Ethics Statement

The protocol was approved by the standing Institutional Review Board, ‘Medisch-ethische Toetsingscommissie Instellingen Geestelijke Gezondheidszorg’ (Review Board for Human Research in Psychiatry), the Netherlands [protocol number 377].

Written informed consent was obtained from each patient, hence, consent obtained from the next of kin was not necessary and not recommended by the Review Board for Human Research in Psychiatry.

Subjects

A 4-year prospective naturalistic study (July 2003–May 2007) was conducted with 209 patients with chronic mental illness in order to determine the genetic risk factors of the four major types of movement disorders (TD, parkinsonism, akathisia, and tardive dystonia), subtypes of TD and parkinsonism, as well as a principal-factor of the movement disorders and their subtypes. To this end, a cohort was drawn from a general psychiatric hospital (GGZ Centraal, Amersfoort, the Netherlands). Full details of the study design and movement disorders have been published previously [21] (Bakker and colleagues, submitted). The cohort was representative of the population of patients with the most severe chronic mental illness requiring long-stay care, given that the hospital serves an epidemiological catchment area, is the only institute providing this type of care in this area, and patients were selected from a comprehensive list of all inpatients.

Of the patients assessed at baseline (N = 207) 93.7% (n = 194) had at least one follow-up and 59.4% (n = 123) had two follow-up assessments. Loss to follow-up was due to patients who were difficult to trace after leaving hospital, died or refused assessment after inclusion.

Assessment

Patients were examined by a trained psychiatrist (PRB), using a standard protocol, described by van Harten and colleagues [22]. In addition, subtypes of movement disorders were assessed using (i) the Abnormal Involuntary Movement Scale (AIMS) [23], [24] with items 1–4 for orofacial and items 5–7 for limb truncal dyskinesia, (ii) the Unified Parkinson Disease Rating Scale (UPDRS) [25] with item c3–c4 for ‘rest tremor’ (rest tremor, and action/postural tremor of hands); item c5 for rigidity; and items c1, c2, c6–c12, and c14 for bradykinesia. This approach has been described previously by 3 members of our research team (AAH, JvO and PvH) [26]–[28].

As movement disorders likely share genetic liability, a genetic association between the combined movement disorders and candidate genes is also required. To determine the association between the combined movement disorder and variation in 10 candidate genes, a principal-factor of the four major types of movement disorders and subtypes of TD and parkinsonism was calculated with the FACTOR procedure in the STATA statistical program [29].

Based on the literature published between 1976 and August 2011, we selected 10 candidate genes (Table S1 and Text S1) that (i) are involved in the dopaminergic and serotonergic systems which have been implicated in the development of movement disorders, and the gene coding for the free radical scavenging enzymes like manganese super oxide dismutase (MnSOD) based on the hypothesis of neuronal degeneration owing to toxic effects of free radicals on TD. Genes involved in the glutamatergic system that may also contribute to cumulative neural damage, were not selected as the extensive number of receptors in this system, like metabotropic receptors (mGluRs) and ionotropic receptors (iGluRs), merit separate analysis.

In addition, variables possibly affecting risk were extracted from patients’ case notes including age, sex, BMI, self-reported handedness, diagnosis according to DSM-IV, ethnic group (classified as white and non-white), duration of hospitalization and history of electroconvulsive therapy (ECT). Negative symptoms were rated using the negative symptom subscale of the Positive and Negative Symptom Severity (PANSS) scale [30]. The MINI sections for alcohol and drug use were administered, and information on tobacco intake (yes/no, number of cigarettes, cigars, etc; descriptors such as ‘light’, ‘mild’, ‘heavy’ and ‘normal’ use of tobacco) was collected. At baseline and at each follow-up assessment, current use of antipsychotic and anticholinergic medication was collected, and the global symptom rating of the Clinical Global Impression – Schizophrenia severity of illness (CGI-SCH SI) scale was completed. All clinical assessments were carried out by a psychiatrist (PRB). Information on current use of the above medication was collected from the hospital and outpatient pharmacy databases.

The diagnosis ‘schizophrenia’ hereafter refers to DSM-IV codes 295.30, 295.10, 295.20, 295.90, 295.60, 295.70, and other diagnoses of ‘psychotic disorder’ to 295.40, 297.1, 298.8, 298.9.

DNA Extraction, Genotyping

Two 10 ml EDTA tubes of peripheral blood were drawn from participants, and genomic DNA was extracted from leucocytes by Autopure LS method (Qiangen) according to the manufacturer’s protocols. We genotyped 31 SNPs (TaqMan® SNP Genotyping Assays method, Applied Biosystems, Foster City, California, USA) in 10 candidate gene regions, including SNPs previously reported as associated with movement disorders and schizophrenia.

Statistical Analyses

Sample Characteristics

Over the period of observation (mean = 1.1 years, SD = 0.64), of the 209 patients included at baseline, 207 participated in the study. One patient developed a brain tumor, another patient died after inclusion. All patients had a history of cumulative antipsychotic intake of minimally 1 year. Attrition rate was low at 9.8% over a 4-year period.

Of the 207 patients, with chronic psychiatric illness requiring long-term admission, 199 participated in the genetic study. To prevent ethnic stratification resulting in spurious associations owing to differences in allele frequencies and risk of movement disorders, only white patients, representing the most prevalent group (168 = 84.4%), were included in the analysis. At baseline, mean age expressed in years was 48.8 (SD 12.4); men 48.6 (SD 12.5) and women 49.1 (SD 12.2). Age at first admission, expressed in years, was 25.1 (SD 8.8); men 23.7 (SD 7.8) and women 27.1 (SD 9.7), respectively. The total duration of admission, expressed in years, was 23.4 (SD 12.9), men 24.4 (SD 12.5) and women 22.0 (SD 13.4). Diagnoses according to DSM-IV Axis I as defined above were: schizophrenia 112 (66.7%), psychosis 9 (5.4%), affective disorder 27 (16.1%), other Axis I diagnosis 11 (6.6%) and no Axis I diagnosis 9 (5.4%).

Association Analyses with SNPs

Six redundant SNPs owing to strong linkage disequilibrium (LD) (Levwontin’s D’ = 1, R-squared = 1) were removed (Table S1): rs879606, rs907094, rs3764353, rs3764352 in PPP1R1B, and rs4606 and rs1819741 in RGS2.

The following SNPs were excluded from analysis, due to deviation from HWE: all movement disorders – rs6280, as well as controls; TD - rs4795390; orofacial dyskinesia - rs4795390, rs1800497; limb truncal dyskinesia - rs1800497; bradykinesia - rs1799732, rs6311.

The (multilevel) regression yielded significant coefficients, after adjustment for age, between tardive dyskinesia and rs6265 (B = 0.19, p = 0.0072) as well as rs988748 (B = 0.18, p = 0.0076); between orofacial dyskinesia and rs6265 (B = 0.24, p = 0.0014) as well as rs988748 (B = 0.23, p = 0.0019); and between limb truncal dyskinesia and rs6314 (B = −0.24, p = 0.0357). After adjustment for age and total antipsychotic DDD, associations were apparent between rest tremor and rs6275 (B = −0.14, p = 0.0140); between rigidity and rs6265 (B = −0.15, p = 0.0482) as well as rs4680 (B = 0.14, p = 0.0303); and between bradykinesia and rs4795390 (B = 0.16, p = 0.0451). Without adjustment, associations were apparent between akathisia and rs4680 (B = 0.13, p = 0.0289); between tardive dystonia and rs1799732 (B = 0.04, p = 0.0494), rs4880 (B = −0.03, p = 0.0399), as well as rs1152746 (B = 0.03, p = 0.0456). After Simes correction for multiple testing of the above mentioned analyses, the number of rejected p-values was zero, with a corrected overall critical p-value of 0.00021 (Figure 1).

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Figure 1. Smile plot summarizing set of multiple analyses after Simes correction for multiple testing.

Corresponding p-values (on a reverse log scale against the corresponding parameter estimates). TD = tardive dyskinesia, OF = orofacial dyskinesia, LT = limb truncal dyskinesia, PK = parkinsonism, RT = rest tremor, RG = rigidity, BK = bradykinesia, AK = akathisia, TDt = tardive dystonia and PF = principal-factor.

https://doi.org/10.1371/journal.pone.0036561.g001

Power calculations showed that our sample was insufficiently powered (0.05%) to identify the betas from our regressions, which were between −0.28 and 0.22.

Limitations

This study had limitations, for which we refer to our previous publications [21] (Bakker and colleagues, submitted) and additional limitations. First, as mentioned before, the relatively small sample size was the major limitation in this study. Still, the power in the current study may be increased as our patients had chronic mental illness, with a mean total duration of admission of 23.4 yrs (SD 12.9), which is a relatively long time for genetically susceptible patients to develop movement disorder. Also, we used continuous measures of movement disorder, which as a so-called intermediate quantitative trait is more informative about the underlying path in complex genetic diseases and thus generates more statistical power [32], [33]. In addition, we used repeated measures for continuous movement disorders, which may give a more stable phenotype, and thus more power.

Second, some authors may argue that association studies of movement disorders in patient with a psychotic disorder will produce non-significant results, as this model is inadequate since movement disorders may share risk alleles with schizophrenia [13]. However, many movement disorders and schizophrenia are complex diseases caused by multiple genetic and environmental factors, which are probably only partly shared, as (i) clinical heterogeneity in schizophrenia is clear, (ii) evidence of pathophysiological and etiological heterogeneity is accumulating [35], [36], and (iii) TD is a predictor for poor outcome of schizophrenia [37]. Hence, it can be hypothesized that patients with movement disorders represent a subgroup of schizophrenia and the above mentioned model is adequate.

Third, some authors may contend that medication is an important confounder, which should have been included in our analysis. However, a confounding mechanism is difficult to envisage, as choice of medication would need to be associated with an SNP and, independently thereof, with the movement disorder outcome. Nevertheless, medication may modify SNP-movement disorder outcomes and may be included in future analyses as an interaction term.

Strengths

We refer to our previous publications [21] (Bakker and colleagues, submitted). The importance of repeated measures should be noted, as case definition of repeated measures, rather than a single cross-sectional measure, for continuous movement disorders better reflects the continuously fluctuating nature in time of movement disorders, and therefore may represent a more suitable standard in future research. To the best of our knowledge only few papers in the literature address this issue.

As the sample size of the current study is small with low power and previous studies show inconsistent results, definite conclusions cannot be made. Yet the question is how to interpret these results. In our opinion, the findings of weak genetic signals need to be replicated in larger study samples, preferably in longitudinal studies which take the fluctuating course of movement disorders and gene-environment interactions into account [38], [39]. Even though the current study is inconclusive, negative studies also ought to be reported as otherwise meta-analytic results in the future can be biased by positive studies that tend to be published more readily.

Various combinations of susceptibility genes may converge on synaptic processing in microcircuits, affecting a final common pathway of dysfunction and related symptoms, and secondary morphological alterations [40], [41]. However, despite growing evidence from genetic association studies, genetics only explains a minor part of schizophrenia, a fact which supports the importance of other interacting factors, such as environmental factors, which play important roles in schizophrenia [38]. Neuropsychiatric disorders may reflect the complex interplay of not only genetic factors, but first and foremost of epigenetic, stochastic, and non-genetic factors [42]. Consequently, at the moment it is too early to describe a genetic pathway of schizophrenia [38] or movement disorders.

An important development in human (pharmaco) genetics since 2005 is the possibility of genome-wide association studies (GWASs) [43] which have the advantage of a ‘hypothesis free’ and hence unbiased approach for examining new DNA variants which influence genetic susceptibility to many common diseases and can thus elucidate as yet unknown pathophysiological mechanisms.

After the choice of candidate genes in the current genetic association study was made, three GWASs of movement disorders were published: (i) the study by Inada e.a. [44] suggesting involvement of the GABA receptor signaling pathway in the development of therapy-resistant tardive dyskinesia, (ii) the study by Akelai e.a. [45] specifying EPF1, NOVA1, and FIGN as promising genes related to antipsychotic-induced parkinsonism, and (iii) the study by Åberg [46] determining an association between parkinsonism and a SNP in ZNF202, a transcriptional repressor controlling the major protein in myelin, PLP1, related both to Pelizaeus-Merzbacher disease with parkinsonism as symptom, and schizophrenia.

The Psychiatric GWAS Consortium (PGC) has suggested that in the near future larger GWAS samples will detect more variants of common susceptibility with smaller effect sizes and that meta-analyses of GWAS should find more conclusive evidence for genetic associations. Meanwhile, new potentially promising genetic techniques are being implemented such as epigenetics and whole-exome sequencing as an alternative study design. Rare variants detected by these next generation sequencing technologies may yield a stronger signal than GWAS approaches. In our view, the common variant common disease/phenotype approach is challenged including the area of pharmacogenetics. Rare variants warrant more attention in future studies. Also, gene-environment-wide interaction studies (GEWIS) approaches are being suggested [47]. It seems legitimate to conclude that these new techniques offer more effective genetic linkage and association studies.

There is a need for more participatory research designs, especially in naturalistic studies in personalized medicine including psychiatry. However, Lehoux and colleagues pose the following question to be answered: ‘what value does personalized medicine bring to health care?’ [48] This important question refers to the unique context of personalized medicine where economic, political and social issues come together.

In conclusion, the findings suggest that selected SNPs are not associated with a susceptibility to movement disorders. However, replication is needed in larger study samples, preferably in longitudinal studies which take the fluctuating course of movement disorders and gene-environment interactions into account. The use of intermediate phenotypes, for example, laboratory based phenotypes [42], or more accurate measures of movement disorders, for example instrument measurement of lingual force variability as proposed by Koning and colleagues [49], which may represent a powerful alternative since instrument measurement detects subclinical movement disorders and is highly reliable. Moreover, (pharmaco) genetic studies may help elucidate common pathways in the development of movement disorders. With this information, an alternative World Health Organization Model List of Essential Medicines may be one that lists the ‘minimal essential biomarkers’ required for optimal pharmacotherapy [50]. However, on balance, our findings should be set in the context of interactions with both other genetic susceptibility loci and environmental factors, and, as rightly stated by Faraone and colleagues [51] “any conclusion about the role of genes and environment must rely not on a single study or class of study but on the converging evidence provided by a variety of research paradigms.”

Future research on movement disorders may be served by the inclusion of all four movement disorder, as performed in the current study, since they may represent pleiotropic effects from (partly) shared genetic factors [52].