National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710004, P. R. China.
Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P. R. China.
Schizophr Bull. 2021 Oct 21;47(6):1642-1652. doi: 10.1093/schbul/sbab023.
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 × 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 × 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.
由于全基因组关联研究（GWAS）鉴定的双相情感障碍（BD）信号通常位于非编码区域，因此理解这些遗传位点的生物学相关性已被证明很复杂。全转录组关联研究（TWAS）为鉴定新的疾病风险基因和揭示以前由 GWAS 鉴定的遗传位点的可能因果基因提供了一种强大的方法。然而，这些方法没有考虑到表观遗传调控在基因表达中的重要性。在这里，我们开发了一种新的基于表观遗传元件的全转录组关联研究（ETWAS），该研究利用表观遗传特征作为先验，测试了遗传变异对基因表达水平的影响，并进一步介导了预测表达与 BD 之间的关联。我们进行了一项包含 20352 例病例和 31358 例对照的 ETWAS，鉴定出 44 个转录组全显著命中。我们发现了 14 个条件独立基因，其中 10 个以前没有与 BD 相关的基因被认为是新的候选基因，例如小脑半球中的 ASB16（P = 9.29×10-8）。我们证明了由遗传调控表达驱动的 BD GWAS 中的几个全基因组显著信号，以及 NEK4 解释了 GWAS 信号的 90.1%。此外，ETWAS 鉴定的基因可以解释遗传力超出了与 GWAS 相关 SNP 解释的范围（P = 5.60×10-66）。通过在表型数据库中查询鉴定基因的最终模型中的 SNPs，我们发现了一些以前与 BD 相关的表型，如精神分裂症和抑郁症。总之，ETWAS 是一种强大的方法，我们鉴定了几个与 BD 相关的新候选基因。
