Gene-based association analysis identifies 190 genes affecting neuroticism

• Published in: Scientific reports Vol. 11; no. 1; p. 2484
• Main Authors: Belonogova, Nadezhda M., Zorkoltseva, Irina V., Tsepilov, Yakov A., Axenovich, Tatiana I.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28-01-2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/208; 631/208/1515; 631/208/205; 631/477; Association analysis; Bioinformatics; Gene expression; Genomes; Humanities and Social Sciences; Mental disorders; multidisciplinary; Neurosis; Non-coding RNA; Pleiotropy; Risk factors; Science; Science (multidisciplinary); Statistical analysis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-82123-5

Neuroticism is a personality trait, which is an important risk factor for psychiatric disorders. Recent genome-wide studies reported about 600 genes potentially influencing neuroticism. Little is known about the mechanisms of their action. Here, we aimed to conduct a more detailed analysis of genes that can regulate the level of neuroticism. Using UK Biobank-based GWAS summary statistics, we performed a gene-based association analysis using four sets of within-gene variants, each set possessing specific protein-coding properties. To guard against the influence of strong GWAS signals outside the gene, we used a specially designed procedure called “polygene pruning”. As a result, we identified 190 genes associated with neuroticism due to the effect of within-gene variants rather than strong GWAS signals outside the gene. Thirty eight of these genes are new. Within all genes identified, we distinguished two slightly overlapping groups obtained from using protein-coding and non-coding variants. Many genes in the former group included potentially pathogenic variants. For some genes in the latter group, we found evidence of pleiotropy with gene expression. Using a bioinformatics analysis, we prioritized the neuroticism genes and showed that the genes that contribute to neuroticism through their within-gene variants are the most appropriate candidate genes.