RsQTL: correlation of expressed SNVs with splicing using RNA-sequencing data

Abstract

RsQTL is a tool for identification of splicing quantitative trait loci (sQTLs) from RNA-sequencing (RNA-seq) data by correlating the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA) with the proportion of molecules spanning local exon-exon junctions at loci with differential intron excision (percent spliced in, PSI). We exemplify the method on sets of RNA-seq data from human tissues obtained though the Genotype-Tissue Expression Project (GTEx). RsQTL does not require matched DNA and can identify a subset of expressed sQTL loci. Due to the dynamic nature of VAFRNA, RsQTL is applicable for the assessment of conditional and dynamic variation-splicing relationships.

Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data

“…In scRNA studies, where the different cells are often in gradual states of progressive processes, VAF RNA analyses can be adopted to study lineages and cellular dynamics. Finally, VAF RNA can be used to study functional SNVs from sets where matched DNA (and, respectively, genotypes) data is not available [29,30]. Ultimately, these analyses apply to expressed SNVs and will not capture loci positioned in transcriptionally silent regions.…”

“…Genetic variants are traditionally called from DNA and often analyzed and interpreted as discrete genotypes (for diploid organisms, homo-or heterozygous). For expressed loci, genetic variation can also be assessed using RNA-seq data [24][25][26][27][28][29][30], by calculating the variant allele fraction (VAF RNA = n var /(n var + n ref ), where n var and n ref are the variant and reference read counts, respectively). VAF RNA is an informative measure of genetic variation for several reasons.…”

Estimating allele-specific expression of SNVs from 10x Genomics Single-Cell RNA-Sequencing Data

“…In scRNA studies, where the different cells are commonly in gradual states of progressive processes, VAFRNA analyses can be adopted to study lineages and cellular dynamics. Third, VAFRNA can be used to study functional SNVs from sets where matched DNA (and, respectively genotypes) is not available [24,25]. Ultimately, these analyses apply to expressed SNVs and will not capture loci positioned in transcriptionally silent regions.…”

“…These include loci exhibiting preferential expression of functional alleles, somatic mutations in cancer, and RNA-editing loci. Second, in contrast to the (static) genotypes, VAFRNA is dynamic and reflects the actual allele content in the system at any particular time, which allows for the assessment of dynamic and progressive processes [23][24][25]. Importantly, through primarily reflecting genetic variation, VAFRNA is an essential component of the genomic interactome and plays a major role in phenotype formation [26][27][28][29][30].…”

“…We have selected this platform due to its growing popularity along with: (1) high throughput (our analysis includes 26,640 cells obtained from three healthy donors), (2) high depth of sequencing (~150,000 sequencing reads per cell), and, (3) support for unique molecular identifiers (UMI) for removal of PCR-related sequencing bias. Because VAFRNA is sensitive to allele-mapping bias [24,25], we use SNV-aware alignments where reads mapped ambiguously due to the variant nucleotide are removed [32]. From the SNV-aware alignments, we systematically assess the ability to estimate VAFRNA using three different thresholds for minimum required number of unique sequencing reads (minR) -3, 5 and 10.…”