AUTHOR=Patro C. Pawan K. , Nousome Darryl ,  The Glioma International Case Control Study (GICC) , Lai Rose K. , Claus Elizabeth B. , Il’yasova Dora , Schildkraut Joellen , Barnholtz-Sloan Jill S. , Olson Sara H. , Bernstein Jonine L. , Johansen Christoffer , Jenkins Robert B. , Melin Beatrice S. , Wrensch Margaret R. , Houlston Richard S. , Bondy Melissa L. 

TITLE=Meta-Analyses of Splicing and Expression Quantitative Trait Loci Identified Susceptibility Genes of Glioma

JOURNAL=Frontiers in Genetics

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.609657

DOI=10.3389/fgene.2021.609657

ISSN=1664-8021

ABSTRACT=Background: 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 alternatively RNA splicing. 
Objective: This study explored splicing quantitative trait loci (sQTL) as molecular QTL and improved the power of QTL mapping through meta-analyses of both cis eQTL and sQTL.
Methods: We first evaluated eQTLs and sQTLs of the CommonMind Consortium and Genotype-Tissue Expression Project 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. 
Results: 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 7 loci, both eQTL and sQTL co-existed (2q33.3, 7p11.2, 11q23.3 15q24.2, 16p12.1, 20q13.33 and 22q13.1), but the target genes were different for 5 of these 7 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 (C2orf80, SEC61G, TMEM25, PHLDB1, RP11-161M6.2, HEATR3, RTEL1-TNFRSF6B and LIME1) had multiple alternatively spliced transcripts. 
Conclusion: 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 evaluates only total gene expression will miss many important target genes.