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Schizophr Res. Author manuscript; available in PMC Jan 1, 2009.
Published in final edited form as:
PMCID: PMC2237895
NIHMSID: NIHMS37867

Allelic association of G72/G30 with schizophrenia and bipolar disorder: a comprehensive meta-analysis

Abstract

The G72/G30 gene complex (G72 also known as D-amino acid oxidase activator, DAOA) and its chromosomal region 13q32–34 have been linked and associated with both schizophrenia (SCZ) and bipolar disorder (BP) in multiple studies, including our initial association report on BP. However, the inconsistency of associated variants across studies is obvious. Previous meta-analyses had small data sets. The present meta-analysis combined 18 association articles published before April of 2007. There were 19 independent studies of SCZ, with 4304 cases, 5423 controls, and 1384 families, and four independent studies of BP with 1145 cases, 1829 controls, and 174 families. Of 15 single nucleotide polymorphisms (SNPs) analyzed in the 95-kb G72/G30 gene region, M18/rs947267 and M22/rs778293 showed association with SCZ in Asians, and M24/rs1421292 with SCZ in Europeans. The associations of C allele at M18 and A allele at M22 with SCZ in Asians survived conservative Bonferroni correction for multiple testing for 15 markers and subgroup analysis (adjusted P = 0.0000253 for M18; adjusted P = 0.009 for M22). No single maker showed evidence of overall association with BP. These results suggest that G72/G30 may influence susceptibility to schizophrenia with weak effects.

Keywords: G72, G30, D-amino acid oxidase activator (DAOA), single nucleotide polymorphism, schizophrenia, bipolar disorder, meta-analysis, association

1. Introduction

Kraepelin’s dichotomy implies that schizophrenia (SCZ) and bipolar disorder (BP) are two separate disease entities with different underlying etiologies (Kendell, 1987; Kraepelin, 1919). However, SCZ and BP show an overlap in clinical symptoms (including increased suicide risk) (Kravariti et al., 2005; Martinez-Aran et al., 2002; Seidman et al., 2002; Van et al., 1999) as well as in genetic etiology as discussed below.

Twin and adoption studies over much of the 20th century provided consistent evidence for a genetic component that increases susceptibility to each disorder (Shih et al., 2004). Systematic diagnosis of relatives using structured instruments suggests that there is a substantial overlap in co-aggregation of other mood disorders (major depression and schizoaffective disorder) in families with an individual suffering from a core disorder of BP or SCZ (Gershon et al., 1982; Gershon et al., 1988; Maier et al., 1993). This implies shared genetic susceptibility loci for SCZ and BP (Cardno et al., 2002; Farmer et al., 1987; Gershon et al., 1982; Gershon et al., 1988; Kendler et al., 1985; Kendler, 1986; Kendler et al., 1993; Maier et al., 1993).

Recent linkage and association data also suggests overlap in genetic etiology for the two disorders (Craddock et al., 2005; Craddock et al., 2006; Potash, 2006). A meta-analysis of all published whole-genome linkage scans before 2000 implicated two regions (on 13q and 22q) significantly linked to each disorder (Badner and Gershon, 2002). Shortly afterward, the G72/G30 gene complex (G72 is now officially known as D-aminoacid oxidase activator, DAOA) was associated, independently, with each disorder (Chumakov et al., 2002; Hattori et al., 2003). An association has been reported in multiple independent studies (Addington et al., 2004; Chen et al., 2004; Fallin et al., 2005; Hall et al., 2004; Hong et al., 2006; Korostishevsky et al., 2004; Korostishevsky et al., 2006; Ma et al., 2006; Schulze et al., 2005; Schumacher et al., 2004; Wang et al., 2004; Williams et al., 2006; Yue et al., 2006; Yue et al., 2007; Zou et al., 2005). However, several studies did not find association with traditional BP or SCZ phenotype (Bakker et al., 2007; Goldberg et al., 2006; Liu et al., 2006; Mulle et al., 2005; Vilella et al., 2007; Wood et al., 2006). In addition, an obvious inconsistency of allelic association(s) has been observed.

In a meta-analysis using Fisher’s chi-square method (combining P values across studies), Detera-Wadleigh and McMahon (Detera-Wadleigh and McMahon, 2006) reviewed seven G72/G30 association studies in SCZ and three in BD, and found that there were several single nucleotide polymorphisms (SNPs) significantly associated with both SCZ and/or BP. In a recent meta-analysis of 11 studies including 2138 cases, 2228 controls and 463 parent-offspring trios, Li and He (Li and He, 2007) found four markers in the G72/G30 gene region (M12/rs3916965, M15/rs2391191, M23/rs3918342 and M24/rs1421292) significantly associated with SCZ. M12, M15 and M23 showed significant evidence of between-study heterogeneity. No evidence of association is shown in the meta-analysis of G72/G30 data in the SchizophreniaGene Database of Schizophrenia Research Forum (http://www.schizophreniaforum.org/res/sczgene/default.asp), which does not include family-based association data.

Since then, 13 more G72/G30 association studies in SCZ and three more in BP have been published (Bakker et al., 2007; Fallin et al., 2005; Goldberg et al., 2006; Hong et al., 2006; Korostishevsky et al., 2006; Liu et al., 2006; Ma et al., 2006; Nicodemus et al., 2007; Schulze et al., 2005; Vilella et al., 2007; Williams et al., 2006; Wood et al., 2006; Yue et al., 2006; Yue et al., 2007), with inconsistent associated alleles or haplotypes. Here we report an updated meta-analysis to re-evaluate the relationship between variants in the G72/G30 gene region and these two major psychiatric disorders.

2. Methods

2.1. Literature searches

Selection of literature was accomplished by searching the PubMed database of the National Center for Biotechnology Information (NCBI) using the keywords “schizophrenia”, “bipolar disorder”, “G72”, “G30” and “DAOA”. All English literature published before April 12, 2007 was analyzed. All references cited in articles on association analyses, reviews and/or meta-analyses were examined to identify potential additional studies not revealed by searching the PubMed. The association studies in SCZ were also extracted from the SchizophreniaGene Database by searching gene of “DAOA” (http://www.schizophreniaforum.org/res/sczgene/default.asp), which was accessed on April 12, 2007.

2.2. Inclusion Criteria

Studies included in our meta-analysis had to fulfill five criteria: (1) published in a peer-reviewed journal; (2) detailed description of the samples tested (including sample size, ancestry of samples, diagnostic criteria for schizophrenia or bipolar disorder (sub-phenotypes or endo-phenotypes not considered); (3) data published or furnished by correspondence. These data had to contain marker information (marker name or position on chromosome), allele frequency in case and control groups in population-based studies or numbers of transmitted and non-transmitted alleles from heterozygous parents to affected offspring in family-based studies; (4) sample not duplicative of other reports; and (5) there had to be at least three studies reporting results for each marker subjected to meta-analysis.

2.3. Statistical Analyses

We performed allelic meta-analyses on SCZ and BP separately. We excluded SNP reports if they violated the Hardy-Weinberg Equilibrium (HWE) in the controls of population-based studies or in the parents of family-based data.

A single report was considered as containing separate association studies when it included unrelated both case-control and family-based data, analyzed each disorder separately, or if it separately analyzed samples from distinct populations. We integrated population-based and family-based association studies into a single meta-analysis using the method described in detail elsewhere (Cho et al., 2005; Kazeem and Farrall, 2005; Lohmueller et al., 2003). Briefly, counts of alleles in case and control groups in population-based studies were summarized in two-by-two tables. For family-based studies, the number of each transmitted allele from heterozygous parents to affected offspring were treated as the number of occurrence of that allele in cases and considering the controls to be a very large population with equal numbers of each allele (to reflect the expected 50:50 transmission ratio (Lohmueller et al., 2003). The transmitted and non-transmitted risk alleles were summarized in two-by-one tables. For each table, the natural logarithms of odds ratios (ORs) and standard errors (SEs) were calculated based on the allelic data (Kazeem and Farrall, 2005). The “risk” alleles were assigned according to the initial reports (Chumakov et al., 2002; Hattori et al., 2003).

Cochran’s chi-square-based Q-statistic test was applied to assess between-study heterogeneity. Q is distributed approximately as a chi-square statistic with k-1 degrees of freedom (k = number of studies). Overall ORs and their 95% CIs were estimated under both the Mantel-Haenszel’s fixed-effects (MANTEL and HAENSZEL, 1959) and DerSimonian-Laird’s random-effects models (DerSimonian and Laird, 1986). The fixed-effects model assumes all of the studies are estimating the same underlying effect and considers only within-study variation, while the random-effects model assumes between-study heterogeneity attributed to both between-study and within-study variance. The random-effects model is more conservative than the fixed-effects model and produces a wider confidence interval. When there is no evidence of heterogeneity, the random-effects model will give similar results to the fixed-effects model. Significance of overall OR was examined using a Z-test, and the P values were adjusted using Bonferroni correction when there were multiple tests.

For a sensitivity analysis, each individual study was removed in turn from the analysis, and meta-analysis was re-performed. This approach can determine whether a finding in the meta-analysis is due to contribution of a single study.

Evidence for publication bias was assessed using Egger’s regression asymmetry statistics with a funnel plot of natural logarithm of OR (Ln(OR)) against inverse SE in each study (Egger et al., 1997).

The meta-analyses were carried out using the program Meta-analysis with Interactive eXplanations (MIX, version 1.54) (Bax et al., 2006). To detect potential effects of different ancestries (European versus Asian) or study design types (case-control versus family-based), overall ORs and SEs were obtained for each group, then heterogeneity of overall ORs between different ancestries or study designs were assessed using a chi-square test with one degree of freedom, similar to the method introduced by Kazeem and Farrall (Kazeem and Farrall, 2005). A significance level was set at P = 0.05 for all tests with exception of P = 0.1 for the Q-test and Egger’s test.

The Haploview program (version 4.0) (Barrett et al., 2005) was used to assess linkage disequilibrium (LD) between marker pairs and construct haplotype blocks on the genotype data from the 30 Centre d’Etude du Polymorphisme Humain (CEPH) trios in the International HapMap Project (http://www.hapmap.org). We focused on the 95-kb G72/G30 gene region from M12 to M24 (HapMap Data Rel21a/phaseII Jan07, On NCBI B35 assembly, dbSNP b125).

3. Results

3.1. Description of Studies

Several studies were excluded because of sample duplication with another study (Yue et al., 2006), lack of sufficient data even after requesting informative data from the authors (Fallin et al., 2005; Goldberg et al., 2006; Hall et al., 2004; Nicodemus et al., 2007). M12, M15, M23 and M24 were excluded for BP because they were analyzed in only one or two studies (Schulze et al., 2005). After these exclusions, a total of 18 articles were used for meta-analysis. There were 16 articles with 19 independent samples on SCZ, and four studies with four independent samples on BP published between 2002 and April, 2007. These studies are summarized in Table 1. The studies on SCZ include 4304 cases and 5423 controls in case-control studies and 1384 families in family-based studies. The four studies on BP include 1145 cases and 1829 controls in case-control studies and 174 families in our family-based association study. The details of most of the case-control studies in SCZ can also be found in the SchizophreniaGene Database. When the genotypes in the Database (calculated based on assuming them in HWE) are different from the original data provided by the authors, we used the original.

Table 1
Characteristics of studies included in the meta-analysis

3.2. Meta-analysis of Studies on Schizophrenia

For all samples combined, seven SNPs revealed significant between-study heterogeneity (P(Q) < 0.1); M18 and M24 showed nominally significant overall association (uncorrected P(Z) < 0.05) (Table 2), which could not survive correction for multiple testing for 15 markers (Bonferroni corrected P > 0.05). No publication bias was found in Egger’s regression analyses.

Table 2
Meta-analyses of allelic association studies of the G72/G30 gene with psychiatric disorders

In the European samples, the minor allele T at M24 revealed nominally significant association evidence (Table 2); four SNPs showed significant between-study heterogeneity (Table 2). In the Asian samples, the minor allele C of M18 and the major allele A of M22 were significantly associated with disease and showed no evidence for between-study heterogeneity (Table 2). The association remained significant after conservative Bonferroni correction for multiple testing for 15 markers and subgroup analyses (Table 2, adjusted P = 0.0000253 for M18; P = 0.009 for M22). The Forest plots of M18 and M22 analyzed in Asian and M24 in combined samples are shown in Figure 1. M18, M22, and M24 are located in three different haplotype blocks (Supplementary Fig. 1, similar LD structure in the Han Chinese HapMap samples not shown). M12 and M15 revealed significant evidence for between-study heterogeneity but not for association in Asian samples (Table 2).

Fig. 1
Forest plots of Ln(OR) with 95% CI for each schizophrenia-associated polymorphism in the G72/G30 gene region. Solid squares and horizontal lines indicate the Ln(OR)s and 95% CIs, respectively. The pooled results are shown by the open diamonds. Results ...

M18 and M22 showed significant heterogeneity between European and Asian samples (M18: P = 0.001; M22: P = 0.002. Supplementary Table 1). The markers rs1935058 and M18 had significant heterogeneity between study designs (population-based versus family-based) (rs1935058: P = 0.04; M18: P = 0.02. Supplementary Table 1).

3.3. Meta-analysis of Studies on BP

None of the five SNPs analyzed was significantly associated with BP (Table 2). All the five SNPs showed significant evidence for heterogeneity between studies in European samples (Table 2) as well as between study designs (Supplementary Table 1). Egger’s regression analyses showed evidence for publication bias for two markers (M19, P = 0.09; rs1341402, P = 0.005). However, power to detect publication bias is generally low (Macaskill et al., 2001), and is further limited when there are few studies (Egger et al., 1997).

4. Discussion

The findings of association of the G72/G30 gene complex with SCZ and BP in multiple studies have been reviewed as the most promising current associations in both disorders (Craddock et al., 2006; Craddock et al., 2005; Maier et al., 2005). However, the associated alleles or haplotypes at the G72/G30 gene region vary across different studies (Supplementary Table 2), further confirmed by significant heterogeneity between individual studies for a large proportion of the markers in the present meta-analysis (Table 2). The heterogeneity may result from: (1) different ancestries (Asian versus European) and (2) study designs (case-control versus family-based) (Supplementary Table 1); (3) incomplete genotyping rates or genotyping error rates in different studies. However, it is impossible to test this possibility as such data can not be obtained for each study (Munafo et al., 2006); (4) population-specific association. For example, M18 and M22 show an association with SCZ in Asians while M24 is associated with SCZ in Europeans, particularly they are in three different haplotype blocks (Supplementary Fig. 1) and not in LD with each other based on HapMap data, implying multiple susceptibility variants in this region, which could be identified by deep resequencing (Topol and Frazer, 2007). However, rare and uncommon variant association(s) may not have been detectable by analysis of individual SNPs in the reviewed association studies which all focused on common variants; (5) SNPs violating Hardy-Weinberg equilibrium included in some studies, which seems inapplicable to the current meta-analysis as we excluded such studies; (6) phenotype heterogeneity due to sample diagnosis and ascertainment. Endo-phenotype or sub-phenotype analysis has been proposed to make the patients with psychiatric disorders more etiologically homogeneous and thus may help to address the non-replications in genetic association studies (Gottesman and Gould, 2003; Hasler et al., 2006); and (7) gene-gene and gene-environment interactions. However, no evidence on this has yet been published.

We found that two markers (M18 and M22) at the G72/G30 gene region were significantly associated with SCZ in Asian samples, even after conservative Bonferroni correction for multiple testing (15 markers, 2 subgroups and the whole combined sample, thus 45 tests). This association has not been detected in previous meta-analyses (Detera-Wadleigh and McMahon, 2006; Li and He, 2007), and needs to be further confirmed in larger sample because of few studies included and the modest power to detect both allelic association and between-study heterogeneity. For example, we had around 30% power to detect pooled effects of M22 with comparable 1500 cases and 1500 controls in Asians (estimated by Genetic Power Calculation, http://pngu.mgh.harvard.edu/~purcell/gpc/). Interestingly, both the minor allele C at M18 and major allele A at M22 showed a “protective” role in the development of disease (OR < 1). One possibility is that the corresponding alleles (A at M18 and G at M22) may tag two different causative/susceptibility alleles because they are not in LD with each other. The T allele at M24 showed a potential association with SCZ in Europeans or in the combined samples (Table 2), consistent with a previous meta-analysis (Li and He, 2007). No publication bias was found for this SNP (data not shown). But sensitivity analysis identified the original study (Chumakov et al., 2002) with a major contribution to the association (after excluding the original study in Canadians, OR = 1.07; 95% CI: 0.99–1.16, unadjusted P = 0.07). This also needs confirmation in larger samples, due to weak effects of the risk allele (OR = 1.1) and no significant association after correction for multiple testing.

Our meta-analysis of BP data cannot draw a definitive conclusion, because few studies were included, the sample size was modest for each SNP, and significant heterogeneity existed between either individual studies or study designs (Table 2 and Supplementary Table 1).

We did not perform haplotypic meta-analysis of the G72/G30 association data, because of different haplotypes and different individual SNPs tested in different studies (Supplementary Table 2), different statistical methods of haplotype analysis employed, and/or lack of an appropriate meta-analytic method for the haplotype data. We did not meta-analyze the genotypic data either, because (1) genotype data were not available for some studies, even after contacting the authors, (2) a decreased number of studies would decrease the power of meta-analyses of the genotype data, and (3) family-based association data could not be included in the genotypic meta-analysis due to lack of appropriate methods.

In conclusion, the present meta-analysis found significant evidence for associations of M18 and M22 with SCZ in Asians, and suggestive evidence for association of M24 with SCZ in Europeans. Thus, G72/G30 may influence susceptibility to schizophrenia with weak effects.

Supplementary Material

01

Supplementary Fig. 1:

Linkage disequilibrium (LD) plot across the G72/G30 gene region. The LD structure was constructed using the genotype data of the 30 Centre d’Etude du Polymorphisme Humain (CEPH) trios in the International HapMap Project. The horizontal black line depicts the 95-kb genomic region spanning the G72/G30 gene. The 15 single nucleotide polymorphisms (SNPs) included in our meta-analysis and the structure of the G72/G30 gene, with vertical lines indicating exons, are shown above the bar indicating the chromosome 13 genomic location in green and in lavender, respectively. A total of 128 SNPs with minor allele frequency ≥ 0.1 in the region between M12 (rs3916965) and M24 (rs1421292) are shown below the black chromosome line. A linkage disequilibrium plot is shown in the bottom part of the figure based on the measure D’/LOD: each square represents the magnitude of linkage disequilibrium for a single pair of SNPs, with red color indicating linkage disequilibrium that is strong (D' > 0.8) and statistically significant (logarithm of odds >2.0), and white indicating D’ < 0.5. This figure was prepared using the program LocusView 2.0 (T. Petryshen, A. Kirby, M. Ainscow, unpublished software; http://www.broad.mit.edu/mpg/locusview). Genomic positions are in accordance with the National Center for Biotechnology Information build 35 of the human genome assembly 17.

02

Supplementary Table 1:

Assessment of heterogeneity between ancestries and between study designs in G72/G30 association studies

aMarkers beginning with M were annotated by Chumakov et al. 2002; the risk alleles reported by Chumakov et al or by Hattori et al were in parentheses. bSignificance test for heterogeneity between European and Asian populations. P-values < 0.05 are in bold type. NA indicates association studies performed only in European samples. cSignificance test for heterogeneity between Case-Control and family-based studies. P-values are in bold type. NA indicates all association studies performed using Case-Control design.

03

Supplementary Table 2:

Haplotype blocks in the G72/G30 gene region reported to be associated with SCZ or BP

Studies included in the meta-analysis are described in detail in Table 1 in the text. Haplotypes showing most significant association in individual study are highlighted in boxes, upward and downward arrows indicate the haplotype with the minimum P-value in each study increasing and decreasing disease risk, respectively. Key: M-, SNP ID assigned by Chumakov et al 2002. *, significant allelic association; ns, not significant at P < 0.05; -, global P-value not reported.

Acknowledgments

This work was supported by the National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Awards (to C Liu and J Shi), Brain Research Foundation at the University of Chicago (to C Liu), and NIH MH065560-02, MH61613-05A1 (to ES Gershon). We are thankful to those authors who kindly provided their raw data or described their data in details upon our request. We thank Dr. Dawei Li at the laboratory of statistical genetics of Rockefeller University, for helping prepare the Forest plot.

Appendix A. Supplementary data

Supplementary data associated with this article is available in the online version, at the Schizophrenia Research website.

Footnotes

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