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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Psychiatr Genet. Author manuscript; available in PMC Oct 24, 2008.
Published in final edited form as:
PMCID: PMC2572255

PDLIM5 and susceptibility to bipolar disorder: a family-based association study and meta-analysis



The postsynaptic density-95/discs large/zone occludens-1 (PDZ) domain and LIM (Lin-11, Isl-1, and Mec-3) domain 5 (PDLIM5) gene has been analyzed as a candidate gene for both schizophrenia (SZ) and bipolar disorder (BP) in Japanese samples. We performed a family-based association study to test the hypothesis that variants in PDLIM5 increase susceptibility to BP in European Americans and a meta-analysis to clarify whether there is a single marker consistently contributing to risk for BP.


Five single nucleotide polymorphisms (SNPs) in the PDLIM5 gene were genotyped in 290 European American BP families. Programs Sibling-Transmission/Disequilibrium Test (sib_tdt) and PDTPHASE were used for allelic and haplotypic association, respectively. We carried out a meta-analysis combing our family-based data and case-control data from two Japanese sample sets and from two genome-wide association (GWA) studies.


Our association analysis showed no single SNP associated with BP. A rare haplotype consisted of rs10008257 and rs2433320 had nominal association (p = 0.045), which failed to survive correction for multiple tests. The meta-analysis identified a significant allelic association at rs2433320 in all combined samples (excluding overlapped samples in GWA: overall OR = 0.897, 95% CI: 0.838-0.961, adjusted p = 0.012) and in all Caucasian samples (excluding overlapped samples in GWA: overall OR = 0.905, 95% CI: 0.843-0.971, adjusted p = 0.032), but not in the Japanese samples.


PDLIM5 may play a minor effect on susceptibility to bipolar disorder in Caucasians. The findings in Japanese need further confirmation in larger independent samples.

Keywords: PDLIM5, bipolar disorder, association study, haplotype, single nucleotide polymorphism


The PDLIM5 gene encodes an enigma-homologue (ENH) protein containing a postsynaptic density-95/discs large/zone occludens-1 (PDZ) domain and three LIM (Lin-11, Isl-1, and Mec-3) domains (Ueki et al., 1999). There are two splicing isoforms of PDLIM5 gene. The long gene spans 216.3 Kb with one non-coding and 12 coding exons while the short gene spans 136.3 Kb with one non-coding and 9 coding exons. Several lines of evidence suggest PDLIM5 as a candidate gene for both schizophrenia (SZ) and bipolar disorder (BP). First, PDLIM5 is significantly upregulated in postmortem brains of patients with SZ or BP from the Stanley Medical Research Institute's (SMRI) brain collections (Iwamoto et al., 2004b; Kato et al., 2005) and abnormally expressed in the peripheral leukocytes derived from patients with each disorder (Iwamoto et al., 2004a; Numata et al., 2007). Second, PDLIM5 is an adopter protein interacting with N-type calcium channel (Maeno-Hikichi et al., 2003), a signaling pathway implicated in the pathophysiology of BP (Warsh et al., 2004). Third, PDLIM5 is mapped to 4q22, a potential linkage region of BP (Detera-Wadleigh et al., 1997) and SZ (Mowry et al., 2000).

Recently, PDLIM5 was reported to be associated with SZ in Japanese (Horiuchi et al., 2006). However, other two Japanese groups failed to confirm this association (Kato et al., 2005; Numata et al., 2007). Kato et al (Kato et al., 2005) reported that two adjacent SNPs, rs10008257 and rs2433320, were associated with BP in two independent Japanese sample sets, respectively. In addition, the haplotype containing these two SNPs was also associated with disease in the combined sample (Kato et al., 2005).

To investigate whether PDLIM5 also increase susceptibility to BP in European Americans, we performed a family-based association. To see whether there is a consistent allelic association across different ethnic samples, we carried out a meta-analysis combing our family-based data and case-control data from two Japanese sample sets and from two latest genome-wide association (GWA) studies (Baum et al., 2008; Wellcome Trust Case Control Consortium 2007).

Materials and methods


A total of 1103 samples from 290 families were recruited in the association study. The participants included 831 samples from the National Institute of Mental Health (NIMH) Genetics Initiative for Bipolar Disorder Waves 1-4 series (37 trios and 180 quads), 45 samples from the Clinical Neurogenetics (CNG) series (7 trios and 6 quads), and 227 samples from the Chicago-Hopkins-Intramural Project (CHIP) series (13 trios and 47 quads). Most of the CNG and NIMH wave 1-2 samples were included in a previous linkage study and showed linkage to 4q22 (Detera-Wadleigh et al., 1997). All patients met DSM-III-R or DSM-IV criteria for bipolar or schizoaffective bipolar type. All samples are Caucasian descendants identified by self-reported ethnicity information and further confirmed by STRUCTURE (Pritchard et al., 2000) analysis using 254 unlinked SNPs (data not shown).

SNP selection and genotyping

A series of bioinformatics tools developed in our laboratory (http://bioinfo.bsd.uchicago.edu/index.html) were used to select tag SNPs (tSNPs) and described elsewhere (Shi et al., 2007). Briefly, we first downloaded the 30 CEPH trios' HapMap genotype data within the 256.3-kb region contains the PDLIM5 gene and 20 kb upstream and downstream (Rel21a/phaseII Jan07, on NCBI B35 assembly, dbSNP b125). SNPs with minor allele frequency (MAF) greater than 0.1 in individuals of European descent from the HapMap Project were examined for linkage disequilibrium (LD). Then, the ldSelect algorithm was used to select tSNPs to represent SNPs in LD with each other (Carlson et al., 2004). This approach clusters the SNPs into different LD bins according to the pairwise correlation coefficient r2 values. A criterion of r2 ≥ 0.85 was chosen to define the bins. Our analysis generated 19 tSNPs and 13 singleton SNPs (only one SNP in each bin). However, in the present replication study, we selected only rs10008257 and rs2433320 (significant association with BP (Kato et al., 2005) or SZ (Horiuchi et al., 2006)), one tSNPs (tagging rs11732668, which showed nominal association with SZ (Horiuchi et al., 2006)), and two other tSNP for a better physical coverage of the gene region.

Genotyping was carried out using TaqMan 5′ exonuclease assays (Applied Biosystems Inc., Foster City, CA, USA). We used PedCheck1.1 (O'Connell and Weeks 1998) to detect any Mendelian inconsistence or genotyping error, and Merlin (Abecasis et al., 2002) to detect unlikely genotypes. All genotype errors were manually resolved by checking the raw genotype data, either assigning a genotype as missing (if unable to be resolved) or correcting the genotype prior to statistical analysis. The average genotyping success rate was > 97.3%.

Statistical analyses


Tests for Hardy-Weinberg equilibrium (HWE) of genotype distribution in founders (unrelated parents in this study) were performed with the software PEDSTATS (Wigginton and Abecasis 2005). Pairwise D' and r2 linkage disequilibrium (LD) between any two markers was analyzed using the program Haploview (version 4.0) (Barrett et al., 2005). Haplotype blocks were identified using the solid spine of LD method in Haploview (Barrett et al., 2005). Allelic association of individual SNPs was tested using the Sibling-Transmission/Disequilibrium Test (sib_tdt) program in the program package ASPEX 2.5 (http://aspex.sourceforge.net/). Haplotypic association tests were carried out using PDTPHASE in the program package UNPHASED (Dudbridge 2003). PDTPHASE analyzes whole pedigrees for association, correcting for the effects of linkage. It gives results for the individual haplotypes and a global probability value for a particular combination of SNPs. Given an association with schizophrenia shown in a previous study (Horiuchi et al., 2006), we also analyzed our data based on the presence of psychosis in affected offspring.


We performed a meta-analysis of SNPs rs10008257 and rs2433320, combining our family-based association data and case-control data from the Japanese sample sets (Kato et al., 2005) and two latest GWA studies (Baum et al., 2008; Wellcome Trust Case Control Consortium 2007). The NIMH samples in Baum et al's GWA study (Baum et al., 2008) overlap substantially with ours, thus data from these two samples sets was separately combined with other data.

Counts of alleles in case and control groups in population-based studies were summarized in two-by-two tables, and numbers of transmitted alleles or haplotypes from heterozygous parents to affected offspring in family-based studies were summarized in two-by-one tables. Odds ratios (ORs) and their 95% confidence intervals (CIs) and standard errors (SEs) were calculated based on the allele or haplotype data (Kazeem and Farrall 2005). 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 the Mantel-Haenszel's fixed-effects model (MANTEL and HAENSZEL 1959) if there is no evidence of between-study heterogeneity, otherwise using DerSimonian-Laird's random-effects model (DerSimonian and Laird 1986). Significance of overall OR was examined using a Z-test. 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 the Egger's regression asymmetry statistics with a funnel plot of natural logarithm of OR (LnOR) 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 samples of ancestry (European versus Asian), overall ORs and SEs were obtained for each group, then heterogeneity of overall ORs between different ancestries was assessed using a chi-square test with 1 degree of freedom, similar to the method introduced by Kazeem and Farrall (Kazeem and Farrall 2005).

A Fisher chi-square method (Fisher 1954) was used to pool global p-values of haplotype associations across 3 sample sets. This method is based on the additivity of independent chi-squares, and on the fact that when a null hypothesis is true, the statistics is distributed as chi-square with 2 degree of freedom.

A significance level was set at P = 0.1 in the Q-test and Egger's test. Two SNPs and 4 haplotypes were meta-analyzed. The precise number of multiple tests is difficult to calculate since the results seem to be correlated. We used a conservative Bonferroni correction for 6 tests (p = 0.0083) to test the significance of overall ORs.

Results and discussion

No deviation from HWE was found in the founders (data not shown). The genomic positions and LD plot of 5 SNPs tested are shown in Figure 1. The results of association analyses are summarized in Table 1. The SNP rs10008257 reveals a borderline association with BP (p = 0.068). A rare haplotype consisted of rs10008257 and rs2433320 shows nominal association (p = 0.045), which is not significant after correction for multiple tests (Bonferroni corrected p = 0.225). Borderline association signals are also seen at the common haplotype GG in individual haplotype association analysis and in a global analysis (Table 1). This rs10008257-rs2433320 haplotype block shows a global p-value of 0.001 in the original Japanese “MDMD” samples set (Kato et al., 2005), and gives nearly significant global p-values in the replication “MPS” samples of Japanese (p = 0.099) (Kato et al., 2005) as well as in our study in European American (p = 0.071). The Fisher p-value that combined global p-values of the rs10008257-rs2433320 haplotype analyses from 3 samples sets is 0.0006. However, the associated haplotype is not the same across all three studies. This makes the significance of this finding difficult to interpret. There is no evidence of association in the subsample with psychotic symptoms (data not shown).

Figure 1
Linkage disequilibrium (LD) plot of 5 single nucleotide polymorphsims in the PDLIM5 gene region on chromosomal region 4q22
Table 1
Allelic and haplotypic association of the PDLIM5 gene with bipolar disorder

The allele A at rs2433320 has a disease-protective role in our meta-analysis under the fixed-effects model (Table 2). This association survives a conservative Bonferroni correction for 6 tests for 4 haplotypes and 2 SNPs in our meta-analysis. When the NIMH samples in the GWA study is excluded, the overall ORs are 0.905 (corrected p = 0.032) in Caucasian samples and 0.897 (corrected p = 0.012) in all samples (Caucasians plus Japanese). When our data, which includes partial NIMH samples, is excluded, the overall ORs are 0.903 (corrected p = 0.018) in Caucasian samples and 0.897 (corrected p = 0.007) in all samples. No heterogeneity between samples from two different ancestries and no publication bias was found by Egger's regression analyses (data not shown). No allelic association was found in the combined Japanese sample (overall OR = 0.779, 95% CI, 0.580-1.046, unadjusted p = 0.097), may due to significant between-study heterogeneity (pQ = 0.087) and underpowered sample size. Therefore, the relationship of rs2433320 with bipolar disorder in Japanese needs further clarification in larger independent Japanese samples.

Table 2
Meta-analysis of association studies between PDLIM5 and bipolar disorder

We did not detect significant haplotypic disease-association under either the random-effects model, or the fixed-effects model (Table 2).

Several limitations exist in the present study. First, 5 SNPs have poor coverage for the gene region (about 50 Kb/SNP). As shown above, at least 19 tSNPs and 13 singletons at the PDLIM5 gene region should be analyzed to test the Common Disease/Common Variants (e.g. MAF > 0.1) (CV/CD) hypothesis in BP. In addition, two common SNPs with potential functional effects on the gene (the synonymous mutation Gln170Gln/rs11097431 and the missense mutation Ala272Thr /rs7690296) should be considered too, although they are respectively tagged by rs2452594 (r2 = 0.847, LOD = 18.8) and rs1543013 (r2 = 1, LOD = 32.8) in the Caucasian samples in the HapMap project, which have been tested in our study. Secondly, rare variants may also account for susceptibility to common disease (the Common Disease/Multiple Rare Variants (CD/MRV) hypothesis) (Pritchard 2001; Pritchard and Cox 2002), which has received experimental support (Cohen et al., 2005; Cohen et al., 2004; Cohen et al., 2006; Fearnhead et al., 2004; Johnson et al., 2007; Meyer et al., 2005; Romeo et al., 2007; Zhu et al., 2005). Resequencing the PDLIM5 gene region may help identify rare variants that confer susceptibility to BP. Finally, our sample has limited capacity to detect alleles with weak effects (e.g., OR < 1.3). With 233 quads and 57 trios, at the significance level of p < 0.01 (to correct for 5 SNPs), and under a multiplicative model, power calculation using the PBAT software (http://www.biostat.harvard.edu/~clange/default.htm) showed our sample set with 80% power to detect association with an allele frequency of 0.1 and 0.5, odds ratio of 1.6 and 1.4, respectively.

To conclude, the present replication study shows a potential association of the PDLIM5 gene with bipolar disorder in European Americans. Our meta-analysis identifies a significant allelic association at rs2433320 in the Caucasians with ancestry of Europe. Previous findings in Japanese need to be confirmed in larger independent samples. Variants in the PDLIM5 gene may influence risk for bipolar disorder with weak effects.


The authors wish to thank all the patients and families who participated in this study. We are grateful to the members of the NIMH Genetics Initiative for Bipolar Disorder consortium, the Chicago-Hopkins-Intramural Project (CHIP) and the Clinical Neurogenetics (CNG) for their efforts in family ascertainment, DNA sample collection and clinical diagnosis. We appreciate that Dr T Kato provided raw genotype data in his group's study on bipolar disorder and TDT data on schizophrenia. This study makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk/. Funding for the project was provided by the Wellcome Trust under award 076113. This study was supported by National Institutes of Health grants MH065560-02 and MH61613-05A1 (to Dr ES Gershon), Brain Research Foundation at the University of Chicago (to C Liu), and NARSAD Young Investigator Awards (to C Liu, and J Shi). Support from the Geraldi Norton Foundation and the Eklund Family are also gratefully acknowledged.

Sponsorships: Supported by National Institutes of Health Grants MH065560-02 and MH61613-05A1 (to Dr ES Gershon), Brain Research Foundation at the University of Chicago (to C.L.), and NARSAD (to J.S. and C.L.).

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