1
SUPPLEMENTARY INFORMATION
Genome-wide association analysis of insomnia complaints identifies risk genes
and genetic overlap with psychiatric and metabolic traits
Table of contents
Supplementary Note ............................................................................................................ 2
1. Phenotype ......................................................................................................................... 2
1.1 Insomnia complaints in UK Biobank ...................................................................... 2
1.2 Validation and optimization of insomnia phenotype in an independent sample:
the Netherlands Sleep Registry ................................................................................ 2
1.2.1 Validation of the insomnia phenotype in an independent sample ............... 2
1.2.2 Confounding with Restless Legs Syndrome? .............................................. 3
1.2.3 The insomnia phenotype is not mainly due to other disorders .................... 4
1.2.4 Multivariate profile cross-validation supports a specific insomnia
phenotype ..................................................................................................... 5
1.3 Insomnia complaints in deCODE ............................................................................. 6
1.4 RLS and insomnia complaints in the Dortmund Health Study ................................ 6
1.5 RLS and insomnia complaints in the ‘Course of Restless Legs Syndrome’ ............ 7
2 SNPs associated with insomnia complaints ..................................................................... 7
2.1 Functional annotation of SNPs associated with insomnia complaints ..................... 7
2.2 Effect of confounding RLS on the insomnia MEIS1 association ............................. 8
3 Genes associated with insomnia complaints ..................................................................... 9
3.1 Associations with other phenotypes ......................................................................... 9
3.2 Gene functions and expression profiles ................................................................. 10
4 Power analysis ............................................................................................................... 10
5 Additional analyses with insomnia complaints and other sleep-related phenotypes in
UK Biobank ................................................................................................................... 11
5.1 Additional insomnia phenotypes .............................................................................. 11
5.2 Additional sleep-related phenotypes ........................................................................ 12
5.3 Conditional analyses ................................................................................................ 13
6 Conditional analyses of RLS on insomnia complaints .................................................. 13
7 Biological annotations ................................................................................................... 14
7.1 Pathway analysis .................................................................................................... 14
7.2 Tissue enrichment analysis .................................................................................... 14
7.2.1 GTEx tissue enrichment analysis ............................................................... 14
7.2.2 BrainSpan tissue enrichment analysis ........................................................ 15
8 Acknowledgements ........................................................................................................ 16
9 References ...................................................................................................................... 17
Supplementary Figures ..................................................................................................... 20
Supplementary Tables ....................................................................................................... 51
Nature Genetics: doi:10.1038/ng.3888
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Supplementary Note
1. Phenotype
1.1 Insomnia complaints in UK Biobank.
In the UK Biobank study, insomnia complaints were measured in 501,755 participants – of
whom 113,006 were included in the present study based on availability of genotype data and
ancestry – through the following touchscreen question: “Do you have trouble falling asleep at
night or do you wake up in the middle of the night?” A help button displayed the following text:
“If this varies a lot, answer this question in relation to the last 4 weeks.” The participants had
four answer possibilities: “never/rarely”, “sometimes”, “usually”, or “prefer not to answer”.
Participants who answered the question with “usually” were considered as cases, and participants
answering “never/rarely” or “sometimes” were considered as controls. Prevalence of insomnia
complaints was 29% in Caucasians with genotype data available in the UK Biobank sample
(Supplementary Table 1). The mean age was 56.92 (sd = 7.94).
1.2 Validation of the insomnia phenotype
Sleep complaints occur not only in Insomnia Disorder (ID), but in other disorders as well. We
therefore systematically evaluated the validity of the UK Biobank question on trouble falling or
staying asleep to discriminate ID.
1.2.1 Validation of the insomnia phenotype in an independent sample
We evaluated how well the UK Biobank question on trouble falling asleep or waking up in the
middle of the night discriminates insomnia cases from controls, using data from participants of
the Netherlands Sleep Registry (NSR)1 in the same age range as UK Biobank participants. The
NSR is a large-scale study using web-based assessment of questionnaires to collect data on sleep
behaviour and additional variables like stress, personality and health. In order to obtain an
estimate of the discriminative validity of the UK Biobank question and the optimal answer
option cut-off, we selected 1,918 participants (72% female, m = 50 (sd = 15) years of age) from
the NSR, who were either without insomnia complaints (n = 1,073) or were likely to have
Insomnia Disorder (n = 845) according to previously established criteria: participants with
absence of insomnia complaints had to score below 6 on the Pittsburgh Sleep Quality Index
(PSQI)2 and below 8 on the Insomnia Severity Index (ISI)
3; Participants with probable ID had to
score at least 6 on the PSQI and at least 15 on the ISI. These cut-offs have shown to have a high
accuracy for discriminating ID cases from controls in clinical and community samples2–5
, here
further optimised by simultaneous application of the criteria from both the ISI and PSQI
questionnaires. We subsequently verified the absence or presence of a diagnosis of ID according
to the golden standard of DSM-56 and ICSD3
7 criteria in all 1,918 participants using an
independent assessment tool: the Duke Structured Interview for Sleep Disorders8 which agreed
with the ISI+PSQI-based diagnosis in 92% of all the participants.
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The single aggregated UK Biobank question about trouble falling asleep and waking up in the
middle of the night was represented by two separate questions in the NSR ("difficulty falling
asleep" and "difficulty staying asleep"), rated on a 5-point Likert-type scale with answer options
“none”, “mild”, “moderate”, “severe”, or “very severe”. Supplementary Figure 2 shows the
Receiver Operating Characteristic (ROC) curve for the accuracy of discriminating probable ID
cases from controls using only the partial information of the highest rating across the two
questions on trouble falling and staying asleep. Using the ISI+PSQI criteria for ID, the area
under the curve (AUC) was 0.998 with maximal discriminative accuracy for the criterion of at
least one moderate complaint (sensitivity 0.98, specificity 0.96, accuracy 0.97). A similarly good
discrimination was obtained using the subsequently assessed structured interview diagnosis of ID
(ROC AUC 0.947, sensitivity 0.94, specificity 0.89, accuracy 0.91, Supplementary Fig. 2), again
with a maximal accuracy for the criterion of at least one moderate complaint. The findings
suggest that the UK Biobank proxy question can discriminate ID well.
1.2.2 Confounding with Restless Legs Syndrome?
We next evaluated whether the UK Biobank insomnia question specifically discriminates ID, or
has a similar sensitivity to Restless Legs Syndrome (RLS), a movement disorder that can disturb
sleep. The NSR validation sample was queried about the four diagnostic criteria for RLS defined
by the International RLS Study Group (IRLSSG)9, as well as the presence of diagnosed disorders
with a structured interview1,8
. Of the 1918 participants, 356 (19%) met all four IRLSSG criteria,
while these could not be attributed to other disorders known to present with similar complaints10
.
To optimally estimate specificity to discriminate RLS in the UK Biobank sample, we
downsampled the NSR to match a 29% prevalence of insomnia complaints (420 out of 1,461).
Supplementary Figure 2 shows the ROC curve for the accuracy of discriminating RLS cases
(242) from controls within this sample according to a “mild”, “moderate”, “severe” or “very
severe” rating of either trouble falling or staying asleep. The area under the curve is 0.602,
indicating a rather poor discrimination, and the cut-off that was highly successful to define ID
performed poor in the discrimination of probable RLS (sensitivity 0.43, specificity 0.74,
accuracy 0.69).
We continued the phenotype validation by investigating the discrimination within the subsample
of ID and/or RLS. Leaving out all controls, within the 554 cases with disordered sleep (RLS
only: n = 141, ID only: n = 413, comorbid RLS+ID, n = 101) the discriminative power of
moderate to very severe trouble falling or staying asleep for the presence of ID alone or
comorbid RLS+ID versus RLS only was good, again with a maximal accuracy for the criterion
of at least one moderate complaint (sensitivity 0.96, specificity 0.97, accuracy 0.97,
Supplementary Fig. 2).
Supplementary Figure 1 and Supplementary Table 2 show in detail how ratings on trouble falling
or staying asleep discriminate ID but hardly RLS. The majority of people having insomnia rate
their trouble falling or staying asleep as moderate to very severe. In contrast, although sleep
quality can be affected, only few RLS patients without comorbid ID rate their trouble falling or
staying asleep as more than mild.
To evaluate whether the low accuracy of discriminating RLS could be secondary to selecting a
sample of insomnia cases and controls with very strict PSQI and ISI selection criteria, we
extended the NSR phenotype validation sample with another 551 cases, randomly selected while
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maintaining the 29% prevalence of insomnia complaints to match the UK Biobank sample, that
had uncertain diagnoses, i.e. some sleep complaints, but in-between the strict criteria for either
ID or control. In the resulting total sample (n = 2,012, 72% female, m = 49 (sd = 16) years of
age), discrimination of the 355 (18%) cases with RLS remained rather poor (AUC = 0.591,
sensitivity 0.40, specificity 0.73, accuracy 0.67). In contrast, there was again a good
discrimination of (less specifically clinical) insomnia according to a validated ISI > 9 cut-off5
(AUC 0.919, sensitivity 0.81, specificity 0.94, accuracy 0.90), again with a maximal accuracy for
the criterion of at least one moderate complaint.
The findings are in agreement with a previous population-based study that suggests relatively
poor discrimination of RLS by sleep complaints: only few RLS patients (13%) experience
restless legs symptoms more than three times a week11
, whereas experiencing sleep problems at
least three times a week is a defining characteristic of ID. Frequent and/or severe trouble falling
asleep or staying asleep is more likely in selected clinical populations of RLS patients, who seek
professional help for their complaints and often have comorbid insomnia. However, even in
clinical populations, IRLSSG severity scores do not necessarily correlate with sleep onset
latency and sleep disturbance12
. The absence of ‘trouble falling or staying asleep’ among the
defining criteria of RLS according to the IRLSSG likely reflects its limited discriminative power.
In addition to the differential sensitivity and specificity of moderate to very severe trouble falling
or staying asleep in discriminating ID or RLS from controls, the a priori population prevalence
of ID (11.1%) is substantially higher than the prevalence of RLS (5.5%), while both increase
with age13–15
. In summary, the phenotype of insomnia complaints excellently represents ID and
causes only minor contamination by RLS (for quantitative details, see Supplementary
Information section 2.2).
1.2.3 The insomnia phenotype is not mainly due to other disorders
The phenotype of trouble falling or staying asleep may also occur in association with other
disorders which could confound the results of the genetic association analyses. We evaluated to
what extent the phenotype is as reflective of other disorders or comorbidities in 1,709 of the
1,918 NSR participants in whom we also systematically assessed current diseases according to
19 categories of the 10th revision of the International Statistical Classification of Diseases and
Related Health Problems (ICD-10)16
. For each disease category, we evaluated contingency tables
on the proportion of cases with/without a disorder and with/without moderate to very severe
insomnia complaints. χ2 tests indicated small effect sizes (d < 0.30) for 17 categories and
moderate effect sizes for 2 categories (d = 0.42 for Diseases of the skin and subcutaneous tissue;
and d = 0.47 for Symptoms, signs, abnormal findings not elsewhere classified). Whereas the
effects were small on average (d = 0.18), ten effects were significant at the P < 0.05 level
because of the large sample size. However, as shown in Supplementary Figure 3, the accuracy of
insomnia complaints to discriminate any ICD-10 disease categories was low (range 0.57-0.63).
The findings indicate that sleep complaints are somewhat more likely to occur in half of the
aggregated ICD-10 disorder classes, yet without strong discriminative properties. However, the
ICD-10 categories are very broad and include many different disorders. Hence, some specific
disorders within the categories, and even more so specific symptoms, may still show an
association with the insomnia phenotype. For example, as shown in Supplementary Figure 3 (and
Fig. 4 in the main article), χ2 tests showed small to medium effect sizes of major depressive
disorder, coronary artery disease and anorexia nervosa for the proportion reporting trouble
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falling or staying asleep, but the discriminative accuracy remained low (respectively 0.63, 0.61
and 0.59).
1.2.4 Multivariate profile cross-validation supports a specific insomnia phenotype
The difference between ID, RLS and controls is not likely to be limited to only trouble falling
and staying asleep. To further support the validity of the one-question UK Biobank insomnia
phenotype, we evaluated how the multivariate profile of possibly related phenotypes of UK
Biobank participants with insomnia differed from those without, and then tested whether this
profile was characteristic for ID or RLS in the NSR validation sample.
UK Biobank included five other sleep-related questions (UK Biobank question numbers 1170-
1220, described in Supplementary Information section 5.2), pertaining to the ease of getting up
in the morning, being an evening versus morning person (chronotype), the frequency of daytime
napping, snoring, and unintentional daytime dozing. Given the high prevalence of depressive
symptoms in people having insomnia17
, we also included two UK Biobank questions about
feeling depressed and loss of interest (20510 and 20514). To allow for a comprehensive
deviation plot irrespective of differences between the scaling of variables, all seven variables
were standardized relative to mean and standard deviation of the answers provided by the control
group. The upper radar-plot in Supplementary Figure 4 shows the multivariate profile of
deviations in the UK Biobank participants with insomnia complaints relative to those without.
Clockwise, the profile shows that people with insomnia complaints have a shorter sleep duration,
get up less easy, do not show a systematically different chronotype, report somewhat more
frequent napping, snoring and unintentional dozing, feel more depressed and have more loss of
interest.
To support the validity of the UK Biobank insomnia phenotype, we evaluated whether its
multivariate profile resembled the profile of well-characterized people with insomnia in the
NSR. For each of the seven UK Biobank variables, corresponding questions were derived from
the Pittsburgh Sleep Quality Index2, the Berlin questionnaire
18, the Munich Chronotype
Questionnaire19
, the Duke Structured Interview for Sleep Disorders8, the Inventory of Depressive
Symptoms20
, and the Center for Epidemiological Studies Depression (CES-D) scale21
. Scores
were again standardized relative to mean and standard deviation of the answers provided by the
NSR control group (n = 927) who did not report any sleep complaint. The group mean deviation
profiles for NSR participants having ID (N = 635), RLS (n = 146) and comorbid RLS+ID (n =
210) are shown in the middle radar-plot in Supplementary Figure 4. The plots suggest striking
similarity between the UK Biobank insomnia phenotype profile and the profiles of the NSR
groups with ID and comorbid RLS+ID. The profile of the NSR participants with RLS deviates
only marginally from controls and does not resemble the insomnia profiles even when zooming
in (Supplementary Fig. 4, lower panel).
The standardized deviation profile for the NSR ID groups (ID only, or comorbid with RLS) was
overall more pronounced than the standardized deviation profile for the UK Biobank insomnia
group (note scale differences in Supplementary Fig. 4). This is possibly related to the fact that
stringent criteria for clinical insomnia were applied in the NSR ID groups. In order to quantify
the similarity of the shapes, the profile of every individual UK Biobank participant was
correlated with each of the three NSR sleep disorder group profiles. The UK Biobank
participants with insomnia complaints showed positive correlations of, on average, r = 0.19 with
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both the NSR ID and comorbid ID+RLS profiles, but negative correlations with the NSR RLS
profile (r = -0.10). In contrast, the UK Biobank control group showed negative correlations of,
on average, r = -0.08 with both the NSR ID and comorbid ID+RLS profiles, and a very small
positive correlation with the NSR RLS profile (r = 0.03). Thus, the multivariate deviation profile
of the UK Biobank participants with insomnia complaints resembled the NSR insomnia profiles
(with and without RLS) and was dissimilar to the NSR RLS profile, while the UK Biobank
control group was dissimilar to the NSR insomnia profiles. In conclusion, profile similarity
supports the validity of the UK Biobank insomnia phenotype.
1.3 Insomnia measure in deCODE
DeCODE had insomnia data available for 7,611 participants (Supplementary Table 10), which
was measured using three questions: “I have difficulty falling asleep at night”, “I wake up too
early and find it difficult to fall back asleep”, and “I wake up often during the night”. The
participants had six answer possibilities: “never/almost never”, “less than once a week”, “once or
twice a week”, “three to five times a week”, “six to seven times a week”, or “do not know”.
Participants who indicated “three to five times a week” or “six to seven times a week” for at least
one of the three questions were considered as cases. Participants who had none of these two
responses for any of the three questions were considered as controls. The sample was recruited
for a study on sleep related disorders. Hence, obstructive sleep apnea was somewhat
overrepresented as compared to population estimates in this age range22
: 41% of cases and 32%
of controls suffered from obstructive sleep apnea, and in addition 24% of cases and 32% of
controls were first degree relatives.
1.4 RLS and insomnia complaints in the Dortmund Health Study
The population-based Dortmund Health Study (DHS) assessed the prevalence and incidence of
the various types of headache as well as other chronic conditions, and their impact on the daily
activities. 2,291 participants were recruited into the study. Of these, 1,312 visited the study
center for an interview and to provide blood samples while the others answered a mailed
questionnaire. Follow-up by mailed questionnaire was performed in 77.8% of the survivors, on
average 2.2 years after baseline.
The questionnaire included the PSQI2 questions 1-4, 6, 8, and 9 which were used to derive a
three-level insomnia severity score. From these seven PSQI items, the ISI3 was estimated using
data from the NSR. In the NSR, a linear regression was performed on a random sample including
half of all participants who filled out both the PSQI and the ISI. Applying the regression
coefficients in the other half of the participants, the coefficient of correlation between their true
ISI and ISI-estimate based on the seven PSQI items was 0.88. The regression coefficients
obtained were subsequently used to estimate the ISI in the COR and DHS samples. COR and
DHS participants with an estimated ISI lower than 8 were regarded not to have insomnia, those
with an estimated ISI between 8 and 14 were regarded as having subclinical insomnia, and those
with an estimated ISI of 15 or more were regarded as having moderate to severe clinical
insomnia3.
RLS was assessed using a validated questionnaire23
that included the minimal criteria for RLS
published by the IRLSSG9. Case status was defined as either a study participant had been
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diagnosed by a medical doctor or reported unpleasant sensations in the legs which occurred only
during periods of rest or when falling asleep, worsened in the evening or at night as compared to
the morning, and were partly or totally relieved by movements. An estimated 7% of the DHS
sample fulfilled the criteria for RLS, 14% for ID and 3% for comorbid RLS and ID
(Supplementary Table 18).
1.5 RLS and insomnia complaints in the ‘Course of Restless Legs Syndrome’ study
The ‘Course of Restless Legs Syndrome’ (COR) Study analyzed changes in RLS severity over a
course of six years and evaluated the impact of these changes on quality of life, sleep quality and
depressive symptoms among 2,751 members of the German and Swiss RLS patient
organizations. A standard set of scales (RLS severity scale IRLS, SF-36, PSQI and the CES-D)
was repeatedly applied using mailed questionnaires. Using the PSQI data, a three-level insomnia
severity score was calculated as in the DHS sample (see section 1.4). An estimated 66% of the
COR sample fulfilled the criteria for moderate to severe clinical ID comorbid with RLS
(Supplementary Table 18). Blood was collected by the patient's medical doctor using a kit
provided by the study center.
2. SNPs associated with insomnia complaints
2.1. Functional annotation of SNPs associated with insomnia complaints
We assessed possible functional mechanisms of the identified significant SNPs and all SNPs in
high LD (r2 ≥ 0.6 in UK Biobank data). Supplementary Table 4 displays the annotation regarding
SNP function (ANNOVAR24
, http://annovar.openbioinformatics.org/), deleteriousness (CADD25
,
http://cadd.gs.washington.edu/), regulatory function (RegulomeDB26
, www.regulomedb.org),
eQTL (GTEx27
, http://www.gtexportal.org/home/; Blood eQTL Browser28
,
http://genenetwork.nl/bloodeqtlbrowser; BIOS QTL browser29,30
,
http://genenetwork.nl/biosqtlbrowser/), and chromatin state (ChrHMM31
,
http://compbio.mit.edu/ChromHMM/). The majority of genome-wide significant SNPs were
annotated as intronic variants. All significant SNPs were unlikely to be deleterious. To explore a
possible regulatory function of the SNPs, we first investigated if the SNPs were positioned in
regulatory element using RegulomeDB26
. None of the SNPs had strong biological evidence to be
part of a regulatory element. Next, we investigated if the SNPs are known to act as expression
quantitative trait loci (eQTLs) on the neighboring genes using the data of Genotype-Tissue
Expression (GTEx) Project27
(multiple tissue types), the Blood eQTL browser28
, and the
Biobank-based Integrative Omics Studies (BIOS) Consortium29,30
(blood samples). Only the
multiple SNPs at the chromosome 6 locus showed a significant association with the abundance
of mRNA transcripts (in blood) of two neighboring genes (PHF10, strongest association for: P =
3.65 × 10-13
and C6orf120, P = 3.81 × 10-13
). In addition, genetic variants can have downstream
effects by influencing DNA methylation levels. We investigated if the genome-wide significant
SNPs affected methylation levels in cis and trans using data from the BIOS Consortium
(http://genenetwork.nl/biosqtlbrowser/). One SNP (rs113851554) in the MEIS1 locus showed
evidence (P = 1.08 × 10-6
, FDR < 0.05) to act as a cis-methylation quantitative trait locus
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(meQTL). Furthermore, Supplementary Table 4 reports the chromatin state (i.e. accessibility of
the SNPs) in different tissues using ChromHMM31
.
2.2. Effect of confounding RLS on the insomnia MEIS1 association
MEIS1 has been shown to have a comparatively strong effect on RLS32
. The most significant
association was found for rs11385155433
which also is the lead SNP of the insomnia complaints
association. By simulation, we assessed to what extend confounding RLS contributes to the
MEIS1 insomnia association. We simulated the hypothesis that RLS alone drives the association,
using i) an estimate on the proportions P(RLS|1) and P(RLS|0) of RLS individuals in the UK
Biobank cases and controls, and ii) information on the effect size (allelic odds ratio OR) of
rs113851554 on RLS.
For the estimate in (i), we applied Bayes’ theorem,
P(RLS|1) = P(1|RLS)P(RLS)/( P(1|RLS)P(RLS) + P(1|nonRLS)P(nonRLS)) (1)
P(RLS|0) = P(0|RLS)P(RLS)/( P(0|RLS)P(RLS) + P(0|nonRLS)P(nonRLS)) (2)
where P(RLS) = 1 – P(nonRLS) = 0.77 is the RLS prevalence as adapted to ethnicity, sex
distribution, and average age in UK Biobank according to the meta-analysis of Ohayon et al.
(2012)15
who determined 5.2% in males and 10% in females of European decent in the age range
of 55-60 years, while P(1|RLS) = 1 - P(0|RLS) and P(0|nonRLS) = 1 - P(1|nonRLS) are the
sensitivity and specificity, respectively, of the UK Biobank question (“trouble of falling or
staying asleep?”, see Supplementary Information section 1.2.1) in identifying RLS. We
determined P(1|RLS) and P(0|nonRLS) in two independent datasets. One was a subset (n =
1,460; 242 RLS) of NSR (see Supplementary Information section 1.2.2), generated by randomly
reducing individuals reporting insomnia complaints (similar to the insomnia complaints question
in UK Biobank), until their proportion equaled the proportion of insomnia complaints in the UK
Biobank population sample (29%), thus minimizing the ascertainment bias for this question in
NSR. The other dataset used for estimating RLS sensitivity and specificity was the DHS
population sample (see Supplementary Information section 1.4). After quality control, 1,008 of
the DHS participants (mean age +/- SD = 52.5 +/- 13.8 years, 47.2% male) had information on
both the RLS status (positive in 97) and the PSQI components 1, 3, 4, and 7. Unfortunately,
PSQI questions 5a and 5b which match the UK Biobank question are not part of DHS. Therefore,
we applied multiple logistic regression to the COR study (see Supplementary Information section
1.4) which comprises the complete PSQI in order to produce a predictor of the answer to the UK
Biobank question in DHS. The combination of PSQI components 1, 3 and 7 performed best
(accuracy of 82%), yielding 307 DHS individuals predicted to have insomnia complaints.
Remarkably, down-sampled NSR and DHS provided almost the same results, that is, an RLS
sensitivity of the UK Biobank question of 0.43 and 0.45, respectively, and a specificity of 0.74
and 0.71, respectively. Taking the means, 0.436 and 0.727 (weighted according to RLS and non-
RLS proportions, respectively), complied well with the rate of insomnia complaints in UK
Biobank (0,436*0,077+(1-0,727)*(1-0,077) = 28.6%), and led to estimates P(RLS|1) and
P(RLS|0) in UKB as 0.118 and 0.061, respectively.
For (ii) we used data of Xiong et al. (2009)33
who analyzed 285 RLS cases and 285 controls
from Canada and identified rs113851554 risk allele frequencies of 0.19 and 0.081, respectively,
corresponding to an allelic odds ratio (ORRLS) of 2.661. It should be noted that the RLS SNP
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9
association in MEIS1 has been derived from a clinical RLS sample with severe complaints. In a
population-based study like the UK Biobank, the effect size of this locus is likely to be smaller.
We also used more recent information on individuals of European descent (N > 10,000;
Schormair et al., personal communication) indicating an ORRLS of 2,153 and risk allele
frequencies of 0.125 and 0.062, respectively. Of note, the latter is close to the EAF = 0.056
reported in the present paper (see Table 1 of the main text).
Hence, if only RLS but not insomnia was associated with rs113851554, the expected risk allele
frequencies xUKB and yUKB in UK Biobank cases and controls, respectively, can be calculated
from a system of four equations with four unknown variables,
xUKB = xRLSP(RLS|1) + yRLS(1-P(RLS|1) (3)
yUKB = xRLSP(RLS|0) + yRLS(1-P(RLS|0) (4)
ORRLS = xRLS(1-yRLS)/(yRLS(1-xRLS)) (5)
EAF = xUKBhcases + yUKBhcontrols (6)
where xRLS and yRLS are the unknown risk allele frequencies in the RLS and non-RLS subgroups
of UK Biobank, hcases = 32,384/(32,384+80,622) and hcontrols = 1-hcases are the proportions of
cases and controls in UK Biobank, and the other parameters are defined as above.
Using eq(5) to substitute yRLS in eq(3) and eq(4), and combing the resulting two equation
according to eq(6), yields a quadratic equation, xRLS = -(b+(b2-4ac)
½)/(2a), where a =
hRLS(ORRLS-1), c = EAF*ORRLS, b = -(a+c+1-EAF), and hRLS = hcasesP(RLS|1)+hcontrolsP(RLS|0)
= the expected proportion of RLS cases in UK Biobank. Knowing xRLS, the other three unknowns
can be derived from eqs(3-5). Thus, for ORRLS = 2.153, we get xRLS = 0.107, yRLS =0.053, xUKB =
0.059, and yUKB = 0.056, and for ORRLS = 2.661, xRLS = 0.124, yRLS =0.050, xUKB = 0.059, and
yUKB = 0.056. Calculating the allele numbers in UK Biobank cases and controls, and performing
a one-sided, 1-df χ2 test, results in a statistic of 7.8 (Schormair et al., personal communication)
and 14.0 (Xiong et al.33
) with P values of 5 × 10-3
and 2 × 10-4
for ORRLS being 2.153 and 2.661,
respectively. These P values are quite different from 2 × 10-18
measured in UK Biobank (see
Table 1 of the main text). Analogously, the predicted odds ratios for insomnia complaints are
1.06 and 1.08, that is, substantially lower than the ORinsomnia = 1.18 that was determined in UK
Biobank.
In the case of the stronger effect estimate, ORRLS = 2.661, we derived maximal 95% confidence
intervals for the predicted ORinsomnia and P value by simulation of the possible sampling errors in
the risk allele frequency determination in RLS and non-RLS by Xiong et al. (200933
, see above)
which underlay the assumed ORRLS, in the prevalence estimate of Ohayon et al. (2012)15
whose
meta-analysis for 7 age groups included 49,432 individuals, and in the RLS specificity and
sensitivity estimates that were based on the DHS and down-sampled NSR (see above).
Moreover, the simulation also included the sampling variance in the proportions of RLS cases in
UK Biobank cases and controls that were predicted from P(RLS|1) and P(RLS|0), respectively.
For the predicted P value, the 95%-CI was [0.056, 1.1 × 10-11
], and for the predicted ORinsomnia is
was [1.04, 1.14]. Both intervals did not include the values measured in UK Biobank (see above).
These results suggest that RLS might contribute to the association signal in the MEIS1 SNPs that
we found in our study on insomnia, but that it cannot explain the complete association signal.
Nature Genetics: doi:10.1038/ng.3888
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3. Genes associated with insomnia complaints
3.1 Associations with other phenotypes
We investigated if the genes that were associated with insomnia complaints in our genome-wide
association study (GWAS) and genome-wide gene association study (GWGAS) have been
associated before with other phenotypes. We utilized the information present in the GWAS
Catalog34
(https://www.ebi.ac.uk/gwas/). Most of the associated genes have been associated with
other phenotypes before (Supplementary Table 7). Of interest is the association between MEIS1
and RLS, which is related to insomnia. In addition, MEIS1 has been related to PR interval and
PR segment, and waist-to-hip ratio; insomnia contributes to the risks of cardiovascular disease
and obesity. Furthermore, HHEX shows a robust association with Type 2 Diabetes, for which
insomnia is a risk factor.
3.2 Gene functions and expression profiles
For all genes that we identified in the full and sex-specific GWASes and GWGASes, we
explored their functions and expression profiles. Brief functional descriptions are reported in
Supplementary Table 8. In addition gene expression profiles are shown in different tissue types
from GTEx27
(http://www.gtexportal.org/home/datasets; Supplementary Fig. 9), different brain
regions from Braineac (http://www.braineac.org/; Supplementary Fig. 10), and temporal
expression in the brain from the Human Brain Transcriptome35
(http://hbatlas.org/;
Supplementary Fig. 11).
In addition, Supplementary Figure 12 and Supplementary Table 9 show the average expression
values in males, females and both sexes of the genes identified in the full and sex-specific
GWASes and GWGASes. Gene expression levels were extracted across all tissues present in
GTEx27
. Gene expression values were log2 transformed and zero-mean normalized in the total set
of samples. We compared the expression levels in males, females and the combined samples.
Gene expression levels were overall consistent across sex.
4. Power analysis
We estimated the likelihood to replicate the genome-wide significant SNPs found in the UK
Biobank insomnia complaints GWAS in the deCODE dataset, based on the UK Biobank SNP
association results, the sample size of UK Biobank, and the sample size of deCODE, using the
method described in36
. Specifically, we estimated the probability that UK Biobank SNPs would
show the same sign in deCODE (P(matchi)) and that the P value in deCODE would be
significant at the α-level (P(pdeCODE,i < α)) of 0.05/15=0.003. The probability of a matching sign
for SNP i, i.e. the probability that it is either positive or negative in both samples, is
( ) ( | |
) (
| |
) [ (
| |
)] [ (
| |
)],
Nature Genetics: doi:10.1038/ng.3888
11
where Φ is the standard normal cumulative distribution function, βi is the estimated effect size on
the liability scale corrected for the winner’s curse, and σUKB,i and σdeCODE,i are the corresponding
standard errors in the respective samples.
The probability that a genome-wide significant SNP i is significant at the alpha-level in the
deCODE sample is
( ) ( | |
(
)) [ (
| |
(
))].
We corrected the estimated effect sizes (on the linear scale) from the UK Biobank insomnia
complaints GWAS with the Bayesian Winner’s Curse correction following a procedure
described by Rietveld and colleagues37
, based on an uninformative prior (τ2 = 10,000,000).
The expected true β of a SNP given the estimated b passing genome-wide significant threshold
αgw assuming a prior distribution of β ~ N(0, τ2) follows
( | ) ( )
( ) ,
where (
) ,
( )
[ ( ( )) ( ( ))] ,
( )
[ ( ( )) ( ( ))] ,
( ) (
)
, and
( ) (
) ,
where we assumed v to be known and set it to the standard error of the SNP effect to be
corrected. To estimate E(β|b, αgw), we have drawn 10 million samples from a random variable X
and computed the fraction of the empirical mean of g1(x) and g2(x) as described in Rietveld et
al.37
. We used these corrected values as the true effect size β to compute P(matchedi) and P(p <
αdeCODE) as described above.
Supplementary Table 15 shows the results of the power analysis. Although the probability to
replicate the sign of the effect of the SNPs was high, the probability to replicate the significance
of the SNPs in the deCODE sample was very low.
5. Additional analyses with insomnia complaints and other sleep-related
phenotypes in UK Biobank
5.1 Additional insomnia phenotypes
We constructed the insomnia phenotype in two additional ways to investigate the robustness of
the results of our GWAS of the full sample. First, we considered insomnia as a continuous trait
with three answering levels of the question (see Supplementary Information section 1.1):
Nature Genetics: doi:10.1038/ng.3888
12
“never/rarely” (n = 27,128), “sometimes” (n = 53,494), and “usually” (n = 32,384). Second, we
selected the two extreme answering options and dichotomized those individuals (“never/rarely”
vs “usually”), which was used in the recent GWAS for sleep disturbance traits by Lane and
colleagus38
. However, we note that these operationalizations of the insomnia phenotype have less
predictive value for Insomnia Disorder (see Supplementary Information section 1.2). We
conducted a GWAS on both phenotypes, using the same analysis method as in the original
GWAS (Online Methods). Both analyses showed the significant locus on chromosome 2
including MEIS1 (Supplementary Figure 6). The top SNP rs113851554 was more strongly
associated than in our original GWAS: P = 5.88× 10-22
(β = 0.087 [SE = 0.009]) and P = 2.02 ×
10-19
(OR = 1.27 [CI = 1.21-1.34]) for the continuous and dichotomous analysis respectively.
The signal on chromosome 4 did not reach genome-wide significance for these two different
operationalizations of the phenotype.
SNP-based heritability (liability scale) was estimated at 0.12 (SE = 0.0097) and 0.18 (SE =
0.015) for the continuous and dichotomous analysis respectively using LD score regression39
.
The higher estimates compared to our original analysis (0.09) can be explained by stronger SNP
effect sizes (Supplementary Figure 7).
The genetic correlations estimated by LD score regression between the insomnia
operationalization used in the main paper and the two other operationalizations were high: 0.98
and 0.97 for the continuous and dichotomous (contrasting the two extreme answering options)
analysis respectively.
5.2 Additional sleep-related phenotypes
We performed GWASes on six other sleep-related phenotypes present in UK Biobank: sleep
duration, getting up in the morning, chronotype, nap during the day, snoring and daytime
dozing/sleeping. Sleep duration was analyzed as hours per day assessed with the question “About
how many hours sleep do you get in every 24 hours? (please include naps)". Getting up in the
morning was measured with the question “On an average day, how easy do you find getting up in
the morning?”, including the answering categories “not at all easy”, “not very easy”, “fairly
easy” and “very easy”. Chronotype was represented by the question “Do you consider yourself to
be?”: “definitely a 'morning' person”, “more a 'morning' than 'evening' person”, “more an
'evening' than a 'morning' person” or “definitely an 'evening' person”. Nap during the day was
assessed with the question “Do you have a nap during the day?”, including answering categories
“never/rarely”, “sometimes” and “usually”. Snoring was defined by the question “"Does your
partner or a close relative or friend complain about your snoring?" where the participant could
answer with “yes” or “no”. Daytime dozing or sleeping was measured by the question “How
likely are you to doze off or fall asleep during the daytime when you don't mean to? (e.g. when
working, reading or driving)”, including the answering options “never/rarely”, “sometimes”,
“often” and “all of the time”. All questions had the additional answering option “prefer not to
answer” and all, except nap during the day, “do not know”.
The association analyses were performed using SNPTEST40
as described in the main text and
Online Methods. Sleep duration, getting up in the morning, chronotype were analyzed as
continuous traits, and the following traits as dichotomous phenotype: snoring, nap during the day
(“never/rarely” and “sometimes” vs. “usually”), and daytime dozing/sleeping (“never/rarely” and
“sometimes” vs. “often” and “all of the time”). Both continuous as dichotomous phenotypes
Nature Genetics: doi:10.1038/ng.3888
13
were analyzed using a linear model (considering computational time). Manhattan plots are
shown in Supplementary Figure 14 and the significant loci of the analyses are displayed in
Supplementary Table 24. In addition, we report the P values of the SNPs that were significantly
associated with insomnia complaints for the six sleep phenotypes (Supplementary Table 26).
None of the insomnia complaints SNPs reached genome-wide association in the other sleep-
related phenotypes.
We calculated the phenotypic correlations between all sleep phenotypes using the Pearson’s
method, and genetic correlations using LD score regression39
(Supplementary Table 24).
Insomnia complaints showed a significant genetic correlation with sleep duration (r2 = -0.47, P =
1.97 × 10-16
), daytime dozing/sleeping (r2 = 0.51, P = 3.25 × 10
-04) and nap during the day (r
2 =
0.42, P = 3.95 × 10-06
). Furthermore, chronotype was genetically correlated with getting up in the
morning (r2 = -0.82, P = 1.07 × 10
-148) and daytime dozing/sleeping with nap during the day (r
2 =
0.72, P = 2.10 × 10-06
).
5.3 Conditional analyses
Additional traits may have confounded our association with insomnia complaints in the UK
Biobank sample. We tested this by adding several traits that are related to insomnia as covariates
to the SNP association analyses of the SNPs significantly associated with insomnia complaints
(one analysis for each trait). The tested traits included waist-to-hip ratio, BMI, Townsend
Deprivation Index, years of education, depressive symptoms and neuroticism. We compared the
conditioned signals with the signal of the unconditioned association analysis in the same sample,
which was smaller than the original insomnia complaints GWAS for some traits as this data was
not available in all individuals (see Online Methods). For all traits, the SNP associations of the
conditional analyses remained the same compared to the unconditional signals (Supplementary
Table 27), suggesting no confounding by any of these traits.
6. Conditional analyses of RLS on insomnia complaints
Besides the MEIS1 locus, we also examined other loci and genes that were significantly
associated with insomnia complaints for possible effects on RLS and for possible effects of
conditioning the two phenotypes on each other. We used the Course of Restless Legs Syndrome
(COR; included in the RLS GWAS by Winkelmann et al.41
) study and the Dortmund Health
Study (DHS) in which information on both RLS and insomnia was available (Supplementary
Table 18; also see Supplementary Section 1.4 and 1.5). In the combined sample (n = 1,985), we
tested the SNPs and the genes significantly associated with insomnia complaints in the UK
Biobank sample for: i) association with insomnia; ii) association with insomnia adding RLS
status as covariate; iii) association with RLS; and iv) association with RLS adding the insomnia
phenotype as covariate. (Of note, the conditional analyses have insufficient value since the
combined sample is oversampled both for insomnia with RLS and for RLS with insomnia; see
main text). We found no significant associations of the loci on chromosome 4 and 6
(Supplementary Table 19). TSNARE1, which was associates with insomnia complaints in
females, did not show an association with insomnia in the DHS and COR sample, but did show
an association with RLS in the full sample and females only (Supplementary Table 20).
Nature Genetics: doi:10.1038/ng.3888
14
Interestingly, IPO7, which was associated with insomnia complaints in females as well, showed
an association with RLS in males, but not in females. Future studies are needed to investigate if
these genes play a role in both insomnia and RLS.
7. Biological annotations
We applied different strategies to investigate the molecular mechanisms underlying insomnia.
First, we performed pathway analysis to explore if pre-defined pathways are related to insomnia
complaints (Supplementary Section 7.1). Second, we tested if the top genes for insomnia
complaints were enriched for genes differentially expressed in specific tissues (Supplementary
Section 7.2). These two analyses make use of sets of genes that are constructed based on current
knowledge of biological functions or tissue specific gene expression. This is in contrast to the
HotNet2 analysis (described in the main text), where we investigated, in a more unbiased
manner, whether small protein-protein interaction subnetworks within the total protein-protein
interaction network are associated with insomnia (Online Methods).
7.1 Pathway analysis
We explored whether pre-defined pathways were associated with insomnia complaints. We
selected all canonical pathways (n = 1,330) and Gene Ontology (GO) pathways (n = 1,454) from
the molecular signature database (MsigDB v5.142
,
http://software.broadinstitute.org/gsea/msigdb/). We performed competitive gene-set analyses in
MAGMA43
(http://ctg.cncr.nl/software/magma) to test whether the genes in a gene set are more
strongly associated with the phenotype of interest than the other genes. The associations
were corrected for dependencies between genes and confounding effects of gene size and gene
density. A resampling-based P value adjustment44
was applied to correct for multiple testing,
using 10,000 permutations.
The MAGMA results for the full and sex-specific analyses showed no pathways that were
significantly associated with insomnia complaints after multiple testing correction
(Supplementary Table 31). These results suggest that the genetic variants related to insomnia
complaints do not cluster in genes of the same pre-defined pathway.
7.2 Tissue enrichment analysis
7.2.1 GTEx tissue enrichment analysis
We tested whether our top genes of the GWGAS (P < 0.05) were related to differentially
expressed genes in specific tissues, using transcriptome data from the Genotype-Tissue
Expression (GTEx; http://www.gtexportal.org/home/) Project27
. To investigate if sex-specific
differences in tissue enrichment exist, we performed a tissue enrichment analysis on both
GWGAS results of the full sample and sex-specific results.
Normalized gene expressions (RPKM) of 53 tissue types were obtained from GTEx V6 release27
(http://www.gtexportal.org/home/datasets). The total of 56,320 mapped genes were filtered on
average RPKM ≥ 1 in at least one tissue type, resulting in 28,520 genes. RPKM was log2
transformed using a pseudocount of 1 (log2(RPKM+1)), followed by z-score normalization.
Nature Genetics: doi:10.1038/ng.3888
15
Genes were considered to be significantly differentially expressed in a tissue if P ≤ 0.05 in
Students T-test after Bonferroni correction for multiple testing, and absolute Fold-difference >
1.5. For the sex-specific analyses, we repeated these steps selecting the samples of one sex only,
and used the output to perform the enrichment analysis of the corresponding sex.
We selected genes with P < 0.05 from our GWGAS results (full, males and females), and used
these three set of genes for the tissue enrichment analyses. Subsequently, the one-sided Fisher’s
exact test was applied to determine the significance in overlap between the tissue-type-genes and
the GWGAS input set of genes. A tissue was considered statistically significant when the P ≤
0.05 for the one-sided Fisher’s exact test after correcting for multiple testing using the Benjamini
and Hochberg method. The enrichment analyses of these top genes showed nominal enrichment
(P < 0.05) for multiple tissues in the three analyses (Supplementary Fig. 16). Only whole blood
showed significant enrichment after multiple testing correction (Benjamini-Hochberg P = 0.041).
These results suggest that blood cells might be of interest in insomnia etiology, although more
investigation is needed in view of the various types of blood cells that exist.
7.2.2 BrainSpan tissue enrichment analysis
Next, we applied transcriptome data from BrainSpan45
(http://www.brainspan.org/) to test
whether the same top genes of the GWGAS as described above were related to differentially
expressed genes in specific brain areas and brain areas over development (considering that the
prevalence of insomnia increases with age).
BrainSpan46
data was used to construct gene sets per brain area and developmental stage based
on the In Situ Hybridization (ISH) genes with corresponding anatomical structures and
developmental stages (http://help.brain-map.org//display/devhumanbrain/Documentation). We
performed the same type of tissue enrichment analysis as for the GTEx data, described above.
The enrichment analyses did not show enrichment in specific brain areas or developmental stage
(Supplementary Fig. 17), suggesting that there is not a specific brain area that is implicated in
insomnia.
Nature Genetics: doi:10.1038/ng.3888
16
8. Acknowledgements
Data on coronary artery disease have been contributed by CARDIoGRAMplusC4D investigators
and have been downloaded from www.CARDIOGRAMPLUSC4D.ORG.
Data on the infant head circumference trait, the childhood body mass index trait, the childhood
obesity trait and the birth weight trait have been contributed by the EGG Consortium and have
been downloaded from www.egg-consortium.org.
The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the
Office of the Director of the National Institutes of Health. Additional funds were provided by the
NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Donors were enrolled at Biospecimen Source
Sites funded by NCI\SAIC-Frederick, Inc. (SAIC-F) subcontracts to the National Disease
Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171), and Science Care,
Inc. (X10S172). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded
through a contract (HHSN268201000029C) to The Broad Institute, Inc. Biorepository operations
were funded through an SAIC-F subcontract to Van Andel Institute (10ST1035). Additional data
repository and project management were provided by SAIC-F (HHSN261200800001E). The
Brain Bank was supported by a supplement to University of Miami grants DA006227 &
DA033684 and to contract N01MH000028. Statistical Methods development grants were made
to the University of Geneva (MH090941 & MH101814), the University of Chicago (MH090951,
MH090937, MH101820, MH101825), the University of North Carolina - Chapel Hill
(MH090936 & MH101819), Harvard University (MH090948), Stanford University
(MH101782), Washington University St Louis (MH101810), and the University of Pennsylvania
(MH101822). The data used for the analyses described in this manuscript were obtained from the
GTEx Portal on 04/07/2016.
We thank the International Genomics of Alzheimer's Project (IGAP) for providing summary
results data for these analyses. The investigators within IGAP contributed to the design and
implementation of IGAP and/or provided data but did not participate in analysis or writing of
this report. IGAP was made possible by the generous participation of the control subjects, the
patients, and their families. The i–Select chips was funded by the French National Foundation on
Alzheimer's disease and related disorders. EADI was supported by the LABEX (laboratory of
excellence program investment for the future) DISTALZ grant, Inserm, Institut Pasteur de Lille,
Université de Lille 2 and the Lille University Hospital. GERAD was supported by the Medical
Research Council (Grant n° 503480), Alzheimer's Research UK (Grant n° 503176), the
Wellcome Trust (Grant n° 082604/2/07/Z) and German Federal Ministry of Education and
Research (BMBF): Competence Network Dementia (CND) grant n° 01GI0102, 01GI0711,
01GI0420. CHARGE was partly supported by the NIH/NIA grant R01 AG033193 and the NIA
AG081220 and AGES contract N01–AG–12100, the NHLBI grant R01 HL105756, the Icelandic
Heart Association, and the Erasmus Medical Center and Erasmus University. ADGC was
supported by the NIH/NIA grants: U01 AG032984, U24 AG021886, U01 AG016976, and the
Alzheimer's Association grant ADGC–10–196728.
Data on anorexia nervosa, autism spectrum disorder, schizophrenia, attention-deficit/
hyperactivity disorder, bipolar disorder and major depressive disorder have been contributed by
the Psychiatric Genomics Consortium (PGC) and have been downloaded from
http://www.med.unc.edu/pgc/results-and-downloads.
Nature Genetics: doi:10.1038/ng.3888
17
Data on subjective well-being, neuroticism and depressive symptoms have been contributed by
the Social Science Genetic Association Consortium (SSGAC) and have been downloaded from
http://www.thessgac.org/data.
Data on childhood intelligence have been contributed by the Childhood Intelligence Consortium
(CHIC) and have been downloaded from http://www.thessgac.org/data.
Data on smoking traits have been contributed by the Tobacco and Genetics Consortium (TaG)
and have been downloaded from http://www.med.unc.edu/pgc/results-and-downloads.
Data on height, hip circumference, waist-to-hip ratio and waist circumference have been
contributed by the Genetic Investigation of ANthropometric Traits (GIANT) and have been
downloaded from
http://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files.
Data on asthma have been contributed by the GABRIEL Consortium and have been downloaded
from https://www.cng.fr/gabriel/results.html.
Data on type 2 diabetes have been contributed by DIAGRAM and have been downloaded from
http://diagram-consortium.org/downloads.html.
Data on anxiety disorders have been contributed by the Anxiety Neuro Genetics STudy
(ANGST) and have been downloaded from http://www.med.unc.edu/pgc/results-and-downloads.
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Supplementary Figures
Supplementary Figure 1 | Distributions of the rating of trouble falling or staying asleep
across the participants of the NSR, UK Biobank and deCODE. Left panel: distributions of
the percentage of participants rating their trouble falling or staying asleep mild, moderate, severe
or very severe within four subpopulations of the NSR: participants without sleep complaints
(None, n = 927, green), with Restless Legs Syndrome only (RLS, n = 146, blue), with Insomnia
Disorder only (ID, n = 635, red) and with comorbid Restless Legs Syndrome and Insomnia
Disorder (ID+RLS, N=210, purple). Ratings discriminate ID but hardly RLS. Trouble falling or
staying asleep is rated as moderate to very severe by the majority of people having insomnia, but
only few RLS patients without comorbid ID rate their trouble falling or staying asleep as more
than mild. Middle panel: distributions of the percentage of UK Biobank participants used in the
analyses that reported to experience trouble falling or staying asleep never/rarely (n = 27,128,
green), sometimes (n = 53,494, green) or usually (n = 32,384, red). The group usually
experiencing trouble falling or staying asleep was regarded likely to suffer from insomnia (red).
Right panel: distributions of the percentage of deCODE participants used in the analyses that
reported to experience trouble falling or staying asleep never or almost never (n = 3,935, green),
less than once a week (n = 1,627, green), once or twice a week (n = 1,606, green), three to five
times a week (n = 1,332, red) or six to seven times a week (n = 1,717, red). The latter two
groups were regarded likely to suffer from insomnia.
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Supplementary Figure 2 | Phenotype validation and optimization.
Receiver Operating Characteristic (ROC) curve for the accuracy of discriminating controls, RLS
and insomnia (defined with two different methods) against each other by at least one very severe,
severe, moderate, mild or null rating of either trouble falling or staying asleep (five markers from
left to right on each curve). Solid line, filled circles: probable Insomnia Disorder (ID; ISI+PSQI
criteria) versus controls. Dashed line, open diamonds: probable ID (ISI+PSQI) versus RLS
(IRLSS). Dash-dotted line, filled triangles: ID (DSM-5+ICSD3 criteria) versus controls. Dotted
line, open squares: RLS (IRLSS) versus controls. Using only the partial information provides
excellent discrimination of cases with probable Insomnia Disorder, validating its representation
by the insomnia complaints phenotype in the GWAS. The figure also shows that for each of the
three ID discrimination ROCs the cut-off with the highest accuracy (i.e., closest proximity to
coordinate [0,1]) is consistently located at the third marker which corresponds to having at least
one moderate complaint.
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Supplementary Figure 3 | Accuracy of moderate to very severe trouble falling and staying
asleep for the discrimination of different disorders in the NSR cohort. Trouble falling and
staying asleep had high discriminative validity for the presence of Insomnia Disorder only, while
the discriminative validity for the presence of Restless Legs Syndrome or any of the ICD-10
disease categories was low (range 0.57-0.63).
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Supplementary Figure 4 |
Radar plots of the average
sleep and mood deviation
profiles. To support the validity
of the UK Biobank insomnia
phenotype, we evaluated
whether its multivariate profile
resembled the profile of well-
characterized people with
insomnia in the NSR. Upper
panel: the UK Biobank
insomnia complaints group
(red) standardized relative to
means and standard deviations
of controls (green line of
reference). Middle panel:
profiles of the NSR participants
fulfilling the
PSQI+ISI+IRLSSG criteria for
ID (red), RLS (blue) and
comorbid RLS+ID (purple)
relative to controls (neither ID
nor RLS; green line of
reference). Lower panel:
magnification of the NSR RLS
group relative to controls. Axes
indicate deviations in the sleep
disorder group means,
expressed in standard
deviations calculated over the
corresponding control group.
The plots suggest a striking
similarity of the shape of the
UK Biobank insomnia
complaints phenotype profile
with the profiles of the NSR
groups with ID or comorbid
ID+RLS, but no similarity with
the NSR group with RLS only.
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Supplementary Figure 5 | Regional association plots for loci with genome-wide significant
association signals for insomnia complaints.
(a-b) Hits of the GWAS including the full sample (32,384 insomnia cases and 80,622 controls),
and hits of the separate GWASes of (c) males (12,863 cases and 40,776 controls) and (d) females
(19,521 cases and 39,846 controls). Plots are created with LocusZoom47
. Color of SNPs
represent LD strength with the index SNP (purple). Bottom panel of the plot shows the name and
location of genes in the UCSC Genome Browser.
a rs113851554 in the full GWAS
b rs574753165 in the full GWAS
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c rs13192566 in the male GWAS
d rs113851554 in the female GWAS
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Supplementary Figure 6 | Manhattan and QQ-plots of insomnia and two additional
insomnia phenotypes in UK Biobank. Association results for the frequency of experiencing
trouble falling asleep or waking up in the middle of the night in 113,006 individuals of European
descent in the UK Biobank study. The phenotype based on this question was constructed in three
different ways: 1) individuals experiencing these complaints usually (cases, n=32,384) contrasted
with those experiencing these complaints never/ rarely or sometimes (controls, n=80,622; results
reported in main manuscript); 2) the three answer possibilities were analysed as continuous trait
(n=113,006); 3) individuals experiencing these complaints usually (cases, n=32,384) contrasted
with those experiencing these complaints never/rarely (controls, n=27,128).
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Supplementary Figure 7 | Effect sizes of the SNP associations contrasted in two UKB
insomnia complaints phenotypes. Effect sizes (absolute value of log10[odds ratio]) of the
association results for the frequency of experiencing trouble falling asleep or waking up in the
middle of the night in individuals in the UK Biobank study. The phenotype based on this
question was constructed in two different ways: 1) individuals experiencing these complaints
usually (cases, n = 32,384) contrasted with those experiencing these complaints never/rarely or
sometimes (controls, n = 80,622; results reported in main manuscript); 2) individuals
experiencing these complaints usually (cases, n = 32,384) contrasted with those experiencing
these complaints never/rarely (controls, n = 27,128).
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Supplementary Figure 8 | QQ-plots of the genome-wide association studies for insomnia
complaints.
All SNPs are shown for the (a) full sample (32,384 insomnia cases and 80,622 controls), (b)
males (12,863 cases and 40,776 controls), and (c) females (19,521 cases and 39,846 controls)
GWASes.
a
b
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c
Supplementary Figure 9 | Gene expression in different tissue types of the genes identified in
the insomnia complaints GWAS and GWGAS.
Expression data figures are extracted from the Genotype-Tissue Expression database
(http://www.gtexportal.org/home/datasets).
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Supplementary Figure 10 | Gene expression in different brain areas of the genes identified
in the insomnia complaints GWAS and GWGAS.
Expression data figures are extracted from the Braineac database (http://www.braineac.org/).
FCTX, frontal cortex; TCTX, temporal cortex; SNIG, substantia nigra; THAL, thalamus;
MEDU, medulla; HIPP, hippocampus; OCTX, occipital cortex; PUTM, putamen; WHMT,
intralobular white matter; CRBL, cerebellar cortex.
MEIS1
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SCFD2
DCBLD1
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MED27
WDR27
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HHEX
RHCG
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IPO7
TSNARE1
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Supplementary Figure 11 | Gene expression in different brain areas over age of the genes
identified in the insomnia complaints GWAS and GWGAS.
Expression data figures are extracted from the Human Brain Transcriptome database
(http://hbatlas.org/). MCX, neocortex; HIP, hippocampus; AMY, amygdala; STR, striatum; MD,
mediodorsal nucleus of the thalamus; CBC, cerebellar cortex.
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Supplementary Figure 12 | Tissue expression of the genes identified by the insomnia
complaints GWAS and GWGAS. The heatmap shows the average expression values in the
tissues from GTEx of the genes identified in the full and sex-specific GWASes and GWGASes.
Genes are labeled according to the GWAS or GWGAS in which it was significantly detected,
either in the female (♂), male (♀) or the joint analysis of all samples (⚥). Gene expression values
were log2 transformed and zero-mean normalized in the total set of samples. The heatmaps for
males and females shows the average expression values in the subset of samples respectively. An
‘x’ indicates the gene expression values that are not available. The corresponding expression
values are reported in Supplementary Table 9.
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Supplementary Figure 13 | Comparison of association results for insomnia complaints and
RLS. SNP P values of the UK Biobank insomnia complaints GWAS are plotted against the RLS
GWAS results reported by Winkelmann et al.41
. The right plot is an amplification of the lower-
left part of the left plot. Contour lines indicate the density of the data in that region. The lines are
colored from green to pink, indicating increasing data density. Dotted lines indicate the P value
thresholds used in the Low P value enrichment tests; from yellow to red P = 0.05, P = 1 × 10-3
, P
= 1 × 10-4
, and P = 1 × 10-5
(note that all SNPs present in both GWASes are displayed, while the
enrichment tests were performed on pruned data).
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Supplementary Figure 14 | Manhattan plots of six sleep-related phenotypes in UK Biobank.
Association results for six phenotypes related to sleep in the individuals of European descent in
the UK Biobank study. Sample sizes and summary statistics of the top SNPs are reported in
Supplementary Table 24.
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Supplementary Figure 15 | Manhattan plot of the interaction effects of sex and additive
SNP effect in the insomnia complaints GWAS.
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Supplementary Figure 16 | GTEx tissue enrichment analysis.
A one-sided Fisher’s exact test was applied to determine the significance in overlap between the GTEx tissue-type gene sets and the
GWGAS input sets of genes (P < 0.05) of the full sample, male and female analyses. For the sex-specific analyses, only the GTEx
samples of the corresponding sex were analyzed. Uncorrected P values are shown. Only Whole blood in the male analysis reached
significance after multiple testing correction using the Benjamini-Hochberg approach.
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Supplementary Figure 17 | BrainSpan brain tissue enrichment analysis.
A one-sided Fisher’s exact test was applied to determine the significance in overlap between the
BrainSpan tissue-type gene sets and the GWGAS input sets of genes (P < 0.05) of the full
sample, male, and female analyses. Uncorrected P values are shown.
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Supplementary Figure 18 | Protein-protein-interaction subnetworks identified by HotNet2.
The genes most strongly related to insomnia (P < 0.1) in the full and sex-specific GWGASes
were used as input to investigate the enrichment of protein-protein interaction networks. (a)
Twelve subnetworks of genes for males (P = 0.01, δ = 0.011846, k = 7, Expected = 8.24) and (b)
nine subnetworks for females (P = 0.02, δ = 0.0141808, k = 7, Expected = 5.61) were identified.
Color represents the heat of the gene, with red indicating more heat.
a
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b
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Supplementary Tables
Supplementary Table 1 | Insomnia complaints measure in the UK Biobank sample included
in the GWAS.
N (%)
total sample
N (%)
males
N (%)
females
Age in years,
mean (SD)
Age in years
males,
mean (SD)
Age in years
females, mean
(SD)
cases 32,384 (28.7%) 12,863 (24.0%) 19,521 (32.9%) 57.76 (7.52) 58.20 (7.81) 57.47 (7.31)
controls 80,622 (71.3%) 40,776 (76.0%) 39,846 (67.1%) 56.58 (8.08) 56.95 (8.07) 56.19 (8.06)
Numbers are shown for all individuals with data available on the question about trouble falling asleep and waking
up in the middle of the night, and genotype data available after quality control (Caucasians only). SD, standard
deviation
Supplementary Table 2 | Distribution of the number of NSR participants (n = 1,918)
meeting the criterion of a moderate to severe rating of either trouble falling or staying
asleep across subpopulations meeting the criteria for Restless Legs Syndrome, Insomnia
Disorder, both, or none. Row percentages and χ2 comparisons between disordered
subpopulations indicate that a moderate to very severe rating of either trouble falling or staying
asleep discriminates ID very well. Contributions of ID and RLS to reporting moderate to very
severe trouble falling or staying asleep was disentangled by multiple logistic regression, which
showed a significant contribution of presence of ID (β = 6.94, SE = 0.32, P < 0.0001) but not of
presence of RLS (β = 0.43, SE = 0.38, P = 0.26) nor the interaction of their presence (β = 1.12,
SE = 1.11, P = 0.31).
NSR diagnoses Trouble falling/staying asleep
0 1
None 889 38 4.1%
RLS 137 9 6.2%
ID 14 621 97.8%
ID+RLS 1 209 99.5%
χ2 RLS versus ID = 639.06, P <0.0001
χ2 RLS versus ID+RLS = 316.23, P <0.0001
χ2 ID versus ID+RLS = 2.70, P =0.10
χ2 RLS versus None = 1.28, P =0.26
χ2 ID versus None = 1356.45, P <0.0001
χ2 Any ID (ID or ID+RLS) versus no ID (None or RLS) = 1677.54, P <0.0001
χ2 Any RLS (RLS or ID+RLS) versus no RLS (None or ID) = 42.38, P <0.0001
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Supplementary Table 3 | Descriptives of the UK Biobank insomnia complaints sample.
Descriptive Controls Insomnia cases
N 80,622 32,384
Males (%) 50.58 39.72
Females (%) 49.42 60.28
Age in years (mean [SD]) 56.57 [8.07] 57.76 [7.51]
Townsend Deprivation Index (mean [SD]) -1.59 [2.92] -1.23 [3.14]
Years of educationa (mean [SD]) 15.35 [4.43] 14.91 [4.43]
Body Mass Index (mean [SD]) 27.35 [4.65] 27.97 [5.22]
Waist-to-hip ratio (mean [SD]) 0.88 [0.090] 0.87 [0.092]
Depressive symptomsb (mean [SD]) 2.44 [1.04] 2.94 [1.53]
Neuroticismc (mean [SD]) 3.65 [3.24] 5.24 [3.69]
SD, standard deviation. aWe mapped the educational qualification according to the 1997 International Standard Classification of Education
(ISCED) of the United Nations Educational, Scientific and Cultural Organization, as described by Okbay et al.
2016 (doi: 10.1038/nature17671); bConstructed by summing the responses to “Over the past two weeks, how often
have you felt down, depressed or hopeless?” and “Over the past two weeks, how often have you had little interest
or pleasure in doing things?”, resulting in a score range of 2-8 with higher scores indicating more depressive
symptoms; cTotal score of the 12 neuroticism items with higher scores indicating more neurotic behaviors (score
range of 0-12).
Supplementary Table 4 | Functional annotations of the SNPs and SNPs in LD that are
associated with insomnia complaints. Annotations are included regarding SNP function,
deleteriousness, regulatory function, eQTL, meQTL, and chromatin state.
Excel file
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Supplementary Table 5 | Credible set analysis of the SNPs at the MEIS1 locus. Posterior
probabilities of being the causal SNP are reported before and after inclusion of functional
annotations (deleteriousness, regulatory function, meQTL, and chromatin state) to prioritize
SNPs.
SNP Chr BP Posterior probability
Posterior probability
incl. functional
annotation
rs375216017 2 66728627 0.146 1.85E-09
rs62144051 2 66730783 0.114 2.82E-03
rs62144053 2 66745864 0.116 3.62E-03
rs62144054 2 66747480 0.110 2.07E-03
rs113851554 2 66750564 0.959 1
rs182588061 2 66757709 0.953 0.986
rs139775539 2 66782432 0.145 1.45E-04
rs11679120 2 66785180 0.189 0.0445
rs115087496 2 66793725 0.130 0.413
rs549771308 2 66795237 0.130 3.00E-08
rs11693221 2 66799986 0.095 7.25E-03
Supplementary Table 6 | Genome-wide gene associations with insomnia complaints. Gene
association results for 18,356 genes tested for insomnia complaints in males, females and the
sex-combined UK Biobank sample using MAGMA.
Excel file
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Supplementary Table 7 | Previous phenotype associations of the genes identified in the
insomnia complaints GWAS and GWGAS.
Gene Phenotype Reference Pubmed ID
MEIS1 Restless Legs Syndrome Winkelmann et al., 2007 17637780
MEIS1 Restless Legs Syndrome Winkelmann et al., 2011 21779176
MEIS1 PR interval Pfeufer et al., 2010 20062060
MEIS1 PR interval Smith et al., 2011 21347284
MEIS1 PR interval Butler et al., 2012 23139255
MEIS1 PR segment Verweij et al., 2014 24850809
MEIS1 Waist-to-hip ratio adjusted for
body mass index Shungin et al., 2015 25673412
SCFD2 Optic nerve measurement (cup
area) Macgregor et al., 2010 20395239
SCFD2
Lifetime average cigarettes
per day in chronic obstructive
pulmonary disease
Siedlinski et al., 2011 21685187
DCBLD1 Lung cancer Lan et al. 2010 23143601
DCBLD1 Height Berndt et al., 2010 23563607
DCBLD1 Colorectal cancer Schumacher et al., 2015 26151821
WDR27 Type 1 diabetes Bradfield et al., 2011 21980299
HHEX Type 2 diabetes Saxena et al., 2007 17463246
HHEX Type 2 diabetes Scott et al., 2007 17463248
HHEX Type 2 diabetes Takeuchi et al., 2009 19401414
HHEX Type 2 diabetes Voight et al., 2010 20581827
HHEX Type 2 diabetes Perry et al., 2012 22693455
HHEX Type 2 diabetes Hara et al., 2013 23945395
HHEX Type 2 diabetes Mahajan et al., 2014 24509480
HHEX Dehydroepiandrosterone
sulphate levels Zhai et al., 2011 21533175
HHEX Multiple sclerosis Sawcer et al., 2011 21833088
TSNARE1 Schizophrenia Ripke et al., 2013 25056061
TSNARE1 Schizophrenia, schizoaffective
disorder or bipolar disorder Sleiman et al. 2013 24166486
TSNARE1 Schizophrenia Ripke et al., 2014 23974872
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Supplementary Table 8 | Gene functions of the genes identified in the insomnia complaints
GWAS and GWGAS.
Gene Analysis hit Function
MEIS1
GWAS full;
GWAS females;
GWGAS full
Homeobox protein that acts as transcriptional regulator and activator.
May be important for normal development.
SCFD2 GWAS full May be involved in protein transport and vesicle docking involved in
exocytosis (inferred from electronic annotation).
DCBLD1 GWGAS full May be involved in oligosaccharide binding (inferred from biological
aspect of ancestor).
MED27 GWGAS full Component of the mediator complex that acts as coactivator of the
regulated transcription of most RNA polymerase II-dependent genes.
WDR27 GWAS males Protein with multiple WD repeats that may form scaffolds for protein-
protein interactions. May play an important role in cell signaling.
HHEX GWGAS males Homeobox protein that acts as transcriptional repressor. May play a
role in hematopoietic differentiation.
RHCG GWGAS males Electroneutral and bidirectional ammonium transporter. May regulate
transepithelial ammonia secretion.
IPO7 GWGAS females Mediates nuclear protein import.
TSNARE1 GWGAS females Involved in snare binding and vesicle docking and fusion.
Supplementary Table 9 | Tissue expression of the genes identified by the insomnia
complaints GWAS and GWGAS. This table includes the average expression values in the
tissues from GTEx of the genes identified in the full and sex-specific GWASes and GWGASes,
which are displayed in Supplementary Figure 14. Gene expression values were log2 transformed
and zero-mean normalized in the total set of samples. The gene expression values for males and
females depict the average expression values in the subset of samples respectively.
Excel file
Supplementary Table 10 | Insomnia complaints measure in deCODE.
N
total sample
N
males
N
females
Age in years,
mean (SD)
Age in years
males,
mean (SD)
Age in years
females, mean
(SD)
cases 3,774 1,983 1,791 55.69 (14.92) 57.45 (14.50) 53.43 (15.06)
controls 3,791 2,064 1,727 51.19 (15.07) 52.51 (14.73) 49.63 (15.34)
SD, standard deviation
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Supplementary Table 11 | SNP associations in the deCODE sample of the SNPs
significantly associated with insomnia complaints in the UK Biobank sample.
FULL MALES FEMALES
rsID Chr BPa EA non-EA EAF OR P OR P OR P
rs375216017 2 66,728,627 G/GTTb GT 0.115 1.09 0.128
rs62144051 2 66,730,783 G A 0.100 1.08 0.201
rs62144053 2 66,745,864 A G 0.100 1.10 0.105
rs62144054 2 66,747,480 A G 0.098 1.07 0.225
rs113851554 2 66,750,564 T G 0.056 1.15 0.061
1.13 0.249
rs182588061 2 66,757,709 T G 0.041 1.04 0.654
rs139775539 2 66,782,432 A AC 0.056 1.08 0.307
1.10 0.385
rs11679120 2 66,785,180 A G 0.055 1.08 0.300
1.09 0.412
rs115087496 2 66,793,725 C G 0.046 1.10 0.210
1.08 0.468
rs549771308 2 66,795,237 C/CTTb CT 0.156 1.08 0.087
rs11693221 2 66,799,986 T C 0.048 1.14 0.093
1.13 0.265
rs574753165 4 53,977,261 G A 0.003 1.30 0.458
rs71554396 6 169,841,072 G/GTTb GT 0.166
1.00 0.968
rs13208844 6 169,961,603 G A 0.148
1.02 0.824
rs13192566 6 169,961,635 C G 0.148
1.02 0.824
Chr, chromosome; BP, base pair; EA, effect allele; EAF, effect allele frequency; OR, odds ratio areported on GRCh37; bTriallelic SNP
Supplementary Table 12 | Gene associations in the deDECODE sample of the genes
significantly associated with insomnia complaints in the UK Biobank sample.
FULL MALES FEMALES
Gene Entrez ID Chr Starta Stop
a N SNPs P P P
MEIS1 4211 2 66,660,257 66,800,891 369 0.0042
DCBLD1 285761 6 117,801,803 117,892,021 340 0.8187
MED27 9442 9 134,734,497 134,957,274 501 0.6026
HHEX 3087 10 94,447,681 94,456,408 16 0.0195
RHCG 51458 15 90,013,638 90,041,799 27 0.0581
TSNARE1 203062 8 143,292,441 143,486,543 928 0.1232
IPO7 10527 11 9,404,169 9,470,674 182 0.1711
Chr, chromosome; Start, start position of gene in base pairs; Stop, stop position of gene in base pairs areported on GRCh37. Bold P values are significant associations after Bonferroni correction (α = 0.007).
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Supplementary Table 13 | Meta-analysis of SNPs significantly associated with insomnia
complaints in UK Biobank together with deCODE.
FULL MALES FEMALES
rsID Chr BPa Z P Dir Z P Dir Z P Dir
rs375216017 2 66,728,627 -5.14 2.82E-07 +-
rs62144051 2 66,730,783 -6.03 1.69E-09 ++
rs62144053 2 66,745,864 6.29 3.13E-10 ++
rs62144054 2 66,747,480 6.14 8.36E-10 ++
rs113851554 2 66,750,564 8.94 3.89E-19 ++ 7.19 6.65E-13 ++
rs182588061 2 66,757,709 6.20 5.59E-10 ++
rs139775539 2 66,782,432 8.34 7.63E-17 ++ 6.88 6.18E-12 ++
rs11679120 2 66,785,180 8.36 6.56E-17 ++ 6.67 2.51E-11 ++
rs115087496 2 66,793,725 7.84 4.43E-15 ++ 6.47 9.91E-11 ++
rs549771308 2 66,795,237 -5.99 2.16E-09 ++
rs11693221 2 66,799,986 7.75 9.37E-15 ++ 6.46 1.08E-10 ++
rs574753165 4 53,977,261 -5.85 5.00E-09 ++
rs71554396 6 169,841,072 5.36 8.23E-08 -+
rs13208844 6 169,961,603 5.59 2.27E-08 -+
rs13192566 6 169,961,635 -5.63 1.80E-08 -+
Chr, chromosome; BP, base pair; Dir, direction of effect
areported on GRCh37. Bold P values are genome-wide significantly associated with insomnia complaints (P < 5 ×
10-8).
Supplementary Table 14 | Meta-analysis of genes significantly associated with insomnia
complaints in UK Biobank together with deCODE.
FULL MALES FEMALES
Gene Entrez ID Chr Starta Stop
a P P P
MEIS1 4211 2 66,660,257 66,800,891 4.61E-08
DCBLD1 285761 6 117,801,803 117,892,021 3.00E-06
MED27 9442 9 134,734,497 134,957,274 2.27E-06
HHEX 3087 10 94,447,681 94,456,408 2.53E-07
RHCG 51458 15 90,013,638 90,041,799 6.35E-07
TSNARE1 203062 8 143,292,441 143,486,543 8.75E-07
IPO7 10527 11 9,404,169 9,470,674 1.06E-06
Chr, chromosome; Start, start position of gene in base pairs; Stop, stop position of gene in base pairs areported on GRCh37. Bold P values show an significant association signal after Bonferroni correction (α = 2.72 ×
10-6) for all genome-wide genes tested in the original GWGAS.
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Supplementary Table 15 | Power analysis to replicate the UK Biobank insomnia complaints
GWAS findings in deCODE
SNP β
UK Biobank
SE
UK Biobank
SE
deCODE
β UK Biobank
corrected for
Winner’s Curse
Probability
significance
Probability
sign match
rs375216017 0.0896 0.0157 0.0536 0.0410 0.0151 0.7754
rs62144051 0.0962 0.0163 0.0565 0.0608 0.0316 0.8591
rs62144053 0.0989 0.0163 0.0566 0.0626 0.0338 0.8658
rs62144054 0.0978 0.0162 0.0573 0.0597 0.0292 0.8511
rs113851554 0.1782 0.0204 0.0728 0.1778 0.3108 0.9927
rs182588061 0.2284 0.0363 0.0874 0.1737 0.1715 0.9765
rs139775539 0.1870 0.0224 0.0736 0.1859 0.3409 0.9942
rs11679120 0.1878 0.0225 0.0742 0.1867 0.3377 0.9941
rs115087496 0.1770 0.0226 0.0789 0.1744 0.2342 0.9865
rs549771308 0.0905 0.0158 0.0471 0.0523 0.0341 0.8663
rs11693221 0.1708 0.0226 0.0765 0.1667 0.2247 0.9853
rs574753165 0.3947 0.0675 0.3517 0.2471 0.0129 0.7588
rs71554396 -0.1208 0.0215 0.0472 -0.0535 0.0358 0.8666
rs13208844 -0.1135 0.0203 0.0490 -0.0501 0.0279 0.8420
rs13192566 -0.1146 0.0204 0.0490 -0.0581 0.0400 0.8803
Supplementary Table 16 | Insomnia-complaints associations of SNPs in MEIS1 previously
associated with Restless Legs Syndrome.
FULL MALES FEMALES
Reference SNP OR
(95% CI) P
OR
(95% CI) P
OR
(95% CI) P
R2 with
rs113851554
Winkelmann
et al. 200732
rs6710341
tagging SNP
haplotype
0.98
(0.95-1.00)
0.07 0.99
(0.96-1.03)
0.83 0.96
(0.93-1.00)
0.03 0.17
Winkelmann
et al. 200732
rs12469063
tagging SNP
haplotype
1.02
(1.00-1.05)
0.03 1.04
(1.01-1.07)
0.02 1.01
(0.98-1.04)
0.41 0.01
Winkelmann
et al. 200732
rs2300478
top SNP
1.02
(1.00-1.05)
0.03 1.04
(1.00-1.07)
0.04 1.01
(0.99-1.04)
0.30 0.18
Nature Genetics: doi:10.1038/ng.3888
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Table 17 | Summary statistics of the six top SNPs identified in the RLS GWAS by
Winkelmann et al. (2011) and their association results in the insomnia complaints
GWAS on the UK Biobank sample.
RLS Insomnia complaints
SNP Chr:BP EA EAF OR (95% CI) P EA/
nonEA EAF OR (95% CI) P
rs2300478 2:66781453 G 0.24 1.68 (1.57-1.81) 3.40E-49 G/T 0.25 1.02 (1.00-1.05) 0.0325
rs6747972 2:68070225 G 0.44 1.23 (1.16-1.31) 9.03E-11 G/A 0.42 0.98 (0.96-1.00) 0.0679
rs9357271 6:38365873 A 0.76 1.47 (1.35-1.47) 7.75E-22 C/T 0.79 1.00 (0.97-1.02) 0.845
rs1975197 9:8846955 T 0.16 1.29 (1.19-1.40) 3.49E-10 A/G 0.18 1.02 (0.99-1.04) 0.144
rs12593813 15:68036852 A 0.68 1.41 (1.32-1.52) 1.37E-22 G/A 0.68 1.01 (0.99-1.03) 0.152
rs3104767 16:52624738 G 0.58 1.35 (1.27-1.43) 9.40E-19 T/G 0.59 0.99 (0.97-1.00) 0.0896
Supplementary Table 18 | Distribution of the participants in the ‘Course of Restless
Legs Syndrome’ (COR) Study and Dortmund Health Study (DHS) that were included in
the conditional analyses of RLS and insomnia complaints.
COR
Insomnia Estimate
no clinically
significant
insomnia
subthreshold
insomnia
clinical
insomnia
not
determined
RLS 60 (7%) 234 (27%) 564 (66%) 193
no RLS 0 0 0 0
DHS
Insomnia Estimate
no clinically
significant
insomnia
subthreshold
insomnia
clinical
insomnia
not
determined
RLS 35 (39%) 31 (34%) 24 (27%) 0
no RLS 435 (53%) 275 (33%) 114 (14%) 20
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Supplementary Table 19 | SNP associations with RLS and insomnia in the DHS and COR samples of the SNPs significantly
associated with insomnia complaints in the UK Biobank sample.
Full analysis
Insomnia
(N = 1,772)
Insomnia conditioned
on RLS (N = 1,772)
RLS
(N = 1,985)
RLS conditioned on
Insomnia (N = 1,772)
Analysis hit Chr BP SNP EA β P β P β P β P
Full 2 66728627 rs375216017 GT 0.181 3.88E-07 0.0365 0.254 2.82 3.36E-19 2.41 3.18E-11
Full 2 66730783 rs62144051 G 0.173 2.10E-06 0.00941 0.774 3.31 1.82E-22 2.94 1.39E-14
Full 2 66745864 rs62144053 A 0.171 1.94E-06 0.0125 0.699 3.19 3.07E-22 2.81 3.85E-14
Full 2 66747480 rs62144054 A 0.168 2.89E-06 0.0107 0.741 3.17 7.24E-22 2.81 4.57E-14
Full/females 2 66750564 rs113851554 T 0.197 6.73E-07 0.0299 0.402 3.39 4.42E-20 2.88 2.16E-12
Full 2 66757709 rs182588061 T 0.158 8.69E-03 -0.0285 0.592 3.88 4.78E-11 3.60 6.80E-08
Full/females 2 66782432 rs139775539 A 0.260 7.13E-09 0.0525 0.197 4.57 4.87E-22 3.74 9.7E-14
Full/females 2 66785180 rs11679120 A 0.258 1.02E-08 0.0450 0.270 4.75 1.20E-22 3.94 3.00E-14
Full/females 2 66793725 rs115087496 C 0.262 8.37E-09 0.0455 0.270 4.84 1.35E-22 4.15 8.66E-15
Full 2 66795237 rs549771308 C 0.140 1.00E-04 0.00372 0.908 2.60 8.61E-17 2.39 6.67E-11
Full/females 2 66799986 rs11693221 T 0.274 1.10E-09 0.0547 0.180 5.06 8.35E-24 4.23 4.40E-15
Full 4 53965702 rs189916659a T 0.0907 0.538 0.0608 0.638 1.15 0.744 1.09 0.8718
Full 4 53977261 rs574753165a G 0.0907 0.538 0.0608 0.638 1.15 0.744 1.09 0.8718
Males 6 169841072 rs71554396 GT -0.0200 0.584 -0.00131 0.967 0.902 0.326 0.898 0.3927
Males 6 169961603 rs13208844 G -0.0278 0.445 -0.00741 0.817 0.893 0.280 0.905 0.4282
Males 6 169961635 rs13192566 C -0.0278 0.445 -0.00741 0.817 0.893 0.280 0.905 0.4282
Males analysis
Insomnia
(N = 690)
Insomnia conditioned
on RLS (N = 690)
RLS
(N = 746)
RLS conditioned on
Insomnia (N = 690)
Analysis hit Chr BP SNP EA β P β P β P β P
Full 2 66728627 rs375216017 GT 0.197 1.23E-03 -0.00343 0.948 3.10 4.98E-10 2.91 1.6E-06
Full 2 66730783 rs62144051 G 0.166 7.61E-03 -0.0529 0.325 3.36 1.29E-10 3.47 7.03E-08
Full 2 66745864 rs62144053 A 0.179 3.48E-03 -0.0361 0.498 3.28 1.30E-10 3.24 1.45E-07
Full 2 66747480 rs62144054 A 0.166 7.19E-03 -0.0464 0.384 3.21 3.03E-10 3.26 1.50E-07
Full/females 2 66750564 rs113851554 T 0.230 6.70E-04 -0.00156 0.979 3.58 8.11E-10 3.33 8.03E-07
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Full 2 66757709 rs182588061 T 0.296 9.16E-03 0.0421 0.663 4.25 1.28E-05 3.09 5.51E-03
Full/females 2 66782432 rs139775539 A 0.268 3.68E-04 -0.0218 0.740 5.12 5.88E-11 4.77 5.71E-08
Full/females 2 66785180 rs11679120 A 0.279 2.74E-04 -0.0292 0.665 5.74 1.98E-11 5.38 4.87E-08
Full/females 2 66793725 rs115087496 C 0.294 1.47E-04 -0.0334 0.624 6.42 3.53E-12 6.29 6.39E-09
Full 2 66795237 rs549771308 C 0.177 3.69E-03 -0.0196 0.710 2.94 3.29E-09 2.84 1.75E-06
Full/females 2 66799986 rs11693221 T 0.301 5.88E-05 -0.0120 0.856 6.14 3.60E-12 5.72 1.85E-08
Full 4 53965702 rs189916659a T 0.0222 0.930 0.171 0.415 0.518 0.352 0.251 0.1125
Full 4 53977261 rs574753165a G 0.0222 0.930 0.171 0.415 0.518 0.352 0.251 0.1125
Males 6 169841072 rs71554396 GT -0.0946 0.126 -0.0446 0.391 0.811 0.230 0.775 0.2666
Males 6 169961603 rs13208844 G -0.121 0.0480 -0.0648 0.209 0.783 0.159 0.802 0.3383
Males 6 169961635 rs13192566 C -0.121 0.0480 -0.0648 0.209 0.783 0.159 0.802 0.3383
Females analysis
Insomnia
(N = 1,082)
Insomnia conditioned
on RLS (N = 1,082)
RLS
(N = 1,239)
RLS conditioned on
Insomnia (N = 1,028)
Analysis hit Chr BP SNP EA β P β P β P β P
Full 2 66728627 rs375216017 GT 0.163 1.76E-04 0.0529 0.187 2.72 6.51E-11 2.25 1.58E-06
Full 2 66730783 rs62144051 G 0.165 2.28E-04 0.0364 0.379 3.38 2.05E-13 2.80 1.44E-08
Full 2 66745864 rs62144053 A 0.156 3.88E-04 0.0328 0.419 3.23 3.79E-13 2.68 2.50E-08
Full 2 66747480 rs62144054 A 0.159 3.04E-04 0.0358 0.380 3.23 4.52E-13 2.68 2.76E-08
Full/females 2 66750564 rs113851554 T 0.169 5.30E-04 0.0405 0.367 3.36 8.58E-12 2.74 2.68E-07
Full 2 66757709 rs182588061 T 0.0864 0.217 -0.0641 0.316 3.82 5.20E-07 3.98 3.64E-06
Full/females 2 66782432 rs139775539 A 0.243 1.27E-05 0.0869 0.0924 4.30 2.02E-12 3.32 1.60E-07
Full/females 2 66785180 rs11679120 A 0.232 2.64E-05 0.0755 0.140 4.25 1.44E-12 3.37 7.68E-08
Full/females 2 66793725 rs115087496 C 0.226 5.20E-05 0.0738 0.153 4.06 9.01E-12 3.30 1.60E-07
Full 2 66795237 rs549771308 C 0.113 0.0107 0.0116 0.776 2.43 3.13E-09 2.22 3.39E-06
Full/females 2 66799986 rs11693221 T 0.240 1.78E-05 0.0793 0.127 4.50 5.82E-13 3.59 4.05E-08
Full 4 53965702 rs189916659a T 0.106 0.559 0.0110 0.946 1.89 0.315 2.37 0.217
Full 4 53977261 rs574753165a G 0.106 0.559 0.0110 0.946 1.89 0.315 2.37 0.217
Males 6 169841072 rs71554396 GT 0.0308 0.493 0.0315 0.439 1.00 0.990 0.985 0.924
Males 6 169961603 rs13208844 G 0.0315 0.483 0.0329 0.417 0.998 0.985 0.979 0.892
Males 6 169961635 rs13192566 C 0.0315 0.483 0.0329 0.417 0.998 0.985 0.979 0.892
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Chr, chromosome; BP, base pair; EA, effect allele. aProxy for rs574753165. Bold P values indicate significance when corrected for 16 SNP tests (α = 3.13 × 10-3).
Supplementary Table 20 | Gene associations with RLS and insomnia in the DHS and COR samples of the SNPs significantly
associated with insomnia complaints in the UK Biobank sample.
Full analysis
Insomnia
(N = 1,772)
Insomnia conditioned
on RLS (N = 1,772)
RLS
(N = 1,985)
RLS conditioned on
Insomnia (N = 1,772)
Analysis
hit
Gene
Symbol Chr Start BP
a Stop BP
a N SNPs P N SNPs P N SNPs P N SNPs P
Full MEIS1 2 66660257 66800891 1,021 9.07E-04 1,021 0.0730 1,060 1.77E-12 1,021 1.68E-10
Full DCBLD1 6 117801803 117892021 647 0.833 647 0.836 664 0.368 647 0.493
Full MED27 9 134734497 134957274 1,535 0.140 1,535 0.100 1,612 0.228 1,535 2.58E-01
Males HHEX 10 94447681 94456408 32 0.0341 32 0.200 35 0.332 32 0.180
Males RHCG 15 90013638 90041799 243 0.740 243 0.704 248 0.0719 243 0.0559
Females TSNARE1 8 143292441 143486543 2,443 0.0249 2,443 0.511 2,528 3.42E-04 2,443 9.52E-04
Females IPO7 11 9404169 9470674 493 0.952 493 0.381 509 0.0162 493 0.0268
Males analysis
Insomnia
(N = 690)
Insomnia conditioned
on RLS (N = 690)
RLS
(N = 746)
RLS conditioned on
Insomnia (N = 690)
Analysis
hit
Gene
Symbol Chr Start BP
a Stop BP
a N SNPs P N SNPs P N SNPs P N SNPs P
Full MEIS1 2 66660257 66800891 804 0.0696 804 0.502 844 3.42E-05 804 8.65E-05
Full DCBLD1 6 117801803 117892021 514 0.773 514 0.531 530 0.465 514 0.475
Full MED27 9 134734497 134957274 1,129 0.136 1,129 0.106 1,160 0.328 1,129 0.475
Males HHEX 10 94447681 94456408 26 0.653 26 0.443 26 0.920 26 0.909
Males RHCG 15 90013638 90041799 158 0.314 158 0.256 160 0.586 158 0.606
Females TSNARE1 8 143292441 143486543 1,947 0.517 1,947 0.515 1,963 0.558 1,947 0.549
Females IPO7 11 9404169 9470674 382 0.097 382 0.469 401 5.70E-03 382 4.85E-03
Females analysis
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Insomnia
(N = 1,082)
Insomnia conditioned
on RLS (N = 1,082)
RLS
(N = 1,239)
RLS conditioned on
Insomnia (N = 1,028)
Analysis
hit
Gene
Symbol Chr Start BP
a Stop BP
a N SNPs P N SNPs P N SNPs P N SNPs P
Full MEIS1 2 66660257 66800891 855 3.93E-03 855 8.99E-03 901 5.94E-09 855 1.14E-06
Full DCBLD1 6 117801803 117892021 592 0.593 592 0.697 607 0.663 592 0.695
Full MED27 9 134734497 134957274 1,247 0.567 1,247 0.483 1,339 0.466 1,247 0.586
Males HHEX 10 94447681 94456408 28 0.0242 28 0.245 32 0.126 28 0.0388
Males RHCG 15 90013638 90041799 200 0.452 200 0.330 207 0.123 200 0.141
Females TSNARE1 8 143292441 143486543 2,004 0.0194 2,004 0.349 2,102 3.38E-03 2,004 6.75E-03
Females IPO7 11 9404169 9470674 413 0.168 413 0.0705 431 0.553 413 0.709
Chr, chromosome; BP, base pair. aReported on GRCh37, Including a window of 2,1 kb. Bold P values indicate significance when corrected for 7 gene tests (α = 7.14 × 10-3).
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Supplementary Table 21 | Sign concordance test of independent SNPs from the UK
Biobank insomnia complaints GWAS and the RLS GWAS by Winkelmann et al. (2011).
P
threshold N SNPs
N SNPs
concordant sign
N SNPs
discordant
sign
% SNPs
concordant sign
P binominal
test
1 37,857 18,858 18,999 49.81 0.472
P thresholds based on RLS GWAS
0.5 19,187 9,463 9,724 49.32 0.0605
0.05 2,044 977 1,067 47.80 0.0490
1 × 10-3 55 18 37 32.73 0.0145
1 × 10-4 6 1 5 16.67 0.219
1 × 10-5 5 1 4 20 0.375
P thresholds based on insomnia complaints GWAS
0.5 19,610 9,751 9,859 49.72 0.445
0.05 2,185 1,110 1,075 50.80 0.467
0.001 78 38 40 48.72 0.910
1.00E-04 9 7 2 77.78 0.180
1.00E-05 1 1 0 100 NA
Independent SNPs were defined by pruning in PLINK (--indep-pairwise 1000 100 0.1). Binominal test for the null
hypothesis of 50% of SNPs will have the same sign (i.e. by chance). Lower P vaues indicate more deviation from
50%. However, because the sample sizes (N SNPs) differ greatly between the tests, the P values of the different test
cannot be compared.
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Supplementary Table 22 | Low P value enrichment tests of independent SNPs from the UK
Biobank insomnia complaints GWAS and the RLS GWAS by Winkelmann et al. (2011).
N SNPs P Fisher’s
Exact Test
P ≥ 0.05 P < 0.05
T = 0.05 P ≥ T 33,762 2,051
0.12 P < T 1,910 134
T = 1 × 10-3
P ≥ T 37,724 78
NA P < T 55 0
T = 1 × 10-4
P ≥ T 37,842 9
NA P < T 6 0
T = 1 × 10-5
P ≥ T 37,851 1
NA P < T 5 0
Independent SNPs were defined by pruning in PLINK (--indep-pairwise 1000 100 0.1).
T, P value threshold. SNPs below this threshold are included in the Fisher’s exact test.
Supplementary Table 23 | Low P value enrichment tests of ranked independent SNPs from
the UK Biobank insomnia complaints GWAS and the RLS GWAS by Winkelmann et al.
(2011).
N SNPs P Fisher’s
Exact Test
R > T R ≤ T
T = 3,200 R > T 31,735 2,922
0.62 R ≤ T 2,922 278
T = 1,600 R > T 34,732 1,525
0.34 R ≤ T 1,525 75
T = 800 R > T 36,281 776
0.08 R ≤ T 776 24
T = 400 R > T 37,065 392
0.08 R ≤ T 392 8
T = 200 R > T 37,459 198
0.29 R ≤ T 198 2
T = 100 R > T 37,658 99
0.23 R ≤ T 99 1
T = 50 R > T 37,757 50
NA R ≤ T 50 0
Independent SNPs were defined by pruning in PLINK (--indep-pairwise 1000 100 0.1).
T, Rank threshold. SNPs below this rank are included in the Fisher’s exact test.
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Supplementary Table 24 | Independent SNPs reaching genome-wide significant association
with six sleep-related phenotypes in the UK Biobank sample.
SNP Chr BPa EA Non-EA Info N EAF P β (SE)
Chronotype
rs76681500 1 77713434 A G 0.99 101,185 0.16 5.27E-13 -0.044 (0.0061)
rs10788873 1 150250534 A T 0.98 101,185 0.48 4.73E-09 0.026 (0.0045)
rs694383 1 182568204 C G 1.00 101,185 0.030 1.61E-14 0.099 (0.013)
rs2050122 1 19989205 C T 1.00 101,185 0.20 4.31E-09 0.033 (0.0056)
rs1075265 2 54354927 G C 0.99 101,185 0.48 1.09E-08 -0.025 (0.0045)
rs55986470 2 239363773 G A 0.98 101,185 0.15 1.53E-09 0.037 (0.0062)
rs371681307 4 103538903 T C 0.97 101,185 0.042 3.65E-08 -0.062 (0.011)
rs542675489 12 120994888 CA C 0.88 101,185 0.40 4.64E-08 -0.026 (0.0048)
rs4821940 22 40659573 C T 1.00 101,185 0.45 9.26E-09 0.026 (0.0045)
Daytime dozing/sleepiness
rs543431433 4 10027795 A T 0.89 112,717 0.0028 4.33E-09 0.26 (0.045)
rs573716401 4 178972961 C G 0.84 112,717 0.0013 2.18E-08 0.42 (0.076)
rs553962214 8 16560526 C T 0.90 112,717 0.0010 1.45E-08 0.41 (0.073)
rs558006880 18 8116743 A G 0.86 112,717 0.0014 1.97E-08 0.35 (0.063)
rs565444861 20 19360120 A G 0.92 112,717 0.0012 2.73E-08 0.36 (0.064)
rs6099524 20 37038113 T C 0.97 112,717 0.0020 1.43E-09 0.29 (0.048)
Getting up in the morning
rs1144566 1 182569626 C T 1 112,866 0.030 2.91E-08 -0.066 (0.012)
rs569778919 2 11026245 C CTTTT
TTTTT
TTTTT
0.89 112,866 0.28 3.76E-08 0.027 (0.0048)
rs9382484 6 55182860 G T 0.98 112,866 0.24 1.07E-08 -0.028 (0.0048)
rs28458909 9 140257189 T C 1 112,866 0.12 6.00E-09 -0.036 (0.0062)
rs72827839 17 46420996 A G 1.00 112,866 0.22 9.07E-10 0.03 (0.0049)
Nap during the day
rs541594711 3 21954839 A C 0.90 113,054 0.0017 1.72E-08 0.3 (0.053)
rs114515123 3 46270326 T G 0.96 113,054 0.0011 3.49E-08 0.35 (0.064)
rs755927998 5 65093989 T C 0.85 113,054 0.0018 6.14E-09 0.32 (0.055)
rs182197129 11 69586227 T C 0.89 113,054 0.0023 6.71E-10 0.3 (0.048)
Sleep duration
rs1380703 2 57941287 G A 0.89 112,411 0.38 1.86E-09 -0.027 (0.0046)
rs62158211 2 114106139 T G 0.99 112,411 0.21 1.14E-12 0.037 (0.0052)
rs61980273 14 94218949 A G 1 112,411 0.039 4.31E-08 0.06 (0.011)
Snoring
rs34888975 16 1695896 TA T 0.99 105,377 0.078 4.65E-08 0.044 (0.008)
The top SNP for each independent significantly associated locus (P < 5 × 10-08) is reported. The six sleep-related
phenotypes are described in Supplementary Information section 5.2.
Chr, chromosome; BP, base pair; EA, effect allele; EAF, effect allele frequency areported on GRCh37
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Supplementary Table 25 | Genetic and phenotypic correlations between the six sleep-
related phenotypes in UK Biobank.
Insomnia
Sleep
duration
Getting
up in
morning
Chrono-
type
Nap
during
day
Snoring
Daytime
dozing/
sleeping
Insomnia
-0.47 -0.13 0.01 0.42 -0.04 0.51
Sleep duration -0.26
-0.16 0.07 0.09 0.06 -0.26
Getting up in morning -0.10 -0.02
-0.82 0.04 0.01 0.04
Chronotype 0.01 0.03 -0.48
-0.20 0.04 -0.21
Nap during day 0.05 0.11 -0.01 -0.02
0.21 0.72
Snoring -0.03 0.02 -0.001 0.02 0.03
0.14
Daytime
dozing/sleeping 0.08 0.002 -0.06 0.003 0.23 0.05
Upper right part of table: genetic correlations calculated with LD Score. Lower left part of table: phenotypic
correlations. Bold genetic correlations are significantly correlated after correction for 21 tests (P < 2.38 × 10-3).
The six sleep-related phenotypes are described in Supplementary Information section 5.2.
Supplementary Table 26 | SNP association results of the SNPs significantly associated with
insomnia complaints in six sleep-related phenotypes in the UK Biobank sample.
SNP Chr BPa
P
Chronotype
P
Daytime
dozing/
sleepiness
P
Getting
up in the
morning
P
Nap
during
the day
P
Sleep
duration
P
Snoring
rs375216017 2 66728627 0.0040 0.11 0.84 0.67 0.29 0.81
rs62144051 2 66730783 0.0080 0.086 0.92 0.99 0.21 0.89
rs62144053 2 66745864 0.0027 0.085 0.74 0.78 0.43 0.94
rs62144054 2 66747480 0.0022 0.079 0.63 0.86 0.38 0.97
rs113851554 2 66750564 0.00029 0.18 0.65 0.99 0.76 0.98
rs182588061 2 66757709 0.39 0.25 0.69 0.75 0.42 0.60
rs139775539 2 66782432 0.0038 0.12 0.88 0.58 0.33 0.85
rs11679120 2 66785180 0.0026 0.18 0.78 0.78 0.24 0.69
rs115087496 2 66793725 0.0046 0.26 0.65 0.72 0.24 0.69
rs549771308 2 66795237 0.0036 0.59 0.31 0.46 0.62 0.97
rs11693221 2 66799986 0.0074 0.35 0.95 0.72 0.23 0.68
rs574753165 4 53977261 0.67 0.74 0.24 0.30 0.11 0.87
rs71554396 6 169841072 0.67 0.94 0.13 1 0.75 0.83
rs13208844 6 169961603 0.69 0.94 0.34 0.85 0.67 1
rs13192566 6 169961635 0.66 0.99 0.30 0.84 0.71 0.93
The six sleep-related phenotypes are described in Supplementary Information section 5.2.
Chr, chromosome; BP, base pair; EA, effect allele.
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Supplementary Table 27 | Significant SNPs from the insomnia complaints GWAS adjusted for characteristics and traits that
have been associated with insomnia.
Original
SNP Chr BP EA non-EA MAF OR (95% CI) P
Full GWAS
32,384 insomnia cases + 80,622 controls
rs375216017 2 66728627 GT G
0.11 1.09 (1.05-1.12) 1.21E-08
rs62144051 2 66730783 G A
0.094 1.10 (1.06-1.13) 3.81E-09
rs62144053 2 66745864 A G
0.095 1.10 (1.06-1.13) 1.20E-09
rs62144054 2 66747480 A G
0.094 1.10 (1.06-1.13) 1.68E-09
rs113851554 2 66750564 T G
0.056 1.19 (1.14-1.24) 2.14E-18
rs182588061 2 66757709 T G
0.02 1.21 (1.13-1.28) 3.18E-10
rs139775539 2 66782432 A AC
0.048 1.19 (1.14-1.24) 7.00E-17
rs11679120 2 66785180 A G
0.047 1.19 (1.14-1.24) 6.18E-17
rs115087496 2 66793725 C G
0.047 1.18 (1.13-1.23) 4.43E-15
rs549771308 2 66795237 C CT
0.12 1.08 (1.05-1.11) 9.51E-09
rs11693221 2 66799986 T C
0.048 1.17 (1.12-1.22) 3.79E-14
rs574753165 4 53977261 G A
0.0053 1.40 (1.24-1.57) 4.98E-09
Male GWAS
19,521 insomnia cases + 39,846 controls
rs71554396 6 1.7E+08 GT G
0.14 0.89 (0.85-0.93) 1.95E-08
rs13208844 6 1.7E+08 G A
0.15 0.89 (0.85-0.93) 2.25E-08
rs13192566 6 1.7E+08 C G
0.15 0.89 (0.85-0.93) 1.80E-08
Female GWAS
12,863 insomnia cases + 40,776 controls
rs113851554 2 66750564 T G
0.056 1.20 (1.14-1.26) 2.25E-12
rs139775539 2 66782432 A AC
0.048 1.21 (1.14-1.28) 4.08E-12
rs11679120 2 66785180 A G
0.048 1.20 (1.14-1.27) 2.09E-11
rs115087496 2 66793725 C G
0.048 1.19 (1.13-1.26) 9.91E-11
rs11693221 2 66799986 T C
0.048 1.19 (1.12-1.25) 2.88E-10
Waist-to-hip ratio BMI
Excl. covariate Incl. covariate Excl. covariate Incl. covariate
SNP OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P
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Full GWAS 32,322 insomnia cases + 80,504 controls 32,276 insomnia cases + 80,418 controls
rs375216017 1.09 (1.05-1.12) 1.30E-08 1.09 (1.05-1.12) 8.54E-09 1.09 (1.06-1.12) 1.01E-08 1.09 (1.06-1.12) 7.47E-09
rs62144051 1.1 (1.06-1.13) 4.21E-09 1.10 (1.06-1.13) 3.19E-09 1.1 (1.06-1.13) 3.13E-09 1.10 (1.06-1.13) 2.89E-09
rs62144053 1.1 (1.06-1.13) 1.34E-09 1.10 (1.06-1.13) 1.07E-09 1.1 (1.07-1.13) 9.03E-10 1.10 (1.07-1.13) 7.40E-10
rs62144054 1.1 (1.06-1.13) 1.90E-09 1.10 (1.06-1.13) 1.47E-09 1.1 (1.07-1.13) 1.23E-09 1.10 (1.07-1.13) 9.92E-10
rs113851554 1.19 (1.14-1.24) 2.13E-18 1.19 (1.14-1.24) 1.17E-18 1.19 (1.14-1.24) 1.46E-18 1.19 (1.14-1.24) 6.64E-19
rs182588061 1.21 (1.13-1.28) 3.55E-10 1.21 (1.13-1.28) 2.73E-10 1.21 (1.14-1.29) 1.40E-10 1.21 (1.14-1.29) 1.04E-10
rs139775539 1.19 (1.14-1.24) 1.12E-16 1.19 (1.14-1.24) 5.05E-17 1.19 (1.14-1.24) 6.05E-17 1.19 (1.14-1.24) 2.80E-17
rs11679120 1.19 (1.14-1.24) 1.05E-16 1.19 (1.14-1.24) 4.95E-17 1.19 (1.14-1.24) 5.98E-17 1.19 (1.14-1.24) 2.73E-17
rs115087496 1.18 (1.13-1.23) 7.65E-15 1.18 (1.13-1.23) 3.80E-15 1.18 (1.13-1.23) 5.52E-15 1.18 (1.13-1.23) 2.80E-15
rs549771308 1.08 (1.05-1.11) 1.05E-08 1.08 (1.05-1.11) 7.62E-09 1.08 (1.05-1.11) 7.66E-09 1.08 (1.05-1.11) 4.83E-09
rs11693221 1.17 (1.12-1.22) 6.51E-14 1.17 (1.12-1.22) 3.34E-14 1.17 (1.12-1.22) 4.81E-14 1.17 (1.12-1.22) 2.55E-14
rs574753165 1.39 (1.24-1.57) 8.10E-09 1.39 (1.24-1.57) 6.24E-09 1.39 (1.23-1.57) 9.40E-09 1.39 (1.23-1.57) 2.64E-09
Male GWAS 12,837 insomnia cases + controls 40,724 12,807 insomnia cases + 40,664 controls
rs71554396 0.89 (0.85-0.93) 2.14E-08 0.89 (0.85-0.93) 2.89E-08 0.89 (0.85-0.93) 2.81E-08 0.89 (0.85-0.93) 3.91E-08
rs13208844 0.89 (0.85-0.93) 2.33E-08 0.89 (0.85-0.93) 3.32E-08 0.89 (0.85-0.93) 2.65E-08 0.89 (0.85-0.93) 3.63E-08
rs13192566 0.89 (0.85-0.93) 1.82E-08 0.89 (0.85-0.93) 2.51E-08 0.89 (0.85-0.93) 2.12E-08 0.89 (0.85-0.93) 2.82E-08
Female GWAS 19,485 insomnia cases + 39,780 controls 19,469 insomnia cases + 39,754 controls
rs113851554 1.2 (1.14-1.26) 2.52E-12 1.20 (1.14-1.26) 1.74E-12 1.2 (1.14-1.26) 2.52E-12 1.20 (1.14-1.26) 9.87E-13
rs139775539 1.21 (1.14-1.28) 5.56E-12 1.21 (1.14-1.28) 3.55E-12 1.21 (1.14-1.28) 4.88E-12 1.21 (1.14-1.28) 2.22E-12
rs11679120 1.2 (1.13-1.27) 2.83E-11 1.20 (1.13-1.27) 1.76E-11 1.2 (1.13-1.27) 2.49E-11 1.20 (1.13-1.27) 1.07E-11
rs115087496 1.19 (1.13-1.26) 1.40E-10 1.19 (1.13-1.26) 9.15E-11 1.19 (1.13-1.26) 1.25E-10 1.19 (1.13-1.26) 5.82E-11
rs11693221 1.19 (1.12-1.25) 4.07E-10 1.19 (1.12-1.25) 2.64E-10 1.19 (1.12-1.25) 3.68E-10 1.19 (1.12-1.25) 1.69E-10
Townsend Deprivation Index Years of education
Excl. covariate Incl. covariate Excl. covariate Incl. covariate
SNP OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P
Full GWAS 32,341 insomnia cases + 80,518 controls 24,829 insomnia cases + 66,669 controls
rs375216017 1.09 (1.06-1.12) 7.90E-09 1.09 (1.06-1.12) 8.12E-09 1.09 (1.05-1.13) 1.71E-07 1.09 (1.05-1.13) 1.75E-07
rs62144051 1.1 (1.06-1.13) 2.60E-09 1.10 (1.06-1.13) 2.82E-09 1.09 (1.06-1.13) 3.36E-07 1.09 (1.06-1.13) 3.66E-07
rs62144053 1.1 (1.07-1.13) 8.26E-10 1.10 (1.07-1.13) 9.86E-10 1.09 (1.06-1.13) 2.74E-07 1.09 (1.06-1.13) 3.02E-07
rs62144054 1.1 (1.07-1.13) 1.17E-09 1.10 (1.07-1.13) 1.38E-09 1.09 (1.06-1.13) 3.40E-07 1.09 (1.06-1.13) 3.61E-07
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rs113851554 1.19 (1.15-1.24) 7.84E-19 1.19 (1.15-1.24) 8.85E-19 1.18 (1.13-1.23) 4.55E-14 1.18 (1.13-1.23) 6.17E-14
rs182588061 1.21 (1.14-1.29) 1.77E-10 1.21 (1.14-1.29) 1.22E-10 1.2 (1.11-1.28) 4.50E-08 1.20 (1.11-1.28) 4.80E-08
rs139775539 1.19 (1.14-1.24) 2.65E-17 1.19 (1.14-1.24) 2.30E-17 1.18 (1.13-1.24) 4.63E-13 1.18 (1.13-1.24) 5.95E-13
rs11679120 1.19 (1.15-1.24) 2.19E-17 1.19 (1.15-1.24) 1.76E-17 1.19 (1.13-1.24) 2.54E-13 1.19 (1.13-1.24) 3.43E-13
rs115087496 1.18 (1.13-1.23) 1.65E-15 1.18 (1.13-1.23) 1.29E-15 1.18 (1.12-1.23) 5.54E-12 1.18 (1.12-1.23) 7.51E-12
rs549771308 1.08 (1.05-1.11) 7.22E-09 1.08 (1.05-1.11) 5.23E-09 1.08 (1.04-1.11) 2.81E-07 1.08 (1.04-1.11) 3.61E-07
rs11693221 1.17 (1.13-1.22) 1.47E-14 1.17 (1.13-1.22) 1.24E-14 1.17 (1.11-1.22) 3.15E-11 1.17 (1.11-1.22) 4.35E-11
rs574753165 1.4 (1.24-1.58) 4.44E-09 1.40 (1.24-1.58) 1.13E-08 1.37 (1.2-1.57) 1.40E-06 1.37 (1.20-1.57) 1.03E-06
Male GWAS 12,844 insomnia cases + 40,725 controls 9,857 insomnia cases + 33,576 controls
rs71554396 0.89 (0.85-0.93) 2.17E-08 0.89 (0.85-0.93) 6.70E-08 0.89 (0.85-0.94) 1.11E-06 0.89 (0.85-0.94) 1.06E-06
rs13208844 0.89 (0.85-0.93) 2.49E-08 0.89 (0.85-0.93) 8.00E-08 0.89 (0.85-0.93) 6.27E-07 0.89 (0.85-0.93) 6.08E-07
rs13192566 0.89 (0.85-0.93) 1.99E-08 0.89 (0.85-0.93) 6.30E-08 0.89 (0.85-0.93) 4.48E-07 0.89 (0.85-0.93) 4.34E-07
Female GWAS 19,497 insomnia cases + 39,793 controls 14,972 insomnia cases + 33,093 controls
rs113851554 1.2 (1.14-1.26) 1.50E-12 1.20 (1.14-1.26) 1.49E-12 1.18 (1.12-1.25) 6.53E-09 1.18 (1.12-1.25) 7.35E-09
rs139775539 1.21 (1.15-1.28) 2.50E-12 1.21 (1.15-1.28) 2.86E-12 1.2 (1.12-1.27) 4.02E-09 1.20 (1.12-1.27) 3.56E-09
rs11679120 1.2 (1.14-1.27) 1.28E-11 1.20 (1.14-1.27) 1.39E-11 1.19 (1.12-1.27) 9.50E-09 1.19 (1.12-1.27) 8.82E-09
rs115087496 1.2 (1.13-1.26) 6.02E-11 1.20 (1.13-1.26) 5.85E-11 1.19 (1.11-1.26) 2.66E-08 1.19 (1.11-1.26) 2.61E-08
rs11693221 1.19 (1.12-1.26) 1.79E-10 1.19 (1.12-1.26) 1.77E-10 1.18 (1.11-1.25) 6.63E-08 1.18 (1.11-1.25) 6.68E-08
Depressive symptoms Neuroticism
Excl. covariate Incl. covariate Excl. covariate Incl. covariate
SNP OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P
Full GWAS 29,918 insomnia cases + 76,337 controls 25,829 insomnia cases + 66,360 controls
rs375216017 1.09 (1.05-1.12) 2.80E-08 1.09 (1.05-1.12) 8.89E-09 1.1 (1.06-1.13) 1.01E-08 1.10 (1.06-1.13) 7.41E-09
rs62144051 1.1 (1.06-1.13) 1.13E-08 1.10 (1.06-1.13) 3.80E-09 1.11 (1.07-1.14) 3.95E-09 1.11 (1.07-1.14) 2.48E-09
rs62144053 1.1 (1.07-1.14) 3.00E-09 1.10 (1.07-1.14) 1.16E-09 1.11 (1.07-1.15) 1.72E-09 1.11 (1.07-1.15) 1.44E-09
rs62144054 1.1 (1.06-1.13) 5.88E-09 1.10 (1.06-1.13) 2.46E-09 1.11 (1.07-1.15) 2.74E-09 1.11 (1.07-1.15) 2.51E-09
rs113851554 1.19 (1.14-1.24) 4.86E-17 1.19 (1.14-1.24) 7.23E-17 1.2 (1.15-1.25) 3.67E-16 1.20 (1.15-1.25) 2.07E-16
rs182588061 1.2 (1.13-1.28) 3.83E-09 1.20 (1.13-1.28) 1.71E-08 1.2 (1.12-1.29) 1.67E-08 1.20 (1.12-1.29) 4.60E-08
rs139775539 1.19 (1.14-1.24) 2.73E-15 1.19 (1.14-1.24) 8.59E-15 1.2 (1.15-1.26) 2.67E-15 1.20 (1.15-1.26) 1.92E-15
rs11679120 1.19 (1.14-1.24) 1.65E-15 1.19 (1.14-1.24) 7.73E-15 1.2 (1.14-1.26) 3.88E-15 1.20 (1.14-1.26) 5.60E-15
rs115087496 1.18 (1.13-1.23) 7.54E-14 1.18 (1.13-1.23) 5.99E-13 1.19 (1.13-1.24) 2.91E-13 1.19 (1.13-1.24) 9.24E-13
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rs549771308 1.08 (1.05-1.11) 1.10E-08 1.08 (1.05-1.11) 4.74E-08 1.08 (1.04-1.11) 3.93E-07 1.08 (1.04-1.11) 4.69E-07
rs11693221 1.17 (1.12-1.22) 9.06E-13 1.17 (1.12-1.22) 5.83E-12 1.18 (1.12-1.23) 1.54E-12 1.18 (1.12-1.23) 4.86E-12
rs574753165 1.37 (1.21-1.56) 8.47E-08 1.37 (1.21-1.56) 5.86E-08 1.32 (1.15-1.51) 1.94E-05 1.32 (1.15-1.51) 9.57E-06
Male GWAS 11,971 insomnia cases + 38,779 controls 10,433 insomnia cases + 34,126 controls
rs71554396 0.89 (0.85-0.93) 5.55E-08 0.89 (0.85-0.93) 9.18E-08 0.88 (0.84-0.92) 3.57E-08 0.88 (0.84-0.92) 5.48E-08
rs13208844 0.89 (0.85-0.93) 7.53E-08 0.89 (0.85-0.93) 8.37E-08 0.89 (0.85-0.93) 1.37E-07 0.89 (0.85-0.93) 3.30E-07
rs13192566 0.89 (0.85-0.93) 7.96E-08 0.89 (0.85-0.93) 8.74E-08 0.89 (0.85-0.93) 1.19E-07 0.89 (0.85-0.93) 2.84E-07
Female GWAS 37,558 insomnia cases + 17,947 controls 15,396 insomnia cases + 32,234 controls
rs113851554 1.21 (1.15-1.27) 1.85E-12 1.21 (1.15-1.27) 1.87E-12 1.22 (1.15-1.29) 8.61E-12 1.22 (1.15-1.29) 5.90E-12
rs139775539 1.22 (1.15-1.29) 2.53E-12 1.22 (1.15-1.29) 4.52E-12 1.23 (1.15-1.3) 1.06E-11 1.23 (1.15-1.3) 8.07E-12
rs11679120 1.21 (1.14-1.28) 1.03E-11 1.21 (1.14-1.28) 2.73E-11 1.22 (1.14-1.3) 5.55E-11 1.22 (1.14-1.3) 9.61E-11
rs115087496 1.21 (1.14-1.28) 2.52E-11 1.21 (1.14-1.28) 1.05E-10 1.21 (1.14-1.29) 2.40E-10 1.21 (1.14-1.29) 9.32E-10
rs11693221 1.2 (1.13-1.27) 1.29E-10 1.20 (1.13-1.27) 4.38E-10 1.21 (1.13-1.28) 3.75E-10 1.21 (1.13-1.28) 1.16E-09
Six different phenotypes were added as covariate (one analysis per covariate) to the original insomnia complaints GWAS to control for any confounding effects
of these phenotypes. SNP P values and odds ratios were calculated for each SNP in SNPTEST with an additive genetic model using logistic regression adjusted
for age, sex in the full GWAS, genotyping array, and principal components, and one of the six phenotypes. Results are compared to insomnia complaints in the
same set of individuals. aWe mapped the educational qualification according to the 1997 International Standard Classification of Education (ISCED) of the United Nations Educational,
Scientific and Cultural Organization, as described by Okbay et al. 2016 (doi: 10.1038/nature17671); bConstructed by summing the responses to “Over the past
two weeks, how often have you felt down, depressed or hopeless?” and “Over the past two weeks, how often have you had little interest or pleasure in doing
things?”, resulting in a score range of 2-8 with higher scores indicating more depressive symptoms; cTotal score of the 12 neuroticism items with higher scores
indicating more neurotic behaviors (score range of 0-12).
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Supplementary Table 28 | Low P value enrichment tests of independent SNPs from the UK
Biobank insomnia complaints GWAS in males and females.
N SNPs P Fisher’s
Exact Test
P ≥ T P < T
T = 0.05 P ≥ T 575,080 30,692
0.80 P < T 30,418 1,612
T = 1 × 10-3
P ≥ T 636,506 633
NA P < T 663 0
T = 1 × 10-4
P ≥ T 637,661 66
NA P < T 75 0
T = 1 × 10-5
P ≥ T 637,787 5
NA P < T 10 0
Independent SNPs were defined by pruning in PLINK (--indep-pairwise 1000 100 0.1).
T, P value threshold. SNPs below this threshold are included in the Fisher’s exact test.
Supplementary Table 29 | Low P value enrichment tests of genes from the UK Biobank
insomnia complaints GWAS in males and females.
N SNPs P Fisher’s
Exact Test
P > T P < T
T = 0.05 P ≥ T 16,021 1,135
0.81 P < T 1,119 76
T = 1 × 10-3
P ≥ T 18,194 96
NA P < T 61 0
T = 1 × 10-4
P ≥ T 18,311 32
NA P < T 8 0
T = 1 × 10-5
P ≥ T 18,343 5
NA P < T 3 0
T, P value threshold. Genes below this threshold are included in the Fisher’s exact test.
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Supplementary Table 30 | Sign concordance test of independent SNPs from the UK
Biobank insomnia complaints GWAS in males and females.
P
threshold N SNPs
N SNPs
concordant sign
N SNPs
discordant sign
% SNPs
concordant sign
P binominal
test
1 637,802 320,533 317,269 50.26 4.39 × 10-5
P thresholds based on the GWAS in males
0.5 319,838 161,136 158,702 50.38 1.69 × 10-5
0.05 32,030 16,072 15,958 50.18 0.53
1 × 10-3 663 346 317 52.19 0.28
1 × 10-4 75 40 35 53.33 0.64
1 × 10-5 10 6 4 60.00 0.75
P thresholds based on the GWAS in males
0.5 320,096 161,010 159,086 50.30 6.77 × 10-4
0.05 32,304 16,384 15,920 50.72 9.99 × 10-3
1 × 10-3 633 304 329 48.03 0.34
1 × 10-4 66 32 34 48.48 0.90
1 × 10-5 5 4 1 80.00 0.38
Independent SNPs were defined by pruning in PLINK (--indep-pairwise 1000 100 0.1). Binominal test for the null
hypothesis of 50% of SNPs will have the same sign (i.e. by chance). Lower P vaues indicate more deviation from
50%. However, because the sample sizes (N SNPs) differ greatly between the tests, the P values of the different test
cannot be compared.
Supplementary Table 31 | Pathway analysis of canonical pathways and Gene Ontology
(GO) pathways. Competitive gene-set analyses of 1,330 canonical pathways and 1,454 GO
pathways from the molecular signature database (MsigDB) were performed using MAGMA.
Excel file
Supplementary Table 32 | Enrichment analysis of HotNet2 subnetworks. Enrichment
analysis of the twelve subnetworks of genes identified for males and nine subnetworks identified
for females by the HotNet2 analysis. Hypergeometric tests were performed to determine the
significance in overlap between the HotNet2 subnetwork and canonical pathways (n = 1,330) and
Gene Ontology (GO) pathways (n = 1,454) from the molecular signature database (MsigDB
v5.1). Pathways are reported that were considered statistically significant (P ≤ 0.05 after
correcting for multiple testing using Benjamini and Hochberg method).
Excel file
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Supplementary Table 33 | 29 traits tested for genetic correlation with insomnia complaints.
Trait N Reference rg (SE) P value
Anxiety disorders 17,310 Otowa et al. 2016 PMID: 26754954 0.59 (0.15) 7.14E-05
Depressive symptoms 161,460 Okbay et al. 2016 PMID: 27089181 0.53 (0.06) 1.03E-17
Neuroticism 170,911 Okbay et al. 2016 PMID: 27089181 0.44 (0.04) 1.20E-25
Major depressive disorder 16,610
Major Depressive Disorder
Working Group of the
Psychiatric GWAS
Consortium 2013; Cross-
Disorder Group of the
Psychiatric Genomics
Consortium 2013
PMID: 22472876;
PMID: 23453885 0.41 (0.12) 6.50E-04
Cigarettes per day 38,181 Tobacco and Genetics
Consortium 2010 PMID: 20418890 0.30 (0.11) 9.30E-03
Type 2 diabetes 69,033 Morris et al. 2012 PMID: 22885922 0.28 (0.08) 3.37E-04
Coronary artery disease 184,305 Nikpay et al. 2015 PMID: 26343387 0.21 (0.07) 3.93E-03
Waist circumference 210,088 Shungin et al. 2015 PMID: 25673412 0.17 (0.04) 2.00E-04
Waist-to-hip ratio 210,088 Shungin et al. 2015 PMID: 25673412 0.16 (0.05) 1.20E-03
Body mass index 236,231 Locke et al. 2015 PMID: 25673413 0.16 (0.04) 3.05E-05
Ever smoker 74,035 Tobacco and Genetics
Consortium 2010 PMID: 20418890 0.14 (0.07) 0.06
Asthma 26,475 Moffatt et al. 2010 PMID: 20860503 0.13 (0.10) 0.22
Hip circumference 210,088 Shungin et al. 2015 PMID: 25673412 0.13 (0.05) 6.00E-03
Bipolar disorder 11,810
Psychiatric GWAS
Consortium Bipolar Disorder
Working Group 2011; Cross-
Disorder Group of the
Psychiatric Genomics
Consortium 2013
PMID: 21926972;
PMID: 23453885 0.03 (0.08) 0.72
Birth length 28,459 van der Valk et al. 2015 PMID: 25281659 0.01 (0.09) 0.87
Attention-deficit/
hyperactivity disorder 5,422
Neale et al. 2010; Cross-
Disorder Group of the
Psychiatric Genomics
Consortium 2013
PMID: 20732625;
PMID: 23453885 0.01 (0.14) 0.95
Birth weight 26,836 Horikoshi et al. 2013 PMID: 23202124 -0.03 (0.09) 0.76
Obesity - childhood 13,848 Bradfield et al. 2012 PMID: 22484627 -0.03 (0.07) 0.67
Schizophrenia 150,064
Schizophrenia Working
Group of the Psychiatric
Genomics Consortium 2014
PMID: 25056061 -0.03 (0.05) 0.48
Autism spectrum disorder 14,528 Psychiatric Genomics
Consortium
http://www.med.unc.e
du/pgc -0.03 (0.08) 0.69
Height 253,288 Wood et al. 2014 PMID: 25282103 -0.05 (0.03) 0.17
Smoking cessation 41,278 Tobacco and Genetics
Consortium 2010 PMID: 20418890 -0.05 (0.10) 0.61
Anorexia nervosa 14,477 Psychiatric Genomics
Consortium
http://www.med.unc.e
du/pgc -0.08 (0.09) 0.35
Body mass index -
childhood 35,668 Felix et al. 2016 PMID: 26604143 -0.10 (0.07) 0.14
Head circumference in 10,678 Taal et al. 2012 PMID: 22504419 -0.18 (0.10) 0.08
Nature Genetics: doi:10.1038/ng.3888
75
infancy
Alzheimer's disease 54,162 Lambert et al. 2013 PMID: 24162737 -0.20 (0.12) 0.11
Intelligence - childhood 12,441 Benyamin et al. 2014 PMID: 23358156 -0.25 (0.09) 7.20E-03
Educational attainment 328,917 Okbay et al. 2016 PMID: 27225129 -0.34 (0.03) 1.81E-22
Subjective well-being 298,420 Okbay et al. 2016 PMID: 27089181 -0.44 (0.07) 5.64E-11
Bold values are significant genetic correlations with insomnia. rg, genetic correlation; SE, standard error
Supplementary Table 34 | 18 phenotypic group differences between individuals with and
without insomnia complaints in the Netherlands Sleep Register.
Trait d (95% CI) P value
Anxiety disorders 1.16 (0.98 – 1.35) 4.06E-88
Depressive symptoms 1.10 (0.91 – 1.29) 2.83E-76
Neuroticism 1.01 (0.73 – 1.30) 7.30E-35
Major depressive disorder 0.46 (0.36 – 0.56) 2.64E-20
Type 2 diabetes 0.09 (0.00 – 0.19) 4.89E-02
Coronary artery disease 0.32 (0.22 – 0.41) 5.70E-11
Body mass index 0.17 (0.01 – 0.34) 2.50E-04
Ever smoker 0.14 (0.05 – 0.24) 3.88E-03
Asthma 0.05 (-0.04 – 0.14) 3.02E-01
Bipolar disorder 0.09 (0.00 – 0.19) 6.00E-02
Attention-deficit/
hyperactivity disorder 0.07 (-0.03 – 0.16) 1.65E-01
Schizophrenia 0.04 (-0.05 – 0.14) 3.71E-01
Autism spectrum disorder 0.06 (-0.04 – 0.15) 2.46E-01
Height -0.23 (-0.23 – -0.23) 6.83E-07
Smoking cessation 0.08 (-0.01 – 0.18) 9.16E-02
Anorexia nervosa 0.25 (0.16 – 0.35) 1.89E-07
Educational attainment -0.46 (-0.52 – -0.40) 3.87E-22
Subjective well-being -0.93 (-1.03 – -0.84) 1.14E-39
Group differences between 1,073 individuals without insomnia
complaints and 845 likely to suffer from insomnia disorder were
evaluated using t-tests (continuous phenotype) or 2-tests
(dichotomous phenotypes). Bold values are significant phenotypic
differences. d, profile of the magnitude of phenotypic group
difference; CI, confidence interval.
Nature Genetics: doi:10.1038/ng.3888