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Identification of common genetic variation that modulates alternative splicing.

ABSTRACT: Alternative splicing of genes is an efficient means of generating variation in protein function. Several disease states have been associated with rare genetic variants that affect splicing patterns. Conversely, splicing efficiency of some genes is known to vary between individuals without apparent ill effects. What is not clear is whether commonly observed phenotypic variation in splicing patterns, and hence potential variation in protein function, is to a significant extent determined by naturally occurring DNA sequence variation and in particular by single nucleotide polymorphisms (SNPs). In this study, we surveyed the splicing patterns of 250 exons in 22 individuals who had been previously genotyped by the International HapMap Project. We identified 70 simple cassette exon alternative splicing events in our experimental system; for six of these, we detected consistent differences in splicing pattern between individuals, with a highly significant association between splice phenotype and neighbouring SNPs. Remarkably, for five out of six of these events, the strongest correlation was found with the SNP closest to the intron-exon boundary, although the distance between these SNPs and the intron-exon boundary ranged from 2 bp to greater than 1,000 bp. Two of these SNPs were further investigated using a minigene splicing system, and in each case the SNPs were found to exert cis-acting effects on exon splicing efficiency in vitro. The functional consequences of these SNPs could not be predicted using bioinformatic algorithms. Our findings suggest that phenotypic variation in splicing patterns is determined by the presence of SNPs within flanking introns or exons. Effects on splicing may represent an important mechanism by which SNPs influence gene function.

SUBMITTER: Hull J

PROVIDER: S-EPMC1904363 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Recently, thanks to the increasing throughput of new technologies, we have begun to explore the full extent of alternative pre-mRNA splicing (AS) in the human transcriptome. This is unveiling a vast layer of complexity in isoform-level expression differences between individuals. We used previously published splicing sensitive microarray data from lymphoblastoid cell lines to conduct an in-depth analysis on splicing efficiency of known and predicted exons. By combining publicly available AS annotation with a novel algorithm designed to search for AS, we show that many real AS events can be detected within the usually unexploited, speculative majority of the array and at significance levels much below standard multiple-testing thresholds, demonstrating that the extent of cis-regulated differential splicing between individuals is potentially far greater than previously reported. Specifically, many genes show subtle but significant genetically controlled differences in splice-site usage. PCR validation shows that 42 out of 58 (72%) candidate gene regions undergo detectable AS, amounting to the largest scale validation of isoform eQTLs to date. Targeted sequencing revealed a likely causative SNP in most validated cases. In all 17 incidences where a SNP affected a splice-site region, in silico splice-site strength modeling correctly predicted the direction of the micro-array and PCR results. In 13 other cases, we identified likely causative SNPs disrupting predicted splicing enhancers. Using Fst and REHH analysis, we uncovered significant evidence that 2 putative causative SNPs have undergone recent positive selection. We verified the effect of five SNPs using in vivo minigene assays. This study shows that splicing differences between individuals, including quantitative differences in isoform ratios, are frequent in human populations and that causative SNPs can be identified using in silico predictions. Several cases affected disease-relevant genes and it is likely some of these differences are involved in phenotypic diversity and susceptibility to complex diseases.

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Identification of common genetic variation that modulates alternative splicing.

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