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

Subjects

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.

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, 2, 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 Sehgal and Mignot4). 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, 8, 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, which 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 h of the night. To date, no replicated associations between common genetic variants and sleep duration (or other sleep parameters) have been reported from GWAS. In this 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 of this phenotype,11, 12 and is the first to show replication in an independent sample.

Materials and 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, 17, 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 (RS I and RS II),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 and II, SHIP, SOF, TwinsUK and 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

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 h in 24.3% of subjects and by at least 2h 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% (s.d. 7.7%), somewhat lower than the previously reported heritability of sleep duration from twin studies,1, 2, 3 while for weekend nights heritability was estimated at only 12.3% (s.d. 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 0500 and 1800 hours were excluded from analysis, where these data were known. Those whose usual sleep duration differed by >2h 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 (Affymetrix, Inc., Santa Clara, CA, USA) 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 18 November 2012) and in a separate query of significant results from >50 gene expression data sets covering multiple tissues (Supplementary Table 4). Evidence for an effect of SNPs of interest on thyroid function was sought through a look-up 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 were 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 (Figure 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 Figure 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, Figures 2 and 3).

Figure 1
figure1

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

PowerPoint slide

Table 2 SNPs with genome-wide significant association with usual sleep duration in discovery cohorts in two independent loci
Figure 2
figure2

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/).

PowerPoint slide

Figure 3
figure3

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/).

PowerPoint slide

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- to 80-kb upstream from paired box gene 8 (PAX8) and, on the opposite strand, 80- to 130-kb upstream from cobalamin synthase W domain-containing protein 2 gene (CBWD) (Figure 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 0.25–0.37, which were associated with an increase in self-reported usual sleep duration of 2.8 (s.e. 0.5) to 3.0 (s.e. 0.5) min per night per copy of the minor allele, explaining an estimated 0.07% of phenotypic heterogeneity. LD 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 described36 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 0.003–0.01).

The direction of effect was positive in all but one cohort (Supplementary Figure 2). Although there was no significant heterogeneity across cohorts, in nine of the European-descent cohorts, the estimated effect was >5.0 min per night per copy of the minor allele, whereas in eight of the cohorts the estimated effect was <2.6 min per night. The former cohorts were on average substantially older, with a mean age of 70 (s.d. 8) years, versus a mean age of 50 (s.d. 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 and TwinsUK) and two were family studies (FHS and 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 3.3–3.7 min 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 and rs1964463); a fourth directly genotyped SNP (rs1191684) was in perfect LD with rs1191685 in the HapMap 2 Yoruba in Ibadan, Nigeria (YRI) sample. Interestingly, these four SNPs have very little LD in the HapMap 2 YRI sample, with r2 values of 0.001–0.04 (Supplementary Figure 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 two out of four 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 (s.e. 3.4) min 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 min 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

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 look-up of these SNPs in the Meta-Thyroid consortium analysis of over 20 000 individuals of European ancestry39 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 data set.42 As a result 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 and 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=0.008–0.011).45 In each case, the minor allele, which is associated with longer sleep duration, is associated with a more favorable metabolic profile, that is, 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). As sleep disturbance is a common symptom in a number of psychiatric illnesses, we also performed a look-up 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 associated with longer usual sleep duration in this 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 (Figure 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 LD 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 (s.e. 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 LD 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 look-up 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=0.0003–0.001).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=0.02–0.05) 49 and schizophrenia (P=0.04–0.08).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 min 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 that had no overlap with the cohorts included in this 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-min shorter sleep per night in homozygotes for the minor allele than in homozygotes for the major allele,8 had a smaller estimated effect in this study of 0.9-min 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-min longer sleep per night per copy of the minor allele,11 had an estimated effect in the present analysis of only 0.9-min 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

Discussion

This GWAS 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- to 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. Although 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 higher52 and lower53 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 whereas 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 GWAS, 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 (Figure 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.

Although 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 min per night, the 0.07% of variance in sleep duration explained by this variant is typical of GWAS. 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 >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 with objective measures such as actigraphy59 and the coarse-grained response options typical of sleep duration questionnaires, as well as extensive socioenvironmental 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 this study is >10-fold larger than the two previously published GWAS of sleep duration, it remains small compared with 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 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 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.

References

  1. 1

    Partinen M, Kaprio J, Koskenvuo M, Putkonen P, Langinvainio H . Genetic and environmental determination of human sleep. Sleep 1983; 6: 179–185.

    CAS  Article  Google Scholar 

  2. 2

    Heath AC, Kendler KS, Eaves LJ, Martin NG . Evidence for genetic influences on sleep disturbance and sleep pattern in twins. Sleep 1990; 13: 318–335.

    CAS  Article  Google Scholar 

  3. 3

    Watson NF, Buchwald D, Vitiello MV, Noonan C, Goldberg J . A twin study of sleep duration and body mass index. J Clin Sleep Med 2010; 6: 11–17.

    PubMed  PubMed Central  Google Scholar 

  4. 4

    Sehgal A, Mignot E . Genetics of sleep and sleep disorders. Cell 2011; 146: 194–207.

    CAS  Article  Google Scholar 

  5. 5

    Toh KL, Jones CR, He Y, Eide EJ, Hinz WA, Virshup DM et al. An hPer2 phosphorylation site mutation in familial advanced sleep phase syndrome. Science 2001; 291: 1040–1043.

    CAS  Article  Google Scholar 

  6. 6

    Xu Y, Padiath QS, Shapiro RE, Jones CR, Wu SC, Saigoh N et al. Functional consequences of a CKIdelta mutation causing familial advanced sleep phase syndrome. Nature 2005; 434: 640–644.

    CAS  Article  Google Scholar 

  7. 7

    He Y, Jones CR, Fujiki N, Xu Y, Guo B, Holder JL Jr et al. The transcriptional repressor DEC2 regulates sleep length in mammals. Science 2009; 325: 866–870.

    CAS  Article  Google Scholar 

  8. 8

    Allebrandt KV, Teder-Laving M, Akyol M, Pichler I, Müller-Myhsok B, Pramstaller P et al. CLOCK gene variants associate with sleep duration in two independent populations. Biol Psychiatry 2010; 67: 1040–1047.

    CAS  Article  Google Scholar 

  9. 9

    Evans DS, Parimi N, Nievergelt CM, Blackwell T, Redline S, Ancoli-Israel S et al. Common genetic variants in ARNTL and NPAS2 and at chromosome 12p13 are associated with objectively measured sleep traits in the elderly. Sleep 2013; 36: 431–446.

    Article  Google Scholar 

  10. 10

    Utge S, Kronholm E, Partonen T, Soronen P, Ollila HM, Loukola A et al2011 Shared genetic background for regulation of mood and sleep: association of GRIA3 with sleep duration in healthy Finnish women. Sleep 34: 1309–1316.

    Article  Google Scholar 

  11. 11

    Gottlieb DJ, O'Connor GT, Wilk JB . Genome-wide association of sleep and circadian phenotypes. BMC Med Genet 2007; 8 (Suppl 1): S9.

    Article  Google Scholar 

  12. 12

    Allebrandt KV, Amin N, Müller-Myhsok B, Esko T, Teder-Laving M, Azevedo RV et al. A K(ATP) channel gene effect on sleep duration: from genome-wide association studies to function in Drosophila. Mol Psychiatry 2011; 18: 122–132.

    Article  Google Scholar 

  13. 13

    Psaty BM, O’Donnell CJ, Gudnason V, Lunetta KL, Folsom AR, Rotter JI et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ Cardiovasc Genet 2009; 2: 73–80.

    Article  Google Scholar 

  14. 14

    The ARIC investigators. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. Am J Epidemiol 1989; 129: 687–702.

    Article  Google Scholar 

  15. 15

    Fried LP, Borhani NO, Enright P, Furberg CD, Gardin JM, Kronmal RA et al. The Cardiovascular Health Study: design and rationale. Ann Epidemiol 1991; 1: 263–276.

    CAS  Article  Google Scholar 

  16. 16

    Dawber TR, Meadors GF, Moore FE . Epidemiological approaches to heart disease: the Framingham study. Am J Public Health 1951; 41: 279–286.

    CAS  Article  Google Scholar 

  17. 17

    Feinleib M, Kannel W, Garrison R, McNamara P, Castelli W . The Framingham Offspring Study: design and preliminary data. Prev Med 1975; 4: 518–525.

    CAS  Article  Google Scholar 

  18. 18

    Splansky GL, Corey D, Yang Q, Atwood LD, Cupples LA, Benjamin EJ et al. The Third Generation Cohort of the National Heart Lung and Blood Institute’s Framingham Heart Study: design recruitment and initial examination. Am J Epidemiol 2007; 165: 1328–1335.

    Article  Google Scholar 

  19. 19

    Köttgen A, Pattaro C, Böger CA, Fuchsberger C, Olden M, Glazer NL et al. New loci associated with kidney function and chronic kidney disease. Nat Genet 2010; 42: 376–384.

    Article  Google Scholar 

  20. 20

    Barker DJP, Osmond C, Forsen TJ, Kajantie E, Eriksson JG . Trajectories of growth among children who have coronary events as adults. N Engl J Med 2005; 353: 1802–1809.

    CAS  Article  Google Scholar 

  21. 21

    Melzer D, Perry JR, Hernandez D, Corsi AM, Stevens K, Rafferty I et al. A genome-wide association study identifies protein quantitative trait loci (pQTLs). PLoS Genet 2008; 4, p e1000072.

    Article  Google Scholar 

  22. 22

    Blank JB, Cawthon PM, Carrion-Petersen ML, Harper L, Johnson JP, Mitson E et al. Overview of recruitment for the osteoporotic fractures in men study (MrOS). Contemp Clin Trials 2005; 26: 557–568.

    Article  Google Scholar 

  23. 23

    Orwoll E, Blank JB, Barrett-Connor E, Cauley J, Cummings S, Ensrud K et al. Design and baseline characteristics of the osteoporotic fractures in men (MrOS) study—a large observational study of the determinants of fracture in older men. Contemp Clin Trials 2005; 26: 569–585.

    Article  Google Scholar 

  24. 24

    Bouchard C . Genetic epidemiology association and sib-pair linkage: results from the Quebec Family Study In Bray GA, Ryan DH (eds) Molecular and Genetic Aspects of Obesity. Louisiana State University Press: Baton Rouge, LA, USA, pp 470–481, 1996.

    Google Scholar 

  25. 25

    Hofman A, van Duijn CM, Franco OH, Ikram MA, Janssen HL, Klaver CC et al. The Rotterdam Study: 2012 objectives and design update. Eur J Epidemiol 2011; 26: 657–686.

    Article  Google Scholar 

  26. 26

    Völzke H, Alte D, Schmidt CO, Radke D, Lorbeer R et al. Study of Health in Pomerania (SHIP) – a community cohort and repeated survey approach to comprehensively assess main health determinants among the general adult population. Int J Epidemiol 2011; 40: 294–307.

    Article  Google Scholar 

  27. 27

    Cummings SR, Nevitt MC, Browner WS, Stone K, Fox KM, Ensrud KE et al. Risk factors for hip fracture in white women. Study of Osteoporotic Fractures Research Group. N Engl J Med 1995; 332: 767–773.

    CAS  Article  Google Scholar 

  28. 28

    Moayyeri A, Hammond CJ, Valdes AM, Spector TD . Cohort Profile: TwinsUK and Healthy Ageing Twin Study. Int J Epidemiol 2012; 42: 76–85.

    Article  Google Scholar 

  29. 29

    Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S . The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 1993; 328: 1230–1235.

    CAS  Article  Google Scholar 

  30. 30

    Raitakari OT, Juonala M, Rönnemaa T, Keltikangas-Järvinen L, Räsänen L, Pietikäinen M et al. Cohort profile: the cardiovascular risk in Young Finns Study. Int J Epidemiol 2008; 37: 1220–1226.

    Article  Google Scholar 

  31. 31

    Rimm EB, Giovannucci EL, Willett WC, Colditz GA, Ascherio A, Rosner B et al. Prospective study of alcohol consumption and risk of coronary disease in men. Lancet 1991; 338: 464–468.

    CAS  Article  Google Scholar 

  32. 32

    Colditz GA, Hankinson SE . The Nurses' Health Study: lifestyle and health among women. Nat Rev Cancer 2005; 5: 388–396.

    CAS  Article  Google Scholar 

  33. 33

    Musunuru K, Lettre G, Young T, Farlow DN, Pirruccello JP, Ejebe KG et al. Candidate gene association resource (CARe): design methods and proof of concept. Circ Cardiovasc Genet 2010; 3: 267–275.

    CAS  Article  Google Scholar 

  34. 34

    Almasy L, Blangero J . Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 1998; 62: 1198–1211.

    CAS  Article  Google Scholar 

  35. 35

    Willer C, Li Y, Abecasis G . METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010; 26: 2190–2191.

    CAS  Article  Google Scholar 

  36. 36

    Yang J, Ferreira T, Morris AP, Medland SE Genetic Investigation of ANthropometric Traits (GIANT) Consortium; DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Madden PA, Heath AC et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet 2012; 44: 369–375, S1-3.

    CAS  Article  Google Scholar 

  37. 37

    Gauderman WJ, Morrison JM QUANTO 1.1: A computer program for power and sample size calculations for genetic-epidemiology studies. http://hydra.usc.edu/gxe, 2006.

  38. 38

    Johnson AD, Handsaker RE, Pulit S, Nizzari MM, O’Donnell CJ, de Bakker PIW . SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 2008; 24: 2938–2939.

    CAS  Article  Google Scholar 

  39. 39

    Porcu E, Medici M, Pistis G, Volpato CB, Wilson SG, Cappola AR et al. A meta-analysis of thyroid-related traits reveals novel loci and gender-specific differences in the regulation of thyroid function. PLoS Genet 2013; 9: e1003266.

    CAS  Article  Google Scholar 

  40. 40

    Nitsch R, Dato VD, Gennaro AD, Cristofaro TD, Abbondante S, De Felice M et al. Comparative genomics reveals a functional thyroid-specific element in the far upstream region of the PAX8 gene. Genomics 2010; 11: 306.

    PubMed  Google Scholar 

  41. 41

    Grundberg E, Small KS, Hedman AK, Nica AC, Buil A, Keildson S et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat Genet 2012; 44: 1084–1089.

    CAS  Article  Google Scholar 

  42. 42

    Ding J, Gudjonsson JE, Liang L, Stuart PE, Li Y, Chen W et al. Gene expression in skin and lymphoblastoid cells: refined statistical method reveals extensive overlap in cis-eQTL signals. Am J Hum Genet 2010; 87: 779–789.

    CAS  Article  Google Scholar 

  43. 43

    Morselli L, Leproult R, Balbo M, Spiegel K . Role of sleep duration in the regulation of glucose metabolism and appetite. Best Pract Res Clin Endocrinol Metab 2010; 24: 687–702.

    CAS  Article  Google Scholar 

  44. 44

    Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 2010; 42: 105–116.

    CAS  Article  Google Scholar 

  45. 45

    Soranzo N, Sanna S, Wheeler E, Gieger C, Radke D, Dupuis J et al. Common variants at 10 genomic loci influence hemoglobin A1C levels via glycemic and nonglycemic pathways. Diabetes 2010; 59: 3229–3239.

    CAS  Article  Google Scholar 

  46. 46

    Neale BM, Medland SE, Ripke S, Asherson P, Franke B, Lesch KP et al. Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2010; 49: 884–897.

    Article  Google Scholar 

  47. 47

    Fehrmann RS, Jansen RC, Veldink JH, Westra HJ, Arends D, Bonder MJ et al. Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes with a major role for the HLA. PLoS Genet 2011; 7: e1002197.

    CAS  Article  Google Scholar 

  48. 48

    Cross-Disorder Group of the Psychiatric Genomics Consortium; Genetic Risk Outcome of Psychosis (GROUP) Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 2013; 381: 1371–1379.

    Article  Google Scholar 

  49. 49

    Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium, Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium Ripke S, Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium Wray NR, Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium Lewis CM, Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium Hamilton SP, Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium Weissman MM, Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium Breen G et al. A mega-analysis of genome-wide association studies for major depressive disorder. Mol Psychiatry. 2013; 18: 497–511.

    Article  Google Scholar 

  50. 50

    Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium. Genome-wide association study identifies five new schizophrenia loci. Nat Genet. 2011; 43: 969–976.

    Article  Google Scholar 

  51. 51

    Ruiz-Llorente S, Carrillo Santa de Pau E, Sastre-Perona A, Montero-Conde C, Gómez-López G, Fagin JA et al. Genome-wide analysis of Pax8 binding provides new insights into thyroid functions. BMC Genomics 2012; 13: 147.

    CAS  Article  Google Scholar 

  52. 52

    Spiegel K, Leproult R, Van Cauter E . Impact of sleep debt on metabolic and endocrine function. Lancet 1999; 354: 1435–1438.

    CAS  Article  Google Scholar 

  53. 53

    Kessler L, Nedeltcheva A, Imperial J, Penev PD . Changes in serum TSH and free T4 during human sleep restriction. Sleep 2010; 33: 1115–1118.

    Article  Google Scholar 

  54. 54

    Krishnan PV, Vadivu AS, Alappatt A, Kameswaran M . Prevalence of sleep abnormalities and their association among hypothyroid patients in an Indian population. Sleep Med 2012; 13: 1232–1237.

    Article  Google Scholar 

  55. 55

    Kales A, Heuser G, Jacobson A, Kales JD, Hanley J, Zweizig JR et al. All night sleep studies in hypothyroid patients before and after treatment. J Clin Endocrinol Metab 1967; 27: 1593–1599.

    CAS  Article  Google Scholar 

  56. 56

    Marinò M, Chiovato L, Pinchera A . Graves’ disease. In Jameson JL, De Groot LJ (eds) Endocrinology. Saunders Elsevier: Philadelphia, PA, USA, pp 1527–1558 2010.

    Google Scholar 

  57. 57

    Opp MR, Krueger JM . Interleukin 1-receptor antagonist blocks interleukin 1-induced sleep and fever. Am J Physiol 1991; 260: R453–R457.

    CAS  PubMed  Google Scholar 

  58. 58

    Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 2010; 42: 937–948.

    CAS  Article  Google Scholar 

  59. 59

    Van den Berg JF, Van Rooij FJ, Vos H, Tulen JH, Hofman A, Miedema HM et al. Disagreement between subjective and actigraphic measures of sleep duration in a population-based study of elderly persons. J Sleep Res 2008; 17: 295–302.

    Article  Google Scholar 

  60. 60

    Gerhman P, Seelig AD, Jacobson IG, Boykp EJ, Hooper TI, Gackstetter GD et al. Predeployment sleep duration and insomnia as risk factors for new-onset mental health disorders following military deployment. Sleep 2013; 36: 1009–1018.

    Article  Google Scholar 

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Acknowledgements

The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung and Blood Institute contracts N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC–55022, R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. The Cardiovascular Health Study was supported by NHLBI contracts HHSN268201200036C, N01-HC-85239, N01-HC-55222, N01-HC-85079, N01-HC-85080, N01-HC-85081, N01-HC-85082, N01-HC-85083, N01-HC-85086; and NHLBI grants HL080295, HL087652, HL105756 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through AG023629 from the National Institute on Aging (NIA). DNA handling and genotyping was supported in part by the National Center for Research Resources grant UL1RR033176, now at the National Center for Advancing Translational Sciences CTSI grant UL1TR000124; the National Institute of Diabetes and Digestive and Kidney Disease grant DK063491 to the Southern California Diabetes Endocrinology Research Center. This research was conducted in part using data and resources from the Framingham Heart Study of the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. The analyses reflect intellectual input and resource development from the Framingham Heart Study investigators participating in the SNP Health Association Resource (SHARe) project. This work was partially supported by the National Heart, Lung and Blood Institute’s Framingham Heart Study (contract no. N01‐HC‐25195) and its contract with Affymetrix, Inc for genotyping services (contract no. N02‐HL‐6‐4278). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA‐II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. The Health, Aging and Body Composition Study supported by NIA contracts N01-AG-62101, N01-AG-62103 and N01-AG-62106 and NIA grants 1R01AG032098-01A1 and 1R01AG030474-01A1. Genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096C. This research was supported in part by the Intramural Research Program of the NIH, NIA. The Helsinki Birth Cohort Study has been supported by grants from the Academy of Finland, the Finnish Diabetes Research Society, Folkhälsan Research Foundation, Novo Nordisk Foundation, Finska Läkaresällskapet, Signe and Ane Gyllenberg Foundation, University of Helsinki, Ministry of Education, Ahokas Foundation, Emil Aaltonen Foundation, Juho Vainio Foundation, and Wellcome Trust (grant number WT089062). The Nurses Health Study and Health Professional Follow-Up Study GWAS were supported by grants from the National Institutes of Health [NCI (CA40356, CA087969, CA055075, CA98233), NIDDK (DK058845, DK070756), NHGRI (HG004399), NHLBI (HL35464)] with additional support from Merck/Rosetta Research Laboratories, North Wales, PA. The Invecchiare in CHIANTI study baseline (1998–2000) was supported as a ‘targeted project’ (ICS110.1/RF97.71) by the Italian Ministry of Health and in part by the U.S. National Institute on Aging (Contracts: 263 MD 9164 and 263 MD 821336). The Osteoporotic Fractures in Men (MrOS) Study is supported by National Institutes of Health funding. The following institutes provide support: the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Institute on Aging (NIA), the National Center for Research Resources (NCRR), and NIH Roadmap for Medical Research under the following grant numbers: U01 AR45580, U01 AR45614, U01 AR45632, U01 AR45647, U01 AR45654, U01 AR45583, U01 AG18197, U01-AG027810, and UL1 RR024140. The National Heart, Lung, and Blood Institute (NHLBI) provides funding for the MrOS Sleep ancillary study ‘Outcomes of Sleep Disorders in Older Men’ under the following grant numbers: R01 HL071194, R01 HL070848, R01 HL070847, R01 HL070842, R01 HL070841, R01 HL070837, R01 HL070838, and R01 HL070839’. The NIAMS provides funding for the MrOS ancillary study ‘GWAS in MrOS and SOF’ under the grant number RC2ARO58973. The Quebec Family Study was funded by multiple grants from the Medical Research Council of Canada and the Canadian Institutes for Health Research. This work was supported by a team grant from the Canadian Institutes for Health Research (FRN-CCT-83028). Funding for the Queensland Institute of Medical Research Twin Study was provided by the Australian National Health and Medical Research Council (241944, 339462, 389927, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 552485, 552498), the Australian Research Council (A7960034, A79906588, A79801419, DP0770096, DP0212016, DP0343921), the FP-5 GenomEUtwin Project (QLG2-CT-2002-01254) and the US National Institutes of Health (NIH grants AA07535, AA10249, AA11998, AA13320, AA13321, AA13326, AA14041, MH66206). A portion of the genotyping on which this study was based (Illumina 370K scans) was carried out at the Center for Inherited Disease Research, Baltimore (CIDR), through an access award to our late colleague Dr Richard Todd (Psychiatry, Washington University School of Medicine, St Louis). Statistical analyses were carried out on the Genetic Cluster Computer, which is financially supported by the Netherlands Scientific Organization (NWO 480-05-003). EMB is supported by NHMRC grant 613608. The generation and management of GWAS genotype data for the Rotterdam Study are supported by the Netherlands Organisation of Scientific Research NWO Investments (no. 175.010.2005.011, 911-03-012). This study is funded by the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) project nr. 050-060-810. The Rotterdam Study is funded by Erasmus MC and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. Henning Tiemeier was supported by the VIDI grant of ZonMw (2009-017.106.370). Karin Hek was supported by a grant from BavoEuropoort. Netherlands Twin Registry funding was obtained from the Netherlands Organization for Scientific Research (NWO: MagW/ZonMW grants 904-61-090, 985-10-002, 904-61-193,480-04-004, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192), Center for Medical Systems Biology (CSMB, NWO Genomics), NBIC/BioAssist/RK(2008.024), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI –NL, 184.021.007), VU University’s Institute for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam (NCA), European Science Foundation (ESF, EU/QLRT-2001-01254), the European Community’s Seventh Framework Program (FP7/2007-2013), ENGAGE (HEALTH-F4-2007-201413); European Science Council (ERC 230374), Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06), the Avera Institute, Sioux Falls, South Dakota (USA), and the National Institutes of Health (NIH, R01D0042157-01A, Grand Opportunity grants 1RC2MH089951-01 and 1RC2 MH089995-01). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. The Study of Health in Pomerania is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103 and 01ZZ0403), the Ministry of Cultural Affairs and the Social Ministry of the Federal State of Mecklenburg-West Pomerania. Genome-wide data have been supported by the Federal Ministry of Education and Research (grant no. 03ZIK012) and a joint grant from Siemens Healthcare, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. The University of Greifswald is a member of the ‘Center of Knowledge Interchange’ program of the Siemens AG and the Caché Campus program of the InterSystems GmbH. The Study of Osteoporotic Fractures is supported by National Institutes of Health funding. The National Institute on Aging (NIA) provides support under the following grant numbers: R01 AG005407, R01 AR35582, R01 AR35583, R01 AR35584, R01 AG005394, R01 AG027574, R01 AG027576, and R01 AG026720. The National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) provides funding for the ancillary study ‘GWAS in MrOS and SOF’ under the grant number RC2ARO58973. TwinsUK was funded by the Wellcome Trust; European Community’s Seventh Framework Programme (FP7/2007-2013), ENGAGE project grant agreement (HEALTH-F4-2007-201413). The study also receives support from the Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy’s & St Thomas’ NHS Foundation Trust in partnership with King’s College London. Genotyping was performed by the Wellcome Trust Sanger Institute, support of the National Eye Institute via an NIH/CIDR genotyping project. This research was supported for the Wisconsin Sleep Cohort Study by the National Heart, Lung, and Blood Institute (R01HL62252) and National Center for Research Resources (1UL1RR025011) and by NS23724. The Young Finns Study has been financially supported by the Academy of Finland: grants 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), and 41071 (Skidi), the Social Insurance Institution of Finland, Kuopio, Tampere and Turku University Hospital Medical Funds (grant 9M048 for 9N035 for TeLeht), Juho Vainio Foundation, Paavo Nurmi Foundation, Finnish Foundation of Cardiovascular Research and Finnish Cultural Foundation, Tampere Tuberculosis Foundation and Emil Aaltonen Foundation.

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Gottlieb, D., Hek, K., Chen, Th. et al. Novel loci associated with usual sleep duration: the CHARGE Consortium Genome-Wide Association Study. Mol Psychiatry 20, 1232–1239 (2015). https://doi.org/10.1038/mp.2014.133

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