Running title: ABCC9 and sleep duration
Karla V. Allebrandt et al.
Supplementary Information for
A KATP channel gene effect on sleep duration: from
genome wide association studies to function in Drosophila
Karla V. Karla V. Allebrandt1 PhD, Najaf Amin2 PhD, Bertram Müller-Myhsok3 MD, Tõnu
Esko4 MSc, Maris Teder-Laving4 MSc, Renata V.D.M. Azevedo5 PhD, Caroline Hayward6
PhD, Josine van Mill7 MD, Nicole Vogelzangs7PhD, Edward W. Green5 PhD, Scott A.
Melville8 PhD, Peter Lichtner9 PhD, H.-Erich Wichmann10 PhD, Ben A. Oostra2 PhD, A.
Cecile J.W. Janssens2 DPhil, Harry Campbell11 PhD, James F. Wilson11 DPhil, Andrew A.
Hicks8 PhD, Peter P. Pramstaller8 MD, Zoran Dogas12 PhD, Igor Rudan11,12 PhD, Martha
Merrow13 PhD, Brenda Penninx7 PhD, Charalambos P. Kyriacou5 PhD, Andres Metspalu4
PhD, Cornelia M. van Duijn2,14 PhD, Thomas Meitinger9 MD and Till Roenneberg1* PhD.
1
Institute of Medical Psychology, Ludwig-Maximilians-University, Munich, Germany;
Genetic epidemiology unit, Department of Epidemiology and Clinical Genetics, Erasmus
MC, Rotterdam, The Netherlands;
3
Max-Planck-Institute of Psychiatry, Munich, Germany;
4
Estonian Genome Center and Institute of Molecular and Cell Biology of University of
Tartu, Estonian Biocentre, Tartu, Estonia;
5
Department of Genetics, University of Leicester, Leicester, UK;
6
Medical Research Council, Human Genetics Unit, IGMM, Edinburgh, Scotland;
7
VU University Medical Center Amsterdam, Amsterdam, The Netherlands;
8
Center for Biomedicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy Affiliated Institute of the University of Lübeck, Lübeck, Germany;
9
Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany.
10
Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany - Institute
of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-University,
Munich, Germany, and Klinikum Großhadern, Munich Germany;
11
Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland;
12
Croatian Centre for Global Health, University of Split Medical School, Split, Croatia;
13
Department of Chronobiology, University of Groningen, Haren, The Netherlands;
14
Centre of Medical Systems Biology, Netherlands Genomics Initiative (NGI), the
Netherlands.
2
This file includes:
Supplementary Figures 1 to 11
Supplementary Tables 1 to 9
Supplementary Materials and Methods
Acknowledgments
Authors’ Contributions
Supplementary References
1
Running title: ABCC9 and sleep duration
Karla V. Allebrandt et al.
SUPPLEMENTARY FIGURES AND TABLES
Figure S1. Study design. Number of investigated SNPs, genotyping platforms, and study
sample characteristics are shown by cohort for EGCUT, ERF, KORA F4, KORCULA,
MICROS, NESDA, and ORCADES. Stage-1 and Stage-2 refer to discovery and replication
phases, respectively. Plots refer to the distributions of chronotype (mid-sleep phase on freedays adjusted for the sleep deficit accumulated during the work-week and normalized for age
and sex – MSFsasc) and average sleep duration (SDav). IND = individuals.
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Running title: ABCC9 and sleep duration
Karla V. Allebrandt et al.
Figure S2. Study-specific and meta-analysis quantile-quantile plots. Quantile–quantile plots
and lambda (λ) values were estimated for the primary analysis using the Cochran–Armitage
trend test and for the confirmatory analyzes using logistic regressions and Cochran–Mantel–
Haenszel stratified tests. Shown are expected P-values plotted against observed P-values
resulting from each single study and for the meta-analysis after genomic control correction.
All cohorts showed low over-dispersion of the chi-square statistics with the following λvalues: EGCUT = 1.00, ERF = 0.99, KORA F4 = 1.01, KORCULA = 0.97, MICROS = 0.99,
NESDA = 0.99, and ORKNEY = 1.03. For the overall meta-analysis, the inflation factor (λ)
was 0.997. If λ is large (for example, > 1.2), there is evidence that the observed test statistics
deviate from the expected. This could be due to true associations but is more likely due to a
systematic bias (for example, population stratification effects). Filled circles indicate observed
association above expected (red line).
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Running title: ABCC9 and sleep duration
Karla V. Allebrandt et al.
Figure S3. Manhattan plot of the meta-analysis. SNPs are plotted on the x-axis according to
their position on each chromosome against associations with sleep duration on the y-axis
(shown as –log10 P). The locus associated with sleep duration (P < 5 x 10−8) in the metaanalysis is indicated.
4
Running title: ABCC9 and sleep duration
Karla V. Allebrandt et al.
Figure S4. Top 100 association signals in the meta-analysis (top panel) with their respective
Rsq (a quality measure that indicates how reliable the imputation is for each SNP; bottom
panel) per GWAS. SNPs are order by chromosomal location, as indicated in the rectangles
above the Rsq values. To filter imputed data we combined a minor allele frequency threshold
with either the imputed information score < 0.3 (Proper_Info in IMPUTE; NESDA cohort) or
the variance ratio < 0.3 (rsq_hat in MACH; other cohorts), for details see Supplementary
Table 3. A large variation in imputation quality was observed across platforms mostly for
SNPs on the chromosomes, 5, 7 – 11, 15 and 18; none of which were among the top 10 loci
we report in Table 1. Only 13% of the 100 top association signals came from SNPs genotyped
in at least one of the independent GWAS.
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Running title: ABCC9 and sleep duration
Karla V. Allebrandt et al.
Figure S5. Genomic positions of SNPs within the ABCC9 region (NCBI build 36, UCSC
hg18, chr12:21683593−22083593). The top panel shows SNP single marker association
results (significances as -log10 of nominal P), and all known genes and predicted transcripts in
the local area. The bottom panel shows the LD structure of the same region. Boxes are shaded
(from white to black) according to the disequilibrium coefficient based on r2 (from weak to
perfect LD). The top ranking SNP (rs11046205 – diamond in the top and bottom panels)
presents moderate LD (0.8 > r2 > 0.5) with only two other SNPs (rs12425757, rs1517284; in
green). The second best hit (rs11046211) in this locus has 6 other SNPs in perfect LD with it
(marked in blue in the bottom panel). Recombination rates in centimorgans (cM) per
megabase
(Mb)
are
indicated
at
the
base
of
the
graphic
(blue
line).
6
Running title: ABCC9 and sleep duration
Karla V. Allebrandt et al.
Figure S6. Forest plots for 8 SNPs from the ABCC9 locus that were among the best 100
associations in the meta-analysis. Contributing effects from each study are represented by
squares, with confidence intervals indicated by horizontal lines. The contributing weight of
each study to the meta-analysis is indicated by the size of the squares. The diamond at the
bottom of each graph represents the meta-analysis pooled estimate. Significance is achieved
at the set level if the diamond is clear of the line of no effect (vertical dashed line).
rs11046205 reached GWA significance (P = 3.6 x 10-8). rs11046206, rs11046207,
rs11046209, rs12369421, rs12371172, and rs2307026 are proxies of rs11046211
(P = 9.90 x 10-6) and had very similar P-values (1 x 10-5 to 1.5 x 10-5).
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Karla V. Allebrandt et al.
Figure S7. Forest plots for 8 SNPs that were among the best ranking loci on the metaanalysis. Contributing effects (beta) from each study are represented by squares, with
confidence intervals indicated by horizontal lines. The contributing weight of each study to
the meta-analysis is indicated by the size of the squares. The diamond at the bottom of each
graph represents the meta-analysis pooled estimate. Significance is achieved at the set level if
the diamond is clear of the line of no effect (vertical dashed line). Plots are ordered by
chromosome SNP position. Except for rs2245601 (at the coding region of the cholinergic
receptor delta subunit – CHRND locus), all SNPs showed consistent effect directions in the
discovery phase. None of the P-values were significant at the genome-wide level (for details
see Table 1).
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Running title: ABCC9 and sleep duration
Karla V. Allebrandt et al.
Figure S8. The top panel shows a regional plot of the CLOCK gene (500 kb up- and
downstream of rs12649507, earlier associated with SDav1) for associations with SDav in the
discovery meta-analysis (−log10 P values on the y axis and the SNP genomic position on the
x axis). We could confirm the earlier association of rs12649507 or rs11932595 with SDav
(only had imputed genotypes) in the larger MICROS sample investigated here, but not in
other cohorts. The low-quality imputation in the region also could not support multi-marker
analysis in the present GWAS and might explain why we did not replicate it in the EGCUT
samples – also used in the earlier candidate study.1 We detected in the discovery metaanalysis other modest association signals in SNPs surrounding this locus (see Table 6). SNPs
are coloured according to the disequilibrium coefficient based on r2 (from weak to perfect
LD). The bottom panel shows clock genes showing associations (P < 0.05; within 20, 100, or
500 kb up- and downstream of the genes) with SDav in our discovery phase meta-analysis.
CSNK1A1 is the gene with the best ranking SNPs.
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Running title: ABCC9 and sleep duration
Karla V. Allebrandt et al.
Figure S9. (A) Average of mean day and (B) night sleep amount, over two days, for the UASSur RNAi knockout lines (black bars) and controls (gray bars). Sleep duration of knockout
UAS-Sur lines was clearly reduced in the night phase compared to controls. During the day,
the V6750 insertion control slept significantly more than the V6750 knockdown, indicating
that there is no daytime compensation for loss of nighttime sleep. CS control flies slept on
average longer than any other strain during the night, while HU control flies slept shorter than
any other strain during the day. Error bars are standard errors of the means. Stars represent
that significant differences were found in relation to other genotypes by one-way ANOVA.
10
Circadian period (hours)
Running title: ABCC9 and sleep duration
26
25
Karla V. Allebrandt et al.
elav-gal4/+ [n=32]
V104241(Sur)/elav [n=20]
V6750 (Sur)/elav [n=11]
24
23
22
Figure S10. Free-running periods of the experimental knockdown lines and the GAL4 driver
control. There were no significant differences in free-running period between the
experimental knockdown lines and the GAL4 driver control were observed (F 2,60 = 1.49,
P = 0.23). These periods were very similar to those obtained from repeated analyses for the
domesticated Canton-S and various wild strains.2 Error bars are standard errors of the means.
11
Running title: ABCC9 and sleep duration
Karla V. Allebrandt et al.
Figure S11. Mean waking locomotor activity of experimental and controls flies during the
light (day; panel A) and dark (night; panel B) phases averaged over 2 days. Error bars are
standard errors of the means.
12
Running title: ABCC9 and sleep duration
Karla V. Allebrandt et al.
Table S1. Study sample characteristics. Sample size, age, BMI, and sleep duration means per gender are shown by cohort for EGCUT, ERF, KORA
F4, KORCULA, MICROS, NESDA, ORCADES, totalling 4 251 subjects, and for the EGCUT replication samples.
Sample size
Mean age y. (range)
Mean BMI ± SD
Mean sleep duration ± SD
Data set (Ethnicity)
total
females (males)
females
males
females
males
EGCUT (Estonian)
924
518 (406)
41.0 (18 - 86) 38.2 (18 - 82)
25.6 ± 5.8
25.7 ± 4.6
7.92 ± 1.11
7.83 ± 1.14
ERF (Dutch)
740
397 (343)
46.7 (19 - 75) 50.0 (20 - 75) 25.8 ± 4.69
27.3 ± 4.21
7.57 ± 1.10
7.26 ± 0.95
KORA F4 (German)
548
279 (269)
54.6 (40 – 65) 55.3 (36 – 66) 26.6 ± 0.87
27.8 ± 4.21
7.30 ± 0.87
7.20 ± 0.81
KORCULA (Croatian)
600
383 (217)
55.6 (18 - 75) 57.3 (22 - 76) 27.8 ± 4.37
28.8 ± 3.56
7.34 ± 1.25
7.41 ± 1.14
MICROS (Italian)
693
374 (319)
39.3 (10 - 75) 41.1 (10 - 75) 24.2 ± 4.73
25.6 ± 4.04
8.20 ± 0.83
7.90 ± 1.07
NESDA (Dutch)
540
358 (182)
39.8 (18 - 66) 44.2 (19 - 65) 24.8 ± 5.00 26.0 ± 4.50
7.50 ± 0.90
7.80 ± 1.00
ORCADES (Scottish)
206
116 (90)
49.8 (18 - 73) 50.0 (17 - 71) 26.8 ± 5.13
26.7 ± 3.48
7.54 ± 0.88
7.26 ± 0.88
EGCUT in silico
536
250 (286)
38.9 (18 - 83) 39.6 (18 - 84) 24.2 ± 4.62 25.53 ± 3.93
7.98 ± 1.07
7.89 ± 1.02
5 949
3 006 (2 943)
39.1 (18 - 55) 34.0 (17 - 55)
25.4 ± 5.2
26.0 ± 5.0
7.93 ± 0.89
8.00 ± 1.00
Summer
3 226
1 654 (1 572)
39.1 (18 - 55) 33.2 (17 - 55)
25.2 ± 5.1
25.7 ± 4.4
7.89 ± 0.89
7.89 ± 0.97
Winter
2 645
1 312 (1 333)
38.9 (18 - 55) 34.4 (17 - 55)
25.5 ± 5.2
26.0 ± 4.7
7.96 ± 0.89
7.89 ± 0.97
25.8 ± 5.3
EGCUT de novo total
females
males
Winter early chronotype 1 322
624 (698)
38.7 (19 - 55)
34.3 (17 -55)
26.01 ± 4.6
8.01 ± 0.87
7.94 ± 0.99
Winter late chronotypes 1 323
688 (635)
39.1 (18 - 55)
34.5 (18 -55) 25.36 ± 5.1 26.05 ± 4.8
7.93 ± 0.90
7.80 ± 0.95
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Running title: ABCC9 and sleep duration
Karla V. Allebrandt et al.
Table S2. Pharmacological sleep agents. Drug groups and correspondent ATC codes used as exclusion criteria when selecting phenotyped subjects
for the independent GWA studies a.
a
Drug groups
Pharmacological ATC codes
Benzodiazepines
N05CD, N05CF
Barbiturates
N01AF, N01AG, N03AA, N05CA, N05CB, N05CX
Imipramine
N06AA02, N06AA03, N06AA06
Nortriptyline
N06AA10
Neuroleptics
N05AK
Phenothiazines
N05AB, N05AC, N05AA
Fluoxetine
N06AB03
Sertraline
N06AB06
Paroxetine
N06AB05
ß-Blockers, propranolol
C07, S01ED
Theophylline
R03DA04
Amphetamine
N06B
With exception of the NESDA cohort, that only excluded subjects using benzodiazepines.
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Karla V. Allebrandt et al.
Table S3. Stage-1 genotyping and imputation details across the different platforms used.
Discovery
samples
Genotyping
Platform
Quality control of genotyped
SNPs
HWE
P
Call rate Call rate
SNP
IND
MAF
Genetic
Imputations
software
NCBI
Build
Analysis
software a
Total of SNPs
for imputation
Total of SNPs
in the metaanalysis
% of
SNPs
imputed
2 412 810
87.2
EGCUT
Illumina 370K
10-6
98%
95%
0.01
MACH 1.0.16
36
ProbABEL
310 000
ERF
Illumina 6K, 318K, 370K,
Affymetrix 250K
10-6
95%
95%
0.01
MACH 1.0.16
36
ProbABEL
up to 487 573
KORA
Affymetrix 1000K
10-5
95%
95%
0.01
MACH 1.0.16
36
ProbABEL
688 765
2 442 744
71.8
KORCULA
Illumina 370K
10-10
98%
98%
0.02
MACH 1.0.15
36
ProbABEL
30 7728
2 369 235
87.0
MICROS
Illumina 318K
10-6
98%
98%
0.01
MACH 1.0.16
36
ProbABEL
292 917
2 375 117
87.7
NESDA
Perlegen 600K
-
95%
95%
0.01
IMPUTE 1.0
35
SNPTEST
435 291
2 403 506
81.9
ORCADES
Illumina 318K
10-10
98%
98%
0.02
MACH 1.0.15
36
ProbABEL
306 207
2 402 555
87.3
EGCUT in silico
370CNV-quad
10-6
98%
95%
0.01
IMPUTE 1.0
36
SNPTEST
188 000
2 412 810
92.2
a
2 408720
79.8
For the cohorts ERF and MICROS a linear mixed model 3 in ProbABEL was used to account for familial relationships. The software incorporates the FASTA method4 and kinship
matrix5 estimated from the genotyped SNPs to correct for relatedness. For KORCULA and ORCADES, the first three principal components were used as covariates in the model to
adjust for relationships. All individual studies association results underwent genomic control before meta-analysis. Other cohorts were comprised of unrelated individuals (EGCUT,
KORA, and NESDA). MAF: minor allele frequency. Imputation error can very between platforms generating false negative and false positive findings, but several quality control
criteria as allele frequency, Hardy-Weinberg equilibrium (HWE), effect direction and association signals for correlated SNPs will help to filter out false positive findings.6 We may miss
though several association signals because of low-quality imputation.
15
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Karla V. Allebrandt et al.
Table S4. TaqMan genotyping efficiency and association results for the meta-analysis top hit (rs11046205) in 3 discovery cohorts that only had
imputed genotypes for this SNP.
Study
sample
Imputed allele
freq A
Genotyped allele
freq A
Concordance
Imputed vs.
genotyped
Imputed / genotyped data
Genotyping
call rate (SNP)
Sample N
EGCUT
0.188
0.216
87%
97%
924 / 924b
KORA
0.185
0.185
87%
96%
548 / 527
KORCULA
0.189
0.195 to 0.196
68% a
96%
600 / 584
The genotyped variants were in Hardy-Weinberg equilibrium (P > 0.05) in all cohorts.
a
Beta
P-values
0.1269 /
0.06 /
0.07
0.1076
0.1452 /
0.2034
0.1779 / 0.0387
0.06 /
0.001
Combined P
(meta)
3.68 x 10-8 /
3.99 x 10-8
0.07 / 0.9
KORCULA genotypes concordance maybe lower than for other cohorts because of
relatedness and demographic variation in this cohort. Missing genotypes were imputed. P-values significance threshold was P ≤ 0.05.
b
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Table S5. List of best 100 associations with SDav in our discovery meta-analysis. The file
included SNP rs codes, genomic position, alleles, variant role, gene, effect (beta), P-values,
effect direction across cohorts, and a list of genes with altered expression associated with the
respective SNPs. To be downloaded as a separate excel file.
Table S6. List of associations (P < 0.05) found in 19 clock genes (within 100 KB up- or
downstream of these genes) with SDav in our discovery meta-analysis. The file included SNP rs
codes, alleles, effect (beta), P-values, effect direction across cohorts, and genomic positions. To
be downloaded as a separate excel file.
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Table S7. Independent Sample Mann-Whitney U-test.
Dependent variable: SDasc
N
SDasc  SEM
Mean rank
Sig.
Grouping by season (Summer / Winter)
EGCUT
361 / 563
924
7.84  0.07
456 / 563
0.759
KORA
288 / 260
548
7.64  0.06
262 / 287
0.061
KORCULA
335 / 291
626
7.50  0.06
307 / 321
0.302
MICROS
284 / 379
663
8.18  0.06
323 / 339
0.285
ORCADES
212 / 218
430
7.56  0.07
207 / 224
0.167
EGCUT replication
3 226 / 2 645
5 871
8.10  0.02
2886 / 2997
0.012*
EGCUT in silico
365 / 171
536
7.95  0.06
272 / 260
0.390
Summer 181 / 180
361
7.84  0.12
172 / 189
0.123
Winter 282 / 281
563
7.83  0.90
286 / 277
0.507
Summer 145 / 143
288
7.58  0.05
143 / 146
0.692
Winter 130 / 130
260
7.70  0.05
129 / 231
0.817
Summer 168 / 167
335
7.45  0.06
181 / 155
0.012*
Winter 146 / 145
291
7.56  0.07
153 / 138
0.124
Summer 141 /142
283
8.14  0.05
145 / 138
0.477
Winter 189 / 190
379
8.20  0.04
207 / 172
0.002**
Summer 106 /106
212
7.51  0.06
108 / 105
0.694
Winter 109 / 109
218
7.61  0.05
107 / 112
0.497
Summer 1613 / 1613
3 226
8.08  0.01
1 683 / 1 544
2.1 x 10-5***
Winter 1322 / 1323
2 645
8.12  0.01
1 375 / 1 271
4.9 x 10-4***
Summer 183 / 182
365
7.98  0.05
193 / 172
0.056
Winter 86 / 85
171
7.89  0.79
84 / 88
0.621
Grouping by chronotype (1st early / 2nd late half)
EGCUT
KORA
KORCULA
MICROS
ORCADES
EGCUT replication
EGCUT in silico
The total N of individuals assessed here was dependent of the availability of the dates of the assessments. Some
of the populations had a slightly larger sample for the phenotype analyses than for the GWAS analyses. Data
from ERF and NESDA were not available. Summer: defined as the period of the respective year with day-light
saving time – DST. Winter: defined as the period without DST. SDasc = average sleep duration normalized for
age and sex. SEM = standard error of the mean. Stars represent that significant differences in SDasc were found
between subgroups of the population.
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Table S8. In silico and de novo genotyping replication samples association results versus
discovery phase meta-analysis results for the ABCC9 SNPs.
EGCUT replication
ABCC9 SNPs
(Chr 12)
rs11046205
De novo
genotyping
Summer / Winter
Av sleep duration
rs11046211
rs11046209
De novo
genotyping
Winter Av sleep
duration:
rs11046205
rs11046211
Effect
allele
rs11046209
rs11046205
In silico total
rs11046211
rs11046209
Beta
SEM
P
Discovery
phase meta
Beta (SEM)
A
3 219 /
2 644
- 0.024 /
0.018
0.027 /
0.031
0.386 /
0.561
0.1565
(0.028)
T
3 203/
2 682
-0.015/
0.014
0.036 /
0.038
0.673 /
0.710
0.2001
(0.045)
A
3 209/
2 681
0.009 /
- 0.006
0.036 /
0.039
0.792 /
0.868
-0.1951
(0.045)
A
1 321 /
1323
0.064 /
- 0.018
0.044 /
0.042
0.149 /
0.671
0.1565
(0.028)
T
1 309 /
1 322
0.064 /
- 0.021
0.056 /
0.053
0.255 /
0.695
0.2001
(0.045)
A
1 313 /
1 313
- 0.054 /
0.025
0.055 /
0.054
0.333 /
0.647
-0.1951
(0.045)
A
53
0.047
0.082
0.568
0.1565
(0.028)
T
536
0.055
0.100
0.585
0.2001
(0.045)
A
536
-0.059
0.099
0.551
-0.1951
(0.045)
1st
half (early) /
2nd half (late) of
the chronotype
distribution
N
All SNPs were in HWE, had minor allele frequency > 0.05, and the call rate per individual was > 98%. P-values
significance threshold was P ≤ 0.05. SEM = standard error of the mean. Summer: defined as the period of the
respective year with day-light saving time – DST. Winter: defined as the period without DST. Av sleep duration
= average sleep duration.
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Table S9. Meta-analyzes results for rs11046205 (ABCC9). Meta-analyzes of discovery cohorts,
before and after de novo genotyping, and meta-analysis of the discovery and replication results.
rs11046205 (effect allele A)
Discovery meta
N
beta
beta_95L
beta_95U
P
4 251
0.1669
0.1076
0.2261
3.44 x 10-8
4 251
0.1715
0.1150
0.2280
2.78 x 10-9
4 251
0.1565
0.1007
0.2123
3.99 x 10-8
6 045
0.1234
0.0784
0.1685
7.94 x 10-8
Discovery meta with KORA-EGCUT de
novo genotyped
Discovery meta with KORA-EGCUTKORCULA de novo genotyped
Discovery meta a, in silico and de novo
W_Early replication
a
Including 3 de novo genotyped cohorts (KORA-EGCUT-KORCULA). W_Early = early chonotypes half of the
replication dataset collected during the wintertime.
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SUPPLEMENTARY MATERIALS AND METHODS
Description of the discovery phase study cohorts
1) The EGCUT is a bio-bank consisting of data of 40 000 individuals from a population based
Estonian cohort.7 GWAS was performed on 924 phenotyped subjects (56% females). The
cohort consisted of two sets of samples: i) 480 population based samples which are part of a
bigger study and were selected randomly from all over the country and ii) 444 individuals
selected as tails from the corrected chronotype distribution (assessed with the Munich
ChronoType Questionnaire) of 5 098 individuals (mid-sleep values normalized for age and
gender were 0.55-2.55 and 5.75-9.12). SDav had a continuous and approximated to normal
distribution for this GWA sample.
2) ERF is a family-based study that includes over 3 000 participants descending from 22
couples living in the Rucphen region in the 19th century.8 All living descendants of these
couples and their spouses were invited to take part in the study. 1 700 individuals from this
population were assessed for sleep duration (see phenotyping for details). After strict
phenotype quality control only 940 respondents were available for the analysis, of which 740
individuals (54% females) were used in the genome-wide analysis.9
3) The KORA F4 research project has evolved from the WHO MONICA study (Monitoring
of Trends and Determinants of Cardiovascular Disease). The KORA genome-wide
association study was done using samples from the KORA S4 survey,10 which is a
population-based sample from the general population living in the region of Augsburg,
Southern Germany. Samples were recruited between 1999-2002 independent from KORA S3
but using the same platform with the same standard operating procedures and based on the
same population. KORA F4 is part of this sample (with a 10-year follow-up) and 1 814
subjects were genome-wide genotyped. All participants had a German passport and were of
European origin. Apart from the standard phenotype exclusion criteria, we excluded subjects
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Karla V. Allebrandt et al.
older than 65 years old. In total, 548 (51% females) individuals who passed phenotype and
genotype quality control could be included in the GWA analysis.
4) KORCULA is a family-based, cross-sectional study on the Dalmatian island of Korcula.
600 subjects (64% females) who passed phenotype and genotype quality control were used
for the GWA analysis.11
5) MICROS is a family-based study that is part of the genomic health care program
'GenNova'. It was carried out in three villages of the Val Venosta, South Tyrol (Italy), in
2001-2003.12 740 subjects (54% females) who passed phenotype and genotype quality control
were included in the GWA analysis.
6) The NESDA study consists of 2 981 unrelated persons including both major depressive
disorder cases as well as healthy controls without a history of major depressive disorder.13
540 healthy controls (66% females) without current or prior major depressive disorder, who
passed phenotype and genotype quality control, were used for the GWA analysis.
7) ORCADES is a family-based collection from an isolated Scottish population. Genetic
diversity is decreased compared to the mainland Scottish samples, consistent with a high
extent of endogamy. The current study included 206 individuals (56% females) who passed
phenotype and genotype quality control.
Characterisation of Drosophila knockdown
Males from the w1118 ; V6750; + RNAi line was crossed to w1118 ; +; Actin>GAL4 / TM6B,
Tb1 females that carry a driver active in all tissues, so that in the F1 generation, flies carrying
the TM6B,Tb1 chromosome represented control non-expressing siblings of the knock-down
flies (that carry Actin>GAL4). Total RNA was extracted from three biological replicates of
two males and two females of each genotype using a column based kit (Norgen Biotek Corp).
The qPCR reaction was performed using a Brilliant II 1-step qPCR kit (Agilent Technologies)
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Karla V. Allebrandt et al.
using 50ng of total RNA following the manufacturer’s instructions on a LightCycler 480
instrument (Roche Applied Science). The dSur RNA was amplified with exon spanning
primers (Forward: acgtggacaatccaagtgaac, Reverse: cgagtgtcctccgtcatgt), and RNA levels
were calculated relative to the RP49 housekeeping gene using four technical replicates for
each reaction. Ratios of target/reference gene were calculated using the LightCycler 480
software v1.5 (Roche Applied Science), and ratios for knockdown and wild-type sibs were
compared, giving an estimated gene knockdown of 89% (t = 2.99, df = 4, P = 0.04). These
results suggest that the V6750 hairpin construct provided an extremely efficient reduction of
dSur to very low levels in tissues such as the neuronal cells targeted by the elav>GAL4 driver
used in this study.
Drosophila behavioural assays to estimate circadian period
Circadian period was calculated using CLEAN spectral analysis of free running activity
rhythms14 for each fly, excluding flies that died during the experiment.
ACKNOWLEDGMENTS
We thank all participants from all cohorts for donating their time and DNA. The ERF study
acknowledges the general practitioners and neurologists for their contributions and P. Veraart
for her help in genealogy, J. Vergeer for the supervision of the laboratory work and P.
Snijders for his help in data collection. We are grateful for T. Kantermann contribution on the
quality control of the MCTQ questionnaires collected within the KORA project, and O.
Polasek, I. Kolcic, and L. Zgaga for their contribution to field work in Croatia. We
acknowledge the invaluable contributions of L. Anderson and the research nurses in Orkney,
the administrative team in Edinburgh and the people of Orkney, and the use of the Wellcome
Trust Clinical Research Facility in Edinburgh, where ORCADES DNA extractions were
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Karla V. Allebrandt et al.
performed. We acknowledge EGCUT personnel, especially M. Hass, and M. Nelis and V.
Soo for their contribution on the genotyping of the EGCUT samples performed in the
Estonian Biocentre Genotyping Core Facility. We acknowledge the High Performance
Computing Center of University of Tartu, for space and facilities to conduct part of the
EGCUT analysis.
AUTHORS’ CONTRIBUTIONS
T.R., K.V.A., C.vD., T.M., B.P., A.M., and M.M. designed the study. K.V.A., M.T-L., A.M.,
B.P., J.vM., M.M., I.R., J.F.W., H.C., C.vD., P.P.P., E.W., Z.D., B.A.O., and A.C.J.W.J.
acquired data. M.T-L., K.V.A., T.E., C.H., N.V., H-E.W., J.F.W., B.A.O. and T.M. performed
genotyping. T.E., K.V.A, N.A., B.M-M, C.H., N.V., and S.A.M. analysed the data.
R.V.D.M.A., E.W.G. and C.P.K. conducted the Drosophila behaviour experiments. K.V.A.,
T.R., and C.P.K. wrote the paper. T.R., T.M., A.M, B.P., J.F.W., H.C., M.M., and C.P.K.
obtained funding. K.V.A, B.M-M, C.vD., T.M and T.R. supervised the study. N.A., K.V.A
and B.M-M had full access to all of the data used in the meta-analysis and take responsibility
for the integrity of the data and the accuracy of the data analysis. K.V.A. and T.R. take
responsibility for the accuracy of the analysis of the phenotype used in the independent
GWAS and replication studies. C.P.K. supervised the Drosophila behavioral experiments and
takes responsibility for their accuracy.
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Karla V. Allebrandt et al.
SUPPLEMENTARY REFERENCES
1.
Allebrandt KV, Teder-Laving M, Akyol M, Pichler I, Muller-Myhsok B,
Pramstaller P et al. CLOCK gene variants associate with sleep duration in two
independent populations. Biol Psychiatry 2010; 67(11): 1040-1047.
2.
Sawyer LA, Hennessy JM, Peixoto AA, Rosato E, Parkinson H, Costa R et al.
Natural variation in a Drosophila clock gene and temperature compensation.
Science 1997; 278(5346): 2117-2120.
3.
Aulchenko YS, de Koning DJ, Haley C. Genomewide rapid association using
mixed model and regression: a fast and simple method for genomewide
pedigree-based quantitative trait loci association analysis. Genetics 2007;
177(1): 577-585.
4.
Chen WM, Abecasis GR. Family-based association tests for genomewide
association scans. Am J Hum Genet 2007; 81(5): 913-926.
5.
Amin N, van Duijn CM, Aulchenko YS. A genomic background based method
for association analysis in related individuals. PLoS ONE 2007; 2: e1274.
6.
Lin P, Hartz SM, Zhang Z, Saccone SF, Wang J, Tischfield JA et al. A new
statistic to evaluate imputation reliability. PLoS ONE 2010; 5(3): e9697.
7.
Nelis M, Esko T, Magi R, Zimprich F, Zimprich A, Toncheva D et al. Genetic
structure of Europeans: a view from the North-East. PLoS One 2009; 4(5):
e5472.
8.
Kayser M, Liu F, Janssens AC, Rivadeneira F, Lao O, van Duijn K et al. Three
genome-wide association studies and a linkage analysis identify HERC2 as a
human iris color gene. Am J Hum Genet 2008; 82(2): 411-423.
9.
Aulchenko YS, Heutink P, Mackay I, Bertoli-Avella AM, Pullen J, Vaessen N
et al. Linkage disequilibrium in young genetically isolated Dutch population.
Eur J Hum Genet 2004; 12(7): 527-534.
10.
Wichmann HE, Gieger C, Illig T. KORA-gen--resource for population genetics,
controls and a broad spectrum of disease phenotypes. Gesundheitswesen 2005;
67 Suppl 1: S26-30.
11.
Polasek O, Marusic A, Rotim K, Hayward C, Vitart V, Huffman J et al.
Genome-wide association study of anthropometric traits in Korcula Island,
Croatia. Croat Med J 2009; 50(1): 7-16.
25
Running title: ABCC9 regulates sleep duration
Karla V. Allebrandt et al.
12.
Pattaro C, Marroni F, Riegler A, Mascalzoni D, Pichler I, Volpato CB et al.
The genetic study of three population microisolates in South Tyrol (MICROS):
study design and epidemiological perspectives. BMC Med Genet 2007; 8: 29.
13.
Boomsma DI, Willemsen G, Sullivan PF, Heutink P, Meijer P, Sondervan D et
al. Genome-wide association of major depression: description of samples for
the GAIN Major Depressive Disorder Study: NTR and NESDA biobank
projects. Eur J Hum Genet 2008; 16(3): 335-342.
14.
Rosato E, Kyriacou CP. Analysis of locomotor activity rhythms in Drosophila.
Nat Protoc 2006; 1(2): 559-568.
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