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

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.Author Summary: Many genetic variants that are associated with diseases also affect gene expression levels. We used a next generation sequencing approach targeting 3′ transcript ends (DeepSAGE) to gain specific insight into how genetic variants affect the expression of genes and the usage and length of 3′-untranslated regions. We detected many associations for antisense and other non-coding transcripts, often in genomic regions containing retrotransposon-derived elements. Some of these variants are also associated with disease. We also identified and confirmed variants that affect the usage of alternative polyadenylation sites, thereby potentially influencing the stability of mRNAs. We conclude that DeepSAGE is useful for detecting eQTL effects on both known and unknown transcripts, and for identifying variants that affect alternative polyadenylation.