A genome-wide segmentation approach for the detection of selection footprints

Abstract

AbstractMotivationIn population genetics, the detection of genomic regions under positive selection is essential to understand the genetic basis of locally adaptive trait variation. We propose a principled approach to detect those regions that combines a robust moment basedFSTestimator with a segmentation algorithm.ResultsOur approach allows for pairwise comparisons of populations and does not require any prior knowledge about the size of the regions to be detected. The procedure runs within seconds even for large genome datasets with millions of SNPs, and provides a complete landscape of theFSTdistribution over the chromosome. The procedure comes with a grounded estimator of the baselineFSTlevel, allowing the detection of regions exhibiting high departures from this reference value. The potential of our procedure is illustrated in two applications in animal and human population genetics. We were able to recover in a matter of seconds regions known to be under selection, often with greater precision than what was reported in previous studies.AvailabilityOur approach is implemented in thefst4pgR package available from the CRAN repository. The Sheep dataset is downloadable from the Zenodo repositoryhttps://doi.org/10.5281/zenodo.237116. The 1000 Genome dataset is downloadable fromftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502