Signatures of genetic variation in human microRNAs point to processes of positive selection and population-specific disease risks

The occurrence of natural variation in human microRNAs has been the focus of numerous studies during the last 20 years. Most of them have been focused on the role of specific mutations in disease, while a minor proportion seek to analyse microRNA diversity in the genomes of human populations. We analyse the latest human microRNA annotations in the light of the most updated catalogue of genetic variation provided by the 1000 Genomes Project. By means of the in silico analysis of microRNA genetic variation we show that the level of evolutionary constraint of these sequences is governed by the interplay of different factors, like their evolutionary age or genomic location. The role of mutations in the shaping of microRNA-driven regulatory interactions is emphasized with the acknowledgement that, while the whole microRNA sequence is highly conserved, the seed region shows a pattern of higher genetic diversity that appears to be caused by the dramatic frequency shifts of a fraction of human microRNAs. We highlight the participation of these microRNAs in population-specific processes by identifying that not only the seed, but also the loop, are particularly differentiated regions among human populations. The quantitative computational comparison of signatures of population differentiation showed that candidate microRNAs with the largest differences are enriched in variants implicated in gene expression levels (eQTLs), selective sweeps and pathological processes. We explore the implication of these evolutionary-driven microRNAs and their SNPs in human diseases, such as different types of cancer, and discuss their role in population-specific disease risk.
This study has been possible thanks to the Chilean Agencia Nacional de Investigación y Desarrollo—ANID (FONDECYT regular nº 1170446), the Chilean Ministry of Education (MAG-1995), grant PID2019-110933 GB-I00/AEI/10. 13039/501100011033 awarded by the Spanish Agencia Estatal de Investigación (AEI), Ministerio de Ciencia, Innovación y Universidades (MCIU, Spain), the support of Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2017 SGR 702), part of the “Unidad de Excelencia María de Maeztu”, funded by the AEI (CEX2018-000792-M). P.V-M is supported by an FPI PhD fellowship (FPI-BES-2016-077706) part of the “Unidad de Excelencia María de Maeztu” funded by MINECO (ref: MDM-2014-0370).