Accepted author manuscript, 16.5 MB, multipart/x-zip
Available under license: CC BY-NC-ND
Rights statement: This is the author’s version of a work that was accepted for publication in Cell. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Cell, 173, 7, 2018 DOI: 10.1016/j.cell.2018.05.046
Accepted author manuscript, 738 KB, PDF document
Available under license: CC BY-NC-ND
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
| <mark>Journal publication date</mark> | 14/06/2018 |
|---|---|
| <mark>Journal</mark> | Cell |
| Issue number | 7 |
| Volume | 173 |
| Number of pages | 11 |
| Pages (from-to) | 1705-1715.e16 |
| Publication Status | Published |
| <mark>Original language</mark> | English |
Schizophrenia and bipolar disorder are two distinct diagnoses that share symptomology. Understanding the genetic factors contributing to the shared and disorder-specific symptoms will be crucial for improving diagnosis and treatment. In genetic data consisting of 53,555 cases (20,129 bipolar disorder [BD], 33,426 schizophrenia [SCZ]) and 54,065 controls, we identified 114 genome-wide significant loci implicating synaptic and neuronal pathways shared between disorders. Comparing SCZ to BD (23,585 SCZ, 15,270 BD) identified four genomic regions including one with disorder-independent causal variants and potassium ion response genes as contributing to differences in biology between the disorders. Polygenic risk score (PRS) analyses identified several significant correlations within case-only phenotypes including SCZ PRS with psychotic features and age of onset in BD. For the first time, we discover specific loci that distinguish between BD and SCZ and identify polygenic components underlying multiple symptom dimensions. These results point to the utility of genetics to inform symptomology and potential treatment.