Mol Biol Rep DOI 10.1007/s11033-014-3770-9

Clock gene variants differentiate mood disorders Monika Paulina Dmitrzak-Weglarz • Joanna Maria Pawlak • Malgorzata Maciukiewicz • Jerzy Moczko • Monika Wilkosc • Anna Leszczynska-Rodziewicz • Dorota Zaremba • Joanna Hauser

Received: 22 May 2013 / Accepted: 20 September 2014  Springer Science+Business Media Dordrecht 2014

Abstract Genetic variations in clock-related genes were hypothesized to be involved to in the susceptibility of mood disorders MD (both unipolar (UPD) and bipolar (BPD) disorders). In our work we investigated role of gene variants form four core period proteins: CLOCK, ARNTL, TIM and PER3. The total sample comprised from 744 mood disorders inpatients (UPD = 229, BPD = 515) and 635 healthy voluntary controls. The 42 SNPs from four genes of interest were genotyped. We used single polymorphisms, haplotypes, SNPs interactions and prediction analysis using classical statistical and machine learning methods. We observed association between two polymorphisms of CLOCK (rs1801260 and rs11932595) with BPDII and two polymorphisms of TIM (rs2291739, rs11171856) with UPD. We also detected ARNTL haplotype variant (rs1160996C/rs11022779G/rs1122780T) to be associated with increased risk of MD, BPD (both types). We established significant epistatic interaction between PER3 (rs2172563) and ARNTL (rs4146388 and rs7107287) in case of BPD. Additionally relation between PER3

(rs2172563) and CLOCK (rs1268271 and rs3805148) appeared in case of UPD. Classification and Regression Trees (C and RT) showed significant predictive value for 10 polymorphisms in all analyzed genes. However we failed to obtain model with sufficient predictive power. During analyses of sleep disturbances sample, we found carriers of homozygote variants (ARNTL: rs11022778 TT, rs1562438 TT, rs1982350 AA and PER3: rs836755 CC) showing more frequent falling asleep difficulties when compare to other genotypes carriers. Our study suggested a putative role of the CLOCK, TIM, ARNTL and PER3 and polymorphisms in MD susceptibility. In our analyses we showed association of specific gene variants with particular types of MD. We also confirmed necessity of performing separate analyzes for BPD and UPD patients. Comprehensive statistical approach is required even with individual symptoms analyses.

M. P. Dmitrzak-Weglarz  J. M. Pawlak  M. Maciukiewicz  A. Leszczynska-Rodziewicz  D. Zaremba  J. Hauser Psychiatric Genetics Unit, Department of Psychiatry, University of Medical Sciences, Poznan, Poland

Introduction

M. P. Dmitrzak-Weglarz (&) Szpitalna St. 27/33, 60-572 Poznan, Poland e-mail: [email protected] J. Moczko Department of Computer Sciences and Statistics, Poznan University of Medical Sciences, Poznan, Poland M. Wilkosc Department of Individual Differences, Institute of Psychology, University of Bydgoszcz, Bydgoszcz, Poland

Keywords Mood disorder  Circadian rhythm  Sleep disturbances  Clock genes  Machine learning methods

Mood disorders (MD), also called affective disorders, constitutes endogenous group of disorders characterized by periodic disturbances of mood, emotion and activity. Disorders mentioned can be manifested by the presence of depressive, hypomanic, manic and mixed states. Such broad range of symptoms means MD include all types of depression and bipolar disorder [1]. As the result MD prevalence equals 20–48 % of the general population [2, 3]. Thus, practice procedures may be optimized by identification of genetic biomarkers facilitated both diagnosis, choice of treatment and response prediction [4].

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Mol Biol Rep Table 1 Sample characteristics CG

MD

BPD

BPDI

BPDII

UPD

N

635

744

515

393

122

229

Age

38.61 ± 12.78

45.05 ± 13.97

44.83 ± 13.79

44.83 ± 14.00

46.01 ± 13.06

45.60 ± 14.33

Gender [female:male]

326:309

472:272

295:220

208:185

87:35

177:52

Average age at onset

–

39.64 ± 23.94

32.19 ± 15.33

30.38 ± 12.82

38.04 ± 20.51

56.39 ± 30.51

Average duration of illness

–

13.25 ± 10.75

14.52 ± 10.99

15.11 ± 11.32

12.55 ± 9.66

9.16 ± 8.73

Family history of mental illness

–

320 (43.01)

251 (48.74)

189 (48.09)

62 (50.82)

69 (30.13)

Decreased need for sleep

–

456 (61.30)

450 (87.38)

356 (90.58)

94 (77.05)

7 (3.06)

Difficulty in falling asleep

–

510 (68.55)

381 (73.98)

285 (72.52)

96 (78.69)

129 (56.33)

Wake up at night

–

481 (64.65)

342 (66.41)

249 (63.36)

93 (76.23)

139 (60.70)

Wake up early

–

508 (68.28)

367 (71.26)

263 (66.92)

104 (85.24)

141 (61.57)

Somnolence

–

174 (23.39)

145 (28.15)

118 (30.02)

27 (22.13)

29 (12.66)

% in the brackets

Primary role of the circadian system in the development of mood disorders is suggested by essential symptoms observed in affective disorders patients (depressed/elevated mood, decreased/increased activity, fear/irritability as well as increased/decreased need for sleep). Moreover fluctuations in mood disorders depth, when typical depressed mood in the morning and his partial evening recovery is seen, is associated with circadian rhythm [5]. Several types of chronotherapeutics interventions (e.g., phototherapy in seasonal depression) and attempts to use melatonin proved to be successful in some patients with MD [6–8]. All of findings presented above suggest presence of both bipolar and unipolar disorder patients group, for whom disease is associated with impaired functioning of the circadian system. The endogenous circadian pacemaker is located in the suprachiasmatic nucleus (SCN) of the hypothalamus [9]. It coordinates physiological activity (e.g., sleep and wakefulness, thermoregulation, and glucose homeostasis and fat metabolism) in the light–dark cycle. It is also involved in number of non-photic behaviors such as physical activities, social interactions and food intake. On cellular level the molecular clock consists of heterodimer between clock homolog protein (CLOCK) and aryl hydrocarbon receptor nuclear translocator-like protein (ARNTL/BMAL1) in mammals and in humans. Complex, by binding to E-box elements in the promoter of three period (PER3) and two cryptochrome (CRY) genes, activates their transcription. A number of other genes, such as nuclear receptor subfamily 1, group D, member 1 (NR1D1), RAR-related orphan receptor A (RORA), and timeless homolog (Drosophila) (TIMELESS), are involved in the feedback loops [10, 11]. Previous studies tested several variants of genes controlling the circadian system for their association with mood disorder [4, 12–19]. However, clear conclusions are difficult to make and results require replications in other

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samples and populations. The aim of the study was to systematically investigate the role of the four core period proteins: CLOCK, ARNTL, TIM and PER3 gene variants (complex analysis of single polymorphisms and haplotypes as well as SNPs interactions). Our data came from Polish patients sample diagnosed with mood disorders. We applied classical statistical and machine learning methods. The results were correlated with clinical subtypes of mood disorders (according to DSM-IV criteria) and interviewed sleep disturbances.

Methods Participants Demographic and clinical information about the group studied is shown in Table 1. For our study we included 744 inpatients (472 female, 272 male), aged 18–84 (mean = 45, SD ± 14) meeting DSM-IV criteria for bipolar disorder (393 BPDI, 122 BPDII) and unipolar disorder: recurrent and first episode of major depression (229 UPD) living in Wielkopolska region of Poland. The lifetime diagnosis was established using SCID-I (Structured Clinical Interview for Axis I clinical disorders for DSM-IV). Family history, for the first and second degree relatives, of bipolar spectrum disorders (including bipolar disorder, schizoaffective disorder and/or major depression) was established in 48 % of patients with BPDI and in 50 % of patients with BPDII and 30 % in UPD. According to the Operational Criteria Checklist (OPCRIT) we established patients with the sleep disturbances [20]. Decreased need for sleep was found in 456 patients, while somnolence appeared for 174 patients. Among all patients 510 had a difficulty in falling asleep, 481 wake up at night and 508 patients declared wake up

Mol Biol Rep

early. Recruited patients were treated in: the Department of Psychiatry, University of Medical Sciences in Poznan, in the Department of Psychiatry, University School of Medicine in Bydgoszcz and in the Psychiatric Hospital in Koscian. The control group (CG) consisted of 635 healthy subjects (326 female, 309 male), aged 18–83 years (mean = 39 years, SD ± 13), from Wielkopolska. We recruited controls from blood donors, students and other volunteers with no history of any psychiatric disorder, substance abuse or serious somatic illnesses. In case of 463 voluntaries such information was obtained on the basis of a statement without psychiatric screening. 172 subjects were assessed using the Polish version of M.I.N.I screen (Mini International Neuropsychiatric Interview) for excluding presence of any serious mental health problems [21]. The study was approved by the Ethics Committee, University of Medical Sciences in Poznan. All study participants were Caucasians of Polish origin and gave the written informed consent. SNP selection and genotyping The DNA was extracted from 10 ml of EDTA anticoagulated whole blood using the salting out method [22]. The SNP selecting included the following criteria: (1) functionality (in experimental studies), (2) high frequency (MAF [ 0.1), (3) indication as tag SNP in Haploview v 4.2 [23] according to HapMap database (Genome Browser release#24 (Phase 1 and 2—full dataset) for Caucasian population or (4) previously reported associations for psychiatric disorders (both positive and negative findings). SNPs chosen include both coding regions of known functionality as well as non-coding regions (introns, UTRs) possibly affecting gene regulation. The polymorphisms of CLOCK, ARNTL, TIM, PER3 genes were genotyped using TaqMan SNP Genotyping assays (Applied Biosystems) and TaqMan Genotyping Master Mix. The list of SNPs analyzed in the ID numbers of TaqMan assays is presented in Table 2. All the assays were validated and predesigned, except for six polymorphisms (rs1554483, rs895682, rs10864315, rs4908694, rs2172563, rs2640909, rs10462021) for which custom assays were designed. The assay for rs1554483 failed to pass the functional test. The amplification for TaqMan SNP genotyping assay plates was done in ABI PRISM 7900HT Sequence Detection System. Data acquisition and analysis was performed using the allelic discrimination analysis module in SDS v2.1 software (Applied Biosystems). For each reaction plate, genomic control DNA samples and non-template controls (water) were included. The control TaqMan SNP genotyping assay was also performed (10 % of randomly chosen samples from both groups) to check for genotyping accuracy. It also enabled to identify identical genotypes in all repeated

samples. The genotyping was performed without knowing the clinical status of the subject and its success rates amounted 94–99 %. Genotyping error rates for all the polymorphisms were \1 %. Statistical analysis The statistical analyses were performed with licensed statistical package STATISTICA v. 10.0, and packages such as Haploview v. 4.2 [23], and QUANTO v. 1.2.4 [24] as well as R statistical software environment [25] and appropriate package SNPassoc [26] where necessary. Nonparametric Evaluation of Quantitative and Qualitative Traits in Population-Based Association Studies with the Log-Additive Genetic Model for complex disease was used [27, 28].

Results HWE analysis Genotype distributions for all studied polymorphisms were in concordance with Hardy–Weinberg equilibrium, except for rs4757142 from ARNTL gene. Due to significant deviation from HWE this polymorphism was eliminated from further analysis (p \ 0.001) (Table 2). Case-control study For computations we used SNPassoc package, tool designed for genetic association analyses. Results are presented in Table 3. In the molecular analysis of 41 polymorphisms from four candidate genes, the significant associations with MD risk and MD subgroups were found as follows: • •

•

polymorphisms rs2291739 and rs11171856 of TIM gene were associated with MD risk SNPs rs1801260 and rs11932595 of CLOCK gene and rs11171856 and rs2279665 of TIM gene were associated with BPDII risk polymorphisms rs2291739 and rs11171856 of TIM gene have demonstrated association with UPD.

Significantly associated polymorphisms mentioned above were connected with a slightly increased risk of disease (OR ratio in the range from 1.03 to 1.48). The possibly protective effect has been demonstrated in the case of ARNTL gene polymorphisms (rs3816360, rs3789327, rs11600996, rs11022780) and PER3 gene rs2640909 polymorphism (OR ratio in the range from 0.67 to 0.84).

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Mol Biol Rep Table 2 Description of analyzed polymorphisms Gene

SNP ID

Chromosomal position

Custom name

Alleles

MAF

HWE

TaqMan assay ID

Function

CLOCK

rs1801260

55996126

3111C/T

T:C

68.1

0.619

C___8746719_20

30 UTR

rs3805148

56001567

-

A:C

64.4

0.590

C__27519005_10

Intron

rs6849474

56013219

-

G:A

66.0

0.539

C__11821338_10

Intron

ARNTL

TIM

PER3

rs1554483*

56016574

-

C:G

-

-

Custom assay

Intron

rs11932595

56018354

-

A:G

56.9

0.955

C____296556_10

Intron

rs12648271

56062879

-

G:C

69.7

0.519

C____251897_10

Intron

rs6850524

56076754

-

G:C

62.8

0.772

C__11821294_10

Intron

rs12649507

56380484

-

A:G

67.0

0.759

C___1836992_10

Intron

rs4340844

56023613

-

A:C

66.2

0.021

C__31137420_10

Intron

rs534654

55984977

-

A:G

84.0

0.615

C____769781_10

Intron

rs1481892 rs4146388

13258497 13263181

-

G:C C:T

72.3 76.3

0.838 0.365

C___1870638_10 C___1870648_10

Intron Intron

rs10766075

13275163

C:T

72.3

0.685

C___1870671_10

Intron

rs4757142**

13282271

A:G

67.3

0.000

C___1870681_10

Intron

rs7396943

13285555

G:C

59.0

0.867

C___1870682_10

Intron

rs11824092

13302870

C:T

60.0

0.498

C___2160476_10

Intron

rs7947951

13312606

G:A

64.5

0.551

C___2160488_10

Intron

rs7937060

13319391

T:C

58.4

0.067

C__29136982_10

Intron

rs1562438

13320776

G:A

69.1

0.252

C___2160492_10

Intron

rs3816360

13324326

G:A

64.9

0.406

C__25813227_10

Intron

rs7126303

13327111

T:C

57.6

0.086

C___2160497_10

Intron

rs3789327

13341892

T:C

56.6

0.271

C___2160503_20

Intron

rs11022778

13347436

T:G

71.4

0.507

C__31248681_10

Intron

rs11600996

13352742

T:C

57.0

0.407

C___2160507_10

Intron

rs11022779

13353386

G:A

82.4

0.011

C___2160509_10

Intron

rs11022780

13353485

T:C

57.1

0.439

C___2160510_10

Intron

rs7107287 rs1982350

13269545 13306707

G:T A:G

74.9 58.6

1.000 0.503

C___1870658_10 C___2160480_10

Intron Intron

rs895682***

13301808

A:G

-

-

Custom assay

Intron

rs2291739

55100920

Pro1018Leu

T:C

58.7

0.001

C__15966257_10

Exon 25

rs2291738

55101548

-

A:G

51.1

0.082

C___3134217_1_

Intron

rs7302060

55115359

-

T:C

58.3

0.065

C___2690225_10

Intron

rs10876890

55120018

-

A:T

53.0

0.039

C___2690213_10

Intron

rs11171856

55128086

-

C:T

50.7

0.012

C__31820742_10

Intron

rs2279665

55113961

Leu38Leu

C:G

50.2

0.116

C__15968332_10

Exon 3

rs836755

7769114

-

T:G

68.2

0.849

C___2510236_20

Intron

rs228727

7770423

-

G:A

66.1

0.856

C__11673507_10

Intron

rs10864315

7772668

-

C:T

67.5

0.852

Custom assay

Intron

rs4908694

7773485

-

C:T

85.3

0.016

Custom assay

Intron

rs228682

7778933

-

T:C

56.1

0.698

C___8881633_20

Intron

rs228642

7785880

-

T:C

60.0

0.567

C___2510264_10

Intron

rs2172563 rs2640909

7796630 7812704

Met1028Thr

G:A T:C

74.0 70.2

0.670 0.845

Custom assay Custom assay

Intron Exon 18

rs10462021

7819720

His1149Arg

A:G

81.7

0.926

Custom assay

Exon 20

* Genotyping assay non functionally tested, ** polymorphism does not in HWE, *** non polymorphic variant in Polish population

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0.56–0.92 NS NS NS NS rs2640909 PER3

* Bold value marks significant p value for polymorphisms associated with increased risk of illness while italicized type marks p value for polymorphisms associated with possibly protective effect

-

0.71 0.1662 0.0160

-

-

NS

NS

-

-

NS

NS

-

-

NS NS 0.51–0.92 0.69 0.1051 0.69–1.00 0.83 0.4600 0.67–0.94 0.80 0.1200 0.0211 rs11022780

0.2215

0.83

0.71–0.97

0.0082

0.0461

NS

NS 0.1698 0.0405

0.0070 0.67–0.97 0.80

NS

0.4056

NS

0.0193 0.77

NS -

0.81 0.1492

NS

0.0071

rs3789327

rs11600996

NS

0.69–0.94

0.0028

0.65–0.92 NS

0.0636

0.0112

NS

NS

0.56–0.99

0.50–0.90

0.74

0.67 0.1035

-

-

-

NS NS 0.74

NS NS -

0.54–1.00

NS

0.1888 0.0494

NS -

-

NS

NS

NS

NS

0.7–1.00

-

0.84

-

0.3253

NS

0.0499

NS -

NS

NS

NS

NS

rs7947951 ARNTL

rs3816360

-

1.09–1.72

1.12–1.76 0.0907 0.0048 NS 0.0089 rs11171856

0.1492

1.23

1.05–1.44

NS

-

NS

NS

-

-

0.0074

0.1035

1.11–1.98

1.40

-

1.48

1.37

NS

0.0907

NS

0.0076

0.51–0.98

-

0.71

NS

0.1642 0.0311

NS -

-

NS

NS NS

NS

0.68–0.99

-

0.82

NS

0.3253 0.0353

NS 1.00–1.40

-

1.19 0.3053

NS NS

0.0451

rs12648271

rs2291739 TIM

-

-

NS

NS

NS

NS

1.08–1.95

1.03–1.84

1.45

1.37 0.1642

0.1051 0.0138

0.0319 -

-

NS

NS NS

NS

-

-

-

-

NS

NS

NS NS

-

-

NS

NS

NS

NS

rs1801260

OR FDR

CLOCK

rs11932595

p value 95 % CI OR p value p value 95 % CI OR FDR p value p value

95 % CI

BPD MD SNP ID Gene

Table 3 The significant results of case–control association study

BPDI

FDR

OR

95 % CI

BPDII

FDR

UPD

FDR

OR

95 % CI

Mol Biol Rep

After applying the false discovery rate (FDR) correction for multiple testing, none of the nominal significant p values survived. Power test using log additive mode of inheritance was performed. Model chosen includes the risk of the disease and frequencies of the risk allele in the population studied as well as controls ratio per case. The power of association tests was estimated at the level about 5 % which is characteristic for complex disorder analyses [29].

Haplotype analysis We examined comparisons r2 (correlation coefficient between the two loci) and D0 (deviation of the observed frequency of a haplotype from the expected) values of the pairwise comparisons between selected genes polymorphisms. Values obtained were used for linkage disequilibrium (LD) estimations. The results are presented in Fig. 1. We applied Westfall-Young permutation test as multiple testing correction [23]. Presence of haplotype variants associated with either increased or decreased risk in whole MD and subtypes patients group (Table 4) was detected using Haploview software. The most significant association for MD risk appeared for haplotype CGT of ARNTL gene, except for exception UPD (p \ 0.02). We also found out ARNTL gene TGC haplotype of the (p = 0.014) potentially protective against BPD, while CLOCK gene AAA haplotype of the (p = 0.024) presumably protective against UPD. However, all significant haplotype variants associations did not remain significant after 1,000 permutations used as correction for multiple comparisons.

SNP: SNP interaction We performed two-dimensional analysis of epistatic SNP– SNP interactions, using log-additive model [26]. P values matrix is visualized with plot on Fig. 2. We observed number of significant interactions between polymorphisms analyzed. Ten pairs of SNPs selected demonstrated the strongest interactions (p \ 0.001, visible as very dark green pixels). Existed pairs however contained SNPs from the same LD block. In case of PER3 and ARNTL genes, real epistatic interaction were observed between rs2172563/ rs4146388 and rs2172563/rs7107287 polymorphisms (p \ 0.01; dark green pixels). In the clinical subtype of mood disorders the analogous analysis was provided. The specific combination of SNPs pairs showed epistatic interaction in selected subgroup of patients (Table 5). Unique SNPs pairs of PER3 and CLOCK genes (rs2172563/ rs1268271 and rs2172563/rs3805148) appeared only in UPD patients.

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Mol Biol Rep

Fig. 1 Relative positions and LD estimates between polymorphisms in the analyzed population. Colored squares correspond to D’ values with numerical estimates given within squares

Table 4 Significant associated haplotype variants Patients

Gene

SNP ID in block

MD

ARNTL

Block 7

BPD

ARNTL

ARNTL

BPDI

BPDII

ARNTL

ARNTL

ARNTL

CLOCK

123

Haplotype variant

Frequency

Case-control frequencies

Chi square

p value

rs11600996

CGT

0.569

0.591, 0.543

6.301

0.012

rs11022779

TGC

0.253

0.238, 0.270

3.726

0.053

rs11022780

TAC

0.173

0.166, 0.181

1.04

0.307 0.028

Block 4 rs11824092

CG

0.591

0.625, 0.561

4.787

rs1982350

TA

0.391

0.363, 0.415

3.24

0.071

-

CA

0.012

0.008, 0.015

1.322

0.250

rs11600996

CGT

0.567

0.606, 0.534

6.087

0.013

rs11022779 rs11022780

TGC TAC

0.260 0.168

0.226, 0.289 0.166, 0.171

5.935 0.043

0.014 0.836

Block 7

Block 7 rs11600996

CGT

0.563

0.596, 0.543

5.432

0.0198

rs11022779

TGC

0.256

0.233, 0.270

3.369

0.0664

rs11022780

TAC

0.177

0.169, 0.181

0.476

0.4902 0.043

Block 6 rs3789327

AT

0.566

0.626, 0.555

4.095

rs11022778

GG

0.286

0.269, 0.289

0.413

0.520

-

GT

0.147

0.105, 0.155

4.037

0.044

Block 7 rs11600996

CGT

0.557

0.633, 0.543

6.463

0.011

rs11022779

TGC

0.260

0.206, 0.270

4.235

0.0396

rs11022780

TAC

0.177

0.157, 0.181

0.809

0.3685

rs534654

GAC

0.364

0.336, 0.369

0.941

0.332

rs1801260 rs3805148

GGA GAA

0.322 0.167

0.396, 0.308 0.156, 0.169

7.225 0.269

0.007 0.604

-

AAA

0.148

0.112, 0.154

2.819

0.093

Block 8

Mol Biol Rep Table 4 continued Patients

Gene

SNP ID in block

UPD

CLOCK

Block 8

Haplotype variant

Frequency

Case-control frequencies

Chi square

p value

rs534654

GAC

0.359

0.336, 0.349

0.133

0.715

rs1801260 rs3805148

GGA GAA

0.323 0.162

0.396, 0.325 0.155, 0.153

4.174 0.006

0.041 0.937

AAA

0.156

0.112, 0.173

5.075

0.024

-

Fig. 2 The p values plot for SNP–SNP interactions

Table 5 Significant SNPs pair interactions with P \ 0.01 MD

BPD

BPDI

BPDII

UPD

PER3/ARNTL

PER3/ARNTL

PER3/ARNTL

PER3/ARNTL

-

rs2172563/rs4146388

rs2172563/rs4146388

rs2172563/rs4146388

rs2172563/rs4146388

PER3/ARNTL

PER3/ARNTL

PER3/ARNTL

PER3/ARNTL

rs2172563/rs7107287

rs2172563/rs7107287

rs2172563/rs7107287

rs2172563/rs7107287

-

PER3/CLOCK

PER3/CLOCK

-

-

rs10462021/rs6849474

rs10462021/rs6849474

PER3/CLOCK

PER3/CLOCK

-

PER3/CLOCK

rs10462021/rs12649507

rs10462021/rs12649507

-

-

-

-

rs2172563/rs12649507 -

PER3/CLOCK rs2172563/rs1268271

-

-

-

Predictive models for MD After genotyping classic case-control study may be performed in order to find link between specific alleles and

-

PER3/CLOCK rs2172563/rs3805148

disease. Moreover we may model relationship between genetics polymorphisms and disease with predictive value using genotyping results. We analysed data available with Classification and Regression Trees (C and RT). Using this

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Mol Biol Rep

number of nodes divided: 10; number of terminal nodes: 11

0 1

ID=1

N=834

1

rs1801260_CLOCK = GG ID=2 1

... N=85

ID=3

= other N=749 1

rs10462021_PER = AG ID=4

0

, ... N=238

ID=5

rs11600996_ARNTL = CT ID=6 1

... N=120

ID=7

= other N=511 1

rs11022778_ARNTL = other N=118 0

= GG ID=12 0

... N=49

= other ID=13 N=462 1

rs10876890_TIM = AT ID=8 1

... N=56

rs1562438_ARNTL = other

ID=9

0

N=64

= CC ID=14

1

, ... N=268

= other ID=15 N=194 1

rs11022779_ARNTL = AA ID=20 1

... N=4

= other ID=21 N=190 0

rs7126303_ARNTL = TT ID=22

... 1

= other ID=23 N=187 0

N=3

rs228642_PER = CC ID=24

1

, ... N=85

= other ID=25 N=102 0

rs7302060_TIM = CC ID=26 1

... N=9

= other ID=27 N=93 0

Fig. 3 Decision tree algorithm to classify subject either to MD patients or to control group based on genotyping variants of all analyzed polymorphism. Classification tree has a characteristic structure in which we distinguish elements such as roots, branches, decision nodes and leaf/end nodes. The nodes correspond to tests

performed on attribute values of the analyzed variables (genotypes). The first node in the graph is the root. The branches reflect the results of the used tests, and the leaves contain the label of each class (0unaffected, 1-affected). N-number of observations

method we try to detect criteria for dividing the individuals of a population into predetermined classes (Fig. 3). In C and RT analysis minimum incorrect amount acceptable equal 100. The growth of the tree was completed with this number of incorrect classifications. K-10 fold crossvalidation for testing the model was implemented. From all analyzed polymorphisms ten only (rs1801260, rs10462021, rs11600996, rs10876890, rs11022778, rs1562438, rs11022779, rs7126303, rs228642, rs7302060) have predictive values. The specific combination of genotypes, presented in Table 6, allows classifying the carrier to either patients or control group. First pure node (ID = 20) consists of a six polymorphisms (rs1801260, rs10462021, rs11022778, rs1562438, rs11022779, rs7126303) (Fig. 1). This node contains four cases with a diagnosis of MD. Remaining terminal nodes were mixed, with no clear discrimination between ill and healthy ones. Estimated

classification accuracy of the model equaled 61 % with 71 % sensitivity and 50 % specificity (Table 7). After constructing model discriminating patients from healthy controls, we tried to build a tree the adherence to the selected type of mood disorders. However no significant model appeared (data not shown).

123

Clock genes and sleep disturbances Sleep disturbances are one of major symptoms of mood disorders course. Thus we provided analysis of the relationship between selected sleep disturbances and ‘‘clock’’ genes polymorphisms. The results obtained (see Table 8) indicated significant associations between ARNTL and PER3 genes polymorphisms and difficulty in falling asleep. Relation appeared in a group of patients declaring sleep disturbances. No other analyzed sleep disturbances

Mol Biol Rep Table 6 List of polymorphisms with predictive value in C and RT analysis Left branch

Right branch

No. of nodes

No. of cases classified as control

No. of cases classified as patients

Selected class

Gene

Distinguishing polymorphism

Genotype assigns to the selected class

1

2

3

834

393

441

1

CLOCK

rs1801260

GG

2

-

-

85

27

58

1

-

-

-

-

3 4

4 6

5 7

749 238

366 130

383 108

1 0

PER3 ARNTL

rs10462021 rs11600996

AG CT

GG -

6

8

9

120

56

64

1

TIM

rs10876890

AT

-

8

-

-

56

19

37

1

-

-

-

-

9

-

-

64

37

27

0

-

-

-

-

7

-

-

118

74

44

0

-

-

-

-

5

12

13

511

236

275

1

ARNTL

rs11022778

GG

-

12

-

-

49

29

20

0

-

-

-

-

13

14

15

462

207

255

1

ARNTL

rs1562438

CC

TT

14

-

-

268

110

158

1

-

-

-

-

15

20

21

194

97

97

1

ARNTL

rs11022779

AA

-

20

-

-

4

0

4

1

-

-

-

-

21

22

23

190

97

93

0

ARNTL

rs7126303

TT

22

-

-

3

0

3

1

-

-

-

-

23

24

25

187

97

90

0

PER3

rs228642

CC

TT

24 25

26

27

85 102

38 59

47 43

1 0

TIM

rs7302060

CC

-

26

-

-

9

2

7

1

-

-

-

-

27

-

-

93

57

36

0

-

-

-

-

Selected class: 0-unaffected, 1-affected

Table 7 Estimation of sensitivity or specificity of C and RT model Anticipated class

1

Authentic class 1

0

True positive

False positive

N = 314

N = 196

0

False negative

Accuracy = 61 %

Sensitivity = 71 %

N = 127

True negative N = 197 Specificity = 50 %

N-number of observations, 0-unaffected, 1-affected

(decreased need for sleep, wake up at night or early, and somnolence) showed associations with any analyzed polymorphisms.

Discussion It was 40 years ago, when attention to the circadian rhythm disturbances in mood disorders was drew for the first time [30]. Since then, progress in understanding the molecular basis of rhythmic processes and biological clock

functioning was done. Molecular biology success enabled discoveries mentioned as well as so-called ‘‘clock genes’’ identification. Genes are responsible for maintain of circadian rhythm. Studies indicate that physiological cyclic processes changes and psychopathological symptoms arise as a result of changes in those genes [31]. Therefore, we provided a comprehensive analysis of the CLOCK, ARNTL, TIM and PER3 genes encoding the four key circadian clock proteins with mood disorders in Polish population. Here we report evidence for a role of circadian system genes in bipolar disorder type II and unipolar disorder. We

123

Mol Biol Rep Table 8 Significant associations between polymorphisms and difficulty in falling asleep Gene

SNP ID

Genotypes

ALL

BPD

BPDI

BPDI

UPD

ARNTL

rs11022778

TT versus GT

0.0125 (0.0190)

0.0051 (0.0351)

0.0008 (0.0056)

NS

NS

ARNTL

rs1562438

TT versus CT

NS

NS

NS

0.0207 (0.03839)

NS

ARNTL

rs1982350

AA versus AG

NS

NS

NS

0.0264 (0.0268)

NS

PER3

rs836755

CC versus AC

0.0188 (0.0188)

0.0123 (0.0138)

NS

NS

NS

Results were obtained using Kruskal–Wallis test, using Wilcoxon pair test with FDR adjustment for p values (in the brackets)

found significant association between two CLOCK SNPs and BPDII and between one TIM SNP and both BPDII and UPD. In UPD additionally we detected another TIM gene SNP to be associated with illness risk. Findings presented supports existence of a connection between specific circadian gene variants and specific type of mood disorders (mainly BPDII and UPD). In contrast two polymorphisms of TIM gene (rs2291739, rs11171856) were evidently associated with risk of UPD. Results by others researchers indicated TIM gene involvement in UPD risk. Utge et al. showed association between three SNP of TIM gene: rs7486220, rs1082214, rs2291739 [9]. Last one of these polymorphisms was also associated with UPD in this paper. On the contrary, in the study presented TIM polymorphisms were associated neither with depression [17, 32, 33], nor with bipolar depression [4, 15, 18, 33]. Among all analyzed CLOCK gene polymorphisms, only two (rs1801260 and rs11932595) showed association with BPDII. Simultaneously none of them were related with UPD, which is in concordance with findings by other researchers [34–36]. Located in the 30 UTR polymorphism T3111C (rs1801260) polymorphism was the most intensive SNP analyzed with-it circadian rhythm disturbances in several illnesses course. In one of the first studies, rs1801260 showed association with morning activity preference [37]. In turn, Benedetti et al. [38], confirmed that individuals with C allele show significantly more frequent preference for: evening activities, the phase shift and reduced sleep time. Moreover polymorphism mentioned, was associated with a higher degree of recurrence of the bipolar disorder as well as with improvement in insomnia after antidepressants treatment [12, 38]. In case of rs11932595 SNP CLOCK gene, Allebrandt et al. reported association with sleep duration in healthy controls [39]. Results mentioned added another putative function for CLOCK gene. However this SNP was not previously analyzed in mood disorders, according to our knowledge. The LD analysis demonstrated the existence of significant haplotype variants built of SNPs form ARNTL and CLOCK genes. Haplotype variant (rs1160996C/rs11022779G/

123

rs1122780T_ARNTL) was associated with increased risk of: MD, BPD and both type of BPD. We observed he protective effect in case of opposite haplotype variant, consist of rs1160996T/rs11022779G/rs1122780C_ARNTL for BPD. In case of rs534654A/rs1801260A/rs3805148A_CLOCK we saw such effect for UPD. The usage of haplotype analysis in association studies, increased both power and sensitivity of tests [40]. Herein, haplotype analyses extend number of SNPs associated both with risk and protective effect. Association analysis did not show relationship CLOCK gene SNPs with UPD risk. In turn the haplotype analysis detected risk and protective haplotype variant existence, what means that obtained results are complementary to each other. Our results are partially concordant with meta-analyses, where any associations (in the allele, genotype, or haplotype analysis) between CLOCK gene and any mood disorders were detected. These data suggested that CLOCK does not play a major role in the pathophysiology of mood disorders. However, protective effect can’t be excluded [36]. Lack of associations after multiple testing correction indicated weak effect of individual polymorphisms. Thus, epistasis analysis, defined as a deviation from sum of individual genes/SNPs independent effects, may be more effective than individual variants separate examination [41]. Therefore we used two dimensional SNP–SNP interactions in our study. In BPD we detected significant epistasis between PER3 (rs2172563) SNP and two SNPs (rs4146388 and rs7107287) of ARNTL gene. Unique SNPs pairs of PER3 (rs2172563) and CLOCK genes (rs1268271 and rs3805148) was observed in UPD. Obtained results are partially concordant with previous papers, indicating interaction between TIM and PER1 genes in MD patients subgroups [9]. Both analyses of individual SNPs and haplotypes as well as SNP–SNP interaction proved mood disorders diversity on genetic level. It confirms variety of statistical approaches to be used. Individual method allows only describing phenomena from a one point of view, whereas different points of view are needed. Traditional parametric statistical approaches such as logistic regression have limited power for modeling high-order non-linear interactions that are important in the etiology of complex disorder [42]. As the result we used Classification and

Mol Biol Rep

Regression Trees (C and RT) method to obtain predictive models for MD and its clinical subtypes [43]. This method is nonparamateric and nonlinear alternative for logistic regression and does not require initial assumptions about the nature of the relationship between predictors and the dependent variables. Significant predictive value was observed for following polymorphisms: CLOCK (rs1801260), ARNTL (rs11600996, rs11022778, rs1562438, rs11022779, rs7126303), TIM (rs10876890, rs7302060) and PER3 (rs10462021, rs228642). Even though we managed to confirm importance of investigated ‘‘clock gene’’ polymorphisms in MD, we failed to produce model with sufficient predictive power. Analyzing wider number of clock genes and their polymorphisms may lead to create stronger models [29]. Number of researchers indicated sleep imbalance as the primary marker of circadian rhythms disturbances in mood disorders course [44, 45]. Carriers of homozygotes variants (ARNTL: rs11022778 TT, rs1562438 TT, rs1982350 AA and PER3: rs836755 CC) have significant more frequent difficulty in falling asleep in sleep disturbances group analyzed. We detected association between rs7107287 (located in ARNTL) and BPDI [15]. In case of rs1868049 marker relation with early morning awakening among depressive man appeared. Among BPD sample association between rs228697 and evening activity preferences appeared [17]. In summary, results obtained overlap with previous reports, collected in Partonen review [46]. Differences appeared probably due to different criteria of tagSNPs selection and stratification effect. On the other hand, obtained results confirm role of ‘‘clock genes’’ in the MD, BPD and UPD predisposition. It also shows that disturbances in circadian rhythm are the result subtle changes in ‘‘clock genes’’ accumulation, which effect may be disclosed by haplotype, interaction or presented by us C and RT analysis. Association between ‘‘clock gene variants’’ with individual symptoms instead of the diagnosis is essential to detect small effects of SNPs, not obtained in classical case-control study. In conclusion, our study suggested a putative role of the CLOCK, TIM, ARNTL and PER3 polymorphisms in mood disorders susceptibility. We showed genetic differentiation of mood disorders. Also we confirmed the need to perform separate analyzes for BP and UPD patients, as well as with symptoms occurred independently from diagnosis. Acknowledgments This research was supported by Grants No N N106 280939, N N402 4671 40, financed by the National Science Centre. Conflict of interest

Authors declare no conflict of interest.

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