Identification of reproducible BCL11A alterations in schizophrenia through individual-level prediction of coexpression
Previous studies have provided evidence for an alteration of genetic coexpression in schizophrenia (SCZ). However, such analyses have thus far lacked biological specificity for individual genes, which may be critical for identifying illness-relevant effects. Therefore, we applied machine learning to...
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| Main Authors: | , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
September 2020
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| In: |
Schizophrenia bulletin
Year: 2020, Volume: 46, Issue: 5, Pages: 1165-1171 |
| ISSN: | 1745-1701 |
| DOI: | 10.1093/schbul/sbaa047 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1093/schbul/sbaa047 Verlag, kostenfrei, Volltext: https://academic.oup.com/schizophreniabulletin/article/46/5/1165/5813924 
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| Author Notes: | Junfang Chen, Han Cao, Tobias Kaufmann, Lars T Westlye, Heike Tost, Andreas Meyer-Lindenberg, and Emanuel Schwarz |
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| 520 | |a Previous studies have provided evidence for an alteration of genetic coexpression in schizophrenia (SCZ). However, such analyses have thus far lacked biological specificity for individual genes, which may be critical for identifying illness-relevant effects. Therefore, we applied machine learning to identify gene-specific coexpression differences at the individual subject level and compared these between individuals with SCZ, bipolar disorder, major depressive disorder (MDD), autism spectrum disorder (ASD), and healthy controls. Utilizing transcriptome-wide gene expression data from 21 independent datasets, comprising a total of 9509 participants, we identified a reproducible decrease of BCL11A coexpression across 4 SCZ datasets that showed diagnostic specificity for SCZ when compared with ASD and MDD. We further demonstrate that individual-level coexpression differences can be combined in multivariate coexpression scores that show reproducible illness classification across independent datasets in SCZ and ASD. This study demonstrates that machine learning can capture gene-specific coexpression differences at the individual subject level for SCZ and identify novel biomarker candidates. | ||
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