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Effect of rs1108580 of DBH and rs1006737 of CACNA1C on Cognition and Tardive Dyskinesia in a North Indian Schizophrenia Cohort

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Abstract

Genetic perturbations in dopamine neurotransmission and calcium signaling pathways are implicated in the etiology of schizophrenia. We aimed to test the association of a functional splice variant each in Dopamine β-Hydroxylase (DBH; rs1108580) and Calcium voltage-gated channel subunit alpha1 C (CACNA1C; rs1006737) genes in these pathways with schizophrenia (506 cases, 443 controls); Abnormal Involuntary Movement Scale (AIMS) scores in subjects assessed for tardive dyskinesia (76 TD-positive, 95 TD-negative) and Penn Computerized Neurocognitive Battery (PennCNB) scores (334 cases, 234 controls). The effect of smoking status and SNP genotypes on AIMS scores were assessed using ANOVA; health status and SNP genotypes on three performance functions of PennCNB cognitive domains were assessed by ANCOVA with age and sex as covariates. Association with Positive and Negative Syndrome Scale (PANSS) scores in the TD cohort and cognitive scores in healthy controls of the cognition cohort were tested by linear regression. None of the markers were associated with schizophrenia. Smoking status [F(2, 139) = 10.6; p = 5 × 10−5], rs1006737 [F(2, 139) = 7.1; p = 0.001], TD status*smoking [F(2, 139) = 8.0; p = 5.0 × 10−4] and smoking status*rs1006737 [F(4, 139) = 2.7; p = 0.03] had an effect on AIMS score. Furthermore, rs1006737 was associated with orofacial [F(2, 139) = 4.6; p = 0.01] and limb-truncal TD [(F(2, 139) = 3.8; p = 0.02]. Main effect of rs1108580 on working memoryprocessing speed [F(2, 544) = 3.8; p = 0.03] and rs1006737 on spatial abilityefficiency [F(1, 550) = 9.4; p = 0.02] was identified. Health status*rs1006737 interaction had an effect on spatial memoryprocessing speed [F(1, 550) = 6.9; p = 0.01]. Allelic/genotypic association (p = 0.01/0.03) of rs1006737 with disorganized/concrete factor and allelic association of rs1108580 (p = 0.04) with a depressive factor of PANSS was observed in the TD-negative subcohort. Allelic association of rs1006737 with sensorimotor dexterityaccuracy (p = 0.03), attentionefficiency (p = 0.05), and spatial abilityefficiency (p = 0.02); allelic association of rs1108580 with face memoryaccuracy (p = 0.05) and emotionefficiency (p = 0.05); and allelic/genotypic association with emotionaccuracy (p = 0.003/0.009) were observed in healthy controls of the cognition cohort. These association findings may have direct implications for personalized medicine and cognitive remediation.

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Data Availability

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

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Acknowledgements

We acknowledge help from Dr. Aravind Kumar of Sandor Life Sciences Pvt. Ltd, Banjara Hills, Hyderabad-500034 for Fluidigm-based genotyping. Skew power transformations using the car package in R was done with help from Prof. Sanford Weisberg, School of Statistics, University of Minnesota. The aligned rank transformation was done with kind help from Prof. James J. Higgins, Department of Statistics, Kansas State University.

Funding

This work was supported by the Department of Biotechnology (DBT), Government of India, New Delhi, India (grant numbers [BT/PR/2425/MED/13/089/2001, B.K.T], [BT/IC-2/Israel/Deshpande/2002, S.N.D], and [BT/IC-2/00/smita/99, S.N.D]) and the Department of Science and Technology – Science and Engineering Research Board, New Delhi, India (grant number SR/S2/JCB-44/2011 – JC Bose phase II, B.K.T).

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Authors and Affiliations

  1. Department of Genetics, University of Delhi South Campus, Benito Juarez Road, New Delhi, 110021, India

    Toyanji Joseph Punchaichira, Prachi Kukshal & B. K. Thelma

  2. Sri Sathya Sai Sanjeevani International Centre for Child Heart Care & Research, Palwal, Haryana, 121102, India

    Prachi Kukshal

  3. Department of Psychiatry, Postgraduate Institute of Medical Education and Research-Dr. Ram Manohar Lohia Hospital, Baba Kharak Singh Marg, Connaught Place, New Delhi, 110001, India

    Triptish Bhatia & Smita Neelkanth Deshpande

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  1. Toyanji Joseph Punchaichira
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Contributions

The study was designed by B.K. Thelma and Toyanji Joseph Punchaichira. Statistical analysis was done by Toyanji Joseph Punchaichira, Triptish Bhatia, and Prachi Kukshal. Visualization of the data as figures was done by Toyanji Joseph Punchaichira. Samples were recruited by Smita Neelkanth Deshpande and her team at RML Hospital. Preparation of the first draft of the manuscript was done by Toyanji Joseph Punchaichira.

Corresponding author

Correspondence to B. K. Thelma.

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Ethical Approval

This study was performed in line with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Approval was granted by the Ethics Committee of Dr. Ram Manohar Lohia Hospital (No.18-15/2002-RMLH(HAI)/3140 dated 5/3/2004; 18-62/06-RMLH(HA-1)/vol.II/63 dated 30/11/2008 and 18-9/2002-RMLH(HA-I)/1088 dated 15/01/2008)).

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Informed consent was obtained from all individual participants included in the study.

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Not applicable.

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The authors declare no competing interests.

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Supplementary Figure 1: Scatter Plot Matrix of cognitive accuracy scores among various domains in the study cohort. Scatter plot matrix depicting accuracy of cognitive variables are presented. While Box plots for SZ subjects are depicted as red, that for healthy controls are depicted as blue for each domain in the top panel. Frequency distribution plots for each domain for healthy controls (blue) and SZ subjects (red) are depicted to the left column. Diagonal plots depict overlay of density distribution of SZ subjects (red) and healthy controls (blue) for that domain. Scatter plots and correlation coefficients between each pair of variables for SZ subjects in red and for healthy controls in blue for cognitive domains are also depicted. Supplementary Figure 2: Scatter Plot Matrix of cognitive processing speed scores among various domains in the study cohort. Scatter plot matrix depicting processing speed of cognitive domains are presented. While Box plots for SZ subjects are depicted as red, that for healthy controls are depicted as blue for each domain in the top panel. Frequency distribution plots for each domain for healthy controls (blue) and SZ subjects (red) are depicted to the left column. Diagonal plots depict overlay of density distribution of SZ subjects (red) and healthy controls (blue) for that domain. Scatterplots and correlation coefficients between each pair of variables for SZ subjects in red and for healthy controls in blue for cognitive domains are also depicted. Supplementary Figure 3: Scatter Plot Matrix of cognitive efficiency scores among various domains in the study cohort. Scatter plot matrix depicting efficiency of cognitive domains are presented. While Box plots for SZ subjects are depicted as red, that for healthy controls are depicted as blue for each domain in the top panel. Frequency distribution plots for each domain for healthy controls (blue) and SZ subjects (red) are depicted to the left column. Diagonal plots depict overlay of density distribution of SZ subjects (red) and healthy controls (blue) for that domain. Scatterplots and correlation coefficients between each pair of variables for SZ subjects in red and for healthy controls in blue for cognitive domains are also depicted. Supplementary Figure 4: Scree plot showing the variance explained by number of components for (a)accuracy (b) processing speed and (c) efficiency of cognitive scores. A scree plot with the percentage of variance explained versus the number of dimensions are depicted for (a) accuracy (b) processing speed and (c) efficiency of cognitive domains are depicted. The percentage of variance explained by each dimension is presented on the top of each bar. Supplementary Figure 5: Correlation plot highlighting the quality of representation (cos2) of cognitive variables to various dimensions of (a) accuracy, (b) processing speed and (c) efficiency of cognitive scores. Correlation plot of cos2of eight cognitive domains on to the five dimensions upon PCA is depicted for (a) accuracy (b) processing speed and (c) efficiency. Correlations are depicted from white (the domain not loaded on to that dimension) to grades of blue from light to dark (depicting partial to almost full loading on to that dimension) highlighting most contributing variables to each dimension. Supplementary Figure 6: The total contribution of cognitive variables to PC1 and PC2 of (a) accuracy, (b) processing speed and (c) efficiency of cognitive scores. This plot depicts contribution of each cognitive domain to the first two dimensions on PCA of (a) accuracy, (b) processing speed and (c) efficiency. The red dotted line depicts average contribution if the variables were uniform. Variables more than this threshold could be considered as important in contributing to the first two dimensions. (PDF 3998 kb)

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Punchaichira, T.J., Kukshal, P., Bhatia, T. et al. Effect of rs1108580 of DBH and rs1006737 of CACNA1C on Cognition and Tardive Dyskinesia in a North Indian Schizophrenia Cohort. Mol Neurobiol 60, 6826–6839 (2023). https://doi.org/10.1007/s12035-023-03496-4

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