SweeD: Likelihood-Based Detection of Selective Sweeps in Thousands of Genomes

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.

Population Genetic Analysis of the DARC Locus (Duffy) Reveals Adaptation from Standing Variation Associated with Malaria Resistance in Humans

“…Though the DARC promoter region has the fewest SNPs in African populations, it has more pairwise differences likely due to two divergent FY Ã O haplotypes in these populations. To further investigate the underlying characteristics that prevents detecting this locus as non-neutral in genome-wide scans of selection, we analyzed statistics from three main classes of selection scans: population differentiation (F ST ), site frequency spectrum (Sweepfinder [52,53]), and linkage disequilibrium (H-scan [54] Tables). For example, using Sweepfinder, a method designed to detect recently completed hard selective sweeps based on the site frequency spectrum, the region is in the 97th percentile (corresponding to P-value = 0.032 in Table 1) genome-wide in African populations.…”

“…We analyzed methods in three main categories of selection detection: population differentiation (F ST ), site frequency spectra (Sweepfinder [52,53]), and linkage disequilibrium (H-scan [54]). Genomic regions that have undergone a recent hard selective sweep are expected to have site frequency spectrums skewed toward rare and high frequency derived variants, increased homozygosity and, if local adaptation, high population differentiation.…”

“…Sweepfinder, a method designed to detect recent hard selective sweeps based on the site frequency spectrum was ran via the SweeD software [52,53]. H-scan, a statistic designed to detect hard and soft sweeps [54], measures the average length of pairwise homozygosity tracts in a population.…”

“…), median, and 95% confidence interval (CI). (PDF) [52,53]. This likelihood is determined via a composite likelihood ratio test where the numerator is the likelihood of the region site frequency spectrum given a hard selective sweep and the denominator is the likelihood of the region given a neutral model.…”

Worldwide Population Structure, Long-Term Demography, and Local Adaptation of Helicobacter pylori

“…First we used the SweeD algorithm (Pavlidis et al 2013), derived from SweepFinder (Nielsen et al 2005) to localize recent events of positive selection, an approach based upon comparison with the "background" site frequency spectrum (SFS) ( Figure S7). The scan for positive selection is carried out by centering the maximized probability of a selective sweep on a slidingwindow locus along the chromosome, and calculating the composite likelihood for each centered locus to fall within a region where the distribution of SNPs deviates from the neutral expectation.…”