SweeD: Likelihood-based detection of selective sweeps in thousands of genomes

Pavlos Pavlidis*, Daniel Živković, Alexandros Stamatakis, Nikolaos Alachiotis

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

322 Citations (Scopus)
119 Downloads (Pure)

Abstract

The advent of modern DNA sequencing technology is the driving force in obtaining complete intra-specific genomes that can be used to detect loci that have been subject to positive selection in the recent past. Based on selective sweep theory, beneficial loci can be detected by examining the single nucleotide polymorphism patterns in intraspecific genome alignments. In the last decade, a plethora of algorithms for identifying selective sweeps have been developed. However, the majority of these algorithms have not been designed for analyzing whole-genome data. We present SweeD (Sweep Detector), an open-source tool for the rapid detection of selective sweeps in whole genomes. It analyzes site frequency spectra and represents a substantial extension of the widely used SweepFinder program. The sequential version of SweeD is up to 22 times faster than SweepFinder and, more importantly, is able to analyze thousands of sequences. We also provide a parallel implementation of SweeD for multi-core processors. Furthermore, we implemented a checkpointing mechanism that allows to deploy SweeD on cluster systems with queue execution time restrictions, as well as to resume long-running analyses after processor failures. In addition, the user can specify various demographic models via the command-line to calculate their theoretically expected site frequency spectra. Therefore, (in contrast to SweepFinder) the neutral site frequencies can optionally be directly calculated from a given demographic model. We show that an increase of sample size results in more precise detection of positive selection. Thus, the ability to analyze substantially larger sample sizes by using SweeD leads to more accurate sweep detection. We validate SweeD via simulations and by scanning the first chromosome from the 1000 human Genomes project for selective sweeps. We compare SweeD results with results from a linkage-disequilibrium-based approach and identify common outliers.

Original languageEnglish
Pages (from-to)2224-2234
Number of pages11
JournalMolecular Biology and Evolution
Volume30
Issue number9
DOIs
Publication statusPublished - Sept 2013
Externally publishedYes

Keywords

  • High-performance computing
  • Positive selection
  • Selective sweep
  • Site frequency spectrum

Fingerprint

Dive into the research topics of 'SweeD: Likelihood-based detection of selective sweeps in thousands of genomes'. Together they form a unique fingerprint.

Cite this