Abstract
In the last decades, genome-wide association studies (GWAS) have uncovered tens of thousands of associations between common genetic variants and complex diseases. However, these statistical associations can rarely be interpreted functionally and mechanistically. As the majority of the disease-associated variants are located far from coding sequences, even the relevant gene is often unclear. A way to gain insight into the relevant mechanisms is to study the genetic determinants of intermediate molecular phenotypes, such as gene expression and transcript structure. We propose a computational strategy to discover genetic variants affecting the relative expression of alternative 3′ untranslated region (UTR) isoforms, generated through alternative polyadenylation, a widespread posttranscriptional regulatory mechanism known to have relevant functional consequences. When applied to a large dataset in which whole genome and RNA sequencing data are available for 373 European individuals, 2,530 genes with alternative polyadenylation quantitative trait loci (apaQTL) were identified. We analyze and discuss possible mechanisms of action of these variants, and we show that they are significantly enriched in GWAS hits, in particular those concerning immune-related and neurological disorders. Our results point to an important role for genetically determined alternative polyadenylation in affecting predisposition to complex diseases, and suggest new ways to extract functional information from GWAS data.
Highlights
Understanding the relationship between human genotypes and phenotypes is one of the central goals of biomedical research
To investigate the effect of human genetic variants on the expression of alternative 3′ untranslated region (UTR) isoforms, we developed a computational approach similar to the one commonly used for Expression quantitative trait loci (eQTL) analysis (Figure 1)
The method we propose combines wide applicability, being based on standard RNA-Seq data, with the high sensitivity allowed by limiting the analysis to a single type of transcript structure variant, namely, 3′ UTR length
Summary
Understanding the relationship between human genotypes and phenotypes is one of the central goals of biomedical research. Genome-wide association studies (GWAS) examine common genetic variants to identify associations with complex traits, including common diseases. Long lists of genetic associations apaQTL Mapping Analysis with disparate traits have been obtained, but their functional interpretation is far from being straightforward (Visscher et al, 2017). Because of linkage disequilibrium, GWAS identify genomic regions carrying multiple variants among which it is not possible to identify the causal ones without additional information. Most loci identified in human GWAS are in noncoding regions, presumably exerting regulatory effects, but usually, we do not know the identity of the affected gene or the molecular mechanism involved
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