Clinical Psychopharmacology and Neuroscience 2020; 18(2): 231-240  
ZNF804A Gene Variants Have a Cross-diagnostic Influence on Psychosis and Treatment Improvement in Mood Disorders
Marco Calabrò1, Laura Mandelli2, Concetta Crisafulli1, Marco Di Nicola3, Roberto Colombo3, Luigi Janiri3, Soo-Jung Lee4, Tae-Youn Jun4, Sheng-Min Wang4, Prakash S. Masand5, Ashwin A. Patkar6, Changsu Han7, Chi-Un Pae4,6,8, Alessandro Serretti2
1Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, 2Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, 3Fondazione Policlinico Universitario “A. Gemelli” - IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy, 4Department of Psychiatry, College of Medicine, The Catholic University of Korea, Seoul, Korea, 5Global Medical Education, New York, NY, 6Department of Psychiatry and Behavioural Sciences, Duke University Medical Center, Durham, NC, USA, 7Department of Psychiatry, College of Medicine, Korea University, Seoul, 8Cell Death Disease Research Center, College of Medicine, The Catholic University of Korea, Seoul, Korea
Correspondence to: Chi-Un Pae
Department of Psychiatry, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 327 Sosa-ro, Wonmi-gu, Bucheon 14647, Korea
Received: October 31, 2018; Accepted: November 21, 2018; Published online: May 31, 2020.
© The Korean College of Neuropsychopharmacology. All rights reserved.

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Objective: Genetic variations in the gene encoding zinc finger protein 804A gene (ZNF804A) have been associated with major depression and bipolar disorder. In this work we focused on the potential influence of ZNF804A variations on the risk of developing specific sub-phenotypes as well as the individual response to available treatments.
Methods: We used two samples of different ethnic origin: a Korean sample, composed by 242 patients diagnosed with major depression and 132 patients diagnosed with bipolar disorder and 326 healthy controls; an Italian sample composed 151 major depression subjects, 189 bipolar disorder subjects and 38 outpatients diagnosed for a primary anxiety disorder.
Results: Our analyses reported an association of rs1344706 with psychotic phenotype in the cross-diagnostic pooled sample (geno p = 4.15 × 10−4, allelic p = 1.06 × 10−4). In the cross-diagnosis Italian sample but not in the Korean one, rs7597593 was involved with depressive symptoms improvement after treatment (geno p = 0.025, allelic p = 0.007).
Conclusion: The present study evidenced the role of ZNF804A alterations in symptoms improvement after treatment. Both manic and depressive symptoms seem to be modulated by ZNF804A, though the latter was observed in the bipolar pooled sample only. The role of this factor is likely related to synaptic development and maintenance; however, further analyses will be needed to better understand the molecular mechanics involved with ZNF804A.
Keywords: ZNF804A; Bipolar disorder; Major depressive disorder; Symptoms improvement; Psychotic disorders.

Several studies evidenced that the genetic background can significantly contribute to the risk of mood disorders [1-5] as well as the individual response to available treatments [6,7]. The specific biological aspects involved in complex disorders are however difficult to identify since every genetic variation seems to give a small contribution to the pathologic phenotype [8]. However a growing body of evidence is suggesting that the genetic variants associated with psychiatric disorders may have a trans-diagnostic effect on specific sub-phenotypes rather than the general disease risk [9].

In this study, we analyzed the potential influence of a gene previously associated with unipolar (major depressive disorder, MDD) or bipolar disorder (BPD) on discrete phenotypic effects of mood disorders [10-12]. The gene we considered is zinc finger protein 804A (ZNF804A, OMIM: 612282), located on chromosome 2q32.1. ZNF804A encodes for a protein containing a C2H2-type zinc finger domain supposed to act as a transcription factor [13,14]. The exact functions of this protein remain unknown so far [15], but some studies suggested that a variant in ZNF804A (rs1344706) can influence brain structure and activity [16-19]. ZNF804A protein is commonly detected through the human brain, especially in the medial temporal lobe and cortex areas [13,15,20]. ZNF804A was associated with the risk for BPD and psychosis [10,11], MDD [12], schizophrenia [21-24], as well as efficacy of antidepressant [25] and antipsychotic drugs [26,27]. These findings were replicated [22,28,29] and confirmed in meta-analyses [24,30] suggesting ZNF804A as a cross- diagnosis risk gene.

In this study, we investigated 7 single nucleotide polymorphisms (SNPs) within ZNF804A in two independent samples of Asian (Korean) and Caucasian (Italian) ancestry. We focused especially on rs1344706, an intronic SNPs solidly associated with psychiatric disorders [31-36], but we also evaluated other SNPs to increase the coverage of the gene.



The Korean sample was composed by 242 patients diagnosed with MDD and 132 patients diagnosed with BPD according to the Diagnostic and Statistical Manual for Mental Disorders, 4th edition (DSM-IV) criteria [37] and 326 healthy controls. Recruitment details and exclusion criteria have been previously reported [38,39]. All the patients were recruited at the Department of Psychiatry of Seoul St. Mary’s Hospital for treatment. Controls were collected among hospital staff and patients; controls did not have to meet criteria for any current or past psychiatric condition. Subjects with severe or unstable medical and/or neurological conditions, in treatment with a long-acting antipsychotic, with current or recent (past six months) comorbidity with alcohol and substance abuse disorders were excluded from the study. All individuals were Koreans of Korean ancestry.

The Italian sample included 151 MDD, 189 BPD, and 38 outpatients diagnosed for a primary anxiety disorder (AD) (including DSM-IV panic disorder, generalized anxiety and social phobia), but presenting with a comorbid clinical depressive episode. Sixty-five healthy controls were also recruited among volunteers and clinical staff. The patients were recruited among subjects admitted to two university tertiary psychiatric care centers in Italy; the psychiatric department of the local health unit in Bologna (University of Bologna) and the psychiatric department of the “A. Gemelli” General Hospital in Rome (Catholic University of Rome). Inclusion criteria for the patients were as follows; age 18 to 75 years, presence of a DSM-IV diagnosis of MDD, BPD or primary AD with comorbid depression, eligibility for a pharmacological treatment with antidepressants. Exclusion criteria were represented by presence of severe or unstable medical conditions, neurological disorders and/or cognitive impairment. All individuals were Italian of Italian ancestry. A subsample of Italian MDD patients (n = 88) was already analyzed with regard to antidepressant response [40]. The ethical committee of The Catholic University of Korea, Bucheon St. Mary’s Hospital approved the study procedures (No. HC10TISI0031); all the subjects were included after they had signed an informed consent.


Based on an agreement prior to the study, rather homogeneous assessment methods were employed in the different centers. Patients and controls were evaluated for psychiatric disorders by the Mini-International Neuropsychiatric Interview (MINI) [37]. Demographic and clinical variables, including age at first illness episode (onset), family history for psychiatric disease, history of suicide attempt and comorbidity with alcohol/substance dependence, were collected by clinical interview and review of clinical charts. Alcohol/substance disorder was evaluated on Italian patients only since alcohol/substance use represented exclusion criteria in the Korean center. Patients with MDD (both Italians and Koreans), those with BPD in current depressive episode (Italians only) and AD patients with comorbid depression (Italians only) were evaluated for symptoms severity at baseline and after 6−8 weeks of treatment by the Hamilton Rating Scale for Depression (HDRS) [41]. In Koreans only, total baseline and endpoint HDRS scores were available for this analysis, while on Italian samples we also had single HDRS items scores. For these subsamples, antidepressant response, remission to treatments was assessed according to Schosser et al. [42] Response to treatment was defined as a 50% improvement of HDRS scores from baseline to endpoint. Remission was defined as a HDRS score of ≤ 7 at the endpoint; resistance as non-response to at least two adequate consecutive antidepressant trials (including the present) [42]. A total of 32 AD, 121 MDD, and 172 BPD patients (all Italian) were also evaluated at baseline for anxiety symptoms by the Hamilton Anxiety Rating Scale [43]. Korean BPD patients, all in a manic episode, were evaluated by the Young Mania Rating Scale (YMRS) over a period of 3 to 6 weeks of treatment [44]. Response was defined as a ≥ 50% reduction from baseline in YMRS score. Remission defined as YMRS total score ≤ 12 [44].

Genetic Analysis

The choice of the SNPs was performed through a literature check of the data available on ZNF804A and its correlation with psychiatric disorders [12,25,35,45-47]. The list of SNPs was further enriched to guarantee its maximum possible coverage of the gene, according to the resources available. Out of 10 SNPs, only 2 SNPs were analyzed in both samples (rs1344706 and rs7597593). Five were genotyped in Koreans only (rs359895, rs1021043, rs17508706, rs1987025, and rs7588907), and 3 on Italians only (rs7605689, rs39317905, and rs7603001) due to ethnic specificity.

For the Korean sample, high-throughput genotyping using a pyrosequencer (Biotage AB, Uppsala, Sweden) was employed to genotype the genomic DNA from subjects blood. Polymerase chain reaction (PCR) primers (Bioneer, Daejeon, Korea) and sequencing primers (Bioneer) used for the pyrosequencing assay were designed through Pyrosequencing Assay Design software ver. 1 (Biotage), 1 primer of each primer set was biotinylated. Genomic DNA of Italian samples was extracted from blood thorough an automated magnetic-beads based nucleic acids extractor (Maxwell; Promega, Madison, WI, USA). Presence of the investigated SNPs within each sample was checked by a multiplex Sequenom MassArray platform (Sequenom Inc., San Diego, CA, USA). Sequenom’s MassARRAY Designer software was used to designs PCR and extension primers (sequences available on request) for each investigated SNP. All analyses on the chosen SNPs were performed by two independent investigators blind to clinical information of the subjects. Samples showing ambiguous alleles were discarded if they showed the same features on repeated genotyping.

Statistical Analysis

Main statistical analyses were performed using the IBM SPSS package for Windows ver. 20.0 (; IBM Co., Armonk, NY, USA). Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium were tested by Haploview 3.2 software for Windows (Broad Institute of MIT and Harvard, Cambridge, MA, USA) [48]. Haplotypes’ analysis was performed in “R” environment (, using the statistics package “haplo.score”. To control for multiple testing we used Bonferroni correction to evaluate the significance of our findings. For the correction we took in consideration the number of variables tested for significance. Significance was considered for p < 0.05/ 23 = 0.0022.


Socio-demographic tables summarizing the characteristics of the samples under investigation are reported below (Tables 1 and 2). All the SNPs were in HWE (data not shown). Frequencies of alleles for rs7597593 did not vary significantly between the two samples (c2 = 0.009, p = 0.925). A significant difference was detected for rs1344706 (c2 = 82.641, p < 0.001), whose allelic distribution is widely known to be different between European and Asian populations (see Hap-Map frequencies on the site of National Center for Biotechnology Information; PMC1310647/).

Psychiatric Diagnosis

ZNF804A SNPs were not associated with psychiatric risk in the cross-diagnosis samples. However, some significant associations were evidenced for rs1344706 with BPD risk in the BPD merged sample (against controls, geno p = 5.77 × 10−6 and allelic p = 2.78 × 10−3). Interestingly, the same SNP, rs1344706, was also correlated as a trend with MDD risk (against controls, geno p = 0.007 and allelic p = 0.014) in the MDD merged sample (pooling the data from Korean and Italian). Table 3 summarizes our findings. No correlations between ZNF804A polymorphisms and AD with comorbid depression were evidenced by single SNP analyses.

ZNF804A polymorphisms were also tested to evidence possible differences between the two main mood disorders (MDD + depressed AD vs. BPD). However, none of the analyzed samples and subsamples evidenced any significant data. Finally, haplotype analysis did not reveal any significant association with diagnoses.

Clinical Features

Several variables were tested for association with ZNF804A SNPs, including age of onset, family history for psychiatric disease, suicide attempt, comorbidity with alcohol/substance disorder (Italian sample only), psychotic phenotype, depressive, manic and anxiety severity at baseline.

In the cross-diagnosis sample (BPD + MDD + AD), pooling Italian and Korean samples, we observed an association between rs1344706 and the psychotic phenotype (geno p = 4.15 × 10−4, allelic p = 1.06 × 10−4). Haplotype analysis did not reveal any significant association between ZNF804A SNPs and clinical features. Table 3 summarizes our findings.

Antidepressant Treatment Efficacy

ZNF804A SNPs were not associated with antidepressant treatment outcome, either in the subsamples of Italian depressed BPD and Italian AD with comorbid depression, or in pooled samples.

However, trends of association were observed between rs7597593 and remission to treatment (allelic p = 0.025), rs7597593 and response to treatment (geno p = 0.025, allelic p = 0.007), and rs7605689 (geno p = 0.012, allelic p = 0.003) in the cross-diagnostic Italian sample. Table 3 summarizes our findings.

Antimanic Treatment Efficacy

The efficacy of antimanic treatment was evaluated in the BPD subsamples of Korean and Italian groups. The pooled data from the two groups was also tested for association.

In the merged sample, our findings evidenced no significant correlations with remission. However, we observed some associations with response. In particular, rs1344706 (geno p = 1.22 × 10−3, allelic p = 7.38 × 10−4) was correlated with this phenotype in the merged sample. Further, the same SNP was also significantly associated with manic symptoms at baseline (geno p = 2.16 × 10−7, allelic p = 1.10 × 10−6) and their improvement (geno p= 3.15 × 10−7, allelic p = 2.54 × 10−6) in the pooled BPD sample. Table 3 summarizes our findings.


The main aim of this work was to investigate the possible role of ZNF804A variants on the modulation specific sub-phenotypes within psychiatric subjects in a cross-diagnostic setting. While the biological role of ZNF804A is still not entirely known, alterations within this gene ap pear to have a role in neurodevelopment: data available in literature links alleles of ZNF804A with alterations in neural activity and connectivity, cognitive effects, and neuroanatomical changes [18].

According to our data, the psychotic phenotype seems to be influenced by ZNF804A rs1344706 genotype in the pooled sample, regardless of the disorder. Analyses on the BPD pooled subsample confirmed the association of this SNP with psychosis; moreover, in this subsample rs1344706 was also associated with BPD risk; further, it may play a role in manic symptoms severity (YMRS) and response to antimanic treatment. This SNP also showed an association with depressive symptoms improvement (HDRS), but only in the pooled bipolar sample.

Literature data report several evidences supporting the role of rs1344706 in psychiatric disorders [12,31-36,46,49], and with antidepressant response [25,40]. Regarding its association with psychosis, there were already some evidences indicating a possible correlation between this SNP and the psychotic phenotype [31], that indicated how rs1344706 may be able to mediate one of ZNF804A mRNA isoform, ZNF804AE3E4, during fetal life [31]. This mediation is the mechanic most likely involved in psychosis development. An in-silico analysis on the effect of this mutation on Human Splicing Finder (HSF) [50] ( revealed that the change T > G may indeed create an enhancer consensus sequence and at the same time the loss of a potential branch point which may affect splicing mechanics. This alteration may be correlated to an early modification of neural nets during fetal neurodevelopment.

Another interesting result we obtained is the rs7597593 association with depressive symptoms improvement (HDRS) in the cross-diagnostic Italian sub-group. This SNP was associated with MDD in literature [12]; however, to our knowledge, it was the first time it was associated with depressive symptoms improvement. Despite the lack of data of this specific SNP, alterations within ZNF804A were already associated with antidepressant response to treatment, even by our group in another sample (only partially overlapping with this one) [25]. Of particular interest, is that our association this time was extended to include a BPD sample (in a cross-diagnostic setting). Also, this association was evidenced even in a BPD subsample (BPD depressed Italian subgroup). Overall, this data imply how ZNF804A action may have an effect on depressive symptoms improvement during treatment, irrespectively of the psychiatric disorder. Indeed, ZNF804A seems able to influence emotion perception mechanics [51], linking its variants with mood alterations.

This SNP is an intron variant, as such it does not directly cause any amminoacidic changes on ZNF804A protein. Also, it does not seem to have any relevant impact on splicing, at least according to HSF [50] ( However, the same software, indicated that the change T > C cause the loss of a silencer consensus sequence, which may somewhat alter the transcriptional regulation of this gene, thus relevantly impacting with its function. It has to be noted that the significant data obtained, derived from our Italian sample. In the Korean sample rs7597593 did not show any relevant association or trend. Ethnic differences may play a role in this discrepancy; however, further analyses should be done on different sample in order to confirm this possibility.

Both the significant SNPs we found seem to exert their influence through a modification of ZNF804A isoforms concentrations rather than an actual modification of the protein. In particular, from in-silico data, these SNPs likely alter mRNA maturation mechanics. This data may hint to the importance of regulating ZNF804A isoforms concentration in neurodevelopment and synaptic maintenance. However, the precise molecular mechanics need further data to be correctly identified.

The main limit of our study is the choice of SNPs between the two laboratories, since only two of them overlapped between Korean and Italian samples. This selection was due to ethnic and logistic issues and somewhat limited our analyses, nevertheless the data obtained may be helpful in the analysis of ZNF804A role in psychiatric disorders and their treatment. Although we have tested two independent samples, the sample size is still relatively small compared to the large scale genetic studies. Secondly, our analyses tested some common SNPs within ZNF804A, thus SNPs other than the ones investigated, rare missense mutations and CNVs were not investigated. As such, we cannot guarantee a complete coverage of this Gene. Further, the trends underlined in this work have to be considered as exploratory and more analyses in larger sample would be needed to further evaluate our data. Also, ethnic differences should be taken in consideration while evaluating these results. Many results observed in the pooled samples were not observed in the ethnic homogenous ones, but trends emerged, probably for the reduction in sample size. It has to be noted, though, that rs1344706 distribution between the two samples did vary significantly. This variation should be taken in consideration when interpreting the results. Finally, the presence of false positives should always be taken in consideration even when statistical correction is applied.

The present study evidenced the role of ZNF804A alterations in symptoms improvement after treatment. Both manic and depressive symptoms seem to be modulated by ZNF804A, though the latter was observed in the bipolar pooled sample only. Its role on the psychotic phenotype was also observed, irrespectively of ethnic origin and psychiatric disorder. Unfortunately, the lack of a biological understanding of ZNF804A function, limit our capacity of correctly evaluate our data in the greater picture that is the neural nets (and the brain in general) physiology. Further efforts should be done to understand the biological functions of ZNF804A.


This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number : HC15C1405 and HI12C0003). We thank all the patients that given their consent to analyzetheir data.

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Author Contributions

Conception and design of the study: Chi-Un Pae and Alessandro Serretti. Acquisition and analysis of data: Chi-Un Pae, Marco Calabrò, Laura Mandelli, Concetta Cristafulli, Soo-Jung Lee and Tae-Youn Jun. Original draft: Marco Calabrò. Final editing of manuscript and intellectual comments: Marco Di Nicola, Roberto Colombo, Luigi Janiri, Sheng-Min Wang, Prakash S. Masand, Ashwin A. Patkar, Changsu Han, Chi-Un Pae and Alessandro Serretti.


Sociodemographic data of the Korean sample

MDD subsample (n = 242) BPD subsample (n = 132) Controls (n = 326)
Age (yr) 43.57 ± 14.81 36.36 ± 11.61 45.36 ± 13.06
Age at onset (yr) 39.75 ± 13.76 26.58 ± 10.20
Baseline HDRS score 22.75 ± 7.30 7.70 ± 4.03
Baseline YMRS score 33.27 ± 9.09
Sex (%)
Female 62.0 34.1 54.9
Male 38 65.9 45.1
ND 0 0 0
Family history (%)
Yes 19.4 31.8
No 80.2 29.6
ND 0.4 38.6
Suicide history (%)
Yes 22.3 16.7
No 77.3 67.4
ND 0.4 15.9
Yes 55.3
No 43.2
ND 1.5
Remission, HDRS (%)
Yes 45.9
No 54.1
ND 0
Response, HDRS (%)
Yes 37.6
No 62.4
ND 0
Remission, YMRS (%)
Yes 90.2
No 9.9
ND 0
Response, YMRS (%)
Yes 95.5
No 4.6
ND 0

Values are presented as mean ± standard deviation or percent only.

MDD, major depressive disorder; BPD, bipolar disorder; HDRS, Hamilton Depression Rating Scale; YMRS, Young Mania Rating Scale; ND, not detected.

Sociodemographic data of the Italian sample

MDD subsample (n = 151) BPD subsample (n = 189) AD subsample (n = 38) Controls (n = 65)
Age (yr) 54.30 ± 15.11 47.27 ± 12.38 41.89 ± 14.56 42.08 ± 12.66
Age at onset (yr) 38.58 ± 18.68 27.05 ± 11.08 31.91 ± 14.66
Baseline HDRS score 19.08 ± 7.91 15.67 ± 6.64 13.33 ± 5.98
Baseline YMRS score 3.97 ± 5.71
Baseline HARS score 17.90 ± 7.79 16.38 ± 6.86 18.97 ± 6.15
Sex (%)
Female 65.6 50.3 63.2 53.9
Male 34.4 49.7 36.8 44.6
ND 0 0 0 1.5
Family history (%)
Yes 57.6 14.3 47.4
No 29.1 11.1 31.6
ND 13.3 74.6 21.1
Suicide history (%)
Yes 24.5 9.0 0
No 58.9 15.9 68.4
ND 16.6 75.1 31.6
Yes 3.3 0.5 0
No 96.7 30.2 100
ND 0 69.3 0
Remission, HDRS (%)
Yes 27.2 54.0 60.5
No 31.8 21.2 18.4
ND 41.1 24.9 21.1
Response, HDRS (%)
Yes 15.2 14.3 26.3
No 43.1 60.9 47.4
ND 41.7 24.9 26.3
Remission, YMRS (%)
Yes 65.1
No 1.6
ND 33.3
Response, YMRS (%)
Yes 14.8
No 18.0
ND 67.2
Remission, HARS (%)
Yes 37.6
No 29.1
ND 33.3
Response, HARS (%)
Yes 5.29
No 61.4
ND 33.3

Values are presented as mean ± standard deviation or percent only.

MDD, major depressive disorder; BPD, bipolar disorder; AD, primary anxiety disorder; HDRS, Hamilton Depression Rating Scale; YMRS, Young Mania Rating Scale; HARS, Hamilton Anxiety Rating Scale; ND, not detected.

Summary of the significant results from analyses

SNP Variable Type of analysis Raw pvalue Analysis details

Mean Statistic value 95% CI
Cross-diagnosis merged sample (MDD + AD + BPD - Kor + Ita)
rs1344706 Psychosis Genotypic 4.154E-04 GG vs. TT B = 1.398, SE = 0.409, p = 0.001, OR = 4.045 1.814−9.020
TG vs. TT B = 1.165, SE = 0.366, p = 0.001, OR = 3.207 1.566−6.570
Allelic 1.063E-04 not G vs. G B = −1.239, SE = 0.351, p = 0.000, OR = 0.290 0.145−0.577
Cross-diagnosis Italian sample (MDD + AD + BPD - Ita)
rs7597593 Symptom improvement (HDRS) Allelic 5.299E-04 C: m = 13.364 Err. = 0.484 12.406−14.322
not C: m = 13.463 Err. = 0.927 11.629−15.297
Bipolar merged sample (BPD - Kor + Ita)
rs1344706 Disease Genotypic 5.769E-06 GG vs. TT B = −1.054, SE = 0.219, p = 0.000, OR = 0.349 0.227−0.535
TG vs. TT B = −0.564, SE = 0.186, p = 0.002, OR = 0.569 0.396−0.819
Allelic 2.783E-05 not G vs. G B = 0.727, SE = 0.175, p = 0.000, OR = 2.069 1.469−2.913
Psychosis Allelic 2.009E-03 not G vs. G B = −1.197, SE = 0.413, p = 0.004, OR = 0.302 0.134−0.679
Antimanic response Genotypic 1.215E-03 GG vs. TT B = 1.862, SE = 0.599, p = 0.002, OR = 6.435 1.990−20.815
TG vs. TT B = 1.072, SE = 0.395, p = 0.007, OR = 2.920 1.346−6.334
Allelic 7.377E-04 not G vs. G B = −1.278, SE = 0.375, p = 0.001, OR = 0.279 0.134−0.581
rs1344706 HDRS score at baseline Genotypic 7.872E-04 GG: m = 9.750 Err. = 0.897 7.985−11.515
TG: m = 11.836 Err. = 0.567 10.719−12.952
TT: m = 13.931 Err. = 0.665 12.623−15.240
Allelic 1.210E-03 G: m = 11.240 Err. = 0.482 10.292−12.188
not G: m = 13.931 Err. = 0.668 12.617−15.246
YMRS score at baseline Genotypic 2.159E-07 GG: m = 26.811 Err. = 2.148 22.582−31.041
TG: m = 19.992 Err. = 1.393 17.249−22.735
TT: m = 11.571 Err. = 1.706 8.212−14.931
Allelic 1.102E-06 G: m = 22.011 Err. = 1.182 19.683−24.339
not G: m = 11.571 Err. = 1.726 8.173−14.970
Symptom improvement (YMRS) Genotypic 3.153E-07 GG: m = 17.363 Err. = 1.290 14.822−19.904
TG: m = 12.738 Err. = 0.821 11.122−14.355
TT: m = 8.259 Err. = 1.024 6.243−10.275
Allelic 2.537E-06 G: m = 14.071 Err. = 0.703 12.685−15.456
not G: m = 8.259 Err. = 1.040 6.211−10.307
Depressed bipolar Italian sample (BPD.D - Ita)
rs7597593 Symptom improvement (HDRS) Allelic 1.636E-03 C: m = 14.799 Err. = 0.437 13.931−15.666
not C: m = 14.674 Err. = 0.826 13.036−16.312

SNP, single nucleotide polymorphism; 95% CI, 95% confidential interval; MDD, major depressive disorder; AD, depressed subsample of anxiety disorder; BPD, bipolar disorder; BPD.D, depressed subsample of BPD; Kor, Korean; Ita, Italian; SE, standard error; OR, odds ratio; HDRS, Hamilton Depression Rating Scale; YMRS, Young Mania Rating Scale.

  1. Dedic N, Pöhlmann ML, Richter JS, Mehta D, Czamara D, Metzger MW, et al. Cross-disorder risk gene CACNA1C differentially modulates susceptibility to psychiatric disorders during development and adulthood. Mol Psychiatry 2018;23:533-543.
    Pubmed KoreaMed CrossRef
  2. Rao S, Yao Y, Zheng C, Ryan J, Mao C, Zhang F, et al. Common variants in CACNA1C and MDD susceptibility: a comprehensive meta-analysis. Am J Med Genet B Neuropsychiatr Genet 2016;171:896-903.
    Pubmed CrossRef
  3. He K, An Z, Wang Q, Li T, Li Z, Chen J, et al. CACNA1C, schizophrenia and major depressive disorder in the Han Chinese population. Br J Psychiatry 2014;204:36-39.
    Pubmed CrossRef
  4. Krishnan V, Nestler EJ. Linking molecules to mood: new insight into the biology of depression. Am J Psychiatry 2010;167:1305-1320.
    Pubmed KoreaMed CrossRef
  5. Vogelzangs N, Duivis HE, Beekman AT, Kluft C, Neuteboom J, Hoogendijk W, et al. Association of depressive disorders, depression characteristics and antidepressant medication with inflammation. Transl Psychiatry 2012;2:e79.
    Pubmed KoreaMed CrossRef
  6. Arias B, Fabbri C, Serretti A, Drago A, Mitjans M, Gastó C, et al. DISC1-TSNAX and DAOA genes in major depression and citalopram efficacy. J Affect Disord 2014;168:91-97.
    Pubmed CrossRef
  7. Perlis RH. Pharmacogenomic testing and personalized treatment of depression. Clin Chem 2014;60:53-59.
    Pubmed CrossRef
  8. Cross-Disorder Group of the Psychiatric Genomics C. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet. 2013;381;1371-1379.
  9. Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell. 2018;173;1705-1715.
    Pubmed KoreaMed CrossRef
  10. Lett TA, Zai CC, Tiwari AK, Shaikh SA, Likhodi O, Kennedy JL, et al. ANK3, CACNA1C and ZNF804A gene variants in bipolar disorders and psychosis subphenotype. World J Biol Psychiatry 2011;12:392-397.
    Pubmed CrossRef
  11. Williams HJ, Craddock N, Russo G, Hamshere ML, Moskvina V, Dwyer S, et al. Most genome-wide significant susceptibility loci for schizophrenia and bipolar disorder reported to date cross-traditional diagnostic boundaries. Hum Mol Genet 2011;20:387-391.
    Pubmed KoreaMed CrossRef
  12. Ou J, Li M, Xiao X. The schizophrenia susceptibility gene ZNF804A confers risk of major mood disorders. World J Biol Psychiatry 2017;18:557-562.
    Pubmed CrossRef
  13. Donohoe G, Morris DW, Corvin A. The psychosis susceptibility gene ZNF804A: associations, functions, and phenotypes. Schizophr Bull 2010;36:904-909.
    Pubmed KoreaMed CrossRef
  14. Gamsjaeger R, Liew CK, Loughlin FE, Crossley M, Mackay JP. Sticky fingers: zinc-fingers as protein-recognition motifs. Trends Biochem Sci 2007;32:63-70.
    Pubmed CrossRef
  15. O'Donovan MC, Craddock NJ, Owen MJ. Genetics of psychosis; insights from views across the genome. Hum Genet 2009;126:3-12.
    Pubmed CrossRef
  16. Lencz T, Szeszko PR, DeRosse P, Burdick KE, Bromet EJ, Bilder RM, et al. A schizophrenia risk gene, ZNF804A, influences neuroanatomical and neurocognitive phenotypes. Neuropsychopharmacology 2010;35:2284-2291.
    Pubmed KoreaMed CrossRef
  17. Esslinger C, Walter H, Kirsch P, Erk S, Schnell K, Arnold C, et al. Neural mechanisms of a genome-wide supported psychosis variant. Science 2009;324:605.
    Pubmed CrossRef
  18. Esslinger C, Kirsch P, Haddad L, Mier D, Sauer C, Erk S, et al. Cognitive state and connectivity effects of the genome-wide significant psychosis variant in ZNF804A. Neuroimage 2011;54:2514-2523.
    Pubmed CrossRef
  19. Rasetti R, Sambataro F, Chen Q, Callicott JH, Mattay VS, Weinberger DR. Altered cortical network dynamics: a potential intermediate phenotype for schizophrenia and association with ZNF804A. Arch Gen Psychiatry 2011;68:1207-1217.
    Pubmed CrossRef
  20. Johnson MB, Kawasawa YI, Mason CE, Krsnik Z, Coppola G, Bogdanović D, et al. Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron 2009;62:494-509.
    Pubmed KoreaMed CrossRef
  21. O'Donovan MC, Craddock N, Norton N, Williams H, Peirce T, Moskvina V, et al. Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nat Genet 2008;40:1053-1055.
    Pubmed CrossRef
  22. Riley B, Thiselton D, Maher BS, Bigdeli T, Wormley B, McMichael GO, et al. Replication of association between schizophrenia and ZNF804A in the Irish Case-Control Study of Schizophrenia sample. Mol Psychiatry 2010;15:29-37.
    Pubmed KoreaMed CrossRef
  23. Steinberg S, Mors O, Børglum AD, Gustafsson O, Werge T, Mortensen PB, et al. Expanding the range of ZNF804A variants conferring risk of psychosis. Mol Psychiatry 2011;16:59-66.
    Pubmed KoreaMed CrossRef
  24. Williams HJ, Norton N, Dwyer S, Moskvina V, Nikolov I, Carroll L, et al. Fine mapping of ZNF804A and genome-wide significant evidence for its involvement in schizophrenia and bipolar disorder. Mol Psychiatry 2011;16:429-441.
    Pubmed KoreaMed CrossRef
  25. Fabbri C, Crisafulli C, Calati R, Albani D, Forloni G, Calabrò M, et al. Neuroplasticity and second messenger pathways in antidepressant efficacy: pharmacogenetic results from a prospective trial investigating treatment resistance. Eur Arch Psychiatry Clin Neurosci 2017;267:723-735.
    Pubmed CrossRef
  26. Mössner R, Schuhmacher A, Wagner M, Lennertz L, Steinbrecher A, Quednow BB, et al. The schizophrenia risk gene ZNF804A influences the antipsychotic response of positive schizophrenia symptoms. Eur Arch Psychiatry Clin Neurosci 2012;262:193-197.
    Pubmed CrossRef
  27. Zhang J, Wu X, Diao F, Gan Z, Zhong Z, Wei Q, et al. Association analysis of ZNF804A (zinc finger protein 804A) rs1344706 with therapeutic response to atypical antipsychotics in first-episode Chinese patients with schizophrenia. Compr Psychiatry 2012;53:1044-1048.
    Pubmed CrossRef
  28. Schwab SG, Kusumawardhani AAAA, Dai N, Qin W, Wildenauer MDB, Agiananda F, et al. Association of rs1344706 in the ZNF804A gene with schizophrenia in a case/control sample from Indonesia. Schizophr Res 2013;147:46-52.
    Pubmed CrossRef
  29. Zhang R, Lu SM, Qiu C, Liu XG, Gao CG, Guo TW, et al. Population-based and family-based association studies of ZNF804A locus and schizophrenia. Mol Psychiatry 2011;16:360-361.
    Pubmed CrossRef
  30. Zhang R, Yan JD, Valenzuela RK, Lu SM, Du XY, Zhong B, et al. Further evidence for the association of genetic variants of ZNF804A with schizophrenia and a meta-analysis for genome-wide significance variant rs1344706. Schizophr Res 2012;141:40-47.
    Pubmed CrossRef
  31. Tao R, Cousijn H, Jaffe AE, Burnet PW, Edwards F, Eastwood SL, et al. Expression of ZNF804A in human brain and alterations in schizophrenia, bipolar disorder, and major depressive disorder: a novel transcript fetally regulated by the psychosis risk variant rs1344706. JAMA Psychiatry 2014;71:1112-1120.
    Pubmed KoreaMed CrossRef
  32. Huang L, Ohi K, Chang H, Yu H, Wu L, Yue W, et al. A comprehensive meta-analysis of ZNF804A SNPs in the risk of schizophrenia among Asian populations. Am J Med Genet B Neuropsychiatr Genet 2016;171B:437-446.
    Pubmed CrossRef
  33. Wei Q, Li M, Kang Z, Li L, Diao F, Zhang R, et al. ZNF804A rs1344706 is associated with cortical thickness, surface area, and cortical volume of the unmedicated first episode schizophrenia and healthy controls. Am J Med Genet B Neuropsychiatr Genet 2015;168B:265-273.
    Pubmed CrossRef
  34. Wickramasinghe A, Tulloch AD, Hayes RD, Chang CK, Broadbent M, Di Forti M, et al. Associations between the schizophrenia susceptibility gene ZNF804A and clinical outcomes in psychosis. Transl Psychiatry 2015;5:e698.
    Pubmed KoreaMed CrossRef
  35. Zhang C, Wang Z, Hong W, Wu Z, Peng D, Fang Y. ZNF804A genetic variation confers risk to bipolar disorder. Mol Neurobiol 2016;53:2936-2943.
    Pubmed CrossRef
  36. Sun Y, Hu D, Liang J, Bao YP, Meng SQ, Lu L, et al. Association between variants of zinc finger genes and psychiatric disorders: systematic review and meta-analysis. Schizophr Res 2015;162:124-137.
    Pubmed CrossRef
  37. Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry 1998;59 Suppl 20:22-33;quiz 34-57.
  38. Mandelli L, Wang SM, Han C, Lee SJ, Patkar AA, Masand PS, et al. The impact of a single nucleotide polymorphism in SIGMAR1 on depressive symptoms in major depressive disorder and bipolar disorder. Adv Ther 2017;34:713-724.
    Pubmed CrossRef
  39. Calabrò M, Mandelli L, Crisafulli C, Sidoti A, Jun TY, Lee SJ, et al. Genes involved in neurodevelopment, neuroplasticity, and bipolar disorder: CACNA1C, CHRNA1, and MAPK1. Neuropsychobiology 2016;74:159-168.
    Pubmed CrossRef
  40. Fabbri C, Marsano A, Albani D, Chierchia A, Calati R, Drago A, et al. PPP3CC gene: a putative modulator of antidepressant response through the B-cell receptor signaling pathway. Pharmacogenomics J 2014;14:463-472.
    Pubmed CrossRef
  41. Zimmerman M, Martinez JH, Young D, Chelminski I, Dalrymple K. Severity classification on the Hamilton Depression Rating Scale. J Affect Disord 2013;150:384-388.
    Pubmed CrossRef
  42. Schosser A, Serretti A, Souery D, Mendlewicz J, Zohar J, Montgomery S, et al. European Group for the Study of Resistant Depression (GSRD)--where have we gone so far: review of clinical and genetic findings. Eur Neuropsychopharmacol 2012;22:453-468.
    Pubmed CrossRef
  43. Thompson E. Hamilton Rating Scale for Anxiety (HAM-A). Occup Med (Lond) 2015;65:601.
    Pubmed CrossRef
  44. Young RC, Biggs JT, Ziegler VE, Meyer DA. A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry 1978;133:429-435.
    Pubmed CrossRef
  45. de Castro-Catala M, Mora-Solano A, Kwapil TR, Cristóbal- Narváez P, Sheinbaum T, Racioppi A, et al. The genome-wide associated candidate gene ZNF804A and psychosis-proneness: evidence of sex-modulated association. PLoS One 2017;12:e0185072.
    Pubmed KoreaMed CrossRef
  46. Wang Q, Ji W, He K, Li Z, Chen J, Li W, et al. Genetic analysis of common variants in the ZNF804A gene with schizophrenia and major depressive disorder. Psychiatr Genet 2018;28:1-7.
    Pubmed CrossRef
  47. Chen X, Zhang Z, Zhang Q, Zhao W, Zhai J, Chen M, et al. Effect of rs1344706 in the ZNF804A gene on the brain network. Neuroimage Clin 2017;17:1000-1005.
    Pubmed KoreaMed CrossRef
  48. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 2005;21:263-265.
    Pubmed CrossRef
  49. Rao S, Yao Y, Ryan J, Jin C, Xu Y, Huang X, et al. Genetic association of rs1344706 in ZNF804A with bipolar disorder and schizophrenia susceptibility in Chinese populations. Sci Rep 2017;7:41140.
    Pubmed KoreaMed CrossRef
  50. Desmet FO, Hamroun D, Lalande M, Collod-Béroud G, Claustres M, Béroud C. Human Splicing Finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Res 2009;37:e67.
    Pubmed KoreaMed CrossRef
  51. Fornito A, Bullmore ET. Connectomic intermediate phenotypes for psychiatric disorders. Front Psychiatry 2012;3:32.
    Pubmed KoreaMed CrossRef

This Article

Cited By Articles
  • CrossRef (0)

Author ORCID Information

Funding Information
  • Health Industry Development Institute
  • Health & Welfare, Republic of Korea

Social Network Service