ArticleFrontiers in genetics2021
Meta-Analyses of Splicing and Expression Quantitative Trait Loci Identified Susceptibility Genes of Glioma.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 12 citations in OpenAlex.
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- Response splicing quantitative trait loci in primary human chondrocytes identify putative osteoarthritis risk genes.Nature communications · 2025Article
- Response splicing QTLs in primary human chondrocytes identifies putative osteoarthritis risk genes.bioRxiv : the preprint server for biology · 2024Article
- Alternative polyadenylation-related genetic variants contribute to bladder cancer risk.Journal of biomedical research · 2023Article
- Regulator of telomere elongation helicase 1 gene and its association with malignancy.Cancer reports (Hoboken, N.J.) · 2023Review
- Ecological Speciation Promoted by Divergent Regulation of Functional Genes Within African Cichlid Fishes.Molecular biology and evolution · 2022Article
- Bridging the splicing gap in human genetics with long-read RNA sequencing: finding the protein isoform drivers of disease.Human molecular genetics · 2022Review
- The Importance of a Genome-Wide Association Analysis in the Study of Alternative Splicing Mutations in Plants with a Special Focus on Maize.International journal of molecular sciences · 2022Review
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4 authors at 2 institutions in 1 country.
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Abstract
backgroundThe 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.
objectiveThis study explored splicing quantitative trait loci (sQTL) as molecular QTL and improved the power of QTL mapping through meta-analyses of both
methodsWe 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.
resultsBetween 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 (
conclusionOur 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.
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