RNA sequencing reveals the role of splicing polymorphisms in regulating human gene expression

Lalonde, Emilie; Ha, Kevin C.H.; Wang, Zibo; Bemmo, Amandine; Kleinman, Claudia L.; Kwan, Tony; Pastinen, Tomi; Majewski, Jacek

doi:10.1101/gr.111211.110

Cited by 111 publications

(110 citation statements)

References 51 publications

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“…To enable a greater number of type 2 diabetes-associated loci to be examined by the more powerful allelic expression approach, future studies may consider using intronic SNPs to measure allelic expression. Allelic expression measurements calculated from intronic SNPs have been shown to be highly correlated with values calculated from linked exonic SNPs (16). While this is, to our knowledge, the first report of a systematic use of allelic expression measurements in human islets to identify likely causal genes at type 2 diabetes GWAS loci, there are other published studies that have reported allelic imbalance in human islets at single loci.…”

Section: Discussionmentioning

confidence: 79%

Targeted Allelic Expression Profiling in Human Islets Identifies cis-Regulatory Effects for Multiple Variants Identified by Type 2 Diabetes Genome-Wide Association Studies

et al. 2014

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Genome-wide association studies (GWAS) have identified variation at >65 genomic loci associated with susceptibility to type 2 diabetes, but little progress has been made in elucidating the molecular mechanisms behind most of these associations. Using samples heterozygous for transcribed single nucleotide polymorphisms (SNPs), allelic expression profiling is a powerful technique for identifying cis-regulatory variants controlling gene expression. In this study, exonic SNPs, suitable for measuring mature mRNA levels and in high linkage disequilibrium with 65 lead type 2 diabetes GWAS SNPs, were identified and allelic expression determined by real-time PCR using RNA and DNA isolated from islets of 36 white nondiabetic donors. A significant allelic expression imbalance (AEI) was identified for 7/14 (50%) genes tested (ANPEP, CAMK2B, HMG20A, KCNJ11, NOTCH2, SLC30A8, and WFS1), with significant AEI confirmed for five of these genes using other linked exonic SNPs. Lastly, results of a targeted islet expression quantitative trait loci experiment support the AEI findings for ANPEP, further implicating ANPEP as the causative gene at its locus. The results of this study support the hypothesis that changes to cis-regulation of gene expression are involved in a large proportion of SNP associations with type 2 diabetes susceptibility.

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Section: Discussionmentioning

confidence: 79%

Targeted Allelic Expression Profiling in Human Islets Identifies cis-Regulatory Effects for Multiple Variants Identified by Type 2 Diabetes Genome-Wide Association Studies

et al. 2014

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“…It is well known that there are many molecular mechanisms that allow different genotypes to influence gene expression. These include allele-specific expression through the modulation of activity of cis-regulatory elements, the regulation of transcript isoform levels through disruption of the splicing machinery, and the modification of chromatin accessibility and transcription factor binding Pickrell et al 2010;Lalonde et al 2011;Degner et al 2012). However, the mechanisms underlying the interaction between these expression-controlling genotypes are poorly understood.…”

Section: Formation Of Evqtlmentioning

confidence: 99%

Genetic Variants Contribute to Gene Expression Variability in Humans

2013

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Expression quantitative trait loci (eQTL) studies have established convincing relationships between genetic variants and gene expression. Most of these studies focused on the mean of gene expression level, but not the variance of gene expression level (i.e., gene expression variability). In the present study, we systematically explore genome-wide association between genetic variants and gene expression variability in humans. We adapt the double generalized linear model (dglm) to simultaneously fit the means and the variances of gene expression among the three possible genotypes of a biallelic SNP. The genomic loci showing significant association between the variances of gene expression and the genotypes are termed expression variability QTL (evQTL). Using a data set of gene expression in lymphoblastoid cell lines (LCLs) derived from 210 HapMap individuals, we identify cis-acting evQTL involving 218 distinct genes, among which 8 genes, ADCY1, CTNNA2, DAAM2, FERMT2, IL6, PLOD2, SNX7, and TNFRSF11B, are cross-validated using an extra expression data set of the same LCLs. We also identify $300 trans-acting evQTL between .13,000 common SNPs and 500 randomly selected representative genes. We employ two distinct scenarios, emphasizing single-SNP and multiple-SNP effects on expression variability, to explain the formation of evQTL. We argue that detecting evQTL may represent a novel method for effectively screening for genetic interactions, especially when the multiple-SNP influence on expression variability is implied. The implication of our results for revealing genetic mechanisms of gene expression variability is discussed.Q UANTITATIVE genetic analysis has long focused on detecting genetic variants that affect organismal phenotypes. This is often done by contrasting mean differences in phenotypes among genotypes. Despite increasing evidence across several species for genetic control of phenotypic variance (Ansel et al. 2008;Hill and Mulder 2010; JimenezGomez et al. 2011), variance differences in phenotypes have been largely ignored. Recently, however, the fact that variance of phenotypes is genotype dependent has inspired the detection of genetic variants associated with phenotypic variability (Pare et al. 2010;Sudmant et al. 2010;Yang et al. 2012).When gene expression level is considered as a heritable, quantitative trait, statistical associations between mean gene expression and genotype can be established to identify those genomic loci associated with or linked to gene expression level (i.e., expression QTL, eQTL) (Montgomery and Dermitzakis 2011). The difference in mean gene expression and its genetic control have been extensively examined in humans (Stranger et al. 2005(Stranger et al. , 2007bPickrell et al. 2010). The difference in variance of gene expression (i.e., gene expression variability) is genetically controlled and likely to be selectable (Raser and O'Shea 2005;Blake et al. 2006;Maheshri and O'Shea 2007;Cheung and Spielman 2009;Zhang et al. 2009). A small number of initial efforts have been m...

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“…Despite these observations, which point to the functional importance of exon usage regulation, there is also conflicting evidence: Splicing factor recognition motifs are short and degenerate, exon usage can vary between individuals because of subtle genetic variations in cis-regulatory regions (15,16), and it has been suggested that a stochastic model of processes in the splicing machinery explains most splicing variation (17). In fact, a comparative analysis of transcriptomes associated with multiple human individuals found a high prevalence for noisy, presumably erroneous, splicing, particularly in low-abundance isoforms (18).…”

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confidence: 99%

Drift and conservation of differential exon usage across tissues in primate species

Reyes¹,

Anders²,

Weatheritt

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

121

107

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Alternative usage of exons provides genomes with plasticity to produce different transcripts from the same gene, modulating the function, localization, and life cycle of gene products. It affects most human genes. For a limited number of cases, alternative functions and tissue-specific roles are known. However, recent high-throughput sequencing studies have suggested that much alternative isoform usage across tissues is nonconserved, raising the question of the extent of its functional importance. We address this question in a genome-wide manner by analyzing the transcriptomes of five tissues for six primate species, focusing on exons that are 1:1 orthologous in all six species. Our results support a model in which differential usage of exons has two major modes: First, most of the exons show only weak differences, which are dominated by interspecies variability and may reflect neutral drift and noisy splicing. These cases dominate the genome-wide view and explain why conservation appears to be so limited. Second, however, a sizeable minority of exons show strong differences between tissues, which are mostly conserved. We identified a core set of 3,800 exons from 1,643 genes that show conservation of strongly tissue-dependent usage patterns from human at least to macaque. This set is enriched for exons encoding protein-disordered regions and untranslated regions. Our findings support the theory that isoform regulation is an important target of evolution in primates, and our method provides a powerful tool for discovering potentially functional tissue-dependent isoforms.alternative isoform regulation | comparative transcriptomics

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RNA sequencing reveals the role of splicing polymorphisms in regulating human gene expression

Cited by 111 publications

References 51 publications

Targeted Allelic Expression Profiling in Human Islets Identifies cis-Regulatory Effects for Multiple Variants Identified by Type 2 Diabetes Genome-Wide Association Studies

Targeted Allelic Expression Profiling in Human Islets Identifies cis-Regulatory Effects for Multiple Variants Identified by Type 2 Diabetes Genome-Wide Association Studies

Genetic Variants Contribute to Gene Expression Variability in Humans

Drift and conservation of differential exon usage across tissues in primate species

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