Department of Biological Sciences, North Carolina State University, Raleigh NC State University, Raleigh, NC, 27695, USA.
Bioinformatics Research Center, North Carolina State University, Raleigh NC State University, Raleigh, NC, 27695, USA.
Sci Rep. 2022 Sep 7;12(1):15151. doi: 10.1038/s41598-022-18506-z.
In this study, we generated whole-transcriptome RNA-Seq from n = 192 genotyped liver samples and used these data with existing data from the GTEx Project (RNA-Seq) and previous liver eQTL (microarray) studies to create an enhanced transcriptomic sequence resource in the human liver. Analyses of genotype-expression associations show pronounced enrichment of associations with genes of drug response. The associations are primarily consistent across the two RNA-Seq datasets, with some modest variation, indicating the importance of obtaining multiple datasets to produce a robust resource. We further used an empirical Bayesian model to compare eQTL patterns in liver and an additional 20 GTEx tissues, finding that MHC genes, and especially class II genes, are enriched for liver-specific eQTL patterns. To illustrate the utility of the resource to augment GWAS analysis with small sample sizes, we developed a novel meta-analysis technique to combine several liver eQTL data sources. We also illustrate its application using a transcriptome-enhanced re-analysis of a study of neutropenia in pancreatic cancer patients. The associations of genotype with liver expression, including splice variation and its genetic associations, are made available in a searchable genome browser.
在这项研究中,我们从 n = 192 个基因分型的肝样本中生成了全转录组 RNA-Seq,并使用这些数据和 GTEx 项目(RNA-Seq)和之前的肝 eQTL(微阵列)研究中的现有数据,创建了人类肝脏中的增强转录组序列资源。对基因型-表达关联的分析表明,与药物反应基因的关联明显富集。这些关联在两个 RNA-Seq 数据集之间基本一致,略有变化,表明获得多个数据集对于生成稳健的资源非常重要。我们进一步使用经验贝叶斯模型比较了肝和另外 20 个 GTEx 组织中的 eQTL 模式,发现 MHC 基因,尤其是 II 类基因,富集了肝特异性 eQTL 模式。为了说明该资源在具有小样本量的 GWAS 分析中增加的效用,我们开发了一种新的元分析技术,以结合多个肝 eQTL 数据源。我们还使用对胰腺癌患者中性粒细胞减少症研究的转录组增强重新分析来说明其应用。基因型与肝表达(包括剪接变异及其遗传关联)的关联可在可搜索的基因组浏览器中获得。