University of Groningen Rhythm & Blues Knapen, Stefan Erik

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University of Groningen

Rhythm & Blues

Knapen, Stefan Erik

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2019

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Knapen, S. E. (2019). Rhythm & Blues: Chronobiology in the pathophysiology and treatment of mood

disorders. Rijksuniversiteit Groningen.

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Chapter 2 Association of circadian

genes with chronotype and

mood disorder, an analysis

of epidemiological and

translational data

S.E. Knapen

1,4

_{, F.J. Bosker}

2

_{, I.M. Nolte}

3

_{, P. Terpstra}

3

_{, R.F. Riemersma-van der Lek}

4

_,

N. Antypa

5

_{, C.A. Hartman}

1,2,4

_{, H. Snieder}

3

_{, B.W.J. Penninx}

6

_{, R.A. Schoevers}

1,2

1. University of Groningen, University Medical Center Groningen, Department of

Psy-chiatry, Research School of Behavioural and Cognitive Neurosciences (BCN),

Gronin-gen, the Netherlands

2. University of Groningen, University Medical Center Groningen, Department of

Psy-chiatry, Groningen, the Netherlands

3. Department of Epidemiology, University of Groningen, University Medical Center

Groningen, the Netherlands

4. Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University

Medical Center Groningen, University of Groningen, Groningen, the Netherlands

5. Department of Clinical Psychology, Institute of Psychology, Leiden University,

Leiden, the Netherlands

6. Department of Psychiatry, EMGO Institute for Health and Care Research and

Neuro-science Campus Amsterdam, the Netherlands

(3)

Abstract

Objective

To identify genes for major depression disorder (MDD) by investigating associations

of genetic markers in 338 circadian genes with chronotype and mood disorder, in the

Netherlands Study of Depression and Anxiety (NESDA), using GAIN-NESDA-NTR GWAS

data encompassing 1352 cases and 1649 controls.

Methods

The genetic markers were first tested for association with chronotype, and second for

association with mood disorder. For markers significantly associated with mood

disor-der, it was tested whether chronotype was mediating this association using the Sobel

test and similarly the mediation effect of mood disorder on the significant

associa-tions between marker and chronotype was assessed. The identified genes including

four previously identified candidate genes for MDD TNF, NPY, C5orf20 (DCANP1) and

SLC6A2 (3) were then subjected to an over-representation analysis to investigate their

relation with biochemical pathways and disease processes.

Results and Discussion

We found 13 genetic markers in six genes from the circadian gene set to be associated

with chronotype, remaining significant after correction for multiple testing. In a

simi-lar fashion we found 59 genetic markers in 18 genes from the circadian gene set to be

associated with mood disorder, also remaining significant after correction for multiple

testing. A subsequent analysis showed that none of these associations were mediated

by the other phenotype. Over-representation analyses failed to identify gene clusters

indicative of specific neuronal processes, but it yielded several significant functional

clusters involved in metabolic disorders.

(4)

Introduction

According to the Global Burden of Disease Study 2010 mood disorders are expected to

become one of the leading causes for disability in the western world (1). Mood

disor-ders have a highly heterogeneous character with both genetic and environmental

fac-tors playing a role. For example, the genetic component of major depressive disorder

(MDD) as estimated from twin studies is approximately 30%, but the genetic profile

(including gene-gene interactions and gene-environment interactions) for MDD is

hith-erto largely unknown. Genome-wide association studies (GWAS) for MDD have been

largely unsuccessful (2–4), although a recent GWAS identified significant associations

with 44 loci (5).

Previously we have used genome-wide association data (GWAS) data from the

Neth-erlands Study of Depression and Anxiety (NESDA) study (6) to explore reported

candi-date gene and single-nucleotide polymorphism (SNP) associations in MDD (7,8). These

studies could only replicate 2 out of 92 (2%) and 9 out of 185 (5%) of the SNPs,

re-spectively. Replication rates improved to 7% and 20%, respectively, when all genetic

markers in the genes were analyzed. The poor replication of candidate genes is most

likely attributable to the heterogeneity of MDD, which is also reflected by the many

pathophysiological hypotheses that have been raised in the past such as

malfunction-ing mono-amine, stress and immune systems. Another major hypothesis of MDD

con-cerns adaptive/restorative processes such as neuroplasticity and neurogenesis. The

latter processes may be particularly important during sleep which is often disturbed

in MDD. All physiological functions including sleep, activity, appetite and secretion of

hormones are controlled by the circadian rhythm which is regulated by the

suprachias-matic nucleus (SCN) (9). The SCN consists of a network of transcriptional-translational

feedback loops that gives a rhythmic expression pattern of clock genes (9,10). This

physiological process is essential for mental well-being in both humans and animals

(11–14). Mood may particularly vary with changes and disruptions of the biological

clock (11,14–17). Furthermore, it is clear that restoring circadian rhythms has a

ben-eficial effect on depressive symptoms. For example, the efficacy of light therapy for

both seasonal affective disorder (SAD) and non-seasonal depression might suggest that

restoring circadian rhythms is relevant for the treatment of mood disorders (18–20). A

non-invasive method to study the circadian rhythm in individuals is studying the

chro-notype of a person (21). Chrochro-notype consists of a spectrum with people being a

morn-ing type, preferrmorn-ing daytime activities and people bemorn-ing an evenmorn-ing type, preferrmorn-ing

nighttime activities. Chronotype has been linked to mood disorders (22–26) and is a

heritable trait (27).

The aim of the present study is to identify genes for mood disorders by investigating

the association between chronotype and mood disorder with genetic markers from

cir-cadian rhythm related genes in the GAIN-NESDA-NTR GWAS sample (6). In addition in

case of a significant association with mood disorder it was tested whether this effect

was mediated by chronotype and vice versa, whether the associations with chronotype

were mediated by mood disorder.

(5)

Material and methods

Population

Subjects were derived from the Netherlands Study of Depression and Anxiety

(NES-DA), an ongoing cohort study (n = 2981 at baseline, age ranging from 18 to 65 years)

including 2329 persons with a lifetime diagnosis of a depressive and/or anxiety

disor-der, as well as 652 healthy controls. Participants were recruited from the community

(19%), general practice (54%) and secondary mental health care (27%). All ethical

committees of the participating universities approved the NESDA research protocol and

all participants provided written informed consent. For a detailed description of NESDA

see Penninx et al. 2008 (28). Participants who participated at the 2-year follow-up

as-sessment were included in this study (n = 2596).

Mood disorder

Mood disorder diagnosis was determined at baseline and 2-year follow-up using the

Composite International Diagnostic Interview (29). Subjects with a lifetime diagnosis of

major depressive disorder, bipolar disorder or dysthymia were classified as the mood

disorder group (n=1352). Subjects from NESDA without lifetime diagnosis of major

de-pressive disorder, bipolar disorder or dysthymia and a low dede-pressive score (<15) on

the Inventory of Depressive Symptomatology (Self-Rating) were classified as subjects

without mood disorder (n=1649) (30).

Chronotype

Chronotype data of both cases and controls were available in the form of the Munich

Chronotype Questionnaire (MCTQ). The MCTQ is a self-report questionnaire consisting of

questions regarding the timing of sleep on free and workdays (31–34). From the MCTQ the

midpoint in time between falling asleep and waking up at free days can be calculated, the

Midpoint of Sleep on Free days (MSF). The MSF needs to be corrected for oversleep on free

days in later chronotypes, as subjects with a later chronotype often sleep longer on free

days due to accumulated sleep debt during work days. This is done by subtracting half of

the difference between sleep duration on free days and workdays and results in the Mid

Sleep on Free days, sleep-debt correct (MSFsc). The MSFsc is a measure of the chronotype

of a person, validated with other chronotype questionnaires and internal phase markers.

Genetic data

DNA was isolated using the GENTRA Puregene kit following the manufacturer’s

proto-cols (6). Genotyping was done with the Affymetrix Perlegen 5.0 (N=1803) and Affymetrix

6.0 (N=2372) arrays (1610 samples were genotyped on both). Genotype calling was

per-formed with the APT-Genotyper software. Sample and SNP QC was done first within, and

then between platforms using the PLINK software (35). With the LiftOver tool

(“http://ge-nome.sph.umich.edu/wiki/LiftOver”) the positions of the SNPs were converted to build

37 (HG19) of the Human reference genome for each platform. Strands were aligned using

Chapter 2: Association of circadian genes with chronotype and mood disorder,

(6)

the 1000 Genomes phase1 release v3 global reference panel. Genetic markers were

ex-cluded if they had ambiguous locations, mismatching alleles with the 1000 Genomes

reference set or the allele frequencies differed more than 0.20 with those from the

ref-erence. Per platform genetic markers were excluded if the minor allele frequency (MAF)

<1%, the Hardy–Weinberg Equilibrium (HWE) p-value < 0.00001 or the call rate <95%.

Samples were removed when their reported sex did not match with their genotypes, call

rate <90% or the F inbreeding value was >0.10 or <−0.10. Subsequently, the data of the

individual’s arrays were merged into a single dataset. The HWE, MAF and the reference

al-lele frequency difference filters were next re-applied in the combined data. C/G and A/T

SNPs with a MAF between 0.35 and 0.50 were removed to avoid incorrect strand

align-ment. Phasing of all samples and imputation of missing calls of genotyped markers was

done with MACH (36). The phased data were next imputed with MINIMAC (37). To avoid

issues arising from merging genotype data from different platforms re-imputed calls for

all genotyped markers were taken (mean concordance between re-imputed markers and

the original genotypes was 0.9868). The resulting genome-wide data set consisted of

31,316,056 imputed genetic markers with a mean imputation quality R

2

_{of 0.38.}

Translational genetic data

In the supplement of Menger et al. (38) rat genes are listed that could be involved in

circadian rhythms. For this study we selected the rat genes mentioned in their suppl.

tables S1-44 and legends of Fig1 and Fig2 resulting in 336 unique Rat Locus Link-IDs

(LLIDs), Using the “Biomart” tool at the Ensembl website (www.ensembl.org) we found

the human homologues of these rat LLID’s. Fourteen of these RAT LLIDs map to 2

HU-MAN Ensembl-IDs. The total of unique HUHU-MAN Ensembl-IDs is potentially 350. Two of

the RAT LLIDs could not be found in Ensembl at all. Eight of the RAT LLIDs could not be

found with a reliable HUMAN homolog. Seven of the human gene equivalents could not

be found on our Illumina gene expression array, leaving us with a set of 333 human

cir-cadian genes to test for expression differences between our experimental conditions.

Besides these 333 genes, we also selected 5 serotonergic genes previously reported to

be involved in circadian rhythm, namely SERT, HTR1A, HTR1B, HTR2C and HTR7 (39–45)

leaving us with a set of 338 circadian and serotonergic candidate genes to be

investi-gated in the GAIN-NESDA-NTR GWAS sample.

Statistical analysis

Phenotype data were prepared using SPSS version 22. Group differences were assessed

by analysis of variance (ANOVA) for continuous data and chi-square analyses on

cate-gorical data. Genotype-phenotype association analysis was performed with SNPTEST

version 2.5 using an additive SNP model (46). Genetic markers in the circadian genes

were first tested for association with chronotype, and second for association with mood

disorder. As chronotype differs between men and women and over age groups, sex and

age at the moment of chronotype assessment are included as covariates. Furthermore

chronotype as measured with the MCTQ can be influenced by external timing factors,

such as the presence of children in the household and whether a subject is currently

employed. These variables were therefore also included as covariates in the analyses.

(7)

In addition three principle components to control for population stratification and four

dummy variables to correct for different genotyping platform were added. Genetic

markers were regarded as significant if the p-value was <0.00015 to correct for

multi-ple testing.

For genetic markers significantly associated with mood disorder, it was tested whether

chronotype was mediating this association using the Sobel’s test with 10,000

boot-strap permutations to determine the p-value. Similarly the mediation effect of mood

disorder on the significant associations between marker and chronotype was assessed.

For these analyses the residuals were first computed for chronotype, corrected for the

same covariates as used in the previous analyses.

Bioinformatics analysis

To investigate the relation of significant genes with biochemical pathways and disease

processes they were subjected to both Genetrail-2V1.5 gene set enrichment (GSEA)

and overrepresentation analyses (ORA) (47). To this end the default categories, GO -

Bi-ological Process, GO - Cellular Component, GO - Molecular Function, KEGG – Pathways

and Wiki Pathways were used. In the Genetrail ORA we used false discovery rate (FDR)

adjustment (48) and a significance level of 0.05, with 2 as minimum size of category

and 700 as maximum size of category.

Results

We found 13 genetic markers in 6 genes from the circadian gene set to be associated

with chronotype, remaining significant after correction for multiple testing. In a similar

fashion we found 59 markers in 18 genes from the circadian gene set to be associated

with mood disorder, also remaining significant after correction for multiple testing. A

subsequent analysis with Sobel’s test showed that two of the associations with

chro-notype might be mediated by the mood disorder, but these did not remain significant

after multiple testing correction for the six genes (table 1). None of the effects of the

genetic markers associated with mood disorder were mediated by chronotype.

In particular many genes associated with mood disorder appeared to be involved in

neuronal signaling and/or plasticity (table 1). When subjecting the genes found to be

significantly associated with circadian rhythm to gene ontology (GO) programs the

in-volvement in circadian rhythmicity was confirmed. Other significant

processes/func-tions were circadian gene expression, transmembrane helix (receptor, transporter and

channel), synapse, corticosteroid receptor and behavior. However, GO analyses of the

genes significantly associated with either chronotype or mood disorder failed to

iden-tify gene clusters indicative of specific neuronal processes. Yet, an ORA of the

signifi-cantly associated circadian genes supplemented with previously identified genes for

MDD TNF, NPY, C5orf20 (DCANP1) and SLC6A2 (7) yielded several significant functional

clusters pointing at a genetic connection between depression and metabolic syndrome

(table 2).

(8)

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Table 1: Circadian genes significantly associated with mood disorder or chronotype (p<0.00015 after

Bon-ferroni correction) and previous results from literature: 1) number of significant genetic markers in the gene in this study, 2) protein function, 3) reported connection with mood disorder, 4) reported connection with hypotheses of depression, 5) references.

*Genetic markers within CACNA2D1 (rs149907348) and CD44 (11:35218084:TA_T) showed suggestive me-diation of the effect of the marker on chronotype by mood disorder (p=0.047 and p=0.041, respectively).

(9)

Table 2: Over-representation analysis of genes significantly associated with (A) chronotype or (B) mood

dis-order + four previously identified MDD genes NPY, TNF, C5orf20 and SLC6A2

an analysis of epidemiological and translational data

ͲŚƌŽŶŽƚǇƉĞ 'K–ĞůůƵůĂƌŽŵƉŽŶĞŶƚ EŽƐŝŐŶŝĨŝĐĂŶƚĐĂƚĞŐŽƌŝĞƐŚĂǀĞďĞĞŶĨŽƵŶĚ tŝŬŝWĂƚŚǁĂǇƐ ŶƌŝĐŚĞĚ DŽŶŽĂŵŝŶĞƚƌĂŶƐƉŽƌƚ ƉсϬ͘Ϭϭϱϰ ^>ϲϮ͕dE& <''WĂƚŚǁĂǇƐ ŶƌŝĐŚĞĚ ĚŝƉŽĐǇƚŽŬŝŶĞƐŝŐŶĂůŝŶŐƉĂƚŚǁĂǇ ƉсϬ͘Ϭϰϰϵ EWz͕dE& ŶƌŝĐŚĞĚ ŝůĂƚĞĚĐĂƌĚŝŽŵǇŽƉĂƚŚǇ ƉсϬ͘Ϭϰϰϵ EϮϭ͕dE& ŶƌŝĐŚĞĚ ,ǇƉĞƌƚƌŽƉŚŝĐĐĂƌĚŝŽŵǇŽƉĂƚŚǇ;,DͿ ƉсϬ͘Ϭϰϰϵ EϮϭ͕dE& 'K–ŝŽůŽŐŝĐĂůWƌŽĐĞƐƐ EŽƐŝŐŶŝĨŝĐĂŶƚĐĂƚĞŐŽƌŝĞƐŚĂǀĞďĞĞŶĨŽƵŶĚ 'K–DŽůĞĐƵůĂƌ&ƵŶĐƚŝŽŶ EŽƐŝŐŶŝĨŝĐĂŶƚĐĂƚĞŐŽƌŝĞƐŚĂǀĞďĞĞŶĨŽƵŶĚ ZĞĂĐƚŽŵĞͲWĂƚŚǁĂǇƐ EŽƐŝŐŶŝĨŝĐĂŶƚĐĂƚĞŐŽƌŝĞƐŚĂǀĞďĞĞŶĨŽƵŶĚ –DŽŽĚĚŝƐŽƌĚĞƌнƉƌĞǀŝŽƵƐůǇŝĚĞŶƚŝĨŝĞĚDŐĞŶĞƐ 'K–ĞůůƵůĂƌŽŵƉŽŶĞŶƚ EŽƐŝŐŶŝĨŝĐĂŶƚĐĂƚĞŐŽƌŝĞƐŚĂǀĞďĞĞŶĨŽƵŶĚ tŝŬŝWĂƚŚǁĂǇƐ ŶƌŝĐŚĞĚ &ŽĐĂůĚŚĞƐŝŽŶ ƉсϬ͘Ϭϯϳϲ K>ϯϭ͕/d'͕s'& ŶƌŝĐŚĞĚ /ŶƚĞŐƌĂƚĞĚWĂŶĐƌĞĂƚŝĐĂŶĐĞƌWĂƚŚǁĂǇ ƉсϬ͘Ϭϯϳϲ &'&ϭ͕dE&͕s'& ŶƌŝĐŚĞĚ DŽŶŽĂŵŝŶĞdƌĂŶƐƉŽƌƚ ƉсϬ͘Ϭϯϳϲ ^>ϲϮ͕dE& <''WĂƚŚǁĂǇƐ ŶƌŝĐŚĞĚ dǇƉĞ//ĚŝĂďĞƚĞƐŵĞůůŝƚƵƐ ƉсϬ͘ϬϬϭϭ Eϭ͕,<Ϯ͕dE& ŶƌŝĐŚĞĚ ŵŝŶŽ ƐƵŐĂƌ ĂŶĚ ŶƵĐůĞŽƚŝĚĞ ƐƵŐĂƌ ŵĞƚĂďŽůŝƐŵ ƉсϬ͘ϬϯϴϬ ,<Ϯ͕W'Dϭ ŶƌŝĐŚĞĚ 'ĂůĂĐƚŽƐĞŵĞƚĂďŽůŝƐŵ ƉсϬ͘ϬϯϴϬ ,<Ϯ͕W'Dϭ ŶƌŝĐŚĞĚ DW<ƐŝŐŶĂůŝŶŐƉĂƚŚǁĂǇ ƉсϬ͘ϬϯϴϬ Eϭ͕&'&ϭ͕dE& ŶƌŝĐŚĞĚ WƌŽƚĞŽŐůǇĐĂŶƐŝŶĐĂŶĐĞƌ ƉсϬ͘ϬϯϴϬ &'&ϭ͕dE&͕s'& ŶƌŝĐŚĞĚ dǇƉĞ/ĚŝĂďĞƚĞƐŵĞůůŝƚƵƐ ƉсϬ͘ϬϯϴϬ WdWZEϮ͕dE& ŶƌŝĐŚĞĚ 'ůǇĐŽůǇƐŝƐͬ'ůƵĐŽŶĞŽŐĞŶĞƐŝƐ ƉсϬ͘ϬϰϯϬ ,<Ϯ͕W'Dϭ ŶƌŝĐŚĞĚ W/ϯ<ͲŬƚƐŝŐŶĂůŝŶŐƉĂƚŚǁĂǇ ƉсϬ͘ϬϰϯϬ K>ϯϭ͕&'&ϭ͕s'& ŶƌŝĐŚĞĚ ^ƚĂƌĐŚĂŶĚƐƵĐƌŽƐĞŵĞƚĂďŽůŝƐŵ ƉсϬ͘ϬϰϯϬ ,<Ϯ͕W'Dϭ ŶƌŝĐŚĞĚ ŵdKZƐŝŐŶĂůŝŶŐƉĂƚŚǁĂǇ ƉсϬ͘ϬϰϯϬ dE&͕s'& 'K–ŝŽůŽŐŝĐĂůWƌŽĐĞƐƐ EŽƐŝŐŶŝĨŝĐĂŶƚĐĂƚĞŐŽƌŝĞƐŚĂǀĞďĞĞŶĨŽƵŶĚ 'K–DŽůĞĐƵůĂƌ&ƵŶĐƚŝŽŶ EŽƐŝŐŶŝĨŝĐĂŶƚĐĂƚĞŐŽƌŝĞƐŚĂǀĞďĞĞŶĨŽƵŶĚ ZĞĂĐƚŽŵĞͲWĂƚŚǁĂǇƐ EŽƐŝŐŶŝĨŝĐĂŶƚĐĂƚĞŐŽƌŝĞƐŚĂǀĞďĞĞŶĨŽƵŶĚ

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Discussion

This study was set out to investigate associations of genes involved in circadian rhythm

with chronotype and mood disorder using GWAS data from the GAIN-NESDA-NTR study

in order to identify genes for MDD through two endophenotypes. When analyzing the

genetic markers from the circadian genes 13 markers in six genes were found to be to be

significantly associated with chronotype after Bonferroni correction (see table 1). In a

similar fashion 59 markers in 18 genes were found to be associated with mood disorder,

also remaining significant after correction for multiple testing. A subsequent analysis

with Sobel’s test showed that only genetic markers within CACNA2D1 (rs149907348)

and CD44 (11:35218084:TA_T) showed suggestive mediation of the effect of the

mark-er on chronotype by mood disordmark-er (p=0.047 and p=0.041, respectively).

The 338 candidate genes from the present study were in large part the human

homo-logues of reported rat circadian genes (38). We have used these human homohomo-logues

before in a gene expression study of seasonal affective disorder (n=15) but were not

able to find significant associations, likely due to the small sample size (89).

Many of the genes associated with mood disorder appeared to be involved in

neuro-nal signeuro-naling and/or plasticity. However, over-representation aneuro-nalysis failed to identify

gene clusters indicative of specific neuronal processes. The functional categories for

the rat circadian genes in Menger et al. (38) seem to be constructed by the authors from

the individual gene descriptions and are difficult to compare with established GO

pro-grams such as Genetrail. Accordingly we also included five serotonergic genes (SERT,

HT1A, HTR1B, HTR2C and HTR7) clearly involved in circadian rhythm (39–45).

Circadian genes that were significantly associated with chronotype or mood disorder

were subjected to a GSEA and ORA. Both types of GO analyses confirmed the

involve-ment in circadian rhythmicity. Other significant processes/functions were circadian

gene expression, transmembrane helix (receptor, transporter and channel), synapse,

corticosteroid receptor and behavior. Because the current and previous candidate gene

study (7) were performed on the same GAIN-NESDA-NTR GWAS data set we have merged

the positive genes from both studies and subjected them again to a GSEA and ORA. The

GSEA did not yield significant results but the perhaps somewhat less stringent ORA

yielded several significant and possibly relevant functional clusters. The first one,

driv-en by TNF and the noradrdriv-enaline transporter, suggests the involvemdriv-ent of monoamine

neurotransmission, which is in line with current hypotheses of MDD (90). The second

one points at the adipocytokine pathway (NPY, TNF), which plays a role in inflammatory

processes, but also in metabolic syndrome (91,92). The third cluster presents a

com-bination of genes from the current and previous study pointing at sugar metabolism

(HK2, PGM1), diabetes I (PTPRN2, TNF) and II (CACNA1C, HK2, TNF), dilated

cardiomyop-athy (CACNA2D1,TNF) and hypertrophic cardiomyopcardiomyop-athy (CACNA2D1,TNF). The results

from the ORA suggest an association of depression with metabolic syndrome

(diabe-tes II, cardiovascular disease and adipocytokine pathways). A meta-analysis by Pan et

al. (93) reported indeed a strong bidirectional association between depressive

disor-ders and metabolic syndrome. In both disordisor-ders dysregulation of the immune system

(11)

and disruptions of the cytokine network have been observed. One of the suggested

mechanisms underlying both disorders might be HPA-axis hyperactivity triggered by

pro-inflammatory cytokines. Another mechanism that has been put forward involves

adipocytokines, such as leptin, adiponectin and resistin. Low levels of leptin have been

associated with depression in humans as well as depressive-like behavior in animals

(94). This has led to the formulation of a “leptin hypothesis of depression” with both

leptin insufficiency and resistance as important factors (94). The MAPK, PI3K-Akt and

mTOR pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) as

identi-fied in the ORA converge to T-cell receptor signaling (95) and metabolic programming

(96). Moreover, focal adhesion as identified in the Wiki pathways analysis might also

hint at a role of the immune system, in which T-lymphocytes migrate along the

connec-tive endothelium following cellular signals to damaged biological tissue (97). It is also

important to note that the clock gene ARNTL2, while not being identified in the ORA

analysis, has been implicated in both diabetes I and II (98,99).

The present study emphasizes the potential of bioinformatics to integrate previously

gathered preclinical information into the analysis of gene associations with mental and

somatic diseases. Globally the incidence of both mood disorder and metabolic

syn-drome related diseases such as obesity, diabetes and cardio-vascular disease is rapidly

increasing. Each condition separately, and their combination even more, is responsible

for an impressive disease burden affecting the lives of many. It is important to note

that the present study was set out to investigate association of circadian genes with

depression through the endophenotypes chronotype and mood disorder, and definitely

not aimed at investigating a genetic association between depression and metabolic

syndrome. Thus the outcome of the ORA came rather unexpected. However, the many

hints at sugar metabolism in the KEGG pathway analysis are in support of current ideas

that the globally increased sugar intake constitutes a risk factor for developing both

metabolic syndrome and mood disorder (100). Yet, it is also clear that the present

re-sults have to be replicated by other studies.

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