The authors have declared that no competing interests exist.
Conceived and designed the experiments: CZ LF. Performed the experiments: SB CH. Analyzed the data: JP XW LW LS. Contributed reagents/materials/analysis tools: QM. Wrote the paper: LF PX.
TheG-protein β3 gene (GNβ3) has been implicated in psychiatric illness through its effects upon intracellular transduction of several neurotransmitter receptors. Multiple studies have investigated the relationship of the C825T polymorphism of the GNβ3 gene (GNβ3 C825T) to depression and antidepressant response. However, the relationship between GNβ3 C825T and depression remains inconsistent. Therefore, here we performed a meta-analysis to investigate the role of GNβ3 C825Tin depression risk.
Published case-control studies examining the association between GNβ3 C825T and depression were systematically searched for through several electronic databases (PubMed, Scopus, Science Direct, Springer, Embase, psyINFO, and CNKI). The association between GNβ3 C825T and depression risk were assessed by odd ratios (ORs) and their 95% confidence intervals (CIs) for each study. Pooled ORs were constructed for allele contrast (C versus T), homozygote (CC versus TT) model, heterozygote (CC versus CT) model, dominant model (CC + CT versus TT), and recessive (CC versus TT+CT) model. In order to evaluate possible biases, a sensitivity analysis was conducted by sequential deletion of individual studies in an attempt to assess the contribution of each individual dataset to the pooled OR.
Nine studies, including 1055 depressed patients and 1325 healthy controls, were included. A significant association between GNβ3 C825Tand depression was found to exist, suggesting that the T-allele of GNβ3 C825Tcan increase susceptibility to depression. After stratification by ethnicity, the same association was found in the Asian subpopulation, but not the Caucasian subpopulation.
This is the first meta-analysis to reveal a relationship between GNβ3 C825T and depression. Asian T-allele carriers of GNβ3 C825T appear to be more susceptible to depression.
Major depressive disorder (MDD) is a prevalent psychiatric disorder characterized by persistent depressed mood and anhedonia [
In particular, reduced G-protein function has been identified in the peripheral blood cells of patients with depression [
In recent years, several genome-wide association studies (GWAS) have discovered statistically significant genetic variations relevant to the etiology of depression, yielding novel insights into genetic risk factors underlying depression [
However, results from these genotyping studies have been contradictory. While some studies have found that the frequency of the T-allele of GNβ3 C825T is significantly higher in depressed patients, several other studies have shown no associations between depression and GNβ3 gene polymorphisms. Therefore, here we performed a meta-analysis to assess the relationship between depression and GNβ3 C825T.
All published studies examining the association between GNβ3 C825T and depression were systematically searched for through several electronic databases (PubMed, Scopus, Science Direct, Springer,Embase, psyINFO, and CNKI) from January 1990 to September 2014 using the following search terms: (“G protein-β-3” OR GNβ3) AND C825T AND (“mood disorders” OR “major depressive disorder” OR MDD OR “depressive episode” OR “depression”).
Only full-length articles meeting the following criteria were included: (i) a case-control design; (ii) evaluating GNβ3 C825T and depression risk; (iii) an adequate description of the diagnostic criteria for patient inclusion and exclusion; and (iv) sufficient reported data for estimating odds ratios (ORs) and their 95% confidence intervals (95% CIs). Abstracts, conference proceedings, case studies, family-based designs, population-based studies of healthy subjects, reviews, and duplicate cohorts were excluded.
Three authors independently extracted data to avoid extraction errors with discrepancies resolved by discussion. The following parameters were extracted from each eligible article: first author, publication year, country of origin, ethnicity (defined as either Asian or Caucasian), diagnostic system, number of cases and controls (male/female), antidepressant therapy, Hardy-Weinberg equilibrium, the available genotype, and allele frequency information for the C825T polymorphism.
All statistical analyses were conducted using Rev Man 5.0.1 and STATA software (version 12.1; Stata Corporation, College Station, Texas, USA). All
In order to evaluate possible biases, a sensitivity analysis was conducted by sequential deletion of individual studies in an attempt to assess the contribution of each individual dataset to the pooled OR. Finally, we estimated publication bias by Egger’s test with a
The study selection procedure is shown in
Hence, nine studies were ultimately included in this meta-analysis based on our inclusion criteria [
Author | Year | Country | Ethnicity | Diagnosis | Ratingscale | Controls(F)mean age±SD | Depression(F)mean age±SD | Genotyping method |
---|---|---|---|---|---|---|---|---|
Alessandro | 2012 | Italy | Caucasian | MDD | - | 76- | 222(161)50.06±15.02 | - |
Anttila | 2007 | Finland | Caucasian | Depression | - | 392(182)44.4±11.1 | 119(65)57.7±14.0 | Taq ManAssay |
Cao | 2007 | China | East Asian | Depression | HAMD-17≥18 | 156(80)54.44±6.542 | 180(96)55.84±8.522 | PCR-RFLP |
Chen | 2011 | China | East Asian | PSD | HAMD-24≥21 | 106(34)60.7 ±13.2 | 53(20)62.9 ±13.8 | PCR-RFLP |
Kunugi | 2002 | Japan | East Asian | Depression | - | 198(104)30.0 ± 8.1 | 68(44)54.6 ± 14.1 | PCR-RFLP |
Lee | 2004 | Korea | East Asian | MDD | HAMD-17>18 | 133(89)43.4±10.2 | 106(78)47.1±13.3 | PCR-RFLP |
Lin | 2001 | Taiwan | East Asian | Depression | - | 153(72)39.8 ±18.1 | 65(40)39.8 ± 13.7 | PCR-RFLP |
Peter | 2000 | Germany | Caucasian | Depression | HAMD-1726.7 ±6.4 | 111(57)47.3 ±12.1 | 88(59)51.6 ±13.0 | PCR-RFLP |
Xiao | 2002 | China | East Asian | Depression | HAMD-17>17 | 100(50)28±7 | 154(93)43±14 | PCR-RFLP |
MDD: major depressive disorder; HAMD: Hamilton Depression Rating Scale; PSD: post-stroke depression.
Author | Diagnosis | Cases | Genotype distribution (%) | Allele frequency (%) | HWE | |||
---|---|---|---|---|---|---|---|---|
CC | CT | TT | C | T | ||||
Alessandro | Control | 76 | 36(47) | 31(41) | 9(12) | 103(68.0) | 49(32.0) | Yes |
MDD | 222 | 86(39) | 115(52) | 21(9) | 287(65.0) | 157(35.0) | ||
Anttila | Control | 392 | 218 (55.6) | 144 (36.7) | 30 (7.7) | 580(74.0) | 204(26.0) | Yes |
Depression | 119 | 63 (52.9) | 46 (38.7) | 10 (8.4) | 172(72.3) | 66(27.7) | ||
Cao | Control | 156 | 44(28.2) | 72(46.2) | 40(25.6) | 160(51.3) | 152(48.7) | Yes |
Depression | 180 | 20(11.1) | 76(42.2) | 84(46.7) | 116(32.2) | 244(67.8) | ||
Chen | Controls | 106 | 29 (27.4) | 41 (38.7) | 36(34.0) | 99(46.7) | 113 (53.3) | Yes |
PSD | 53 | 8(15.1) | 22 (41.5) | 23 (43.4) | 38 (35.8) | 68 (64.2) | ||
Kunugi | Control | 198 | 49 (24.7) | 90 (45.5) | 59 (29.8) | 188 (47.5) | 208 (52.5) | Yes |
Depression | 68 | 16 (23.5) | 32 (47.1) | 20 (29.4) | 64 (47.1) | 72 (52.9) | ||
Lee | Control | 133 | 43 (32.3) | 62 (46.6) | 28 (21.1) | 148(56.0) | 118(44.0) | Yes |
MDD | 106 | 19 (17.9) | 60 (56.6) | 27 (25.5) | 98(46.0) | 114(54.0) | ||
Lin | Control | 153 | 31 (20.0) | 90 (59.0) | 32 (21.0) | 152(52.0) | 154(48.0) | Yes |
Depression | 65 | 16 (25.0) | 36 (55.0) | 13 (20.0) | 68(52.3) | 62(47.7) | ||
Peter | Control | 111 | 57 (52.0) | 46 (41.0) | 8 (7.0) | 160(72.0) | 62(28.0) | Yes |
Depression | 88 | 33 (38.0) | 36 (41.0) | 19 (21.0) | 102(58.0) | 74(42.0) | ||
Xiao | Control | 100 | 27(27.0) | 51(51.0) | 22(22.0) | 105(52.5) | 95(47.5) | Yes |
Depression | 154 | 35(22.7) | 49(31.8) | 70(44.8) | 119(38.6) | 189(61.4) |
HWE: Hardy-Weinberg equilibrium; MDD: major depressive disorder; PSD:post-stroke depression.
The nine case-control studies, consisting of 1055 depressed cases and 1325 controls, were pooled together to assess the association between depression and GNβ3 C825T. On the basis of the random effects model, the pooled OR for the T-allele of GNβ3 C825T showed a significant correlation with depression risk under the allele model (C-allele versus T-allele: OR = 1.39, 95% CI = 1.13–1.72, Z = 3.10,
Overall and subgroup forest plots showing the summary effect sizes and heterogeneity findings for (A) C-allele versus T-allele and (B) the recessive model (CC versus CT+TT).
Overall and subgroup forest plots showing the summary effect sizes and heterogeneity findings for (A) TT homozygosity versus CC homozygosity, (B) the heterozygote model (CC versus CT), and (C) the dominant model (CC + CT versus TT).
A subgroup analysis was performed based on ethnicity. The ethnicity-stratified analysis indicated that GNβ3 C825T is strongly related to depression risk in the Asian subpopulation under all genetic models except for the heterozygote model (CC versus CT: OR = 1.35, 95% CI = 0.87–2.08,
C-allele vs. T-allele | CC vs. TT | CC vs. CT | CC vs. CT + TT | CC + CT vs. TT | ||||||
---|---|---|---|---|---|---|---|---|---|---|
OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | ||||||
Overall | 1.39(1.13, 1.72) | 0.002 | 1.84 (1.20, 2.83) | 0.005 | 1.32(1.08, 1.62) | 0.007 | 1.53(1.15,2.04) | 0.003 | 1.54 (1.08, 2.18) | 0.02 |
By ethnicity | ||||||||||
Caucasian | 1.31 (0.95, 1.80) | 0.10 | 1.63(0.70, 3.78) | 0.26 | 1.28(0.95, 1.72) | 0.11 | 1.44(0.99, 2.09) | 0.051 | 1.43 (0.61, 3.36) | 0.42 |
East Asian | 1.43 (1.08, 1.90) | 0.01 | 1.96(1.16, 3.31) | 0.01 | 1.35(0.87, 2.08) | 0.18 | 1.58 (1.03, 2.42) | 0.04 | 1.59(1.07, 2.36) | 0.02 |
Significant heterogeneity was found among ORs in overall comparisons (I2 = 64%, Tau2 = 0.06 for allele model; I2 = 61%, Tau2 = 0.26 for homozygote model; I2 = 61%, Tau2 = 0.17 for dominant model), while no heterogeneity was found under the heterozygote model (I2 = 37%, Tau2 = 12.76). To determine the origins of the heterogeneity, subgroup analysis on ethnicity was carried out as described above. However, significant heterogeneity remained among the Asian and Caucasian subpopulations.
Sensitivity analyses were conducted with the leave-one-out method to assess the degree to which each individual study influenced the results of the overall analysis. The results of the sensitivity analysis confirmed that no single study influenced the pooled ORs (
To our knowledge, this is the first meta-analysis to demonstrate a relationship between GNβ3 C825T and depression. We used 5 models to estimate the relationship between G protein-β-3 gene C825T polymorphism and depression. A significant association between T-allele withinGNβ3 C825T and depression were found both in the homozygote and heterozygote genotype variation. The results of the dominant model and the recessive model supported CT genotype and TT genotype respectively could increase the risk of depression. Notably, compared with cohorts without the variation, the frequency of the GNβ3 C825T TT genotypes in depressed patients was significantly higher than that of healthy controls with an increase of depression by 84 percent; the heterozygote variation (CT) caused an increase of depression by 32 percent as well. The results of our meta-analysis among all the 5 models showed that GNβ3 C825T polymorphism increased a risk of depression and the sensitivity analyses further confirmed the stability of the results, suggesting that GNβ3 C825T may be an important heritable factor underlying the genetic mechanism of depression. Our results also show a significant association between the T-allele of GNβ3 C825T and depression risk in Asians, but not in Caucasians.
GNβ3 C825T has been shown to be predictive of depressive mood in a young, healthy Western population [
Thus far, the majority of psychiatric studies have focused on investigating the function and expression of G-proteins in affective disorders. G-proteins are composed of three subunits, which can dissociate into Gα and Gβγ units after receptor activation. The Gβ subunit is further subdivided into three subtypes: 1, 2, and 3 [
Ever since Siffert et al. first identified a genetic variant (C825T) in exon 10 of the G-protein gene [
Significant heterogeneity was found among ORs in the allele model, homozygote model, and dominant model. Possible factors underlying this high heterogeneity may include age, gender, and ethnicity. However, no differences were detected after an ethnicity-based subgroup analysis. Gender differences were also considered; however, due to the lack of reported data, we could not perform this analysis. Notably, Anttila et al. has previously identified an association between GNβ3 C825T and depression risk in females but an opposing trend in males [
Several limitations should be mentioned with respect to our findings. Firstly, the number of included studies was not sufficient for a comprehensive analysis of GNβ3 C825T and depression risk in the Caucasian subpopulation. Thus, more studies are needed to explore the relationship between GNβ3 C825T and depression in Caucasians. Secondly, only English studies were included in the meta-analysis. This may have been a source of publication bias although no such publication bias was found in our meta-analysis. Thirdly, we did not analyze the possible impact of gender differences, which may explain the observed heterogeneity. Finally, one study by Chen et al., which mainly targeted PSD patients, was not excluded from this study, as it could be classified into depression. The sensitivity analyses indicated that this study did not influence the effect size or conclusions.
In conclusion, this is the first meta-analysis to reveal a relationship between GNβ3 C825T and depression. We found that Asian T-allele carriers of GNβ3 C825T are more susceptible to depression. In contrast, no significant association between T-allele carriers of GNβ3 C825T and depression risk was found in Caucasians. These results may provide clinicians and public health administrators with an important screening tool for assessing depression. As many factors have been associated with depression risk, additional factors (such as gender, age, ethnicity, and environmental factors) should be taken into consideration in future studies on this topic.
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We thank the scientific editors at Impactys (