An integrative analysis of genome-wide association study and regulatory SNP annotation datasets identified candidate genes for bipolar disorder

• Main Authors: Xin Qi, Yan Wen, Ping Li, Chujun Liang, Bolun Cheng, Mei Ma, Shiqiang Cheng, Lu Zhang, Li Liu, Om Prakash Kafle, Feng Zhang
• Format: Article
• Language: English
• Published: SpringerOpen 2020-02-01
• Series: International Journal of Bipolar Disorders
• Subjects: Bipolar disorder; Regulatory SNP; Genome-wide association studies
• Online Access: https://doi.org/10.1186/s40345-019-0170-z

Abstract Background Bipolar disorder (BD) is a complex mood disorder. The genetic mechanism of BD remains largely unknown. Methods We conducted an integrative analysis of genome-wide association study (GWAS) and regulatory SNP (rSNP) annotation datasets, including transcription factor binding regions (TFBRs), chromatin interactive regions (CIRs), mature microRNA regions (miRNAs), long non-coding RNA regions (lncRNAs), topologically associated domains (TADs) and circular RNAs (circRNAs). Firstly, GWAS dataset 1 of BD (including 20,352 cases and 31,358 controls) and GWAS dataset 2 of BD (including 7481 BD patients and 9250 controls) were integrated with rSNP annotation database to obtain BD associated SNP regulatory elements and SNP regulatory element-target gene (E–G) pairs, respectively. Secondly, a comparative analysis of the two datasets results was conducted to identify the common rSNPs and also their target genes. Then, gene sets enrichment analysis (FUMA GWAS) and HumanNet-XC analysis were conducted to explore the functional relevance of identified target genes with BD. Results After the integrative analysis, we identified 52 TFBRs target genes, 44 TADs target genes, 55 CIRs target genes and 21 lncRNAs target genes for BD, such as ITIH4 (P dataset1  = 6.68 × 10−8, P dataset2  = 6.64 × 10−7), ITIH3 (P dataset1  = 1.09 × 10−8, P dataset2  = 2.00 × 10−7), SYNE1 (P dataset1  = 1.80 × 10−6, P dataset2  = 4.33 × 10−9) and OPRM1 (P dataset1  = 1.80 × 10−6, P dataset2  = 4.33 × 10−9). Conclusion We conducted a large-scale integrative analysis of GWAS and 6 common rSNP information datasets to explore the potential roles of rSNPs in the genetic mechanism of BD. We identified multiple candidate genes for BD, supporting the importance of rSNP in the development of BD.