Journal of Affective Disorders Reports (Apr 2022)
Genome-wide association study for suicide in high–risk isolated historical population from North East India
Abstract
Background: Suicide with complex etiology and increased global prevalence is a challenge in genome-based biomarker discovery. The present study explores whole-genome association in a small, isolated, historical tribal population of Idu Mishmi from Dibang Valley of Arunachal Pradesh, India with a high rate of suicide. Methods: Microarray genotyping (∼660,000 markers) was performed on 72 well-annotated un-related cases and controls for suicide risk. A logistic regression model adjusted for age, sex and descent was used to identify known/novel genetic markers implicated in suicide risk. STRING network analysis was implemented on significant genes found from this study, earlier re-tested SNPs genes in the same population and reported candidate genes for functional inference. Results: We found 12 SNPs (all intronic, some downstream or upstream transcript variants) from nine genes with suggestive significance (p-value <0.00001) in the pathogenesis of suicide. Among the 12 SNPs, the variant (rs6454748) of GABRR2 gene was earlier reported for alcohol abuse. Genes GRM7, SIRT2 and RAPGEF4 from this study are strongly embedded within candidate/known genes in the STRING network and all genes we found in this study were implicated earlier with some other psychiatry disorders. Limitation: Small sample size and considering GWAS significance threshold criteria at p-value < 0.00001 instead of stringent <10−8 in view of the exploratory nature of the study. Conclusion: The present study illustrated the advantage of small, historical populations in eliciting genetics of complex diseases like suicide. Beside, network analysis provided an opportunity in validation of earlier candidate genes and pathways, beside proposing novel pathways in suicide risk. This study provided a framework of gene functional interactions for further exploration.
