Supplementary information
Genome-wide association study identifies SESTD1 as a novel risk gene for
lithium responsive bipolar disorder
Supplementary Methods
Swedish Quality Register for bipolar disorder (BipoläR)
2
Phenotype definition and assessment
2
Quality control for genotyping
5
Test of Hardy-Weinberg Equilibrium for imputed variant rs116323614
6
Heritability estimation for lithium-responsive bipolar disorder (BD)
6
Supplementary Tables and Figures
Table S1. Subject characteristics for patients with lithium assessment and genotype
8
Table S2. Corresponding number of sample of the objectively and subjectively defined
assessments for lithium response
9
GWAS comparing lithium responding with lithium non-responding BD patients
(Summary statistics in Table S3, quantile-quantile and Manhattan plots in Fig S1, associated
genetic regions in Table S4)
10
GWAS comparing lithium responding BD patients and healthy controls
(Summary statistics in Table S5, quantile-quantile and Manhattan plots in Fig S2, associated
genetic regions in Table S6, region plots of the most associated regions in meta-analysis in
Figure S3)
16
Table S7. Association results of top associated loci from meta-analyses for each sub-sample
23
Table S8. Predicted genotype frequencies for rs116323614 in each sample and tests of HardyWeinberg Equilibrium
24
Table S9. Association results for rs116323614 in male and female samples
25
Table S10. Univariate heritability estimates of lithium-responsive BD
26
References
27
1
Supplemental Methods
Swedish Quality Register for BD (BipoläR)
Swedish Quality Register for BD (BipoläR) contains individualized data on diagnoses (i.e.,
BD type 1, type 2, not otherwise specified, or schizoaffective disorder bipolar type), medical
intervention, and outcomes. It also captures basic clinical epidemiological data as well as
longitudinal data on the natural history and clinical course of the disease. Participation is
voluntary for the clinician as well as for the patients. The diagnoses were made according to
the DSM-IV-TR, but the use of structured interviews varies between participating units,
which include both private and public psychiatric outpatient health care units in Sweden.
Psychiatrists who register patients have often specialized in the treatment of mood disorders
and treatment of BDs in particular. Hence, BipoläR contains much more detailed phenotypic
information than other Swedish national registers and provides good validity and high data
quality. Patients were followed-up annually 2005–2013. Until June 2013 when the data were
extracted, 6429 BD patients were registered in BipoläR with the mean total follow-up time
3.1 years (SD=1.7, range 1-9 years).
Phenotype definition and assessment
Phenotype definition for Swedish sample
Subjective assessment included two branches. Participants from Stanley were interviewed
over the phone by trained nurses using a structured questionnaire. Provided a person had
taken lithium for at least 12 months at any point in life, he or she would be asked about the
therapeutic effect regardless of potential side effects. Responses were categorized into four
groups: 1) “Complete remission. No further episodes, became well” (N=660, 62.1%); 2)
2
“Clearly improved, but continued to suffer from mood episodes, or needed additional
treatment” (N=264, 24.9%); 3) “No or questionable treatment effect” (N=86, 8.1%); 4) “Do
not know or do not want to answer” (N=52, 4.9%).
Patients from S:t Göran were assessed by a psychiatrist using a standardized interview
protocol (the Affective Disorders Evaluation) which was previously used in the Systematic
Treatment Enhancement Program of Bipolar Disorder Program (STEP-BD).1 Response was
also categorized into four groups: 1) “Complete response” (N=77, 33.3%); 2) “Markedly
improved or somewhat improved, but continued to suffer from mood episodes, or needed
additional treatment” (N=20, 8.7%); 3) “No or doubtful treatment effect” (N=13, 5.6%). 4)
“No data or used lithium too short time” (N=121, 52.4%).
By adding an objective assessment lithium response in the Swedish sample, we aimed for a
phenotype definition that would correspond more closely to the UK-BDRN subgroup with
excellent and beneficial response to lithium (group 1 and 2 in the UK samples, see below).
We assessed the effectiveness of lithium in preventing mood episodes by using recurrence
data at yearly longitudinal follow-ups extracted from Swedish Quality Register from May
2004 until June 2013. Subjects who had used lithium for at least one year were included.
Responders were defined as having no mood episodes during follow-up (N=159, 16.9%),
while non-responders were those that had at least one mood episode during follow-up (N=780,
83.1%). The extent to which subjective assessment and objective assessment were in line with
each other is shown in Supplementary Table S2.
Phenotype definition for UK sample
Lithium response information for participants from BDRN was collected by interviews and
reviews of clinical notes and was originally categorized into five groups:
3
1) “Objective evidence for excellent response to lithium prophylaxis” (i.e., frequency of
episodes reduced to <10% of frequency after lithium prophylaxis and/or 2 or more
episodes of illness occurring within weeks of cessation of lithium. This could only be
rated if at least 3 episodes of illness had occurred before lithium prophylaxis and
lithium response had been observed for at least 5 years.) (N=47, 3.0%);
2) “Objective evidence for beneficial response” (i.e., clear reduction in number and/or
severity of episodes following introduction of lithium prophylaxis. This could only be
rated if at least 3 episodes of illness had occurred before lithium prophylaxis and
lithium response had been observed for at least 3 years) (N=117, 7.4%);
3) “Subjective good response” (i.e., self-reported complete or partial remission, but with
an observation period too short to meet objective criteria (<=3 years)) (N=738, 46.8%);
4) “Unsure of response” (i.e., have been on lithium only for a couple of months, or had it
stopped after a brief period due to side effects) (N=603, 38.2%);
5) “No evidence of response to lithium” (i.e., no reduction in number and severity of
episodes following introduction of lithium prophylaxis) (N =73, 4.6%).
Harmonizing the datasets
We treat lithium response as a dichotomous trait based on the subjective and objective
measurements, respectively.
By using subjectively defined lithium response we maximize the sample size. In the Swedish
sample, a total of 1120 subjects had available assessments of lithium response together with
genotyping data that passed quality control. We compared patients who reported complete
remission on lithium (Group 1), N=737, 65.8%) with those who reported partial or no
response (Group 2) and 3), N=383, 34.2%). For the UK subjects, we defined the UK groups
1), 2) and 3) as subjective responders (N=902, 57.2%), and the UK groups 4) and 5) as
4
subjective non-responders to lithium (N=676, 42.8%). This method of categorization is
similar to dichotomous definitions proposed in several previous clinical and genetic papers.2-5
By using objectively defined lithium response, we arrive at a narrower phenotype definition
of lithium response. The definition of objective response in the Swedish sample is given
above. For the UK sample, we categorized the UK groups 1) and 2) as objective responders
(N=164, 10.4% of the total UK sample) and the UK group 5) as objective non-responders
(N=73, 4.6% of the total UK sample). Groups 3) and 4) lack long-term data. With longer
observation, cases in these groups might end up in the response or the non-response group.
We therefore chose to exclude groups 3) and 4) from the objective assessment of UK data.
Quality control for Genotyping
Swedish sample
The quality control exclusionary measures for Swedish subjects were: genotype missingness
rate >5%, ancestry outliers identified via multidimensional scaling (MDS), suspected sample
error or contamination (i.e., subject heterozygosity rate >10%), ambiguous genetic sex, and a
randomly selected member of any pair of subjects identified as related (pi-hat > 0.20).
Exclusionary measures for SNPs were: marked deviations from Hardy-Weinberg equilibrium
(P<1×10-6), SNP missingness rate >5%, minor allele frequency (MAF) <1%, differential
missingness based on affection status (P<1×10-6), and differential missingness based on
haplotype (P<1×10-10).
UK sample
The quality control exclusionary parameters for the BDRN sample were: subject
heterozygosity rate >15%, subject missingness rate >2%, ambiguous genetic sex, SNP
5
missingness rate >2%, MAF<1%, marked departure from Hardy-Weinberg equilibrium
(P<5×10-5), differential missingness for SNPs between cases and controls (P<1×10-3) and
differential missingness based on haplotype (P<1×10-10), population outliers identified via
multidimensional scaling, and a random member of each pair of related subjects (defined as
pi-hat >0.10).
Test of Hardy-Weinberg Equilibrium for imputed variant rs116323614
As the calculation for genotype frequencies is not straightforward in dosage (imputed) data, we
used the method provided in the paper “Approximate and Exact Tests of Hardy-Weinberg
Equilibrium Using Uncertain Genotypes”.6 Regarding potential skewing in genotype
distributions, we performed the exact tests of Hardy-Weinberg Equilibrium (HWE). The
results are shown in Table S8.
Heritability estimation for lithium-responsive BD
We first combined the two datasets (Sweden wave 2 and UK) and excluded variants only
existing in one sample or with ambiguous base pair position and strand. We then used GCTA
version 1.24 to filter for cryptic relatedness between individuals (cutoff value 0.025). A total
of 10 786 individuals and 382 330 SNPs were included in the final dataset.
To test for systematic discrepancy between the genotypes produced by the two microarrays
and validate the combined dataset, we did a benchmark analysis with GCTA to estimate the
heritability of BD. The result showed that the heritability of BD was 0.32 (95% CI 0.28 to
0.36) in this sample, which was similar to a previous estimate (0.25) in a study applying the
GCTA method.7
6
We then estimated the heritability of lithium-responsive BD phenotype with subjectively and
objectively defined lithium response, respectively. We specify the prevalence 0.30 since
previous literatures have reported that full lithium responders are about one-third among
lithium treated patients.8,9 We established statistical significance using the likelihood-ratio test
of specific hypothesis (H0: SNP-heritability = 0) and reported the asymptotic 95% CI
(calculated as 1.96 times the standard error).
7
Table S1. Subject characteristics for patients with lithium assessment and genotype
Sweden (N=1822)*
61.5
Ever taken lithium (%)
Lithium
Lithium nonresponders
responders
Subjective measurement (available lithium assessment and genotype)
Sample size
737
383
38.5
Sex (% male)
38.6
Mean age at sampling
52.1±13.9
48.8±13.3
(Standard deviation)
53.7
Bipolar disorder type I (%)
59.1
Objective measurement (available lithium assessment and genotype)
UK BDRN (N=2577)
61.2
Lithium
Lithium nonresponders
responders
902
33.5
676
26.5
48.9±12.3
47.9±12.0
74.7
68.9
Sample size
159
780
164
73
37.8
28.8
Sex (% male)
39.6
43.2
Mean age at sampling
57.7±11.9
52.2±11.9
49.6±13.7
48.6±12.4
(Standard deviation)
50.3
83.6
Bipolar disorder type I (%)
57.9
80.5
* Swedish sample consists of subjects participating in both the Stanley study and S.t Göran Project
8
Table S2. Corresponding number of sample of the two different assessments for lithium
response
Objective assessment
Responder
Non-responder
NA
Total
Responder
135
324
129
588
Subjective assessment
Non-responder
17
319
2
338
NA
7
137
275
419
Total
159
780
406
1345
The numbers in the table refer to subjects with available lithium assessment and genotype (passed genotyping
quality control).
Abbreviations: NA, not applicable, could not be placed into either category.
9
Table S3. Summary statistics for GWAS comparing lithium responding with lithium
non-responding bipolar disorder patients (Quantile-quantile and Manhattan plots
shown in Fig S1)
Subjective
measurement
Objective
measurement
Responders
Non-responders
λGC
Quantile-quantile plot
Manhattan plot
Responders
Non-responders
λGC
Quantile-quantile plot
Manhattan plot
Swedish sample
Affymetrix 6.0
OmniExpress arrays
(wave 1)
(wave 2)
149
588
45
338
0.97
1.02
A
B
I
II
159
780
1.01
E
V
UK
BDRN
Metaanalysis
902
676
1.00
C
III
164
73
1.00
F
VI
1639
1059
0.99
D
IV
323
853
0.97
G
VII
* λGC: genomic inflation factor, calculated by the median observed χ2 statistic divided by expectation under the
null
*The quantile-quantile and Manhattan plots for all analyses are in Supplementary Figure S1
A
B
10
C
D
E
F
G
11
I
II
III
12
IV
V
VI
13
VII
Fig S1: Quantile-quantile plot for association analyses of lithium responders vs nonresponders; A: Sweden wave 1 subjective assessment, B: Sweden wave 2 subjective
assessment, C: UK-BDRN subjective assessment, D: Meta-analysis for subjective assessment,
E: Sweden wave 2 objective assessment, F: UK-BDRN objective assessment, G: Metaanalysis for objective assessment.
Manhattan plot for association analyses of lithium responders vs non-responders; I: Sweden
wave 1 subjective assessment, II: Sweden wave 2 subjective assessment, III: UK-BDRN
subjective assessment, IV: Meta-analysis for subjective assessment, V: Sweden wave 2
objective assessment, VI: UK-BDRN objective assessment, VII: Meta-analysis for objective
assessment.
14
Table S4. Summary of top loci for each analysis comparing lithium responders vs nonresponders and genes located in these regions
Chr
Index SNP
A1/A2
Freq
Sweden wave 1, subjective assessment
OR
P-values
N
Position
KB
0.80
4.67
5.23×10-6
68
129841278-129886304
45
rs34521094
C/G
4
0.89
rs4858400
A/G
3
0.85
rs13085296
C/T
3
0.90
Sweden wave 2, subjective assessment
rs56177802
T/C
2
0.73
rs10013531
C/A
4
0.53
7.52
4.67
5.41
6.73×10-6
8.86×10-6
1.49×10-5
10
243
120
189959142-189992773
22417440-22580784
166748961-167275775
34
163
527
2.14
1.60
2.03×10-9
2.54×10-6
61
106
190955006-191038244
184452303-184499131
83
47
0.64
1.63
2.60×10-6
44
73588067-73717421
129
rs7185701
A/G
16
0.87
Sweden wave 2, objective assessment
1.99
3.52×10-6
138
6652748-6785191
132
12
17
rs11060299
rs3743991
C/T
T/C
6
rs114221506
G/A
0.88
0.43
1.13×10-6
170
31770265-32625494
855
19
rs141183405
G/A
0.78
0.50
1.44×10-6
27
32747302-32979847
233
13
rs113653486
C/T
0.91
0.40
1.73×10-6
118
50224143-50771235
547
1
rs56207132
C/T
0.85
0.91
2.64×10-6
66
50602495-51512469
910
Genes
ZNF84,ZNF26,TMEM132
D,MIR1244-3
FRG2
MIR4273,FRG2C
ZBBX,WDR49,SERPINI2
C2orf88
FRG2
SAP30BP,RECQL5,MYO1
5B,LOC643008,LOC1001
30933,LLGL2,ITGB4
RBFOX1
NOTCH4, MHC, many
genes
ZNF507,KIR3DP1,KIR2D
L4,DPY19L3
TRIM13, ST13P4,
MIR3613, many genes
OR4F16,OR4F29,LOC100
133331,LOC100132287,F
AF1,ELAVL4,DMRTA2,C
DKN2C
UK-BDRN, subjective assessment
20
rs28691794
C/T
0.90
0.49
2.11×10-6
65
61150190-61213367
63
2
3
rs10856800
rs150265641
C/G
G/T
0.47
0.94
1.43
2.12
2.32×10-6
2.34×10-6
49
112
20716754-20752681
149155106-149252704
36
98
19
rs8113341
A/G
0.20
0.66
2.60×10-6
88
10125941-10193325
67
UK-BDRN, objective assessment
rs1956691
C/T
14
0.83
rs11620153
A/G
13
0.47
3.97
0.35
2.21×10-6
2.89×10-6
240
175
58171917-58448472
66592665-66911823
277
319
MIR133A2,MIR11,C20orf200,C20orf166
No genes
WWTR1,TM4SF4
RDH8,KIR3DP1,KIR2DL4
,COL5A3,C3P1,C19orf66,
ANGPTL6
No genes
PCDH9
SPATA12,MIR4273,IL17R
rs13095395
C/T
3.44 3.76×10-6 129
56965656-57261340
296 D,HESX1,FRG2C,ARHGE
3
0.81
F3,APPL1
-6
rs1392230
G/T
3.44
5.60×10
117
118411464-118585110
174
No genes
3
0.81
We used LD clumping to aggregate association findings into genomic regions. Position=hg19 coordinates. Genes in these
regions or the 20-kb flanking regions were identified using gene tracks from the UCSC Genome Browser.
Abbreviations: Chr, chromosome; Index SNP, the single-nucleotide polymorphism with the strongest association in the
genomic region; A1/A2, reference and alternate alleles; Freq, frequency of reference alleles; OR, odds ratio; N, number of
SNPs in the reported region; MHC, major histocompatibility complex.
15
Table S5. Summary statistics for GWAS comparing lithium responders with controls
(Quantile-quantile and Manhattan plots shown in Fig S2)
Subjective
measurement
Objective
measurement
Responders
Controls
λGC
Quantile-quantile plot
Manhattan plot
Responders
Controls
λGC
Quantile-quantile plot
Manhattan plot
Swedish sample
Affymetrix 6.0
OmniExpress arrays
(wave 1)
(wave 2)
149
588
2215
1271
1.01
1.04
A
B
I
II
159
1271
1.01
E
V
Cardiff
sample
Metaanalysis
902
5413
1.04
C
III
164
5413
1.01
F
VI
1565
8899
1.05
D
IV
323
6684
1.01
G
VII
* λGC: genomic inflation factor, calculated by the median observed χ2 statistic divided by expectation under the
null
*The quantile-quantile and Manhattan plots for all analyses are in Supplementary Figure S2
A
B
16
C
D
E
F
G
17
I
II
III
18
IV
V
VI
19
VII
Fig S2: Quantile-quantile plot for association analyses of lithium responders vs controls; A:
Sweden wave 1 subjective assessment, B: Sweden wave 2 subjective assessment, C: UKBDRN subjective assessment, D: Meta-analysis for subjective assessment, E: Sweden wave 2
objective assessment, F: UK-BDRN objective assessment, G: Meta-analysis for objective
assessment
Manhattan plot for association analyses of lithium responders vs controls; I: Sweden wave 1
subjective assessment, II: Sweden wave 2 subjective assessment, III: UK-BDRN subjective
assessment, IV: Meta-analysis for subjective assessment, V: Sweden wave 2 objective
assessment, VI: UK-BDRN objective assessment, VII: Meta-analysis for objective assessment
20
Table S6. Summary of top loci for each analysis comparing lithium responders vs controls
and genes located in these regions
Chr
Index SNP
A1/A2 Freq
Sweden wave 1, subjective assessment
OR
P-value
N
Position
KB
Genes
No genes
ZNF84,ZNF26,MIR1
244-3
MDH1B,LOC200726
,DYTN
9
rs10979017
C/G
0.99
0.20
1.08×10-8
4
110461462-110497099
36
12
rs146499272
C/T
0.98
0.17
2.71×10-7
38
84520335-85027937
508
2
rs115920983
C/A
0.99
0.15
4.73×10-7
4
207525730-207593030
67
13
rs9542739
T/C
0.12
2.23
5.48×10-7
90
71993107-72366052
37
3
DACH1
Sweden wave 2, subjective assessment
18
rs1442378
T/C
0.33
1.55
5.19×10-8
45
4050546-4071783
21
7
rs6466030
T/C
0.62
0.68
4.04×10-7
388
104557060-105064593
507
rs4887200
G/C
15
0.95
rs2091672
A/T
2
0.30
Sweden wave 2, objective assessment
0.41
0.66
7.57×10-7
7.77×10-8
13
196
88180809-88537816
140567355-140782556
357
215
3
rs73186618
C/T
0.99
0.12
4.93×10-7
27
19791860-20004093
212
11
rs386419745
-/AC
0.94
0.26
7.40×10-7
29
107643375-107797271
154
12
rs187180438
G/A
0.99
0.07
9.32×10-7
2
121791447-121909328
118
rs71455013
T/A
11
0.84
UK-BDRN, subjective assessment
0.42
1.26×10-6
30
22795964-22810983
15
16
rs141589271
A/C
0.96
2.93
1.36×10-6
1
81171896-81171896
0
3
rs3936575
A/G
0.24
0.74
1.41×10-6
136
21644870-21783136
138
51
18095574-18479387
384
23
7477518-7819352
342
7
rs201537822
T/-
0.96
0.55
1.56×10-6
7
rs193121099
C/T
0.99
0.39
2.11×10-6
DLGAP1
SRPK2,MLL5,
LOC723809,LOC100
216545,LHFPL3
NTRK3
No genes
RAB5A,MIR4273,
FRG2C,EFHB,
C3orf48
SLC35F2,RAB39
ZNF84,ZNF26,RNF3
4,MIR12443,KDM2B,ANAPC5
GAS2
PKD1L2
ZNF385D,MIR4273,
FRG2C
No genes
RPA3,MIOS,LOC729
852,COL28A1
UK-BDRN, objective assessment
TEX12,PTS,
C11orf34,BCO2
XIRP1,WDR48,TTC2
rs142153631
C/A
1.35×10-7
222
38809692-39545741
736
3
0.99 0.20
1A, many genes
UQCR11,TCF3,
PLK5P,MEX3D,
rs77866734
C/T
1.39×10-7
13
1528365-1642221
114
19
0.98 0.23
MBD3,KIR3DP1,KIR
2DL4,ADAMTSL5
PSTK,LOC399815,
rs28498397
T/C
2.21×10-7
228 124304753-124872079 567
IKZF5,FLJ46361,
10
0.98 0.24
many genes
We used LD clumping to aggregate association findings into genomic regions. Position=hg19 coordinates. Genes in these
11
rs146727601
TA/-
0.99
0.22
1.22×10-7
19
112060319-112384063
324
regions or the 20-kb flanking regions were identified using gene tracks from the UCSC Genome Browser.
Abbreviations: Chr, chromosome; Index SNP, the single-nucleotide polymorphism with the strongest association in the
genomic region; A1/A2, reference and alternate alleles; Freq, frequency of reference alleles; OR, odds ratio; N, number of
SNPs in the reported region; MHC, major histocompatibility complex.
21
a
b
Figure S3. Region plots of the most associated region in meta-analysis results comparing
lithium responders vs controls. SNPs are represented from genome build hg19/1000 Genomes
Nov 2014 EUR. The purple diamond marks the most highly associated SNPs. (a)
rs116323614 (p=2.74 x 10-8; OR=3.14). (b) rs146727601 (p=1.33 x 10-8; OR=3.98).
22
Table S7. Association results of top associated loci from meta-analyses for each sub-sample
Index SNP
A1/A2 Sample
Freq
Responders vs non-responders, subjective assessments
rs73918339
T/C
Sweden wave 1
0.92
Sweden wave 2
0.91
UK BDRN
0.90
rs7240206
C/G
Sweden wave 1
0.11
Sweden wave 2
0.09
UK BDRN
0.09
rs116927879
G/A
Sweden wave 1
0.89
Sweden wave 2
0.86
UK BDRN
0.84
rs78295376
T/C
Sweden wave 1
0.87
Sweden wave 2
0.90
UK BDRN
0.91
Responders vs non-responders, objective assessments
rs438475
G/A
Sweden wave 2
0.88
UK BDRN
0.87
rs113262272
A/Sweden wave 2
0.71
UK BDRN
0.71
rs809
C/T
Sweden wave 2
0.54
UK BDRN
0.48
rs181812561
G/A
Sweden wave 2
0.98
UK BDRN
0.98
Responders vs controls, subjective assessments
rs12144699
G/A
Sweden wave 1
0.96
Sweden wave 2
0.95
UK BDRN
0.96
rs9834970
T/C
Sweden wave 1
0.50
Sweden wave 2
0.50
UK BDRN
0.51
rs12493050
G/A
Sweden wave 1
0.20
Sweden wave 2
0.20
UK BDRN
0.20
rs4947962
G/C
Sweden wave 1
0.11
Sweden wave 2
0.11
UK BDRN
0.11
Responders vs controls, objective assessments
rs146727601
-/TA
Sweden wave 2
0.01
UK
0.02
rs116323614
A/G
Sweden wave 2
0.02
UK
0.03
rs77866734
C/T
Sweden wave 2
0.99
UK
0.98
rs142643109
T/G
Sweden wave 2
0.99
UK
0.98
INFO
OR
95% CI
P-value
0.72
0.93
0.76
0.83
0.91
0.91
0.90
0.82
0.86
0.74
0.86
0.78
0.72
0.66
0.49
1.48
0.56
0.57
1.73
1.77
1.41
0.73
0.61
0.52
0.24-2.18
0.46-0.96
0.37-0.66
0.61-3.56
0.40-0.78
0.44-0.74
0.81-3.70
1.32-2.38
1.14-1.74
0.30-1.77
0.42-0.88
0.39-0.71
0.56
0.03
2.11×10-6
0.39
6.70×10-4
2.54×10-5
0.16
1.36×10-4
0.001
0.49
0.008
2.21×10-5
0.99
0.96
0.82
0.73
0.98
0.99
0.66
0.64
0.43
0.73
1.89
2.01
0.56
0.73
0.13
0.06
0.31-0.60
0.39-1.37
1.36-2.64
1.21-3.35
0.44-0.72
0.49-1.09
0.05-0.33
0.00-4.29
1.13×10-6
0.33
1.63×10-4
0.007
7.64×10-6
0.13
1.43×10-5
0.20
0.77
0.75
0.71
0.97
1.00
1.00
1.00
0.95
1.01
0.94
0.97
0.94
0.56
0.55
0.62
0.68
0.83
0.84
1.22
1.25
1.28
1.35
1.54
1.27
0.33-0.97
0.37-0.82
0.48-0.81
0.53-0.87
0.71-0.96
0.76-0.93
0.92-1.61
1.04-1.50
1.14-1.44
0.95-1.91
1.23-1.94
1.08-1.48
0.04
0.003
4.66×10-4
0.002
0.01
9.24×10-4
0.17
0.02
3.72×10-5
0.09
2.19×10-4
0.003
0.74
0.82
0.86
0.79
0.90
0.64
0.74
0.75
2.84
4.53
2.84
3.30
0.51
0.23
0.25
0.29
1.15-7.14
2.56-7.69
1.41-5.88
2.00-5.26
0.18-1.43
0.13-0.39
0.09-0.68
0.16-0.51
0.02
1.22×10-7
0.004
1.97×10-6
0.20
1.39×10-7
0.006
1.85×10-5
Abbreviations: Index SNP, the single-nucleotide polymorphism with the strongest association in each meta-analysis; A1/A2,
reference and alternate alleles; Freq, frequency of reference alleles; INFO, imputation info score; OR, odds ratio; CI,
confidence interval.
23
Table S8. Predicted genotype frequencies for rs116323614 in each sample and tests of HardyWeinberg Equilibrium
Sample
N
Sweden wave 1
Sweden wave 2
UK
3141
3755
8035
Number of subjects by imputed genotype
AA(%)
AG(%)
GG(%)
2 (0.0007)
132 (0.04) 3007 (0.96)
2 (0.0006)
167 (0.04) 3586 (0.95)
6 (0.0007)
441 (0.05) 7588 (0.94)
Exact Tests of HardyWeinberg Equilibrium
P=0.45
P=0.84
P=0.40
Method for calculation was from “Approximate and Exact Tests of Hardy-Weinberg Equilibrium Using Uncertain
Genotypes”.6
24
Table S9. Association results for rs116323614 in male and female samples
Ref
allele
Freq
OR
SE
95% CI
P-value
Test for
difference
between
ORs
Sample
Sex
No of
responder vs.
control
Sweden
wave 2
Male
Female
63:632
96:639
0.02
0.02
2.16
3.53
0.58
0.48
0.69-6.73
1.38-9.04
0.19
0.008
P=0.51
UK
Male
Female
71:2805
93:2608
0.03
0.03
3.00
3.43
0.42
0.31
1.32-6.83
1.87-6.30
0.009
8.63×10-5
P=0.80
Male
MetaFemale
analysis
All*
134:3437
189:3247
323:6684
0.03
0.03
0.03
2.67
3.46
3.10
0.34
0.26
0.21
1.37-5.22
2.07-5.78
2.07-4.64
0.004
2. 19×10-6
3.93×10-8
P=0.55
* Association analysis with adjustment for sex.
Abbreviations: Ref allele Freq, frequency of reference alleles; OR, odds ratio; SE, standard error; 95% CI, 95% confidence
interval.
25
Table S10. Univariate heritability estimates of lithium-responsive BD
Phenotype
Cases/Controls*
Heritability (h2)*
95%CI
P-value*
BD
3824 / 6247
32%
28 to 36
<0.01
Subjective definition
1430 / 6247
29%
23 to 36
<0.01
Objective definition
307 / 6247
25%
0 to 51
0.03
Lithium-responsive BD
* Numbers of cases and controls are after excluding one of each pair of individual with cryptic relatedness using GCTA; h 2 is
SNP-heritability on the liability scale; P-values are from likelihood tests of null hypothesis of heritability being 0.
Abbreviations: BD, bipolar disorder; CI, confidence interval
26
References
1.
Sachs GS, Thase ME, Otto MW, Bauer M, Miklowitz D, Wisniewski SR et al. Rationale,
design, and methods of the systematic treatment enhancement program for bipolar disorder
(STEP-BD). Biological psychiatry 2003; 53(11): 1028-1042.
2.
Garnham J, Munro A, Slaney C, Macdougall M, Passmore M, Duffy A et al. Prophylactic
treatment response in bipolar disorder: results of a naturalistic observation study. Journal of
affective disorders 2007; 104(1-3): 185-190.
3.
Squassina A, Manchia M, Borg J, Congiu D, Costa M, Georgitsi M et al. Evidence for
association of an ACCN1 gene variant with response to lithium treatment in Sardinian patients
with bipolar disorder. Pharmacogenomics 2011; 12(11): 1559-1569.
4.
Grof P, Duffy A, Cavazzoni P, Grof E, Garnham J, MacDougall M et al. Is response to
prophylactic lithium a familial trait? The Journal of clinical psychiatry 2002; 63(10): 942-947.
5.
Squassina A, Manchia M, Congiu D, Severino G, Chillotti C, Ardau R et al. The
diacylglycerol kinase eta gene and bipolar disorder: a replication study in a Sardinian sample.
Molecular psychiatry 2009; 14(4): 350-351.
6.
Shriner D. Approximate and exact tests of Hardy-Weinberg equilibrium using uncertain
genotypes. Genetic epidemiology 2011; 35(7): 632-637.
7.
Cross-Disorder Group of the Psychiatric Genomics Consortium, Lee SH, Ripke S, Neale BM,
Faraone SV, Purcell SM et al. Genetic relationship between five psychiatric disorders
estimated from genome-wide SNPs. Nature genetics 2013; 45(9): 984-994.
8.
Rybakowski JK. Lithium in neuropsychiatry: A 2010 update. The World Journal of Biological
Psychiatry 2011; 12(5): 340-348.
9.
Garnham J, Munro A, Slaney C, MacDougall M, Passmore M, Duffy A et al. Prophylactic
treatment response in bipolar disorder: Results of a naturalistic observation study. Journal of
affective disorders; 104(1): 185-190.
27