Genetic control of RNA splicing and its distinct role in complex trait variation

Abstract

Most genetic variants identified from genome-wide association studies (GWAS) in humans are noncoding, indicating their role in gene regulation. Previous studies have shown considerable links of GWAS signals to expression quantitative trait loci (eQTLs) but the links to other genetic regulatory mechanisms, such as splicing QTLs (sQTLs), are underexplored. Here, we introduce an sQTL mapping method, testing for heterogeneity between isoform-eQTLeffects (THISTLE), with improved power over competing methods. Applying THISTLE together with a complementary sQTL mapping strategy to brain transcriptomic (n = 2,865) and genotype data, we identified 12,794 genes with cis-sQTLs at P < 5 × 10−8, approximately 61% of which were distinct from eQTLs. Integrating the sQTL data into GWAS for 12 brain-related complex traits (including diseases), we identified 244 genes associated with the traits through cis-sQTLs, approximately 61% of which could not be discovered using the corresponding eQTL data. Our study demonstrates the distinct role of most sQTLs in the genetic regulation of transcription and complex trait variation.

Skipper analysis of RNA-protein interactions highlights depletion of genetic variation in translation factor binding sites

“…58 Enrichment of disease variants occurring in RNA-binding protein binding sites is well documented, yet such work usually aggregates signal at the level of whole transcriptomes. 31,36,50 By contrast, the transcripts with sequence-dependent RPS19 intron binding we identified under selective constraint could point to pathways that underlie erythroid susceptibility to mutations found in Diamond-Blackfan anemia patients as well as differing clinical presentations.…”

“…Indeed, mutation of RBP binding sites induce splicing changes in accordance with the corresponding RBP's distinct gene regulatory role. [35][36][37][38] To examine alternative splicing regulation at the level of individual RBPs, we overlapped 5′ and 3′ splice sites flanking alternative cassette exons with the expanded set of enriched windows called by Skipper. We stratified the alternative exons by whether an RBP of interest bound the alternative splice site window, the constitutive splice site window, or both and searched for stereotyped RBP binding patterns (Figure 4a).…”

Isoform-level transcriptome-wide association uncovers extensive novel genetic risk mechanisms for neuropsychiatric disorders in the human brain

“…Recent work has highlighted the promise of alternative splicing as a mechanism underlying complex trait biology not fully captured through eQTLs 23,25,26,70 . Splicing-QTL integration is a promising vehicle to prioritize alternative exons or splice sites that may explain the genetic association with a trait, but a single exon or splice site can correspond to multiple isoforms of the same gene.…”

Bidirectional genetic overlap between bipolar disorder and intelligence

“…To identify genes associated with the risk loci, we conducted expression quantitative trait loci (eQTL) analyses of all the significant SNPs in each independent genomic region (conjFDR < 0.01 and at r 2 < 0.2), to identify genes associated with the risk loci in multiple datasets. We used datasets of postmortem postnatal human brain tissues (dorsolateral prefrontal cortex (DLPFC) or hippocampus) such as psychENCODE ( n = 1387 for DLPFC) [ 34 ], BrainMeta ( n = 2865 for cortex) [ 35 ], BrainSeq ( n = 477 for hippocampus) [ 36 ] and Genotype-Tissue Expression (GTEx; n = 175 for DLPFC and n = 165 for hippocampus) [ 37 ], datasets of prenatal cortex tissues from O’Brien et al ( n = 120) [ 38 ] and Walker et al ( n = 201) [ 39 ] studies, as well as single-cell eQTLs resources (including mature midbrain dopaminergic neurons (from 175 donors) and serotonergic-like neurons (from 161 donors) differentiated from human induced pluripotent stem cells (iPSC) lines [ 40 ]; and excitatory neurons and inhibitory neurons from 196 individuals by single nuclei RNA-sequencing [ 41 ]). Detailed information about the sample characteristics, expression quantification, and normalization, as well as statistical analysis in each eQTL dataset, can be found in the original publications.…”