DeepSAGE Reveals Genetic Variants Associated with Alternative Polyadenylation and Expression of Coding and Non-coding Transcripts

Daria V Zhernakova,Rick Jansen,Jan H Veldink,Anastasios Mastrokolias,Jouke J Hottenga,Gonneke Willemsen,Leonard H Van Den Berg,Yavuz Ariyurek,Dorret I Boomsma,Gert-Jan B Van Ommen,Brenda W Penninx,Peter A C 'T Hoen,Harm-Jan Westra,Eco J De Geus,Cisca Wijmenga,Eleonora De Klerk,Lude Franke,Shoaib Amini,Johan T Den Dunnen

doi:10.1371/journal.pgen.1003594

Abstract

Many disease-associated variants affect gene expression levels (expression quantitative trait loci, eQTLs) and expression profiling using next generation sequencing (NGS) technology is a powerful way to detect these eQTLs. We analyzed 94 total blood samples from healthy volunteers with DeepSAGE to gain specific insight into how genetic variants affect the expression of genes and lengths of 3′-untranslated regions (3′-UTRs). We detected previously unknown cis-eQTL effects for GWAS hits in disease- and physiology-associated traits. Apart from cis-eQTLs that are typically easily identifiable using microarrays or RNA-sequencing, DeepSAGE also revealed many cis-eQTLs for antisense and other non-coding transcripts, often in genomic regions containing retrotransposon-derived elements. We also identified and confirmed SNPs that affect the usage of alternative polyadenylation sites, thereby potentially influencing the stability of messenger RNAs (mRNA). We then combined the power of RNA-sequencing with DeepSAGE by performing a meta-analysis of three datasets, leading to the identification of many more cis-eQTLs. Our results indicate that DeepSAGE data is useful for eQTL mapping of known and unknown transcripts, and for identifying SNPs that affect alternative polyadenylation. Because of the inherent differences between DeepSAGE and RNA-sequencing, our complementary, integrative approach leads to greater insight into the molecular consequences of many disease-associated variants.

Highlights

Genome-wide association studies (GWAS) have associated genetic variants, such as single nucleotide polymorphisms (SNPs) and copy number variants (CNVs), with numerous diseases and complex traits
We used a generation sequencing approach targeting 39 transcript ends (DeepSAGE) to gain specific insight into how genetic variants affect the expression of genes and the usage and length of 39-untranslated regions
We identified and confirmed variants that affect the usage of alternative polyadenylation sites, thereby potentially influencing the stability of messenger RNAs (mRNA)

Summary

Introduction

Genome-wide association studies (GWAS) have associated genetic variants, such as single nucleotide polymorphisms (SNPs) and copy number variants (CNVs), with numerous diseases and complex traits. The mechanisms through which genetic variants affect disease phenotypes or physical traits often remain unclear. To gain insight into these mechanisms, we have combined genotype data with gene expression data by conducting expression quantitative trait locus (eQTL) mapping. High-throughput generation sequencing (NGS) has become available, which allows quantification of expression levels by counting mRNA fragments (RNA-seq) or sequence tags (including serial analysis of gene expression (SAGE), cap analysis of gene expression (CAGE), and massively parallel signature sequencing (MPSS)) [7]. Two NGS eQTL studies have been published [8,9], both of which used RNA-seq. RNA-seq is a versatile technique, the coverage in the ultimate 39-end is usually lower due to the fragmentation and random hexamer priming steps involved

Methods

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Discussion

Conclusion