Association of GWAS and candidate gene loci of dopaminergic system with major depression, schizophrenia and bipolar disorder in the Pakistani population

doi:10.21203/rs.3.rs-1664067/v1

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Association of GWAS and candidate gene loci of dopaminergic system with major depression, schizophrenia and bipolar disorder in the Pakistani population

https://doi.org/10.21203/rs.3.rs-1664067/v1

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The dopaminergic pathways control neural signals that modulate mood and behaviour along and have a vital role in the aetiology of major depression (MDD), schizophrenia (SHZ) and bipolar disorder (BD). Genome-wide association studies (GWAS) have reported several dopaminergic pathway’s and other genetic loci’s association with these disorders, therefore, the present study was conducted to analyse the GWAS and candidate gene loci of the dopaminergic and cognitive system genes in MDD, SHZ, and BD, in the Pakistani population. A total of 1237 subjects [MDD n = 479; BD n = 222; SHZ n = 146; and controls n = 390], were screened for eleven genetic variants through polymerase chain reaction (PCR) techniques. Univariant followed by multivariant logistic regression analysis were applied to determine the genetic association. Significant risk associations were observed for rs4532 and rs1799732 with MDD; and rs1006737 and rs2238056 with BD. However, after applying multiple test corrections rs4532 and rs1799732 association did not remain significant for MDD. Moreover, a protective association was found for three variants DRD4-120bp, rs10033951 and rs2388334 in the current cohort. Thus, in conclusion, the current study revealed the risk association of rs1006737 and rs2238056 with BD and the protective effect of DRD4-120bp in MDD and BD, of rs2388334 in BD and of rs10033951 in MDD, BD, and SHZ in the current Pakistani cohort.

Psychiatric conditions

Mood disorders

Dopaminergic system

Association studies

Genetic variants

Tandem repeats

population studies

Psychiatric disorders are one of the leading causes of morbidity worldwide. Among common psychiatric conditions, major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SHZ) are categorised as the most complex psychiatric disorders, with overlapping clinical features and shared genetic determinants (Soares & Gershon, 2000; Torrey et al., 2000). These psychiatric conditions present with disruptive psychopathologies that involve behaviour, thought processing, perception, cognitive function, motor activity, mood, and emotions (Soares & Gershon, 2000; Torrey et al., 2000). Genetic factors are known to play an important role and influence the susceptibility to these psychiatric conditions but the elucidation of genetic factors is always challenging as these disorders are polygenic (Wang et al., 2017). The complex genetic heterogeneity of MDD, BD, and SHZ, predisposes the individuals to overlapping clinical conditions or features, thus making it hard to distinguish them clinically (Feczko et al., 2019).

A number of Genome-Wide Association Studies (GWAS), meta-GWAS and familial studies of psychiatric conditions have been conducted to unravel the genetic determinants of these disorders (Craddock & Sklar, 2013; Ikeda et al., 2018; Major Depressive Disorder Working Group of the Psychiatric et al., 2013). The GWAS identified genetic loci and candidate genes that are associated with MDD, BD and SHZ aetiology and which belong to multiple neuronal pathways regulating learning, memory, motor activity and cognitive functions (International HapMap, 2003; Liu et al., 2020; Major Depressive Disorder Working Group of the Psychiatric et al., 2013; Segurado et al., 2003; Sklar et al., 2008; Stahl et al., 2019).

However, it has also been reported that several loci, that are found to be associated with a higher risk of BD, were also associated with MDD and SHZ, similarly, genetic determinants of SHZ overlap with BD and MDD loci (Cross-Disorder Group of the Psychiatric Genomics, 2013). Some of the shared genetic markers between these psychiatric conditions (MDD, BD and SHZ), as reported by several GWAS and population-based association studies, regulate and belong to the dopaminergic neurotransmission (Allen et al., 2008; Cross-Disorder Group of the Psychiatric Genomics, 2013; Klein et al., 2019; Seifuddin et al., 2012; Stahl et al., 2019; Tunbridge et al., 2019).

The dopaminergic system is regulated by the excitatory neurotransmitter dopamine (DA), which is the most abundant neurotransmitter in the brain. This neurotransmitter is involved in regulating different brain functions such as behaviour, executive memory, reward, pleasure, decision, critical thinking, and voluntary motor movements of the body (Bombin et al., 2008; Chang et al., 2013; Klein et al., 2019). The DA hypothesis in mood disorders postulates that DA dysregulation and signalling abnormalities underlie, psychosis, SHZ, mood, and affective disorders such as MDD and BD (Kapur & Mann, 1992; Kesby et al., 2018; Singh, 1970; Tissot, 1975; Wittenborn, 1974). This defines DA signalling as of potential relevance in the pathophysiology and progression of psychiatric conditions like MDD, BD, and SHZ (Diehl & Gershon, 1992; Dunlop & Nemeroff, 2007; Fochtmann & Fink, 1992; Howes et al., 2012; Howes et al., 2013).

Therefore, the current case-control association study was conducted to replicate in the Pakistani population, the findings of selected GWAS identified and case-control association studies reported genetic variants of the dopaminergic system genes that have a direct or indirect role in the regulation of DA and cognitive functions, and to assess the potential genetic overlap in the aetiology of MDD, BD and SHZ.

2.1. Subjects Recruitment

In total n = 1237 subjects, including MDD (n = 479), BD (n = 222), SHZ (n = 146), and healthy controls (n = 390), participated in the study. Patients were recruited from the Psychiatry department of Pakistan Institute of Medical Sciences (PIMS) hospital Islamabad, Pakistan. Patients were diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), (American Psychiatric Association, 2013), by a qualified registered psychiatrist of the psychiatry department of PIMS. The healthy controls were also recruited from PIMS, conducting a questionnaire-based interview and were confirmed by psychiatrists, as not suffering from any psychiatric conditions.

2.1.1. Inclusion and Exclusion Criteria

To be included in the study, patients had to fulfil DSM-5 criteria for the relevant psychiatric illness, the participants (cases and controls) had to be between the ages of 18–65 years and not suffer from any metabolic disorders. In addition, controls with a positive family history of psychiatric disorders did not have any medical condition that potentially interferes with the brain function (thyroid disorder, uncontrolled hypertension, Hepatitis C, Human Immunodeficiency virus-HIV), and drug abusers were also not included in the current study.

2.1.2. Sample Collection and Processing

A blood sample of 3ml from each subject was collected in Ethylenediaminetetraacetic acid (EDTA) tube (1 Becton Drive, Franklin Lakes, NJ). The genomic DNA was extracted according to the standard phenol-chloroform DNA extraction protocol (Sambrook & Russell, 2001). The extracted DNA samples were stored at -20 ºC until further use.

2.2. Genetic Variants and Genotypying

Eleven genetic variants previously reported in GWAS and candidate gene studies in different psychiatric disorders were selected for association analysis. Ten single nucleotide polymorphism (SNP) and one variable number of tandem repeat (VNTR), belonging to ten genes, [Calcium voltage-gated channel subunit alpha1 C (CACNA1c), dopamine receptor 1(DRD1), dopamine receptor 2 (DRD2), dopamine receptor 3 (DRD3), dopamine receptor 4 (DRD4), dopamine receptor 5 (DRD5), phosphofurin acidic cluster sorting protein 1 (PACS1), tetratricopeptide repeat and ankyrin repeat-containing 1 (TRANK1), follistatin like 5 (FSTL5), and POU Class 3 Homeobox 2 (POU3F2)]. The selection criteria for the genes and variants were based on associations observed in GWAS, meta-analysis, and candidate gene studies in psychiatric disorders in different populations (Supplementary Table S1).

Seven variants [CACNA1c (rs1006737, rs2238056), DRD1 (rs4532), DRD2 (rs1799732), DRD3 (rs6280), DRD4 (120bp VNTR), DRD5 (rs10033951)], were screened in MDD, SHZ and BD. While, an additional four variants [PACS1 (rs10896090), TRANK1 (rs9834970), FSTL5 (rs11724116), POU3F2 (rs2388334)], were selected from the recent GWAS in the BD cohort of European, North American, and Australian populations (Stahl et al., 2019), and were screened in the BD cohort of the present study.

The genotyping was performed by using different PCR techniques, 2 of the SNPs were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), and 8 SNPs were genotyped by tetra- amplification refractory mutation system-PCR (TETRA-ARMS-PCR), while the VNTR was genotyped by standard polymerase chain reaction (PCR). The amplified fragments of less than 100 base pairs were analysed on a 2% agarose gel and fragments larger than 100 base pairs were visualized on 3% agarose gel. The details of primers and thermal profiles are mentioned in (Supplementary Table S2).

2.3. Statistical Analysis

All the statistical analyses were done using R (v 4.0.0), (R Core Team, 2020). Fisher t-test was applied for continuous variables and the χ² test was applied for categorical variables, in comparison between cases and controls. Hardy-Weinberg equilibrium (HWE), was also determined using the χ² test. Genotypes of variants were coded additively (Additive model) and univariate logistic regression analysis was applied to determine the association of each variant with the cases. All the results were adjusted for age and sex (multivariate logistic regression analysis). Quanto-v.1.2.4 (Gauderman, 2002) was used for the power calculation of the SNPs. The threshold significance was kept at a P-value ≤ 0.05 in all statistical tests, while the adjusted P-value after multiple testing was set at ≤ 0.007 in Tables 2 & 5 and ≤ 0.004 in Table 3.

2.3.1. Genotype and Allele Frequencies comparison with 1000 Genome and ALFA

The SNPs data of controls of the current study were also analysed in comparison to the 1000 Genome PJL (Punjabi in Lahore), data (The 1000 Genomes Project Consortium) and Allele Frequency Aggregator (ALFA) –data of South Asians (SAS), (L. Phan, 2020), and determine the differences and/or similarities between the genotype calls and allele frequencies.

3.1. Descriptive Characteristics of the Subjects

The descriptive characteristics of all cases and controls are summarised in Table 1. The age of the cases was comparatively older than the healthy controls, MDD (P < 2.2e-16), SHZ (P = 7.51e-10), and BD (P = 1.47e-13). In the current cohort, the mean age at onset ± SD (AAO) was observed to be at a later age in MDD (28.89 ± 11.03) and at an earlier age in SHZ (23.43 ± 8.75) and BD (23.75 ± 9.81). Sex differences were only observed in MDD (P = 2.37e-08) and SHZ (P = 0.01), compared to the controls. Moreover, the majority of the participants in the present study were Punjabi in ethnicity (Punjab province of Pakistan), though a statistical difference has been observed in SHZ (P = 0.02), compared to the controls. In addition, more tobacco users were found in cases compared to controls, MDD (P = 4.60e-05), BD (P = 4.22e-10), and SHZ (P < 2.2e-16), and the cannabis users were only found in cases.

Table 1

Descriptive statistics of the studied cohort
Demographic Factors		CON	MDD	BPD	SHZ	MDD vs CON P	BPD vs CON P	SHZ vs CON P
Sr. no.	Total (n)	390	479	222	146
1	Age (years)	24.06 ± 6.11	32.80 ± 11.50	30.47 ± 11.43	29.85 ± 10.12	< 2.2e-16	1.47e-13	7.51e-10
2	Age at onset (years)	NA	28.89 ± 11.03	23.75 ± 9.81	23.43 ± 8.75	NA	NA	NA
3	Sex (Male) (Female)	224(57.0%) 166(43.0%)	183(38.0%) 296(62.0%)	132(59.0%) 90(41.0%)	102(70.0%) 44(30.0%)	2.37e-08*	0.68*	0.01*
4	Ethnicity (Punjabi)	267(68.50%)	321(67.01%)	166(75.00%)	115(79.00%)	0.70*	0.12*	0.02*
5	Tobacco users	29(7.43%)	81(16.91%)	58(26.13%)	52(35.62%)	4.60e-05*	4.22e-10*	< 2.2e-16*
6	Cannabis abusers	0	16(3.34%)	25(11.26%)	12(8.22%)	NA	NA	NA

Values are n (%), or mean ± SD; P-value from Welch Two Sample t-test and *P-value from Pearson's Chi-squared test χ² with Yates' continuity correction. NA, not applicable; Con, Controls, MDD, Major depressive disorder, BPD, Bipolar disorder, SHZ, Schizophrenia

. Genetic Association of the Variants

In total 11 genetic variants were genotyped in the present cohort and the genotype of 3 of the SNPs was found in deviation from HWE, rs10033951 (P = 0.02), rs11724116 (P < 0.0001), and rs2388334 (P = 0.03), (Supplementary Table S4).

3.1.1. Association with MDD

Univariate logistic regression analysis did show significant association of rs1006737 [Odds Ratio (OR) (95% Confidence Interval (CI) = 1.05(1.00-1.11), P = 0.040], rs4532 [OR(95% CI) = 1.09(1.03–1.15), P = 0.002], and rs1799732 [OR(95%CI) = 1.10(1.01–1.18), P = 0.029], with higher risk of MDD susceptibility. The results remained consistent when adjusted for age and sex (multivariate logistic regression analysis), for rs4532 [OR(95%CI) = 1.06(1.01–1.11), P = 0.039], and rs1799732 [OR(95%CI) = 1.10(1.02–1.17), P = 0.012]. However, after applying multiple test correction (Bonferroni correction), the P values did not remain significant for any of the SNP (Table 2).

Table 2

Association of genetic variants with MDD
MDD							Univariate Analysis			Multivariate* Analysis
Genes	Variants	Allele change	Minor Allele	Genotype Call rate %	Control (n = 390) (AA, Aa, aa)	Cases (n = 479) (AA, Aa, aa)	OR	95% CI	P-value	OR	95% CI	P-value
CACNA1c	rs1006737	G > A	A	91.0%	255 (137, 88, 30)	436 (203,163,70)	1.05	1.00- 1.11	0.040	1.04	0.99–1.09	0.07
CACNA1c	rs2238056	T > C	T	95.0%	330 (33, 100, 197)	455 (64,63,328)	1.04	0.99–1.09	0.10	1.04	0.99–1.08	0.11
DRD1	rs4532	T > C	C	96.0%	348 (150, 171, 27)	460 (147,269,44)	1.09	1.03–1.15	0.002	1.06	1.01–1.11	0.039
DRD2	rs1799732	GG > G	DEL	96.6%	359 (309, 45, 5)	463 (371,82,10)	1.10	1.01–1.18	0.029	1.10	1.02–1.17	0.012
DRD3	rs6280	C > T	T	96.0%	370 (66,180,124)	460 (91,201,168)	1.00	0.96–1.05	0.832	1.01	0.97–1.05	0.57
DRD4	120bp VNTR	1R > 2R	2R	95.8%	359 (24,164,171)	459 (26,320,113)	0.85	0.80–0.90	3.68e-08	0.86	0.82–0.91	8.88e-08
DRD5	rs10033951	C > T	T	93.3%	353 (3,129,221)	447 (11,229,207)	0.85	0.80–0.91	2.14e-06	0.90	0.85–0.95	3.0e-04

*Multivariate (adjusted for sex and age); A, Major Allele; a, Minor Allele; P-value from Pearson's Chi-squared χ² test with Yates' continuity correction. OR, odd ratio; CI, confidence interval; MDD, Major Depressive Disorder. The threshold significance was kept at a P-value < 0.05 in all statistical tests and the adjusted P-value after multiple test corrections was set at ≤ 0.007. The significant values are bold and underlined.

In addition, 2 of the variants, DRD4 (120bpVNTR), and rs10033951 were observed to have a significant protective effect towards MDD susceptibility in both univariate logistic regression analysis, DRD4 (120bpVNTR) [OR(95%CI) = 0.85(0.80–0.90), P = 3.68e-08], rs10033951 [OR(95%CI) = 0.85(0.80–0.91), P = 2.14e-0], and in multivariate logistic regression analysis, DRD4 (120bpVNTR) [OR(95%CI) = 0.86(0.82–0.91), P = 8.88e-08], rs10033951 [OR(95%CI) = 0.90(0.85–0.95), P = 3.0e-04], (Table 2).

3.1.2. Association with BD

In BD, both of the CACNA1c SNPs [rs1006737, rs2238056], showed significant association with higher risk of susceptibility, univariate logistic regression analysis; rs1006737 [OR(95%CI) = 1.15(1.08–1.22), P = 7.52e-06], rs2238056 [OR(95%CI) = 1.12(1.05–1.19), P = 4.45e-4], and the results remained significant when adjusted for age and sex (multivariate logistic regression); rs1006737 [OR(95%CI) = 1.14(1.07–1.20), P = 1.3e-05], rs2238056 [OR(95%CI) = 1.11(1.05–1.18), P = 3.44e-4], (Table 3).

Table 3

Association of genetic variants with BD
BD							Univariate Analysis			Multivariate* Analysis
Genes	Variants	Allele change	Minor Allele	Genotype Call rate %	Control (AA, Aa, aa)	Cases (AA, Aa, aa)	OR	95% CI	P value	OR	95% CI	P value
CACNA1c	rs1006737	G > A	A	73%	255 (137, 88, 30)	189 (65, 79, 45)	1.15	1.08–1.22	7.52e-06***	1.14	1.07–1.20	1.3E-05***
CACNA1c	rs2238056	T > C	T	88%	330 (33, 100, 197)	208 (19, 24, 165)	1.12	1.05–1.19	4.45E-4***	1.11	1.05–1.18	3.44E-4***
DRD1	rs4532	T > C	C	92%	348 (150, 171, 27)	215 (80, 127, 8)	1.01	0.95–1.08	0.72	1.01	0.95–1.08	0.77
DRD2	rs1799732	GG > G	DEL	94%	359 (309, 45, 5)	217 (186, 31, 00)	0.98	0.88–1.09	0.75	1.00	0.93–1.08	0.88
DRD3	rs6280	C > T	T	96%	370 (66,180,124)	216 (33, 106, 77)	1.02	0.97–1.08	0.43	1.02	0.97–1.07	0.45
DRD4	120bp VNTR	1R > 2R	2R	92%	359 (24,164,171)	206 (11, 137, 58)	0.89	0.83–0.94	4.30E-4***	0.9	0.84–0.96	9.02E-4***
DRD5	rs10033951	C > T	T	92%	353 (3,129,221)	208 (17, 113, 78)	0.79	0.74–0.85	2.72E-11***	0.81	0.76–0.86	1.98E-10***
PACS1	rs10896090	A > G	G	85%	307 (236, 64, 7)	215 (163, 49, 3)	1.00	0.92–1.09	0.97	1.01	0.93–1.09	0.94
TRANK1	rs9834970	T > C	C	85%	305 (132, 148, 25)	214 (112, 88, 14)	0.94	0.87-1.00	0.054	0.94	0.89–1.01	0.09
FSTL5	rs11724116	C > T	T	81%	287 (170, 56, 61)	210 (111, 75, 24)	0.98	0.93–1.04	0.62	0.97	0.92–1.02	0.27
POU3F2	rs2388334	A > G	G	84%	302 (51, 183, 68)	214 (42, 168, 4)	0.84	0.77–0.90	2.89e-06***	0.85	0.80–0.91	1.41e-05***

*Multivariate (adjusted for sex and age); A, Major Allele; a, Minor Allele; P-value from Pearson's Chi-squared χ² test with Yates' continuity correction. OR, odd ratio; CI, confidence interval; BD, Bipolar Disorder. The significant values are bold and underlined. The threshold significance was kept at a P-value < 0.05 in all statistical tests and the adjusted P-value after multiple test corrections was set at ≤ 0.004.

Three of the variants, rs2388334, DRD4 (120bpVNTR) repeat and rs10033951, were found to afford a significant protective effect against BD susceptibility. Univariate logistic regression; rs2388334 [OR(95%CI) = 0.84(0.77–0.90), P = 2.89e-06], DRD4(120bpVNTR) [OR(95%CI) = 0.89(0.83–0.94), P = 4.30e-4], rs10033951 [OR(95%CI) = 0.79(0.74–0.85), P = 2.72e-11], multivariate logistic regression; rs2388334 [OR(95%CI) = 0.85(0.80–0.91), P = 1.41e-05], DRD4 (120bpVNTR) [OR(95%CI) = 0.90(0.84–0.96), P = 9.02e-4], and rs10033951 [OR(95%CI) = 0.81(0.76–0.86), P = 1.98e-10].

3.1.3. Association with SHZ

In SHZ, only rs10033951 revealed protective association, univariate logistic regression analysis: [OR(95%CI) = 0.86(0.80–0.93), P = 1.60e-04] and multivariate logistic regression analysis: [OR(95%CI) = 0.89(0.83–0.95), P = 0.0013], (Table 4).

Table 5

Association of genetic variants with SHZ
SHZ							Univariate Analysis			Multivariate* Analysis
Genes	Variants	Allele change	Minor Allele	Genotype Call rate %	Control (n = 390) (AA, Aa, aa)	Cases (n = 146) (AA, Aa, aa)	OR	95% CI	P value	OR*	95% CI	P* value
CACNA1c	rs1006737	G > A	A	72%	255 (137, 88, 30)	130 (62,49,19)	1.04	0.97–1.11	0.24	1.03	0.97–1.09	0.37
CACNA1c	rs2238056	T > C	T	88%	330 (33, 100, 197)	139 (14,26,99)	1.05	0.99–1.12	0.09	1.04	0.98–1.10	0.15
DRD1	rs4532	T > C	C	91%	348 (150, 171, 27)	139 (63,74,2)	0.95	0.89–1.02	0.15	0.95	0.89–1.01	0.09
DRD2	rs1799732	GG > G	DEL	94%	359 (309, 45, 5)	144 (129,13,2)	0.95	0.86–1.05	0.36	0.96	0.88–1.06	0.47
DRD3	rs6280	C > T	T	96%	370 (66,180,124)	143 (30,74,39)	0.96	0.91–1.02	0.17	0.95	0.90-1.00	0.06
DRD4	120bp VNTR	1R > 2R	2R	94%	359 (24,164,171)	144 (10,73,61)	0.97	0.91–1.03	0.36	0.96	0.91–1.02	0.24
DRD5	rs10033951	C > T	T	93%	353 (3,129,221)	142 (4,74,64)	0.86	0.80–0.93	1.60e-04 ***	0.89	0.83–0.95	0.0013 **

*Multivariate (adjusted for sex and age); A, Major Allele; a, Minor Allele; P-value from Pearson's Chi-squared χ² test with Yates' continuity correction. OR, odd ratio; CI, confidence interval; SHZ, Schizophrenia. The threshold significance was kept at a P-value < 0.05 in all statistical tests and the adjusted P-value after multiple test corrections was set at ≤ 0.007. The significant values are bold and underlined.

3.2. Comparison with 1000 Genomes (PJL) and ALFA SAS data

The genotype and allele frequencies of the studied genetic variants were retrieved from the 1000 Genome PJL (Punjabi in Lahore) and compared with the current data (Supplementary Table S4).

Significant differences were observed with 7 of the 10 variants, in genotype frequencies; rs2238056 [χ²(P) = 23.31(< 0.0001)], rs4532 [χ²(P) = 12.21(0.02)], rs6280 [χ²(P) = 13.50(0.001)], rs10033951 [χ²(P) = 15.90(< 0.0001)], rs9834970 [χ²(P) = 19.13(< 0.0001)], rs11724116 [χ²(P) = 27.89(< 0.0001)], rs238833 [χ²(P) = 64.40(< 0.0001)], and in 6 of the 10 variants in allele frequencies; rs2238056 [χ²(P) = 43.68(< 0.0001)], rs6280 [χ²(P) = 12.96(0.0003)], rs10033951 [χ²(P) = 13.70(< 0.0001)], rs9834970 [χ²(P) = 15.44(< 0.0001)], rs11724116 [χ²(P) = 5.42(< 0.02)], rs2388334 [χ²(P) = 47.71(< 0.0001)].

Allele frequencies were also compared with the ALFA SAS data, significant differences were observed in the same 6 variants, which showed differences with 1000 Genome PJL data, except rs4532, which did not show allele frequency difference with 1000 Genome PJL data while significant deviation was observed in comparison to the ALFA SAS allele frequency data of rs4532 [χ²(P) = 195.9(< 0.0001)].

Additionally, the allele frequency data from 1000 Genome PJL were also compared to the ALFA SAS data to determine the differences in an allele frequency distribution between them. The significant differences were observed among 2 of the 10 variants, rs4532 [χ²(P) = 48.15(< 0.0001)], and rs6280 [χ²(P) = 53.24(< 0.0001)].

The association analysis of GWAS and candidate gene loci belonging to dopaminergic system genes, in the current psychiatric cohort of the Pakistani population revealed a significant association for CACNA1c polymorphisms (rs1006737, rs2238056), DRD1-rs4532 DRD2-rs1799732, DRD4-120bp, and DRD5-rs10033951. Among CACNA1c polymorphisms, rs1006737 was associated with a higher risk in both MDD and BD, while, rs2238056 was found to be associated with BD susceptibility only. However, the association did not survive for rs1006737 in MDD when the P-value was adjusted for age and sex, suggesting the possible impact of these demographic variables in this association with MDD, while the results remained significant in BD.

Similar to the current findings previously reported studies by He et al, (2014) in Chinese Han and Green et al, (2010) in the British populations, reported the rs1006737 association with MDD (Green et al., 2010; He et al., 2014), and in the European-British population, the association of both the polymorphisms; rs1006737, rs2238056 were reported with BD (Ferreira et al., 2008; Gershon et al., 2014; Green et al., 2010; Starnawska et al., 2016). However, studies conducted on Americans(Frazier et al., 2014), Germans(Kloiber et al., 2012), Europeans (Cross-Disorder Group of the Psychiatric Genomics, 2013) and Swedish population (Lavebratt et al., 2010) found contrasting results for rs1006737 association with MDD and in Southern Taiwanese population with BD (Jan et al., 2014). Whereas, a study of SHZ by Nie et al, (2015), reported the association of rs1006737 in European and Asian populations (Nie et al., 2015), while, the present study failed to establish a significant association of CACNA1c polymorphisms in SHZ, which points to the inconsistent association results of CACNA1c polymorphism in different populations and ethnicities.

CACNA1c is a calcium channel gene that encodes the alpha subunit (α1C) of the L-type voltage-gated calcium channel (LTCC). The function of the alpha subunit is to determine the kinetics, conductance, voltage dependence and pharmacology of the LTCC (Heyes et al., 2015). LTCC is involved in various aspects of neuronal development, signalling, and also in the establishment of maintenance of connectivity during and after neuronal development (Spitzer, 2006). The role of CACNA1c polymorphisms as a pathophysiological factor in the progression of psychiatric disorders has been reported by Yoshimizu et al, (2015) and Starnawska et al, (2016) in clinical and functional studies (Starnawska et al., 2016; Yoshimizu et al., 2015). Both polymorphisms (rs1006737 and rs2238056), are located in intron 3 of CACNA1c, which has important gene expression-regulatory functions, through interaction with the transcription start site. This intron also carries CpG islands which underwent a DNA methylation shift due to hypermethylation in the presence of rs2238056 (C > T), (Starnawska et al., 2016). This explains the impact of the polymorphisms (rs1006737 and rs2238056), and the role of intron 3 in the expression of CACNA1c, by regulating the 3D genomic architect associated with the chromosomal looping, in the presence of risk alleles of rs1006737 (A) and rs2238056 (T), (Starnawska et al., 2016). Thus the altered LTCC are unable to regulate the calcium influx and efflux which are important steps in neurotransmission, as calcium ions are also needed for the fusion of neurotransmitter vesicles to release neurotransmitters (dopamine, acetylcholine etc.) in the synaptic cleft (Imbrici et al., 2013), suggesting the contribution of CACNA1c polymorphisms in the genetic aetiology of psychiatric conditions by altering the functional activity of LTCC in different brain circuits and CACNA1c expression.

DRD1 is an extensively studied gene, yet very few reported association studies are available regarding rs4532, which is located on the 5′ UTR (untranslated region) (Pan et al., 2014). This locus has been reported to be associated with the therapeutic response to antipsychotics (Potkin et al., 2003), the positive symptoms of SHZ (psychotic symptoms), and with the prefrontal cortex (PFC), related cognitive functions (Rybakowski et al., 2005; Williams & Castner, 2006). In the initial analysis, DRD1-rs4532 and DRD2-rs1799732 were found to be associated with the risk of MDD in the current cohort, however, the association did not survive for any of these SNPs in MDD, after applying multiple test corrections. The reported association studies in different populations have reported contrasting results to our current findings. A study in the Estonian population has reported no association of rs4532 with MDD (Koks et al., 2006), while, a study of the Sardinian population, reported a significant association of rs4532 with BD (Severino et al., 2005). Whereas in SHZ, the role of rs4532 is still controversial and its role in the manifestation of SHZ is yet to be established in different populations, however, a meta-analysis study conducted in 2014, reported no association of rs4532 in SHZ (Pan et al., 2014), which supports the current findings in SHZ of the present study.

DRD2 SNP rs1799732 is a functional polymorphism in a promoter region of the DRD2 gene, involving the insertion (INS)/deletion (DEL) of a cytosine (-141C INDEL), which affects the receptor density by modulating the transcription and transcription-binding-factors (TBF), and reduces DRD2 expression 20–40% (Arinami et al., 1997). This polymorphism has been reported for its association with MDD in the Chinese Han population (He et al., 2013), while a study in Caucasian populations reported contradictory results in MDD (Furlong et al., 1998; Leszczynska-Rodziewicz et al., 2005). In BD several association studies have reported a lack of risk association of rs1799732 in different ethnicities including Polish (Leszczynska-Rodziewicz et al., 2005), Asian (Han Chinese), and Caucasians (Furlong et al., 1998; Li et al., 1999; Stober et al., 1998). A meta-analysis by Zou et al, (2012), has also reported no association between rs1799732 and mood disorders (BD & MDD) in Caucasians (Zou et al., 2012). Whereas, studies in SHZ have reported risk association of this SNP, rs1799732 in Chinese Han (Xiao et al., 2013), British Caucasian (Breen et al., 1999), and Brazilian population (Cordeiro et al., 2009). Though an ethnicity-based meta-analysis for SHZ, revealed the significant association of rs1799732 with a higher risk of SHZ susceptibility in Asians (Wang et al., 2016), and in Chinese Han (Zhao et al., 2016), but not in Caucasian, Japanese and Indian populations. (Arinami et al., 1997; Zhao et al., 2016).

In the present study, we have also observed that 2 of the studied variants (DRD4-120bp repeat and DRD5-rs10033951), were found to be associated with a protective effect. DRD4-120bp (2R = longer repeat or 120bp duplication), showed a significantly lower risk association with MDD and BD susceptibility, while the association was not found with SHZ. The DRD4 encodes dopamine receptor 4 (D4), which is involved in inhibitory neurotransmission (Andersen et al., 1990; Civelli et al., 1993; Missale et al., 1998; Niznik & Van Tol, 1992). The DRD4-120bp repeat is a promoter region tandem duplication polymorphism (functional polymorphism), having consensus binding sites for transcription factors. This suggests the role of the duplication sequence in conferring differential transcriptional activity (Seaman et al., 1999), by enhancing the binding capacity of certain transcription factors such as Sp1 (specificity protein 1) in the duplicated form (2R), (Ronai et al., 2004). The Sp1 transcription factor binds to a GC-rich region and can activate or repress the transcriptional activity in response to the physiological or/and pathological stimuli (Infantino et al., 2011). However, functional studies by McCracken et al, (2000)d Souza et al, (2004) reported the lower transcriptional activity of longer repeat (2R), than the shorter allele (1R), suggesting that the duplication allele might have a regulatory role in DRD4 expression providing an underlying biological mechanism in the aetiology of neuropsychiatric disorders (D'Souza et al., 2004; McCracken et al., 2000).

The DRD4-120bp repeat sequence is known to have a difference in allele frequencies in different populations. Significant risk association of duplication sequence (2R), in MDD, has been reported in the Chinese population (Lai et al., 2010) and in SHZ in the Chinese and North Indian populations (Li et al., 2004; Srivastava et al., 2006; Xing et al., 2003), while the non-significant results were reported in Danish SHZ patients (Olsen et al., 2005). The association of DRD4-120bp tandem duplication polymorphism has not been reported in case-control association studies of BD, however, an association study on novelty seeking (NS) behaviour analysis in BD patients has reported the association of shorter repeat (homozygous 1R) with higher NS scoring in BD (Rogers et al., 2004). In parallel, the DRD5 variant, rs10033951 was found as a protective factor in all the studied groups, (MDD, BD and SHZ), in the present study. The literature on the DRD5 variant is very limited and this locus has not been well studied in different ethinicities for MDD, BD and SHZ. However this variant has been explored in different antipsychotics analysis in psychiatric conditions (Hwang et al., 2012). In addition, DRD genetic variants specifically DRD4-48bp VNTR had shown differences in allelic distribution in different ethnic groups from Pakistan (Mansoor et al., 2008). Therefore further replication studies of DRD4-120bp and DRD5 rs10033951 for psychiatric conditions in different population is suggested. On the contrary, the DRD3 polymorphism rs6280 which is a missense variant (Ser9Gly), showed no risk association in either of the disorders (MDD, BD and SHZ), in our population. However, an association study by Chang et al., (2013) in Chinese BD patients, comorbid with or without anxiety disorders reported a significant association of rs6280 in BD comorbid with an anxiety disorder (Chang et al., 2013). Whereas association studies in SHZ did report a significant association of rs6280 with SHZ, however, all meta-analyses conducted for this variant failed to confirm a significant risk association with SHZ in different ethnicities (www.szgene.org, 2010), which comply with the current findings in SHZ.

Furthermore, 4 variants (POU3F2-rs2388334, PACS1-rs10896090, TRANK1-rs9834970, FSTL5-rs11724116), that were recently identified to be associated with BD cohorts of European, North American and Australian populations (Stahl et al., 2019), were screened in the present BD cohort of the Pakistani population. We observed that rs10896090 showed no risk association with BD in the present study, while it was reported to have a significant risk association with BD in a GWAS (Stahl et al., 2019). On the other hand, rs9834970, rs11724116 and rs2388334, were found with lower risk (protective-effect) in BD in the same GWAS (Stahl et al., 2019), however, in the present BD cohort of the Pakistani population, only rs2388334 was found to have a protective effect in BD susceptibility, which supports the previously reported GWAS findings (Stahl et al., 2019).

In the comparative analysis of the genotype and allele frequencies of all the studied genetic variants in the current study to the 1000 Genomes (PJL), significant differences were observed in genotype frequencies or allele frequencies of 6 variants (rs2238056, rs4532, rs6280, rs10033951, rs9834970, rs11724116 and rs2388334). All these variants were found in HWE except, rs10033951, rs11724116 and rs2388334. Similar differences were observed in comparison to ALFA SAS data, except rs4532, which did not show allele frequency differences with 1000 Genome Project PJL data but showed marked deviation in comparison to ALFA SAS allele frequency data. The deviation from 1000 Genome PJL reported frequencies might be due to the smaller sample size (n = 96), of that study as compared to the current study cohort size (n = 390) which was 3–4 folds larger than the 1000 genome PJL Pakistani data set, in addition, PJL included samples of Punjabi origin only from Lahore, which is g geographically separated (about 400–450 km apart) from Islamabad and Rawalpindi. As opposed to this the ALFA SAS reported frequencies were based on a larger sample size and included data of all South Asian populations but unlike the 1000 Genome, it did not describe the geographical location of the ethnicities they have included from Pakistan. In the present study, the majority of the participants, geographically belong to Rawalpindi and Islamabad, while the rest (20–33%) belongs to other geographical areas of Pakistan. Thus sample size and geographical differences might be the possible reasons for the observed deviation and differences.

The present study also had a few limitations, first, the majority of the participants of the current study were of Punjabi ethnic background, secondly, the control group that was collected was from the same geographical location as of the patients, therefore there are chances of overlooking the population stratification because three variants (rs10033951, rs11724116 and rs2388334) showed deviation from HWE of the control population. There is a chance of differences in the origins of the studied population. Thirdly, the sample size was relatively small in BD and SHZ groups. It is always very challenging with smaller cohort sizes, to decide on a genetic association study in multifactorial disorders, due to the diversity of population genetics. Since both the genetic and environmental backgrounds vary for different populations and ethnicities, thus the generalization of these current findings to other populations is limited. Despite all the reported association of the studied variants with the phenotypes, it must be admitted that most of the genetic association analyses in the present study were underpowered (See Supplementary Table 3), which might explain why some of the variants did not replicate the risk association observed in previous studies. Thus, the present study only provides preliminary explanations for the observed associations of the studied variants with MDD, BD and SHZ as these variants were screened for the first time in these psychiatric conditions in the Pakistani population. Future replication studies with larger cohort sizes in multiple ethnic populations are warranted to confirm these findings and functional studies may lead to a better understanding of the underlying genetic architectures of these psychiatric conditions (MDD, BD, and SHZ).

In conclusion, the present study was a replication study of the GWAS and association studies identified variants, in MDD, BD and SHZ in the Pakistani population. The present study explains evidence of some degree of genetic overlap between MDD, BD and SHZ, the underpinnings of susceptibility to these psychiatric conditions. We observed BD associated risk allele at CACNA1c polymorphisms (rs1006737), which also confers the risk of MDD susceptibility, whereas the risk allele at DRD5 polymorphism (rs10033951), found to act as a protective factor in all the 3 psychiatric conditions (MDD, BD and SHZ), in the current Pakistani cohort. However, the role of studied variants in SHZ susceptibility remains elusive due to the contradictory findings on the association of these SNPs in SHZ. We did observe deviation of our results from previously reported findings thus explaining the contribution of divergent genetic and geographical backgrounds of the different populations in clinical heterogeneity in psychiatric illnesses.

Statements and Declaration

The study conforms to the Helsinki Declaration and was approved by the Ethics Review Board of the Department of Biosciences, COMSATS University Islamabad, Pakistan. The study was supported by the Higher Education Commission of Pakistan (grant no. 3738), given to (Maleeha Azam) MA, and the Pakistan Academy of Sciences (grant no. 5-9/PAAS/1082) given to RQ. This research was supported by COMSATS University Islamabad (CUI), Pakistan’s core grant to (Raheel Qamar) RQ.

Conflicts of Authors

None

Funding Source

The study was supported by the Higher Education Commission of Pakistan (grant no. 3738), given to (Maleeha Azam) MA, and the Pakistan Academy of Sciences (grant no. 5-9/PAAS/1082) given to RQ. This research was supported by COMSATS University Islamabad (CUI), Pakistan’s core grant to (Raheel Qamar) RQ.

Conflicts of Authors

None

Ethical Approval

The current study conforms to the Helsinki Declaration and was approved by the Ethics Review Board of the Department of Biosciences, COMSATS University Islamabad, Pakistan. All the subjects were informed about the study objectives and written consent was obtained from the subjects or the family member of the patient. The privacy rights of all the participants were kept under consideration and their identity was anonymised.

Acknowledgement

We thank all the subjects for their cooperation and participation in the study. We are also grateful to Dr Rizwan Taj, head of the psychiatry department, and the staff members of the Pakistan Institute of Medical Sciences (PIMS) Islamabad, for their valuable support in sample collection.

Allen, N. C., Bagade, S., McQueen, M. B., Ioannidis, J. P., Kavvoura, F. K., Khoury, M. J., Tanzi, R. E., & Bertram, L. (2008, Jul). Systematic meta-analyses and field synopsis of genetic association studies in schizophrenia: the SzGene database. Nat Genet, 40(7), 827-834. https://doi.org/10.1038/ng.171
American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). American Psychiatric Association. https://www.healthdirect.gov.au/types-of-mental-illness
Andersen, P. H., Gingrich, J. A., Bates, M. D., Dearry, A., Falardeau, P., Senogles, S. E., & Caron, M. G. (1990, Jun). Dopamine receptor subtypes: beyond the D1/D2 classification. Trends Pharmacol Sci, 11(6), 231-236. https://doi.org/10.1016/0165-6147(90)90249-8
Arinami, T., Gao, M., Hamaguchi, H., & Toru, M. (1997, Apr). A functional polymorphism in the promoter region of the dopamine D2 receptor gene is associated with schizophrenia. Hum Mol Genet, 6(4), 577-582. https://doi.org/10.1093/hmg/6.4.577
Bombin, I., Arango, C., Mayoral, M., Castro-Fornieles, J., Gonzalez-Pinto, A., Gonzalez-Gomez, C., Moreno, D., Parellada, M., Baeza, I., Graell, M., Otero, S., Saiz, P. A., & Patino-Garcia, A. (2008, Sep 5). DRD3, but not COMT or DRD2, genotype affects executive functions in healthy and first-episode psychosis adolescents. Am J Med Genet B Neuropsychiatr Genet, 147B(6), 873-879. https://doi.org/10.1002/ajmg.b.30710
Breen, G., Brown, J., Maude, S., Fox, H., Collier, D., Li, T., Arranz, M., Shaw, D., & StClair, D. (1999, Aug 20). -141 C del/ins polymorphism of the dopamine receptor 2 gene is associated with schizophrenia in a British population. Am J Med Genet, 88(4), 407-410. https://doi.org/10.1002/(sici)1096-8628(19990820)88:4<407::aid-ajmg19>3.0.co;2-3
Chang, Y. H., Lee, S. Y., Chen, S. L., Tzeng, N. S., Wang, T. Y., Lee, I. H., Chen, P. S., Huang, S. Y., Yang, Y. K., Ko, H. C., & Lu, R. B. (2013, Dec). Genetic variants of the BDNF and DRD3 genes in bipolar disorder comorbid with anxiety disorder. J Affect Disord, 151(3), 967-972. https://doi.org/10.1016/j.jad.2013.08.017
Civelli, O., Bunzow, J. R., & Grandy, D. K. (1993). Molecular diversity of the dopamine receptors. Annu Rev Pharmacol Toxicol, 33, 281-307. https://doi.org/10.1146/annurev.pa.33.040193.001433
Cordeiro, Q., Siqueira-Roberto, J., Zung, S., & Vallada, H. (2009, Jun). Association between the DRD2-141C Insertion/Deletion polymorphism and schizophrenia. Arq Neuropsiquiatr, 67(2A), 191-194. https://doi.org/10.1590/s0004-282x2009000200004
Craddock, N., & Sklar, P. (2013, May 11). Genetics of bipolar disorder. Lancet, 381(9878), 1654-1662. https://doi.org/10.1016/S0140-6736(13)60855-7
Cross-Disorder Group of the Psychiatric Genomics, C. (2013, Apr 20). Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet, 381(9875), 1371-1379. https://doi.org/10.1016/S0140-6736(12)62129-1
D'Souza, U. M., Russ, C., Tahir, E., Mill, J., McGuffin, P., Asherson, P. J., & Craig, I. W. (2004, Nov 1). Functional effects of a tandem duplication polymorphism in the 5'flanking region of the DRD4 gene. Biol Psychiatry, 56(9), 691-697. https://doi.org/10.1016/j.biopsych.2004.08.008
Diehl, D. J., & Gershon, S. (1992, Mar-Apr). The role of dopamine in mood disorders. Compr Psychiatry, 33(2), 115-120. https://doi.org/10.1016/0010-440x(92)90007-d
Dunlop, B. W., & Nemeroff, C. B. (2007, Mar). The role of dopamine in the pathophysiology of depression. Arch Gen Psychiatry, 64(3), 327-337. https://doi.org/10.1001/archpsyc.64.3.327
Feczko, E., Miranda-Dominguez, O., Marr, M., Graham, A. M., Nigg, J. T., & Fair, D. A. (2019, Jul). The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes. Trends Cogn Sci, 23(7), 584-601. https://doi.org/10.1016/j.tics.2019.03.009
Ferreira, M. A., O'Donovan, M. C., Meng, Y. A., Jones, I. R., Ruderfer, D. M., Jones, L., Fan, J., Kirov, G., Perlis, R. H., Green, E. K., Smoller, J. W., Grozeva, D., Stone, J., Nikolov, I., Chambert, K., Hamshere, M. L., Nimgaonkar, V. L., Moskvina, V., Thase, M. E., Caesar, S., Sachs, G. S., Franklin, J., Gordon-Smith, K., Ardlie, K. G., Gabriel, S. B., Fraser, C., Blumenstiel, B., Defelice, M., Breen, G., Gill, M., Morris, D. W., Elkin, A., Muir, W. J., McGhee, K. A., Williamson, R., MacIntyre, D. J., MacLean, A. W., St, C. D., Robinson, M., Van Beck, M., Pereira, A. C., Kandaswamy, R., McQuillin, A., Collier, D. A., Bass, N. J., Young, A. H., Lawrence, J., Ferrier, I. N., Anjorin, A., Farmer, A., Curtis, D., Scolnick, E. M., McGuffin, P., Daly, M. J., Corvin, A. P., Holmans, P. A., Blackwood, D. H., Gurling, H. M., Owen, M. J., Purcell, S. M., Sklar, P., Craddock, N., & Wellcome Trust Case Control, C. (2008, Sep). Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet, 40(9), 1056-1058. https://doi.org/10.1038/ng.209
Fochtmann, L., & Fink, M. (1992, Nov-Dec). Role of dopamine in mood disorders. Compr Psychiatry, 33(6), 417-418. https://doi.org/10.1016/0010-440x(92)90065-x
Frazier, T. W., Youngstrom, E. A., Frankel, B. A., Zunta-Soares, G. B., Sanches, M., Escamilla, M., Nielsen, D. A., & Soares, J. C. (2014, May). Candidate gene associations with mood disorder, cognitive vulnerability, and fronto-limbic volumes. Brain Behav, 4(3), 418-430. https://doi.org/10.1002/brb3.226
Furlong, R. A., Coleman, T. A., Ho, L., Rubinsztein, J. S., Walsh, C., Paykel, E. S., & Rubinsztein, D. C. (1998, Sep 7). No association of a functional polymorphism in the dopamine D2 receptor promoter region with bipolar or unipolar affective disorders. Am J Med Genet, 81(5), 385-387. https://www.ncbi.nlm.nih.gov/pubmed/9754623
Gauderman, W. J. (2002, Jan 15). Sample size requirements for matched case-control studies of gene-environment interaction. Stat Med, 21(1), 35-50. https://doi.org/10.1002/sim.973
Gershon, E. S., Grennan, K., Busnello, J., Badner, J. A., Ovsiew, F., Memon, S., Alliey-Rodriguez, N., Cooper, J., Romanos, B., & Liu, C. (2014, Aug). A rare mutation of CACNA1C in a patient with bipolar disorder, and decreased gene expression associated with a bipolar-associated common SNP of CACNA1C in brain. Mol Psychiatry, 19(8), 890-894. https://doi.org/10.1038/mp.2013.107
Green, E. K., Grozeva, D., Jones, I., Jones, L., Kirov, G., Caesar, S., Gordon-Smith, K., Fraser, C., Forty, L., Russell, E., Hamshere, M. L., Moskvina, V., Nikolov, I., Farmer, A., McGuffin, P., Wellcome Trust Case Control, C., Holmans, P. A., Owen, M. J., O'Donovan, M. C., & Craddock, N. (2010, Oct). The bipolar disorder risk allele at CACNA1C also confers risk of recurrent major depression and of schizophrenia. Mol Psychiatry, 15(10), 1016-1022. https://doi.org/10.1038/mp.2009.49
He, K., An, Z., Wang, Q., Li, T., Li, Z., Chen, J., Li, W., Wang, T., Ji, J., Feng, G., Lin, H., Yi, Q., & Shi, Y. (2014, Jan). CACNA1C, schizophrenia and major depressive disorder in the Han Chinese population. Br J Psychiatry, 204(1), 36-39. https://doi.org/10.1192/bjp.bp.113.126979
He, M., Yan, H., Duan, Z. X., Qu, W., Gong, H. Y., Fan, Z. L., Kang, J. Y., Li, B. C., & Wang, J. M. (2013). Genetic distribution and association analysis of DRD2 gene polymorphisms with major depressive disorder in the Chinese Han population. Int J Clin Exp Pathol, 6(6), 1142-1149. https://www.ncbi.nlm.nih.gov/pubmed/23696934
Heyes, S., Pratt, W. S., Rees, E., Dahimene, S., Ferron, L., Owen, M. J., & Dolphin, A. C. (2015, Nov). Genetic disruption of voltage-gated calcium channels in psychiatric and neurological disorders. Prog Neurobiol, 134, 36-54. https://doi.org/10.1016/j.pneurobio.2015.09.002
Howes, O. D., Kambeitz, J., Kim, E., Stahl, D., Slifstein, M., Abi-Dargham, A., & Kapur, S. (2012, Aug). The nature of dopamine dysfunction in schizophrenia and what this means for treatment. Arch Gen Psychiatry, 69(8), 776-786. https://doi.org/10.1001/archgenpsychiatry.2012.169
Howes, O. D., Williams, M., Ibrahim, K., Leung, G., Egerton, A., McGuire, P. K., & Turkheimer, F. (2013, Nov). Midbrain dopamine function in schizophrenia and depression: a post-mortem and positron emission tomographic imaging study. Brain, 136(Pt 11), 3242-3251. https://doi.org/10.1093/brain/awt264
Hwang, R., Tiwari, A. K., Zai, C. C., Felsky, D., Remington, E., Wallace, T., Tong, R. P., Souza, R. P., Oh, G., Potkin, S. G., Lieberman, J. A., Meltzer, H. Y., & Kennedy, J. L. (2012, Apr 27). Dopamine D4 and D5 receptor gene variant effects on clozapine response in schizophrenia: replication and exploration. Prog Neuropsychopharmacol Biol Psychiatry, 37(1), 62-75. https://doi.org/10.1016/j.pnpbp.2011.11.018
Ikeda, M., Takahashi, A., Kamatani, Y., Okahisa, Y., Kunugi, H., Mori, N., Sasaki, T., Ohmori, T., Okamoto, Y., Kawasaki, H., Shimodera, S., Kato, T., Yoneda, H., Yoshimura, R., Iyo, M., Matsuda, K., Akiyama, M., Ashikawa, K., Kashiwase, K., Tokunaga, K., Kondo, K., Saito, T., Shimasaki, A., Kawase, K., Kitajima, T., Matsuo, K., Itokawa, M., Someya, T., Inada, T., Hashimoto, R., Inoue, T., Akiyama, K., Tanii, H., Arai, H., Kanba, S., Ozaki, N., Kusumi, I., Yoshikawa, T., Kubo, M., & Iwata, N. (2018, Mar). A genome-wide association study identifies two novel susceptibility loci and trans population polygenicity associated with bipolar disorder. Mol Psychiatry, 23(3), 639-647. https://doi.org/10.1038/mp.2016.259
Imbrici, P., Camerino, D. C., & Tricarico, D. (2013). Major channels involved in neuropsychiatric disorders and therapeutic perspectives. Front Genet, 4, 76. https://doi.org/10.3389/fgene.2013.00076
Infantino, V., Convertini, P., Iacobazzi, F., Pisano, I., Scarcia, P., & Iacobazzi, V. (2011, Aug 19). Identification of a novel Sp1 splice variant as a strong transcriptional activator. Biochem Biophys Res Commun, 412(1), 86-91. https://doi.org/10.1016/j.bbrc.2011.07.047
International HapMap, C. (2003, Dec 18). The International HapMap Project. Nature, 426(6968), 789-796. https://doi.org/10.1038/nature02168
Jan, W. C., Yang, S. Y., Chuang, L. C., Lu, R. B., Lu, M. K., Sun, H. S., & Kuo, P. H. (2014, Mar). Exploring the associations between genetic variants in genes encoding for subunits of calcium channel and subtypes of bipolar disorder. J Affect Disord, 157, 80-86. https://doi.org/10.1016/j.jad.2013.12.044
Kapur, S., & Mann, J. J. (1992, Jul 1). Role of the dopaminergic system in depression. Biol Psychiatry, 32(1), 1-17. https://doi.org/10.1016/0006-3223(92)90137-o
Kesby, J. P., Eyles, D. W., McGrath, J. J., & Scott, J. G. (2018, Jan 31). Dopamine, psychosis and schizophrenia: the widening gap between basic and clinical neuroscience. Transl Psychiatry, 8(1), 30. https://doi.org/10.1038/s41398-017-0071-9
Klein, M. O., Battagello, D. S., Cardoso, A. R., Hauser, D. N., Bittencourt, J. C., & Correa, R. G. (2019, Jan). Dopamine: Functions, Signaling, and Association with Neurological Diseases. Cell Mol Neurobiol, 39(1), 31-59. https://doi.org/10.1007/s10571-018-0632-3
Kloiber, S., Czamara, D., Karbalai, N., Muller-Myhsok, B., Hennings, J., Holsboer, F., & Lucae, S. (2012, Aug). ANK3 and CACNA1C--missing genetic link for bipolar disorder and major depressive disorder in two German case-control samples. J Psychiatr Res, 46(8), 973-979. https://doi.org/10.1016/j.jpsychires.2012.04.017
Koks, S., Nikopensius, T., Koido, K., Maron, E., Altmae, S., Heinaste, E., Vabrit, K., Tammekivi, V., Hallast, P., Kurg, A., Shlik, J., Vasar, V., Metspalu, A., & Vasar, E. (2006, Apr). Analysis of SNP profiles in patients with major depressive disorder. Int J Neuropsychopharmacol, 9(2), 167-174. https://doi.org/10.1017/S1461145705005468
L. Phan, Y. J., H. Zhang, W. Qiang, E. Shekhtman, D. Shao, D. Revoe, R. Villamarin, E. Ivanchenko, M. Kimura, Z. Y. Wang, L. Hao, N. Sharopova, M. Bihan, A. Sturcke, M. Lee, N. Popova, W. Wu, C. Bastiani, M. Ward, J. B. Holmes, V. Lyoshin, K. Kaur, E. Moyer, M. Feolo, and B. L. Kattman. . (2020). ALFA: Allele Frequency Aggregator. National Center for Biotechnology Information, U.S. National Library of Medicine, . Retrieved 10 Mar. 2020 from www.ncbi.nlm.nih.gov/snp/docs/gsr/alfa/.
Lai, J. H., Zhu, Y. S., Huo, Z. H., Sun, R. F., Yu, B., Wang, Y. P., Chai, Z. Q., & Li, S. B. (2010, Nov 4). Association study of polymorphisms in the promoter region of DRD4 with schizophrenia, depression, and heroin addiction. Brain Res, 1359, 227-232. https://doi.org/10.1016/j.brainres.2010.08.064
Lavebratt, C., Aberg, E., Sjoholm, L. K., & Forsell, Y. (2010, Sep). Variations in FKBP5 and BDNF genes are suggestively associated with depression in a Swedish population-based cohort. J Affect Disord, 125(1-3), 249-255. https://doi.org/10.1016/j.jad.2010.02.113
Leszczynska-Rodziewicz, A., Hauser, J., Dmitrzak-Weglarz, M., Skibinka, M., Czerski, P., Zakrzewska, A., Kosmowska, M., & Rybakowski, J. K. (2005, Jun). Lack of association between polymorphisms of dopamine receptors, type D2, and bipolar affective illness in a Polish population. Med Sci Monit, 11(6), CR289-295. https://www.ncbi.nlm.nih.gov/pubmed/15917720
Li, T., Chen, C. K., Hu, X., Ball, D., Lin, S. K., Chen, W., Sham, P. C., Loh el, W., Murray, R. M., & Collier, D. A. (2004, Aug 15). Association analysis of the DRD4 and COMT genes in methamphetamine abuse. Am J Med Genet B Neuropsychiatr Genet, 129B(1), 120-124. https://doi.org/10.1002/ajmg.b.30024
Li, T., Liu, X., Sham, P. C., Aitchison, K. J., Cai, G., Arranz, M. J., Deng, H., Liu, J., Kirov, G., Murray, R. M., & Collier, D. A. (1999, Jun 30). Association analysis between dopamine receptor genes and bipolar affective disorder. Psychiatry Res, 86(3), 193-201. https://doi.org/10.1016/s0165-1781(99)00034-7
Liu, Y. P., Wu, X., Xia, X., Yao, J., & Wang, B. J. (2020, Aug 8). The genome-wide supported CACNA1C gene polymorphisms and the risk of schizophrenia: an updated meta-analysis. BMC Med Genet, 21(1), 159. https://doi.org/10.1186/s12881-020-01084-0
Major Depressive Disorder Working Group of the Psychiatric, G. C., Ripke, S., Wray, N. R., Lewis, C. M., Hamilton, S. P., Weissman, M. M., Breen, G., Byrne, E. M., Blackwood, D. H., Boomsma, D. I., Cichon, S., Heath, A. C., Holsboer, F., Lucae, S., Madden, P. A., Martin, N. G., McGuffin, P., Muglia, P., Noethen, M. M., Penninx, B. P., Pergadia, M. L., Potash, J. B., Rietschel, M., Lin, D., Muller-Myhsok, B., Shi, J., Steinberg, S., Grabe, H. J., Lichtenstein, P., Magnusson, P., Perlis, R. H., Preisig, M., Smoller, J. W., Stefansson, K., Uher, R., Kutalik, Z., Tansey, K. E., Teumer, A., Viktorin, A., Barnes, M. R., Bettecken, T., Binder, E. B., Breuer, R., Castro, V. M., Churchill, S. E., Coryell, W. H., Craddock, N., Craig, I. W., Czamara, D., De Geus, E. J., Degenhardt, F., Farmer, A. E., Fava, M., Frank, J., Gainer, V. S., Gallagher, P. J., Gordon, S. D., Goryachev, S., Gross, M., Guipponi, M., Henders, A. K., Herms, S., Hickie, I. B., Hoefels, S., Hoogendijk, W., Hottenga, J. J., Iosifescu, D. V., Ising, M., Jones, I., Jones, L., Jung-Ying, T., Knowles, J. A., Kohane, I. S., Kohli, M. A., Korszun, A., Landen, M., Lawson, W. B., Lewis, G., Macintyre, D., Maier, W., Mattheisen, M., McGrath, P. J., McIntosh, A., McLean, A., Middeldorp, C. M., Middleton, L., Montgomery, G. M., Murphy, S. N., Nauck, M., Nolen, W. A., Nyholt, D. R., O'Donovan, M., Oskarsson, H., Pedersen, N., Scheftner, W. A., Schulz, A., Schulze, T. G., Shyn, S. I., Sigurdsson, E., Slager, S. L., Smit, J. H., Stefansson, H., Steffens, M., Thorgeirsson, T., Tozzi, F., Treutlein, J., Uhr, M., van den Oord, E. J., Van Grootheest, G., Volzke, H., Weilburg, J. B., Willemsen, G., Zitman, F. G., Neale, B., Daly, M., Levinson, D. F., & Sullivan, P. F. (2013, Apr). A mega-analysis of genome-wide association studies for major depressive disorder. Mol Psychiatry, 18(4), 497-511. https://doi.org/10.1038/mp.2012.21
Mansoor, A., Mazhar, K., & Qamar, R. (2008, Jun). VNTR polymorphism of the DRD4 locus in different Pakistani ethnic groups. Genet Test, 12(2), 299-304. https://doi.org/10.1089/gte.2007.0120
McCracken, J. T., Smalley, S. L., McGough, J. J., Crawford, L., Del'Homme, M., Cantor, R. M., Liu, A., & Nelson, S. F. (2000, Sep). Evidence for linkage of a tandem duplication polymorphism upstream of the dopamine D4 receptor gene (DRD4) with attention deficit hyperactivity disorder (ADHD). Mol Psychiatry, 5(5), 531-536. https://doi.org/10.1038/sj.mp.4000770
Missale, C., Nash, S. R., Robinson, S. W., Jaber, M., & Caron, M. G. (1998, Jan). Dopamine receptors: from structure to function. Physiol Rev, 78(1), 189-225. https://doi.org/10.1152/physrev.1998.78.1.189
Nie, F., Wang, X., Zhao, P., Yang, H., Zhu, W., Zhao, Y., Chen, B., Valenzuela, R. K., Zhang, R., Gallitano, A. L., & Ma, J. (2015, Dec). Genetic analysis of SNPs in CACNA1C and ANK3 gene with schizophrenia: A comprehensive meta-analysis. Am J Med Genet B Neuropsychiatr Genet, 168(8), 637-648. https://doi.org/10.1002/ajmg.b.32348
Niznik, H. B., & Van Tol, H. H. (1992, Oct). Dopamine receptor genes: new tools for molecular psychiatry. J Psychiatry Neurosci, 17(4), 158-180. https://www.ncbi.nlm.nih.gov/pubmed/1450188
Olsen, L., Timm, S., Wang, A. G., Soeby, K., Jakobsen, K. D., Clemmensen, S., Lokke, A., Fossum, M., Parnas, J., Hemmingsen, R., Rasmussen, H. B., & Werge, T. (2005, Feb 1). Association of the 120-bp duplication in the dopamine D4 receptor gene and schizophrenia in a sample of Danish subjects. Schizophr Res, 73(1), 133-135. https://doi.org/10.1016/j.schres.2004.08.010
Pan, Y., Yao, J., & Wang, B. (2014). Association of dopamine D1 receptor gene polymorphism with schizophrenia: a meta-analysis. Neuropsychiatr Dis Treat, 10, 1133-1139. https://doi.org/10.2147/NDT.S63776
Potkin, S. G., Basile, V. S., Jin, Y., Masellis, M., Badri, F., Keator, D., Wu, J. C., Alva, G., Carreon, D. T., Bunney, W. E., Jr., Fallon, J. H., & Kennedy, J. L. (2003, Jan). D1 receptor alleles predict PET metabolic correlates of clinical response to clozapine. Mol Psychiatry, 8(1), 109-113. https://doi.org/10.1038/sj.mp.4001191
R Core Team. (2020). R: The R Project for Statistical Computing. the R Development Core Team. https://www.r-project.org/
Rogers, G., Joyce, P., Mulder, R., Sellman, D., Miller, A., Allington, M., Olds, R., Wells, E., & Kennedy, M. (2004, Apr 1). Association of a duplicated repeat polymorphism in the 5'-untranslated region of the DRD4 gene with novelty seeking. Am J Med Genet B Neuropsychiatr Genet, 126B(1), 95-98. https://doi.org/10.1002/ajmg.b.20133
Ronai, Z., Guttman, A., Keszler, G., & Sasvari-Szekely, M. (2004, Apr). Capillary electrophoresis study on DNA-protein complex formation in the polymorphic 5' upstream region of the dopamine D4 receptor (DRD4) gene. Curr Med Chem, 11(8), 1023-1029. https://doi.org/10.2174/0929867043455503
Rybakowski, J. K., Borkowska, A., Czerski, P. M., Kapelski, P., Dmitrzak-Weglarz, M., & Hauser, J. (2005, Nov). An association study of dopamine receptors polymorphisms and the Wisconsin Card Sorting Test in schizophrenia. J Neural Transm (Vienna), 112(11), 1575-1582. https://doi.org/10.1007/s00702-005-0292-6
[Record #168 is using a reference type undefined in this output style.]
Seaman, M. I., Fisher, J. B., Chang, F., & Kidd, K. K. (1999, Dec 15). Tandem duplication polymorphism upstream of the dopamine D4 receptor gene (DRD4). Am J Med Genet, 88(6), 705-709. https://doi.org/10.1002/(sici)1096-8628(19991215)88:6<705::aid-ajmg22>3.0.co;2-f
Segurado, R., Detera-Wadleigh, S. D., Levinson, D. F., Lewis, C. M., Gill, M., Nurnberger, J. I., Jr., Craddock, N., DePaulo, J. R., Baron, M., Gershon, E. S., Ekholm, J., Cichon, S., Turecki, G., Claes, S., Kelsoe, J. R., Schofield, P. R., Badenhop, R. F., Morissette, J., Coon, H., Blackwood, D., McInnes, L. A., Foroud, T., Edenberg, H. J., Reich, T., Rice, J. P., Goate, A., McInnis, M. G., McMahon, F. J., Badner, J. A., Goldin, L. R., Bennett, P., Willour, V. L., Zandi, P. P., Liu, J., Gilliam, C., Juo, S. H., Berrettini, W. H., Yoshikawa, T., Peltonen, L., Lonnqvist, J., Nothen, M. M., Schumacher, J., Windemuth, C., Rietschel, M., Propping, P., Maier, W., Alda, M., Grof, P., Rouleau, G. A., Del-Favero, J., Van Broeckhoven, C., Mendlewicz, J., Adolfsson, R., Spence, M. A., Luebbert, H., Adams, L. J., Donald, J. A., Mitchell, P. B., Barden, N., Shink, E., Byerley, W., Muir, W., Visscher, P. M., Macgregor, S., Gurling, H., Kalsi, G., McQuillin, A., Escamilla, M. A., Reus, V. I., Leon, P., Freimer, N. B., Ewald, H., Kruse, T. A., Mors, O., Radhakrishna, U., Blouin, J. L., Antonarakis, S. E., & Akarsu, N. (2003, Jul). Genome scan meta-analysis of schizophrenia and bipolar disorder, part III: Bipolar disorder. Am J Hum Genet, 73(1), 49-62. https://doi.org/10.1086/376547
Seifuddin, F., Mahon, P. B., Judy, J., Pirooznia, M., Jancic, D., Taylor, J., Goes, F. S., Potash, J. B., & Zandi, P. P. (2012, Jul). Meta-analysis of genetic association studies on bipolar disorder. Am J Med Genet B Neuropsychiatr Genet, 159B(5), 508-518. https://doi.org/10.1002/ajmg.b.32057
Severino, G., Congiu, D., Serreli, C., De Lisa, R., Chillotti, C., Del Zompo, M., & Piccardi, M. P. (2005, Apr 5). A48G polymorphism in the D1 receptor genes associated with bipolar I disorder. Am J Med Genet B Neuropsychiatr Genet, 134B(1), 37-38. https://doi.org/10.1002/ajmg.b.30116
Singh, M. M. (1970). A unifying hypothesis on the biochemical basis of affective disorder. Psychiatr Q, 44(4), 706-724. https://doi.org/10.1007/BF01563010
Sklar, P., Smoller, J. W., Fan, J., Ferreira, M. A., Perlis, R. H., Chambert, K., Nimgaonkar, V. L., McQueen, M. B., Faraone, S. V., Kirby, A., de Bakker, P. I., Ogdie, M. N., Thase, M. E., Sachs, G. S., Todd-Brown, K., Gabriel, S. B., Sougnez, C., Gates, C., Blumenstiel, B., Defelice, M., Ardlie, K. G., Franklin, J., Muir, W. J., McGhee, K. A., MacIntyre, D. J., McLean, A., VanBeck, M., McQuillin, A., Bass, N. J., Robinson, M., Lawrence, J., Anjorin, A., Curtis, D., Scolnick, E. M., Daly, M. J., Blackwood, D. H., Gurling, H. M., & Purcell, S. M. (2008, Jun). Whole-genome association study of bipolar disorder. Mol Psychiatry, 13(6), 558-569. https://doi.org/10.1038/sj.mp.4002151
Soares, J. C., & Gershon, S. (2000, Mar). The diagnostic boundaries of bipolar disorder. Bipolar Disord, 2(1), 1-2. https://doi.org/10.1034/j.1399-5618.2000.020101.x
Spitzer, N. C. (2006, Dec 7). Electrical activity in early neuronal development. Nature, 444(7120), 707-712. https://doi.org/10.1038/nature05300
Srivastava, V., Varma, P. G., Prasad, S., Semwal, P., Nimgaonkar, V. L., Lerer, B., Deshpande, S. N., & Bk, T. (2006, Feb). Genetic susceptibility to tardive dyskinesia among schizophrenia subjects: IV. Role of dopaminergic pathway gene polymorphisms. Pharmacogenet Genomics, 16(2), 111-117. https://doi.org/10.1097/01.fpc.0000184957.98150.0f
Stahl, E. A., Breen, G., Forstner, A. J., McQuillin, A., Ripke, S., Trubetskoy, V., Mattheisen, M., Wang, Y., Coleman, J. R. I., Gaspar, H. A., de Leeuw, C. A., Steinberg, S., Pavlides, J. M. W., Trzaskowski, M., Byrne, E. M., Pers, T. H., Holmans, P. A., Richards, A. L., Abbott, L., Agerbo, E., Akil, H., Albani, D., Alliey-Rodriguez, N., Als, T. D., Anjorin, A., Antilla, V., Awasthi, S., Badner, J. A., Baekvad-Hansen, M., Barchas, J. D., Bass, N., Bauer, M., Belliveau, R., Bergen, S. E., Pedersen, C. B., Boen, E., Boks, M. P., Boocock, J., Budde, M., Bunney, W., Burmeister, M., Bybjerg-Grauholm, J., Byerley, W., Casas, M., Cerrato, F., Cervantes, P., Chambert, K., Charney, A. W., Chen, D., Churchhouse, C., Clarke, T. K., Coryell, W., Craig, D. W., Cruceanu, C., Curtis, D., Czerski, P. M., Dale, A. M., de Jong, S., Degenhardt, F., Del-Favero, J., DePaulo, J. R., Djurovic, S., Dobbyn, A. L., Dumont, A., Elvsashagen, T., Escott-Price, V., Fan, C. C., Fischer, S. B., Flickinger, M., Foroud, T. M., Forty, L., Frank, J., Fraser, C., Freimer, N. B., Frisen, L., Gade, K., Gage, D., Garnham, J., Giambartolomei, C., Pedersen, M. G., Goldstein, J., Gordon, S. D., Gordon-Smith, K., Green, E. K., Green, M. J., Greenwood, T. A., Grove, J., Guan, W., Guzman-Parra, J., Hamshere, M. L., Hautzinger, M., Heilbronner, U., Herms, S., Hipolito, M., Hoffmann, P., Holland, D., Huckins, L., Jamain, S., Johnson, J. S., Jureus, A., Kandaswamy, R., Karlsson, R., Kennedy, J. L., Kittel-Schneider, S., Knowles, J. A., Kogevinas, M., Koller, A. C., Kupka, R., Lavebratt, C., Lawrence, J., Lawson, W. B., Leber, M., Lee, P. H., Levy, S. E., Li, J. Z., Liu, C., Lucae, S., Maaser, A., MacIntyre, D. J., Mahon, P. B., Maier, W., Martinsson, L., McCarroll, S., McGuffin, P., McInnis, M. G., McKay, J. D., Medeiros, H., Medland, S. E., Meng, F., Milani, L., Montgomery, G. W., Morris, D. W., Muhleisen, T. W., Mullins, N., Nguyen, H., Nievergelt, C. M., Adolfsson, A. N., Nwulia, E. A., O'Donovan, C., Loohuis, L. M. O., Ori, A. P. S., Oruc, L., Osby, U., Perlis, R. H., Perry, A., Pfennig, A., Potash, J. B., Purcell, S. M., Regeer, E. J., Reif, A., Reinbold, C. S., Rice, J. P., Rivas, F., Rivera, M., Roussos, P., Ruderfer, D. M., Ryu, E., Sanchez-Mora, C., Schatzberg, A. F., Scheftner, W. A., Schork, N. J., Shannon Weickert, C., Shehktman, T., Shilling, P. D., Sigurdsson, E., Slaney, C., Smeland, O. B., Sobell, J. L., Soholm Hansen, C., Spijker, A. T., St Clair, D., Steffens, M., Strauss, J. S., Streit, F., Strohmaier, J., Szelinger, S., Thompson, R. C., Thorgeirsson, T. E., Treutlein, J., Vedder, H., Wang, W., Watson, S. J., Weickert, T. W., Witt, S. H., Xi, S., Xu, W., Young, A. H., Zandi, P., Zhang, P., Zollner, S., e, Q. C., Consortium, B., Adolfsson, R., Agartz, I., Alda, M., Backlund, L., Baune, B. T., Bellivier, F., Berrettini, W. H., Biernacka, J. M., Blackwood, D. H. R., Boehnke, M., Borglum, A. D., Corvin, A., Craddock, N., Daly, M. J., Dannlowski, U., Esko, T., Etain, B., Frye, M., Fullerton, J. M., Gershon, E. S., Gill, M., Goes, F., Grigoroiu-Serbanescu, M., Hauser, J., Hougaard, D. M., Hultman, C. M., Jones, I., Jones, L. A., Kahn, R. S., Kirov, G., Landen, M., Leboyer, M., Lewis, C. M., Li, Q. S., Lissowska, J., Martin, N. G., Mayoral, F., McElroy, S. L., McIntosh, A. M., McMahon, F. J., Melle, I., Metspalu, A., Mitchell, P. B., Morken, G., Mors, O., Mortensen, P. B., Muller-Myhsok, B., Myers, R. M., Neale, B. M., Nimgaonkar, V., Nordentoft, M., Nothen, M. M., O'Donovan, M. C., Oedegaard, K. J., Owen, M. J., Paciga, S. A., Pato, C., Pato, M. T., Posthuma, D., Ramos-Quiroga, J. A., Ribases, M., Rietschel, M., Rouleau, G. A., Schalling, M., Schofield, P. R., Schulze, T. G., Serretti, A., Smoller, J. W., Stefansson, H., Stefansson, K., Stordal, E., Sullivan, P. F., Turecki, G., Vaaler, A. E., Vieta, E., Vincent, J. B., Werge, T., Nurnberger, J. I., Wray, N. R., Di Florio, A., Edenberg, H. J., Cichon, S., Ophoff, R. A., Scott, L. J., Andreassen, O. A., Kelsoe, J., Sklar, P., & Bipolar Disorder Working Group of the Psychiatric Genomics, C. (2019, May). Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat Genet, 51(5), 793-803. https://doi.org/10.1038/s41588-019-0397-8
Starnawska, A., Demontis, D., Pen, A., Hedemand, A., Nielsen, A. L., Staunstrup, N. H., Grove, J., Als, T. D., Jarram, A., O'Brien, N. L., Mors, O., McQuillin, A., Borglum, A. D., & Nyegaard, M. (2016, Jun 7). CACNA1C hypermethylation is associated with bipolar disorder. Transl Psychiatry, 6(6), e831. https://doi.org/10.1038/tp.2016.99
Stober, G., Jatzke, S., Heils, A., Jungkunz, G., Knapp, M., Mossner, R., Riederer, P., & Lesch, K. P. (1998). Insertion/deletion variant (-141C Ins/Del) in the 5' regulatory region of the dopamine D2 receptor gene: lack of association with schizophrenia and bipolar affective disorder. Short communication. J Neural Transm (Vienna), 105(1), 101-109. https://doi.org/10.1007/s007020050041
The 1000 Genomes Project Consortium. A Deep Catalog of Human Genetic Variation. https://www.internationalgenome.org/
Tissot, R. (1975). The common pathophysiology of monaminergic psychoses: a new hypothesis. Neuropsychobiology, 1(4), 243-260. https://doi.org/10.1159/000117498
Torrey, E. F., Webster, M., Knable, M., Johnston, N., & Yolken, R. H. (2000, Aug 3). The stanley foundation brain collection and neuropathology consortium. Schizophr Res, 44(2), 151-155. https://doi.org/10.1016/S0920-9964(99)00192-9
Tunbridge, E. M., Narajos, M., Harrison, C. H., Beresford, C., Cipriani, A., & Harrison, P. J. (2019, Oct 15). Which Dopamine Polymorphisms Are Functional? Systematic Review and Meta-analysis of COMT, DAT, DBH, DDC, DRD1-5, MAOA, MAOB, TH, VMAT1, and VMAT2. Biol Psychiatry, 86(8), 608-620. https://doi.org/10.1016/j.biopsych.2019.05.014
Wang, T., Zhang, X., Li, A., Zhu, M., Liu, S., Qin, W., Li, J., Yu, C., Jiang, T., & Liu, B. (2017). Polygenic risk for five psychiatric disorders and cross-disorder and disorder-specific neural connectivity in two independent populations. Neuroimage Clin, 14, 441-449. https://doi.org/10.1016/j.nicl.2017.02.011
Wang, Y., Liu, L., Xin, L., Fan, D., Ding, N., Hu, Y., Cai, G., Wang, L., Xia, Q., Li, X., Yang, X., Zou, Y., & Pan, F. (2016, Aug). The -141C Ins/Del and Taq1A polymorphism in the dopamine D2 receptor gene may confer susceptibility to schizophrenia in Asian populations. J Clin Neurosci, 30, 1-7. https://doi.org/10.1016/j.jocn.2015.10.052
Williams, G. V., & Castner, S. A. (2006, Apr 28). Under the curve: critical issues for elucidating D1 receptor function in working memory. Neuroscience, 139(1), 263-276. https://doi.org/10.1016/j.neuroscience.2005.09.028
Wittenborn, J. R. (1974, May). Deductive approaches to the catecholamine hypothesis of affective disorders. J Nerv Ment Dis, 158(5), 320-324. https://doi.org/10.1097/00005053-197405000-00002
www.szgene.org. (2010). Meta-Analysis of All Published SZ Association Studies (Case-Control Only). http://www.szgene.org/meta.asp?geneID=224
Xiao, L., Shen, T., Peng, D. H., Shu, C., Jiang, K. D., & Wang, G. H. (2013, Aug). Functional -141C Ins/Del polymorphism in the dopamine D2 receptor gene promoter and schizophrenia in a Chinese Han population. J Int Med Res, 41(4), 1171-1178. https://doi.org/10.1177/0300060513483415
Xing, Q. H., Wu, S. N., Lin, Z. G., Li, H. F., Yang, J. D., Feng, G. Y., Wang, M. T., Yang, W. W., & He, L. (2003, Dec 1). Association analysis of polymorphisms in the upstream region of the human dopamine D4 receptor gene in schizophrenia. Schizophr Res, 65(1), 9-14. https://doi.org/10.1016/s0920-9964(03)00064-1
Yoshimizu, T., Pan, J. Q., Mungenast, A. E., Madison, J. M., Su, S., Ketterman, J., Ongur, D., McPhie, D., Cohen, B., Perlis, R., & Tsai, L. H. (2015, Feb). Functional implications of a psychiatric risk variant within CACNA1C in induced human neurons. Mol Psychiatry, 20(2), 284. https://doi.org/10.1038/mp.2014.181
Zhao, X., Huang, Y., Chen, K., Li, D., Han, C., & Kan, Q. (2016, Sep). -141C insertion/deletion polymorphism of the dopamine D2 receptor gene is associated with schizophrenia in Chinese Han population: Evidence from an ethnic group-specific meta-analysis. Asia Pac Psychiatry, 8(3), 189-198. https://doi.org/10.1111/appy.12206
Zou, Y. F., Wang, F., Feng, X. L., Li, W. F., Tian, Y. H., Tao, J. H., Pan, F. M., & Huang, F. (2012, Feb). Association of DRD2 gene polymorphisms with mood disorders: a meta-analysis. J Affect Disord, 136(3), 229-237. https://doi.org/10.1016/j.jad.2010.11.012

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Association of GWAS and candidate gene loci of dopaminergic system with major depression, schizophrenia and bipolar disorder in the Pakistani population

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Abstract

1. Introduction

2. Methodology

2.1. Subjects Recruitment

2.1.1. Inclusion and Exclusion Criteria

2.1.2. Sample Collection and Processing

2.2. Genetic Variants and Genotypying

2.3. Statistical Analysis

2.3.1. Genotype and Allele Frequencies comparison with 1000 Genome and ALFA

3. Results

3.1. Descriptive Characteristics of the Subjects

3.1.1. Association with MDD

3.1.2. Association with BD

3.1.3. Association with SHZ

3.2. Comparison with 1000 Genomes (PJL) and ALFA SAS data

4. Discussion

Declarations

References

Tables

Competing Interests

Supplementary Files

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