Genome-Wide Identification of Splicing Quantitative Trait Loci (sQTLs) in Diverse Ecotypes of Arabidopsis thaliana

Abstract

Alternative splicing (AS) of pre-mRNAs contributes to transcriptome diversity and enables plants to generate different protein isoforms from a single gene and/or fine-tune gene expression during different development stages and environmental changes. Although AS is pervasive, the genetic basis for differential isoform usage in plants is still emerging. In this study, we performed genome-wide analysis in 666 geographically distributed diverse ecotypes of Arabidopsis thaliana to identify genomic regions [splicing quantitative trait loci (sQTLs)] that may regulate differential AS. These ecotypes belong to different microclimatic conditions and are part of the relict and non-relict populations. Although sQTLs were spread across the genome, we observed enrichment for trans-sQTL (trans-sQTLs hotspots) on chromosome one. Furthermore, we identified several sQTL (911) that co-localized with trait-linked single nucleotide polymorphisms (SNP) identified in the Arabidopsis genome-wide association studies (AraGWAS). Many sQTLs were enriched among circadian clock, flowering, and stress-responsive genes, suggesting a role for differential isoform usage in regulating these important processes in diverse ecotypes of Arabidopsis. In conclusion, the current study provides a deep insight into SNPs affecting isoform ratios/genes and facilitates a better mechanistic understanding of trait-associated SNPs in GWAS studies. To the best of our knowledge, this is the first report of sQTL analysis in a large set of Arabidopsis ecotypes and can be used as a reference to perform sQTL analysis in the Brassicaceae family. Since whole genome and transcriptome datasets are available for these diverse ecotypes, it could serve as a powerful resource for the biological interpretation of trait-associated loci, splice isoform ratios, and their phenotypic consequences to help produce more resilient and high yield crop varieties.

Stress-Induced Changes in Alternative Splicing Landscape in Rice: Functional Significance of Splice Isoforms in Stress Tolerance

“…The functional impact of genetic variations in shaping AS events with stress-adaptive consequences has also been revealed in plants. For example, microsatellites are involved in modulating AS of miRNA genes under different stress conditions [ 178 ], and ‘splicing QTLs’ (sQTLs) potentially regulate AS of various stress-responsive genes [ 179 ]. However, the significance of microsatellites and sQTLs in regulating AS for rice stress tolerance has yet to be determined.…”

Genome‐wide discovery of natural variation in pre‐mRNA splicing and prioritising causal alternative splicing to salt stress response in rice

“…These differences suggest that the mechanism of splice site recognition may differ between plants (intron definition) and animals (exon definition) (Barbazuk et al ., 2008). Although many studies on AS are based on expressed sequence tags (ESTs) and cDNAs in plants, only a few studies focus on AS variations at the plant population level (Chen et al ., 2018; Khokhar et al ., 2019). With advancements in next‐generation sequencing technology, it is feasible to obtain RNA‐seq datasets for AS analysis, but it is challenging to accurately determine and quantify AS due to short reads and the complexity of splicing when evaluating a large population.…”

Splice-site Strength Estimation: A simple yet powerful approach to analyse RNA splicing

“…A recent study utilized the same RNA-Seq data from the 1001 genome project and carried out isoform-based splicing QTL analysis 35 using sQTLseekeR 36 based UlfasQTL 37 analysis and concluded that majority of splicing variation in Arabidopsis is due to trans QTL [35][36][37] . This finding is in contrast to our current studies as well as earlier genetic studies 38 that argued for cis-regulatory variation as primary determinants of splicing.…”

“…Supplementary Table 4. Comparison the of GWAS results between isoform-based approach (Khokhar et al, 2019) and our approach, for the same genes. Supplementary Table 5.…”