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Single Nucleotide Polymorphisms Can Create Alternative Polyadenylation Signals and Affect Gene Expression through Loss of MicroRNA-Regulation

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Title
Single Nucleotide Polymorphisms Can Create Alternative Polyadenylation Signals and Affect Gene Expression through Loss of MicroRNA-Regulation
Published in
PLoS Computational Biology, August 2012
DOI 10.1371/journal.pcbi.1002621
Pubmed ID
Authors

Laurent F. Thomas, Pål Sætrom

Abstract

Alternative polyadenylation (APA) can for example occur when a protein-coding gene has several polyadenylation (polyA) signals in its last exon, resulting in messenger RNAs (mRNAs) with different 3' untranslated region (UTR) lengths. Different 3'UTR lengths can give different microRNA (miRNA) regulation such that shortened transcripts have increased expression. The APA process is part of human cells' natural regulatory processes, but APA also seems to play an important role in many human diseases. Although altered APA in disease can have many causes, we reasoned that mutations in DNA elements that are important for the polyA process, such as the polyA signal and the downstream GU-rich region, can be one important mechanism. To test this hypothesis, we identified single nucleotide polymorphisms (SNPs) that can create or disrupt APA signals (APA-SNPs). By using a data-integrative approach, we show that APA-SNPs can affect 3'UTR length, miRNA regulation, and mRNA expression--both between homozygote individuals and within heterozygote individuals. Furthermore, we show that a significant fraction of the alleles that cause APA are strongly and positively linked with alleles found by genome-wide studies to be associated with disease. Our results confirm that APA-SNPs can give altered gene regulation and that APA alleles that give shortened transcripts and increased gene expression can be important hereditary causes for disease.

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Geographical breakdown

Country Count As %
United States 4 5%
Australia 1 1%
Italy 1 1%
China 1 1%
Brazil 1 1%
Unknown 78 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 28%
Researcher 14 16%
Student > Master 11 13%
Professor > Associate Professor 8 9%
Student > Doctoral Student 8 9%
Other 12 14%
Unknown 9 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 53%
Biochemistry, Genetics and Molecular Biology 16 19%
Computer Science 5 6%
Medicine and Dentistry 4 5%
Engineering 3 3%
Other 0 0%
Unknown 12 14%