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G-protein β3 subunit genetic variation moderates five-year depressive symptom trajectories of primary care attendees
Journal of Affective Disorders 165 (2014) 64–68
Contents lists available at ScienceDirect
Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Brief report
G-protein β3 subunit genetic variation moderates five-year depressive symptom trajectories of primary care attendees Chad A. Bousman a,b,c,d,n, Maria Potiriadis b, Ian P. Everall a,d,e, Jane M. Gunn b a
The University of Melbourne, Department of Psychiatry, VIC, Australia The University of Melbourne, Department of General Practice, Parkville, VIC, Australia c Swinburne University of Technology, Centre for Human Psychopharmacology, Hawthorne, VIC, Australia d Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia e NorthWestern Mental Health, Melbourne, VIC, Australia b
art ic l e i nf o
a b s t r a c t
Article history: Received 21 January 2014 Received in revised form 17 April 2014 Accepted 17 April 2014 Available online 24 April 2014
Background: Genetic variation in the G-protein β3 subunit (GNB3) has previously been associated with gene splicing that has been further linked to increased signal transduction and major depressive disorder. However, the effect of GNB3 genetic variation on depressive symptom trajectories is currently unknown. The aim of the present study is to examine whether genetic variation in GNB3 moderates depressive symptom trajectories among 301 primary care attendees enrolled in the Diagnosis, Management and Outcomes of Depression in Primary Care (diamond) prospective cohort study. Methods: Depressive symptoms were assessed using three measures: (1) DSM-IV criteria, (2) Primary Care Evaluation of Mental Disorders Patient Health Questionnaire-9 (PHQ-9), and (3) Center for Epidemiologic Studies Depression Scale (CESD). DSM-IV criteria were measured at baseline, 24, 36, 48, and 60 months post-baseline, whereas, PHQ-9 and CESD measurements were taken at baseline, 12, 24, 36, 48, and 60 months post-baseline. Two haplotype-tagging single nucleotide polymorphisms [rs5443 (C825T) and rs5440] spanning the GNB3 gene including 1 Kb upstream and downstream of the gene boundaries were genotyped. Results: Five-year PHQ-9 and CESD depressive symptom trajectories were moderated by rs5440. Carriers of the rs5440 GG genotype had more favourable depressive symptom trajectories compared to AG or AA genotype carriers. The rs5443 polymorphism did not moderate depressive symptom trajectories, regardless of the measure used. Limitations: Generalizability to depressed populations outside of the primary care setting may be limited. Conclusions: These results provide novel evidence suggesting genetic variation in the 5-prime region of GNB3 moderates depressive symptom trajectories among primary care attendees. & 2014 Elsevier B.V. All rights reserved.
Keywords: GNB3 Polymorphism Depression Longitudinal
1. Introduction Heterotrimeric guanine nucleotide-binding proteins (G proteins) consist of three subunits, α, β, and γ that are attached to the cell surface plasma membrane and serve as a signalling link between numerous ligands (e.g., neurotransmitters), their receptors (i.e. G protein-coupled receptors), and downstream effectors (Neves et al., 2002). Upon receptor activation, signalling can occur by way of the Gα protein or Gβγ protein complex. Although Gα proteins regulate the majority of this cell signalling, neurotransmitters associated with mood (e.g., serotonin, dopamine, epinephrine, and acetylcholine) n Corresponding author at: The University of Melbourne, Department of Psychiatry, 30 Royal Parade, Level 4, Parkville, VIC, Australia. Tel.: þ 61 3 9035 6667. E-mail address: [email protected] (C.A. Bousman).
http://dx.doi.org/10.1016/j.jad.2014.04.044 0165-0327/& 2014 Elsevier B.V. All rights reserved.
have receptors that signal through the Gβγ protein complex. In addition, many effector proteins of the Gβγ protein complex such as phosphatidylinositol 3-kinase (PI3Kβ), phospholipase C-β (PLCβ), glycogen synthase kinase-3 (GSK3), and calcium channels have been implicated in the pathophysiology of affective disorders (Kim et al., 2005; Tanis and Duman, 2007). To date, a synonymous polymorphism [rs5433 (C825T)] in exon 10 of the β3 subunit of the heterotrimeric G-protein (GNB3) has received considerable attention since it was first identified in 1998 (Siffert et al., 1998). The T allele of this polymorphism has been associated with a splice variant that is linked to increased signal transduction (Siffert et al., 1998) and major depressive disorder (MDD) (Lopez-Leon et al., 2008). However, no other polymorphisms in or near GNB3 have been examined for their association with depression phenotypes and no study to our knowledge has
C.A. Bousman et al. / Journal of Affective Disorders 165 (2014) 64–68
assessed the effect of GNB3 genetic variation on depressive symptom trajectories. 1.1. Aims of the study The present study aimed to examine whether genetic variation in GNB3 moderates depressive symptom trajectories. The main hypothesis was that carriers of the rs5443 T allele would have greater rates of major depressive disorder and more severe depressive symptom trajectories over a five-year observation period compared to CC carriers. We also sought to explore variation in the 5-prime region, a region that has not been examined in depression, of the GNB3 gene.
2. Material and methods 2.1. Study population Participants were enrolled in the Diagnosis, Management and Outcomes of Depression in Primary Care (diamond) study, an ongoing prospective cohort that commenced in 2005 (Gunn et al., 2008). Details of the methods have been published elsewhere (Gunn et al., 2008, 2013; Potiriadis et al., 2008). Briefly, primary care patients were eligible for the diamond cohort if they were: (a) aged 18–75 years, (b) able to read English, (c) not terminally ill, (d) did not reside in a nursing home and (e) scored 16 or higher on the Center for Epidemiologic Studies Depression Scale [CES-D; (Radloff, 1977)]. Participants were assessed annually using postal surveys and/or computer-assisted telephone interviews. In 2011 (cohort year 6), participants enrolled in the cohort were invited to provide a saliva sample for DNA extraction and genotyping. 2.2. Measures Depressive symptoms were assessed using three different serial measures of depression: (1) DSM-IV criteria (American Psychiatric Association, 1994), (2) Primary Care Evaluation of Mental Disorders Patient Health Questionnaire-9 (PHQ-9) (Kroenke et al., 2001), and (3) CESD (Radloff, 1977). DSM-IV criteria for MDD were assessed at baseline and 24, 36, 48, and 60 months post-baseline using the Composite International Diagnostic Interview (CIDI) Auto version 2.1 (WHO, 1997) by a trained research assistant. PHQ-9 and CESD measures were given at baseline and 12, 24, 36, 48, and 60 months post-baseline. At baseline, demographics, smoking status, family history of depression, age of depression onset, health status (Ware et al., 1996), quality of life (World Health Organization, 1998), child abuse exposure (MacMillan et al., 1997), and personality (Moran et al., 2003) were assessed. Suicide ideation and attempt were assessed using items from the CIDI. Panic and other anxiety syndromes were assessed using the anxiety module of the PHQ (Spitzer et al., 1999). Alcohol and drug abuse/dependence (i.e. cannabis, opioid, sedative, cocaine, amphetamine, hallucinogens, inhalants) was assessed using the CIDI Auto version 2.1 (WHO, 1997). The current use of antidepressants, anxiolytics, antipsychotics, and herbal/alternative medications as well as self-reported lifetime presence of the most common chronic conditions seen in primary care including: hypertension, stroke, diabetes, heart disease, asthma, dermatitis and/or cancer were also assessed. 2.3. Polymorphism selection, DNA extraction and genotyping Two haplotype-tagging single nucleotide polymorphisms (htSNPs) covering the GNB3 gene including 1 Kb upstream and downstream of the gene boundaries were selected [rs5443 (C825T) and rs5440]. htSNPs were selected using the International Haplotype Map
65
(HapMap) Project (release 27) and Tagger (de Bakker et al., 2005). The minimum pairwise linkage disequilibrium (LD) rate and minor allele frequency (MAF) were set at 0.80 and 0.15, respectively. DNA was recovered from stabilized saliva samples and genotyped as part of a larger genotyping project with the Sequenom MassARRAY MALDITOF genotyping system (Sequenom Inc., San Diego, CA) as described in detail elsewhere (Bousman et al., 2014). 2.4. Statistical analysis Power calculations were conducted using the GnPower 3.1.3 repeated measures within-between interaction module (Faul et al., 2007) with the following parameters: α¼0.008 (0.05/6 comparisons), 1 β¼ 0.80, number of measures¼6, number of groups¼ 3, and correlation between measures¼0.50. PLINK (Purcell et al., 2007) was used to detect departures from Hardy Weinberg Equilibrium (HWE), determine minor allele frequency (MAF), and estimate pairwise linkage disequilibrium (LD) measures r2 and D0 . To estimate the presence of population stratification, MAFs from the three HapMap phase III population (Northern/Western European, CEU; Han Chinese, CHB; Yoruba in Nigeria, YRI) were extracted for both GNB3 SNPs and compared to the observed MAFs using a two-sample z-test. Comparison of categorical and continuous baseline characteristics by genotypes was done using chi-square and analysis of variance (ANOVA), respectively. Linear mixed models fitted with restricted maximum likelihood were used to determine trajectory differences in MDD rates, PHQ-9 and CESD symptom severity over the 60-month follow-up period by each of the selected GNB3 polymorphisms. The mixed models approach enables use of all repeated measurements, accounts for clustering of participants within primary care sites, and provides unbiased estimates in the presence of missing data. Prior to creation of interaction terms and modelling, genotype variables and covariates were centred (Kraemer and Blasey, 2004). All models included fixed effects of time, genotype, and a time genotype interaction term as well as random effects of individual, primary care site, intercept and time. Adjusted models included relevant covariates as well as covariate time and covariate genotype interaction terms. Covariance models used for the random and repeated effects were unstructured and first-order autoregressive, respectively. Sensitivity analyses, excluding participants missing follow-up assessments, were also conducted to test the robustness of findings. The Benjamini and Hochberg (B–H) step-up procedure (Benjamini and Hochberg, 1995) was used to adjust for multiple comparisons. All analyses were performed using SPSS 21.0 (IBM, Armonk, NY). 2.5. Ethics All procedures were conducted in accord with principles expressed in the Declaration of Helsinki and obtained approval from the University of Melbourne Human Research Ethics Committee (Ethics ID 1135247.1).
3. Results A total of 306 participants consented and were genotyped. We excluded from the present analysis, five participants for whom genotyping failed for one or both of the GNB3 SNPs examined. This resulted in a sample of 301 participants included in the analysis (Table 1). Power calculations showed this sample size was sufficient for detection of an interaction effect of moderate magnitude (eta squared40.08). Both htSNPs were in HWE (rs5443: p ¼0.195; rs5440: p¼ 0.689) and MAFs were 410% (rs5443: T ¼29%; rs5440: A ¼49%). LD was not present between the two SNPs (r2 ¼0.08, D0 ¼0.45) and as such both htSNPs were treated as independent. Concurring with
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Table 1 Participant characteristics for the full sample and by genotype. Baseline variables
Age, mean (sd) years Sex, % (n) female Education, % (n) tertiary Family history of depression, % (n)a Depression age of onset, mean (sd) yearsb Suicide behaviour Ideation, % (n) 1 or more attempts, % (n) Co-morbid psychiatric disorders PHQ panic syndrome, % (n) PHQ other anxiety syndrome, % (n) Co-morbid medical conditions, ever present Hypertension, % (n) Stroke, % (n) Diabetes, % (n) Heart disease, % (n) Cancer, % (n) Asthma, % (n) Dermatitis, % (n) Co-morbid substance use Current smoker, % (n) Alcohol abuse/dependence, % (n)c Any drug abuse/dependence, % (n)c Medication use Any antidepressant, % (n) Any anxiolytic, % (n) Any antipsychotic, % (n) Any herbal/alternative medication, % (n)d St. John's Wort, % (n) Quality of life and functioning WHOQOL BREF physical, mean (sd) WHOQOL BREF social, mean (sd) Self-rated health, % (n) good to excellent Childhood abuse Severe physical abuse, % (n)b Severe sexual abuse, % (n)b Personality SAPAS total score, mean (sd)d
p
Full sample
rs5443 (C825T)
(n¼301)
CC (n¼154)
CT (n¼116)
TT (n¼ 31)
50 69 25 74 17
49 71 24 74 17
52 62 23 74 18
46 81 32 76 15
(12) (207) (74) (191) (20)
(12) (110) (37) (101) (20)
(12) (72) (27) (71) (21)
p
rs5440 GG (n¼ 79)
GA (n¼ 147)
AA (n¼ 75)
50 68 23 77 16
50 71 21 72 19
(11) (25) (10) (19) (14)
0.054 0.079 0.596 0.978 0.715
50 68 31 73 17
(12) (54) (24) (50) (19)
(12) (100) (34) (95) (19)
(13) (53) (16) (46) (21)
0.991 0.919 0.314 0.716 0.647
35 (105) 1 (4)
37 (57) 2 (3)
31 (36) 1 (1)
40 (12) 0 (0)
0.478 0.588
36 (28) 1 (1)
35 (51) 1 (1)
35 (26) 3 (2)
0.985 0.363
17 (52) 20 (61)
15 (23) 19 (29)
20 (23) 25 (28)
19 (6) 13 (4)
0.560 0.284
17 (13) 17 (13)
21 (30) 24 (35)
12 (9) 18 (13)
0.261 0.347
14 1 2 3 7 17 13
16 1 2 1 7 18 14
15 2 3 5 8 14 14
7 0 0 0 3 23 10
0.348 0.510 0.346 0.072 0.625 0.459 0.822
13 (10) 1 91) 5 (4) 5 (4) 8 (6) 17 (13) 13 (10)
14 1 2 2 6 16 13
16 0 0 1 8 19 15
(12) (0) (0) (1) (6) (14) (11)
0.839 0.420 0.063 0.323 0.847 0.848 0.920
(43) (3) (7) (8) (21) (50) (40)
(24) (1) (3) (2) (11) (27) (21)
(17) (2) (4) (6) (9) (16) (16)
(2) (0) (0) (0) (1) (7) (3)
(21) (2) (3) (3) (9) (23) (19)
23 (70) 13 (39) 6 (17)
19 (29) 13 (19) 7 (10)
27 (31) 15 (17) 3 (3)
32 (10) 10 (3) 13 (4)
0.147 0.655 0.094
27 (21) 8 (6) 3 (2)
24 (35) 17 (24) 8 (11)
19 (61) 12 (9) 6 (4)
0.489 0.156 0.268
61 26 10 34 10
55 25 10 33 9
66 28 10 37 11
71 26 3 32 7
(22) (8) (1) (10) (2)
0.083 0.784 0.352 0.717 0.668
58 20 10 40 15
63 30 10 27 7
59 25 8 45 9
(44) (19) (6) (33) (7)
0.762 0.275 0.853 0.016 0.145
(182) (79) (29) (103) (29)
(84) (38) (16) (50) (14)
(76) (33) (12) (43) (13)
(46) (16) (8) (31) (12)
(92) (44) (15) (39) (10)
55 (17) 49 (24) 61 (108)
55 (17) 48 (24) 62 (96)
54 (19) 49 (24) 58 (67)
60 (16) 53 (18) 65 (20)
0.231 0.631 0.676
56 (19) 51 (22) 62 (49)
54 (18) 46 (24) 56 (82)
58 (15) 51 (22) 69 (52)
0.347 0.208 0.139
47 (140) 29 (85)
26 (40) 31 (46)
33 (38) 24 (27)
30 (9) 23 (7)
0.442 0.434
31 (24) 27 (21)
30 (43) 30 (43)
27 (20) 22 (16)
0.900 0.503
2.7 (2.0)
2.8 (1.7)
2.6 (1.6)
2.8 (1.6)
0.598
2.6 (1.5)
2.7 (1.7)
3.0 (1.7)
0.445
SAPAS: Standardised Assessment of Personality-Abbreviated Scale. PHQ: Patient Health Questionnaire. WHOQOL-BREF: World Health Organization Quality of Life-BREF. a
n¼ 257. n¼ 294. c n¼297. d n¼298. b
HapMap phase III population data, MAFs of both SNPs in the current sample did not differ (p4 0.07) from those reported in the CEU or CHB population but did differ from the YRI population (p o0.001). Depressive symptom trajectories were moderated by rs5440 but not rs5443 (C825T) genotype (Fig. 1). We observed a greater reduction in PHQ-9 (F¼4.41, df ¼2, 289.1, raw p ¼0.013, B–H p ¼ 0.039) and CESD (F¼ 5.47, df¼2, 287.6, raw p¼0.005, B–H p¼0.030) depressive symptoms over time for carriers of the rs5440 GG genotype compared to participants with either the GA or AA genotype. After adjustment for significant covariates (Table 1), the moderating effect of rs5440 remained (PHQ-9: F¼4.05, df¼2, 284.9, p¼ 0.019; CESD; F¼5.13, df¼ 2, 283.2, p¼ 0.006). Sensitivity analysis (n¼255) did not change any of the results observed in the full sample.
4. Discussion Our findings suggest that genetic variation in GNB3 moderates fiveyear depressive symptom trajectories among primary care attendees.
Carriers of the rs5440 GG genotype showed a more favourable depressive symptom trajectory than carriers of the GA or AA genotypes but we did not observe an effect for rs5443 (C825T). The rs5440 polymorphism is located approximately 500 bp upstream from the 5-prime end of GNB3 within the 3-prime untranslated region of the neighbouring leprecan-like 2 (LEPREL2) gene. We selected this polymorphism as a ‘tag’ for genetic variation within the 5-prime region of GNB3, a region in low LD with rs5443. Although, no reports of rs5440 have been published, a recent study of an elderly (age465 years) sample (Evans et al., 2013) reported an association between several sleep traits and a polymorphism (rs1047776) 207 bp upstream and in moderate LD (r2 ¼0.62) with rs5440. Interestingly, the rs1047776 allele associated with unfavourable sleep traits (e.g., long wake episodes while in bed) is in LD with the rs5440 A allele, which was associated with unfavourable depressive symptom trajectories in the current study. However, post-hoc examination showed that the rs5440 A allele was associated with the PHQ-9 F¼2.41, df ¼9.43, p ¼0.009) but not CESD (F¼1.00, df ¼9.56, p¼ 0.437) sleep question; suggesting the rs5440 A allele effect is not being driven exclusively by sleep problems.
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67
Fig. 1. Depressive symptom severity and MDD rates over 60-months (five years) by GNB3 rs5443 and rs5440 polymorphisms. Error bars represent standard error of the mean. PHQ-9: Primary Care Evaluation of Mental Disorders Patient Health Questionnaire-9; CESD: Center for Epidemiologic Studies Depression Scale.
Contrary to our primary hypothesis, rs5443 (C825T) did not moderate depressive symptom trajectories or MDD diagnosis. No study to date has looked at the effect of rs5443 on depression trajectories but a meta-analysis of all published case-control studies suggested 1.38 (95% CI ¼1.13–1.69) increased odds of MDD for every rs5443 T allele carried (Lopez-Leon et al., 2008). Discordance between our findings and those previously reported may be due to clinical differences in study populations. In the current study, participants with clinically relevant depressed mood (CESD 416) were recruited from primary care settings, of which half (49%) met criteria for MDD. In contrast, previous studies have recruited exclusively MDD patients and compared them to healthy controls. Post-hoc comparison of participants with (n¼ 148) and without (n ¼ 153) MDD at baseline showed T allele frequency among those with MDD (32%) was greater than those without (27%), albeit not significant (p¼ 0.125). In addition, examination of depressive symptom trajectories by rs5443 among participants meeting criteria for MDD at baseline (n ¼148) showed no significant effects (PHQ-9: F¼0.40, df ¼8.03, p ¼ 0.921; CESD: F¼1.20, df ¼8.41, p ¼0.293). As such, our results suggest that
rs5443 is not likely associated with depressive symptom course but replication of these findings are required before firm conclusions can be made. Three key caveats should be acknowledged. First, the sample size was relatively small and we only had statistical power to detect medium size differences (eta squared40.08) in depressive symptom trajectories. Second, the effect that rs5440 has on GNB3 gene and protein expression is not clear. A recent examination of gene expression levels of GNB3 and neighbouring genes using HapMap CEU lymphoblastoid cell lines (Evans et al., 2013) suggested that the rs1047776 variant in LD with rs5440 was not correlated (Spearman r ¼ 0.10, p¼ 0.33) with GNB3 expression. However, similar examinations using human brain tissue should be conducted before ruling out an association between rs5440 and GNB3 gene and protein expression. In addition, it is likely that an unmeasured polymorphism in LD with rs5440 is the ‘true’ moderator and that rs5440 is only a proxy for an as of yet unidentified polymorphism. Third, we attempted to rule out a range of alternative explanations for our results by examining a range of potential confounders but we cannot exclude the possibility of unmeasured
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confounding. For example, we only examined genetic variation in one of several genes coding for G proteins. It is possible that genetic variation in other G proteins modulated the effect observed. Further investigation of genetic variation in genes coding for Gα and Gγ is warranted; albeit previous investigations of the Gα subunit have not been promising (Ram et al., 1997; Zill et al., 2002). Role of funding source No funding body had a role in study design, the collection, analysis, and interpretation of data, the writing of the manuscript, or the decision to submit this manuscript for publication.
Conflict of interest None.
Acknowledgements The diamond study is funded by the National Health and Medical Research Council (IDs 299869, 454463, 566511 and 1002908) and the Victorian Centre for Excellence in Depression and Related Disorders, an initiative between beyondblue and the Victorian Government. The collection of DNA and genotyping was funded by the LEW Carty Chartable Fund (ID 7284). No funding body had a role in the study design, the collection, analysis, and interpretation of data, or the writing of the manuscript for publication. We acknowledge the 30 dedicated GPs, their patients and practice staff for making this research possible. We thank the diamond project team, including associate investigators and researchers involved in the diamond study: Ms. Aves Middleton, Ms. Konstancja Densley, Professor Helen Herrman, Professor Christopher Dowrick, Dr. Gursharan Chana and casual research staff.
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Contents lists available at ScienceDirect
Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Brief report
G-protein β3 subunit genetic variation moderates five-year depressive symptom trajectories of primary care attendees Chad A. Bousman a,b,c,d,n, Maria Potiriadis b, Ian P. Everall a,d,e, Jane M. Gunn b a
The University of Melbourne, Department of Psychiatry, VIC, Australia The University of Melbourne, Department of General Practice, Parkville, VIC, Australia c Swinburne University of Technology, Centre for Human Psychopharmacology, Hawthorne, VIC, Australia d Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia e NorthWestern Mental Health, Melbourne, VIC, Australia b
art ic l e i nf o
a b s t r a c t
Article history: Received 21 January 2014 Received in revised form 17 April 2014 Accepted 17 April 2014 Available online 24 April 2014
Background: Genetic variation in the G-protein β3 subunit (GNB3) has previously been associated with gene splicing that has been further linked to increased signal transduction and major depressive disorder. However, the effect of GNB3 genetic variation on depressive symptom trajectories is currently unknown. The aim of the present study is to examine whether genetic variation in GNB3 moderates depressive symptom trajectories among 301 primary care attendees enrolled in the Diagnosis, Management and Outcomes of Depression in Primary Care (diamond) prospective cohort study. Methods: Depressive symptoms were assessed using three measures: (1) DSM-IV criteria, (2) Primary Care Evaluation of Mental Disorders Patient Health Questionnaire-9 (PHQ-9), and (3) Center for Epidemiologic Studies Depression Scale (CESD). DSM-IV criteria were measured at baseline, 24, 36, 48, and 60 months post-baseline, whereas, PHQ-9 and CESD measurements were taken at baseline, 12, 24, 36, 48, and 60 months post-baseline. Two haplotype-tagging single nucleotide polymorphisms [rs5443 (C825T) and rs5440] spanning the GNB3 gene including 1 Kb upstream and downstream of the gene boundaries were genotyped. Results: Five-year PHQ-9 and CESD depressive symptom trajectories were moderated by rs5440. Carriers of the rs5440 GG genotype had more favourable depressive symptom trajectories compared to AG or AA genotype carriers. The rs5443 polymorphism did not moderate depressive symptom trajectories, regardless of the measure used. Limitations: Generalizability to depressed populations outside of the primary care setting may be limited. Conclusions: These results provide novel evidence suggesting genetic variation in the 5-prime region of GNB3 moderates depressive symptom trajectories among primary care attendees. & 2014 Elsevier B.V. All rights reserved.
Keywords: GNB3 Polymorphism Depression Longitudinal
1. Introduction Heterotrimeric guanine nucleotide-binding proteins (G proteins) consist of three subunits, α, β, and γ that are attached to the cell surface plasma membrane and serve as a signalling link between numerous ligands (e.g., neurotransmitters), their receptors (i.e. G protein-coupled receptors), and downstream effectors (Neves et al., 2002). Upon receptor activation, signalling can occur by way of the Gα protein or Gβγ protein complex. Although Gα proteins regulate the majority of this cell signalling, neurotransmitters associated with mood (e.g., serotonin, dopamine, epinephrine, and acetylcholine) n Corresponding author at: The University of Melbourne, Department of Psychiatry, 30 Royal Parade, Level 4, Parkville, VIC, Australia. Tel.: þ 61 3 9035 6667. E-mail address: [email protected] (C.A. Bousman).
http://dx.doi.org/10.1016/j.jad.2014.04.044 0165-0327/& 2014 Elsevier B.V. All rights reserved.
have receptors that signal through the Gβγ protein complex. In addition, many effector proteins of the Gβγ protein complex such as phosphatidylinositol 3-kinase (PI3Kβ), phospholipase C-β (PLCβ), glycogen synthase kinase-3 (GSK3), and calcium channels have been implicated in the pathophysiology of affective disorders (Kim et al., 2005; Tanis and Duman, 2007). To date, a synonymous polymorphism [rs5433 (C825T)] in exon 10 of the β3 subunit of the heterotrimeric G-protein (GNB3) has received considerable attention since it was first identified in 1998 (Siffert et al., 1998). The T allele of this polymorphism has been associated with a splice variant that is linked to increased signal transduction (Siffert et al., 1998) and major depressive disorder (MDD) (Lopez-Leon et al., 2008). However, no other polymorphisms in or near GNB3 have been examined for their association with depression phenotypes and no study to our knowledge has
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assessed the effect of GNB3 genetic variation on depressive symptom trajectories. 1.1. Aims of the study The present study aimed to examine whether genetic variation in GNB3 moderates depressive symptom trajectories. The main hypothesis was that carriers of the rs5443 T allele would have greater rates of major depressive disorder and more severe depressive symptom trajectories over a five-year observation period compared to CC carriers. We also sought to explore variation in the 5-prime region, a region that has not been examined in depression, of the GNB3 gene.
2. Material and methods 2.1. Study population Participants were enrolled in the Diagnosis, Management and Outcomes of Depression in Primary Care (diamond) study, an ongoing prospective cohort that commenced in 2005 (Gunn et al., 2008). Details of the methods have been published elsewhere (Gunn et al., 2008, 2013; Potiriadis et al., 2008). Briefly, primary care patients were eligible for the diamond cohort if they were: (a) aged 18–75 years, (b) able to read English, (c) not terminally ill, (d) did not reside in a nursing home and (e) scored 16 or higher on the Center for Epidemiologic Studies Depression Scale [CES-D; (Radloff, 1977)]. Participants were assessed annually using postal surveys and/or computer-assisted telephone interviews. In 2011 (cohort year 6), participants enrolled in the cohort were invited to provide a saliva sample for DNA extraction and genotyping. 2.2. Measures Depressive symptoms were assessed using three different serial measures of depression: (1) DSM-IV criteria (American Psychiatric Association, 1994), (2) Primary Care Evaluation of Mental Disorders Patient Health Questionnaire-9 (PHQ-9) (Kroenke et al., 2001), and (3) CESD (Radloff, 1977). DSM-IV criteria for MDD were assessed at baseline and 24, 36, 48, and 60 months post-baseline using the Composite International Diagnostic Interview (CIDI) Auto version 2.1 (WHO, 1997) by a trained research assistant. PHQ-9 and CESD measures were given at baseline and 12, 24, 36, 48, and 60 months post-baseline. At baseline, demographics, smoking status, family history of depression, age of depression onset, health status (Ware et al., 1996), quality of life (World Health Organization, 1998), child abuse exposure (MacMillan et al., 1997), and personality (Moran et al., 2003) were assessed. Suicide ideation and attempt were assessed using items from the CIDI. Panic and other anxiety syndromes were assessed using the anxiety module of the PHQ (Spitzer et al., 1999). Alcohol and drug abuse/dependence (i.e. cannabis, opioid, sedative, cocaine, amphetamine, hallucinogens, inhalants) was assessed using the CIDI Auto version 2.1 (WHO, 1997). The current use of antidepressants, anxiolytics, antipsychotics, and herbal/alternative medications as well as self-reported lifetime presence of the most common chronic conditions seen in primary care including: hypertension, stroke, diabetes, heart disease, asthma, dermatitis and/or cancer were also assessed. 2.3. Polymorphism selection, DNA extraction and genotyping Two haplotype-tagging single nucleotide polymorphisms (htSNPs) covering the GNB3 gene including 1 Kb upstream and downstream of the gene boundaries were selected [rs5443 (C825T) and rs5440]. htSNPs were selected using the International Haplotype Map
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(HapMap) Project (release 27) and Tagger (de Bakker et al., 2005). The minimum pairwise linkage disequilibrium (LD) rate and minor allele frequency (MAF) were set at 0.80 and 0.15, respectively. DNA was recovered from stabilized saliva samples and genotyped as part of a larger genotyping project with the Sequenom MassARRAY MALDITOF genotyping system (Sequenom Inc., San Diego, CA) as described in detail elsewhere (Bousman et al., 2014). 2.4. Statistical analysis Power calculations were conducted using the GnPower 3.1.3 repeated measures within-between interaction module (Faul et al., 2007) with the following parameters: α¼0.008 (0.05/6 comparisons), 1 β¼ 0.80, number of measures¼6, number of groups¼ 3, and correlation between measures¼0.50. PLINK (Purcell et al., 2007) was used to detect departures from Hardy Weinberg Equilibrium (HWE), determine minor allele frequency (MAF), and estimate pairwise linkage disequilibrium (LD) measures r2 and D0 . To estimate the presence of population stratification, MAFs from the three HapMap phase III population (Northern/Western European, CEU; Han Chinese, CHB; Yoruba in Nigeria, YRI) were extracted for both GNB3 SNPs and compared to the observed MAFs using a two-sample z-test. Comparison of categorical and continuous baseline characteristics by genotypes was done using chi-square and analysis of variance (ANOVA), respectively. Linear mixed models fitted with restricted maximum likelihood were used to determine trajectory differences in MDD rates, PHQ-9 and CESD symptom severity over the 60-month follow-up period by each of the selected GNB3 polymorphisms. The mixed models approach enables use of all repeated measurements, accounts for clustering of participants within primary care sites, and provides unbiased estimates in the presence of missing data. Prior to creation of interaction terms and modelling, genotype variables and covariates were centred (Kraemer and Blasey, 2004). All models included fixed effects of time, genotype, and a time genotype interaction term as well as random effects of individual, primary care site, intercept and time. Adjusted models included relevant covariates as well as covariate time and covariate genotype interaction terms. Covariance models used for the random and repeated effects were unstructured and first-order autoregressive, respectively. Sensitivity analyses, excluding participants missing follow-up assessments, were also conducted to test the robustness of findings. The Benjamini and Hochberg (B–H) step-up procedure (Benjamini and Hochberg, 1995) was used to adjust for multiple comparisons. All analyses were performed using SPSS 21.0 (IBM, Armonk, NY). 2.5. Ethics All procedures were conducted in accord with principles expressed in the Declaration of Helsinki and obtained approval from the University of Melbourne Human Research Ethics Committee (Ethics ID 1135247.1).
3. Results A total of 306 participants consented and were genotyped. We excluded from the present analysis, five participants for whom genotyping failed for one or both of the GNB3 SNPs examined. This resulted in a sample of 301 participants included in the analysis (Table 1). Power calculations showed this sample size was sufficient for detection of an interaction effect of moderate magnitude (eta squared40.08). Both htSNPs were in HWE (rs5443: p ¼0.195; rs5440: p¼ 0.689) and MAFs were 410% (rs5443: T ¼29%; rs5440: A ¼49%). LD was not present between the two SNPs (r2 ¼0.08, D0 ¼0.45) and as such both htSNPs were treated as independent. Concurring with
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Table 1 Participant characteristics for the full sample and by genotype. Baseline variables
Age, mean (sd) years Sex, % (n) female Education, % (n) tertiary Family history of depression, % (n)a Depression age of onset, mean (sd) yearsb Suicide behaviour Ideation, % (n) 1 or more attempts, % (n) Co-morbid psychiatric disorders PHQ panic syndrome, % (n) PHQ other anxiety syndrome, % (n) Co-morbid medical conditions, ever present Hypertension, % (n) Stroke, % (n) Diabetes, % (n) Heart disease, % (n) Cancer, % (n) Asthma, % (n) Dermatitis, % (n) Co-morbid substance use Current smoker, % (n) Alcohol abuse/dependence, % (n)c Any drug abuse/dependence, % (n)c Medication use Any antidepressant, % (n) Any anxiolytic, % (n) Any antipsychotic, % (n) Any herbal/alternative medication, % (n)d St. John's Wort, % (n) Quality of life and functioning WHOQOL BREF physical, mean (sd) WHOQOL BREF social, mean (sd) Self-rated health, % (n) good to excellent Childhood abuse Severe physical abuse, % (n)b Severe sexual abuse, % (n)b Personality SAPAS total score, mean (sd)d
p
Full sample
rs5443 (C825T)
(n¼301)
CC (n¼154)
CT (n¼116)
TT (n¼ 31)
50 69 25 74 17
49 71 24 74 17
52 62 23 74 18
46 81 32 76 15
(12) (207) (74) (191) (20)
(12) (110) (37) (101) (20)
(12) (72) (27) (71) (21)
p
rs5440 GG (n¼ 79)
GA (n¼ 147)
AA (n¼ 75)
50 68 23 77 16
50 71 21 72 19
(11) (25) (10) (19) (14)
0.054 0.079 0.596 0.978 0.715
50 68 31 73 17
(12) (54) (24) (50) (19)
(12) (100) (34) (95) (19)
(13) (53) (16) (46) (21)
0.991 0.919 0.314 0.716 0.647
35 (105) 1 (4)
37 (57) 2 (3)
31 (36) 1 (1)
40 (12) 0 (0)
0.478 0.588
36 (28) 1 (1)
35 (51) 1 (1)
35 (26) 3 (2)
0.985 0.363
17 (52) 20 (61)
15 (23) 19 (29)
20 (23) 25 (28)
19 (6) 13 (4)
0.560 0.284
17 (13) 17 (13)
21 (30) 24 (35)
12 (9) 18 (13)
0.261 0.347
14 1 2 3 7 17 13
16 1 2 1 7 18 14
15 2 3 5 8 14 14
7 0 0 0 3 23 10
0.348 0.510 0.346 0.072 0.625 0.459 0.822
13 (10) 1 91) 5 (4) 5 (4) 8 (6) 17 (13) 13 (10)
14 1 2 2 6 16 13
16 0 0 1 8 19 15
(12) (0) (0) (1) (6) (14) (11)
0.839 0.420 0.063 0.323 0.847 0.848 0.920
(43) (3) (7) (8) (21) (50) (40)
(24) (1) (3) (2) (11) (27) (21)
(17) (2) (4) (6) (9) (16) (16)
(2) (0) (0) (0) (1) (7) (3)
(21) (2) (3) (3) (9) (23) (19)
23 (70) 13 (39) 6 (17)
19 (29) 13 (19) 7 (10)
27 (31) 15 (17) 3 (3)
32 (10) 10 (3) 13 (4)
0.147 0.655 0.094
27 (21) 8 (6) 3 (2)
24 (35) 17 (24) 8 (11)
19 (61) 12 (9) 6 (4)
0.489 0.156 0.268
61 26 10 34 10
55 25 10 33 9
66 28 10 37 11
71 26 3 32 7
(22) (8) (1) (10) (2)
0.083 0.784 0.352 0.717 0.668
58 20 10 40 15
63 30 10 27 7
59 25 8 45 9
(44) (19) (6) (33) (7)
0.762 0.275 0.853 0.016 0.145
(182) (79) (29) (103) (29)
(84) (38) (16) (50) (14)
(76) (33) (12) (43) (13)
(46) (16) (8) (31) (12)
(92) (44) (15) (39) (10)
55 (17) 49 (24) 61 (108)
55 (17) 48 (24) 62 (96)
54 (19) 49 (24) 58 (67)
60 (16) 53 (18) 65 (20)
0.231 0.631 0.676
56 (19) 51 (22) 62 (49)
54 (18) 46 (24) 56 (82)
58 (15) 51 (22) 69 (52)
0.347 0.208 0.139
47 (140) 29 (85)
26 (40) 31 (46)
33 (38) 24 (27)
30 (9) 23 (7)
0.442 0.434
31 (24) 27 (21)
30 (43) 30 (43)
27 (20) 22 (16)
0.900 0.503
2.7 (2.0)
2.8 (1.7)
2.6 (1.6)
2.8 (1.6)
0.598
2.6 (1.5)
2.7 (1.7)
3.0 (1.7)
0.445
SAPAS: Standardised Assessment of Personality-Abbreviated Scale. PHQ: Patient Health Questionnaire. WHOQOL-BREF: World Health Organization Quality of Life-BREF. a
n¼ 257. n¼ 294. c n¼297. d n¼298. b
HapMap phase III population data, MAFs of both SNPs in the current sample did not differ (p4 0.07) from those reported in the CEU or CHB population but did differ from the YRI population (p o0.001). Depressive symptom trajectories were moderated by rs5440 but not rs5443 (C825T) genotype (Fig. 1). We observed a greater reduction in PHQ-9 (F¼4.41, df ¼2, 289.1, raw p ¼0.013, B–H p ¼ 0.039) and CESD (F¼ 5.47, df¼2, 287.6, raw p¼0.005, B–H p¼0.030) depressive symptoms over time for carriers of the rs5440 GG genotype compared to participants with either the GA or AA genotype. After adjustment for significant covariates (Table 1), the moderating effect of rs5440 remained (PHQ-9: F¼4.05, df¼2, 284.9, p¼ 0.019; CESD; F¼5.13, df¼ 2, 283.2, p¼ 0.006). Sensitivity analysis (n¼255) did not change any of the results observed in the full sample.
4. Discussion Our findings suggest that genetic variation in GNB3 moderates fiveyear depressive symptom trajectories among primary care attendees.
Carriers of the rs5440 GG genotype showed a more favourable depressive symptom trajectory than carriers of the GA or AA genotypes but we did not observe an effect for rs5443 (C825T). The rs5440 polymorphism is located approximately 500 bp upstream from the 5-prime end of GNB3 within the 3-prime untranslated region of the neighbouring leprecan-like 2 (LEPREL2) gene. We selected this polymorphism as a ‘tag’ for genetic variation within the 5-prime region of GNB3, a region in low LD with rs5443. Although, no reports of rs5440 have been published, a recent study of an elderly (age465 years) sample (Evans et al., 2013) reported an association between several sleep traits and a polymorphism (rs1047776) 207 bp upstream and in moderate LD (r2 ¼0.62) with rs5440. Interestingly, the rs1047776 allele associated with unfavourable sleep traits (e.g., long wake episodes while in bed) is in LD with the rs5440 A allele, which was associated with unfavourable depressive symptom trajectories in the current study. However, post-hoc examination showed that the rs5440 A allele was associated with the PHQ-9 F¼2.41, df ¼9.43, p ¼0.009) but not CESD (F¼1.00, df ¼9.56, p¼ 0.437) sleep question; suggesting the rs5440 A allele effect is not being driven exclusively by sleep problems.
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Fig. 1. Depressive symptom severity and MDD rates over 60-months (five years) by GNB3 rs5443 and rs5440 polymorphisms. Error bars represent standard error of the mean. PHQ-9: Primary Care Evaluation of Mental Disorders Patient Health Questionnaire-9; CESD: Center for Epidemiologic Studies Depression Scale.
Contrary to our primary hypothesis, rs5443 (C825T) did not moderate depressive symptom trajectories or MDD diagnosis. No study to date has looked at the effect of rs5443 on depression trajectories but a meta-analysis of all published case-control studies suggested 1.38 (95% CI ¼1.13–1.69) increased odds of MDD for every rs5443 T allele carried (Lopez-Leon et al., 2008). Discordance between our findings and those previously reported may be due to clinical differences in study populations. In the current study, participants with clinically relevant depressed mood (CESD 416) were recruited from primary care settings, of which half (49%) met criteria for MDD. In contrast, previous studies have recruited exclusively MDD patients and compared them to healthy controls. Post-hoc comparison of participants with (n¼ 148) and without (n ¼ 153) MDD at baseline showed T allele frequency among those with MDD (32%) was greater than those without (27%), albeit not significant (p¼ 0.125). In addition, examination of depressive symptom trajectories by rs5443 among participants meeting criteria for MDD at baseline (n ¼148) showed no significant effects (PHQ-9: F¼0.40, df ¼8.03, p ¼ 0.921; CESD: F¼1.20, df ¼8.41, p ¼0.293). As such, our results suggest that
rs5443 is not likely associated with depressive symptom course but replication of these findings are required before firm conclusions can be made. Three key caveats should be acknowledged. First, the sample size was relatively small and we only had statistical power to detect medium size differences (eta squared40.08) in depressive symptom trajectories. Second, the effect that rs5440 has on GNB3 gene and protein expression is not clear. A recent examination of gene expression levels of GNB3 and neighbouring genes using HapMap CEU lymphoblastoid cell lines (Evans et al., 2013) suggested that the rs1047776 variant in LD with rs5440 was not correlated (Spearman r ¼ 0.10, p¼ 0.33) with GNB3 expression. However, similar examinations using human brain tissue should be conducted before ruling out an association between rs5440 and GNB3 gene and protein expression. In addition, it is likely that an unmeasured polymorphism in LD with rs5440 is the ‘true’ moderator and that rs5440 is only a proxy for an as of yet unidentified polymorphism. Third, we attempted to rule out a range of alternative explanations for our results by examining a range of potential confounders but we cannot exclude the possibility of unmeasured
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confounding. For example, we only examined genetic variation in one of several genes coding for G proteins. It is possible that genetic variation in other G proteins modulated the effect observed. Further investigation of genetic variation in genes coding for Gα and Gγ is warranted; albeit previous investigations of the Gα subunit have not been promising (Ram et al., 1997; Zill et al., 2002). Role of funding source No funding body had a role in study design, the collection, analysis, and interpretation of data, the writing of the manuscript, or the decision to submit this manuscript for publication.
Conflict of interest None.
Acknowledgements The diamond study is funded by the National Health and Medical Research Council (IDs 299869, 454463, 566511 and 1002908) and the Victorian Centre for Excellence in Depression and Related Disorders, an initiative between beyondblue and the Victorian Government. The collection of DNA and genotyping was funded by the LEW Carty Chartable Fund (ID 7284). No funding body had a role in the study design, the collection, analysis, and interpretation of data, or the writing of the manuscript for publication. We acknowledge the 30 dedicated GPs, their patients and practice staff for making this research possible. We thank the diamond project team, including associate investigators and researchers involved in the diamond study: Ms. Aves Middleton, Ms. Konstancja Densley, Professor Helen Herrman, Professor Christopher Dowrick, Dr. Gursharan Chana and casual research staff.
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