Transcriptome-wide association study identifies new susceptibility genes and pathways for depression.
Xiaoyan LiXi SuJiewei LiuHuijuan LiMing Linull nullWenqiang LiXiong-Jian LuoPublished in: Translational psychiatry (2021)
Depression is the most prevalent mental disorder with substantial morbidity and mortality. Although genome-wide association studies (GWASs) have identified multiple risk variants for depression, due to the complicated gene regulatory mechanisms and complexity of linkage disequilibrium (LD), the biological mechanisms by which the risk variants exert their effects on depression remain largely unknown. Here, we perform a transcriptome-wide association study (TWAS) of depression by integrating GWAS summary statistics from 807,553 individuals (246,363 depression cases and 561,190 controls) and summary-level gene-expression data (from the dorsolateral prefrontal cortex (DLPFC) of 1003 individuals). We identified 53 transcriptome-wide significant (TWS) risk genes for depression, of which 23 genes were not implicated in risk loci of the original GWAS. Seven out of 53 risk genes (B3GALTL, FADS1, TCTEX1D1, XPNPEP3, ZMAT2, ZNF501 and ZNF502) showed TWS associations with depression in two independent brain expression quantitative loci (eQTL) datasets, suggesting that these genes may represent promising candidates. We further conducted conditional analyses and identified the potential risk genes that driven the TWAS association signal in each locus. Finally, pathway enrichment analysis revealed biologically pathways relevant to depression. Our study identified new depression risk genes whose expression dysregulation may play a role in depression. More importantly, we translated the GWAS associations into risk genes and relevant pathways. Further mechanistic study and functional characterization of the TWS depression risk genes will facilitate the diagnostics and therapeutics for depression.
Keyphrases
- genome wide
- depressive symptoms
- gene expression
- sleep quality
- small molecule
- prefrontal cortex
- multiple sclerosis
- risk assessment
- genome wide association
- single cell
- bioinformatics analysis
- copy number
- deep learning
- big data
- physical activity
- working memory
- electronic health record
- resting state
- subarachnoid hemorrhage
- functional connectivity
- hepatitis c virus
- cerebral ischemia
- hiv testing
- breast cancer risk