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

Abstract

MotivationIn 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 based FST estimator 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 the FST distribution over the chromosome. The procedure comes with a grounded estimator of the baseline FST level, 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 the fst4pg R package available from the CRAN repository. The Sheep dataset is downloadable from the Zenodo repository https://doi.org/10.5281/zenodo.237116. The 1000 Genome dataset is downloadable from ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502