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DOI: 10.1055/a-2603-0871
Influence of Glutamate Neurotransmission Genes on the Outcomes of Antipsychotic Treatments
- Abstract
- Introduction
- Experimental procedures
- Results
- Discussion
- Financial support and sponsorship
- Contributors
- References
Abstract
Introduction
Traditionally, the aetiology of schizophrenia has been attributed to dopaminergic neurotransmission, but more recent information points to the role of glutamate pathways. Glutamatergic involvement in schizophrenia might be extensible to drug response. The aim of the study was to explore whether the variation in glutamate receptors, transporters and metabolism can influence the outcome of drug treatments.
Methods
A total of 45 polymorphisms in the genes GRIN1, GRIN2A, GRIN2B, GRIN3A, GRIA1, GRIK2, GRM2, GRM3, GRM5, GRM8, SLC1A1, SLC1A3 and GAD1 were genotyped in 258 patients with schizophrenia. Efficacy and side effects were evaluated with the Positive and Negative Symptoms Scale and the UKU scale, respectively, at baseline and after 12 weeks.
Results
The analysis revealed associations between outcomes, including response and adverse effects and genetic variants in several genes (GAD1, GRIA1, GRIN2A, GRIN3A, GRIK2, GRM2, GRM5, GRM8 and SLC1A3). An association of rs1864205 in GRIA1 with autonomic side effects bordered statistical significance after correction for multiple comparisons.
Discussion
Our results suggest that genetic variation in glutamatergic pathways can influence the efficacy and safety of antipsychotic drugs.
#
Introduction
Schizophrenia is a common disorder with an estimated prevalence of 1% of the worldwide population [1]. Its main clinical manifestations are positive symptoms, defined as an excess of normal cognitive processes, including hallucinations and delusions, and negative symptoms, which are a degradation of normal behaviour such as apathy, lack of emotion, initiative and self-care. In addition to this classical biaxial classification, the disorder also presents additional symptoms, remarkably a decline in cognitive function such as impaired processing speed, working memory or executive functions. Schizophrenia can have a negative impact on interpersonal relations, as well as educational and professional functioning. Moreover, affected patients are subject to stigmatization and discrimination on top of the disabilities inherent to the condition.
Dopaminergic overactivation has been the standing paradigm regarding the aetiology of schizophrenia [2]. This hypothesis was supported by the fact that certain dopaminergic drugs, such as amphetamine, can trigger episodes very similar to positive symptoms in exposed, otherwise healthy, people. Varying degrees of antagonism on the dopamine receptor D2 (DRD2) is a common characteristic of antipsychotics [3] [4], although newer drugs also rely on other mechanisms. However, this dopaminergic paradigm does not explain psychotic manifestations other than positive symptoms. More recently, a different idea has taken form in the concept of glutamate dysregulation. Ketamine, an antiglutamatergic drug, can trigger both positive and negative symptoms, and this glutamatergic paradigm could be a better explanation of the physiological changes of the schizophrenic brain [5] [6].
Genetic variants in several genes involved in glutamatergic transmission and homeostasis have been associated with schizophrenia and pharmacotherapy response in previous studies. Alterations in the regulation and expression of genes encoding N-methyl-D-aspartate (NMDA) receptor subunits have been observed in patients with schizophrenia [7] [8] [9] [13]. Genetic variation in Glutamate Receptor, Ionotropic, NMDA-activated 2 A and 2B (GRIN2A and GRIN2B) subunits has been investigated regarding the risk of schizophrenia and response to clozapine [10] [11] [12] [13] [14]. The gene encoding glutamate receptor, ionotropic, AMPA-activated 1 (GRIA1) is suspected to influence the risk of schizophrenia, as it is located in 5q33, a region that has been associated with schizophrenia in genome-wide association studies (GWAS) [15]. Several genetic polymorphisms within this gene have been previously linked to the disorder and with treatment response [15] [16] [17]. The gene encoding glutamate receptor, ionotropic, kainate-activated 2 (GRIK2) is located in a genomic region associated with schizophrenia [18]. Expression of GRIK2 is reduced in people with schizophrenia [19]. Metabotropic receptors are also promising candidates for modifiers of the disorder. Schizophrenia patients have been found to have decreased methylation of the glutamate receptor, metabotropic 2 and 5 (GRM2 and GRM5) [20]. Genetic variation in GRM3 has been associated with positive and negative symptoms [21]. Regarding GRM8, several polymorphisms in this gene have been associated with schizophrenia in different studies [22] [23]. Regarding transporters, genetic polymorphisms in the solute carrier 1A1 (SLC1A1) has been associated with the risk of schizophrenia [24] [25] [26], whereas genetic variation in SLC1A3 has been associated with cognitive decline in patients with schizophrenia [27] [28]. Patients with schizophrenia also show altered expression of SLC1A1 in their prefrontal cortex [29]. Glutamate decarboxylase 1(GAD1) is an important regulator of glutamate, and some common polymorphisms have been found to be more represented in schizophrenia patients [30]. Some preliminary information also link this gene to treatment-resistant schizophrenia [31].
Polymorphisms affecting glutamatergic transmission may lead to qualitatively different characteristics of the disease that could confer differential response to medication, regarding either efficacy or side effects. Furthermore, the exact mechanism of action of atypical antipsychotics is not totally understood, and it is possible that they might interact with glutamatergic receptors. Limited evidence suggests that antipsychotic treatments can regulate the expression of glutamate transporters [32]. Therefore, there are multiple mechanisms through which genetic polymorphisms in glutamatergic genes can influence the outcome of currently used antipsychotics. Involvement of glutamatergic genes in schizophrenia has been seen in GWAS, such as GRM7, which has been reported to influence positive symptoms [33]. The GWAS approach is a strategy with the strong benefit that it does not require a prior hypothesis, and therefore can unveil genetic effects even when the current understanding of the pathology and its mechanisms is incomplete, as well as discover associations outside of the suspected mechanisms. However, GWAS analyse thousands of positions and require very strict statistical corrections to account for multiple comparisons. As a result, true associations can fail to reach genome-wide significance, and some known associations in candidate genes have not shown up in GWAS results [34]. This caveat can be dealt with candidate genes studies, which can find these associations with much smaller sample sizes, at the cost of missing the effect of polymorphisms outside the hypothesis.
The aim of this study was to explore the influence of genetic variation in genes encoding glutamate receptors, or influencing glutamate neurotransmission, on the response or safety of antipsychotic treatments. To achieve this objective, we investigated a selection of polymorphisms in glutamate-related genes in a cohort with detailed information on the outcome of antipsychotic treatments.
#
Experimental procedures
Sample
Patients were recruited in the mental health wards of three hospitals (Hospital Clínic and Hospital de la Santa Creu i Sant Pau, Barcelona; Hospital Universitario, Granada) and were mostly outpatients. Patients whose treatment was guided by a pharmacogenetic test [35] were excluded to avoid confounding by the intervention. Only the patients whose treatment was not changed according to pharmacogenetic information were included in the present study. Two hundred fifty-eight patients met these criteria and thus were eligible to be included in the study. The sample included patients of Caucasian ethnicity with disorders within the schizophrenia spectrum (DSM-V). Fifty-eight percent of patients were male, with an average age of 45.5 years (SD=13.76). All patients were on monotherapy with different antipsychotics: clozapine (44%), risperidone (12%), aripiprazole (7%), olanzapine (13%), quetiapine (6%), paliperidone (12%), other antipsychotics (5%) (see [Table 1]). A sample size of 120 was required to detect associations of strong effects (F=0.35), assuming an allelic frequency of 0.05 at a desired statistical power of 90%. This sample has between 80 and 99% statistical power to detect associations of moderate or large effect size assuming a minor allele frequency of 0.05 (F2=0.15–0.35, respectively, calculated with G*Power v.3.1.9.4). The patients were not taking any other drug for the duration of the study other than the antipsychotic of choice, with the sole exception of lorazepam, which was given to 17% of the patients with sleeping difficulties. This percentage was lower (6%) among patients using clozapine or olanzapine. Doses of 1 to 1.5 mg/day were used, and administration was as needed to control insomnia. All participants gave their written informed consent for the study, which was carried out in accordance with the provisions of the Helsinki Declaration. The study was approved by the scientific research ethics committee of Hospital Clinic de Barcelona (Reg. 2011/6635).
|
Sex |
|
|
Males |
58% |
|
Age |
|
|
Mean |
45.5 |
|
SD1 |
13.76 |
|
Disease statistics (name WIP) |
|
|
Average initial PANSS |
94.34 |
|
Average final PANSS |
68.4 |
|
Average initial UKUs |
7.54 |
|
Average final UKUs |
3.51 |
|
Treatment |
|
|
Clozapine |
44% |
|
Olanzapine |
13% |
|
Risperidone |
12% |
|
Paliperidone |
12% |
|
Aripiprazole |
7% |
|
Quetiapine |
6% |
|
Other |
5% |
|
Average dose (ol.eq.)2 |
11.1 |
|
Diagnostic |
|
|
Schizophrenia |
76% |
|
Delusional disorder |
21% |
|
Schizoafective disorder |
3% |
|
Total N |
258 |
1 (SD) – Standard Deviation, 2 (ol.eq.) – olanzapine equivalent. PANSS: Positive and Negative Symptoms Scale; UKU: Udvalg for Kliniske Undersøgelser.
#
Biochemical parameters
Plasma concentrations of the antipsychotic and its main metabolites were determined at 6 weeks after starting the treatment to check adherence. Concentrations were measured by a validated high-performance liquid chromatography, using UV diode array detection and solid-phase extraction on cyano cartridges. Compounds were separated on a C8 reversed-phase column with a gradient of acetonitrile and phosphate buffer 50 mM, pH 3.8 and detected at 210 and 278 nm. To assure high precision and accuracy of the method, an internal standard was included in the sample preparation and quantitative evaluation. The within- and between-day precision expressed as coefficient of variation (CV)% was<10%. The quality of analyses was subject to a programme of internal quality control and external quality assessment [36]. The dose used for each patient was recorded, and results were standardized among different antipsychotic agents by converting the values to their equivalent olanzapine dose [37]). Doses were titrated from week 0 to 4 and maintained thereafter for the duration of the study.
#
Clinical parameters
Single appraisal of response and side effects might be confounded by different starting situations, therefore all outcome measures were computed as the difference between the value at the moment of treatment evaluation, that happened after 12 weeks, and the values at the start of the treatment. Response was assessed with the Positive and Negative Symptoms Scale (PANSS) [38]. While a 20% improvement in the PANSS score is often considered a threshold for treatment response, there is no real consensus about what percentage would be appropriate [39]. Therefore, the difference in PANSS from baseline to 12 weeks was considered as a continuous variable for the statistical analyses, which also provides a quantitative insight into the magnitude of the improvement. Adverse events were tracked with the Udvalg for Kliniske Undersøgelser (UKU) side effects rating scale [40] at the same time points. PANSS is a common way to assess the general severity of the illness across its two main axes (positive and negative symptoms) as well as a third dimension representing general psychopathology. Each axis is rated from 1 to 7, with higher scores indicating more severe symptoms. The three values can be added together to get a summary score. UKUs are a less widespread method of assessing side effects of psychotropic medications. It works by assessing a number of items representing different kinds of adverse effects, and each one is scored from 0 (representing absence) to 3 (representing greatest severity). In this study, the UKU scores were grouped into four categories: psychological, neurologic, autonomic and other side effects. A fifth and final category, total UKU, captures the overall emergence of any side effect, as it combines the scores of all the UKU categories.
#
Polymorphism selection and genotyping
A candidate gene approach was used. A selection of glutamate genes previously associated with schizophrenia and/or treatment response (see introduction) were included in the study: GRIN1, GRIN2A, GRIN2B, GRIN3A, GRIA1, GRIK2, GRM2, GRM3, GRM5, GRM8, SLC1A1, SLC1A3 and GAD1. Forty-five TagSNPs within the candidate genes were selected using the ENSEMBL Genome Browser (www.ensembl.org), SNPinfo (snpinfo.niehs.nih.gov) and LDlink (ldlink.nci.nih.gov) using the parameters MAF≥0.05 and linkage of R2≥0.8. A complete list of the genes and their polymorphisms is detailed in [Table 2]. The SNPs have been chosen for their ability to capture genetic variability within the specified genes. Whole blood samples were taken from all study participants. DNA was extracted with a commercial kit (QIAmp DNA Mini Kit, Qiagen) following the manufacturers’ recommendations. Genotyping was performed with a MassARRAY platform (CEGEN-PRB2-ISCIII, University of Santiago de Compostela, Spain).
|
Gene |
Polymorphism |
Gene |
Polymorphism |
|---|---|---|---|
|
GAD1 |
rs2058725 |
GRIN3A |
rs1004362 |
|
GAD1 |
rs769407 |
GRIN3A |
rs1323423 |
|
GRIA1 |
rs10515697 |
GRIN3A |
rs1323427 |
|
GRIA1 |
rs1864205 |
GRIN3A |
rs1983812 |
|
GRIA1 |
rs1994862 |
GRIN3A |
rs2050639 |
|
GRIK2 |
rs2227281 |
GRIN3A |
rs2050641 |
|
GRIK2 |
rs2518224 |
GRIN3A |
rs2417290 |
|
GRIK2 |
rs2518302 |
GRM2 |
rs60972621 |
|
GRIK2 |
rs513216 |
GRM2 |
rs75938458 |
|
GRIK2 |
rs9404130 |
GRM3 |
rs1468412 |
|
GRIK2 |
rs995640 |
GRM3 |
rs1989796 |
|
GRIN1 |
rs2301364 |
GRM3 |
rs6465084 |
|
GRIN1 |
rs34547970 |
GRM5 |
rs10501686 |
|
GRIN1 |
rs4880215 |
GRM5 |
rs1391873 |
|
GRIN1 |
rs71387806 |
GRM5 |
rs2648640 |
|
GRIN2A |
rs10518151 |
GRM5 |
rs4628675 |
|
GRIN2A |
rs10870198 |
GRM5 |
rs596370 |
|
GRIN2A |
rs6497524 |
GRM8 |
rs1008907 |
|
GRIN2A |
rs9931155 |
GRM8 |
rs1419489 |
|
GRIN2B |
rs11055624 |
GRM8 |
rs2299516 |
|
GRIN2B |
rs12582848 |
SLC1A1 |
rs1471786 |
|
GRIN2B |
rs1806201 |
SLC1A3 |
rs13154397 |
|
GRIN2B |
rs219934 |
#
Statistical analysis
Linear regression analyses were performed for quantitative phenotypes (differences in PANSS, and total, psychological, neurologic, autonomic and other UKUs). A model consisting of gender, age, dose (olanzapine equivalent) and smoking habits (number of daily cigarettes) as covariates and the genotype of the selected polymorphisms as predictor variables was used for the analyses. P-values were corrected for multiple comparisons using the False Discovery Rate method. All analyses were performed using PLINK 1.07 (Shaun Purcell, May 10th, 2010). Separate analyses were conducted for the total sample, including all treatments (ALL cohort, N=258), for the subgroup of patients treated with clozapine (CLOZ cohort, N=114) and the patients using drugs other than clozapine (OTHER cohort, N=144).
#
#
Results
Five samples were excluded after quality control analyses for having less than 95% SNP and/or sample successful call rate. All genotyped polymorphisms were in Hardy-Weinberg equilibrium.
Genetic associations with treatment response
Associations were observed between treatment response and polymorphisms in GRIK2 (rs995640 [p=0.05] and rs2227281 [p=0.05]), GRIN2A (rs9931155 [p=0.02] and rs6497524 [p=0.04]) and GRIN2B (rs11055624 [p=0.05] and rs12582848 [p=0.05]) when considering all patients. Within the CLOZ group, improvement in PANSS was associated with SLC1A3 rs13154397 [p=0.02], GRIK2 rs995640 [p=0.03] and rs2227281 [p=0.03] variants, and with GRIN3A rs2050639 [p=0.03] and GRIN2A rs6497524 [p=0.04] variants. No association was detected between the genetic variants investigated and efficacy when analysing the OTHER group. None of the previously reported results remained significant after correction for multiple comparisons (adjusted p>0.05 in all instances). These results are presented in [Table 3].
|
ALL1 |
CLOZ2 |
OTHER3 |
|||||
|---|---|---|---|---|---|---|---|
|
GENE |
SNP |
BETA |
P4 |
BETA |
P4 |
BETA |
P4 |
|
GAD1 |
rs2058725 |
2.42 |
0.18 |
6.73 |
0.08 |
1.07 |
0.60 |
|
GAD1 |
rs769407 |
− 2.43 |
0.18 |
− 5.05 |
0.14 |
− 0.77 |
0.72 |
|
GRIA1 |
rs10515697 |
0.95 |
0.59 |
0.19 |
0.96 |
1.80 |
0.35 |
|
GRIA1 |
rs1864205 |
0.57 |
0.74 |
2.84 |
0.49 |
− 0.72 |
0.70 |
|
GRIA1 |
rs1994862 |
1.25 |
0.48 |
0.22 |
0.96 |
2.05 |
0.27 |
|
GRIK2 |
rs2227281 |
− 3.30 |
0.05 |
− 9.23 |
0.03 |
− 1.93 |
0.28 |
|
GRIK2 |
rs2518224 |
− 2.43 |
0.38 |
− 3.70 |
0.65 |
− 1.86 |
0.51 |
|
GRIK2 |
rs2518302 |
0.24 |
0.90 |
1.84 |
0.72 |
− 0.59 |
0.77 |
|
GRIK2 |
rs513216 |
− 0.87 |
0.59 |
− 0.94 |
0.80 |
− 0.97 |
0.58 |
|
GRIK2 |
rs9404130 |
2.18 |
0.48 |
− 5.36 |
0.54 |
4.28 |
0.17 |
|
GRIK2 |
rs995640 |
− 3.36 |
0.05 |
− 9.60 |
0.03 |
− 2.03 |
0.26 |
|
GRIN1 |
rs2301364 |
0.45 |
0.84 |
− 2.97 |
0.47 |
2.21 |
0.40 |
|
GRIN1 |
rs34547970 |
1.21 |
0.65 |
− 1.01 |
0.83 |
2.87 |
0.38 |
|
GRIN1 |
rs4880215 |
− 1.31 |
0.43 |
− 0.66 |
0.86 |
− 0.81 |
0.66 |
|
GRIN1 |
rs71387806 |
− 1.33 |
0.68 |
− 0.92 |
0.89 |
− 2.24 |
0.54 |
|
GRIN2A |
rs10518151 |
2.83 |
0.34 |
− 11.17 |
0.12 |
5.93 |
0.06 |
|
GRIN2A |
rs10870198 |
− 1.71 |
0.33 |
− 3.31 |
0.42 |
− 1.03 |
0.58 |
|
GRIN2A |
rs6497524 |
− 3.07 |
0.04 |
− 6.25 |
0.04 |
− 2.09 |
0.22 |
|
GRIN2A |
rs9931155 |
− 3.40 |
0.02 |
− 4.83 |
0.10 |
− 2.57 |
0.13 |
|
GRIN2B |
rs11055624 |
3.39 |
0.05 |
4.93 |
0.15 |
2.06 |
0.29 |
|
GRIN2B |
rs12582848 |
− 3.31 |
0.05 |
− 2.30 |
0.51 |
− 3.10 |
0.10 |
|
GRIN2B |
rs1806201 |
2.15 |
0.41 |
4.27 |
0.49 |
1.31 |
0.63 |
|
GRIN2B |
rs219934 |
0.68 |
0.68 |
2.30 |
0.49 |
1.27 |
0.50 |
|
GRIN3A |
rs1004362 |
1.04 |
0.62 |
− 1.09 |
0.86 |
0.65 |
0.77 |
|
GRIN3A |
rs1323423 |
2.81 |
0.17 |
5.39 |
0.30 |
1.24 |
0.57 |
|
GRIN3A |
rs1323427 |
− 3.51 |
0.06 |
− 6.86 |
0.14 |
− 0.77 |
0.71 |
|
GRIN3A |
rs1983812 |
− 1.44 |
0.36 |
− 4.10 |
0.33 |
− 2.57 |
0.11 |
|
GRIN3A |
rs2050639 |
− 2.35 |
0.17 |
− 9.19 |
0.03 |
0.97 |
0.61 |
|
GRIN3A |
rs2050641 |
− 2.56 |
0.18 |
− 6.19 |
0.15 |
0.73 |
0.74 |
|
GRIN3A |
rs2417290 |
1.36 |
0.45 |
− 3.99 |
0.40 |
1.77 |
0.36 |
|
GRM2 |
rs60972621 |
− 0.42 |
0.79 |
− 5.92 |
0.06 |
2.81 |
0.13 |
|
GRM2 |
rs75938458 |
− 0.45 |
0.92 |
− 15.07 |
0.18 |
4.60 |
0.32 |
|
GRM3 |
rs1468412 |
− 0.10 |
0.96 |
− 2.01 |
0.68 |
0.14 |
0.94 |
|
GRM3 |
rs1989796 |
2.02 |
0.21 |
0.59 |
0.88 |
2.60 |
0.12 |
|
GRM3 |
rs6465084 |
− 0.32 |
0.88 |
− 7.13 |
0.20 |
1.30 |
0.54 |
|
GRM5 |
rs10501686 |
1.02 |
0.52 |
0.16 |
0.97 |
1.83 |
0.29 |
|
GRM5 |
rs1391873 |
1.44 |
0.39 |
− 0.48 |
0.87 |
3.14 |
0.12 |
|
GRM5 |
rs2648640 |
− 0.55 |
0.76 |
5.77 |
0.14 |
− 2.73 |
0.17 |
|
GRM5 |
rs4628675 |
1.41 |
0.40 |
1.23 |
0.75 |
2.20 |
0.23 |
|
GRM5 |
rs596370 |
− 1.02 |
0.55 |
3.33 |
0.42 |
− 2.42 |
0.19 |
|
GRM8 |
rs1008907 |
− 0.54 |
0.72 |
− 1.15 |
0.70 |
− 0.41 |
0.82 |
|
GRM8 |
rs1419489 |
− 0.54 |
0.73 |
− 2.52 |
0.42 |
0.34 |
0.85 |
|
GRM8 |
rs2299516 |
1.35 |
0.38 |
0.65 |
0.85 |
1.79 |
0.31 |
|
SLC1A1 |
rs1471786 |
− 0.56 |
0.80 |
− 1.69 |
0.65 |
0.63 |
0.81 |
|
SLC1A3 |
rs13154397 |
1.88 |
0.21 |
6.62 |
0.02 |
− 0.19 |
0.91 |
1 (ALL) – group of all the study participants, 2 (CLOZ) – group of participants using clozapine, 3 (OTHER) – group of participants not using clozapine, 4 (P) – uncorrected p-value. Bold indicates a p-value≤0.05. SNPs: single nucleotide polymorphisms; PANSS: Positive and Negative Symptoms Scale
#
Genetic associations with side effects
Variants in several genes (GRIN3A, GRIK2, GRM8, GRIN2A, SLC1A3, GRM5, GRIA1, GRM2 and GAD1) showed associations with adverse effects. A nominal association was observed between GRIN3A rs1004362 [p=0.03] and total adverse effects in the ALL cohort, but not in any of the subgroups. An association with GRIN3A rs2417290 [p=0.04] was found only in the OTHER subgroup . The GRIN3A rs1004362 variant was also found to be associated with psychologic side effects in the ALL [p=0.02] and CLOZ [p=0.03] groups, and was associated with other UKUs in the CLOZ subgroup [p=0.02]. Other polymorphisms in GRIN3A also showed association with different kinds of adverse -effects only in the CLOZ subgroup (rs1323423 [p=0.02] for psychologic UKUs, rs2050641 [p=0.02] and rs1323427 [p=0.03] for autonomic UKUs). Genetic polymorphisms in GRIK2 were also related to side effects, as rs995640 [p=0.03] and rs9404130 [p=0.03] were associated with total side effects in the CLOZ sample, but this effect was not seen in the ALL or OTHER groups. The GRIK2 rs995640 variant was found to be associated with autonomic side effects in the patients of the ALL [p=0.02] and OTHER [p=0.03] subgroups, but not in the CLOZ sample. Another polymorphism in GRIK2, rs2518224, was associated with other adverse effects only in the CLOZ sample [p=0.04]. The GRM8, rs2299516 variant showed association with total adverse effects only in the CLOZ sample [p=0.04]. This polymorphism along with rs1008907 in the same gene were also associated with psychologic side effects only in patients of the CLOZ subgroup [p=0.03]. A third polymorphism in GRM8, rs1419489, showed association with other adverse effects in the CLOZ sample [p=0.03] as well as in the ALL sample [p=0.05]. Two polymorphisms in GRIN2A (rs6497524 and rs9931155) were associated with psychologic side effects in ALL [rs6497524 p=0.01, rs9931155 p=0.03] and OTHER patients [rs6497524 p=0.01, rs9931155 p=0.01]. GRIN2A rs6497524 was also associated with autonomic side effects in OTHER patients [p=0.01], but this was not detected in any of the other two subgroups. The SLC1A3 rs13154397 polymorphism was associated with neurologic adverse effects in the ALL [p=0.01] and OTHER samples [p=0.03] The GRM5 rs4628675 polymorphism was associated with neurologic side effects in the ALL group [p=0.05], and the polymorphism rs2648640 in the same gene was associated with other adverse effects in the OTHER subgroup [p=0.03]. Finally, the GRIA1 rs1864205 polymorphism was associated with autonomic side effects in the ALL and CLOZ samples [p<0.01 for both groups], and the association in CLOZ remained borderline significant after correction for multiple comparisons (p=0.05). Two other polymorphisms showed association with autonomic UKUs GRM2 rsr60972621 in the ALL sample [p=0.02] and GAD1 rs2058725, in the OTHER subgroup [p=0.05]. After correction for multiple comparisons, all adjusted p-values were higher than 0.05, except for the association between GRIA1 rs1864205 and autonomic side effects that bordered the significance threshold (adjusted p=0.05). A summary of these results can be seen in [Table 4].
|
UKUT1 |
UKUP2 |
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
ALL6 |
CLOZ7 |
OTHER8 |
ALL6 |
CLOZ7 |
OTHER8 |
||||||||
|
GENE |
SNP |
BETA |
P9 |
BETA |
P9 |
BETA |
P9 |
BETA |
P9 |
BETA |
P9 |
BETA |
P9 |
|
GAD1 |
rs2058725 |
0.78 |
0.34 |
1.17 |
0.52 |
0.79 |
0.38 |
0.03 |
0.95 |
0.43 |
0.68 |
0.05 |
0.93 |
|
GAD1 |
rs769407 |
–1.34 |
0.09 |
–1.59 |
0.31 |
–1.08 |
0.23 |
–0.54 |
0.23 |
–1.06 |
0.24 |
–0.31 |
0.55 |
|
GRIA1 |
rs10515697 |
–0.34 |
0.67 |
0.62 |
0.73 |
–0.28 |
0.75 |
–0.34 |
0.47 |
1.23 |
0.25 |
–0.64 |
0.21 |
|
GRIA1 |
rs1864205 |
0.16 |
0.84 |
2.11 |
0.22 |
–0.71 |
0.42 |
–0.08 |
0.87 |
0.23 |
0.82 |
–0.31 |
0.53 |
|
GRIA1 |
rs1994862 |
–0.19 |
0.81 |
0.61 |
0.74 |
–0.10 |
0.91 |
–0.35 |
0.44 |
1.23 |
0.25 |
–0.67 |
0.18 |
|
GRIK2 |
rs2227281 |
–0.37 |
0.63 |
–3.39 |
0.06 |
0.57 |
0.51 |
–0.11 |
0.81 |
–1.62 |
0.13 |
0.36 |
0.46 |
|
GRIK2 |
rs2518224 |
1.09 |
0.39 |
–1.55 |
0.67 |
1.20 |
0.37 |
0.86 |
0.23 |
1.97 |
0.36 |
0.60 |
0.43 |
|
GRIK2 |
rs2518302 |
–0.73 |
0.42 |
–2.68 |
0.18 |
–0.02 |
0.99 |
0.26 |
0.61 |
–1.23 |
0.30 |
0.77 |
0.16 |
|
GRIK2 |
rs513216 |
0.72 |
0.33 |
–0.89 |
0.57 |
1.27 |
0.13 |
0.12 |
0.77 |
–1.29 |
0.16 |
0.55 |
0.23 |
|
GRIK2 |
rs9404130 |
0.95 |
0.50 |
7.69 |
0.03 |
0.11 |
0.94 |
–0.52 |
0.52 |
3.40 |
0.11 |
–0.91 |
0.28 |
|
GRIK2 |
rs995640 |
–0.97 |
0.22 |
–3.83 |
0.03 |
–0.30 |
0.73 |
–0.35 |
0.44 |
–2.07 |
0.05 |
0.04 |
0.94 |
|
GRIN1 |
rs2301364 |
–0.08 |
0.94 |
–0.75 |
0.69 |
0.28 |
0.81 |
0.97 |
0.08 |
0.49 |
0.65 |
1.15 |
0.07 |
|
GRIN1 |
rs34547970 |
0.49 |
0.68 |
–0.73 |
0.74 |
1.19 |
0.40 |
1.07 |
0.11 |
0.44 |
0.72 |
1.35 |
0.09 |
|
GRIN1 |
rs4880215 |
0.33 |
0.66 |
0.51 |
0.77 |
0.54 |
0.50 |
0.22 |
0.59 |
–0.32 |
0.74 |
0.62 |
0.17 |
|
GRIN1 |
rs71387806 |
0.51 |
0.72 |
2.69 |
0.38 |
–0.45 |
0.77 |
0.38 |
0.64 |
0.93 |
0.59 |
<0.01 |
1.00 |
|
GRIN2A |
rs10518151 |
0.14 |
0.91 |
–1.82 |
0.62 |
0.51 |
0.71 |
1.18 |
0.12 |
0.75 |
0.72 |
1.31 |
0.09 |
|
GRIN2A |
rs10870198 |
0.06 |
0.93 |
0.07 |
0.97 |
0.17 |
0.84 |
–0.22 |
0.61 |
–1.42 |
0.18 |
0.14 |
0.76 |
|
GRIN2A |
rs6497524 |
–1.07 |
0.12 |
–0.10 |
0.95 |
–1.22 |
0.10 |
–0.98 |
0.01 |
–0.43 |
0.60 |
–1.15 |
0.01 |
|
GRIN2A |
rs9931155 |
–0.60 |
0.36 |
1.00 |
0.46 |
–0.73 |
0.32 |
–0.78 |
0.03 |
0.44 |
0.58 |
–1.09 |
0.01 |
|
GRIN2B |
rs11055624 |
0.83 |
0.27 |
0.63 |
0.69 |
0.71 |
0.39 |
0.54 |
0.21 |
1.53 |
0.08 |
0.05 |
0.91 |
|
GRIN2B |
rs12582848 |
–0.53 |
0.47 |
0.65 |
0.68 |
–0.76 |
0.35 |
–0.17 |
0.67 |
0.58 |
0.52 |
–0.32 |
0.49 |
|
GRIN2B |
rs1806201 |
0.12 |
0.87 |
–1.31 |
0.65 |
0.51 |
0.73 |
0.47 |
0.54 |
–0.18 |
0.91 |
0.39 |
0.66 |
|
GRIN2B |
rs219934 |
0.72 |
0.60 |
1.95 |
0.19 |
0.67 |
0.41 |
0.07 |
0.86 |
1.29 |
0.13 |
0.41 |
0.38 |
|
GRIN3A |
rs1004362 |
–2.10 |
0.03 |
–4.16 |
0.08 |
–1.82 |
0.08 |
–1.25 |
0.02 |
–2.90 |
0.03 |
–1.05 |
0.07 |
|
GRIN3A |
rs1323423 |
–1.02 |
0.27 |
–3.18 |
0.12 |
–0.52 |
0.62 |
–0.77 |
0.14 |
–2.77 |
0.02 |
–0.33 |
0.57 |
|
GRIN3A |
rs1323427 |
–0.85 |
0.32 |
–2.46 |
0.20 |
0.31 |
0.76 |
–0.41 |
0.40 |
–0.50 |
0.66 |
0.05 |
0.93 |
|
GRIN3A |
rs1983812 |
0.52 |
0.48 |
–3.00 |
0.08 |
0.09 |
0.91 |
0.34 |
0.40 |
–1.54 |
0.13 |
0.16 |
0.72 |
|
GRIN3A |
rs2050639 |
–0.69 |
0.38 |
–0.77 |
0.68 |
0.00 |
1.00 |
–0.18 |
0.70 |
0.46 |
0.67 |
0.08 |
0.87 |
|
GRIN3A |
rs2050641 |
–0.56 |
0.51 |
–1.51 |
0.39 |
0.52 |
0.61 |
–0.35 |
0.48 |
–0.18 |
0.86 |
0.05 |
0.93 |
|
GRIN3A |
rs2417290 |
–1.46 |
0.07 |
–0.84 |
0.66 |
–1.83 |
0.04 |
–0.69 |
0.14 |
–0.90 |
0.42 |
–0.79 |
0.12 |
|
GRM2 |
rs60972621 |
–0.48 |
0.50 |
–1.50 |
0.31 |
0.26 |
0.75 |
–0.09 |
0.82 |
0.11 |
0.90 |
–0.03 |
0.95 |
|
GRM2 |
rs75938458 |
–2.74 |
0.16 |
–8.17 |
0.26 |
–1.24 |
0.53 |
–0.01 |
0.99 |
1.70 |
0.68 |
0.30 |
0.78 |
|
GRM3 |
rs1468412 |
–1.37 |
0.12 |
–1.58 |
0.43 |
–1.27 |
0.19 |
–0.28 |
0.57 |
–1.40 |
0.23 |
–0.03 |
0.96 |
|
GRM3 |
rs1989796 |
0.92 |
0.21 |
1.52 |
0.38 |
0.76 |
0.35 |
0.52 |
0.22 |
1.45 |
0.15 |
0.33 |
0.47 |
|
GRM3 |
rs6465084 |
–1.51 |
0.11 |
–1.57 |
0.49 |
–1.56 |
0.13 |
–0.01 |
0.98 |
0.21 |
0.88 |
–0.12 |
0.84 |
|
GRM5 |
rs10501686 |
–0.23 |
0.74 |
–1.73 |
0.29 |
0.62 |
0.40 |
–0.25 |
0.53 |
–1.06 |
0.25 |
0.18 |
0.67 |
|
GRM5 |
rs1391873 |
–0.19 |
0.80 |
0.65 |
0.64 |
–0.93 |
0.29 |
–0.30 |
0.47 |
–0.38 |
0.63 |
–0.42 |
0.40 |
|
GRM5 |
rs2648640 |
0.52 |
0.51 |
2.19 |
0.22 |
0.05 |
0.96 |
0.00 |
0.99 |
1.15 |
0.26 |
–0.39 |
0.43 |
|
GRM5 |
rs4628675 |
–0.04 |
0.96 |
–1.69 |
0.34 |
1.00 |
0.20 |
–0.25 |
0.55 |
–1.08 |
0.29 |
0.24 |
0.59 |
|
GRM5 |
rs596370 |
0.34 |
0.66 |
2.74 |
0.14 |
–0.31 |
0.70 |
0.16 |
0.70 |
1.33 |
0.21 |
–0.15 |
0.73 |
|
GRM8 |
rs1008907 |
–0.21 |
0.75 |
–1.67 |
0.21 |
–0.01 |
0.99 |
0.10 |
0.79 |
–1.47 |
0.05 |
0.57 |
0.20 |
|
GRM8 |
rs1419489 |
–0.65 |
0.35 |
–2.52 |
0.07 |
–0.17 |
0.83 |
0.09 |
0.82 |
–1.24 |
0.12 |
0.47 |
0.29 |
|
GRM8 |
rs2299516 |
0.92 |
0.18 |
3.07 |
0.04 |
0.39 |
0.60 |
–0.01 |
0.99 |
2.02 |
0.02 |
–0.53 |
0.21 |
|
SLC1A1 |
rs1471786 |
–0.50 |
0.61 |
–0.92 |
0.59 |
–0.09 |
0.94 |
<0.01 |
1.00 |
–0.46 |
0.64 |
0.44 |
0.50 |
|
SLC1A3 |
rs13154397 |
0.21 |
0.75 |
0.67 |
0.60 |
–0.15 |
0.84 |
–0.13 |
0.72 |
–0.21 |
0.78 |
–0.21 |
0.62 |
1 (UKUT) – Total UKU, 2 (UKUP) – Psychological UKU, 3 (UKUN) – Neurologic UKU, 4 (UKUA) – Automonic UKU, (5) UKUO – Other UKU, 6 (ALL) – group of all the study participants, 7 (CLOZ) – group of participants using clozapine, 8 (OTHER) – group of participants not using clozapine, 9 (P) – uncorrected p-value. Bold indicates a p-value≤0.05. UKUs: Udvalg for Kliniske Undersøgelser; SNPs: Single nucleotide polymorphisms
|
UKUN3 |
UKUA4 |
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
ALL6 |
CLOZ7 |
OTHER8 |
ALL6 |
CLOZ7 |
OTHER8 |
||||||||
|
GENE |
SNP |
BETA |
P9 |
BETA |
P9 |
BETA |
P9 |
BETA |
P9 |
BETA |
P9 |
BETA |
P9 |
|
GAD1 |
rs2058725 |
0.06 |
0.84 |
0.51 |
0.42 |
–0.23 |
0.53 |
0.46 |
0.09 |
0.71 |
0.37 |
0.49 |
0.05 |
|
GAD1 |
rs769407 |
–0.10 |
0.75 |
0.04 |
0.94 |
0.00 |
1.00 |
–0.21 |
0.43 |
0.24 |
0.72 |
–0.41 |
0.10 |
|
GRIA1 |
rs10515697 |
0.22 |
0.49 |
0.31 |
0.60 |
0.38 |
0.32 |
–0.16 |
0.55 |
–0.39 |
0.61 |
–0.08 |
0.76 |
|
GRIA1 |
rs1864205 |
–0.03 |
0.94 |
0.25 |
0.67 |
–0.08 |
0.82 |
0.74 |
<0.01 |
2.26 |
<0.01 |
0.13 |
0.60 |
|
GRIA1 |
rs1994862 |
0.21 |
0.50 |
0.31 |
0.61 |
0.36 |
0.33 |
–0.16 |
0.52 |
–0.38 |
0.63 |
–0.09 |
0.71 |
|
GRIK2 |
rs2227281 |
0.19 |
0.54 |
–0.42 |
0.50 |
0.42 |
0.24 |
–0.37 |
0.14 |
–0.88 |
0.26 |
–0.28 |
0.23 |
|
GRIK2 |
rs2518224 |
0.82 |
0.10 |
0.59 |
0.62 |
0.82 |
0.14 |
–0.36 |
0.37 |
–1.88 |
0.22 |
–0.32 |
0.38 |
|
GRIK2 |
rs2518302 |
–0.54 |
0.12 |
–0.63 |
0.35 |
–0.50 |
0.22 |
–0.22 |
0.44 |
–0.53 |
0.55 |
–0.09 |
0.75 |
|
GRIK2 |
rs513216 |
0.37 |
0.19 |
–0.05 |
0.92 |
0.59 |
0.08 |
0.25 |
0.30 |
0.44 |
0.51 |
0.12 |
0.60 |
|
GRIK2 |
rs9404130 |
0.54 |
0.33 |
0.78 |
0.52 |
0.48 |
0.44 |
–0.20 |
0.66 |
2.81 |
0.07 |
–0.65 |
0.11 |
|
GRIK2 |
rs995640 |
0.04 |
0.90 |
–0.41 |
0.50 |
0.20 |
0.59 |
–0.58 |
0.02 |
–1.26 |
0.10 |
–0.51 |
0.03 |
|
GRIN1 |
rs2301364 |
–0.41 |
0.27 |
–0.21 |
0.75 |
–0.51 |
0.27 |
–0.62 |
0.06 |
–1.22 |
0.13 |
–0.26 |
0.41 |
|
GRIN1 |
rs34547970 |
–0.45 |
0.32 |
–0.29 |
0.70 |
–0.51 |
0.36 |
–0.34 |
0.39 |
–1.03 |
0.28 |
0.09 |
0.81 |
|
GRIN1 |
rs4880215 |
0.21 |
0.46 |
0.43 |
0.48 |
0.10 |
0.75 |
0.11 |
0.65 |
0.41 |
0.58 |
0.09 |
0.70 |
|
GRIN1 |
rs71387806 |
0.20 |
0.71 |
0.54 |
0.61 |
0.18 |
0.78 |
0.40 |
0.40 |
0.68 |
0.61 |
0.17 |
0.70 |
|
GRIN2A |
rs10518151 |
–0.14 |
0.79 |
–0.28 |
0.83 |
–0.20 |
0.73 |
0.02 |
0.96 |
–1.36 |
0.39 |
0.30 |
0.44 |
|
GRIN2A |
rs10870198 |
0.29 |
0.33 |
0.61 |
0.35 |
0.19 |
0.56 |
0.11 |
0.67 |
0.39 |
0.63 |
0.06 |
0.78 |
|
GRIN2A |
rs6497524 |
0.16 |
0.54 |
–0.26 |
0.60 |
0.41 |
0.18 |
–0.35 |
0.12 |
0.18 |
0.77 |
–0.49 |
0.01 |
|
GRIN2A |
rs9931155 |
0.15 |
0.54 |
–0.17 |
0.72 |
0.43 |
0.14 |
–0.16 |
0.47 |
0.28 |
0.64 |
–0.23 |
0.26 |
|
GRIN2B |
rs11055624 |
–0.03 |
0.92 |
–0.54 |
0.32 |
0.14 |
0.69 |
0.12 |
0.64 |
0.10 |
0.89 |
0.03 |
0.90 |
|
GRIN2B |
rs12582848 |
–0.28 |
0.31 |
0.29 |
0.60 |
–0.44 |
0.18 |
–0.23 |
0.36 |
–0.55 |
0.42 |
–0.06 |
0.80 |
|
GRIN2B |
rs1806201 |
0.28 |
0.55 |
–0.04 |
0.96 |
0.20 |
0.72 |
–0.36 |
0.49 |
–1.53 |
0.24 |
–0.27 |
0.59 |
|
GRIN2B |
rs219934 |
0.18 |
0.51 |
0.64 |
0.22 |
0.50 |
0.13 |
–0.06 |
0.81 |
0.23 |
0.73 |
–0.07 |
0.76 |
|
GRIN3A |
rs1004362 |
0.22 |
0.55 |
0.01 |
0.99 |
0.28 |
0.52 |
–0.34 |
0.27 |
0.17 |
0.87 |
–0.49 |
0.08 |
|
GRIN3A |
rs1323423 |
0.43 |
0.23 |
0.49 |
0.48 |
0.41 |
0.34 |
–0.12 |
0.69 |
0.14 |
0.88 |
–0.22 |
0.44 |
|
GRIN3A |
rs1323427 |
–0.25 |
0.44 |
–0.55 |
0.39 |
0.06 |
0.89 |
–0.20 |
0.45 |
–1.75 |
0.03 |
0.29 |
0.27 |
|
GRIN3A |
rs1983812 |
–0.13 |
0.65 |
–0.39 |
0.51 |
–0.17 |
0.60 |
0.37 |
0.11 |
–1.36 |
0.07 |
0.11 |
0.61 |
|
GRIN3A |
rs2050639 |
–0.25 |
0.41 |
–0.33 |
0.58 |
0.02 |
0.95 |
–0.19 |
0.45 |
–1.37 |
0.08 |
0.14 |
0.58 |
|
GRIN3A |
rs2050641 |
0.20 |
0.56 |
0.05 |
0.93 |
0.55 |
0.20 |
–0.38 |
0.17 |
–1.71 |
0.02 |
0.15 |
0.58 |
|
GRIN3A |
rs2417290 |
0.18 |
0.58 |
0.33 |
0.60 |
0.13 |
0.73 |
–0.21 |
0.43 |
0.65 |
0.42 |
–0.48 |
0.05 |
|
GRM2 |
rs60972621 |
–0.06 |
0.82 |
–0.14 |
0.78 |
0.03 |
0.92 |
–0.55 |
0.02 |
–1.22 |
0.06 |
–0.22 |
0.32 |
|
GRM2 |
rs75938458 |
–0.87 |
0.22 |
–4.11 |
0.10 |
–0.61 |
0.43 |
–0.87 |
0.16 |
–5.63 |
0.07 |
–0.09 |
0.86 |
|
GRM3 |
rs1468412 |
–0.43 |
0.20 |
–0.10 |
0.87 |
–0.46 |
0.25 |
–0.08 |
0.77 |
0.28 |
0.74 |
–0.13 |
0.63 |
|
GRM3 |
rs1989796 |
0.33 |
0.25 |
0.53 |
0.36 |
0.30 |
0.37 |
–0.04 |
0.87 |
–0.70 |
0.35 |
0.09 |
0.69 |
|
GRM3 |
rs6465084 |
–0.62 |
0.10 |
–0.94 |
0.21 |
–0.53 |
0.22 |
–0.20 |
0.51 |
–0.88 |
0.37 |
–0.04 |
0.89 |
|
GRM5 |
rs10501686 |
0.48 |
0.07 |
0.77 |
0.17 |
0.44 |
0.15 |
–0.29 |
0.20 |
–1.26 |
0.07 |
0.12 |
0.56 |
|
GRM5 |
rs1391873 |
–0.10 |
0.73 |
0.33 |
0.49 |
–0.32 |
0.38 |
0.15 |
0.56 |
0.65 |
0.28 |
–0.25 |
0.30 |
|
GRM5 |
rs2648640 |
–0.19 |
0.53 |
–0.35 |
0.57 |
–0.08 |
0.81 |
0.44 |
0.10 |
1.46 |
0.06 |
0.16 |
0.51 |
|
GRM5 |
rs4628675 |
0.55 |
0.05 |
0.68 |
0.27 |
0.60 |
0.06 |
–0.29 |
0.23 |
–0.97 |
0.21 |
0.07 |
0.76 |
|
GRM5 |
rs596370 |
–0.15 |
0.60 |
–0.14 |
0.83 |
–0.14 |
0.66 |
0.22 |
0.37 |
1.28 |
0.12 |
–0.02 |
0.93 |
|
GRM8 |
rs1008907 |
–0.21 |
0.42 |
–0.27 |
0.57 |
–0.25 |
0.45 |
0.07 |
0.77 |
0.09 |
0.87 |
<0.01 |
0.98 |
|
GRM8 |
rs1419489 |
–0.22 |
0.40 |
–0.26 |
0.60 |
–0.20 |
0.54 |
–0.05 |
0.83 |
–0.14 |
0.82 |
–0.05 |
0.81 |
|
GRM8 |
rs2299516 |
0.32 |
0.21 |
0.69 |
0.20 |
0.24 |
0.43 |
0.30 |
0.18 |
0.37 |
0.58 |
0.25 |
0.24 |
|
SLC1A1 |
rs1471786 |
–0.35 |
0.34 |
–0.46 |
0.44 |
–0.31 |
0.52 |
0.18 |
0.58 |
0.11 |
0.88 |
0.23 |
0.47 |
|
SLC1A3 |
rs13154397 |
0.66 |
0.01 |
0.74 |
0.09 |
0.64 |
0.03 |
0.13 |
0.55 |
0.50 |
0.37 |
–0.07 |
0.75 |
|
UKUO5 |
|||||||
|---|---|---|---|---|---|---|---|
|
ALL6 |
CLOZ7 |
OTHER8 |
|||||
|
GENE |
SNP |
BETA |
P9 |
BETA |
P9 |
BETA |
P9 |
|
GAD1 |
rs2058725 |
0.32 |
0.25 |
–0.44 |
0.39 |
0.64 |
0.06 |
|
GAD1 |
rs769407 |
–0.32 |
0.24 |
–0.82 |
0.06 |
–0.11 |
0.75 |
|
GRIA1 |
rs10515697 |
–0.09 |
0.77 |
–0.44 |
0.43 |
–0.03 |
0.93 |
|
GRIA1 |
rs1864205 |
–0.43 |
0.13 |
–0.69 |
0.19 |
–0.35 |
0.31 |
|
GRIA1 |
rs1994862 |
0.15 |
0.61 |
–0.45 |
0.42 |
0.28 |
0.42 |
|
GRIK2 |
rs2227281 |
–0.14 |
0.62 |
–0.54 |
0.33 |
0.03 |
0.93 |
|
GRIK2 |
rs2518224 |
–0.25 |
0.59 |
–2.22 |
0.04 |
0.13 |
0.80 |
|
GRIK2 |
rs2518302 |
–0.08 |
0.82 |
–0.40 |
0.53 |
0.03 |
0.94 |
|
GRIK2 |
rs513216 |
–0.19 |
0.39 |
–0.01 |
0.99 |
–0.23 |
0.38 |
|
GRIK2 |
rs9404130 |
0.71 |
0.16 |
0.40 |
0.72 |
0.78 |
0.18 |
|
GRIK2 |
rs995640 |
–0.20 |
0.42 |
–0.15 |
0.79 |
–0.14 |
0.62 |
|
GRIN1 |
rs2301364 |
–0.16 |
0.62 |
0.18 |
0.74 |
–0.34 |
0.43 |
|
GRIN1 |
rs34547970 |
–0.03 |
0.94 |
0.18 |
0.77 |
–0.14 |
0.80 |
|
GRIN1 |
rs4880215 |
–0.24 |
0.34 |
–0.04 |
0.93 |
–0.28 |
0.36 |
|
GRIN1 |
rs71387806 |
–0.46 |
0.35 |
0.46 |
0.60 |
–0.72 |
0.23 |
|
GRIN2A |
rs10518151 |
–0.35 |
0.44 |
–0.74 |
0.48 |
–0.24 |
0.65 |
|
GRIN2A |
rs10870198 |
–0.06 |
0.81 |
0.38 |
0.47 |
–0.13 |
0.68 |
|
GRIN2A |
rs6497524 |
–0.20 |
0.38 |
0.41 |
0.31 |
–0.40 |
0.16 |
|
GRIN2A |
rs9931155 |
–0.07 |
0.74 |
0.48 |
0.21 |
–0.22 |
0.43 |
|
GRIN2B |
rs11055624 |
0.15 |
0.57 |
–0.55 |
0.22 |
0.47 |
0.15 |
|
GRIN2B |
rs12582848 |
0.02 |
0.95 |
0.31 |
0.49 |
–0.12 |
0.70 |
|
GRIN2B |
rs1806201 |
0.43 |
0.26 |
0.52 |
0.43 |
0.29 |
0.57 |
|
GRIN2B |
rs219934 |
0.03 |
0.91 |
–0.23 |
0.58 |
–0.04 |
0.89 |
|
GRIN3A |
rs1004362 |
–0.50 |
0.14 |
–1.63 |
0.02 |
–0.20 |
0.63 |
|
GRIN3A |
rs1323423 |
–0.33 |
0.32 |
–1.21 |
0.05 |
–0.01 |
0.98 |
|
GRIN3A |
rs1323427 |
–0.06 |
0.85 |
0.49 |
0.41 |
–0.36 |
0.35 |
|
GRIN3A |
rs1983812 |
0.01 |
0.96 |
0.31 |
0.57 |
0.11 |
0.71 |
|
GRIN3A |
rs2050639 |
–0.11 |
0.70 |
0.59 |
0.30 |
–0.38 |
0.29 |
|
GRIN3A |
rs2050641 |
0.06 |
0.84 |
0.37 |
0.50 |
–0.13 |
0.74 |
|
GRIN3A |
rs2417290 |
–0.52 |
0.08 |
–1.05 |
0.07 |
–0.33 |
0.35 |
|
GRM2 |
rs60972621 |
–0.01 |
0.96 |
–0.26 |
0.54 |
0.15 |
0.63 |
|
GRM2 |
rs75938458 |
–0.67 |
0.29 |
1.86 |
0.37 |
–0.75 |
0.29 |
|
GRM3 |
rs1468412 |
–0.41 |
0.19 |
–0.28 |
0.65 |
–0.48 |
0.20 |
|
GRM3 |
rs1989796 |
0.22 |
0.41 |
0.31 |
0.57 |
0.17 |
0.59 |
|
GRM3 |
rs6465084 |
–0.48 |
0.16 |
0.24 |
0.73 |
–0.68 |
0.09 |
|
GRM5 |
rs10501686 |
–0.25 |
0.29 |
–0.15 |
0.75 |
–0.23 |
0.41 |
|
GRM5 |
rs1391873 |
–0.02 |
0.94 |
0.04 |
0.91 |
–0.06 |
0.87 |
|
GRM5 |
rs2648640 |
0.53 |
0.05 |
–0.08 |
0.87 |
0.69 |
0.03 |
|
GRM5 |
rs4628675 |
–0.14 |
0.57 |
–0.27 |
0.59 |
–0.05 |
0.88 |
|
GRM5 |
rs596370 |
0.28 |
0.29 |
0.25 |
0.64 |
0.26 |
0.40 |
|
GRM8 |
rs1008907 |
–0.15 |
0.52 |
–0.02 |
0.97 |
–0.35 |
0.25 |
|
GRM8 |
rs1419489 |
–0.47 |
0.05 |
–0.85 |
0.03 |
–0.43 |
0.15 |
|
GRM8 |
rs2299516 |
0.26 |
0.27 |
0.03 |
0.95 |
0.40 |
0.17 |
|
SLC1A1 |
rs1471786 |
–0.14 |
0.69 |
–0.15 |
0.76 |
–0.12 |
0.78 |
|
SLC1A3 |
rs13154397 |
–0.07 |
0.74 |
–0.35 |
0.33 |
0.03 |
0.91 |
#
#
Discussion
We hypothesized that polymorphisms in glutamatergic genes might affect the outcomes of antipsychotic treatments in schizophrenia patients. This hypothesis was put to the test by analysing polymorphisms in genes related to glutamate pathways and comparing the results with standardized variables (PANSS and UKU) in a cohort of patients using antipsychotic medications. While statistically significant results regarding efficacy or side effects have been found for glutamatergic genes, studies up until now have been centred on particular drugs, particular side effects and/or particular genes. This study is unique in that it observes a wider range of treatment outcomes, such as efficacy and different classes of adverse effects.
We used a candidate-gene strategy to maximise the possibilities of finding associations of moderate effects that may be missed in genomic studies. The study revealed several trends for the association with polymorphisms in genes controlling glutamatergic neurotransmission. Most of the associations were found on the subgroup of patients treated with clozapine (CLOZ), particularly for efficacy. The associations of GRIK2 variants with PANSS, GRIA1 variants with autonomic UKUs and GRM8 variants with other UKUs were stronger in the CLOZ sample, as represented by the lower p-values observed in this cohort. Additionally, the association between the GRIA1 rs1864205 variants and autonomic side effects was statistically significant even after correcting for multiple comparisons (adjusted p value=0.05), but only in the CLOZ subgroup. An association for the same polymorphism was found in the total (ALL) cohort but it was no longer significant after FDR corrections. This suggests an effect on autonomic adverse drug reactions that would be specific to clozapine, or at least more relevant for clozapine than for other drugs. GRIK2, GRIA1, GRM8 and GRIN2A could be pharmacogenetic biomarkers for patients using clozapine, provided that these associations are replicated in future studies. GRIA1 expression has been reported to be reduced in animal models of hypertension [41], providing a link between this receptor and autonomic phenotypes. GRIA1 genetic variants have also been associated with haloperidol efficacy [42]. While we did not find GRIA1 variants associated with treatment response, the patients in our study were treated mainly with second generation antipsychotics which may explain the discrepancy.
Our sample contains an abundance of treatment-resistant patients who have failed antipsychotic treatment in the past. Since clozapine is only used in patients with previous failure to antipsychotic medication, the CLOZ subgroup represents a group of patients with treatment-resistant schizophrenia. Clozapine also represents the final step in the prescription of antipsychotic drugs, because at this point, there are no more alternative drugs. The results of this study, therefore, are particularly relevant for the subset of the population where pharmacogenetic information that can improve the treatment is most needed. The identification of glutamatergic genes as possible factors influencing the improvement of PANSS and UKU scores is an encouraging step forward in the development of better tools for personalizing antipsychotic treatments, but this and the other findings in this study should be validated with bigger sample sizes. As expected, no gene variant was found to individually predict the success or failure of antipsychotic treatment. Given the multitarget profile of most antipsychotics, it is likely that the integration of different clinical and genetic variables is required to predict efficacy.
Our study has several limitations. Sample size was moderate, making it difficult to identify associations with variants of small effect or with rare polymorphisms. While the statistical power was deemed sufficient to detect large to moderate effects, small effects would require bigger sample sizes to be detected. Since drug response is a polygenic trait, small effect sizes from factors are, in fact, likely. Therefore, it is possible that some small effects remain undetected. In addition, the patients were treated with different antipsychotics, although the majority (96.1%) were second-generation drugs. Even though doses were standardized to their olanzapine equivalents, their different profiles regarding their affinity to neurotransmitter receptors and mechanism of action were potential confounders. The separate analysis of the CLOZ sample led us to overcome the heterogeneity issue, at the cost of a further reduced sample size. Some patients used lorazepam concurrently with their antipsychotic medication. This can lead to drug interactions like increased sedation. However, the use was sporadic and low doses (1–1.5 mg/day) were used. Furthermore, no increased sedation was observed in these patients. None of the results, except one, was statistically significant after multiple comparison corrections, and replication in independent samples are required to confirm their validity, as is the case in most candidate-gene studies. A heterogeneous range of side effects was included in the category of “Other” side-effects, which may have hampered the finding of specific associations. Finally, since non-Caucasian patients had to be excluded from the analyses due to the low numbers recruited, the findings of this study might not apply to other ethnicities.
In summary, this article provides evidence suggesting that genes affecting glutamatergic signalling, particularly GRIA1, might affect both efficacy and safety of antipsychotic treatments, and hints at a bigger effect in treatment-resistant patients, represented here by the CLOZ sample. Nevertheless, these findings should be replicated to confirm the association and to provide a better estimation of their clinical utility as response predictors.
#
Financial support and sponsorship
This study was supported by grants from Institute Carlos III (FIS PI11/02006 and g FIS PI16/01029), ISCIII-SGEFI/FEDER
#
Contributors
Study design: Marc Cendrós, Rosa Catalán, MD, María J Arranz, PhD
Molecular biology studies: Marc Cendrós, Mercè Torra, PhD, Mercè Brunet, PhD, , Alexandre Serra-Llovich, Marta H. Hernandez
Sample recruitment and clinical data obtention: Rosa Catalán, MD, Rafael Penadés, PhD, Alexandre González-Rodríguez, MD, PhD, Josefina Perez-Blanco, MD
Statistical analyses: Marc Cendrós, Rosa Catalán, MD, Natalia Cullell, PhD, María J Arranz, PhD
Manuscript draft: Marc Cendrós, Rosa Catalán, MD, Natalia Cullell, PhD, María J Arranz, PhD
All authors contributed to and have approved the final manuscript.
#
#
Conflicts of interest
Marc Cendrós is employed by Eugenomic S.L., a company that offers testing, counselling and data interpretation on pharmacogenetics. The company was not involved in the study, and did not have any influence in any way on the results.
# These authors are equal corresponding authors: María J. Arranz and Rosa Catalán
-
References
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Correspondence
Publication History
Received: 24 April 2024
Accepted: 27 February 2025
Article published online:
17 June 2025
© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).
Georg Thieme Verlag
Rüdigerstraße 14, 70469 Stuttgart, Germany
-
References
- 1 Institute of health Metrics and Evaluation (IHME). Global Health Data Exchange (GHDx) http://ghdx.healthdata.org/gbd-results-tool?params=gbd-api-2019-permalink/27a7644e8ad28e739382d31e77589dd7
- 2 Howes OD, Kapur S. The dopamine hypothesis of schizophrenia: Version III--the final common pathway. Schizophr Bull 2009; 35: 549-562
- 3 Madras BK. History of the discovery of the antipsychotic dopamine D2 receptor: A basis for the dopamine hypothesis of schizophrenia. J Hist Neurosci 2013; 22: 62-78
- 4 Nord M, Farde L. Antipsychotic occupancy of dopamine receptors in schizophrenia. CNS Neurosci Ther 2011; 17: 97-103
- 5 Uno Y, Coyle JT. Glutamate hypothesis in schizophrenia. Psychiatry Clin Neurosci 2019; 73: 204-215
- 6 Hu W, MacDonald ML, Elswick DE. et al. The glutamate hypothesis of schizophrenia: Evidence from human brain tissue studies. Ann N Y Acad Sci 2015; 1338: 38-57
- 7 Fachim HA, Loureiro CM, Corsi-Zuelli F. et al. GRIN2B promoter methylation deficits in early-onset schizophrenia and its association with cognitive function. Epigenomics 2019; 11: 401-410
- 8 Kristiansen LV, Patel SA, Haroutunian V. et al. Expression of the NR2B-NMDA receptor subunit and its Tbr-1/CINAP regulatory proteins in postmortem brain suggest altered receptor processing in schizophrenia. Synapse 2010; 64: 495-502
- 9 Ibrahim HM, Hogg AJ, Healy DJ. et al. Ionotropic glutamate receptor binding and subunit mRNA expression in thalamic nuclei in schizophrenia. Am J Psychiatry 2000; 157: 1811-1823
- 10 Myers SJ, Yuan H, Kang JQ. et al. Distinct roles of GRIN2A and GRIN2B variants in neurological conditions. F1000Res 2019; 8 F1000 Faculty Rev-1940
- 11 Yang Y, Li W, Zhang H. et al. Association study of N-methyl-D-aspartate receptor subunit 2B (GRIN2B) polymorphisms and schizophrenia symptoms in the Han Chinese population. PLoS One 2015; 10: e0125925
- 12 Martucci L, Wong AH, De Luca V. et al. N-methyl-D-aspartate receptor NR2B subunit gene GRIN2B in schizophrenia and bipolar disorder: Polymorphisms and mRNA levels. Schizophr Res 2006; 84: 214-221
- 13 Chiu HJ, Wang YC, Liou YJ. et al. Association analysis of the genetic variants of the N-methyl D-aspartate receptor subunit 2b (NR2b) and treatment-refractory schizophrenia in the Chinese. Neuropsychobiology 2003; 47: 178-181
- 14 Hong CJ, Yu YW, Lin CH. et al. Association analysis for NMDA receptor subunit 2B (GRIN2B) genetic variants and psychopathology and clozapine response in schizophrenia. Psychiatr Genet 2001; 11: 219-222
- 15 Magri C, Gardella R, Barlati SD. et al. Glutamate AMPA receptor subunit 1 gene (GRIA1) and DSM-IV-TR schizophrenia: A pilot case-control association study in an Italian sample. Am J Med Genet B Neuropsychiatr Genet 2006; 141B: 287-293
- 16 Kang WS, Park JK, Kim SK. et al. Genetic variants of GRIA1 are associated with susceptibility to schizophrenia in Korean population. Mol Biol Rep 2012; 39: 10697-10703
- 17 Crisafulli C, Chiesa A, De Ronchi D. et al. Influence of GRIA1, GRIA2 and GRIA4 polymorphisms on diagnosis and response to antipsychotic treatment in patients with schizophrenia. Neurosci Lett 2012; 506: 170-174
- 18 Bah J, Quach H, Ebstein RP. et al. Maternal transmission disequilibrium of the glutamate receptor GRIK2 in schizophrenia. Neuroreport 2004; 15: 1987-1991
- 19 Ibrahim HM, Hogg AJ, Healy DJ. et al. Ionotropic glutamate receptor binding and subunit mRNA expression in thalamic nuclei in schizophrenia. Am J Psychiatry 2000; 157: 1811-1823
- 20 Kordi-Tamandani DM, Dahmardeh N, Torkamanzehi A. Evaluation of hypermethylation and expression pattern of GMR2, GMR5, GMR8, and GRIA3 in patients with schizophrenia. Gene 2013; 515: 163-166
- 21 Reynolds GP, McGowan OO, Dalton CF. Pharmacogenomics in psychiatry: The relevance of receptor and transporter polymorphisms. Br J Clin Pharmacol 2014; 77: 654-672
- 22 Li W, Ju K, Li Z. et al. Significant association of GRM7 and GRM8 genes with schizophrenia and major depressive disorder in the Han Chinese population. Eur Neuropsychopharmacol 2016; 26: 136-146
- 23 Zhang L, Zhong X, An Z. et al. Association analysis of the GRM8 gene with schizophrenia in the Uygur Chinese population. Hereditas 2014; 151: 140-144
- 24 Fiorentino A, Sharp SI, McQuillin A. Association of rare variation in the glutamate receptor gene SLC1A2 with susceptibility to bipolar disorder and schizophrenia. Eur J Hum Genet 2015; 23: 1200-1206
- 25 Spangaro M, Bosia M, Zanoletti A. et al. Exploring effects of EAAT polymorphisms on cognitive functions in schizophrenia. Pharmacogenomics 2014; 15: 925-932
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