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Inferring causative variants in microRNA target sites.

ABSTRACT: MicroRNAs (miRNAs) regulate genes post transcription by pairing with messenger RNA (mRNA). Variants such as single nucleotide polymorphisms (SNPs) in miRNA regulatory regions might result in altered protein levels and disease. Genome-wide association studies (GWAS) aim at identifying genomic regions that contain variants associated with disease, but lack tools for finding causative variants. We present a computational tool that can help identifying SNPs associated with diseases, by focusing on SNPs affecting miRNA-regulation of genes. The tool predicts the effects of SNPs in miRNA target sites and uses linkage disequilibrium to map these miRNA-related variants to SNPs of interest in GWAS. We compared our predicted SNP effects in miRNA target sites with measured SNP effects from allelic imbalance sequencing. Our predictions fit measured effects better than effects based on differences in free energy or differences of TargetScan context scores. We also used our tool to analyse data from published breast cancer and Parkinson's disease GWAS and significant trait-associated SNPs from the NHGRI GWAS Catalog. A database of predicted SNP effects is available at http://www.bigr.medisin.ntnu.no/mirsnpscore/. The database is based on haplotype data from the CEU HapMap population and miRNAs from miRBase 16.0.

SUBMITTER: Thomas LF

PROVIDER: S-EPMC3167593 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Inferring causative variants in microRNA target sites.

Thomas Laurent F LF Saito Takaya T Sætrom Pål P

Nucleic acids research 20110621 16

MicroRNAs (miRNAs) regulate genes post transcription by pairing with messenger RNA (mRNA). Variants such as single nucleotide polymorphisms (SNPs) in miRNA regulatory regions might result in altered protein levels and disease. Genome-wide association studies (GWAS) aim at identifying genomic regions that contain variants associated with disease, but lack tools for finding causative variants. We present a computational tool that can help identifying SNPs associated with diseases, by focusing on S ...[more]

PMID: 21693556

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Project description:MicroRNAs (miRNAs) control the abundance of the majority of the vertebrate transcriptome. The recognition sequences, or target sites, for bilaterian miRNAs are found predominantly in the 3' untranslated regions (3'UTRs) of mRNAs, and are amongst the most highly conserved motifs within 3'UTRs. However, little is known regarding the evolutionary pressures that lead to loss and gain of such target sites. Here, we quantify the selective pressures that act upon miRNA target sites. Notably, selective pressure extends beyond deeply conserved binding sites to those that have undergone recent substitutions. Our approach reveals that even amongst ancient animal miRNAs, which exert the strongest selective pressures on 3'UTR sequences, there are striking differences in patterns of target site evolution between miRNAs. Considering only ancient animal miRNAs, we find three distinct miRNA groups, each exhibiting characteristic rates of target site gain and loss during mammalian evolution. The first group both loses and gains sites rarely. The second group shows selection only against site loss, with site gains occurring at a neutral rate, whereas the third loses and gains sites at neutral or above expected rates. Furthermore, mutations that alter the strength of existing target sites are disfavored. Applying our approach to individual transcripts reveals variation in the distribution of selective pressure across the transcriptome and between miRNAs, ranging from strong selection acting on a small subset of targets of some miRNAs, to weak selection on many targets for other miRNAs. miR-20 and miR-30, and many other miRNAs, exhibit broad, deeply conserved targeting, while several other comparably ancient miRNAs show a lack of selective constraint, and a small number, including mir-146, exhibit evidence of rapidly evolving target sites. Our approach adds valuable perspective on the evolution of miRNAs and their targets, and can also be applied to characterize other 3'UTR regulatory motifs.

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Inferring causative variants in microRNA target sites.

Publications

Inferring causative variants in microRNA target sites.

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