Splicing QTL analysis focusing on coding sequences reveals mechanisms for disease susceptibility loci

Abstract

Splicing quantitative trait loci (sQTLs) are one of the major causal mechanisms in genome-wide association study (GWAS) loci, but their role in disease pathogenesis is poorly understood. One reason is the complexity of alternative splicing events producing many unknown isoforms. Here, we propose two approaches, namely integration and selection, for this complexity by focusing on protein-structure of isoforms. First, we integrate isoforms with the same coding sequence (CDS) and identify 369-601 integrated-isoform ratio QTLs (i2-rQTLs), which altered protein-structure, in six immune subsets. Second, we select CDS incomplete isoforms annotated in GENCODE and identify 175-337 isoform-ratio QTL (i-rQTL). By comprehensive long-read capture RNA-sequencing among these incomplete isoforms, we reveal 29 full-length isoforms with unannotated CDSs associated with GWAS traits. Furthermore, we show that disease-causal sQTL genes can be identified by evaluating their trans-eQTL effects. Our approaches highlight the understudied role of protein-altering sQTLs and are broadly applicable to other tissues and diseases.

Brain Region-Dependent Alternative Splicing of Alzheimer Disease (AD)-Risk Genes Is Associated With Neuropathological Features in AD

“…Genetic variants (single nucleotide polymorphisms [SNPs]) could be regulators of the identified ES events. In particular, SNPs within ES exons and their flanking introns can change splicing patterns by remodeling the binding affinities of splicing factors; such variants are termed sQTLs [ 31 ]. Through integrative analysis of WGS and RNA-Seq, we revealed 418 significant sQTLs associated with total of 44 ES events (44% of the total set) observed from frontal and temporal regions ( Supplementary Table 3 ).…”

CiDRE+ M2c macrophages hijacked by SARS-CoV-2 cause COVID-19 severity

“…The obtained fastq files were aligned to the GRCh38 primary assembly using minimap2 v2.17 with reference to the splice junctions in the GENCODE38 annotation. We used the flair pipeline ( 70 ) to identify the full-length of the novel transcripts and filtered them using the following criteria: 1) isoforms expressing more than 50 reads in total, 2) isoforms whose 5′ end was located within 100 bp from the FANTOM CAGE peak (TSS peak based on a relaxed 0.14 threshold by TSS classifier), 3) isoforms whose 3′ end is located within 100 bp from the TES of PolyASite2.0, and 4) isoforms evaluated as protein coding isoforms by CPAT v3.0.4 (coding probability ≥ 0.364) ( 71 ).…”

Functional Genetics to Understand the Etiology of Autoimmunity

“…Inaccurate isoform quantification is partially caused by incomplete reference datasets we use for isoform quantification [ 48 ]. For example, some disease-causing isoforms have incomplete coding sequences in the GENCODE annotation [ 49 ]. Furthermore, even if all constituent exons are identified, complete isoform reconstruction from short-read data remains challenging [ 50 ].…”