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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

2

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


4

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


5

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


6

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


7

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


8

(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


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.


10

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

( ) ( | |

) (

| |

) [ (

| |

)] [ (

| |

)],


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):


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


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).


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.


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.


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.


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.


21

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.


22

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).


23

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.


24

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


25

c rs13192566 in the male GWAS

d rs113851554 in the female GWAS


26

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).


27

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).


28

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


29

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).


30


31


32


33

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


http://www.braineac.org/

34

SCFD2

DCBLD1


35

MED27

WDR27


36

HHEX

RHCG


37

IPO7

TSNARE1


38

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.


39


40


41


42


43

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.


44

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).


45

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.


46

Supplementary Figure 15 | Manhattan plot of the interaction effects of sex and additive

SNP effect in the insomnia complaints GWAS.


47

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.


48

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.


49

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


50

b


51

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


52

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


53

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


54

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


55

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


56

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).


57

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.


58

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


59

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


60

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 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 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)


on RLS (N = 690)

RLS

(N = 746)

RLS conditioned on

Insomnia (N = 690)


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


61

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)


on RLS (N = 1,082)

RLS

(N = 1,239)

RLS conditioned on



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 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 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 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


62

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)


on RLS (N = 1,772)

RLS

(N = 1,985)

RLS conditioned on


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)


on RLS (N = 690)

RLS

(N = 746)

RLS conditioned on

Insomnia (N = 690)

Analysis

hit

Gene

Symbol Chr Start BP

a Stop BP


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


63

Insomnia

(N = 1,082)


on RLS (N = 1,082)

RLS

(N = 1,239)

RLS conditioned on


Analysis

hit

Gene

Symbol Chr Start BP

a Stop BP


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).


64

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.


65

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


T, Rank threshold. SNPs below this rank are included in the Fisher’s exact test.


66

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


67

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.


68

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


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


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


69

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




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


70

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


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




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


71

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


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).


72

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


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.


73

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


74

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-



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-



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


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.


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