Natural Selection on Human MicroRNA Binding Sites Inferred from Single Nucleotide Polymorphism Data

By combining SNP data with population genetics, MDC’s bioinformatics and systems biology expert Nikolaus Rajewsky together with Kevin Chen (Center for Comparative Functional Genomics, Department of Biology, New York University) have developed a novel approach toward the study of gene regulation. Their study has recently been published in Nature Genetics doi 10.1038/ng1910).
A fundamental problem in biology is to understand how natural selection has shaped the evolution of gene regulation. Here we use single nucleotide polymorphism (SNP) genotype data and population genetics techniques to study an entire layer of short, cis -regulatory sites in the human genome. MicroRNAs (miRNAs) are a class of small non-coding RNAs that post-transcriptionally repress mRNAs through cis -regulatory sites in 3’ untranslated regions (UTRs). We show that stronger negative selection acts on computationally predicted conserved miRNA binding sites than on other conserved sequence motifs in 3’ UTRs, thus providing independent support for the target prediction model and explicitly demonstrating the contribution of miRNAs to Darwinian fitness. Our techniques extend to non-conserved miRNA sites and we estimate that 30-50% of these are functional when the mRNA and miRNA are co-expressed. As we show that polymorphisms in predicted miRNA sites are likely to be deleterious, they are candidates for causal variants of human disease. We believe that our approach can be extended to studying other classes of cis- regulatory sites. (Abstract from Nature Genetics doi 10.1038/ng1910)


Pamela Cohen
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