larrad; Winfried März; Wendy L McArdle; Ruth McPherson; Christa Meisinger; Thomas Meitinger; Olle Melander; Karen L Mohlke; Vincent E Mooser; Mario A Morken; Narisu Narisu; David M Nathan; Matthias Nauck; Chris O'Donnell; Konrad Oexle; Nazario Olla; James S Pankow; Felicity Payne; John F Peden; Nancy L Pedersen; Leena Peltonen; Markus Perola; Ozren Polasek; Eleonora Porcu; Daniel J Rader; Wolfgang Rathmann; Samuli Ripatti; Ghislain Rocheleau; Michael Roden; Igor Rudan; Veikko Salomaa; Richa Saxena; David Schlessinger; Heribert Schunkert; Peter Schwarz; Udo Seedorf; Elizabeth Selvin; Manuel Serrano-Ríos; Peter Shrader; Angela Silveira; David Siscovick; Kjioung Song; Timothy D Spector; Kari Stefansson; Valgerdur Steinthorsdottir; David P Strachan; Rona Strawbridge; Michael Stumvoll; Ida Surakka; Amy J Swift; Toshiko Tanaka; Alexander Teumer; Gudmar Thorleifsson; Unnur Thorsteinsdottir; Anke Tönjes; Gianluca Usala; Veronique Vitart; Henry Völzke; Henri Wallaschofski; Dawn M Waterworth; Hugh Watkins; H-Erich Wichmann; Sarah H Wild; Gonneke Willemsen; Gordon H Williams; James F Wilson; Juliane Winkelmann; Alan F Wright; Carina Zabena; Jing Hua Zhao; Stephen E Epstein; Jeanette Erdmann; Hakon H Hakonarson; Sekar Kathiresan; Kay-Tee Khaw; Robert Roberts; Nilesh J Samani; Mark D Fleming; Robert Sladek; Gonçalo Abecasis; Michael Boehnke; Philippe Froguel; Leif Groop; Mark I McCarthy; W H Linda Kao; Jose C Florez; Manuela Uda; Nicholas J Wareham; Inês Barroso; James B Meigs
Journal:  Diabetes       Date:  2010-09-21       Impact factor: 9.461

9.  A genome-wide association study identifies protein quantitative trait loci (pQTLs).

Authors:  David Melzer; John R B Perry; Dena Hernandez; Anna-Maria Corsi; Kara Stevens; Ian Rafferty; Fulvio Lauretani; Anna Murray; J Raphael Gibbs; Giuseppe Paolisso; Sajjad Rafiq; Javier Simon-Sanchez; Hana Lango; Sonja Scholz; Michael N Weedon; Sampath Arepalli; Neil Rice; Nicole Washecka; Alison Hurst; Angela Britton; William Henley; Joyce van de Leemput; Rongling Li; Anne B Newman; Greg Tranah; Tamara Harris; Vijay Panicker; Colin Dayan; Amanda Bennett; Mark I McCarthy; Aimo Ruokonen; Marjo-Riitta Jarvelin; Jack Guralnik; Stefania Bandinelli; Timothy M Frayling; Andrew Singleton; Luigi Ferrucci
Journal:  PLoS Genet       Date:  2008-05-09       Impact factor: 5.917

10.  Mapping cis- and trans-regulatory effects across multiple tissues in twins.

Authors:  Elin Grundberg; Kerrin S Small; Åsa K Hedman; Alexandra C Nica; Alfonso Buil; Sarah Keildson; Jordana T Bell; Tsun-Po Yang; Eshwar Meduri; Amy Barrett; James Nisbett; Magdalena Sekowska; Alicja Wilk; So-Youn Shin; Daniel Glass; Mary Travers; Josine L Min; Sue Ring; Karen Ho; Gudmar Thorleifsson; Augustine Kong; Unnur Thorsteindottir; Chrysanthi Ainali; Antigone S Dimas; Neelam Hassanali; Catherine Ingle; David Knowles; Maria Krestyaninova; Christopher E Lowe; Paola Di Meglio; Stephen B Montgomery; Leopold Parts; Simon Potter; Gabriela Surdulescu; Loukia Tsaprouni; Sophia Tsoka; Veronique Bataille; Richard Durbin; Frank O Nestle; Stephen O'Rahilly; Nicole Soranzo; Cecilia M Lindgren; Krina T Zondervan; Kourosh R Ahmadi; Eric E Schadt; Kari Stefansson; George Davey Smith; Mark I McCarthy; Panos Deloukas; Emmanouil T Dermitzakis; Tim D Spector
Journal:  Nat Genet       Date:  2012-09-02       Impact factor: 38.330

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  51 in total

1.  The Rotterdam Study: 2016 objectives and design update.

Authors:  Albert Hofman; Guy G O Brusselle; Sarwa Darwish Murad; Cornelia M van Duijn; Oscar H Franco; André Goedegebure; M Arfan Ikram; Caroline C W Klaver; Tamar E C Nijsten; Robin P Peeters; Bruno H Ch Stricker; Henning W Tiemeier; André G Uitterlinden; Meike W Vernooij
Journal:  Eur J Epidemiol       Date:  2015-09-19       Impact factor: 8.082

2.  Genetic Correlation Analysis Suggests Association between Increased Self-Reported Sleep Duration in Adults and Schizophrenia and Type 2 Diabetes.

Authors:  Enda M Byrne; Philip R Gehrman; Maciej Trzaskowski; Henning Tiemeier; Allan I Pack
Journal:  Sleep       Date:  2016-10-01       Impact factor: 5.849

Review 3.  Disparities in Hypertension Among African-Americans: Implications of Insufficient Sleep.

Authors:  Naima Covassin; Eddie L Greene; Prachi Singh; Virend K Somers
Journal:  Curr Hypertens Rep       Date:  2018-06-08       Impact factor: 5.369

Review 4.  Genetics of the human circadian clock and sleep homeostat.

Authors:  Liza H Ashbrook; Andrew D Krystal; Ying-Hui Fu; Louis J Ptáček
Journal:  Neuropsychopharmacology       Date:  2019-08-10       Impact factor: 7.853

Review 5.  Pleiotropic genetic effects influencing sleep and neurological disorders.

Authors:  Olivia J Veatch; Brendan T Keenan; Philip R Gehrman; Beth A Malow; Allan I Pack
Journal:  Lancet Neurol       Date:  2017-02       Impact factor: 44.182

6.  Transcriptional Signatures of Sleep Duration Discordance in Monozygotic Twins.

Authors:  N F Watson; D Buchwald; J J Delrow; W A Altemeier; M V Vitiello; A I Pack; M Bamshad; C Noonan; S A Gharib
Journal:  Sleep       Date:  2017-01-01       Impact factor: 5.849

7.  Self-Reported Sleep Duration and Pattern in Old Order Amish and Non-Amish Adults.

Authors:  Man Zhang; Kathleen A Ryan; Emerson Wickwire; Teodor T Postolache; Huichun Xu; Melanie Daue; Soren Snitker; Toni I Pollin; Alan R Shuldiner; Braxton D Mitchell
Journal:  J Clin Sleep Med       Date:  2019-09-15       Impact factor: 4.062

8.  Common variants in DRD2 are associated with sleep duration: the CARe consortium.

Authors:  Brian E Cade; Daniel J Gottlieb; Diane S Lauderdale; David A Bennett; Aron S Buchman; Sarah G Buxbaum; Philip L De Jager; Daniel S Evans; Tibor Fülöp; Sina A Gharib; W Craig Johnson; Hyun Kim; Emma K Larkin; Seung Ku Lee; Andrew S Lim; Naresh M Punjabi; Chol Shin; Katie L Stone; Gregory J Tranah; Jia Weng; Kristine Yaffe; Phyllis C Zee; Sanjay R Patel; Xiaofeng Zhu; Susan Redline; Richa Saxena
Journal:  Hum Mol Genet       Date:  2015-10-13       Impact factor: 6.150

9.  The Rotterdam Study: 2018 update on objectives, design and main results.

Authors:  M Arfan Ikram; Guy G O Brusselle; Sarwa Darwish Murad; Cornelia M van Duijn; Oscar H Franco; André Goedegebure; Caroline C W Klaver; Tamar E C Nijsten; Robin P Peeters; Bruno H Stricker; Henning Tiemeier; André G Uitterlinden; Meike W Vernooij; Albert Hofman
Journal:  Eur J Epidemiol       Date:  2017-10-24       Impact factor: 8.082

Review 10.  The genetics of obstructive sleep apnoea.

Authors:  Sutapa Mukherjee; Richa Saxena; Lyle J Palmer
Journal:  Respirology       Date:  2017-11-07       Impact factor: 6.424

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