Journal
SCHIZOPHRENIA BULLETIN
Volume 47, Issue 6, Pages 1642-1652Publisher
OXFORD UNIV PRESS
DOI: 10.1093/schbul/sbab023
Keywords
gene expression prediction; epigenetic regulation; bipolar disorder; candidate gene; missing heritability
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Funding
- National Natural Science Foundation of China [31970569, 31871264]
- Innovative Talent Promotion Plan of Shaanxi Province for Young SciTech New Star [2018KJXX-010]
- Fundamental Research Funds for the Central Universities
- High-Performance Computing Platform of Xi'an Jiaotong University
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The research focuses on identifying genes associated with bipolar disorder through a new transcriptome-wide association study method. This method, called ETWAS, takes into account genetic variants and epigenetic features to uncover novel candidate genes overlooked by previous studies. Several genes were identified as potentially linked to BD, shedding light on the complex genetic factors contributing to the disorder.
Since the bipolar disorder (BD) signals identified by genome-wide association study (GWAS) often reside in the non-coding regions, understanding the biological relevance of these genetic loci has proven to be complicated. Transcriptome-wide association studies (TWAS) providing a powerful approach to identify novel disease risk genes and uncover possible causal genes at loci identified previously by GWAS. However, these methods did not consider the importance of epigenetic regulation in gene expression. Here, we developed a novel epigenetic element-based transcriptome-wide association study (ETWAS) that tested the effects of genetic variants on gene expression levels with the epigenetic features as prior and further mediated the association between predicted expression and BD. We conducted an ETWAS consisting of 20 352 cases and 31 358 controls and identified 44 transcriptome-wide significant hits. We found 14 conditionally independent genes, and 10 genes that did not previously implicate with BD were regarded as novel candidate genes, such as ASB16 in the cerebellar hemisphere (P = 9.29 x 10(-8)). We demonstrated that several genome-wide significant signals from the BD GWAS driven by genetically regulated expression, and NEK4 explained 90.1% of the GWAS signal. Additionally, ETWAS identified genes could explain heritability beyond that explained by GWAS-associated SNPs (P = 5.60 x 10(-66)). By querying the SNPs in the final models of identified genes in phenome databases, we identified several phenotypes previously associated with BD, such as schizophrenia and depression. In conclusion, ETWAS is a powerful method, and we identified several novel candidate genes associated with BD.
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