AUTHOR=Singh Minali , Pradhan Dibyabhabha , Kkani Poornima , Prasad Rao Gundugurti , Dhagudu Naveen Kumar , Kumar Lov , Ramasubramanian Chellamuthu , Kumar Srinivasan Ganesh , Sonttineni Surekha , Mohan Kommu Naga 

TITLE=Genome-scale copy number variant analysis in schizophrenia patients and controls from South India

JOURNAL=Frontiers in Molecular Neuroscience

VOLUME=Volume 16 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/molecular-neuroscience/articles/10.3389/fnmol.2023.1268827

DOI=10.3389/fnmol.2023.1268827

ISSN=1662-5099

ABSTRACT=Copy Number Variants (CNVs) are among the main genetic factors identified in schizophrenia (SZ) through genome scale studies mostly in Caucasians. However, there are no genome-scale CNV reports from India till date. To address this shortcoming, we generated for the first time, genome-scale CNV data on 168 each of  SZ patients and controls from South India. Sixty-three different CNVs were identified in 56 patients and 46 controls with significantly higher proportion of medium-sized deletions (100 kb – 1 Mb) after multiple testing (FDR = 2.7E-4) in patients. Thirteen CNVs were previously reported however, when searched against GWAS, transcriptome, exome and DNA methylation studies, another 17 with candidate genes were identified. Of the total 30, twenty-eight were present in 38 patients and twelve in 27 controls indicating a significantly higher representation in the former (p=1.87E-5).  Only 4q35.1-q35.2 duplications were significant (p=0.020) and observed in 11controls and two patients. Among the others that are not significant, a few examples of patient-specific and previously reported CNVs include deletions of 11q14.1 (DLG2), 22q11.21 and 14q21.1 (LRFN5). 16p13.3 deletion (RBFOX1), 3p14.2 duplication (CADPS) and 7p11.2 duplication (CCT6A) were some of the novel CNVs containing candidate  genes. However, these observations needs to be replicated in a larger sample size. In conclusion, this report constitutes an important foundation for future CNV studies in a relatively unexplored population. In addition, the data illustrate an advantage in using an integrated approach for better identification of candidate CNVs for SZ and other mental health disorders.