Patro C Pawan K, Nousome Darryl, Lai Rose K
Department of Neurology and Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA, United States.
Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences, Rockville, MD, United States.
Front Genet. 2021 Apr 15;12:609657. doi: 10.3389/fgene.2021.609657. eCollection 2021.
The functions of most glioma risk alleles are unknown. Very few studies had evaluated expression quantitative trait loci (eQTL), and insights of susceptibility genes were limited due to scarcity of available brain tissues. Moreover, no prior study had examined the effect of glioma risk alleles on alternative RNA splicing.
This study explored splicing quantitative trait loci (sQTL) as molecular QTL and improved the power of QTL mapping through meta-analyses of both eQTL and sQTL.
We first evaluated eQTLs and sQTLs of the CommonMind Consortium (CMC) and Genotype-Tissue Expression Project (GTEx) using genotyping, or whole-genome sequencing and RNA-seq data. Alternative splicing events were characterized using an annotation-free method that detected intron excision events. Then, we conducted meta-analyses by pooling the eQTL and sQTL results of CMC and GTEx using the inverse variance-weighted model. Afterward, we integrated QTL meta-analysis results (Q < 0.05) with the Glioma International Case Control Study (GICC) GWAS meta-analysis (case:12,496, control:18,190), using a summary statistics-based mendelian randomization (SMR) method.
Between CMC and GTEx, we combined the QTL data of 354 unique individuals of European ancestry. SMR analyses revealed 15 eQTLs in 11 loci and 32 sQTLs in 9 loci relevant to glioma risk. Two loci only harbored sQTLs (1q44 and 16p13.3). In seven loci, both eQTL and sQTL coexisted (2q33.3, 7p11.2, 11q23.3 15q24.2, 16p12.1, 20q13.33, and 22q13.1), but the target genes were different for five of these seven loci. Three eQTL loci (9p21.3, 20q13.33, and 22q13.1) and 4 sQTL loci (11q23.3, 16p13.3, 16q12.1, and 20q13.33) harbored multiple target genes. Eight target genes of sQTLs (, , , , , , , and ) had multiple alternatively spliced transcripts.
Our study revealed that the regulation of transcriptome by glioma risk alleles is complex, with the potential for eQTL and sQTL jointly affecting gliomagenesis in risk loci. QTLs of many loci involved multiple target genes, some of which were specific to alternative splicing. Therefore, quantitative trait loci that evaluate only total gene expression will miss many important target genes.
大多数胶质瘤风险等位基因的功能尚不清楚。很少有研究评估表达定量性状位点（eQTL），并且由于可用脑组织的稀缺，对易感基因的了解有限。此外，以前没有研究检查过胶质瘤风险等位基因对可变RNA剪接的影响。
本研究探索剪接定量性状位点（sQTL）作为分子QTL，并通过对eQTL和sQTL的荟萃分析提高QTL定位的能力。
我们首先使用基因分型、全基因组测序和RNA测序数据评估了CommonMind联盟（CMC）和基因型-组织表达项目（GTEx）的eQTL和sQTL。使用一种无注释方法来表征可变剪接事件，该方法可检测内含子切除事件。然后，我们使用逆方差加权模型汇总CMC和GTEx的eQTL和sQTL结果进行荟萃分析。之后，我们使用基于汇总统计的孟德尔随机化（SMR）方法，将QTL荟萃分析结果（Q<0.05）与胶质瘤国际病例对照研究（GICC）全基因组关联研究（GWAS）荟萃分析（病例：12496例，对照：18190例）相结合。
在CMC和GTEx之间，我们合并了354名欧洲血统独特个体的QTL数据。SMR分析揭示了与胶质瘤风险相关的11个位点中的15个eQTL和9个位点中的32个sQTL。两个位点仅含有sQTL（1q44和16p13.3）。在七个位点中，eQTL和sQTL共存（分别为2q33.3、7p11.2、11q23.3、15q24.2、16p12.1、20q13.33和22q13.1），但这七个位点中的五个位点的靶基因不同。三个eQTL位点（9p21.3、20q13.33和22q13.1）和4个sQTL位点（11q23.3、16p13.3、16q12.1和20q13.33）含有多个靶基因。sQTL的八个靶基因（基因名称未给出）有多个可变剪接转录本。
我们的研究表明，胶质瘤风险等位基因对转录组的调控是复杂的，eQTL和sQTL有可能共同影响风险位点的胶质瘤发生。许多位点的QTL涉及多个靶基因，其中一些是可变剪接特有的。因此，仅评估总基因表达的定量性状位点会遗漏许多重要的靶基因。
