Abstract
Schizophrenia is a severe mental disorder with a large genetic component. Recent genome-wide association studies (GWAS) have identified many schizophrenia-associated common variants. For most of the reported associations, however, the underlying biological mechanisms are not clear. The critical first step for their elucidation is to identify the most likely disease genes as the source of the association signals. Here, we describe a general computational framework of post-GWAS analysis for complex disease gene prioritization. We identify 132 putative schizophrenia risk genes in 76 risk regions spanning 120 schizophrenia-associated common variants, 78 of which have not been recognized as schizophrenia disease genes by previous GWAS. Even more significantly, 29 of them are outside the risk regions, likely under regulation of transcriptional regulatory elements contained therein. These putative schizophrenia risk genes are transcriptionally active in both brain and the immune system, and highly enriched among cellular pathways, consistent with leading pathophysiological hypotheses about the pathogenesis of schizophrenia. With their involvement in distinct biological processes, these putative schizophrenia risk genes, with different association strengths, show distinctive temporal expression patterns, and play specific biological roles during brain development.
| Original language | English |
|---|---|
| Pages (from-to) | 1587-1600 |
| Number of pages | 14 |
| Journal | Genetics |
| Volume | 204 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 2016 |
| Externally published | Yes |
Keywords
- Disease risk gene prioritization
- GWAS
- Schizophrenia
Fingerprint
Dive into the research topics of 'Integrated post-GWAS analysis sheds new light on the disease mechanisms of schizophrenia'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver